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https://api.github.com/repos/pandas-dev/pandas/issues/8802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8802/events | https://github.com/pandas-dev/pandas/issues/8802 | 48,628,307 | MDU6SXNzdWU0ODYyODMwNw== | 8,802 | Version 0.15 MultiIndex forces Datetime.date objects to Timestamp objects | {
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"color": "AD7... | closed | false | null | [] | null | 4 | 2014-11-13T11:59:35Z | 2014-11-13T15:07:16Z | 2014-11-13T12:50:49Z | NONE | null | I recently updated Pandas and found this strange behaviour which broke some of my existing code.
I was using a column of Datetime.date objects as a the second level in a two-level MulitIndex.
However, when setting the index with the latest version, the Datetime.date objects are converted to Timestamp objects with 00:... | {
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... | open | false | null | [] | null | 8 | 2014-11-13T16:41:53Z | 2020-04-10T02:36:37Z | null | CONTRIBUTOR | null | `DateParseError` is raised when Series with DateTimeIndex is plotted using bar-plot on axis that already contains another plot:
```
import pandas as pd
df = pd.Series([4,2,1,3,5], index=pd.to_datetime(['2004','2005','2006','2007','2008']))
df.plot()
df.plot(kind='bar')
```
Both of plots work independently, but being ... | {
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"description": "Related to indexing on s... | closed | false | null | [] | null | 13 | 2014-11-13T17:32:54Z | 2014-11-26T13:47:28Z | 2014-11-20T15:20:53Z | CONTRIBUTOR | null | ``` python
x = pd.Index([1, 2, 3])
y = pd.Index(['a', 'b', 'c'])
x.union(y)
```
Sorry no stack trace (on a remote server). Raises with an AttributeError and a message about no attribute is_monotonic_increasing. This is on 0.15.1
I think I saw a thing in the release notes about adding that property in 0.15.1 and I can... | {
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... | 5 | 2014-11-13T17:49:29Z | 2021-08-23T15:19:57Z | null | CONTRIBUTOR | null | Running 0.15.1
```
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify DataFrame in place using non-NA values from passed DataFrame. Aligns on indices
Parameters :
other : DataFrame, or object coercible into a DataFrame
join : {‘left’, ‘right’, ‘oute... | {
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] | closed | false | null | [] | null | 4 | 2014-11-13T20:37:20Z | 2014-11-14T15:36:40Z | 2014-11-14T15:36:26Z | NONE | null | Fix for #3505
This PR allows to directly write filters to ga.py `read_ga` function.
Currently only **OR** filters are supported. with this PR we can support directly writing the filters as a string, allowing for example **AND** filtering.
**Example here**
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"descript... | closed | false | null | [] | null | 1 | 2014-11-13T22:06:21Z | 2014-11-13T22:29:18Z | 2014-11-13T22:28:46Z | CONTRIBUTOR | null | Consider the following:
```
> df['lifespan'].head()
0 -7 days +13:01:00
1 -26 days +00:01:00
2 -18 days +00:01:00
3 -7 days +13:01:00
4 -42 days +00:01:00
Name: flight_time_lifespan, dtype: timedelta64[ns]
```
```
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Stacking should now work as expected for the cases described in the bug report (#8584). So we can pass a list of both ints and strs to `DataFrame.stack`, provided the ints are level names. Mixed ints and strs when the ints aren't level names raises `ValueError`, but that's unchanged.
I've added a new hi... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-14T01:16:21Z | 2014-11-14T12:45:52Z | 2014-11-14T12:45:36Z | CONTRIBUTOR | null | Not a huge issue, just skipping an incompatible test when testing with matplotlib 1.2.
closes #8648.
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https://api.github.com/repos/pandas-dev/pandas/issues/8811 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8811/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8811/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8811/events | https://github.com/pandas-dev/pandas/pull/8811 | 48,719,395 | MDExOlB1bGxSZXF1ZXN0MjQ0MTUyMDk= | 8,811 | DOC: Added get_group() example to grouby.html | {
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"id": 233160... | closed | false | null | [] | null | 4 | 2014-11-14T01:36:36Z | 2014-11-15T09:55:31Z | 2014-11-15T09:55:31Z | CONTRIBUTOR | null | Added a get_group example in the Iterating through groups section of grouby.html to demonstrate selecting
single group from a DataFrameGrouBy object
Fixed typo in README
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https://api.github.com/repos/pandas-dev/pandas/issues/8812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8812/events | https://github.com/pandas-dev/pandas/pull/8812 | 48,726,830 | MDExOlB1bGxSZXF1ZXN0MjQ0MTkzNDg= | 8,812 | VIS: plot with `use_index=False` shouldn't plot the index name | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2014-11-14T03:42:40Z | 2014-11-16T14:09:39Z | 2014-11-16T14:09:36Z | CONTRIBUTOR | null | Fixes #8558. As far as I can tell the two plot types where this applies are line and bar plots, if there any others they should also be easy to fix. Bar and line plots no longer include the index name:
``` python
import pandas as pd
from pandas import Series
import matplotlib.pyplot as plt
s = Series([1, 2, 3], index... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8813/events | https://github.com/pandas-dev/pandas/issues/8813 | 48,729,062 | MDU6SXNzdWU0ODcyOTA2Mg== | 8,813 | BUG: Timedelta * Series -> TypeError | {
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"followers_url": "https://api.github.com/users/jr... | 17 | 2014-11-14T04:23:32Z | 2014-11-26T02:30:02Z | 2014-11-26T02:30:02Z | MEMBER | null | I encountered this today when rerunning a script that worked prior to the 0.15 arrival of Timedelta:
```
In [2]: pd.to_timedelta(10, unit='D') * pd.Series(range(10))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8814/events | https://github.com/pandas-dev/pandas/issues/8814 | 48,748,795 | MDU6SXNzdWU0ODc0ODc5NQ== | 8,814 | API: preferred way to check if column/Series has Categorical dtype | {
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What is the preferred way to check for categorical dtype?
I now answered:
```
In [42]: isinstance(df.cat_column.dtype, pd.core.common.CategoricalDtype)
Out[42]: True
In [43]: pd.core.common.is_categorical_dty... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8815 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8815/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8815/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8815/events | https://github.com/pandas-dev/pandas/issues/8815 | 48,761,643 | MDU6SXNzdWU0ODc2MTY0Mw== | 8,815 | API/DOC: unclear scope of index.asof | {
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The docstring of `Index.asof` says:
```
In [73]: pd.Index.asof?
Type: instancemethod
String form: <unbound method Index.asof>
File: c:\anaconda\lib\site-packages\pandas\core\index.py
De... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8816 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8816/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8816/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8816/events | https://github.com/pandas-dev/pandas/pull/8816 | 48,786,651 | MDExOlB1bGxSZXF1ZXN0MjQ0Mzk5OTI= | 8,816 | StataReader: Support sorting categoricals | {
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... | closed | false | null | [] | null | 25 | 2014-11-14T13:07:03Z | 2014-11-18T13:23:22Z | 2014-11-18T13:23:22Z | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8817/events | https://github.com/pandas-dev/pandas/issues/8817 | 48,799,419 | MDU6SXNzdWU0ODc5OTQxOQ== | 8,817 | Unexpected shift() and tshift() behavior with StaticTzInfo datetimes | {
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``` python
import pandas as pd
import pytz
import datetime
dt = datetime.datetime(2014, 11, 14, 0)
dt_est = pytz.timezo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8818 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8818/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8818/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8818/events | https://github.com/pandas-dev/pandas/issues/8818 | 48,805,437 | MDU6SXNzdWU0ODgwNTQzNw== | 8,818 | BUG: retbins options not working with cut | {
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... | closed | false | null | [] | null | 1 | 2014-11-14T15:59:40Z | 2014-11-14T16:02:16Z | 2014-11-14T16:02:07Z | NONE | null | ```
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(100))
pd.cut(s, 10, retbins=True)
```
This throws
```
ValueError: Wrong number of items passed 2, placement implies 100
```
Am I doing something obviously wrong?
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https://api.github.com/repos/pandas-dev/pandas/issues/8819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8819/events | https://github.com/pandas-dev/pandas/issues/8819 | 48,830,810 | MDU6SXNzdWU0ODgzMDgxMA== | 8,819 | ENH: pd.factorize to accept a Dataframe | {
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... | 1 | 2014-11-14T19:36:58Z | 2021-04-11T05:33:22Z | null | CONTRIBUTOR | null | from #8626 discussion
cc @fkaufer
allow `pd.factorize` to take a Dataframe and process with tuples of columns.
further allow `DataFrame.factorize` with a `subset` argument which just calls `pd.factorize`
impl is below (e.g. simple fast-zipping then factorizing after dense conversion)
just needs some tests
```
In [41... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8820/events | https://github.com/pandas-dev/pandas/issues/8820 | 48,850,962 | MDU6SXNzdWU0ODg1MDk2Mg== | 8,820 | io.sql issues with Oracle DB | {
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... | 14 | 2014-11-14T23:00:23Z | 2020-05-16T03:37:49Z | null | NONE | null | With Oracle Database Express Edition 11g Release 2 in use as a SQL DB I have tried to reproduce **Writing DataFrames** example from http://pandas.pydata.org/pandas-docs/stable/io.html#io-sql
``` python
import pandas as pd
from cStringIO import StringIO
from sqlalchemy import create_engine
csv_sample = '''id,Date,Col_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8821 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8821/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8821/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8821/events | https://github.com/pandas-dev/pandas/issues/8821 | 48,858,102 | MDU6SXNzdWU0ODg1ODEwMg== | 8,821 | categories grouping needed for tools.plotting.scatter_matrix | {
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I've modified scatter_matrix a a bit so it supports this functionality. See... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8822/events | https://github.com/pandas-dev/pandas/pull/8822 | 48,860,995 | MDExOlB1bGxSZXF1ZXN0MjQ0ODI1NjM= | 8,822 | BUG: Error when creating sparse dataframe with nan column label | {
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"... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/jr... | 19 | 2014-11-15T01:33:18Z | 2015-04-11T18:35:21Z | 2015-04-11T18:35:12Z | CONTRIBUTOR | null | Right now the following raises an exception:
```
from pandas import DataFrame, Series
from numpy import nan
nan_colname = DataFrame(Series(1.0,index=[0]),columns=[nan])
nan_colname_sparse = nan_colname.to_sparse()
```
This is because sparse dataframes use a dictionary to store information about columns, with the colu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8823/events | https://github.com/pandas-dev/pandas/pull/8823 | 48,864,995 | MDExOlB1bGxSZXF1ZXN0MjQ0ODQxMjU= | 8,823 | ENH: Allow get_dummies to return SparseDataFrame | {
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"de... | closed | false | null | [] | null | 5 | 2014-11-15T03:13:47Z | 2015-04-13T13:24:45Z | 2015-04-13T13:24:45Z | CONTRIBUTOR | null | For dataframes with a large number of unique values, `get_dummies` can use enormous amounts of memory. This provides a `sparse` flag to `get_dummies` which returns a much more memory-efficient structure.
For example:
```
import pandas as pd
df1 = pd.get_dummies(pd.DataFrame({'a':range(1000)}, dtype='category').a, sp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8824/events | https://github.com/pandas-dev/pandas/issues/8824 | 48,893,286 | MDU6SXNzdWU0ODg5MzI4Ng== | 8,824 | Yahoo options failing again in the docs | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2014-11-15T09:58:09Z | 2015-03-06T00:06:18Z | 2015-03-06T00:06:18Z | MEMBER | null | See http://pandas-docs.github.io/pandas-docs-travis/remote_data.html#yahoo-finance-options and http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0151-api
@dstephens99
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https://api.github.com/repos/pandas-dev/pandas/issues/8825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8825/events | https://github.com/pandas-dev/pandas/issues/8825 | 48,919,170 | MDU6SXNzdWU0ODkxOTE3MA== | 8,825 | Pandas Series should provide to_excel method | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-15T17:26:14Z | 2016-12-05T14:39:14Z | 2016-12-05T14:39:14Z | CONTRIBUTOR | null | Hello,
Pandas Series provide `to_csv`method but they doesn't provide `to_excel` method.
Adding `to_excel` will avoid user to convert a `pandas.core.series.Series` to a `pandas.core.frame.DataFrame` in order to output Excel file
DataFrames provide
```
df.to_clipboard df.to_hdf df.to_period df.to_string
d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8826/events | https://github.com/pandas-dev/pandas/issues/8826 | 48,919,979 | MDU6SXNzdWU0ODkxOTk3OQ== | 8,826 | ENH: Add Series.to_list, alias for tolist | {
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"followers_url": "https://api.github... | 2 | 2014-11-15T17:33:31Z | 2018-12-13T01:47:43Z | 2018-12-13T01:47:43Z | CONTRIBUTOR | null | Hello,
IPython autocomplete (tab) shows
```
In [1]: ts.to
ts.to_clipboard ts.to_frame ts.to_period ts.to_string
ts.to_csv ts.to_hdf ts.to_pickle ts.to_timestamp
ts.to_dense ts.to_json ts.to_sparse ts.tolist
ts.to_dict ts.to_msgpack ts.to_sql
```
maybe `tolist` shou... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8827/events | https://github.com/pandas-dev/pandas/pull/8827 | 48,935,614 | MDExOlB1bGxSZXF1ZXN0MjQ0OTUxNjk= | 8,827 | BUG: sql_schema does not generate dialect-specific DDL | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-11-15T22:51:12Z | 2014-12-05T09:07:50Z | 2014-11-17T08:31:13Z | CONTRIBUTOR | null | Closes #8697 .
sqlalchemy `CreateTable` needs to be called slightly differently to create dialect-specific SQL. See http://stackoverflow.com/questions/2128717/sqlalchemy-printing-raw-sql-from-create .
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https://api.github.com/repos/pandas-dev/pandas/issues/8828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8828/events | https://github.com/pandas-dev/pandas/pull/8828 | 48,946,608 | MDExOlB1bGxSZXF1ZXN0MjQ0OTgxNTY= | 8,828 | BUG: query modifies the frame when you compare with `=` | {
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... | 15 | 2014-11-16T05:01:57Z | 2015-12-09T15:18:25Z | 2015-12-09T15:18:25Z | CONTRIBUTOR | null | Fix for #8664. The simplest way to fix this involved adding an `assignment_allowed=True` arg to the internal `eval()` function. All existing behaviour should be preserved. If adding the arg isn't okay I'm not quite sure how else to do it, as it's only once we reach the internal eval function that the expression is actu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8829/events | https://github.com/pandas-dev/pandas/issues/8829 | 48,960,795 | MDU6SXNzdWU0ODk2MDc5NQ== | 8,829 | Pandas Series should provide to_html method | {
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... | 13 | 2014-11-16T08:27:31Z | 2021-04-11T05:34:01Z | null | CONTRIBUTOR | null | Hello,
Pandas Series provide `to_json` method but they doesn't provide `to_html` method.
Adding `to_html` will avoid user to convert a `pandas.core.series.Series` to a `pandas.core.frame.DataFrame` in order to output HTML file using `s.to_frame().to_html(...)` but directly use `s.to_html(...)`
see also https://github... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8830/events | https://github.com/pandas-dev/pandas/pull/8830 | 48,986,353 | MDExOlB1bGxSZXF1ZXN0MjQ1MDE5NzA= | 8,830 | BLD: fix odd usage of cython imports, xref (GH8602) | {
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easy enough, that import should not have been there. we don't import from other pyx files unless its explicity exported (which is in very few cases).
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https://api.github.com/repos/pandas-dev/pandas/issues/8831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8831/events | https://github.com/pandas-dev/pandas/issues/8831 | 48,989,992 | MDU6SXNzdWU0ODk4OTk5Mg== | 8,831 | Pandas(0.15.1) cannot generate an executable file(.exe) by cx_Freeze,why? | {
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... | closed | false | null | [] | null | 1 | 2014-11-16T14:26:57Z | 2014-11-16T14:29:50Z | 2014-11-16T14:29:36Z | NONE | null | I put the pandas upgrade from 0.13 to 0.15,when I use cx_freeze to generate the executable file，I get the error “AttributeError: 'module' object has no attribute '**pyx_capi**‘ ”，but no such error in version 0.13，why？The following are the contents of the file：
import os,pandas as pd
print('hello world！！！')
os.system('p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8832 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8832/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8832/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8832/events | https://github.com/pandas-dev/pandas/pull/8832 | 48,990,143 | MDExOlB1bGxSZXF1ZXN0MjQ1MDI0Njc= | 8,832 | Fix unrecognized 'Z' UTC designator | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8833 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8833/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8833/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8833/events | https://github.com/pandas-dev/pandas/issues/8833 | 48,990,493 | MDU6SXNzdWU0ODk5MDQ5Mw== | 8,833 | BUG: unhelpful parser exception when passing names and dtype | {
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In [1]: data = """1.0 1
...: 2.0 2
...: 3.0 3"""
In [2]: read_csv(StringIO(data),sep='\s+')
Out[2]:
1.0 1
0 2 2
1 3 3
In [3]: read_csv(StringIO(data),sep='\s+',header=None)
Out[3]:
0 1
0 1 1
1 2 2
2 3 3
In [4]: read_csv(StringIO(data),sep='\s+',header=None,names=['a','b'])
Out[4]:
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8834/events | https://github.com/pandas-dev/pandas/pull/8834 | 49,012,107 | MDExOlB1bGxSZXF1ZXN0MjQ1MDU3NTg= | 8,834 | BUG: Bug in csv parsing when passing dtype and names and the parsed data is a different data type (GH8833) | {
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```
In [5]: data = """1.0,a
...: nan,b
...: 3.0,c
...: """
In [8]: read_csv(StringIO(data),sep=',',names=['A','B'])
Out[8]:
A B
0 1 a
1 NaN b
2 3 c
In [9]: read_csv(StringIO(data),sep=',',names=['A','B'],dtype={'A' : int})
ValueError: cannot safely convert passed user dtype of <i8... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8835 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8835/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8835/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8835/events | https://github.com/pandas-dev/pandas/pull/8835 | 49,016,641 | MDExOlB1bGxSZXF1ZXN0MjQ1MDc0MjQ= | 8,835 | DOC GH3508 (bis) added basic documentation of google analytics in remot... | {
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closes #3508
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https://api.github.com/repos/pandas-dev/pandas/issues/8836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8836/events | https://github.com/pandas-dev/pandas/pull/8836 | 49,034,128 | MDExOlB1bGxSZXF1ZXN0MjQ1MTQwMTg= | 8,836 | FIX: Correct Categorical behavior in StataReader | {
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xref #8816
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https://api.github.com/repos/pandas-dev/pandas/issues/8837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8837/events | https://github.com/pandas-dev/pandas/pull/8837 | 49,034,568 | MDExOlB1bGxSZXF1ZXN0MjQ1MTQxMDk= | 8,837 | ENH: Timestamp: Support addl datetime classmethods | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-17T04:13:57Z | 2014-11-17T10:56:49Z | 2014-11-17T10:56:44Z | CONTRIBUTOR | null | - `utcfromtimestamp`,`timestamp`, and `combine` now supported
Closes #5351.
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https://api.github.com/repos/pandas-dev/pandas/issues/8838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8838/events | https://github.com/pandas-dev/pandas/issues/8838 | 49,069,456 | MDU6SXNzdWU0OTA2OTQ1Ng== | 8,838 | Cannot add column to copy of DataFrame | {
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The original DataFrame looks something like this:
``` python
>>> df.head()
```
| | date | text1 | number1 | text2 | text3 | text4 |
| --- | --- | --- | --- | --- | --- | --- |
| 0 | 2014-10-02 | foo | NaN | NaN | NaN | NaN |
| 1 | 20... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8839 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8839/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8839/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8839/events | https://github.com/pandas-dev/pandas/issues/8839 | 49,100,718 | MDU6SXNzdWU0OTEwMDcxOA== | 8,839 | ENH: add axis parameter to between_time, at_time | {
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"followers_url": "https://api.github... | 2 | 2014-11-17T15:09:52Z | 2018-11-19T01:21:33Z | 2018-11-19T01:21:33Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26973393/between-time-not-working-on-a-pandas-panel/26974754#26974754)
xref #2141 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8840 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8840/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8840/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8840/events | https://github.com/pandas-dev/pandas/pull/8840 | 49,101,471 | MDExOlB1bGxSZXF1ZXN0MjQ1NDE3NzU= | 8,840 | TST: add tests for index.groupby (GH5620) | {
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Also added a docstring.
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https://api.github.com/repos/pandas-dev/pandas/issues/8841 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8841/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8841/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8841/events | https://github.com/pandas-dev/pandas/pull/8841 | 49,113,833 | MDExOlB1bGxSZXF1ZXN0MjQ1NDgyODk= | 8,841 | ENH: Allow unicode separators in to_csv & read_csv | {
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... | 14 | 2014-11-17T16:40:39Z | 2015-05-09T16:07:09Z | 2015-05-09T16:07:09Z | CONTRIBUTOR | null | - single byte UTF8 separators will always work if utf8 encoding is specified (i.e. seps in range(256))
- single byte delimiters with encodings will always work in Python 3, but will not work in Python 2 if they are multibyte when coerced to UTF8
- multi byte delimiters with encodings will not work on reads with C engin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8842 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8842/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8842/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8842/events | https://github.com/pandas-dev/pandas/issues/8842 | 49,118,275 | MDU6SXNzdWU0OTExODI3NQ== | 8,842 | ME breakpoints in fama french factors library | {
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In this zip, there is only one file "ME_Breakpoints.txt". How should this be handed?
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https://api.github.com/repos/pandas-dev/pandas/issues/8843 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8843/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8843/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8843/events | https://github.com/pandas-dev/pandas/pull/8843 | 49,143,342 | MDExOlB1bGxSZXF1ZXN0MjQ1NjU2NDA= | 8,843 | BUG: Implement step in slice StringMethod | {
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Turned out to be dead simple too :)
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https://api.github.com/repos/pandas-dev/pandas/issues/8844 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8844/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8844/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8844/events | https://github.com/pandas-dev/pandas/issues/8844 | 49,155,022 | MDU6SXNzdWU0OTE1NTAyMg== | 8,844 | BUG: .stack(dropna=False) looks through views incorrectly for dataframe views with multi-index columns | {
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In the example below, `[11]` is incorrectly reflecting columns in `dfa` that should not be visible to `dfa1`. Note that this is not a problem when the columns are not a multi-index (`[5]` and `[6]`), or when `dropna=True` (the default; `[10]` and `[12]`).
```
Python 3.4.2 (v3.4.2:ab2c023a9432, Oct 6 2014,... | {
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xref #841
xref #7873
xref #7223
xref #8815
Building on @immerrr's excellent refactor in #8753, I would like to propose adding a `get_nearest` method to pandas.Index that does nearest neighbor lookups. The idea is that nearest neighbor lookups are usually the desirable/sane thing to do when using inexact in... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8846 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8846/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8846/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8846/events | https://github.com/pandas-dev/pandas/issues/8846 | 49,196,328 | MDU6SXNzdWU0OTE5NjMyOA== | 8,846 | splitting pandas dataframe - np.array_split error | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-18T08:56:24Z | 2014-11-18T11:56:11Z | 2014-11-18T11:56:11Z | NONE | null | I just noticed that after upgrading to numpy 1.9.0, when I'm trying to split dataframe with pandas 0.15.1 with the code:
```
split_dfs = np.array_split(big_df,8)
```
I get the error:
```
Traceback (most recent call last):
File "./test.py", line 127, in <module>
split_dfs = np.array_split(big_df,8)
File "... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8848 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8848/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8848/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8848/events | https://github.com/pandas-dev/pandas/issues/8848 | 49,217,783 | MDU6SXNzdWU0OTIxNzc4Mw== | 8,848 | ENH: add missing methods to .dt for Period | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-18T12:26:04Z | 2016-02-27T16:03:10Z | 2016-02-27T16:02:50Z | CONTRIBUTOR | null | methods
- to_timestamp
- asfreq
properties
- start_time/end_time
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https://api.github.com/repos/pandas-dev/pandas/issues/8849 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8849/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8849/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8849/events | https://github.com/pandas-dev/pandas/issues/8849 | 49,234,997 | MDU6SXNzdWU0OTIzNDk5Nw== | 8,849 | reindex from a duplicate axis: inconsistent behaviour | {
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] | open | false | null | [] | null | 4 | 2014-11-18T14:14:21Z | 2021-04-11T05:35:58Z | null | NONE | null | The behaviour below occurs in version `'0.15.1'`.
When a series has a duplicate index, the method `reindex` will raise an exception, unless the index passed to `reindex` is identical to the series' index.
I propose that when a series has a duplicate index, the method `reindex` should always raise an exception, becaus... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8850/events | https://github.com/pandas-dev/pandas/issues/8850 | 49,239,816 | MDU6SXNzdWU0OTIzOTgxNg== | 8,850 | BUG: Accessing DataFrame multi-index column *seems* to modify its content | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-18T14:48:27Z | 2014-11-20T22:55:08Z | 2014-11-20T22:55:08Z | NONE | null | In the example below, `[13]` gives a different result than `[11]`, even though you would think that `[12]` has no effect on data.
```
Python 2.7.8 (default, Oct 19 2014, 16:02:00)
Type "copyright", "credits" or "license" for more information.
IPython 2.1.0 -- An enhanced Interactive Python.
? -> Introduction... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8851 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8851/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8851/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8851/events | https://github.com/pandas-dev/pandas/issues/8851 | 49,293,388 | MDU6SXNzdWU0OTI5MzM4OA== | 8,851 | API/ENH: df.stack(level=[0,1], dropna=False) behavior | {
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... | 3 | 2014-11-18T21:08:25Z | 2018-07-06T21:15:40Z | 2018-07-06T21:15:39Z | CONTRIBUTOR | null | `df.stack(level=[0,1], dropna=False)` appears to be equivalent to `df.stack(level=0, dropna=False).stack(level=0, dropna=False)`. While this makes a certain amount of sense, it results in additional rows that in many cases are probably not expected/desired. In particular, `df.stack(level=[0,1], dropna=False)` is not eq... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8852 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8852/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8852/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8852/events | https://github.com/pandas-dev/pandas/issues/8852 | 49,305,593 | MDU6SXNzdWU0OTMwNTU5Mw== | 8,852 | BUG: plot with kind=scatter fails when checking if an array is in the DataFrame | {
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"followers_url": "https://api.github.com/users/jr... | 16 | 2014-11-18T22:59:04Z | 2014-12-03T22:33:53Z | 2014-12-03T22:33:53Z | CONTRIBUTOR | null | ``` python
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
c = np.array([1, 0])
df.plot(kind='scatter', x='A', y='B', c=c, cmap='spring')
```
fails with a TypeError.
we check `elif c in self.data.columns` which tries to hash the array.
(I know I can just add the array as a column to the DataFrame)
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https://api.github.com/repos/pandas-dev/pandas/issues/8853 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8853/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8853/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8853/events | https://github.com/pandas-dev/pandas/pull/8853 | 49,323,281 | MDExOlB1bGxSZXF1ZXN0MjQ2NjMwMzg= | 8,853 | BUG: type change breaks BlockManager integrity | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2014-11-19T03:07:16Z | 2014-11-21T01:00:59Z | 2014-11-20T22:55:08Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/8850
on master:
```
>>> cols = MultiIndex.from_tuples([('1st', 'a'), ('2nd', 'b'), ('3rd', 'c')])
>>> df = DataFrame([[1.0, 2, 3], [4.0, 5, 6]], columns=cols)
>>> df['2nd'] = df['2nd'] * 2.0 # type change in block manager
/usr/lib/python3.4/site-packages/numpy/lib/funct... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8854 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8854/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8854/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8854/events | https://github.com/pandas-dev/pandas/pull/8854 | 49,334,475 | MDExOlB1bGxSZXF1ZXN0MjQ2Njc2Mjc= | 8,854 | BUG: Panel4D setitem by indexer to Series fails for mixed-dtype | {
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... | 2 | 2014-11-19T06:02:11Z | 2015-05-09T16:06:55Z | 2015-05-09T16:06:55Z | CONTRIBUTOR | null | This is another tweak to `_setitem_with_indexer` that allows it better to comprehend higher dimensional frames of mixed-dtype. With this tweak setting by indexer to a Series now works. Unfortunately setting to a DataFrame is still broken. I plan to open an issue for that.
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https://api.github.com/repos/pandas-dev/pandas/issues/8855 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8855/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8855/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8855/events | https://github.com/pandas-dev/pandas/pull/8855 | 49,340,728 | MDExOlB1bGxSZXF1ZXN0MjQ2Njk2NDI= | 8,855 | BUG: DataFrame.stack(..., dropna=False) with partial MultiIndex. | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-11-19T07:09:27Z | 2014-12-02T20:13:13Z | 2014-12-02T11:14:42Z | CONTRIBUTOR | null | Closes #8844
Fixes `DataFrame.stack(..., dropna=False)` when the columns consist of a "partial" `MultiIndex`, i.e. one in which the `labels` don't reference all the `levels`.
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``` python
In [11]: s = pd.Series(np.arange(10), pd.MultiIndex.from_product(([0, 1], list('abcde'))))
In [12]: s
Out[12]:
0 a 0
0 b 1
0 c 2
0 d 3
0 e 4
1 a 5
1 b 6
1 c 7
1 d 8
1 e 9
dtype: int64
In [13]: s.iloc[::4] = 100; s
Out[13]:
0 a 100
0 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8857 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8857/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8857/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8857/events | https://github.com/pandas-dev/pandas/pull/8857 | 49,413,091 | MDExOlB1bGxSZXF1ZXN0MjQ2OTg4Mzg= | 8,857 | Clarify encoding kwarg on to_csv | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8858/events | https://github.com/pandas-dev/pandas/pull/8858 | 49,415,930 | MDExOlB1bGxSZXF1ZXN0MjQ3MDAxNTI= | 8,858 | DOC: Clean up Stata documentation | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8859 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8859/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8859/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8859/events | https://github.com/pandas-dev/pandas/issues/8859 | 49,424,939 | MDU6SXNzdWU0OTQyNDkzOQ== | 8,859 | reading only header row to help map data types to file headers | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8860 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8860/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8860/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8860/events | https://github.com/pandas-dev/pandas/issues/8860 | 49,455,442 | MDU6SXNzdWU0OTQ1NTQ0Mg== | 8,860 | Maintaining the order of the categorical variable that is passed into pd.crosstab | {
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Currently when we do a `crosstab`, the distinct values in each column is reported in the lexical order. But crosstabs are usually useful when we have categorical data (that may have an inherent ordering).
``` python
import pandas as pd
d = {'MAKE' : pd.Series(['Honda', 'Acura', 'T... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8861/events | https://github.com/pandas-dev/pandas/issues/8861 | 49,462,823 | MDU6SXNzdWU0OTQ2MjgyMw== | 8,861 | need help to clarify ewma() using adjust =TRUE | {
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Can someone please help me understand how `adjust = True` in the function `ewma()` works mathematically?
I understand the idea of simple exponential smoothing and it's basic formula when `adjust = FALSE`,
then alpha is constant, as in the equation:
![daum_equation_1416434433349](https://cloud.githubusercontent.co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8862 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8862/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8862/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8862/events | https://github.com/pandas-dev/pandas/issues/8862 | 49,515,792 | MDU6SXNzdWU0OTUxNTc5Mg== | 8,862 | Import Error: cannot import name TimeDelta | {
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How to resolve this??
thanks
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https://api.github.com/repos/pandas-dev/pandas/issues/8863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8863/events | https://github.com/pandas-dev/pandas/issues/8863 | 49,540,003 | MDU6SXNzdWU0OTU0MDAwMw== | 8,863 | PERF: provide c-parsing of timedelta string formats | {
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... | 1 | 2014-11-20T12:28:00Z | 2015-06-18T16:15:21Z | 2015-06-18T16:15:21Z | CONTRIBUTOR | null | string Timedelta parsing is done via regexes now
would have a large perf boost to in-line parse.
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https://api.github.com/repos/pandas-dev/pandas/issues/8864 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8864/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8864/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8864/events | https://github.com/pandas-dev/pandas/issues/8864 | 49,602,809 | MDU6SXNzdWU0OTYwMjgwOQ== | 8,864 | Undesired auto sort when adding two multiindexes | {
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timestamp sequence attribute
2014-11-12 17:02:23.635000-06:00 0 price
count
quantity
idx1 =
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```
>>> pd.Timestamp('2000-01-01') - pd.Timestamp('1999-01-01')
datetime.timedelta(365)
```
Somewhat related: #8813
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In [15]: s = Series(np.arange(8),index=pd.MultiIndex.from_product([list('ab'),range(2),pd.date_range('20130101',periods=2)],names=['one','two','three']))
In [16]: s
Out[16]:
one two three
a 0 2013-01-01 0
2013-01-02 1
1 2013-01-01 2
2013-01-02 3
b 0 2013-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8867 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8867/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8867/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8867/events | https://github.com/pandas-dev/pandas/pull/8867 | 49,644,532 | MDExOlB1bGxSZXF1ZXN0MjQ4MTUwMTQ= | 8,867 | BUG: missing nose import for skip test | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-21T02:24:20Z | 2014-11-21T14:05:16Z | 2014-11-21T14:05:11Z | CONTRIBUTOR | null | nose.SkipTest used but nose not imported.
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https://api.github.com/repos/pandas-dev/pandas/issues/8868 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8868/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8868/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8868/events | https://github.com/pandas-dev/pandas/issues/8868 | 49,675,835 | MDU6SXNzdWU0OTY3NTgzNQ== | 8,868 | BUG: sort=False ignored when grouping with a categorical column | {
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I stumbled upon the following behavior of groubpy with categorical, which seems at least inconsistent with the way groupby usually operates.
When grouping on a string type column with `sort=False`, the order of the groups is the order in which the keys first appear in the column.
However, when group... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8869 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8869/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8869/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8869/events | https://github.com/pandas-dev/pandas/issues/8869 | 49,679,661 | MDU6SXNzdWU0OTY3OTY2MQ== | 8,869 | BUG: Multi grouper containing a categorical not dropped from index when using groupby with as_index=False | {
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... | 3 | 2014-11-21T10:14:22Z | 2018-05-01T15:09:11Z | 2018-05-01T15:09:11Z | NONE | null | Hello,
The following example definitely seems like a bug. The grouper is not dropped from the index of the resulting DataFrame, even when `as_index = False`.
Actually, even the aggregation step completely fails, so there may be more to it, as shown in this example.
``` python
import pandas as pd
d = {'foo': [10, 8, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8870 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8870/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8870/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8870/events | https://github.com/pandas-dev/pandas/issues/8870 | 49,685,038 | MDU6SXNzdWU0OTY4NTAzOA== | 8,870 | BUG: groupby with categorical drops empty groups when aggregating over a series | {
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- [ ] sorting via a returned Interval-like-Index (string based)
Hello,
When grouping a DataFrame over more than one column including a categorical, the empty groups are kept in the aggregation result. A test for this behaviour was int... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8871/events | https://github.com/pandas-dev/pandas/issues/8871 | 49,701,359 | MDU6SXNzdWU0OTcwMTM1OQ== | 8,871 | Inconsistent behavior when accessing None value | {
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] | closed | false | null | [] | null | 10 | 2014-11-21T14:23:26Z | 2019-10-06T00:47:41Z | 2019-10-06T00:47:41Z | NONE | null | I'm working with OpenStreetMap data queried from a postgresql database. When I extract one row and try to access the name column, the resulting value is inconsistent based on the access method:
the row:
```
osm_id 1895277228
name None
admin_level -1
leisure ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8872 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8872/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8872/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8872/events | https://github.com/pandas-dev/pandas/issues/8872 | 49,716,217 | MDU6SXNzdWU0OTcxNjIxNw== | 8,872 | ImportError: DLL load failed using latest | {
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] | closed | false | null | [] | null | 2 | 2014-11-21T16:19:59Z | 2014-11-21T17:33:14Z | 2014-11-21T17:33:14Z | NONE | null | I am building a GUI application that uses pandas for processing data. I am now trying to deploy this application using cx_freeze.
Initially I encountered the issue as described in https://github.com/pydata/pandas/issues/8602 and fixed by https://github.com/pydata/pandas/pull/8830.
I was using pandas as installed thro... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8873 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8873/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8873/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8873/events | https://github.com/pandas-dev/pandas/issues/8873 | 49,722,063 | MDU6SXNzdWU0OTcyMjA2Mw== | 8,873 | ERR: bytes partially converted with convert_objects(convert_numeric=True) | {
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```
In [3]: df = pd.DataFrame([b'1',b'1'])
In [4]: df.convert_objects(convert_numeric=True)
Out[4]:
0
0 NaN
1 1
```
Python 3, obviously; happens on Windo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8874 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8874/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8874/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8874/events | https://github.com/pandas-dev/pandas/issues/8874 | 49,740,597 | MDU6SXNzdWU0OTc0MDU5Nw== | 8,874 | HDF corrupts data when using complib='blosc:zlib' | {
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"followers_url": "https://api.github.... | 8 | 2014-11-21T19:49:36Z | 2015-06-12T21:50:02Z | 2015-06-12T21:50:02Z | CONTRIBUTOR | null | I'm not sure if this is supported or not -- it isn't in the doc string for HDFStore, but it seems to be allowed by the HDFStore (nothing is raised).
Unfortunately so far I can only get it to show the bad behavior on a proprietary dataset, which is storing a `pd.Panel` which contains items of of mixed types.
Some of... | {
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... | 9 | 2014-11-22T02:16:30Z | 2021-04-11T05:39:14Z | null | MEMBER | null | - [ ] Add `__inverse__` to Index.
- [x] Change duplicated to return np.array (PR #9112)
Related to #7979
Because `Index` is no more the subclass of `np.array`, `Index.duplicated` returns `Index` with `dtype=object`. And `Index` don't accepts logical not.
```
idx = pd.Index([1, 2, 1, 3])
idx.duplicated()
# Index(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8876 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8876/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8876/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8876/events | https://github.com/pandas-dev/pandas/pull/8876 | 49,770,150 | MDExOlB1bGxSZXF1ZXN0MjQ4ODI4NzE= | 8,876 | DOC: Add where and mask to API doc | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8877/events | https://github.com/pandas-dev/pandas/pull/8877 | 49,772,532 | MDExOlB1bGxSZXF1ZXN0MjQ4ODM5MjI= | 8,877 | DOC: Suppress warnings in visualization.rst | {
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I understand there are 2 warnings:
1. Warning in /Users/sin/Documents/Git/pandas/doc/source/visualization.rst at block ending on line 1275
This is raised from `mpl` as plotting empty axes for demonstration purpose. Thus simply suppressed.
2. Warning in /Users/sin/Documents/Git/pa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8878/events | https://github.com/pandas-dev/pandas/issues/8878 | 49,784,326 | MDU6SXNzdWU0OTc4NDMyNg== | 8,878 | python make.py html failure | {
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Traceback (most recent call last):
File "/home/nils/local/bin/sphinx-build", line 5, in <module>
from pkg_resources import load_entry_point
File "/usr/lib/python2.7/site-packages/pkg_resources.py", line 2829, in <module>
working_set.require(**requires**)... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8880/events | https://github.com/pandas-dev/pandas/issues/8880 | 49,797,278 | MDU6SXNzdWU0OTc5NzI3OA== | 8,880 | PERF/API/BUG: index memory not releasing when manipulated by numpy functions | {
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... | 0 | 2014-11-22T16:45:33Z | 2021-04-11T05:40:34Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/27074469/memory-leak-in-python-pandas-reshuffling-index
```
In [1]: df = DataFrame(np.random.randn(10000))
In [2]: def f(df):
...: for dfi in np.array_split(df,100):
...: shuffle = dfi.reindex(np.random.permutation(dfi.index))
...: one = shuffle.iloc[:50... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8881 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8881/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8881/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8881/events | https://github.com/pandas-dev/pandas/issues/8881 | 49,807,330 | MDU6SXNzdWU0OTgwNzMzMA== | 8,881 | ENH: Could Numba be used to make the groupby aggregation functions faster? | {
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... | 8 | 2014-11-22T22:48:30Z | 2017-09-24T19:31:39Z | 2017-09-24T19:31:39Z | NONE | null | I have been doing some experiments with Numba and groupby operations such as max() and sum().
Using Numba might make them faster, though my analysis in incomplete as my functions are returning results as ndarrays, rather than Pandas Series (I don't know how large the overhead of creating the Series result is, and can'... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8882 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8882/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8882/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8882/events | https://github.com/pandas-dev/pandas/issues/8882 | 49,828,875 | MDU6SXNzdWU0OTgyODg3NQ== | 8,882 | Release the GIL in Cython code | {
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... | 4 | 2014-11-23T15:43:06Z | 2015-06-30T10:55:04Z | 2015-06-30T10:55:04Z | CONTRIBUTOR | null | Pandas could release the GIL in parts of the Cython code. This would enable the parallel use of multiple cores with threading.
From off-line conversation with @jreback @cpcloud
http://lbolla.info/blog/2013/12/23/python-threads-cython-gil
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https://api.github.com/repos/pandas-dev/pandas/issues/8883 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8883/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8883/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8883/events | https://github.com/pandas-dev/pandas/pull/8883 | 49,841,803 | MDExOlB1bGxSZXF1ZXN0MjQ5MDc1NjA= | 8,883 | REF: refactor **kwargs usage | {
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among changes:
[tests/test_generic.py](https://github.com/pydata/pandas/blob/0a2ea0ac39a74771d54b91522728f6092b0ef897/pandas/tests/test_generic.py#L603) `downcast` is misspelled `donwcast`, but no error is raised, because of how `**kwargs` is used.
[core/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8884 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8884/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8884/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8884/events | https://github.com/pandas-dev/pandas/pull/8884 | 49,856,928 | MDExOlB1bGxSZXF1ZXN0MjQ5MTM5MDY= | 8,884 | BUG/ENH: cleanup for Timedelta arithmetic | {
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... | closed | false | null | [] | {
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Fixes #5963
Fixes #5436
If the other argument has a dtype attribute, I assume that it is ndarray-like
and convert the `Timedelta` into a `np.timedelta64` object. Alternatively, we
could just return `NotImplemented` and let the other type handle it, but this
has the bonus of making `Timedelta` compatible w... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8885 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8885/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8885/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8885/events | https://github.com/pandas-dev/pandas/pull/8885 | 49,876,431 | MDExOlB1bGxSZXF1ZXN0MjQ5MjQ0NjA= | 8,885 | ENH/VIS: Pass DataFrame column to size argument in DataFrame.scatter | {
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... | 15 | 2014-11-24T10:47:40Z | 2015-05-14T15:47:47Z | 2015-05-09T20:11:46Z | CONTRIBUTOR | null | Originally discussed in #8244. Currently this only uses the column from the dataframe if the `s` argument is a string, supporting int column names is ambiguous and liable to break existing code, but if people can think of a good way to do this I'll try to add it.
Example output:
``` python
import pandas as pd
import ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8886 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8886/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8886/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8886/events | https://github.com/pandas-dev/pandas/issues/8886 | 49,912,167 | MDU6SXNzdWU0OTkxMjE2Nw== | 8,886 | Strange output from timedelta_range for integer start/end | {
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... | 6 | 2014-11-24T16:40:16Z | 2021-04-11T05:40:57Z | null | MEMBER | null | Here I expected a TimedeltaIndex with 11 elements from 0 hours through 11 hours:
```
>>> pd.timedelta_range(0, 10, freq='H')
<class 'pandas.tseries.tdi.TimedeltaIndex'>
['0 days']
Length: 1, Freq: <Hour>
```
It appears that `freq` is taken to refer only to the step size, not the start/stop, and the unit defaults to `... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8887 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8887/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8887/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8887/events | https://github.com/pandas-dev/pandas/issues/8887 | 49,924,654 | MDU6SXNzdWU0OTkyNDY1NA== | 8,887 | ERR: HDF5 serialization of datelike-object dtypes should raise | {
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... | 4 | 2014-11-24T18:28:40Z | 2020-05-16T20:14:08Z | null | NONE | null | UPDATE:
```
In [157]: problem_date = old.dob.loc[4231354]
In [158]: problem_date
Out[158]: datetime.date(2939, 6, 2)
In [159]: test_series = pd.Series([problem_date])
In [160]: pd.to_datetime(test_series)
Out[160]:
0 2939-06-02
dtype: object
```
It seems this is the source of the problem. I think there may be ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8888 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8888/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8888/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8888/events | https://github.com/pandas-dev/pandas/issues/8888 | 49,929,979 | MDU6SXNzdWU0OTkyOTk3OQ== | 8,888 | Implement StringMethod slice_replace | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-11-24T19:17:29Z | 2015-01-08T01:59:16Z | 2015-01-08T01:59:16Z | CONTRIBUTOR | null | The `StringMethod` `slice_replace` currently raises `NotImplementedError`. Couldn't find an issue for it, so opening this. PR with implementation in the works.
Could use thoughts on how to handle slices that return an empty string on some subset of strings - should the replacement string appear or not? This could happ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8889 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8889/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8889/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8889/events | https://github.com/pandas-dev/pandas/issues/8889 | 49,931,852 | MDU6SXNzdWU0OTkzMTg1Mg== | 8,889 | Timezone Problem in MultiIndexing | {
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"descript... | closed | false | null | [] | null | 7 | 2014-11-24T19:34:04Z | 2014-11-25T15:03:46Z | 2014-11-25T15:03:46Z | NONE | null | ```
dt = datetime.datetime(2014,11,24,12,0,0)
dt = dt.replace(tzinfo=td.CHICAGO_TZ)
mi = pd.MultiIndex.from_product([[dt],[1],[2]])
print mi
print
print mi.get_level_values(0)[0]
print
```
When I use get_level_values function and try to filter dataframe based on time index(which is the first level), it always loses ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8890 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8890/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8890/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8890/events | https://github.com/pandas-dev/pandas/issues/8890 | 49,945,943 | MDU6SXNzdWU0OTk0NTk0Mw== | 8,890 | BUG: iterator of DatetimeIndex broken with tzoffset timezone | {
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The trigger is the `tzoffset` timezone. This bug can be reproduced as follows:
```
In [86]: index = pd.date_range("2012-01-01", periods=3, freq='H', tz=dateutil.tz.tzoffset(None, -28800))
In [87]: index
Out[87]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00-08:00, ..., 2012-01-01 02:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8891 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8891/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8891/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8891/events | https://github.com/pandas-dev/pandas/issues/8891 | 49,960,180 | MDU6SXNzdWU0OTk2MDE4MA== | 8,891 | IO: ensure compatibility with 01-24 date formats | {
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"f... | 1 | 2014-11-25T00:06:54Z | 2019-12-18T03:04:45Z | null | CONTRIBUTOR | null | In many engineering and measurement applications, the time is counted from 01-24 to indicate the end of a measurement or integration period.
This issue is to propose that pandas IO functions support importing such timestamps from CSV, Excel etc. out of the box and furthermore, Series can have a flag indicating the ref... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8892 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8892/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8892/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8892/events | https://github.com/pandas-dev/pandas/issues/8892 | 50,050,997 | MDU6SXNzdWU1MDA1MDk5Nw== | 8,892 | Cannot print DataFrame with non-ascii column names in ipython 3.0.0 | {
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"color": "44444... | closed | false | null | [] | null | 2 | 2014-11-25T16:34:46Z | 2019-10-09T05:49:56Z | 2019-10-09T05:49:56Z | NONE | null | ``` python
import pandas
df = pandas.DataFrame({u'превед':[1,2,3]})
df
```
I get:
```
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-1-114199e6a3fa> in <module>()
3 print pandas.__version__
4 df = pandas.DataFrame({u'превед':[1,2,3]})
----> 5 df
/home/gutman/tr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8893 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8893/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8893/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8893/events | https://github.com/pandas-dev/pandas/issues/8893 | 50,059,008 | MDU6SXNzdWU1MDA1OTAwOA== | 8,893 | MultiIndexing Issue | {
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"descript... | closed | false | null | [] | null | 4 | 2014-11-25T17:31:50Z | 2014-11-26T00:44:18Z | 2014-11-26T00:44:18Z | NONE | null | Let 'b' be a large dataframe with MulitiIndex.
I pull the 4th block based on the 1st level of the index, and I got the block fine.
> > > a = b.ix[b.index.levels[0][4]]
Then I printed the index of the block, which also seemed fine, code and result as follows:
> > > print a.index
sequence attribute
4 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8894 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8894/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8894/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8894/events | https://github.com/pandas-dev/pandas/pull/8894 | 50,095,740 | MDExOlB1bGxSZXF1ZXN0MjUwNDc2ODI= | 8,894 | fix to_datetime default error suppression | {
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... | 5 | 2014-11-25T22:54:50Z | 2015-05-09T16:06:43Z | 2015-05-09T16:06:43Z | NONE | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8895/events | https://github.com/pandas-dev/pandas/pull/8895 | 50,102,982 | MDExOlB1bGxSZXF1ZXN0MjUwNTIxOTM= | 8,895 | modified: pandas/io/ga.py | {
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"description": "pandas-gbq compatability",
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"name": "IO Google",
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] | closed | false | null | [] | null | 5 | 2014-11-26T00:23:40Z | 2015-03-25T23:51:50Z | 2015-03-25T23:20:41Z | NONE | null | ```
ENH
I've made a number of alterations to this module to improve the
usability of it.
1. A feature has been added to the 'filters' keyword, in that
compound filters can now be created with AND and OR combinations
as in the API, while still allowing the user to create
the... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8896/events | https://github.com/pandas-dev/pandas/pull/8896 | 50,107,461 | MDExOlB1bGxSZXF1ZXN0MjUwNTUwNDE= | 8,896 | COMPAT: windows compat for tests for dtype inference in parser xref (GH8833) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8897/events | https://github.com/pandas-dev/pandas/issues/8897 | 50,128,734 | MDU6SXNzdWU1MDEyODczNA== | 8,897 | read_html can't parse the output of to_html() when index has name | {
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"default": false,
"description": "read_html, to_html, Styl... | open | false | {
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... | 2 | 2014-11-26T07:52:28Z | 2015-03-06T00:05:24Z | null | NONE | null | Here is the code:
```
import pandas as pd
import numpy as np
import io
df = pd.DataFrame(np.random.rand(6, 3), columns=list("ABC"))
df.index = list("abcdef")
df.index.name = "index"
df2 = pd.read_html(df.to_html(), index_col=0)
print df2[0]
```
Here is the output:
```
A B C index Unnamed:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8898/events | https://github.com/pandas-dev/pandas/pull/8898 | 50,186,806 | MDExOlB1bGxSZXF1ZXN0MjUwODgzOTM= | 8,898 | WIP: add multi-multi index support to join | {
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... | 4 | 2014-11-26T14:45:41Z | 2017-02-25T16:08:07Z | 2015-05-09T16:06:08Z | NONE | null | closes #6360
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https://api.github.com/repos/pandas-dev/pandas/issues/8899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8899/events | https://github.com/pandas-dev/pandas/issues/8899 | 50,219,871 | MDU6SXNzdWU1MDIxOTg3MQ== | 8,899 | DataFrame.describe() add number of missing values | {
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] | closed | false | null | [] | null | 2 | 2014-11-26T19:46:57Z | 2014-11-27T18:18:00Z | 2014-11-26T20:45:54Z | CONTRIBUTOR | null | Hi,
I suggest to add the number of missing values to
pandas.DataFrame.describe()
regards
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https://api.github.com/repos/pandas-dev/pandas/issues/8900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8900/events | https://github.com/pandas-dev/pandas/issues/8900 | 50,220,554 | MDU6SXNzdWU1MDIyMDU1NA== | 8,900 | pandas.DataFrame.is.na() | {
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"color": "0052cc"... | closed | false | null | [] | null | 3 | 2014-11-26T19:54:45Z | 2014-11-27T18:12:58Z | 2014-11-26T20:44:22Z | CONTRIBUTOR | null | Hi,
I suggest a method is.na() for pandas.DataFrame that gives back an boolean array.
If a value in the DataFrame is NaN the corresponding element is 1 otherwise 0.
That would make it easy to determine the number of missing elements in a DataFrame.
regards
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https://api.github.com/repos/pandas-dev/pandas/issues/8901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8901/events | https://github.com/pandas-dev/pandas/issues/8901 | 50,222,941 | MDU6SXNzdWU1MDIyMjk0MQ== | 8,901 | Subset of DataFrame should be a DataFrame? | {
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] | closed | false | null | [] | null | 3 | 2014-11-26T20:19:50Z | 2014-11-28T09:41:50Z | 2014-11-26T20:48:08Z | CONTRIBUTOR | null | Hi,
I realized that having a pandas.DataFame object DF with columns 'a','b','c'
DF['a'] is not a DataFrame but a pandas.core.series.Series
Is there a reason for that? I think, it would make the dealing with DataFrames more convenient when a DataFrame would be returned. With header and indexes.
regards
| {
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