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https://api.github.com/repos/pandas-dev/pandas/issues/9302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9302/events | https://github.com/pandas-dev/pandas/issues/9302 | 54,789,287 | MDU6SXNzdWU1NDc4OTI4Nw== | 9,302 | Plotting secondary axis | {
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Pandas 0.15.2 documentation explains how to create one plot with two axes (http://pandas.pydata.org/pandas-docs/dev/visualization.html?highlight=visualization#plotting-on-a-secondary-y-axis). For example:
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https://api.github.com/repos/pandas-dev/pandas/issues/9303 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9303/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9303/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9303/events | https://github.com/pandas-dev/pandas/pull/9303 | 54,793,544 | MDExOlB1bGxSZXF1ZXN0Mjc2MjQ1MDY= | 9,303 | BUG 9188: concat of all-nan with empty frame produces object dtype | {
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```
In [4]: df_1 = pd.DataFrame({"Row":[0,1,1], "EmptyCol":np.nan, "NumberCol":[1,2,3]})
In [5]: df_2 = pd.DataFrame(columns = df_1.columns)
In [6]: df_concat = pd.concat([df_1, df_2], axis=0)
In [7]: df_1.dtypes
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https://api.github.com/repos/pandas-dev/pandas/issues/9304 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9304/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9304/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9304/events | https://github.com/pandas-dev/pandas/issues/9304 | 54,794,700 | MDU6SXNzdWU1NDc5NDcwMA== | 9,304 | additional keys in groupby indices when NAs are present | {
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In [386]: h = pd.DataFrame({'a':[1,2,1,np.nan,1], 'b':[1,2,3,3,2], 'c':[2,3,1,4,2]})
In [387]: gh=h.groupby(['a', 'b'])
In [388]: gh.groups.keys()
Out[388]: [(1.0, 2), (nan, 3), (1.0, 3), (1.0, 1), (2.0, 2)]
In [389]: gh.indices.keys()
Out[389]: [(1.0, 2), (1.0, 3), (2.0, 3), (1.0, 1), (2.0, 2)] # Incorrect
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https://api.github.com/repos/pandas-dev/pandas/issues/9305 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9305/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9305/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9305/events | https://github.com/pandas-dev/pandas/issues/9305 | 54,824,049 | MDU6SXNzdWU1NDgyNDA0OQ== | 9,305 | RFE: support dateutil 2.x | {
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] | closed | false | null | [] | null | 3 | 2015-01-19T23:12:39Z | 2015-01-20T12:53:41Z | 2015-01-20T11:15:12Z | CONTRIBUTOR | null | Pandas requires dateutil 1.5. This version was released in 2010. There is also a dateutil 2.x series that is currentlu updated. It would be nice if pandas supports datautil 2.x as linux distributions are strating to move away from dateutil 1.5
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https://api.github.com/repos/pandas-dev/pandas/issues/9306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9306/events | https://github.com/pandas-dev/pandas/issues/9306 | 54,838,165 | MDU6SXNzdWU1NDgzODE2NQ== | 9,306 | BUG: API Change with skiprows from v0.14.1 to v0.15.2 | {
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] | closed | false | null | [] | null | 5 | 2015-01-20T02:58:14Z | 2015-01-23T00:32:57Z | 2015-01-23T00:32:57Z | NONE | null | Whereas this worked in v0.14.1:
```
In [1]: import pandas as pd
In [2]: from StringIO import StringIO
In [3]: pd.__version__
Out[3]: '0.14.1'
In [4]: data = '#header\n\na,b,c\n1,2,3\n4,5,6'
In [5]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
In [6]: df
Out[6]:
b c
a
1 2 3
4 5 6
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9307/events | https://github.com/pandas-dev/pandas/pull/9307 | 54,845,996 | MDExOlB1bGxSZXF1ZXN0Mjc2NTUxNjg= | 9,307 | Fix plotting memory leak | {
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Specifically, a `plotf` function object is stored in `ax._plot_data` for the purposes of potentially redrawing if the data needs ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9308 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9308/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9308/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9308/events | https://github.com/pandas-dev/pandas/pull/9308 | 54,851,795 | MDExOlB1bGxSZXF1ZXN0Mjc2NTgzNjk= | 9,308 | BUG: 0/frame numeric ops buggy (GH9144) | {
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closes #8445
---
Here's the results from testing the vbenches related to DataFrames (I also added 6 vbenches).
```
Invoked with :
--ncalls: 10
--repeats: 10
-------------------------------------------------------------------------------
Test name | head[ms] | base[m... | {
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- `Timestamp` shows nanoseconds when it isn't 0
```
dt = pd.Timestamp(datetime.datetime.now())
h = pd.Timedelta(hours=1)
dt
#2015-01-21 00:01:05.260660
dt + offsets.Nano()
#2015-01-21 00:01:05.260660001
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9310 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9310/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9310/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9310/events | https://github.com/pandas-dev/pandas/pull/9310 | 54,894,264 | MDExOlB1bGxSZXF1ZXN0Mjc2ODQ0MzY= | 9,310 | DOC: update install.rst, required version of dateutil is 1.5 or higher G... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9311/events | https://github.com/pandas-dev/pandas/issues/9311 | 54,908,220 | MDU6SXNzdWU1NDkwODIyMA== | 9,311 | BUG: first() changes datetime64 data | {
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``` python
import p... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9312/events | https://github.com/pandas-dev/pandas/issues/9312 | 54,913,681 | MDU6SXNzdWU1NDkxMzY4MQ== | 9,312 | BUG date_range for Annual interval gives end of year instead of start | {
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] | closed | false | null | [] | null | 14 | 2015-01-20T17:33:49Z | 2015-01-26T01:24:00Z | 2015-01-25T23:15:41Z | CONTRIBUTOR | null | When Creating an Annual date range from the start of a year, the actual dates generated are for the END of that year, and do not include the datetime provided. You would expect to get a range starting from `start_date` and a series of subsequent dates one year apart
``` python
pd.date_range(datetime.datetime(2006, 1, ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9313/events | https://github.com/pandas-dev/pandas/issues/9313 | 54,914,245 | MDU6SXNzdWU1NDkxNDI0NQ== | 9,313 | ENH date_range yearly frequency flag 'A' is confusing, 'Y' should be accepted | {
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It would seem more consistent if it accepted "Y" as well/instead of "A", following the pattern:
hour -> hourly -> H
day -> daily -> D
week -> weekly -> W
year -> yearly -> Y
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https://api.github.com/repos/pandas-dev/pandas/issues/9314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9314/events | https://github.com/pandas-dev/pandas/issues/9314 | 54,914,654 | MDU6SXNzdWU1NDkxNDY1NA== | 9,314 | ENH date_range should accept dateutil frequency constants | {
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... | 4 | 2015-01-20T17:41:15Z | 2020-05-23T04:29:14Z | null | CONTRIBUTOR | null | Currently date_range accepts string flags for `freq=`.
dateutil (which is already a pandas dependency) has constants for `YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY` which are used in the dateutil equivalent of date_range, `rrule`.
it would be a nice feature if these were accepted as well as the strin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9315/events | https://github.com/pandas-dev/pandas/issues/9315 | 54,923,638 | MDU6SXNzdWU1NDkyMzYzOA== | 9,315 | bug: read_csv and german umlaut (ä,ö,ü) in filename | {
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```
import pandas
df = pandas.read_csv('C:\\exampleä\\test.csv')
```
thows an `OSError: File b'C:\\example\xc3\xa4\\test.csv' does not exist`.
This `df = pandas.read_excel('C:\exampleä\test.xlsx', 'test')` and this `open(r'C:\exampleä\test.csv', newline='')` works perfectly.
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https://api.github.com/repos/pandas-dev/pandas/issues/9316 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9316/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9316/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9316/events | https://github.com/pandas-dev/pandas/pull/9316 | 54,928,244 | MDExOlB1bGxSZXF1ZXN0Mjc3MDUyNzg= | 9,316 | Doc: API docstrings for indexers (GH6920) | {
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Closes #6920
For now, I just copied the relevant parts of the tutorial docs (in indexing.rst).
I should also include `__getitem__` for `[]`
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https://api.github.com/repos/pandas-dev/pandas/issues/9317 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9317/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9317/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9317/events | https://github.com/pandas-dev/pandas/issues/9317 | 54,931,604 | MDU6SXNzdWU1NDkzMTYwNA== | 9,317 | _metadata has bizzare behaviour in python 3.3 | {
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Users using python 3.3 are reporting that when "non _metadata" attributes are copied they propagate to the new copied dataframe. Furtheremore, when persisting to json they ar... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9318 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9318/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9318/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9318/events | https://github.com/pandas-dev/pandas/pull/9318 | 54,934,012 | MDExOlB1bGxSZXF1ZXN0Mjc3MDg3NTY= | 9,318 | DOC: delete removed Timedelta properties (see GH9257) from API overview | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-01-20T20:20:35Z | 2015-01-21T08:43:49Z | 2015-01-21T08:43:49Z | MEMBER | null | Related to #9257. The removed TimedeltaProperties were not removed from the api.rst overview.
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``` python
In [1]: sample_df = pd.DataFrame(
[[1,2,3],[4,5,6],[7,8,9],[10,11,12]],
columns=['a', 'b', 'c']
)
In [2]: sample_df.dtypes
Out[2]:
a int64
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https://api.github.com/repos/pandas-dev/pandas/issues/9320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9320/events | https://github.com/pandas-dev/pandas/issues/9320 | 54,980,151 | MDU6SXNzdWU1NDk4MDE1MQ== | 9,320 | DataFrame.to_hdf append feature does behave properly | {
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... | open | false | null | [] | null | 10 | 2015-01-21T05:44:25Z | 2017-06-08T08:26:21Z | null | NONE | null | I create a DataFrame and save it to disk as an hdf5 file, as follows:
df.to_hdf(fname, 'table')
This behaves as expected. But when I try to append to the existing hdf5 file,
df.to_hdf(fname, 'table',append=True)
pandas returns an error:
ValueError: Can only append to Tables
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https://api.github.com/repos/pandas-dev/pandas/issues/9321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9321/events | https://github.com/pandas-dev/pandas/pull/9321 | 54,985,296 | MDExOlB1bGxSZXF1ZXN0Mjc3Mzk0NjQ= | 9,321 | ENH: plot method accessors | {
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This PR adds plotting sub-methods like `df.plot.scatter()` as an alternative to using `df.plot(kind='scatter')`.
I've added meaningful function signatures and documentation for a few of these methods, but I would greatly appreciate help to fill in the rest -- this is a lot of documentation to assemble/rec... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9322 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9322/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9322/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9322/events | https://github.com/pandas-dev/pandas/pull/9322 | 54,988,082 | MDExOlB1bGxSZXF1ZXN0Mjc3NDEwMDY= | 9,322 | ENH/DOC: reimplement Series delegates/accessors using descriptors | {
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This PR fixes the API docs to use `Series.str` and `Series.dt` instead of `StringMethods` and `DatetimeProperties`.
It will need a rebase once #9318 is merged.
CC @jorisvandenbossche @jreback
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https://api.github.com/repos/pandas-dev/pandas/issues/9323 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9323/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9323/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9323/events | https://github.com/pandas-dev/pandas/issues/9323 | 54,990,386 | MDU6SXNzdWU1NDk5MDM4Ng== | 9,323 | _repr_html_ returns None | {
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I think this may be to do with `DataFrame._repr_html_()`
If I run the following on 0.15.2:
```
import pandas as pd
import numpy as np
print('pandas version ', pd.__version__)
rng = pd.d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9324 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9324/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9324/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9324/events | https://github.com/pandas-dev/pandas/issues/9324 | 55,006,442 | MDU6SXNzdWU1NTAwNjQ0Mg== | 9,324 | Panel to_frame should have which axis are columns and index in the new DF | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2015-01-21T11:22:02Z | 2018-07-06T20:21:06Z | 2017-07-10T21:06:48Z | NONE | null | to_frame should have more control (be explicit) over what axis becomes index and the columns in the DF.
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https://api.github.com/repos/pandas-dev/pandas/issues/9325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9325/events | https://github.com/pandas-dev/pandas/issues/9325 | 55,028,180 | MDU6SXNzdWU1NTAyODE4MA== | 9,325 | Bug in indexing time series with datetime object? | {
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"gists_url": "https://... | [] | closed | false | null | [] | null | 2 | 2015-01-21T14:51:24Z | 2015-01-21T16:52:25Z | 2015-01-21T16:52:25Z | CONTRIBUTOR | null | Hi I have encountered a very strange issue in one of my programs.
I have a dataframe with a
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2007-01-02, ..., 2014-12-15]
Length: 2914, Freq: None, Timezone: None
```
When I try to slice the DataFrame then this does not work:
```
df2[dt.date(2007,1,1):]
```
while ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9326/events | https://github.com/pandas-dev/pandas/issues/9326 | 55,029,013 | MDU6SXNzdWU1NTAyOTAxMw== | 9,326 | Performance regression in .ffill() | {
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] | closed | false | null | [] | null | 5 | 2015-01-21T14:57:44Z | 2015-01-22T01:09:06Z | 2015-01-22T01:09:06Z | CONTRIBUTOR | null | Under pandas 0.12:
``` python
In [4]: from pandas import *
In [5]: from numpy.random import randn
In [6]: In [14]: df = DataFrame({'one' : Series(randn(3), index=['a', 'b', 'c']),
....: 'two' : Series(randn(4), index=['a', 'b', 'c', 'd']),
....: 'three' : Series(randn(3), index=... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9327 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9327/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9327/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9327/events | https://github.com/pandas-dev/pandas/issues/9327 | 55,037,974 | MDU6SXNzdWU1NTAzNzk3NA== | 9,327 | read_hdf does not work with auto_close argument | {
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"descript... | closed | false | null | [] | null | 3 | 2015-01-21T16:02:45Z | 2015-01-21T16:08:02Z | 2015-01-21T16:04:51Z | NONE | null | When trying to create DataFrame from HDF Store with `auto_close=True` or `False` it breaks with error:
```
somedf = pd.DataFrame({'a':[1,2], 'b':[3,4]})
somedf.to_hdf('hdf_file', 'somekey', format='t', mode='w')
newdf = pd.read_hdf('hdf_file', 'somekey', auto_close=True)
newdf
```
error:
```
d:\Anaconda\envs\py2k\li... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9328/events | https://github.com/pandas-dev/pandas/issues/9328 | 55,048,404 | MDU6SXNzdWU1NTA0ODQwNA== | 9,328 | OMP: Error #134: Cannot set thread affinity mask. | {
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] | closed | false | null | [] | null | 5 | 2015-01-21T17:16:43Z | 2016-02-18T19:42:37Z | 2015-01-22T23:36:46Z | NONE | null | This issue has been reported in the past, however there were some problems reproducing it. (https://github.com/pydata/pandas/issues/6720). I'm providing some code that hopefully helps catch the bug (finally!).
The error appears to be related to the division of a large Series or DataFrame that was built by pd.read_hdf(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9329/events | https://github.com/pandas-dev/pandas/issues/9329 | 55,064,565 | MDU6SXNzdWU1NTA2NDU2NQ== | 9,329 | Partial indexing of MultiIndex fails with empty tuple | {
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] | closed | false | null | [] | null | 2 | 2015-01-21T19:23:06Z | 2015-01-23T02:14:09Z | 2015-01-23T02:14:09Z | NONE | null | Indexing the entire MultiIndex works with the empty tuple, and partial indexing works with non-empty tuples, but partial indexing with an empty tuple doesn't. Here's a repro:
```
import pandas as pd
import numpy as np
import itertools
ix_str = pd.MultiIndex.from_tuples(list(itertools.product([('a')], ['a','b','c'])))... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9330/events | https://github.com/pandas-dev/pandas/issues/9330 | 55,078,406 | MDU6SXNzdWU1NTA3ODQwNg== | 9,330 | False negative on .equals() after read_hdf() | {
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```
import pandas as pd
df = pd.DataFrame({'B':[1,2], 'A':[str('x'), str('y')]}) # str() - just to be sure this is not linked to unicode
print 'df:'
print df
df.to_hdf('hdf_file', 'key', format='t', mode='w')
d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9331/events | https://github.com/pandas-dev/pandas/pull/9331 | 55,107,321 | MDExOlB1bGxSZXF1ZXN0Mjc4MTM5NTQ= | 9,331 | BUG: don't sort unique values from categoricals | {
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CC @jreback @TomAugspurger @JanSchulz
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https://api.github.com/repos/pandas-dev/pandas/issues/9332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9332/events | https://github.com/pandas-dev/pandas/pull/9332 | 55,119,141 | MDExOlB1bGxSZXF1ZXN0Mjc4MjA5MDM= | 9,332 | FIX: Fixed some issues around vb_suite | {
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```
./test_perf.sh -b master -t HEAD
```
But I got some errors (`InvalidGitRepositoryError` and Benchmark duplication errors), and finally I got tests run by fixing following issues.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9333/events | https://github.com/pandas-dev/pandas/pull/9333 | 55,140,564 | MDExOlB1bGxSZXF1ZXN0Mjc4MzQxNTE= | 9,333 | Fix docs in groupby.tail | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-01-22T11:06:37Z | 2015-02-04T14:57:51Z | 2015-02-04T07:34:03Z | CONTRIBUTOR | null | The old docs is wrong where head() & tail() return the same result.
Change input of the example to see group data clearer.
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https://api.github.com/repos/pandas-dev/pandas/issues/9334 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9334/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9334/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9334/events | https://github.com/pandas-dev/pandas/issues/9334 | 55,160,561 | MDU6SXNzdWU1NTE2MDU2MQ== | 9,334 | Windows wheel package (.whl) on Pypi | {
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Currently it is possible to download Windows wheel packages for pandas here: http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas
It would be great if the wheels were directly available on the Pypi server https://pypi.python.org/pypi/ so that they can be installed... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9335 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9335/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9335/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9335/events | https://github.com/pandas-dev/pandas/pull/9335 | 55,164,952 | MDExOlB1bGxSZXF1ZXN0Mjc4NDk1OTg= | 9,335 | Fix missing text on Index documentation page. | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-01-22T15:16:03Z | 2015-03-05T23:29:47Z | 2015-03-05T23:29:43Z | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9336 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9336/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9336/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9336/events | https://github.com/pandas-dev/pandas/issues/9336 | 55,167,924 | MDU6SXNzdWU1NTE2NzkyNA== | 9,336 | Problem mixing datetime64 with other types in DataFrame column | {
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"closed_at": "2019-01-26T00:51:26Z",
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"created_at": "2018-03-29T12:00:12Z",
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"followers_url": "https://api.github... | 2 | 2015-01-22T15:39:56Z | 2018-05-29T01:34:34Z | 2018-05-29T01:34:34Z | NONE | null | Given a DataFrame with a column of type datetime64, there is a problem assigning values of other types to slice of said column:
in [148]:
```
import datetime as dt
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.random((3, 2)), columns = ['a', 'b' ])
df['c'] = pd.NaT
df.c
Out[148]:
0 NaT
1 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9337 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9337/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9337/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9337/events | https://github.com/pandas-dev/pandas/issues/9337 | 55,170,073 | MDU6SXNzdWU1NTE3MDA3Mw== | 9,337 | Problem indexing into first 2-levels of 3-level Index | {
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... | 2 | 2015-01-22T15:55:49Z | 2020-03-31T04:37:10Z | 2020-03-31T04:37:10Z | NONE | null | First create a DataFrame with a 3-level index and then index into it using a single tuple from the outer two levels:
```
import pandas as pd
import itertools
ix = pd.MultiIndex.from_product([range(3), pd.date_range('20140101', '20140102'), list('ab')])
df = pd.DataFrame(0, index = ix, columns = ['A', 'B'])
outer_level... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9338 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9338/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9338/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9338/events | https://github.com/pandas-dev/pandas/pull/9338 | 55,190,392 | MDExOlB1bGxSZXF1ZXN0Mjc4NjU2NjE= | 9,338 | GH 9016: Bitwise operation weirdness | {
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"followers_url": "https://api.github.com/users/jr... | 11 | 2015-01-22T18:30:00Z | 2015-02-05T16:54:48Z | 2015-02-05T11:37:26Z | CONTRIBUTOR | null | Series now supports bitwise op for integral types.
I have made the changes in wrapper() itself instead of na_op() because it looked to me like wrapper is controlling the input and output fill and dtype. Once that is taken care of, na_op() seems to be doing the right thing by itself and so I did not have to change any... | {
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so existing code will work unchanged.
This is effectively reopening issue #9192
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https://api.github.com/repos/pandas-dev/pandas/issues/9340 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9340/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9340/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9340/events | https://github.com/pandas-dev/pandas/issues/9340 | 55,216,293 | MDU6SXNzdWU1NTIxNjI5Mw== | 9,340 | API: Interpolate at new values | {
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... | 36 | 2015-01-22T22:10:34Z | 2021-04-12T05:03:37Z | null | NONE | null | First time I used the .interpolate() method I thought that it receives a new index and then interpolates on it, similar to [scipy.interpolate.interp1d](http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d)
From scipy web:
``` python
from scipy import interpolat... | {
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... | closed | false | null | [] | null | 1 | 2015-01-22T23:09:06Z | 2015-01-22T23:17:37Z | 2015-01-22T23:16:44Z | NONE | null | It seems that the `to_datetime` function always assumes that a timestamp is in local time and converts it to UTC. This does not seem to be the case with converting from strings.
This code:
``` python
from datetime import datetime
import time
import pandas as pd
now = datetime.now()
ts = time.mktime(now.timetuple())
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... | closed | false | null | [] | null | 1 | 2015-01-22T23:48:22Z | 2015-01-22T23:51:45Z | 2015-01-22T23:51:22Z | NONE | null | Shouldn't `convert_objects(convert_dates='coerce')` be coercing a series of strings to NaTs?
``` python
df = pd.DataFrame({'date': ['a', 'b', 'c', 'd']})
df.convert_objects(convert_dates='coerce')
date
0 a
1 b
2 c
3 d
```
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"f... | 1 | 2015-01-23T08:04:35Z | 2016-08-02T10:05:27Z | 2016-08-02T10:05:27Z | MEMBER | null | #9322 will make `Series.str` raise an exception if it is accessed on Series instances with non-object dtype. In principle, the exception should really be raised for any non-strictly string-like data, but that's not practical until pandas has a true string dtype to use (currently we abuse `np.object_` for this purpose).... | {
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This may be a new feature, not a b... | {
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applying a groupby aggregator.
closes #9311
closes #6620
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... | 22 | 2015-01-23T20:57:00Z | 2017-04-09T15:28:52Z | 2017-04-09T15:28:52Z | MEMBER | null | Looking at the API docs, neither the function `pd.unique` nor the order of the unique values from `unique` is mentioned. I would like to:
1. Add `pd.unique` to API > General functions > Data manipulations
2. Note that `Series.unique()` and `unique()` return values in order of appearance
3. Add unit tests to verify the ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9348 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9348/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9348/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9348/events | https://github.com/pandas-dev/pandas/pull/9348 | 55,345,528 | MDExOlB1bGxSZXF1ZXN0Mjc5NjM2MDY= | 9,348 | Fix gbq client to only return results when jobCompleted is True. | {
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] | closed | false | null | [] | null | 1 | 2015-01-23T23:15:05Z | 2015-01-23T23:40:41Z | 2015-01-23T23:40:28Z | NONE | null | When polling for a long-running gbq job to determine if it is complete, we should only return results once `query_results['jobCompleted']` is `True`, not just when the `jobCompleted` key exists. Otherwise, the gbq client thinks results exist and it will start attempting to parse the results, leading to a weird KeyErro... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9349 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9349/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9349/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9349/events | https://github.com/pandas-dev/pandas/issues/9349 | 55,379,076 | MDU6SXNzdWU1NTM3OTA3Ng== | 9,349 | pd.to_timedelta returns Timedelta(0) | {
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"descript... | closed | false | null | [] | null | 1 | 2015-01-24T17:55:40Z | 2015-01-24T18:06:37Z | 2015-01-24T18:06:12Z | CONTRIBUTOR | null | Hello,
I noticed:
```
In [1]: pd.to_timedelta('00:00:00.1')
Out[1]: Timedelta('0 days 00:00:00.100000')
```
but
```
In [2]: pd.to_timedelta('0.1')
Out[2]: Timedelta('0 days 00:00:00')
```
same for
```
In [3]: pd.to_timedelta(0.1)
Out[3]: Timedelta('0 days 00:00:00')
```
I was expecting it returns `Timedelta('0 d... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9350 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9350/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9350/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9350/events | https://github.com/pandas-dev/pandas/pull/9350 | 55,385,082 | MDExOlB1bGxSZXF1ZXN0Mjc5ODI0NTQ= | 9,350 | Changed uin8 to uint8 in response to issue #9266 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9351 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9351/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9351/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9351/events | https://github.com/pandas-dev/pandas/issues/9351 | 55,387,420 | MDU6SXNzdWU1NTM4NzQyMA== | 9,351 | DataFrame.hist() does not get along with matplotlib.pyplot.tight_layout() | {
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I.e. the following code:
```
pd.DataFrame({'a':np.random.randn(100),
'b':np.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9352 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9352/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9352/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9352/events | https://github.com/pandas-dev/pandas/pull/9352 | 55,415,281 | MDExOlB1bGxSZXF1ZXN0Mjc5OTE4NjI= | 9,352 | ENH: StringMethods now supports ljust and rjust | {
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-01-25T13:12:41Z | 2015-01-29T23:28:30Z | 2015-01-29T11:12:01Z | MEMBER | null | Derived from #9111.
- Add `StringMethods.ljust` and `StringMethods.rjust` to be consistent with standard `str`.
- Add `fillchar` option to `StringMethods.pad` and `StringMethods.center` to be consistent with standard `str.center`
- https://docs.python.org/2/library/string.html#string.center
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https://api.github.com/repos/pandas-dev/pandas/issues/9353 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9353/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9353/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9353/events | https://github.com/pandas-dev/pandas/issues/9353 | 55,429,380 | MDU6SXNzdWU1NTQyOTM4MA== | 9,353 | to_timedelta returns an object of different type depending of Python version | {
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```
import pandas as pd
from datetime import timedelta
td=pd.to_timedelta('00:00:15')
```
Python 2
```
td
Timedelta('0 days 00:00:15')
isinstance(td, timedelta)
True
```
but with Python 3
```
td
numpy.timedelta64(15000000000,'ns')
isinstance(td, timedelta)
False
```
Kind regards
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"followers_url": "https://api.github.com/users/jr... | 3 | 2015-01-25T23:33:12Z | 2015-01-28T11:09:07Z | 2015-01-28T11:09:00Z | CONTRIBUTOR | null | xref #7784
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... | 1 | 2015-01-26T01:23:36Z | 2015-05-09T16:06:25Z | 2015-05-09T16:06:25Z | CONTRIBUTOR | null | rebase of #6632.
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This ticket aims to implement this option in the pivot table API. I implemented the three types cited above for a start.
I chose "display_value" for the argument name. One c... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9357 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9357/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9357/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9357/events | https://github.com/pandas-dev/pandas/pull/9357 | 55,463,316 | MDExOlB1bGxSZXF1ZXN0MjgwMTUxMjQ= | 9,357 | Deprecating the trellis rplot module (GH3445) | {
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"followers_url": "https://api.github.com/users/jr... | 13 | 2015-01-26T09:50:07Z | 2015-03-05T08:58:32Z | 2015-03-05T08:55:49Z | MEMBER | null | Closes #3445
---
Start for #3445. Then the current examples should get a seaborn or ggplot alternative.
Questions:
- explicitely refer to seaborn and ggplot as alternatives? (as there may be other packages that could feel neglected that way?)
- I now raise the warning on import of the module (`import pandas.tools.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9358 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9358/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9358/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9358/events | https://github.com/pandas-dev/pandas/pull/9358 | 55,499,872 | MDExOlB1bGxSZXF1ZXN0MjgwMzcxNDI= | 9,358 | CLN: Bring pandas up to date with pandas-datareader | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-01-26T16:03:18Z | 2015-03-07T21:19:39Z | 2015-03-05T23:28:23Z | CONTRIBUTOR | null | Per: pydata/pandas-datareader#15
Fixes #9010, #9026
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https://api.github.com/repos/pandas-dev/pandas/issues/9359 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9359/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9359/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9359/events | https://github.com/pandas-dev/pandas/issues/9359 | 55,534,561 | MDU6SXNzdWU1NTUzNDU2MQ== | 9,359 | FEATURE: Load recursive dictionaries | {
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] | closed | false | null | [] | null | 3 | 2015-01-26T20:42:04Z | 2015-02-04T21:14:15Z | 2015-02-04T12:08:38Z | NONE | null | I have fought with trying to load recursive dictionaries (with labels that have depth) for about an hour now, and [this seems to be the solution](http://stackoverflow.com/questions/28157564/please-explain-odd-behavior-with-pandas-dataframes-of-multi-level-index)
```
import pandas as pd
test = {'a': tuple(range(10)),
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9360 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9360/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9360/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9360/events | https://github.com/pandas-dev/pandas/pull/9360 | 55,588,425 | MDExOlB1bGxSZXF1ZXN0MjgwOTA2NjA= | 9,360 | BUG: Fix buffer overflows in tokenizer.c that caused python to segfault with certain | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9361 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9361/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9361/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9361/events | https://github.com/pandas-dev/pandas/issues/9361 | 55,606,484 | MDU6SXNzdWU1NTYwNjQ4NA== | 9,361 | bug in repeat() method with MultiIndex - fixed in 0.15 | {
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I found a bug in version 0.14, which has since been fixed in 0.15. I thought I'd report it because I couldn't find a mention of it and it might have been fixed silently. Also I couldn't find a specific test for this bug (as far as I could see).
Here is a test that fails with the bug:
``` python
import pandas as ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9362 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9362/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9362/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9362/events | https://github.com/pandas-dev/pandas/pull/9362 | 55,608,789 | MDExOlB1bGxSZXF1ZXN0MjgxMDI4MTY= | 9,362 | TST add a test for repeat() method with MultiIndex, referenced in #9361 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9363 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9363/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9363/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9363/events | https://github.com/pandas-dev/pandas/issues/9363 | 55,644,496 | MDU6SXNzdWU1NTY0NDQ5Ng== | 9,363 | assignment with dataframe.loc fails (in some cases) | {
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More context:
``` python
import pandas as pd
import numpy as np
data = pd.DataFrame(np.zeros(2), columns=['A'])
data.loc[:].A = 'HELLO'
print data
data = pd.DataFrame(np.zeros(2), columns=['A'])
data.A.loc[:] = 'HELLO'
print data
data = pd.DataFrame(np... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9364/events | https://github.com/pandas-dev/pandas/pull/9364 | 55,679,240 | MDExOlB1bGxSZXF1ZXN0MjgxNDc0Mzc= | 9,364 | PERF: nunique perf improved by using len(unique) rather than value_counts | {
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```
In [1]: s=pandas.Series(range(100)*1000)
In [2]: %timeit s.nunique()
1000 loops, best of 3: 1.43 ms per loop
```
After:
```
In [1]: s=pandas.Series(range(100)*1000)
In [4]: %timeit s.nunique()
1000 loops, best of 3: 440 µs per loop
```
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"description": "Related ... | open | false | null | [] | null | 4 | 2015-01-28T07:49:55Z | 2021-04-12T05:03:49Z | null | MEMBER | null | If we're willing to construct KDTrees when necessary, we can support efficient nearest neighbor queries even in multiple dimensions (i.e., on a MultiIndex) and even for unsorted indexes.
For an example of what you can do with this, take a look at this question I just answered on SO: http://stackoverflow.com/a/2818694... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9366 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9366/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9366/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9366/events | https://github.com/pandas-dev/pandas/issues/9366 | 55,785,758 | MDU6SXNzdWU1NTc4NTc1OA== | 9,366 | DataFrame modification operations should return number of rows modified | {
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"gi... | [] | closed | false | null | [] | null | 8 | 2015-01-28T17:14:10Z | 2015-02-04T13:29:42Z | 2015-01-29T10:51:28Z | NONE | null | For situations where an operation on a DataFrame causes a modification to the data in that DataFrame - it would be extremely useful to know how many rows are affected by the modification.
From a debugging, data flow audit perspective - having this "modified rows" value would avoid an extra data query to determine thos... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9367 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9367/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9367/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9367/events | https://github.com/pandas-dev/pandas/issues/9367 | 55,813,567 | MDU6SXNzdWU1NTgxMzU2Nw== | 9,367 | Natural name convention broken for "size" in non-interactive mode. | {
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```
$ python -c "import pandas; df=pandas.DataFrame({'size':range(3)}); print df.size; print df['size']"
```
prints
```
3
0 0
1 1
2 2
Name: size, dtype: int64
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9368 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9368/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9368/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9368/events | https://github.com/pandas-dev/pandas/issues/9368 | 55,843,677 | MDU6SXNzdWU1NTg0MzY3Nw== | 9,368 | Multiplying Together Series with MultiIndexes | {
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"de... | closed | false | null | [] | null | 2 | 2015-01-29T01:37:25Z | 2015-01-30T13:39:00Z | 2015-01-30T13:38:42Z | NONE | null | I have a question regarding multiplying two Series that have MultiIndexes. In pandas, I can multiply these two Series and it makes sense:
``` python
>>> iterables = [
['A', 'B'],
['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']
]
>>> s1 = pd.Series(range(12), index=pd.MultiIndex.from_product(iterables... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9369 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9369/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9369/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9369/events | https://github.com/pandas-dev/pandas/issues/9369 | 55,858,570 | MDU6SXNzdWU1NTg1ODU3MA== | 9,369 | BUG: binary comparison of numpy.int/float and Series | {
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"description": "Unexpected or buggy dtyp... | closed | false | null | [] | {
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"created_at": "2015-02-26T19:29:05Z",
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"followers_url": "https://api.github.com/users/jorisva... | 14 | 2015-01-29T05:35:07Z | 2018-10-26T10:59:59Z | 2018-10-25T23:55:55Z | NONE | null | This only happens with the numpy object is on the left. It doesn't matter if it's an int or a float. This error does not get raised with DataFrames.
After more poking around, It looks like this actually comres from a change in numpy, between versions 1.8.2 and 1.9.0.
``` python
import pandas as pd
import numpy as np
... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9370 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9370/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9370/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9370/events | https://github.com/pandas-dev/pandas/issues/9370 | 55,862,379 | MDU6SXNzdWU1NTg2MjM3OQ== | 9,370 | Support label based slicing even for unsorted indexes? | {
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"color": "AD... | closed | false | null | [] | null | 2 | 2015-01-29T06:34:31Z | 2015-01-29T10:08:49Z | 2015-01-29T10:08:49Z | MEMBER | null | By sorting the index under the hood, we could support label based slicing even for unordered indexes.
Does this seem like a good idea? Here is what the implementation would look like, roughly:
``` python
def slice_indexer_anyorder(idx, start=None, end=None):
# preprocessing takes time O(n * log(n)) and space 2 * ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9371 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9371/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9371/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9371/events | https://github.com/pandas-dev/pandas/issues/9371 | 55,882,498 | MDU6SXNzdWU1NTg4MjQ5OA== | 9,371 | Performance degradation in __setitem__ from 0.12 to 0.15.2 | {
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"color": "a1... | closed | false | null | [] | null | 10 | 2015-01-29T10:34:17Z | 2015-01-29T20:48:47Z | 2015-01-29T10:45:10Z | CONTRIBUTOR | null | ```
In [1]: import pandas as pd
In [2]: print pd.__version__
0.12.0
In [3]: s = pd.Series({'AAPL': 20.})
In [4]: %timeit s['AAPL'] = 25.
1000000 loops, best of 3: 1.29 µs per loop
```
VS
```
In [1]: import pandas as pd
In [2]: print pd.__version__
0.15.2
In [3]: s = pd.Series({'AAPL': 20.})
In [4]: %timeit s['A... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9372 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9372/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9372/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9372/events | https://github.com/pandas-dev/pandas/issues/9372 | 55,949,113 | MDU6SXNzdWU1NTk0OTExMw== | 9,372 | to_excel doesn't support dateutil.relativedelta | {
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... | 6 | 2015-01-29T20:03:24Z | 2019-01-21T05:26:28Z | 2019-01-21T04:41:39Z | CONTRIBUTOR | null | Hello,
to_excel doesn't support `dateutil.relativedelta`
```
import pandas as pd
from dateutil.relativedelta import relativedelta
df = pd.DataFrame([relativedelta(years=2)])
df.to_excel('out.xls')
```
raises `Exception: Unexpected data type <type 'instance'>`
```
df.to_excel('out.xlsx')
```
raises `AttributeError:... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9373 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9373/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9373/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9373/events | https://github.com/pandas-dev/pandas/issues/9373 | 55,969,625 | MDU6SXNzdWU1NTk2OTYyNQ== | 9,373 | Errors while unstacking the DataFrame with MultiIndex | {
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"gists... | [] | closed | false | null | [] | null | 1 | 2015-01-29T22:51:47Z | 2015-01-30T10:33:39Z | 2015-01-29T23:05:07Z | NONE | null | Dear Pandas community, this is a duplicate of SO's [question](http://stackoverflow.com/questions/28226074/unstack-pandas-dataframe-with-multiindex). I have seen a lots of similar questions across the web, but unfortunately I was not able to find the answers that could help me.
What I'm trying to do is just to reshape ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9374 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9374/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9374/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9374/events | https://github.com/pandas-dev/pandas/issues/9374 | 55,978,843 | MDU6SXNzdWU1NTk3ODg0Mw== | 9,374 | BUG: inserting a Categorical with the wrong length into a DataFrame is allowed | {
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"followers_url": "https://api.github.com/users/wesm... | 2 | 2015-01-30T00:33:48Z | 2019-11-16T22:00:33Z | 2019-11-16T22:00:33Z | MEMBER | null | This leaves the DataFrame in a very weird/buggy state:
```
In [33]: cat = pd.Categorical.from_codes([0, 1, 1, 0, 1, 2], ['a', 'b', 'c'])
In [34]: df = pd.DataFrame()
In [35]: df['bar'] = range(10)
In [36]: df['foo'] = cat
In [37]: df
Out[37]:
bar foo
0 0 a
1 1 b
2 2 b
3 3 a
4 4 b
5 5... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9375 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9375/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9375/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9375/events | https://github.com/pandas-dev/pandas/issues/9375 | 55,992,645 | MDU6SXNzdWU1NTk5MjY0NQ== | 9,375 | Reading Panel4D stored in HDF5 | {
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"descript... | closed | false | null | [] | null | 3 | 2015-01-30T04:09:19Z | 2015-01-31T11:26:26Z | 2015-01-30T13:26:23Z | NONE | null | ```
pd.__version__ ### '0.15.2'
store = pd.HDFStore('synthetic_networks.h5',mode='w', table=True)
controls = pd.Panel4D(labels=['a', 'b', 'c'],
items=arange(100),
major_axis=arange(121),
minor_axis=arange(121),
dtype='float64')
store.appe... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9376 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9376/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9376/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9376/events | https://github.com/pandas-dev/pandas/issues/9376 | 56,006,652 | MDU6SXNzdWU1NjAwNjY1Mg== | 9,376 | read_csv: date_parser called once with arrays and then many times with strings (from each single row) as arguments | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-01-30T08:25:22Z | 2015-02-01T14:58:46Z | 2015-02-01T14:58:46Z | CONTRIBUTOR | null | Pandas version info:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: EN
pandas: 0.15.2
nose: 1.3.4
Cython: 0.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9377 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9377/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9377/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9377/events | https://github.com/pandas-dev/pandas/pull/9377 | 56,021,555 | MDExOlB1bGxSZXF1ZXN0MjgzNTM5MzM= | 9,377 | DOC: Clarify how date_parser is called (GH9376) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9378 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9378/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9378/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9378/events | https://github.com/pandas-dev/pandas/issues/9378 | 56,049,555 | MDU6SXNzdWU1NjA0OTU1NQ== | 9,378 | New data format: ROOT files | {
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"... | closed | false | null | [] | null | 17 | 2015-01-30T15:52:28Z | 2019-12-22T16:56:23Z | 2019-12-22T16:56:23Z | NONE | null | Hi,
I've recently written a tiny python package for loading/saving ROOT files as pandas DataFrames: [root_pandas](https://github.com/ibab/root_pandas).
ROOT is the main data format used by particle physicists.
Do you think it might be worth adding `read_root` and `to_root` functions to pandas itself?
This could convin... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9379 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9379/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9379/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9379/events | https://github.com/pandas-dev/pandas/issues/9379 | 56,067,823 | MDU6SXNzdWU1NjA2NzgyMw== | 9,379 | pandas.read_csv() won't read back in complex number dtypes from pandas.DataFrame.to_csv() | {
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"f... | 5 | 2015-01-30T18:31:55Z | 2019-10-21T19:34:16Z | null | NONE | null | How can I read back in dataframes from CSV files which I export using `to_csv()` that have complex numbers?
test case:
```
data = pd.DataFrame([1+2j,2+3j,3+4j],columns=['a'])
print 'a='
print data['a']
print 'a*2='
print data['a']*2
filename = 'testcase1.csv'
data.to_csv(filename)
print "\nReadback..."
data2 = pd.r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9380 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9380/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9380/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9380/events | https://github.com/pandas-dev/pandas/pull/9380 | 56,106,638 | MDExOlB1bGxSZXF1ZXN0Mjg0MDY4Njk= | 9,380 | bug in groupby when key space exceeds int64 bounds | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-01-31T01:34:08Z | 2015-02-07T13:07:06Z | 2015-01-31T16:23:42Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9096
also improves performance when there is `int64` overflow; complete groupby benchmarks [here](https://gist.github.com/behzadnouri/e142fbd65f41357a7360).
```
-------------------------------------------------------------------------------
Test name ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9381 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9381/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9381/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9381/events | https://github.com/pandas-dev/pandas/issues/9381 | 56,120,358 | MDU6SXNzdWU1NjEyMDM1OA== | 9,381 | DataFrame.from_records do not support record have sub array? | {
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"de... | closed | false | null | [] | null | 2 | 2015-01-31T10:15:49Z | 2017-10-23T13:12:11Z | 2015-01-31T16:13:55Z | NONE | null | The code is like below, which i thought it should work, but it doesn't, it raise an exception saying `Data must be 1-dimensional`. Is this by design or an defect? if by design how should i walk around this? pandas version is 0.15.1
``` py
dt_type = np.dtype([
('k', 'i4'),
('v', 'i4', 2)
])
dt = np.array([(123... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9382/events | https://github.com/pandas-dev/pandas/issues/9382 | 56,137,026 | MDU6SXNzdWU1NjEzNzAyNg== | 9,382 | Don't make dropping missing rows a default behavior for HDF append()? | {
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"followers_url": "https://api.github.com/users/jr... | 8 | 2015-01-31T19:55:54Z | 2015-07-31T22:37:48Z | 2015-07-31T22:37:48Z | CONTRIBUTOR | null | Hi All,
At the moment, the default behavior for the HDF append() function ( docs: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.HDFStore.append.html?highlight=append#pandas.HDFStore.append ) is to silently drop all rows that are all NaN except for the index.
As I understand it from a PyData exchange ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9383 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9383/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9383/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9383/events | https://github.com/pandas-dev/pandas/pull/9383 | 56,143,399 | MDExOlB1bGxSZXF1ZXN0Mjg0MjI2ODk= | 9,383 | DOC: further clean-up of removed timedelta attributes in whatsnew docs | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-01-31T23:02:42Z | 2015-02-02T22:11:59Z | 2015-02-02T22:11:48Z | MEMBER | null | Follow-up #9318 (and that one from #9257).
whatsnew v0.15.0 was giving some errors now, so made it into a code-block
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https://api.github.com/repos/pandas-dev/pandas/issues/9384 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9384/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9384/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9384/events | https://github.com/pandas-dev/pandas/issues/9384 | 56,145,838 | MDU6SXNzdWU1NjE0NTgzOA== | 9,384 | example/finance.py throws error | {
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```
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/finance.py:485: MatplotlibDeprecationWarning: This function has been deprecated in 1.4 in favor of `quotes_historical_yahoo_ochl`, which maintains the or... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9385 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9385/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9385/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9385/events | https://github.com/pandas-dev/pandas/pull/9385 | 56,145,907 | MDExOlB1bGxSZXF1ZXN0Mjg0MjM3NzQ= | 9,385 | GH 9384: example/finance.py throws error | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9387 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9387/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9387/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9387/events | https://github.com/pandas-dev/pandas/pull/9387 | 56,159,352 | MDExOlB1bGxSZXF1ZXN0Mjg0MjkzMjU= | 9,387 | ENH: Add StringMethods.zfill | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2015-02-01T10:40:53Z | 2015-02-05T13:39:22Z | 2015-02-05T11:28:38Z | MEMBER | null | - Move `Ticks` related tests to under `TestTicks`.
- Added some comprehensive test cases.
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https://api.github.com/repos/pandas-dev/pandas/issues/9389 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9389/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9389/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9389/events | https://github.com/pandas-dev/pandas/issues/9389 | 56,168,004 | MDU6SXNzdWU1NjE2ODAwNA== | 9,389 | example/regressions.py throws error | {
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```
Traceback (most recent call last):
File "examples/regressions.py", line 12, in <module>
dateRange = DatetimeIndex(start, periods=N)
File "/Users/vyomkesh/code/pandas/pandas/util/decorators.py", line 88, in wrapper
return func(*args,... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9390 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9390/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9390/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9390/events | https://github.com/pandas-dev/pandas/pull/9390 | 56,168,110 | MDExOlB1bGxSZXF1ZXN0Mjg0MzI5OTc= | 9,390 | GH 9389: fix for example/regressions.py error | {
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Looks like using the date_range method fixes the error.
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Apart from fixing the examples, I think we should think for a moment what we want to do with these examples. Why do we want them? What is their function?
And depending on that, d... | {
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Deleting examples dir as this should be part of documentation
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"followers_url": "https://api.github.com/users/jr... | 10 | 2015-02-01T19:49:18Z | 2015-02-08T11:26:44Z | 2015-02-08T11:26:44Z | CONTRIBUTOR | null | I have table names that include `_`, `.` and `-`, up until Pandas .14 I used to quote the table name like this:
``` python
def quote_identifier(s, errors="ignore"):
encodable = s.encode("utf-8", errors).decode("utf-8")
nul_index = encodable.find("\x00")
if nul_index >= 0:
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```
$ gdb python
GNU gdb (Ubuntu 7.7.1-0ubuntu5~14.04.2) 7.7.1
Copyright (C) 2014 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
The... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9395 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9395/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9395/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9395/events | https://github.com/pandas-dev/pandas/pull/9395 | 56,261,201 | MDExOlB1bGxSZXF1ZXN0Mjg0ODMyMjE= | 9,395 | MAINT: get rid of some compiler warnings | {
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"followers_url": "https://api.github.com/users/jr... | 9 | 2015-02-02T16:50:50Z | 2015-07-03T09:06:00Z | 2015-07-02T19:09:35Z | CONTRIBUTOR | null | - uninitialized pointer
- unused variables
- unused function
- ISO C prototypes
- `cimport *`
- useless cast before `snprintf`
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https://api.github.com/repos/pandas-dev/pandas/issues/9396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9396/events | https://github.com/pandas-dev/pandas/issues/9396 | 56,275,671 | MDU6SXNzdWU1NjI3NTY3MQ== | 9,396 | BUG?: div method with timedelta | {
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``` python
import datetime
In [24]: s = pd.Series(pd.to_datetime(['2015-01-01', '2015-02-02']))
In [25]: s
Out[25]:
0 2015-01-01
1 2015-02-02
dtype: datetime64[ns]
In [26]: s.diff() / datetime.timedelta(1)
Out[26]:
0 NaN
1 32
dtype: float64
In [27]: s.diff().div(datetime.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9397 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9397/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9397/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9397/events | https://github.com/pandas-dev/pandas/issues/9397 | 56,295,027 | MDU6SXNzdWU1NjI5NTAyNw== | 9,397 | Shortcut for getting examples of the first few groups from a GroupBy? | {
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"f... | 7 | 2015-02-02T21:22:23Z | 2021-04-12T05:06:24Z | null | MEMBER | null | For visualization/testing purposes, I'm often interested in looking at the first example group(s) from a groupby operation.
Is there a convenient shortcut for this? The best I could come up with is `pd.concat([x for _, x in itertools.islice(group, 3)])` which seemed awkward to me.
Note that this is a _different_ use-... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9398 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9398/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9398/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9398/events | https://github.com/pandas-dev/pandas/pull/9398 | 56,314,049 | MDExOlB1bGxSZXF1ZXN0Mjg1MTUzMzA= | 9,398 | PERF: improves performance and memory usage of DataFrame.duplicated | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2015-02-03T00:12:12Z | 2015-03-08T22:34:02Z | 2015-03-03T00:49:35Z | CONTRIBUTOR | null | on master:
```
In [1]: np.random.seed(2718281)
In [2]: n = 1 << 20
In [3]: t = pd.date_range('2015-01-01', freq='S', periods=n // 64)
In [4]: xs = np.random.randn(n // 64).round(2)
In [5]: df = DataFrame({'a':np.random.randint(- 1 << 8, 1 << 8, n),
...: 'b':np.random.choice(t, n),
...: ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9399/events | https://github.com/pandas-dev/pandas/issues/9399 | 56,326,189 | MDU6SXNzdWU1NjMyNjE4OQ== | 9,399 | BUG: Exception raised for a duplicate MultiIndex level name | {
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... | 3 | 2015-02-03T02:43:17Z | 2021-04-12T05:05:32Z | null | CONTRIBUTOR | null | The following two tests seem inconsistent. Why does the first test for a `KeyError` while the second tests for a `ValueError`? Also, the first error message, `Level foo not found`, doesn't seem correct.
`test_index.py`:
```
def test_duplicate_names(self):
self.index.names = ['foo', 'foo']
assertRa... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9400/events | https://github.com/pandas-dev/pandas/issues/9400 | 56,376,366 | MDU6SXNzdWU1NjM3NjM2Ng== | 9,400 | Improve error message in plotting.py's _plot | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2015-02-03T13:20:06Z | 2015-02-08T13:35:47Z | 2015-02-08T13:35:47Z | CONTRIBUTOR | null | This a minor enhancement proposal. At the moment I cannot submit a pull request. I will probably have time to create one during the next week.
This is a snippet from `tools/plotting.py`: https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2269-2283
``` python
def _plot(data, x=None, y=None, subplo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/9401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9401/events | https://github.com/pandas-dev/pandas/pull/9401 | 56,378,656 | MDExOlB1bGxSZXF1ZXN0Mjg1NTI2NTc= | 9,401 | TST/DOC: Add procedure for TestPickle | {
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"followers_url": "https://api.github.com/users/jr... | 6 | 2015-02-03T13:41:38Z | 2015-02-15T01:33:36Z | 2015-02-11T22:58:13Z | MEMBER | null | Added procedure to `TestPickle`. Based on #9291, I think updating `setup.py` is likely to be ommitted.
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