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https://api.github.com/repos/pandas-dev/pandas/issues/10103 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10103/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10103/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10103/events | https://github.com/pandas-dev/pandas/pull/10103 | 75,248,363 | MDExOlB1bGxSZXF1ZXN0MzUxMzQ2MTI= | 10,103 | ENH: add expand kw to str.get_dummies | {
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Though this still under work (needs #10089 to simplify `get_dummies` flow), would like to discuss followings.
~~#### `.str.extract` note: overlaps with #11386~~
~~Currently it returns `Series` for a single group and `DataFrame` for multiples. To support `expand` kw, we have to choose :~~
~~1. Add `exp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10104 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10104/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10104/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10104/events | https://github.com/pandas-dev/pandas/issues/10104 | 75,252,794 | MDU6SXNzdWU3NTI1Mjc5NA== | 10,104 | support sqlalchemy connections | {
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"followers_url": "https://api.github.com/users/jorisva... | 1 | 2015-05-11T15:06:09Z | 2015-05-12T08:32:24Z | 2015-05-12T08:32:16Z | CONTRIBUTOR | null | I'd like to be able to manage my own connections and transactions with sqlalchemy:
``` python
def foo(connection):
pd.read_sql_query(query, con=connection)
def bar(connection, data):
data.to_sql(name='foo', con=connection)
def main():
with engine.connection() as conn, conn.begin():
bar(foo(), con... | {
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``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.16.1'
In [3]: f = pd.DataFrame({'a': [0, 10, 20, 30, 40], 'b': [5, 4 ,3, 2, 1]},
index=[1, 2, 3, 4, 4])
In [4]: f.dtypes
Out[4]:
a int64
b int64
dtype: object
In [6]: pd.DataFrame(colum... | {
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@jorisvandenbossche this covers `Series`, `DataFrame` and `Panel`, please take a look
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https://api.github.com/repos/pandas-dev/pandas/issues/10108 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10108/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10108/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10108/events | https://github.com/pandas-dev/pandas/pull/10108 | 75,376,773 | MDExOlB1bGxSZXF1ZXN0MzUxNzU1OTg= | 10,108 | BUG: categorical doesn't handle display.width of None in Python 3 (GH10087) | {
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I think this is fairly straightforward, however I am not sure if the prior code comparing `display.width` to 0 was correct... is `display.width` ever supposed to be set to 0? It looks suspiciously similar to a snippet from `Series.__unicode__()`, but that is looking at `display.max_rows`. Maybe it was br... | {
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In the spirit of the excellent assign method, wondering if there is support for some tidyr style transformations?
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"followers_url": "https://api.github.... | 3 | 2015-05-12T12:34:22Z | 2015-06-02T19:26:11Z | 2015-05-13T12:28:09Z | CONTRIBUTOR | null | mostly remove unused stuff
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https://api.github.com/repos/pandas-dev/pandas/issues/10112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10112/events | https://github.com/pandas-dev/pandas/issues/10112 | 75,678,078 | MDU6SXNzdWU3NTY3ODA3OA== | 10,112 | Pandas ExcelWriter set_column fails to format numbers after DataFrame.to_excel used | {
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] | closed | false | null | [] | null | 4 | 2015-05-12T17:33:21Z | 2020-05-07T22:16:26Z | 2020-05-07T22:16:26Z | NONE | null | I have tried the example code found on the xlsxwriter webpage at http://xlsxwriter.readthedocs.org/en/latest/example_pandas_column_formats.html
However it does not format the columns as expected - they simply appear unformatted (no numeric rounding, percentages.)
It seems that calling set_column is being ignored. Is ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10113 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10113/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10113/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10113/events | https://github.com/pandas-dev/pandas/issues/10113 | 75,685,248 | MDU6SXNzdWU3NTY4NTI0OA== | 10,113 | DataFrame.to_json() creates invalid JSON with MultiIndex | {
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"default": fa... | closed | false | null | [] | null | 1 | 2015-05-12T17:59:31Z | 2015-05-12T19:06:45Z | 2015-05-12T19:06:33Z | NONE | null | Pandas will dump MultiIndex dataframes as invalid JSON in 0.16.0. For example this dataframe:
```
0
a c 1
b d 2
```
Will dump as: `{"0":{"["a","c"]":1,"["b","d"]":2}}`. You can see that the keys aren't escaping their double quote character and this isn't readable as JSON.
The following snippet illustrates how... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10114 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10114/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10114/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10114/events | https://github.com/pandas-dev/pandas/issues/10114 | 75,704,982 | MDU6SXNzdWU3NTcwNDk4Mg== | 10,114 | BUG: filter with a multi-grouping including a datetimelike fails | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-05-12T19:05:06Z | 2015-08-15T23:41:09Z | 2015-08-15T23:41:09Z | CONTRIBUTOR | null | this was changed in #9921 (worked prior to 0.16.0)
cc @evanpw
so using a multiple grouper where one item is a datetimelike fails as the `name in self.indices` fails. Instead `.get_group()` can be used to fix
```
In [1]: df = DataFrame({'A' : np.arange(5), 'B' : ['foo','bar','foo','bar','bar'], 'C' : Timestamp('201301... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10115 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10115/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10115/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10115/events | https://github.com/pandas-dev/pandas/issues/10115 | 75,720,160 | MDU6SXNzdWU3NTcyMDE2MA== | 10,115 | BUG: MultiIndex.drop_duplicates() drops names | {
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In [12]: import pandas as pd
In [13]: mi = pd.MultiIndex.from_tuples([('A','a',1), ('A','a',2)],
names=['Upper','Lower','Num'])
In [14]: mi.drop_duplicates()
Out[14]:
MultiIndex(levels=[['A'], ['a'], [1, 2]],
labels=[[0, 0], [0, 0], [0, 1]])
In [16]: mi.names
Ou... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10116 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10116/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10116/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10116/events | https://github.com/pandas-dev/pandas/pull/10116 | 75,738,342 | MDExOlB1bGxSZXF1ZXN0MzUyNzA1MzI= | 10,116 | BUG: drop_duplicates drops name(s). | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-05-12T21:05:54Z | 2015-08-07T00:15:40Z | 2015-06-23T14:11:23Z | CONTRIBUTOR | null | Closes #10115.
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https://api.github.com/repos/pandas-dev/pandas/issues/10117 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10117/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10117/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10117/events | https://github.com/pandas-dev/pandas/issues/10117 | 75,743,708 | MDU6SXNzdWU3NTc0MzcwOA== | 10,117 | resample gives AmbiguousTimeError when index ends on a DST boundary | {
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... | closed | false | null | [] | {
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"followers_url": "https://api.github... | 4 | 2015-05-12T21:22:27Z | 2018-09-23T12:11:02Z | 2018-09-23T12:11:02Z | CONTRIBUTOR | null | Here's the bug:
``` python
In [27]: idx = pd.date_range("2014-10-25 22:00:00", "2014-10-26 00:30:00",
freq="30T", tz="Europe/London")
In [28]: series = pd.Series(np.random.randn(len(idx)), index=idx)
In [31]: series
Out[31]:
2014-10-25 22:00:00+01:00 -0.874014
2014-10-25 22:30:00+01... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10118 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10118/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10118/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10118/events | https://github.com/pandas-dev/pandas/issues/10118 | 75,811,021 | MDU6SXNzdWU3NTgxMTAyMQ== | 10,118 | automatic inversion of x axis by pandas.plot(...) | {
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My example code blow creates three plots, only some, not all, of which shows inverted x axis. I think this behavior is very confusing for users even if there was some rationale behind it. IMHO... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10119/events | https://github.com/pandas-dev/pandas/issues/10119 | 75,904,265 | MDU6SXNzdWU3NTkwNDI2NQ== | 10,119 | Series.plot() ignores label keyword | {
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"followers_url": "https://api.github.... | 9 | 2015-05-13T08:42:46Z | 2015-06-02T19:25:20Z | 2015-05-19T12:27:19Z | CONTRIBUTOR | null | Using pandas 0.16.1, tested in several environments:
``` python
>>> import pandas as pd
>>> s = pd.Series([0, 1])
>>> ax = s.plot(label='stuff')
>>> assert ax.lines[0].get_label() == 'stuff'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AssertionError
```
This is a regression since 0.16.0. ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10120/events | https://github.com/pandas-dev/pandas/issues/10120 | 75,915,306 | MDU6SXNzdWU3NTkxNTMwNg== | 10,120 | ERROR: test_creating_and_reading_multiple_sheets (pandas.io.tests.test_excel.ExcelReaderTests) | {
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## ERROR: test_creating_and_reading_multiple_sheets (pandas.io.tests.test_excel.ExcelReaderTests)
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.16.1/work/pandas-0.16.1-python3_3/lib/pandas/io/tests/test_excel.py", line 474, in test_creating_and_reading_multiple_sheets
for sheet... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10121 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10121/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10121/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10121/events | https://github.com/pandas-dev/pandas/issues/10121 | 75,916,364 | MDU6SXNzdWU3NTkxNjM2NA== | 10,121 | Multiple test errors related to time/date/timezones | {
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## ERROR: test moving from daylight savings to standard time
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.16.1/work/pandas-0.16.1-python3_3/lib/pandas/tseries/tests/test_offsets.py", line 3898, in test_fallback_plural
expected_utc_offset=hrs_post
File "/var/tmp/portage/dev-p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10122 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10122/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10122/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10122/events | https://github.com/pandas-dev/pandas/pull/10122 | 75,925,251 | MDExOlB1bGxSZXF1ZXN0MzUzMTA3Nzc= | 10,122 | parsing latitude and longitude in wb.get_countries | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10123 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10123/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10123/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10123/events | https://github.com/pandas-dev/pandas/issues/10123 | 75,938,031 | MDU6SXNzdWU3NTkzODAzMQ== | 10,123 | Sort broken after unique -> Series | {
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"followers_url": "https://api.github.com/users/jorisva... | 6 | 2015-05-13T10:34:46Z | 2015-05-13T11:18:10Z | 2015-05-13T10:43:14Z | NONE | null | I see #1807 issue reoccurring in my setup:
```
>>> Series(Series([1,3,4]).unique())
0 1
1 3
2 4
dtype: int64
```
And this doesn't return anything:
```
>>> Series(Series([1,3,4]).unique()).sort()
```
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
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https://api.github.com/repos/pandas-dev/pandas/issues/10124 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10124/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10124/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10124/events | https://github.com/pandas-dev/pandas/pull/10124 | 75,960,058 | MDExOlB1bGxSZXF1ZXN0MzUzMTkwOTE= | 10,124 | BUG: Filter/transform fail in some cases when multi-grouping with a datetime-like key | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10125/events | https://github.com/pandas-dev/pandas/issues/10125 | 76,031,756 | MDU6SXNzdWU3NjAzMTc1Ng== | 10,125 | COMPAT: openpyxl >= 2.2 failing | {
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a branch to repro on travis: https://github.com/jreback/pandas/commit/262c5f13c9cde338ae5c8d93e0de1db00d28c5e9
```
nosetests pandas.io.tests.test_excel
E...................S./usr/lib64/python3.4/site-packages/openpyxl/styles/styleable.py:111: UserWarning: Use formatting objects such as font... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10126 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10126/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10126/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10126/events | https://github.com/pandas-dev/pandas/issues/10126 | 76,042,551 | MDU6SXNzdWU3NjA0MjU1MQ== | 10,126 | masking empty DataFrame | {
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"followers_url": "https://api.github.... | 6 | 2015-05-13T16:21:21Z | 2015-12-17T19:09:51Z | 2015-06-03T19:03:09Z | CONTRIBUTOR | null | In pandas 0.16.1, I get a ValueError when trying to mask an empty DataFrame:
``` python
>>> import pandas as pd
>>> df = pd.DataFrame()
>>> df[df>0]
Traceback (most recent call last):
File "<ipython-input-3-efe84c9ebabc>", line 1, in <module>
df[df>0]
File "C:\Python27\lib\site-packages\pandas\core\frame.py"... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10127 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10127/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10127/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10127/events | https://github.com/pandas-dev/pandas/issues/10127 | 76,054,725 | MDU6SXNzdWU3NjA1NDcyNQ== | 10,127 | BUG: MultiIndex.from_arrays doesn't raise if arrays are unequal lengths | {
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... | 2 | 2015-05-13T16:58:51Z | 2016-12-19T00:26:09Z | 2016-12-19T00:26:08Z | CONTRIBUTOR | null | ``` python
In [62]: index = pd.MultiIndex.from_arrays([['a', 'b', 'c'], [1, 2]])
In [63]: index
Out[63]:
MultiIndex(levels=[['a', 'b', 'c'], [1, 2]],
labels=[[0, 1, 2], [0, 1]])
```
Probably not what the user wants, and we rely on them being the same length in, e.g. `index.shape` which will raise a ValueE... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10128 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10128/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10128/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10128/events | https://github.com/pandas-dev/pandas/issues/10128 | 76,106,491 | MDU6SXNzdWU3NjEwNjQ5MQ== | 10,128 | ENH: Support groupby.resample chained operations on MultiIndexed DataFrames | {
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... | 5 | 2015-05-13T20:05:26Z | 2021-04-18T06:45:53Z | null | NONE | null | #10063 doesn't appear to have been completely fixed. While the exact example I gave now works (the resample), what if instead of a resample I had wanted to do a tshift? I would have wanted to be able to do `df.groupby(level='symbol').tshift(1, 'D')`, which still returns an empty DataFrame. Here, there's no aggregating ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10129 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10129/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10129/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10129/events | https://github.com/pandas-dev/pandas/issues/10129 | 76,164,213 | MDU6SXNzdWU3NjE2NDIxMw== | 10,129 | API: Implement pipe protocol, a method for extensible method chaining | {
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"followers_url": "https://api.github.... | 59 | 2015-05-13T23:43:47Z | 2015-06-06T03:14:27Z | 2015-06-06T03:14:27Z | CONTRIBUTOR | null | Today @shoyer and I were talking about a new "protocol" that will let us sidestep the whole macro / method chaining issue. The basic idea is that pandas objects define a `pipe` method (ideally other libraries will implement this to, assuming this is useful).
Based on the discussions below, we're leaning towards a met... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10130 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10130/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10130/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10130/events | https://github.com/pandas-dev/pandas/pull/10130 | 76,201,260 | MDExOlB1bGxSZXF1ZXN0MzUzODkwNDc= | 10,130 | CLN: clean up unused imports | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10131 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10131/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10131/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10131/events | https://github.com/pandas-dev/pandas/pull/10131 | 76,207,720 | MDExOlB1bGxSZXF1ZXN0MzUzOTAxNzY= | 10,131 | BUG: fix Series.plot label setting | {
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Basically `Series.plot(label='foo')` isn't actually setting the label.
I want to look at one more thing. With this fix you'd need
``` python
s.plot(label='foo', legend=True
```
to get the legend to actually show up. I can't remember what t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10132 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10132/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10132/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10132/events | https://github.com/pandas-dev/pandas/pull/10132 | 76,317,672 | MDExOlB1bGxSZXF1ZXN0MzU0MTExNDc= | 10,132 | BUG: get_group fails when multi-grouping with a categorical | {
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``` .python
>>> df = pd.DataFrame({'a' : pd.Categorical('xyxy'), 'b' : 1, 'c' : 2})
>>> df.groupby(['a', 'b']).get_group(('x', 1))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/evanpw/Workspace/pandas/pandas/core/groupby.py", line 601, in get_group
inds = self._get... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10133 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10133/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10133/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10133/events | https://github.com/pandas-dev/pandas/pull/10133 | 76,350,971 | MDExOlB1bGxSZXF1ZXN0MzU0MTg5MDQ= | 10,133 | BUG: Strings with exponent but no decimal point parsed as integers in python csv engine | {
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"followers_url": "https://api.github.... | 7 | 2015-05-14T12:57:00Z | 2015-06-10T13:40:52Z | 2015-06-09T10:49:18Z | CONTRIBUTOR | null | Fixes GH #9565. I also made the handling of out-of-range integers consistent with the C engine: parse as a string rather than a double.
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https://api.github.com/repos/pandas-dev/pandas/issues/10134 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10134/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10134/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10134/events | https://github.com/pandas-dev/pandas/issues/10134 | 76,382,294 | MDU6SXNzdWU3NjM4MjI5NA== | 10,134 | Can't create DataFrame from SQLite3 cursor | {
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```
cursor = sqlite.execute(sql)
pd.DataFrame(cursor)
/usr/lib/python3/dist-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
255 copy=copy)
256 elif isinstan... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10135 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10135/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10135/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10135/events | https://github.com/pandas-dev/pandas/pull/10135 | 76,383,773 | MDExOlB1bGxSZXF1ZXN0MzU0MjgxODQ= | 10,135 | (WIP) PERF: improve .str perf for all-string values (about 2x-) | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-05-14T14:53:02Z | 2015-11-13T05:40:17Z | 2015-05-14T15:55:15Z | MEMBER | null | Related to #10081. Make a short path using numpy's string funcs when all the target values are strings. Otherwise, use current path.
Followings are current comparison results:
```
import pandas as pd
import numpy as np
import string
import random
np.random.seed(1)
s = [''.join([random.choice(string.ascii_letters + ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10136 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10136/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10136/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10136/events | https://github.com/pandas-dev/pandas/pull/10136 | 76,402,795 | MDExOlB1bGxSZXF1ZXN0MzU0MzQxNTQ= | 10,136 | DOC: consistent imports (GH9886) part II | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10137 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10137/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10137/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10137/events | https://github.com/pandas-dev/pandas/issues/10137 | 76,408,763 | MDU6SXNzdWU3NjQwODc2Mw== | 10,137 | Typo in pandas/pandas/io/parsers.py | {
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```
missing = pd.Categorical(list('aaa'), categories=['a', 'b'])
dense = pd.Categorical(list('abc'))
values = np.arange(len(dense))
df = pd.DataFrame({'missing': missing, 'dense': dense, 'values': values})... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10139 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10139/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10139/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10139/events | https://github.com/pandas-dev/pandas/pull/10139 | 76,493,772 | MDExOlB1bGxSZXF1ZXN0MzU0NjMzOTA= | 10,139 | BUG: declare and use correctly self.unique_check | {
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] | closed | false | null | [] | null | 9 | 2015-05-14T21:20:10Z | 2015-06-30T10:55:21Z | 2015-05-29T08:40:01Z | MEMBER | null | I am not sure I understood the last comment in [the previous pull request](https://github.com/pydata/pandas/pull/9526), but anyway this is a rebased version.
In the previous PR I proposed to simply make the `__get__` of `is_unique` directly rely on `self.initialized`, but I am no more so sure it is a good idea, since ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10140 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10140/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10140/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10140/events | https://github.com/pandas-dev/pandas/issues/10140 | 76,519,053 | MDU6SXNzdWU3NjUxOTA1Mw== | 10,140 | Grouping by pd.cut() results creates a categorical index | {
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This is basically what I do in my code (simplified for... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10141 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10141/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10141/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10141/events | https://github.com/pandas-dev/pandas/pull/10141 | 76,556,304 | MDExOlB1bGxSZXF1ZXN0MzU0NzkyODY= | 10,141 | CLN: clean up unused imports part II | {
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"followers_url": "https://api.github.... | 1 | 2015-05-15T02:03:35Z | 2015-06-02T19:26:12Z | 2015-05-15T13:09:15Z | CONTRIBUTOR | null | found quite a few more under `pandas/tseries/` ... this should be it for now :)
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https://api.github.com/repos/pandas-dev/pandas/issues/10143 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10143/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10143/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10143/events | https://github.com/pandas-dev/pandas/issues/10143 | 76,635,005 | MDU6SXNzdWU3NjYzNTAwNQ== | 10,143 | ENH: Iterate over HDF store hierarchically | {
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"followers_url": "https://api.github... | 8 | 2015-05-15T07:42:27Z | 2018-06-26T22:53:49Z | 2018-06-26T22:53:49Z | CONTRIBUTOR | null | I am relatively new to pandas, but have been using raw HDF5 files for a long itme, so apologies if this feature or something equivalent already exists.
As stated in the documentation, it is possible to create hierarchies of data stores:
``` python
store.put('/first_group/df1', df1)
store.put('/first_group/df2', df2)
... | {
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... | 10 | 2015-05-15T10:32:42Z | 2021-04-18T06:48:01Z | 2021-04-18T06:48:01Z | CONTRIBUTOR | null | Are BDay and bday the same? Is one of them deprecated or something?
```
In [48]: pd.datetools.BDay?
Init signature: pd.datetools.BDay(self, n=1, normalize=False, **kwds)
Docstring: DateOffset subclass representing possibly n business days
File: c:\python27\lib\site-packages\pandas\tseries\offsets.py
Typ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10145 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10145/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10145/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10145/events | https://github.com/pandas-dev/pandas/issues/10145 | 76,685,752 | MDU6SXNzdWU3NjY4NTc1Mg== | 10,145 | DOC: add nlargest/nsmallest to API.rst | {
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"followers_url": "https://api.github.... | 5 | 2015-05-15T10:34:53Z | 2015-06-02T19:25:20Z | 2015-05-26T10:32:56Z | CONTRIBUTOR | null | At the moment there's no easy way to partially sort the DataFrame to get top N rows, could be useful feature.
There's partial sorting available for numpy within the [bottleneck](http://berkeleyanalytics.com/bottleneck/reference.html#bottleneck.partsort) library.
Here's link to SO question about numpy partial sort - h... | {
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## FAIL: test_sum (pandas.tests.test_series.TestSeries)
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.16.1/work/pandas-0.16.1-python3_3/lib/pandas/tests/test_series.py", line 2302, in test_sum
self._check_stat_op('sum', np.sum)
File "/var/tmp/portage/dev-python/pandas-0.16.1/... | {
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import pandas as pd
import numpy as np
from pandas.util.testing import assert_frame_equal
df = pd.DataFrame(np.arange(12).reshape(-1, 3),
columns=['a', 'b', 'c'])
df.index = pd.MultiIndex.from_tuples(
[(1, 2000), (1, 2001), (2, 2000), (2, 2001)],
names=['id', 'year'])
s = pd.Seri... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10148 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10148/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10148/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10148/events | https://github.com/pandas-dev/pandas/issues/10148 | 76,753,914 | MDU6SXNzdWU3Njc1MzkxNA== | 10,148 | BUG: Merge on multiple indices fails with incorrect error message | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-05-15T15:05:32Z | 2015-05-16T14:36:34Z | 2015-05-15T16:28:39Z | NONE | null | I have a 2 DataFrame objects that I am trying to merge on a hierarchical index in one (top below) object with two columns in another (bottom below):
However, merging these via the following:
```
ecmo_by_year = pd.merge(pd.DataFrame(ecmo_count, columns=['ECMO']), runs_sex,
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https://api.github.com/repos/pandas-dev/pandas/issues/10149 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10149/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10149/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10149/events | https://github.com/pandas-dev/pandas/issues/10149 | 76,837,115 | MDU6SXNzdWU3NjgzNzExNQ== | 10,149 | BUG: Index.union cannot handle array-likes | {
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"followers_url": "https://api.github.... | 0 | 2015-05-15T19:51:00Z | 2015-06-02T19:25:20Z | 2015-06-01T11:45:10Z | MEMBER | null | Though API doc says `Index.union` can accept `Index` or array-like, it raises errors.
- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.union.html
```
import numpy as np
import pandas as pd
idx = pd.Index([1, 2, 3])
idx.union([2, 3, 4])
# AttributeError: 'list' object has no attribute 'dtype'
idx.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10150 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10150/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10150/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10150/events | https://github.com/pandas-dev/pandas/issues/10150 | 76,839,233 | MDU6SXNzdWU3NjgzOTIzMw== | 10,150 | BUG/API: inconsistent name handling in value_counts | {
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```
s = pd.Series([1, 2, 1], name='a')
idx = pd.Index([1, 2, 1], name='a')
result = s.value_counts()
result.name, result.index.name
# (None, None) # should be ('a', None)?
result... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10151 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10151/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10151/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10151/events | https://github.com/pandas-dev/pandas/issues/10151 | 76,849,957 | MDU6SXNzdWU3Njg0OTk1Nw== | 10,151 | CLEAN: cleanup up platform / python version checks | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-05-15T20:34:09Z | 2015-07-11T20:06:14Z | 2015-07-11T20:06:14Z | CONTRIBUTOR | null | we have in various locations
`os.name, platform.system(), sys.platform`
for detecting platform. Recently add are these functions
in `pandas/compat/__init__.py` as `is_platform_*` which are reasonable to standardize on
however, need to find & fix all the other references to make this nice and clean
this includes cod... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10152 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10152/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10152/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10152/events | https://github.com/pandas-dev/pandas/pull/10152 | 76,894,561 | MDExOlB1bGxSZXF1ZXN0MzU1NjAxNzI= | 10,152 | TST: use compat.long in test_tslib for py3 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10153 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10153/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10153/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10153/events | https://github.com/pandas-dev/pandas/issues/10153 | 77,076,877 | MDU6SXNzdWU3NzA3Njg3Nw== | 10,153 | Support categorical variables with CSVs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10154 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10154/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10154/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10154/events | https://github.com/pandas-dev/pandas/issues/10154 | 77,091,519 | MDU6SXNzdWU3NzA5MTUxOQ== | 10,154 | to_datetime, inconsistent behavior with invalid dates. | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2015-05-16T17:52:41Z | 2015-07-07T10:19:58Z | 2015-07-07T10:19:58Z | CONTRIBUTOR | null | Consider Feb 29 1991 (2291991) and March 32 1991 (3321991), Both are invalid dates.
```
pd.to_datetime(2291991, format="%m%d%Y", coerce=True, exact=True)
```
Returns a TypeError: ValueError: day is out of range for month
While
```
pd.to_datetime(3321991, format="%m%d%Y", coerce=True, exact=True)
```
Returns NaT. W... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10155 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10155/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10155/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10155/events | https://github.com/pandas-dev/pandas/issues/10155 | 77,092,567 | MDU6SXNzdWU3NzA5MjU2Nw== | 10,155 | Mean overflows for integer dtypes | {
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for convenience, that me just put is some large number
test1 = pd.Series(20150515061816532, index=list(range(500)), dtype='int64')
test1.describe()
Out[152]:
count 5.000000e+02
**mean -1.674297e+16**
std 0.000000e+00
min 2.015052e+16
25% 2.015052e+16
50% 2.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10156 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10156/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10156/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10156/events | https://github.com/pandas-dev/pandas/issues/10156 | 77,133,838 | MDU6SXNzdWU3NzEzMzgzOA== | 10,156 | .remove_category(np.nan) fails on Categorical with floats | {
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"followers_url": "https://api.github.... | 2 | 2015-05-16T21:12:36Z | 2015-06-09T23:46:30Z | 2015-06-09T23:46:30Z | NONE | null | Trying to remove a `nan` category from Categorical series fails if categories are made of floats.
In the docs it says:
> Note: As integer Series can’t include NaN, the categories were converted to _object_.
So it is probably linked to this with `float` remaining `float` and `nan` != `nan`.
If this is intended behav... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10157 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10157/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10157/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10157/events | https://github.com/pandas-dev/pandas/pull/10157 | 77,136,426 | MDExOlB1bGxSZXF1ZXN0MzU1Nzc1MTA= | 10,157 | BUG: Index.union cannot handle array-likes | {
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"followers_url": "https://api.github.... | 5 | 2015-05-16T21:26:19Z | 2015-06-02T19:26:12Z | 2015-06-01T11:45:10Z | MEMBER | null | Closes #10149.
- Added explicit tests for array-likes in set-ops, `intersection`, `union`, `difference` and `sym_diff`.
- `TimedeltaIndex` previously did additional input check which is inconsistent with others. Made it work as the same manner as others.
- `MultiIndex` set-ops should only accept list-likes of tupl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10158 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10158/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10158/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10158/events | https://github.com/pandas-dev/pandas/pull/10158 | 77,137,402 | MDExOlB1bGxSZXF1ZXN0MzU1Nzc1NzU= | 10,158 | BUG: Index.name is lost during timedelta ops | {
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"followers_url": "https://api.github.... | 3 | 2015-05-16T21:31:49Z | 2015-06-02T19:26:12Z | 2015-05-18T11:59:45Z | MEMBER | null | Closes #9926.
CC @hsperr I've changed `common._maybe_match_names` to work with not-named objects, and hopefully can use in #9965 also.
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https://api.github.com/repos/pandas-dev/pandas/issues/10159 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10159/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10159/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10159/events | https://github.com/pandas-dev/pandas/issues/10159 | 77,194,541 | MDU6SXNzdWU3NzE5NDU0MQ== | 10,159 | Read from Regex Function | {
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] | closed | false | null | [] | null | 2 | 2015-05-17T02:58:00Z | 2015-05-18T11:06:09Z | 2015-05-18T11:06:04Z | NONE | null | I'm working on a project involving log file analysis and wrote a method which takes a regex, a file name and list of column names, parses the data and returns a pandas DataFrame.
I haven't extensively tested this yet, but the code below successfully reads in an apache log file using the regex below and returns a DataF... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10160 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10160/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10160/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10160/events | https://github.com/pandas-dev/pandas/issues/10160 | 77,224,825 | MDU6SXNzdWU3NzIyNDgyNQ== | 10,160 | pd.DataFrame.from_dict fails when index is numpy.datetime | {
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"followers_url": "https://api.github.... | 2 | 2015-05-17T05:48:48Z | 2015-06-10T20:01:31Z | 2015-06-10T20:01:31Z | NONE | null | Suppose that we want to create a dataframe from a dictionary.
``` python
import pandas as pd,numpy as np
#1) Form dataframe
ix=pd.date_range('1/1/2015',periods=5,freq='D')
df0=pd.DataFrame(np.random.normal(size=(len(ix),5)),index=ix)
#2) Split dataframe into 2 dictionaries, based on sign
d={'+':{},'-':{}}
for loc in d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10161 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10161/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10161/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10161/events | https://github.com/pandas-dev/pandas/issues/10161 | 77,316,447 | MDU6SXNzdWU3NzMxNjQ0Nw== | 10,161 | duplicated() performance and bug on long rows regression from 0.15.2->0.16.0 | {
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also on a private data set that works on 0.15.2 i get an error on 0.16.0 and 0.16.1 on the same operation.
code:
``` python
import pandas,numpy
df = pandas.DataFrame({'A': [1 for x in range(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10162 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10162/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10162/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10162/events | https://github.com/pandas-dev/pandas/issues/10162 | 77,640,072 | MDU6SXNzdWU3NzY0MDA3Mg== | 10,162 | No output of xticklabels when plotting to gridspec subplots | {
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The xticklabels for the first axes defined in a gridspec are not visible when plotting with pandas. The corresponding plot using pyplot.plot does have the labels.
pandas 0.16
matplotlib 1.4.3
``` python
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotl... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10163 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10163/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10163/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10163/events | https://github.com/pandas-dev/pandas/pull/10163 | 77,657,162 | MDExOlB1bGxSZXF1ZXN0MzU2MzMxODQ= | 10,163 | DOC: clarify purpose of DataFrame.from_csv (GH4191) | {
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xref #9568, #4916, #4191
However, while writing this up, I started to doubt a bit if this is necessary.
`DataFrame.from_csv` is implemented as a round-trip method together with `to_csv`. If you use a plain `df.to_csv(path)`, you cannnot read it in as `pd.read_csv(path)` to get exactly the same. You at ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10164 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10164/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10164/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10164/events | https://github.com/pandas-dev/pandas/issues/10164 | 77,669,248 | MDU6SXNzdWU3NzY2OTI0OA== | 10,164 | bug: read_csv incorrect output with skipfooter and skip_blank_lines=True | {
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```
>>> import pandas as pd
>>> import StringIO
>>> test_csv = StringIO.StringIO('a,b,c\n1,2,3\n\nend\n') ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10165 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10165/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10165/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10165/events | https://github.com/pandas-dev/pandas/issues/10165 | 77,695,503 | MDU6SXNzdWU3NzY5NTUwMw== | 10,165 | ERR: better error reporting with .transform and an invalid output from a UDF | {
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I think we can detect the returned values and if they are not scalars, give a nicer error message. Further a doc-note would be helpful.
```
In [4]: cols = pd.MultiIndex.from_tuples([('syn', 'A'), ('mis', 'A'), ('non', 'A'), ('syn', 'C'), ('mis', 'C'), ('non', 'C'),
('syn'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10166 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10166/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10166/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10166/events | https://github.com/pandas-dev/pandas/pull/10166 | 77,780,145 | MDExOlB1bGxSZXF1ZXN0MzU2NjY1NDI= | 10,166 | DOC: Reorder arguments in shared fillna docstring | {
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] | closed | false | null | [] | null | 1 | 2015-05-18T19:43:07Z | 2015-05-18T20:31:00Z | 2015-05-18T20:30:57Z | CONTRIBUTOR | null | The docstring listed 'method' before 'value' which is not consistent
with the order of the arguments when calling the method.
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https://api.github.com/repos/pandas-dev/pandas/issues/10167 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10167/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10167/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10167/events | https://github.com/pandas-dev/pandas/issues/10167 | 77,889,322 | MDU6SXNzdWU3Nzg4OTMyMg== | 10,167 | 'logging' name causing issues | {
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# !/usr/bin/env python
print "hello"
Then
> > python
> > import pandas
you will get the following error
AttributeError: 'module' object has no attribute 'getLogger'
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https://api.github.com/repos/pandas-dev/pandas/issues/10168 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10168/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10168/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10168/events | https://github.com/pandas-dev/pandas/pull/10168 | 77,961,085 | MDExOlB1bGxSZXF1ZXN0MzU3MDAxMzU= | 10,168 | Delete ez_setup.py | {
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Up to date versions of Python 2 and 3 provide setuptools and pip out of the box `python -m ensurepip --upgrade`. For other Python versions, we can simply point users to the up to date instructions at https://packaging.python.org/en/latest/installing... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10169 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10169/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10169/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10169/events | https://github.com/pandas-dev/pandas/issues/10169 | 78,043,810 | MDU6SXNzdWU3ODA0MzgxMA== | 10,169 | Reindex MultiIndex of type float | {
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"color": "02... | closed | false | null | [] | null | 3 | 2015-05-19T11:14:33Z | 2015-05-20T10:55:42Z | 2015-05-20T10:55:42Z | NONE | null | Hi,
There is a strange behavior when I want to reindex a Multi-Index (float64) of a Series: some of the original values are dropped from the reindexed object.
Here is the test case:
``` python
>>> import pandas as pd
>>> import numpy as np
>>> df_g=pd.Series(index=pd.MultiIndex.from_tuples([(10.,4.2),(11.,5.2),(14,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10170 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10170/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10170/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10170/events | https://github.com/pandas-dev/pandas/issues/10170 | 78,118,888 | MDU6SXNzdWU3ODExODg4OA== | 10,170 | Display of a datetime column/index with both second and sub-second resolution | {
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```
In [121]: df = pd.DataFrame({'datetime': pd.date_range('1/1/2000 09:00:00', freq="5ms",periods=10), 'float': np.arange(0,0.05,0.005)})
In [122]: df
Out[122]:
datetime float
0 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10171/events | https://github.com/pandas-dev/pandas/pull/10171 | 78,142,327 | MDExOlB1bGxSZXF1ZXN0MzU3NDQ2NDg= | 10,171 | BUG: consistent datetime display format with < ms #10170 | {
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```
In [3]: Series(date_range('20130101 09:00:00',periods=5,freq='D'))
Out[3]:
0 2013-01-01 09:00:00
1 2013-01-02 09:00:00
2 2013-01-03 09:00:00
3 2013-01-04 09:00:00
4 2013-01-05 09:00:00
dtype: datetime64[ns]
In [4]: Series(date_range('20130101 09:00:00',periods=5,freq='s'))
Out[4]:
0 20... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10172 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10172/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10172/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10172/events | https://github.com/pandas-dev/pandas/pull/10172 | 78,164,170 | MDExOlB1bGxSZXF1ZXN0MzU3NTA1MDU= | 10,172 | BUG: mean overflows for integer dtypes (fixes #10155) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10173/events | https://github.com/pandas-dev/pandas/issues/10173 | 78,249,798 | MDU6SXNzdWU3ODI0OTc5OA== | 10,173 | Does this need to be reopened? It is not in the latest release. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10174 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10174/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10174/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10174/events | https://github.com/pandas-dev/pandas/issues/10174 | 78,430,450 | MDU6SXNzdWU3ODQzMDQ1MA== | 10,174 | Support 'interpolation' keyword in pandas.DataFrame.quantile and pandas.Series.quantile | {
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This would make it consistent with numpy.percentile options (new in 1.9). See http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html
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... | 2 | 2015-05-20T09:42:45Z | 2020-09-22T01:57:12Z | null | NONE | null | ```
df = pd.DataFrame({'a':[55,66,77],'b':[100,111,112],'ch':['x','y','z']})
df.index = pd.Index([10,11,12])
In [6]: df
Out[6]:
a b ch
10 55 100 x
11 66 111 y
12 77 112 z
In [7]: df.loc[10,('b','ch')]
....
ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>
In [8]: df.ix... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10176 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10176/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10176/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10176/events | https://github.com/pandas-dev/pandas/issues/10176 | 78,476,593 | MDU6SXNzdWU3ODQ3NjU5Mw== | 10,176 | How to use loc WITHOUT enlargement in pandas >0.14 | {
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"color": "00... | closed | false | null | [] | null | 11 | 2015-05-20T10:56:23Z | 2015-05-20T17:18:08Z | 2015-05-20T10:59:32Z | CONTRIBUTOR | null | It appears that `loc` now enlarges the result if an index is not found - a behavior I find confusing, because
``` python
df.loc[['missing_label']]
```
gives a `KeyError` while
``` python
df.loc[['existing_label', 'missing_label']]
```
works just fine. Apparently this was changed in 0.15. I have older code that r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10177 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10177/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10177/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10177/events | https://github.com/pandas-dev/pandas/issues/10177 | 78,495,486 | MDU6SXNzdWU3ODQ5NTQ4Ng== | 10,177 | BUG: concat on axis=0 with categorical | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2015-05-20T11:57:53Z | 2015-06-27T22:04:40Z | 2015-06-27T22:04:40Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/9426, fixed in #9597
`axis=1` is ok
```
df1 = pandas.DataFrame(numpy.random.randn(6, 3), columns=["a", "b", "c"])
df2 = pandas.DataFrame(numpy.random.randn(7, 4), columns=["g", "h", "a", "c"])
df2['h'] = pandas.Series(pandas.Categorical(["one", "one", "two", "one", "two", ... | {
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To test
``` python
df = DataFrame({'date_text':["2015-05-18" for i in range(10**6)]})
%timeit pd.to_datetime(df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10179 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10179/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10179/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10179/events | https://github.com/pandas-dev/pandas/pull/10179 | 78,572,359 | MDExOlB1bGxSZXF1ZXN0MzU4NDQ4ODA= | 10,179 | BUG: concat on axis=0 with categorical (GH10177) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10180 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10180/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10180/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10180/events | https://github.com/pandas-dev/pandas/issues/10180 | 78,623,198 | MDU6SXNzdWU3ODYyMzE5OA== | 10,180 | Segfault when writing data out of order to pd.HDFStore via append | {
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] | closed | false | null | [] | null | 13 | 2015-05-20T17:47:02Z | 2015-11-24T11:40:51Z | 2015-11-24T11:40:50Z | NONE | null | I am trying to append chunks of data to an (initially empty) HDF5 frame with pd.HDFStore. The chunks come in out of order, and sometimes certain orders produce segfaults. This script seems to consistently segfault after loading file 31 (_update, see comments for better example_). You will notice that by looking at the ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10181 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10181/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10181/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10181/events | https://github.com/pandas-dev/pandas/issues/10181 | 78,907,988 | MDU6SXNzdWU3ODkwNzk4OA== | 10,181 | Empty dataframe not equal to another empty dataframe in 0.16.1 | {
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"followers_url": "https://api.github.... | 5 | 2015-05-21T09:22:18Z | 2015-06-07T22:22:40Z | 2015-06-07T22:22:40Z | CONTRIBUTOR | null | I searched the issues list for "empty dataframe" but did not find anything matching.
In pandas 0.16.1, the result of adding 2 empty dataframes is not equal to another empty dataframe.
This code, in pandas 0.15.2, does what is expected:
``` python
import pandas as pd
pd.show_versions()
df1 = pd.DataFrame()
df... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10182/events | https://github.com/pandas-dev/pandas/issues/10182 | 78,917,570 | MDU6SXNzdWU3ODkxNzU3MA== | 10,182 | Setting option 'display.max_seq_items' to None results in TypeError when pprinting index | {
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"followers_url": "https://api.github.... | 5 | 2015-05-21T09:44:11Z | 2015-06-10T06:47:12Z | 2015-06-10T06:47:12Z | NONE | null | According to the docstring, the following is allowed:
``` Python
pd.set_option('display.max_seq_items', None)
```
With 0.16.1 this results in a TypeError when pretty printing an index. This is because the None value is compared against lenght of the index to see if we need to limit output.
``` Python
TypeError: unor... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10183/events | https://github.com/pandas-dev/pandas/pull/10183 | 78,950,327 | MDExOlB1bGxSZXF1ZXN0MzU5MjM2Mzg= | 10,183 | FIX printing index with display.max_seq_items=None (GH10182) | {
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"followers_url": "https://api.github.... | 11 | 2015-05-21T11:20:38Z | 2015-06-10T06:47:12Z | 2015-06-10T06:47:12Z | MEMBER | null | Fixes #10182
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https://api.github.com/repos/pandas-dev/pandas/issues/10184 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10184/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10184/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10184/events | https://github.com/pandas-dev/pandas/issues/10184 | 78,955,747 | MDU6SXNzdWU3ODk1NTc0Nw== | 10,184 | read_csv does not set index name on empty dataframe | {
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"followers_url": "https://api.github.... | 5 | 2015-05-21T11:38:34Z | 2015-06-09T12:19:32Z | 2015-06-09T12:19:32Z | NONE | null | If foo.csv contains
``` csv
x,y
1,2
```
then you get the normal behavior:
``` py
>>> df = pd.read_csv('foo.csv', index_col=0)
>>> list(df.columns)
['y']
>>> df.index.name
'x'
```
but if you remove the data row "1,2" in the CSV the index name gets dropped:
``` py
>>> df = pd.read_csv('foo.csv', index_col=0)
>>> lis... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10185 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10185/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10185/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10185/events | https://github.com/pandas-dev/pandas/pull/10185 | 79,003,849 | MDExOlB1bGxSZXF1ZXN0MzU5MzUzNzc= | 10,185 | DOC: Add Index.difference to API doc | {
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"followers_url": "https://api.github.... | 7 | 2015-05-21T13:51:22Z | 2015-06-02T19:26:12Z | 2015-05-23T06:41:18Z | MEMBER | null | Add `Index.difference` to api doc rather than deprecated `Index.diff` (#8226)
And is it OK to remove `Index.diff` alias because #6581 lists #8227 (which closes #8226)?
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https://api.github.com/repos/pandas-dev/pandas/issues/10186 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10186/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10186/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10186/events | https://github.com/pandas-dev/pandas/issues/10186 | 79,013,965 | MDU6SXNzdWU3OTAxMzk2NQ== | 10,186 | API: Set ops for CategoricalIndex | {
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... | open | false | null | [] | {
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... | 8 | 2015-05-21T14:17:09Z | 2021-06-17T03:07:31Z | null | MEMBER | null | Derived from #10157. Would like to clarify what these results should be. Basically, I think:
- The result must be `CategoricalIndex`
- The result should have the same values as the result of normal `Index` which has the same original values.
- Result's `category` should only include categories which the result actual... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10187 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10187/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10187/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10187/events | https://github.com/pandas-dev/pandas/issues/10187 | 79,041,961 | MDU6SXNzdWU3OTA0MTk2MQ== | 10,187 | API: concat of frames with multi-index with non-matching names | {
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Small example:
```
In [41]: df1 = pd.DataFrame([[1,2], [3,4]], index=pd.MultiIndex.from_tuples([('a', 1), ('a',... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10188 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10188/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10188/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10188/events | https://github.com/pandas-dev/pandas/pull/10188 | 79,062,120 | MDExOlB1bGxSZXF1ZXN0MzU5NTAyNzM= | 10,188 | BUG: Adding empty dataframes should result in empty blocks #10181 | {
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"followers_url": "https://api.github.... | 10 | 2015-05-21T16:08:46Z | 2015-06-16T15:40:21Z | 2015-06-07T22:22:40Z | CONTRIBUTOR | null | Fixes #10181
I added a couple of lines to the dataframe test `test_operators` to assert that adding two empty dataframes returns a dataframe that is equal (both using `assert_frame_equal` as well as `.equals`) to an empty dataframe.
I couldn't get vbench to install (I am running on windows) - the error was:
```
c:... | {
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"followers_url": "https://api.github.... | 2 | 2015-05-21T18:13:28Z | 2015-06-02T19:26:12Z | 2015-05-23T06:40:55Z | CONTRIBUTOR | null | …fixes
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https://api.github.com/repos/pandas-dev/pandas/issues/10190 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10190/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10190/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10190/events | https://github.com/pandas-dev/pandas/issues/10190 | 79,154,876 | MDU6SXNzdWU3OTE1NDg3Ng== | 10,190 | pandas to_hdf function get Illegal instruction | {
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Any idea for this?
``` python
import pandas... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10191 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10191/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10191/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10191/events | https://github.com/pandas-dev/pandas/issues/10191 | 79,201,153 | MDU6SXNzdWU3OTIwMTE1Mw== | 10,191 | Interpolation works for MultiIndex of strings and float but not DateTime | {
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"gi... | [] | closed | false | null | [] | null | 2 | 2015-05-21T23:22:54Z | 2015-05-22T21:06:40Z | 2015-05-22T21:06:40Z | NONE | null | I believe I found a bug in interpolation for MultiIndex where linear interpolation works on MultiIndex that contain strings and floats but not for DateTimes. It sounds like linear interpolation on MultiIndexes is not officially supported until next major release however I thought I'd raise this finding in case it helps... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10192 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10192/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10192/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10192/events | https://github.com/pandas-dev/pandas/issues/10192 | 79,361,850 | MDU6SXNzdWU3OTM2MTg1MA== | 10,192 | Performance drop when using timezone-aware DateTimeIndex | {
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"f... | 3 | 2015-05-22T08:53:39Z | 2015-10-23T20:52:42Z | 2015-10-23T20:52:42Z | NONE | null | It seems that pandas.DataFrame operations on Index with timezone-aware dates is order of magnitude slower than on regular datetimes.
for a 500k datetimes created with pandas.date_range, and using DataFrame.shift() to compute deltas between dates, timings goes from **17ms** for standard datetimes to **16seconds** for t... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-05-22T09:44:57Z | 2015-06-18T15:36:30Z | 2015-06-18T15:36:30Z | CONTRIBUTOR | null | Another somewhat degenerate case that works in 0.15.2, fails in 0.16.1
``` python
import pandas as pd
pd.show_versions()
ts = pd.TimeSeries()
ts[pd.datetime(2012, 1, 1)] = 47
ts2 = pd.TimeSeries(0, pd.date_range('2011-01-01', '2011-01-01'))[:0]
ts2[pd.datetime(2012, 1, 1)] = 47
```
0.15.2 output:
``` python
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https://api.github.com/repos/pandas-dev/pandas/issues/10195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10195/events | https://github.com/pandas-dev/pandas/issues/10195 | 79,402,173 | MDU6SXNzdWU3OTQwMjE3Mw== | 10,195 | BUG: TypeError when trying to set values in empty DataFrame using boolean key | {
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``` python
import numpy as np
import pandas as pd
pd.show_versions()
df = pd.DataFrame([[2, 2]], index=pd.DatetimeIndex(['2011-01-01']))
df2 = pd.DataFrame([[1, 1]], index=pd.DatetimeIndex(['2011-01-01']))
df[df > df2] = 4... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10196/events | https://github.com/pandas-dev/pandas/pull/10196 | 79,402,706 | MDExOlB1bGxSZXF1ZXN0MzYwMTk2Mzk= | 10,196 | BUG: Raise TypeError only if key DataFrame is not empty #10126 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10197/events | https://github.com/pandas-dev/pandas/issues/10197 | 79,419,203 | MDU6SXNzdWU3OTQxOTIwMw== | 10,197 | false warning when using to_sql with a dataframe using MultiIndex | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2015-05-22T11:34:19Z | 2015-05-22T15:35:03Z | 2015-05-22T14:24:16Z | NONE | null | Hi,
I get a warning that confused me, and in my opinion is wrong.
See code - that should produce warning with further comments.
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10198 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10198/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10198/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10198/events | https://github.com/pandas-dev/pandas/issues/10198 | 79,463,744 | MDU6SXNzdWU3OTQ2Mzc0NA== | 10,198 | Reading of fixed width file is detecting incorrect number of columns | {
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I have been trying to use the `read_fwf()` dataframe method in pandas 0.16.0. I have supplied a list of tuples for the _colspec_ parameter. There are 9 tuples specified. I have also supplied a list to the _names_ parameter, with 9 column names.
Here's some code:
```
df = pd.read_fwf('c:/6starts.tab',
header... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10199/events | https://github.com/pandas-dev/pandas/pull/10199 | 79,594,577 | MDExOlB1bGxSZXF1ZXN0MzYwNjU1MDU= | 10,199 | PERF: releasing the GIL, #8882 | {
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"followers_url": "https://api.github.com/users/jr... | 23 | 2015-05-22T20:19:47Z | 2015-06-30T16:58:13Z | 2015-06-30T10:55:04Z | CONTRIBUTOR | null | closes #8882
closes #10139
This is now implemented for all groupbys with the factorization part as well (which is actually a big part fo the time)
```
In [2]: N = 1000000
In [3]: ngroups = 1000
In [4]: np.random.seed(1234)
In [5]: df = DataFrame({'key' : np.random.randint(0,ngroups,size=N),
...: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10200/events | https://github.com/pandas-dev/pandas/pull/10200 | 79,606,303 | MDExOlB1bGxSZXF1ZXN0MzYwNjgzMjI= | 10,200 | DOC: minor doc fix for Series.append; indices can overlap | {
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"followers_url": "https://api.github.... | 5 | 2015-05-22T20:57:41Z | 2015-06-02T19:26:12Z | 2015-05-25T11:29:35Z | CONTRIBUTOR | null | Old docstring seemed to be incorrect; overlapping indices are supported e.g.
``` python
>>> import pandas as pd
>>> x = pd.Series(['A', 'B', 'C'])
>>> y = pd.Series(['D', 'E', 'F'])
>>> x.append(y)
0 A
1 B
2 C
0 D
1 E
2 F
dtype: object
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10201/events | https://github.com/pandas-dev/pandas/issues/10201 | 79,620,590 | MDU6SXNzdWU3OTYyMDU5MA== | 10,201 | pandas diff().hist() produces inaccurate plot | {
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My data are Super Rugby differential scores. The matrix of data is below.
``` python
#Create base matrix of score differentials across Teams (row) x Round (col)
S15_Base = np.matrix([[-10,6,-24,0,28,43,-12,4... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/10202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10202/events | https://github.com/pandas-dev/pandas/pull/10202 | 79,845,528 | MDExOlB1bGxSZXF1ZXN0MzYwODc0NDI= | 10,202 | DOC: Updated to mention axis='index' and axis='columns | {
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"followers_url": "https://api.github.... | 1 | 2015-05-23T13:20:21Z | 2015-06-02T19:26:12Z | 2015-05-25T14:17:18Z | CONTRIBUTOR | null | Updated docs throughout DataFrame methods to mention
that axis can be set to 'index' or 'column' instead of 0 or 1
which improves readability significantly.
Fixes #9658
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