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https://api.github.com/repos/pandas-dev/pandas/issues/8702 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8702/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8702/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8702/events | https://github.com/pandas-dev/pandas/pull/8702 | 47,513,259 | MDExOlB1bGxSZXF1ZXN0MjM3MjI0Nzc= | 8,702 | BUG: setitem fails on mixed-type Panel4D | {
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```
In [1]: from pandas.util import testing as tmd
In [2]: p4d = tmd.makePanel4D()
In [3]: p4d['foo'] = 'bar'
In [4]: p4d.iloc[0,0,0,0] = 0
---------------------------------------------------------------------------
AttributeError ... | {
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"followers_url": "https://api.github.com/users/jre... | 2 | 2014-11-01T19:20:29Z | 2014-11-05T21:18:58Z | 2014-11-05T21:18:58Z | CONTRIBUTOR | null | According to the doc of read_sql, dialect can be string or a csv.Dialect instance.
However this is not the case. If you look at the code, it can only really take a csv.Dialect instance. i.e. this doesn't work:
``` python
fh = io.StringIO(u"pomme\tpoire\n1\t\2")
pd.read_csv(fh, dialect='excel-tab')
```
but, this work... | {
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```
In [12]: data.tail()
Out[12]:
country currency_unit rgdpe \
countrycode year
ZWE 2007-01-01 Zimbabwe Zimbabwe Dollar 45666.082031
2008-... | {
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"followers_url": "https://api.github.com/users/jre... | 9 | 2014-11-02T02:13:15Z | 2014-11-02T21:51:43Z | 2014-11-02T21:51:43Z | CONTRIBUTOR | null | CLN: move block2d_to_blocknd support code to core/internal.py
TST/BUG: support Categorical reshaping via .unstack
closes #8704
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I tried making `unbox_if_zerodim` usage more implicit but there's just too much to test when this is introduced (think every me... | {
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closes #8625
This is a work in progress, but it's far enough along that I'd love to get
some feedback.
TODOs (more called out in the code):
- [ ] documentation
- [x] docstrings
- [x] implement an IntervalTree under the hood to handle index methods
- [x] index methods handle point queries properly:
- [... | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-02T09:42:50Z | 2016-02-27T14:41:27Z | 2016-02-27T14:41:27Z | CONTRIBUTOR | null | `np.isscalar` doesn't detect a lot of types that are scalar from pandas point of view, such as dates, times, `None`, `pd.Period`, so `pd.lib.isscalar` should be preferred instead.
Adding the issue as requested in #8706.
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need to be able to combine like categorical (e.g. ordered,categories are ==)
into a 2-d structure so can store in a single CategoricalBlock
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"followers_url": "https://api.github.com/users/jre... | 3 | 2014-11-02T13:27:10Z | 2014-11-02T21:28:33Z | 2014-11-02T21:28:33Z | NONE | null | Hello,
when I try to get a DataFrame (or sub Panel) from a Panel using index as list it raises the following error:
```
TypeError: unhashable type: 'list'
```
See the code
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import pandas.io.data as web
start = "2010-01-01"
end = "2010-01-30"
df... | {
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"followers_url": "https://api.github.com/users/jr... | 31 | 2014-11-02T15:03:00Z | 2017-02-22T18:38:09Z | 2017-02-22T16:06:33Z | CONTRIBUTOR | null | This raises
```
s = Series(range(5),pd.timedelta_range('1day',periods=5))
s.plot()
```
This will show the timedeltas with a formatted (albeit string index)
```
s.index = s.index.format()
s.plot()
```
wonder if we can just register a converter somehow? like #8614
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... | 3 | 2014-11-02T15:06:20Z | 2021-04-11T05:23:25Z | null | CONTRIBUTOR | null | ```
s = Series(list('abbc')).astype('category')
```
This raises
```
s.hist()
```
This works
```
s.value_counts().plot()
```
I think this should work, beyond this, anyone have ideas on what/how to plot categorical data?
cc @JanSchulz
cc @TomAugspurger
cc @jorisvandenbossche
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https://api.github.com/repos/pandas-dev/pandas/issues/8713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8713/events | https://github.com/pandas-dev/pandas/issues/8713 | 47,542,583 | MDU6SXNzdWU0NzU0MjU4Mw== | 8,713 | DataReader should cache queries. | {
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each request using `pandas.io.data.DataReader` ends by a server request.
That's not reasonable.
Maybe a cache for DataReader should be considered.
Kind regards
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``` python
In [35]: s = pd.Series(np.arange(6), index=list('abcdef'))
In [36]: s
Out[36]:
a 0
b 1
c 2
d 3
e 4
f 5
dtype: int64
In [37]: s.loc['e'::-1]
Out[37]: Series([], dtype: int64)
In [38]: s.iloc[4::-1]
Out[38]:
e 4
d 3
c 2
b 1
a 0
dtype: int... | {
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... | 4 | 2014-11-02T21:48:09Z | 2021-04-11T05:23:45Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26701849/pandas-groupby-and-finding-maximum-in-groups-returning-value-and-count)
Thinking that this is a nice general idiom that might deserve a function:
`df.loc[df.groupby(level='host').idxmax()['no']]`
maybe:
a) `df.groupby(level='host').idxmax(loc='no')` ? (e.g. have... | {
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I wasn't sure where to put the test, hopefully this is right.
Follow-up of #8718
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https://api.github.com/repos/pandas-dev/pandas/issues/8720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8720/events | https://github.com/pandas-dev/pandas/issues/8720 | 47,589,218 | MDU6SXNzdWU0NzU4OTIxOA== | 8,720 | BUG/ERR: adding column with float label to Int64Index gives ValueError | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2014-11-03T12:36:48Z | 2019-12-31T15:04:45Z | 2019-12-31T15:04:45Z | MEMBER | null | When adding a column with a float label to a DataFame with integer column index, you get a `ValueError: cannot insert 0.5, already exists` error with an incorrect message:
```
In [15]: df = pd.DataFrame(np.random.randn(8,4))
In [16]: df
Out[16]:
0 1 2 3
0 -0.482266 -1.239583 -1.17765... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8721 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8721/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8721/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8721/events | https://github.com/pandas-dev/pandas/pull/8721 | 47,604,681 | MDExOlB1bGxSZXF1ZXN0MjM3NjYzNzE= | 8,721 | ERR: leftover from renaming outtype to orient in to_dict | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8722/events | https://github.com/pandas-dev/pandas/issues/8722 | 47,606,031 | MDU6SXNzdWU0NzYwNjAzMQ== | 8,722 | Warning/Exception when querying dtype with use_inf_as_null | {
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"followers_url": "https://api.github.com/users/jre... | 1 | 2014-11-03T15:31:11Z | 2014-11-04T01:04:30Z | 2014-11-04T01:04:30Z | CONTRIBUTOR | null | I see `Exception TypeError: 'data type not understood' in 'util._checknull_old' ignored` in the ipython notebook warning color after turning on `use_inf_as_null`.
The code to rep... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8723/events | https://github.com/pandas-dev/pandas/issues/8723 | 47,616,484 | MDU6SXNzdWU0NzYxNjQ4NA== | 8,723 | Formatting dates & timestamps | {
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... | open | false | null | [] | null | 11 | 2014-11-03T16:54:06Z | 2021-04-11T05:24:21Z | null | CONTRIBUTOR | null | The following:
``` python
import pandas.io.data as web
df = web.DataReader(["AAPL", "MSFT", "EBAY", "AMZN", "CSCO", "GOOG", "YHOO", "AOL", "IBM"], "yahoo", start='2014-10-1').to_frame()['Close']
df.index = df.index.droplevel('minor')
df = df.reset_index()
# > df.head()
# Date Close
#0 2014-10-01 99.18
#1 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8724/events | https://github.com/pandas-dev/pandas/pull/8724 | 47,632,146 | MDExOlB1bGxSZXF1ZXN0MjM3ODMyNjk= | 8,724 | DOC: Select row with maximum value from each group | {
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"followers_url": "https://api.github.com/users/jre... | 2 | 2014-11-03T19:16:17Z | 2014-11-04T01:15:48Z | 2014-11-03T23:20:50Z | CONTRIBUTOR | null | Re: http://stackoverflow.com/q/26701849/190597
Included is a cookbook recipe for finding maximal rows per group.
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https://api.github.com/repos/pandas-dev/pandas/issues/8725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8725/events | https://github.com/pandas-dev/pandas/issues/8725 | 47,658,379 | MDU6SXNzdWU0NzY1ODM3OQ== | 8,725 | BUG: dtype compat with get_dummies | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-03T23:20:15Z | 2016-09-02T11:31:08Z | 2016-09-02T11:30:54Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26724872/pandas-map-values-of-categorical-variable-to-a-predefined-list-of-dummy-columns)
I think should not coerce to floats but be an int (or a categorical)
```
In [14]: df = pd.DataFrame({'hour': [0, 1, 3, 8, 13, 14], 'val': np.random.randn(6)})
In [15]: df['hour_cat']... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8726/events | https://github.com/pandas-dev/pandas/pull/8726 | 47,658,521 | MDExOlB1bGxSZXF1ZXN0MjM3OTk5NjY= | 8,726 | COMPAT: Compat issue is DataFrame.dtypes when options.mode.use_inf_as_null is True (GH8722) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8727/events | https://github.com/pandas-dev/pandas/issues/8727 | 47,731,634 | MDU6SXNzdWU0NzczMTYzNA== | 8,727 | ENH: Support for multiple comment characters with readers | {
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"description": "read_csv... | closed | false | null | [] | {
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"f... | 3 | 2014-11-04T16:19:30Z | 2018-07-06T21:53:45Z | 2018-07-06T21:53:45Z | NONE | null | I would be very pleased if Pandas supported multiple comment characters when reading data from files. According to:
```
import pandas as pd
df = pd.read_table("data.dat", comment=("#","@"), delim_whitespace=True)
```
I don't know if this is requires a minor or major implementation effort?
Best,
Erik
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https://api.github.com/repos/pandas-dev/pandas/issues/8728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8728/events | https://github.com/pandas-dev/pandas/pull/8728 | 47,733,781 | MDExOlB1bGxSZXF1ZXN0MjM4NDQyODU= | 8,728 | BUG: The 'jobComplete' key may be present but False in the BigQuery query results | {
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Fixes an issue when looking for 'totalRows' on a large dataset that is not finished in between two subsequent checks.
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https://api.github.com/repos/pandas-dev/pandas/issues/8729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8729/events | https://github.com/pandas-dev/pandas/issues/8729 | 47,759,319 | MDU6SXNzdWU0Nzc1OTMxOQ== | 8,729 | Chunksize not working in pandas.DataFrame.to_sql | {
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] | closed | false | null | [] | null | 23 | 2014-11-04T20:12:39Z | 2015-12-18T21:35:46Z | 2014-11-28T13:17:06Z | NONE | null | I recently updated the panda version to 0.15 from 0.14 . I use the below code, but the chunksize parameter is not working,It doesn't write the records in batches to the Database. I totally have 50,000 records in my DataFrame and it takes long time to execute. Thanks in advance
from sqlalchemy import create_engine
imp... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8730/events | https://github.com/pandas-dev/pandas/issues/8730 | 47,763,967 | MDU6SXNzdWU0Nzc2Mzk2Nw== | 8,730 | Improve clarity around when SettingWithCopyWarning can be ignored (if ever?) | {
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"followers_url": "https://api.github.com/users/jr... | 14 | 2014-11-04T20:55:20Z | 2015-03-31T22:39:59Z | 2015-03-15T19:52:37Z | CONTRIBUTOR | null | I [Edit: thought I got] a `SettingWarningCopyWarning` when running the following code:
``` python
frame[columnone][frame[columntwo]>x]=y
```
The docs seem to imply that I can safely ignore this error.
``` python
# passed via reference (will stay)
In [273]: dfb['c'][dfb.a.str.startswith('o')] = 42
```
Is it therefor... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8731/events | https://github.com/pandas-dev/pandas/issues/8731 | 47,771,172 | MDU6SXNzdWU0Nzc3MTE3Mg== | 8,731 | BUG: incorrectly output index ordering with an ordered Categorical and pivot | {
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from [SO](http://stackoverflow.com/questions/26741204/pandas-pivot-table-alphabetically-sorts-categorical-data-incorrectly-when-addi)
```
In [32]: df = DataFrame({'Sales' : [100,120,220], 'Month' : ['January','January','January'], 'Year' : [2013,2014,2013]})
In [33]: df
Out[33]:
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https://api.github.com/repos/pandas-dev/pandas/issues/8732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8732/events | https://github.com/pandas-dev/pandas/issues/8732 | 47,772,616 | MDU6SXNzdWU0Nzc3MjYxNg== | 8,732 | PERF: directly astype with numpy if series is already nansafe | {
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... | 1 | 2014-11-04T22:07:56Z | 2020-04-25T14:07:14Z | null | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/26744370/most-efficient-way-to-convert-pandas-series-of-integers-to-strings)
so the null check is pretty cheap. if no nulls, then can just bypass nansafe an use the underlying numpy routine. should be a nice speedup.
``
In [13]: arr = np.random.randint(1,10,size=1000000)
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https://api.github.com/repos/pandas-dev/pandas/issues/8733 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8733/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8733/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8733/events | https://github.com/pandas-dev/pandas/issues/8733 | 47,783,796 | MDU6SXNzdWU0Nzc4Mzc5Ng== | 8,733 | pandas.core.groupby.SeriesGroupBy.hist doesn't take kwargs | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8734/events | https://github.com/pandas-dev/pandas/issues/8734 | 47,798,278 | MDU6SXNzdWU0Nzc5ODI3OA== | 8,734 | Inconsistent mean over booleans | {
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... | closed | false | null | [] | null | 1 | 2014-11-05T04:22:40Z | 2014-11-05T06:33:22Z | 2014-11-05T06:32:55Z | NONE | null | It appears that the mean aggregate function is inconsistent when operating on boolean values.
```
from pandas import DataFrame
df1 = DataFrame({'a': [1, 1], 'bools': [True, True]})
df2 = DataFrame({'a': [1, 1], 'bools': [True, False]})
print df1.mean().bools # 1.0
print df2.mean().bools # 0.5
print df1.groupby('a')... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8735 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8735/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8735/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8735/events | https://github.com/pandas-dev/pandas/issues/8735 | 47,798,365 | MDU6SXNzdWU0Nzc5ODM2NQ== | 8,735 | Strange output from `DataFrame.apply` when applied func creates a dict | {
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"followers_url": "https://api.github.com/users/jr... | 4 | 2014-11-05T04:24:31Z | 2015-12-13T18:33:06Z | 2015-08-28T18:16:56Z | CONTRIBUTOR | null | Just had something odd come up while trying to come up with something for [this SO question](http://stackoverflow.com/questions/26747689/mapping-pandas-dataframe-rows-to-a-pandas-series).
If we use `DataFrame.apply()` to try and create dictionaries from the rows of a dataframe, it seems to return the `dict.values()` m... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8736/events | https://github.com/pandas-dev/pandas/issues/8736 | 47,833,804 | MDU6SXNzdWU0NzgzMzgwNA== | 8,736 | INT: clean/consolidate dtype upcasting and selection | {
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... | 1 | 2014-11-05T13:08:33Z | 2018-07-06T21:51:24Z | 2018-07-06T21:51:23Z | CONTRIBUTOR | null | discussion #8714
consolidate array dtype upcasting selection in `common/_concat_compat` and
`internals/get_empty_dtype_and_na`
cc @immerrr
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https://api.github.com/repos/pandas-dev/pandas/issues/8737 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8737/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8737/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8737/events | https://github.com/pandas-dev/pandas/issues/8737 | 47,856,693 | MDU6SXNzdWU0Nzg1NjY5Mw== | 8,737 | Support for empty slice in multi indices | {
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... | 4 | 2014-11-05T19:20:27Z | 2016-08-01T20:38:04Z | 2016-08-01T20:38:04Z | CONTRIBUTOR | null | For example:
``` python
In [1]: import pandas as pd
In [2]: pd.read_csv('/root')
---------------------------------------------------------------------------
IOError Traceback (most recent call last)
...
IOError: Initializing from file failed
```
The ones I've noticed so far can be f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8739/events | https://github.com/pandas-dev/pandas/pull/8739 | 47,885,077 | MDExOlB1bGxSZXF1ZXN0MjM5MzYwOTE= | 8,739 | BUG: Bug in slicing a multi-index level with an empty-list (GH8737) | {
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```
In [5]: multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
In [6]: df = DataFrame(np.random.randn(5, 6), index=range(5), columns=multi_index).sortlevel(0,axis=1)
In [7]: df
Out[7]:
bar baz foo
alpha bet... | {
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Note that this is already currently inconsistent for int and float... | {
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Changed failure to get quote time from raising RemoteDataError to setting to np.nan
@jorisvandenbossche This fixes the doc error you noted on #8631
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index = pd.to_datetime(pd.Series([
'2014-10-26 07:35:49',
'2014-10-26 07:45:08',
'2014-10-26 08:04:58'
]))
df = pd.DataFrame(np.arange(len(index)), index=index)
df = df.tz_localize('Asia/Krasnoyarsk', ambiguous='NaT')
df.resample('D')
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... | 32 | 2014-11-06T11:08:01Z | 2021-04-11T05:28:41Z | null | CONTRIBUTOR | null | Motivating from [SO](http://stackoverflow.com/questions/26762100/reconstruct-a-categorical-variable-from-dummies-in-pandas)
This is the inverse of `pd.get_dummies`. So maybe `invert_dummies` is better?
I think this name makes more sense though.
This seems a reasonable way to do it. Am I missing anything?
```
In [46... | {
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... | 5 | 2014-11-06T17:05:22Z | 2021-04-11T05:29:09Z | null | NONE | null | What about including a method to get the start and stop after a regex search of items in a DataFrame . Perhaps using .str.extract?
Returning the start as a new column would perhaps be as follows:
```
df['start'] = df['string'].str.extract(pattern, output = 'start')
```
an alternative suggestion from jkitchen on Stac... | {
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... | 12 | 2014-11-06T17:34:58Z | 2020-01-30T20:23:56Z | 2018-07-06T21:49:52Z | NONE | null | What about implementing the following slice function? The df.start and df.end columns contain the start and end index required to slice the df.string.
```
df.sliced = df.string[df.start:df.end]
```
Currently we can slice columns with a fixed start and end index
```
df.sliced = df.string.str.slice(1, -1)
```
However... | {
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... | 7 | 2014-11-07T11:46:37Z | 2019-04-08T18:35:42Z | 2018-07-06T21:34:08Z | NONE | null | Would be nice to have the query method support partial string matching, so you could do the equivalence of df[df['A'].str.contains("abc")] using query: df.query("A contains 'abc'").
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... | 4 | 2014-11-07T15:22:20Z | 2020-04-26T17:33:14Z | null | CONTRIBUTOR | null | Unlike `pandas.DataFrame`, `pandas.Series` does not have the methods to be converted to an html table.
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It can be useful to set it to `[df.values]` to treat the single indices in the same way as hierarchical indices thanks to duck typing.
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Fixes a problem with read_csv(delim_whitespace=True) and
read_table(engine='c') when lines being skipped have trailing
whitespace.
closes #8679
closes #8681
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Some of this refactoring may be useful for #8613, so I'd like someone to look through this.
cc'ing @shoyer, @jreback and @jorisvandenbossche .
TODO:
- add tests for label slicing with negative step (ix, loc)
- [x] int64index
- [x] float64index
- [x] objectindex
- [x] fix datetime/period ... | {
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```
In [18]: s = pd.Series(['abcde', 'fghij'])
In [19]: s.str.slice?
Definition: s.str.slice(self, start=None, stop=None, step=1)
Slice substrings from each element in array
Parameters
----------
start : int or None
stop : int or None
Returns
-------
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https://api.github.com/repos/pandas-dev/pandas/issues/8756 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8756/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8756/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8756/events | https://github.com/pandas-dev/pandas/pull/8756 | 48,172,158 | MDExOlB1bGxSZXF1ZXN0MjQxMDM3MDc= | 8,756 | DOC: remove unused matplotlib directives from conf.py | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8757/events | https://github.com/pandas-dev/pandas/issues/8757 | 48,175,569 | MDU6SXNzdWU0ODE3NTU2OQ== | 8,757 | BUG: pd.Timedelta does not accept np.int32, etc. | {
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Is it the intended behavior of pd.Timedelta to only accept np.int64, python ints, and python floats? I ran into this when I tried to construct a list of pd.Timedeltas from the np.int32 array returned by pd.DatetimeIndex.dayofyear.
``` python
>>> import pandas as pd
>>> impo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8759 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8759/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8759/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8759/events | https://github.com/pandas-dev/pandas/pull/8759 | 48,183,730 | MDExOlB1bGxSZXF1ZXN0MjQxMDczNjA= | 8,759 | Update tutorials.rst | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8760 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8760/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8760/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8760/events | https://github.com/pandas-dev/pandas/issues/8760 | 48,189,906 | MDU6SXNzdWU0ODE4OTkwNg== | 8,760 | TST: require unittest2 for testing on Python 2.6? | {
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How about adding the module as a testing requirement on Python 2.6?
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-09T13:08:09Z | 2014-11-11T05:06:50Z | 2014-11-09T22:04:09Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/40415856
https://travis-ci.org/pydata/pandas/jobs/40425986
cc @dstephens99
These are hitting a `ValueError` on multiple-pr's I have seen. Maybe they are getting partial data?
So need to skip on that particular error (not exactly sure where that is coming from).
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... | 11 | 2014-11-09T17:22:49Z | 2020-05-16T03:36:07Z | null | NONE | null | In [3]: pd.show_versions()
INSTALLED VERSIONS
commit: 9a66dbb
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.0-62-g9a66dbb
nose: None
Cython: 0.20.1post0
numpy: 1.8.1
scipy: 0.13.3
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-09T23:20:25Z | 2014-12-01T00:09:33Z | 2014-12-01T00:09:33Z | CONTRIBUTOR | null | ``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 9), index=range(10))
df.plot()
```
Results in this plot (all the same font size):
``` python
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``` python
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 9), index=range(10))
df.plot(fontsize=7)
```
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closes #8633
xref #7621
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cc @jnmclarty
```
.SS.E
======================================================================
ERROR: test_wdi_search (pandas.io.tests.test_wb.TestWB)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/util/testing... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-10T03:32:07Z | 2015-02-09T00:44:01Z | 2014-11-10T12:43:19Z | CONTRIBUTOR | null | Fixes a previously poorly designed test in io.wb.
Closes #8768
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https://api.github.com/repos/pandas-dev/pandas/issues/8770 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8770/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8770/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8770/events | https://github.com/pandas-dev/pandas/issues/8770 | 48,244,423 | MDU6SXNzdWU0ODI0NDQyMw== | 8,770 | BUG: categorical column dropped from groupby agg result when as_index=False | {
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... | 7 | 2014-11-10T09:22:12Z | 2018-07-06T21:30:07Z | 2018-07-06T21:30:00Z | NONE | null | Hello everyone, updating to Pandas 0.15.1 breaks my code because of the following behaviour. When performing an aggregation operation on a groupby object containing a categorical, with as_index=False, this column is now dropped from the aggregation result. This may be an unexpected consequence of #8585 , and certainly ... | {
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``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.14.1'
In [2]: pd.Timestamp("2014-11-10 11:00Z")
Out[2]: Timestamp('2014-11-10 11:00:00+0000', tz='UTC')
```
Beginning with pandas 0.15.0, it seems to be broken:
``` python
In [1]: import pandas as pd;pd.__ve... | {
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``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.15.0'
In [2]: index = pd.date_range(pd.Timestamp("2014-10-25 03:00", tz="Europe/Paris"), periods=10, freq="3H")
In [3]: index
Out[3]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-10-25 03:00:... | {
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"description": "pandas-gbq compatability... | closed | false | null | [] | null | 5 | 2014-11-10T12:43:23Z | 2014-11-11T14:41:39Z | 2014-11-11T14:08:22Z | CONTRIBUTOR | null | Cannot get io.ga to work since update to 14.1 (using 0.15.0 right now):
TypeError: list indices must be integers, not Index
``` python
import pandas.io.ga as ga
df = ga.read_ga(
property_id = "UA-XXX",
profile_id = "XXX",
metrics = ['users'],
dimensions = ['dayOfWeek','hour'],
start_date... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8774 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8774/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8774/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8774/events | https://github.com/pandas-dev/pandas/issues/8774 | 48,262,006 | MDU6SXNzdWU0ODI2MjAwNg== | 8,774 | 'date_range' no longer has a true "24H" frequency | {
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"followers_url": "https://api.github.com/users/jorisva... | 4 | 2014-11-10T13:01:45Z | 2018-04-09T15:13:16Z | 2018-04-09T15:13:16Z | NONE | null | The semantic of the frequencies "D" and "24H" in 'date_range' has changed in pandas 0.15.0: they are now both equivalent to a "calendar daily" frequency, ie local time is kept the same across DST transition:
``` python
In [1]: import pandas as pd;pd.__version__
Out[1]: '0.15.0'
In [2]: list(pd.date_range(pd.Timestamp(... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8775 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8775/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8775/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8775/events | https://github.com/pandas-dev/pandas/issues/8775 | 48,263,687 | MDU6SXNzdWU0ODI2MzY4Nw== | 8,775 | df.info() ignores index hashtable memory usage | {
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"f... | 4 | 2014-11-10T13:22:33Z | 2017-04-25T21:51:22Z | 2017-04-25T21:51:22Z | NONE | null | another problem with memory report (#7619) in df.info() like #8578 (locked) and fix to multindex hidden in #8456 (see [whatsnew](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#enhancements)). it count the space used by index hashtable. for each entry the klib hashtable keeps a copy of the index data as well ... | {
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... | 17 | 2014-11-10T14:45:09Z | 2017-02-17T15:26:53Z | null | MEMBER | null | From [SO](http://stackoverflow.com/questions/26845727/pandas-plot-multiple-columns-of-a-timeseries-with-labels-example-from-pandas-d/26845829#26845829), I noticed that `Series.plot()` plots on the current axis if no ax is specified, and `DataFrame.plot` not (so creates a new figure when no ax is specified).
Some thing... | {
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... | closed | false | null | [] | null | 1 | 2014-11-10T15:05:55Z | 2014-11-10T23:48:07Z | 2014-11-10T23:48:07Z | NONE | null | Hello, I'm trying to apply this
```
from scipy.spatial import distance
def myrolling_apply(df):
print type(df)
print df
print df.dtypes
a = df['predicted'].values
b = df['actual'].values
#dst = distance.euclidean(a,b)
return(df)
rrbug = pd.read_csv('bug.csv')
rrbug = pd.rolling_apply(rrbu... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8778 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8778/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8778/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8778/events | https://github.com/pandas-dev/pandas/issues/8778 | 48,279,884 | MDU6SXNzdWU0ODI3OTg4NA== | 8,778 | problem with to_sql with NA | {
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``` python
data = [True, None, False]
df = pd.DataFrame(a, columns=['test'])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 0 to 2
Data columns (total 1 columns):
test 2 non-null object
dtypes: obj... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8779/events | https://github.com/pandas-dev/pandas/issues/8779 | 48,283,644 | MDU6SXNzdWU0ODI4MzY0NA== | 8,779 | "TypeError: list indices must be integers, not Index" when using Google Analytics IO | {
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... | closed | false | null | [] | null | 3 | 2014-11-10T16:32:05Z | 2014-11-11T14:08:34Z | 2014-11-10T23:56:22Z | NONE | null | I recently upgraded from pandas 14.1 to 15.0 and now I am having trouble with the Google Analytics IO tool. Even when running simple queries like:
``` python
ga_data = ga.read_ga(metrics='sessions',
dimensions='date',
start_date='2014-06-06',
end_date='2014-10-25',
filters='sourceMedium==google / organ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8780/events | https://github.com/pandas-dev/pandas/issues/8780 | 48,287,901 | MDU6SXNzdWU0ODI4NzkwMQ== | 8,780 | Problem building 0.15.1 on Solaris 11u2 | {
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Python 2.7
Compiler: gcc 4.8.2
There is a Solaris bug (CR 16837431) where /usr/lib/python2.7/conf/Makefile contains paths to build compilers and not installed compilers. I adjusted the shell environment:
```
export CC=gcc
export CXX=g++
export LDSHARED="gcc -shared -Wall... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8781 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8781/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8781/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8781/events | https://github.com/pandas-dev/pandas/pull/8781 | 48,314,827 | MDExOlB1bGxSZXF1ZXN0MjQxNzgwMTY= | 8,781 | Allowing empty slice in multi-indices | {
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"followers_url": "https://api.github.com/users/jr... | 8 | 2014-11-10T21:14:40Z | 2014-11-11T00:12:45Z | 2014-11-11T00:11:42Z | CONTRIBUTOR | null | This solves #8737 in the case where there is at least one boolean indexer. The patch of #8739 does not allow the following:
``` python
import pandas as pd
import numpy as np
multi_index = pd.MultiIndex.from_product((['foo', 'bar', 'baz'], ['alpha', 'beta']))
df = pd.DataFrame(np.random.randn(5, 6), index=range(5), co... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8782 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8782/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8782/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8782/events | https://github.com/pandas-dev/pandas/pull/8782 | 48,339,485 | MDExOlB1bGxSZXF1ZXN0MjQxOTM3Mjg= | 8,782 | Do not convert series when looking at sub-daily frequencies. | {
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"followers_url": "https://api.github.com/users/jr... | 7 | 2014-11-11T02:06:09Z | 2014-11-14T19:31:56Z | 2014-11-14T19:31:48Z | CONTRIBUTOR | null | Fixes #8772 by not converting values from UTC to local timezone when trying to infer sub-daily frequencies. Across a DST transition this will result in a non-unique deltas and an inferred frequency of None.
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This is a potential resolution to GH8336 It borrows the same code flow from _get_formatted_index in
pandas.core.format
``` python
import pandas as pd
pd.show_versions()
df = pd.DataFrame({
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"C": ["foo", "foo", "foo", "foo", "foo"],
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https://github.com/datalyze-solutions/pandas/tree/qt4/pandas/sandbox
Its still in sandbox. An example can be found in tests named TestApp.py. I just testet Qt4 and python2.7, Qt5 might be interesting.
Any reviews are welcome! :smiley:
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``` python
from datetime import datetime
import pandas as pd
import numpy as np
result = np.empty(3, dtype=[('name', 'object'),
('value', 'int64'),
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Moving into Pandas from R/Matlab/Stata. One feature I'm finding I really miss: a field generated during a merge that reports, for each row, whether that row came from the left dataset, the right dataset, or was successfully merged.
I do a lot of work in social science where our data is VERY dirty. For exa... | {
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"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-11T23:16:38Z | 2014-12-04T11:30:36Z | 2014-12-04T11:30:36Z | NONE | null | This should work:
``` python
with pandas.HDFStore('file.h5') as f:
print(f)
```
as a more convenient form of
``` python
with contextlib.closing(pandas.HDFStore('file.h5')) as f:
print(f)
```
AFAIK this is a easy as adding the following to `HDFStore`
``` python
def _enter_(self): pass
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https://api.github.com/repos/pandas-dev/pandas/issues/8792 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8792/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8792/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8792/events | https://github.com/pandas-dev/pandas/pull/8792 | 48,453,331 | MDExOlB1bGxSZXF1ZXN0MjQyNjExMTc= | 8,792 | TST: Add test for get_data_google dtypes | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8793 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8793/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8793/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8793/events | https://github.com/pandas-dev/pandas/pull/8793 | 48,463,007 | MDExOlB1bGxSZXF1ZXN0MjQyNjUzMjQ= | 8,793 | ENH: serialization of categorical to HDF5 (GH7621) | {
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"followers_url": "https://api.github.com/users/jr... | 15 | 2014-11-12T01:55:30Z | 2014-11-17T00:34:50Z | 2014-11-17T00:34:49Z | CONTRIBUTOR | null | This is implemented by storing the codes directly in the table. And a metadata VLArray of the categories.
Query and appending work as expected. The only quirk is that I don't allow you to append to a table unless the new data has exactly the same categories. Otherwise the codes become meaningless.
This has the nice pr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8794 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8794/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8794/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8794/events | https://github.com/pandas-dev/pandas/issues/8794 | 48,491,032 | MDU6SXNzdWU0ODQ5MTAzMg== | 8,794 | ENH: add .normalize() to Timestamp? | {
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... | 19 | 2014-11-12T09:45:34Z | 2015-03-11T12:11:02Z | 2015-03-11T12:11:02Z | MEMBER | null | From http://stackoverflow.com/questions/26882499/reset-time-part-of-a-pandas-timestamp#26882499
There is now a `DatetimeIndex.normalize()` method to set times to midnight.
Would it be useful to also have this method available on the individual `Timestamp` object?
Further, this method could also be added to the `.dt`... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8795/events | https://github.com/pandas-dev/pandas/issues/8795 | 48,513,243 | MDU6SXNzdWU0ODUxMzI0Mw== | 8,795 | BUG: Grouper(key='A') gives AttributeError when applying function | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-11-12T14:04:22Z | 2014-12-05T02:34:01Z | 2014-12-05T02:34:01Z | MEMBER | null | I was playing a bit with `pd.Grouper`. Shouldn't the following work:
```
In [109]: df = pd.DataFrame({'A':[0,0,1,1,2,2], 'B':[1,2,3,4,5,6]})
In [110]: df
Out[110]:
A B
0 0 1
1 0 2
2 1 3
3 1 4
4 2 5
5 2 6
In [111]: df.groupby(pd.Grouper(key='A')).sum()
-------------------------------------------------... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8796/events | https://github.com/pandas-dev/pandas/issues/8796 | 48,518,462 | MDU6SXNzdWU0ODUxODQ2Mg== | 8,796 | DataFrame.interpolate() is not equivalent to scipy.interpolate.interp1d | {
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When using with simple data, the differences are small (see images). However, when used with real-world data, the differences can be large enough to throw off some algorithms that depend on the values... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8797/events | https://github.com/pandas-dev/pandas/issues/8797 | 48,528,297 | MDU6SXNzdWU0ODUyODI5Nw== | 8,797 | Series .equals does not ignore NaNs | {
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```
import pandas as pd
import numpy as np
a = pd.DataFrame([{'A': 1, 'B': 'b', 'C': np.NAN}, {'A': 1, 'B': 'b', 'C': 1}])
b = pd.DataFrame([{'A': 1, 'B': 'b', 'C': np.NA... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8798/events | https://github.com/pandas-dev/pandas/issues/8798 | 48,577,102 | MDU6SXNzdWU0ODU3NzEwMg== | 8,798 | Panda 0.15.1 matplotlib 1.4.2 setting default font.size messes up labels. | {
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"followers_url": "https://api.github.com/users/jorisva... | 5 | 2014-11-12T23:20:56Z | 2016-07-04T10:58:42Z | 2016-07-04T10:58:42Z | CONTRIBUTOR | null | ```
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as m
print 'Matplotlib version:', m.__version__
Matplotlib version: 1.4.2
print 'Pandas version:', pd.__version__
Pandas version: 0.15.1
plt.style.use('ggplot')
m.rc("text.latex",preamble=r"\usepackage{cmbright}")
m.rcParams['font.size'] = 20... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8799/events | https://github.com/pandas-dev/pandas/issues/8799 | 48,578,859 | MDU6SXNzdWU0ODU3ODg1OQ== | 8,799 | Panda 0.15.1 matplotlib 1.4.2 setting boxplot heading/subheading | {
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... | 2 | 2014-11-12T23:41:02Z | 2018-07-06T21:26:34Z | 2018-07-06T21:26:34Z | CONTRIBUTOR | null | Hi,
the heading and subheading in the DataFrame.boxplot and DataFrame.hist are generated automatically. I have several issues here.
1) Would like to have the option to turn it on/off/modify content.
2) Default distance between subheading and heading should be larger
3) Default generation of heading/subheading should... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8800/events | https://github.com/pandas-dev/pandas/pull/8800 | 48,600,147 | MDExOlB1bGxSZXF1ZXN0MjQzNDc0NDk= | 8,800 | Removing typo as Renaming | {
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https://api.github.com/repos/pandas-dev/pandas/issues/8801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/8801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/8801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/8801/events | https://github.com/pandas-dev/pandas/issues/8801 | 48,615,642 | MDU6SXNzdWU0ODYxNTY0Mg== | 8,801 | Series.mask should accept same arguments as Series.where | {
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"closed_at": "2015-05-11T15:46:03Z",
"closed_issues": 237,
"created_at": "2014-10-07T14:39:07Z",
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"events_url": "https://api.github.com/users/jreback/events{/privacy}",
"followers_url": "https://api.github.com/users/jr... | 1 | 2014-11-13T09:38:39Z | 2015-04-04T18:28:30Z | 2015-04-04T18:28:30Z | CONTRIBUTOR | null | `Series.mask` just performs a mask operation. `Series.where` accepts substitute values. (In the meantime, workaround is simple - just invert the where selection).
Version 0.15.1 docs:
```
Series.mask(cond)
Returns copy whose values are replaced with nan if the inverted condition is True
Series.where(cond, other... | {
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