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https://api.github.com/repos/pandas-dev/pandas/issues/6601 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6601/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6601/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6601/events | https://github.com/pandas-dev/pandas/pull/6601 | 29,200,541 | MDExOlB1bGxSZXF1ZXN0MTM0MzM3ODY= | 6,601 | BUG: Bug in popping from a Series (GH6600) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6602 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6602/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6602/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6602/events | https://github.com/pandas-dev/pandas/issues/6602 | 29,201,456 | MDU6SXNzdWUyOTIwMTQ1Ng== | 6,602 | makeMissingDataFrame is broken | {
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```
[~/]
[18]: pd.version.version
[18]: '0.13.1-254-g150f323'
from pandas.util.testing import _create_missing_idx
df = pd.util.testing.makeDataFrame()
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https://api.github.com/repos/pandas-dev/pandas/issues/6603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6603/events | https://github.com/pandas-dev/pandas/pull/6603 | 29,202,113 | MDExOlB1bGxSZXF1ZXN0MTM0MzQ3Njk= | 6,603 | ENH: Make sure makeMissing* works as expected. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6604 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6604/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6604/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6604/events | https://github.com/pandas-dev/pandas/pull/6604 | 29,209,884 | MDExOlB1bGxSZXF1ZXN0MTM0Mzk2Nzg= | 6,604 | ENH: Allow update to use an on keyword. Allow one to many update. | {
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```
df = DataFrame([[np.nan, 'A'],
[np.nan, 'A'],
[np.nan, 'A'],
[1.5, 'B'],
[2.2, 'C'],
[3.1, 'C'],
[1.2,' B']], columns=['number', 'name'])
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https://api.github.com/repos/pandas-dev/pandas/issues/6605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6605/events | https://github.com/pandas-dev/pandas/pull/6605 | 29,212,642 | MDExOlB1bGxSZXF1ZXN0MTM0NDEzOTA= | 6,605 | Change Panel.shift to use NDFrame.shift | {
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"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
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"labels_url": "htt... | 12 | 2014-03-11T20:10:41Z | 2014-06-29T18:16:04Z | 2014-04-01T20:55:05Z | CONTRIBUTOR | null | closes #4867
This brings `Panel.shift` in line with `DataFrame.shift`. The shifted data retains the same dimensions/indexes and doesn't drop the empty rows. So I have two concerns.
1. This is an API change and could conceivably break existing code.
2. The perf regresses quite a bit.
Here is the vbench.
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6606/events | https://github.com/pandas-dev/pandas/issues/6606 | 29,213,920 | MDU6SXNzdWUyOTIxMzkyMA== | 6,606 | tzinfo lost when concatenating multiindex arrays | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6607/events | https://github.com/pandas-dev/pandas/issues/6607 | 29,219,934 | MDU6SXNzdWUyOTIxOTkzNA== | 6,607 | BUG: read_table() ignores dtype argument when multi-character separator is specified | {
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"labels_url": "htt... | 26 | 2014-03-11T22:19:14Z | 2014-04-23T22:23:11Z | 2014-04-23T22:23:11Z | CONTRIBUTOR | null | related #4363
closes #3374
Here is a minimal example:
``` ipython
In [21]: df = pd.DataFrame({'A': range(5), 'B': rand(5)})
In [22]: df
Out[22]:
A B
0 0 0.402616
1 1 0.880696
2 2 0.184491
3 3 0.832732
4 4 0.393917
[5 rows x 2 columns]
In [23]: df.to_csv('test.csv', sep=' ')
In [24]: pd.read_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6608/events | https://github.com/pandas-dev/pandas/issues/6608 | 29,223,533 | MDU6SXNzdWUyOTIyMzUzMw== | 6,608 | BUG: strange timeseries plot behavior | {
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``` python
s1 = pd.Series([1, 2, 3], index=[datetime.datetime(1995, 12, 31), datetime.datetime(2000, 12, 31), datetime.datetime(2005, 12, 31)])
s2 = pd.Series([1, 2, 3], index=[datetime.datetime(1997, 12, 31), datetime.datetime(2003, 12, 31), datetime.datetime(2... | {
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```
In [44]: s = pd.Series(['a', 0, np.nan])
In [45]: s.str.contains('a', na=False)
Out[45]:
0 True
1 False
2 False
dtype: bool
In [46]: s.str.match('a', na=False)
Out[46]:
0 True
1 NaN
2 NaN
dtype: object
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6610/events | https://github.com/pandas-dev/pandas/issues/6610 | 29,237,544 | MDU6SXNzdWUyOTIzNzU0NA== | 6,610 | to_csv drops values if DataFrame is int64 but assigned array of float64 | {
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Minimal Reproducible Example on my system (Ubuntu 13.04, python 2.7.4, pandas 0.13.1, numpy 1.8.0):
``` python
import pandas as... | {
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The main reason is that _reindex takeable_ is conceptually different from _reindex_: the latter can introduce new rows wi... | {
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- floats (decimal.Decimal) were not converted due to a small bug in the `coerce_float` arg. This is now OK.
- MySQL has no real BOOL type (it is just an alias for tiny int). So I changed the tests for that (so a bool column is converted to int64 (or fl... | {
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As discussed in #6612, `reindex(..., takeable=True)` as released in 0.13.0 didn't do anything that's not achieved by `.take`, `.iloc` and `.copy` attrs/methods, but also contained a bug which propagated to `iloc` when the latter was changed to use _reindex/takeable_ internally.
The proposed solution is t... | {
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fix https://github.com/pydata/pandas/issues/6589
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```
>>> pd.__version__
'0.13.1-420-g6899ed6'
>>> df2 = pd.DataFrame([1], columns=pd.MultiIndex.from_arrays([[1],[2]]))
>>> df2
1
2
0 1
[1 rows x 1 columns]
>>> df2.columns
MultiIndex(levels=[[1], [2]],
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https://github.com/travis-ci/travis-ci/issues/1989
jreback/pandas@5989bbe
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https://api.github.com/repos/pandas-dev/pandas/issues/6620 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6620/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6620/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6620/events | https://github.com/pandas-dev/pandas/issues/6620 | 29,292,735 | MDU6SXNzdWUyOTI5MjczNQ== | 6,620 | groupby producing incorrect results applying functions to int64 columns due to internal cast to float | {
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groupby often casts int64 data to floats before applying functions to the grouped data, and then casts the result back to an int64. This produces incorrect results.
Here is a simple example:
``` python
import pandas as pd
import numpy as np
label = np.array([1, 2, 2, 3],dtype=np.int)
data = np.array([1,... | {
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"labels_url": "htt... | 10 | 2014-03-12T21:20:11Z | 2014-05-10T19:49:51Z | 2014-05-10T19:49:51Z | CONTRIBUTOR | null | regression in groupby nth, it raises an index error on MI in master. This is my fault (need to add tests, should also test for axis!=0).
http://stackoverflow.com/questions/22361385/integer-position-based-indexing-of-multi-index-dataframes
reason this raise is as I use obj.loc[[[]] :s
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https://api.github.com/repos/pandas-dev/pandas/issues/6622 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6622/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6622/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6622/events | https://github.com/pandas-dev/pandas/pull/6622 | 29,317,093 | MDExOlB1bGxSZXF1ZXN0MTM1MDIwNTM= | 6,622 | BUG: Error in to_stata when DataFrame contains non-string column names | {
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"labels_url": "htt... | 14 | 2014-03-13T00:37:24Z | 2014-07-16T08:58:09Z | 2014-03-13T22:19:16Z | CONTRIBUTOR | null | closes #4558
to_stata does not work correctly when used with non-string names. Since
Stata requires string names, the proposed fix attempts to rename columns using
the string representation of the column name used. A warning is raised if
the column name is changed.
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https://api.github.com/repos/pandas-dev/pandas/issues/6623 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6623/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6623/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6623/events | https://github.com/pandas-dev/pandas/issues/6623 | 29,323,059 | MDU6SXNzdWUyOTMyMzA1OQ== | 6,623 | BUG: timeseries plot mouse-over indicates incorrect time | {
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"followers_url": "https://api.github.com/users/jr... | 2 | 2014-03-13T03:12:00Z | 2014-06-12T21:04:04Z | 2014-04-30T03:54:36Z | CONTRIBUTOR | null | In pandas 0.13.1 this code produces a plot that, when "moused over", indicates incorrect dates on the 't' (x) axis, despite having the correct axis label.
``` python
pd.Series([1, 2, 3], index=pd.date_range('2014-01-01', periods=3, freq='D')).plot()
```
![screenshot](https://f.cloud.github.com/assets/5356340/2405879/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6624/events | https://github.com/pandas-dev/pandas/issues/6624 | 29,323,337 | MDU6SXNzdWUyOTMyMzMzNw== | 6,624 | Join 2 data-frames on a common column (values mismatch) | {
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... | 5 | 2014-03-13T03:21:26Z | 2021-04-11T04:14:44Z | 2021-04-11T04:14:44Z | NONE | null | i'm using pandas to read and join the 2 dataframe, but the data are not aligned on the column i chose for the join. [Here](http://nbviewer.ipython.org/gist/epifanio/9126057) is reproducible example.
_Note at index 8 and 16, the values of SVSPP['_left'] and SVSPP do not match.
with my real data the mismatch is true fo... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6625 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6625/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6625/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6625/events | https://github.com/pandas-dev/pandas/issues/6625 | 29,331,860 | MDU6SXNzdWUyOTMzMTg2MA== | 6,625 | describe() for boolean series | {
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```
In [27]: import pandas as pd
In [28]: s = pd.Series([True, True, False])
In [29]: s.describe()
Out[29]:
count 3
mean 0.6666667
std 0.5773503
min False
25% 0.5
50% 1
75% 1
max True
dty... | {
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... | 1 | 2014-03-13T12:21:31Z | 2018-07-06T21:21:00Z | 2018-07-06T21:21:00Z | CONTRIBUTOR | null | see #6516
after #6516 it is possible to add a `CategoricalGrouper` subclassing `Grouper`, like how `TimeGrouper` does. It won't be user visible, but will 'clean' up things a bit.
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https://api.github.com/repos/pandas-dev/pandas/issues/6627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6627/events | https://github.com/pandas-dev/pandas/pull/6627 | 29,347,165 | MDExOlB1bGxSZXF1ZXN0MTM1MTkyMzE= | 6,627 | SQL: fix meta/has_table | {
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- the `meta` attribute is not updated automatically, with the consequence that eg when you delete a table from sql directly, the `has_table` function does not work anymore:
- I added a test for that
- I converted the `meta` attribute to one which is always updated ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6628 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6628/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6628/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6628/events | https://github.com/pandas-dev/pandas/pull/6628 | 29,350,178 | MDExOlB1bGxSZXF1ZXN0MTM1MjEwNDk= | 6,628 | BUG: Bug in fillna with limit and value specified | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6629 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6629/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6629/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6629/events | https://github.com/pandas-dev/pandas/issues/6629 | 29,354,979 | MDU6SXNzdWUyOTM1NDk3OQ== | 6,629 | "Awkward" Integer-indexing in MultiIndex DataFrames | {
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`df.xs(df.index.levels[N][K], level=N)`
or... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6630 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6630/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6630/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6630/events | https://github.com/pandas-dev/pandas/issues/6630 | 29,370,652 | MDU6SXNzdWUyOTM3MDY1Mg== | 6,630 | Non-aligned x-axes when plotting two series on the same axes | {
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... | 5 | 2014-03-13T17:22:27Z | 2020-04-25T14:00:28Z | null | NONE | null | I have the following `DataFrame` that I want to create a boxplot for:
```
estimates.head()
Out[162]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 \
DATE
2009-04-28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2009-04-29 6 6 6 6 6 6 6 6 6 6 6 6 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6631 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6631/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6631/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6631/events | https://github.com/pandas-dev/pandas/issues/6631 | 29,389,813 | MDU6SXNzdWUyOTM4OTgxMw== | 6,631 | set_index/reindex bugs with time indices | {
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"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
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"labels_url": "htt... | 2 | 2014-03-13T21:10:09Z | 2014-05-12T13:34:58Z | 2014-05-12T13:34:58Z | CONTRIBUTOR | null | ```
[14]: pd.version.version
[14]: '0.13.1-426-ge19b2eb'
```
Noticed these. This one's minor. `set_index` seems to drop frequency information for DatetimeIndex. I'm not sure what other metadata is ignored.
```
import statsmodels.api as sm
import pandas as pd
index = pd.PeriodIndex(start='1959Q1', end='2009Q3')
dta = ... | {
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"followers_url": "https://api.github.com/users/jr... | 12 | 2014-03-13T22:50:51Z | 2015-01-26T01:23:15Z | 2015-01-25T22:57:34Z | CONTRIBUTOR | null | Often I find myself wanting to throw away values which aren't in a given set when doing `value_counts`...
This basically just does this but inside value_counts (before possibly sorting):
```
pd.value_counts(x).reindex(base, fill_value=0) # same as Series method
```
_I previously thought this was trickier to impleme... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6633/events | https://github.com/pandas-dev/pandas/issues/6633 | 29,397,737 | MDU6SXNzdWUyOTM5NzczNw== | 6,633 | Series.plot broken for Int64Index | {
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] | closed | false | null | [] | null | 2 | 2014-03-13T23:06:27Z | 2014-03-14T11:45:01Z | 2014-03-14T11:44:51Z | CONTRIBUTOR | null | Sorry to do my daily issue bombardment, but I live on the edge for better or worse. I thought this should work. It does if it's a single column DataFrame ie., `y.to_frame().plot()`
```
[~/]
[121]: y = pd.util.testing.makeStringSeries()
[~/]
[122]: y.reset_index(drop=True, inplace=True)
[~/]
[123]: y.plot()
---------... | {
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"followers_url": "https://api.github.com/users/jr... | 11 | 2014-03-14T06:21:17Z | 2014-04-30T16:29:24Z | 2014-04-30T16:29:24Z | CONTRIBUTOR | null | I wrote this so my fault. This probably means some stuff with break when passing stuff with unicode.
Will add example.
The reason is to include integers as strings before getting dummies, maybe should just drop that functionality?
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https://api.github.com/repos/pandas-dev/pandas/issues/6635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6635/events | https://github.com/pandas-dev/pandas/issues/6635 | 29,416,767 | MDU6SXNzdWUyOTQxNjc2Nw== | 6,635 | Date range generation is incorrect or misconstrued | {
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>>> from_time = dateutil.parser.parse('2014-02-12 18:30:00')
>>> to_time = dateutil.parser.parse('2014-03-14 18:29:59')
>>> pandas.date_range(from_time, to_time, freq='MS', tz='Asia/Kolkata')
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: MS, Timezone: Asia/Kolkata
```
Why are there no dates being g... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6636 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6636/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6636/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6636/events | https://github.com/pandas-dev/pandas/issues/6636 | 29,457,471 | MDU6SXNzdWUyOTQ1NzQ3MQ== | 6,636 | Speed up DatetimeConverter for plotting | {
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More speci... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6637/events | https://github.com/pandas-dev/pandas/pull/6637 | 29,458,645 | MDExOlB1bGxSZXF1ZXN0MTM1ODUwMzg= | 6,637 | ENH: Have inferred_freq set offset. Have freq always try to infer. | {
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"default"... | closed | false | null | [] | null | 5 | 2014-03-14T18:21:27Z | 2014-07-04T19:23:46Z | 2014-03-14T19:37:45Z | CONTRIBUTOR | null | I'm on the fence about this. Having a property with side effects doesn't quite sit right to me. But I think it kinda makes sense here. If it didn't set `freq`, then it really doesn't make sense for `freq` to exist. I'd just use `inferred_freq` all of the time.
Now calling `freq` always tries to infer the frequency. Wh... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6638/events | https://github.com/pandas-dev/pandas/issues/6638 | 29,472,143 | MDU6SXNzdWUyOTQ3MjE0Mw== | 6,638 | frequency issues | {
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```
from pandas.tseries import frequencies
frequencies._is_annual('AS') # oversight?
# can I go from quarterly to annual?
frequencies.is_subsample('Q', 'A')... | {
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```
In [3]: df = DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})
In [7]: df.columns = MultiIndex.from_tuples([('a','one'),('a','two'),('b','three')])
In [8]: df
Out[8]:
a b
one two three
0 2 5 1
1 1 4 3
2 1 3 2
3 1 2 4
[4 r... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6640 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6640/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6640/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6640/events | https://github.com/pandas-dev/pandas/issues/6640 | 29,476,340 | MDU6SXNzdWUyOTQ3NjM0MA== | 6,640 | DEPR: deprecate rows/cols to pivot | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6641 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6641/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6641/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6641/events | https://github.com/pandas-dev/pandas/issues/6641 | 29,477,299 | MDU6SXNzdWUyOTQ3NzI5OQ== | 6,641 | DEPR: create issues for the current FutureWarnings in pandas | {
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ideally also link the issues back to the original deprecation. Off the top of my head.
their are more though.
Here is what I found (line numbers as of commit 70de129):
- [x] Deprecate load and save in favor of pickle and to_... | {
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- added ability to specify the used column name for the index column in `to_sql` (analoguous to `to_csv`). Good idea?
- I only did it for the new sqlalchemy function, not the legacy one. Only problem is that it starts ... | {
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```
['./bench/bench_join_panel.py',
'./pandas/sandbox/qtpandas.py',
'./pandas/tests/test_common.py',
'./pandas/tests/test_frame.py',
'./pandas/tests/test_generic.py',
'./pandas/core/series.py',
'./pandas/core/format.py',
'./pandas/core/internals.py',
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`width`: Specify the bar width. In previous versions, static value 0.5 was passed to matplotlib and it cannot be overwritten.
`pos`: Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1(ri... | {
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closes #6643
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#6544
```
In [1]: s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
In [2]: m = s.max()
In [3]: x = m.to_datetime()
In [4]: x - s
Out[4]:
0 2 days
1 1 days
2 0 days
dtype: timedelta64[ns]
In [5]: m - s
TypeEr... | {
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This fixes the performance bottleneck described in #6636 by using the vectorized epoch2num for DataIndex arrays. The included test ensures that the DatetimeConverter produces the same results as the Matplotlib's date2num for individual Timestamp objects as well as DatetimeIndex array objects.
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- use new api in 10min.rst
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] | closed | false | null | [] | null | 2 | 2014-03-17T08:32:34Z | 2014-03-22T21:06:03Z | 2014-03-22T21:06:03Z | NONE | null | Like the title said，we use pandas to handle data from mysql，after some groupby and apply operation，we than send them back to mysql.
The problem is time used to run the same script increased a lot after we update pandas to the newest version.
Could you give any suggestion to the possible reasion for this?
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docs and api
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By default, area plot is being stacked. When area plot is not stacked (`stacked=False`), alpha value is set to 0.5 to show overlapped area if not configured specifically. As a side benefi... | {
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Admittedly the conditional in `__sub__` is a little gross now.
I didn't add release notes as this is really just patching up a yet-to-be released item, but please let me know if I should.
Also, the test only runs on numpy >= 1.7, is that okay?
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```
test_boxplot (__main__.TestDataFramePlots) ... > /usr/local/lib/python2.7/dist-packages/pandas-0.13.1_436_g4216178-py2.7-linux-i686.egg/pandas/core/internals.py(64)__init__()
-> '%d' % (len(values), len(items)))
(Pdb) bt
/usr/lib/python2.7/unittest/case.py(331)run()... | {
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Related to #4803, I prepared some codes to add a `matplotlib.Table` to `DataFrame` and `Series` plot using `table` keyword. `table` can take followings as an input:
- `False`: Do nothing (default).
- `True`: Draw a table using the data called `plot` method. Data will be transposed to meet the matplotlib's... | {
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```
ret["returns_abs"] = ret.returns.a... | {
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Even plotting 80/89 rows works fine:
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If none provided, falls back to default behavior.
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The new keyword prevents the conversion from escaping column names
Currently, a sequence like $e^x$ gets properly formatted when you display a dataframe in an ipython notebook, but gets escaped when converted to latex.
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Suppose I have a timeseries indexed by a PeriodIndex at day resolution, and I want to numerically integrate it over a range whose start- and end-point are known to a higher time resolution (say, to the exact second).
As the first step towards this, it'd help to take a slice of my timeseries in which the values... | {
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The test relies on drawing at least 1 draw out of 10 uniform draw to be larger than 0.5; even if this didn't cause the failure originally, it should still be fixed. I set a seed that's known to produce the des... | {
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```
ic = lambda x: pd.Int64Index(np.arange(x))
In [11]: df1.iloc[:,~ic(len(df1.columns)).isin(np.r_[0,3:5])]
```
could borrow from `query` and support parsing I think
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## Test code
```
import pandas as pd
import numpy as np
import timeit
xdim = 10000
ydim = 500
repeats = 10
df = pd.DataFrame(np.random.rand(xdim,ydim))
# axis 0 shift
s = timeit.default_timer()
for i in range(repeats):
df1 = df.shift(1,axis=0)
e = timeit.default_timer()
print "Axis 0 shift",e-s
# ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6673 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6673/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6673/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6673/events | https://github.com/pandas-dev/pandas/issues/6673 | 29,803,585 | MDU6SXNzdWUyOTgwMzU4NQ== | 6,673 | Bug in date_range with semi-open interval and freq='B'? | {
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"followers_url": "https://api.github... | 5 | 2014-03-20T09:20:04Z | 2018-07-31T13:13:28Z | 2018-07-31T13:13:28Z | CONTRIBUTOR | null | #5637 has some examples as well
When the end date in `date_range` is not a valid business day it appears that `date_range` first sets it to a valid date _before_ deciding whether to return the last date depending on whether the interval is closed or open on the right. This results in the date range being incorrectly t... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6674 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6674/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6674/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6674/events | https://github.com/pandas-dev/pandas/issues/6674 | 29,806,731 | MDU6SXNzdWUyOTgwNjczMQ== | 6,674 | peculiar performance drop for inplace rename after dropna | {
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Profiling indicates this is related to garbage collection (see below).
Furthermore, the expected performance is recovered by avoiding the dropna method.
The following short ex... | {
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... | 5 | 2014-03-20T20:37:45Z | 2021-04-11T04:21:52Z | null | MEMBER | null | I often find myself writing code that looks like this:
``` python
start_group = df[df.str_col == 'start'].index.values + 1
end_group = df[df.str_col == 'end'].index.values - 1
for s, e in zip(start_group, end_group):
some_function(df[s:e])
```
to get frame subsets between repeated start and end indicators from a ... | {
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"labels_url": "htt... | 1 | 2014-03-21T00:06:53Z | 2014-03-21T12:46:08Z | 2014-03-21T12:46:08Z | MEMBER | null | Current behavior is to use the index over the columns. I think it makes more sense to prefer the columns.
Current behavior:
```
In [7]: n = 10
In [8]: df = DataFrame(randint(40, 50, size=(n, 2)), columns=list('ab'))
In [9]: df.index.name = 'a'
In [10]: df
Out[10]:
a b
a
0 43 40
1 43 46
2 41 42
3 40 4... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6678 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6678/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6678/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6678/events | https://github.com/pandas-dev/pandas/pull/6678 | 29,883,192 | MDExOlB1bGxSZXF1ZXN0MTM4MjU2Njg= | 6,678 | BUG: legend behaves inconsistently when plotting to the same axes | {
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"labels_url": "htt... | 15 | 2014-03-21T07:30:24Z | 2014-06-12T19:37:46Z | 2014-04-22T13:10:31Z | MEMBER | null | There seems to be some inconsistencies related to `DataFrame.plot` and `Series.plot` legend behaviors.
_Problems:_
- When `DataFrame.plot` or `Series.plot` plots data on the same axes repeatedly:
- If the target axes already has a legend, line plot always appends its legend to existing one ignoring `legend` kw and e... | {
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"followers_url": "https://api.github.com/users/jr... | 5 | 2014-03-21T13:57:55Z | 2014-06-26T08:01:41Z | 2014-03-22T21:02:43Z | CONTRIBUTOR | null | The following code:
```
df=DataFrame(data=[1,2,True,4.5], columns=["A"])
df["B"]=[1,1,1,2]
print df.groupby(df["B"]).sum()
print df.groupby(df["B"]).max()
```
gives as ouput
```
B
B
1 3
2 2
[2 rows x 1 columns]
A B
B
1 2.0 1
2 4.5 2
[2 rows x 2 columns]
```
As we can see, the sum() opera... | {
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related #6645
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related to #5645 as well
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https://api.github.com/repos/pandas-dev/pandas/issues/6682 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6682/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6682/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6682/events | https://github.com/pandas-dev/pandas/pull/6682 | 29,919,095 | MDExOlB1bGxSZXF1ZXN0MTM4NDY5MjQ= | 6,682 | BUG: Bug in binary operations with a rhs of a Series not aligning (GH6681) | {
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```
In [11]: index=MultiIndex.from_product([list('abc'),
['one','two','three'],
[1,2,3]],
names=['first','second','third'])
In [12]: df = DataFrame(np.arange(27*3).reshape(2... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6683 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6683/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6683/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6683/events | https://github.com/pandas-dev/pandas/issues/6683 | 29,920,664 | MDU6SXNzdWUyOTkyMDY2NA== | 6,683 | Poor performance for .loc and .iloc compared to .ix | {
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.ix takes 4.54897093773 sec
.iloc takes 111.531260967 sec
.loc takes 92.80142... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6684 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6684/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6684/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6684/events | https://github.com/pandas-dev/pandas/issues/6684 | 29,923,749 | MDU6SXNzdWUyOTkyMzc0OQ== | 6,684 | Bug: Export to Stata NaN not converted to "." | {
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I noticed that when exporting data to stata the `NaN` values are not always converted to Stata missing values but instead left blank. This somehow confuses Stata which does not allow using the `destring` command to solve the problem nor using `replace value=. if value==.`.
As an Example I downloaded the [World De... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6685/events | https://github.com/pandas-dev/pandas/pull/6685 | 29,944,202 | MDExOlB1bGxSZXF1ZXN0MTM4NTk5ODg= | 6,685 | BUG: NaN values not converted to Stata missing values (GH6684) | {
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Stata does not correctly handle NaNs, and so these must be replaced with Stata
missing values (. by default). The fix checks floating point columns for nan
and replaces these with the Stata numeric code for (.).
The write_index option was also being ignored by omission. This has been fixed and
numerous ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6688 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6688/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6688/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6688/events | https://github.com/pandas-dev/pandas/issues/6688 | 29,968,667 | MDU6SXNzdWUyOTk2ODY2Nw== | 6,688 | to_excel fails when there are lists, dict, objects in DF | {
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I'm running a pretty recent build of Pandas ('0.13.1-213-gc174c3d') on Python 2.7.5 on OS X 0.9.2.
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this only happened on the cythonized methods and not the python agg ones (this is why count worked, but sum did not)
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"labels_url": "htt... | 7 | 2014-03-23T00:01:35Z | 2014-06-17T15:37:54Z | 2014-03-26T20:50:44Z | MEMBER | null | Closes #4525.
I modified the `BarPlot` internal alignment to meet line coordinates. Previously, `BarPlot` doesn't pass `align` keyword to matplotlib (thus matplotlib uses `align='edge'`), but it adjusts bar locations to looks like `align='center'`.
Now `BarPlot` pass `align=center` to matplotlib by default and ticks ... | {
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] | closed | false | null | [] | null | 2 | 2014-03-23T05:10:06Z | 2014-05-05T13:25:59Z | 2014-05-05T13:25:55Z | NONE | null | Wondering if there is a way to insert a pandas DF into Cassandra?
Here is the info of the python driver:
https://github.com/datastax/python-driver
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"labels_url": "htt... | 11 | 2014-03-23T18:42:34Z | 2014-06-17T07:59:48Z | 2014-03-25T11:49:20Z | CONTRIBUTOR | null | closes #6680 -- Deprecate `cols` in `drop_duplicates()` and `duplicated()` (rename to `subset`)
closes #6645 -- Deprecate `cols` in `to_csv()` and `to_excel()` (rename to `columns`)
releated to #6686
Related #5505 -- Deprecate `cols` and `rows` in `pivot_table()` and `crosstab()` (rename to `columns` and `index`)
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```
======================================================================
ERROR: test suite for <class 'statsmodels.tsa.tests.test_expsmoothing.TestExpSmoothing'>
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib... | {
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... | [] | closed | false | null | [] | null | 3 | 2014-03-24T09:08:20Z | 2014-03-24T11:09:36Z | 2014-03-24T11:09:36Z | NONE | null | Hello,
I have a strange performance issue that I want to share with you...
When I have a TimeSeries (let's call it "ts"), I discovered that it is faster to apply it on ts.values than on ts (see example 1). Is that logical ?
This have a large impact on performance when running for example the "apply function after a gr... | {
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data = {'a':[1,1,2,2],'b':['Y','Y','N','Y'], 'c':['Y','Y','N','Y']}
df = pandas.DataFrame(data)
def countY(columnName, group): return len(group[group[columnName] == 'Y'])
df.groupby('a').apply(partial(countY, 'b'))
A... | {
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... | 34 | 2014-03-24T11:06:01Z | 2021-10-02T08:10:51Z | null | CONTRIBUTOR | null | I've code which run fine about ~10 month ago and now fails due to running out of memory. The code basically does this:
```
groups = [[1,2,3][222,333,444, 555], ...] # from a 1.4MB json file
# about 30k such groups, results in ~70k replacements
for replace_list in groups:
replacement = sorted(replace_list)[0]
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```
>>> import pandas as pd
>>> import numpy as np
>>> print(pd.__version__)
0.11.0
>>> df = pd.DataFrame(np.random.rand(20,4), columns=["a","b","c","d"])
>>> df["group"] = ['a', 'b', 'c', 'd'] * 5
>>> def _top10cites(group):
... m = group['a'].q... | {
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... | 3 | 2014-03-24T19:31:57Z | 2017-02-22T18:00:30Z | 2017-02-22T18:00:30Z | NONE | null | According to the [documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#setting-with-enlargement) for pandas 0.13+, assigning to a non-existent key via the .ix or .loc operations should create a new row in the DataFrame. Using pandas 0.13.1 with Python 2.7.5, I noticed that this doesn't seem to be th... | {
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"closed_at": "2014-05-30T12:17:57Z",
"closed_issues": 755,
"created_at": "2014-01-03T20:19:04Z",
"creator": null,
"description": "2014-02-3: previous version released",
"due_on": "2014-05-31T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
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"labels_url": "htt... | 12 | 2014-03-25T00:06:37Z | 2014-04-06T15:47:41Z | 2014-04-06T15:47:14Z | CONTRIBUTOR | null | itertools.islice would be faster than n*comparisons here:
https://github.com/pydata/pandas/blob/88c4c5526b38194f00a00c207ee5388dfd508092/pandas/core/frame.py#L759
Not sure whether to amortize an `import itertoosl` or not.
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