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https://api.github.com/repos/pandas-dev/pandas/issues/2101 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2101/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2101/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2101/events | https://github.com/pandas-dev/pandas/issues/2101 | 7,765,604 | MDU6SXNzdWU3NzY1NjA0 | 2,101 | Bug in set_index or drop | {
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"followers_url": "https://api.github.com/users/wesm/... | 7 | 2012-10-22T13:14:30Z | 2012-11-03T00:23:14Z | 2012-11-03T00:22:54Z | CONTRIBUTOR | null | I'm not sure if the bug is in the drop or that it shouldn't have let me set this MultiIndex since it's non-unique. var1 is just a combination of var2 and var3. In any event the result given back by this is garbage. I wanted to drop all the rows where the count of var1 == 1. Since there's not to my knowledge a way to dr... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2102 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2102/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2102/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2102/events | https://github.com/pandas-dev/pandas/issues/2102 | 7,767,051 | MDU6SXNzdWU3NzY3MDUx | 2,102 | DOC: add info on masking data as missing/invalid based on filters | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-10-22T14:04:40Z | 2012-12-14T02:52:07Z | 2012-12-14T02:52:07Z | CONTRIBUTOR | null | Tags: Communit, Docs
Show how to implement:
- if condition in column 1 is true: mark data in column 2 as missing / invalid
- what numpy procedure shall be used to execute the mask?
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https://api.github.com/repos/pandas-dev/pandas/issues/2103 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2103/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2103/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2103/events | https://github.com/pandas-dev/pandas/issues/2103 | 7,769,554 | MDU6SXNzdWU3NzY5NTU0 | 2,103 | Setting a MultiIndex after concat does not work properly | {
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```
df = pandas.DataFrame([["A","a", 1.2],["A","b", 2.2],["B", "a", 1.1],["C", "a", 1.2]], columns=["var1","var2", "var3"])
df2 = pandas.DataFrame([["A","c", 1.2],["B", "c", 4.1],["C", "c", 2.2]], columns=["var1","var2", "var3"])
df3 = pandas.... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2104 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2104/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2104/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2104/events | https://github.com/pandas-dev/pandas/pull/2104 | 7,775,027 | MDExOlB1bGxSZXF1ZXN0MjczMTcwNg== | 2,104 | Unicode III : revenge of the character planes | {
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there's a lot of issues to point out. fair warning given.
#1994 cleaned up things to use basestring and unicode() over str() where needed
#2005 consolidated and implemented unicode-friendly pretty-printing
The attempt here is to ensure that the internal... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2105 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2105/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2105/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2105/events | https://github.com/pandas-dev/pandas/issues/2105 | 7,793,895 | MDU6SXNzdWU3NzkzODk1 | 2,105 | broken y-axis | {
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"f... | 9 | 2012-10-23T15:53:22Z | 2020-04-15T11:53:32Z | 2016-10-12T18:44:32Z | CONTRIBUTOR | null | from SO: http://stackoverflow.com/questions/13030402/pandas-plot-function-ignores-timezone-of-timeseries
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https://api.github.com/repos/pandas-dev/pandas/issues/2107 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2107/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2107/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2107/events | https://github.com/pandas-dev/pandas/issues/2107 | 7,808,002 | MDU6SXNzdWU3ODA4MDAy | 2,107 | data_frame.xs() with MultiIndex throws exception when explicitly setting level= to the first level | {
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```
market_caps
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 409938 entries, (20111230, '00036110') to (20121019, 'Y8564W10')
> Data columns:
> market_cap 409938 non-null values
> dtypes: float64(1)
```
market_caps.index.names
```
> ['date', 'security_id']
I want to... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2108 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2108/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2108/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2108/events | https://github.com/pandas-dev/pandas/issues/2108 | 7,810,916 | MDU6SXNzdWU3ODEwOTE2 | 2,108 | AttributeError np._asarray_tublesafe in tseries/converter | {
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pandas/tseries/converter.py", line 157, in convert
values = np._asarray_tuplesafe(values)
AttributeError: 'module' object has no attribute '_asarray_tuplesafe'
It can be easily fixed by adding:
from pandas.core.common import _asarray_tuple
and change ... | {
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import pandas
import numpy as np
```
define the mask function
```
def mask(self,condition):
new_self = self.copy()
new_self[~condition.values] = np.nan
return new_self
pandas.DataFrame.mask = mask
index = pandas.date_range('1/1/2000',periods=8,)
columns = ['A','B','C','D']
df = pandas.DataFrame(np.ra... | {
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import pandas
import numpy as np
index = pandas.date_range('1/1/2000',periods=8,)
columns = ['A','B','C','D']
df = pandas.DataFrame(np.random.randn(len(index),len(columns)),index=index
printdf
```
```
A B C D
2000-01-01 0.752832 0.083465-0.273210 1.128781
2000-01-02 0.895254 0.401056 1.473770 1.998924
2000-01-03 ... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2111 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2111/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2111/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2111/events | https://github.com/pandas-dev/pandas/pull/2111 | 7,829,069 | MDExOlB1bGxSZXF1ZXN0Mjc1NzM1OQ== | 2,111 | BUG: setup.py error message lacks cache_dir argument | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2112/events | https://github.com/pandas-dev/pandas/issues/2112 | 7,832,742 | MDU6SXNzdWU3ODMyNzQy | 2,112 | Adding columns in groupby | {
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"f... | 1 | 2012-10-24T16:21:33Z | 2021-01-10T14:15:08Z | 2021-01-10T14:15:08Z | MEMBER | null | Possible to simplify?
xref http://stackoverflow.com/questions/12389898/python-pandas-how-to-add-a-totally-new-column-to-a-data-frame-inside-of-a-group
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https://api.github.com/repos/pandas-dev/pandas/issues/2113 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2113/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2113/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2113/events | https://github.com/pandas-dev/pandas/issues/2113 | 7,835,024 | MDU6SXNzdWU3ODM1MDI0 | 2,113 | Random seg fault with groupby | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-10-24T17:41:03Z | 2012-10-25T12:21:58Z | 2012-10-24T18:16:29Z | NONE | null | I came across this seg fault when I was trying to figure out the syntax to get column means. The below example triggers the seg fault, but strangely only about 30% of the time. About 10% of the time I get a reasonable error. The randomness of the result is very strange to me because the same command was run every ti... | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-10-24T18:53:20Z | 2012-11-04T22:04:13Z | 2012-11-04T22:04:13Z | NONE | null | I have an array of non-negative numbers, that when used with `rolling_sum` or `rolling_mean` produce an output array that has a small negative number in it.
The test looks like this:
``` python
import numpy as np
import pandas
data = np.load('data.npy')
assert all(data >= 0)
sums = pandas.rolling_sum(data, 2, min_... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2116 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2116/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2116/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2116/events | https://github.com/pandas-dev/pandas/issues/2116 | 7,855,557 | MDU6SXNzdWU3ODU1NTU3 | 2,116 | "ValueError: Could not get frequency alias for plotting" if DataFrame freq attr is None | {
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``` python
In [50]: df = pandas.read_csv("pandas_test.csv", parse_dates={'tstamp':[0,1]}, index_col='tstamp')
In [51]: df
Out[51]:
flow
tstamp
2008-01... | {
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"followers_url": "https://api.github.com/users/wesm/... | 2 | 2012-10-25T13:06:57Z | 2012-11-03T19:11:45Z | 2012-11-03T19:11:45Z | CONTRIBUTOR | null | from [stackoverflow](http://stackoverflow.com/questions/13068551/pandas-slice-on-hierarchical-index-without-a-copy/)
``` python
In [25]: cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:>>> index = pd.MultiIndex.from_arrays([['a','a', 'b', 'b'], [1,2,1,2]],
:... ... | {
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"f... | 2 | 2012-10-25T13:46:14Z | 2020-05-11T18:37:51Z | 2020-05-11T18:37:51Z | CONTRIBUTOR | null | so don't have to reindex twice.
http://stackoverflow.com/questions/13064400/resample-to-datetimeindex-raises-an-error
or furthermore, arbitrary non-reg freq resampling?
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https://api.github.com/repos/pandas-dev/pandas/issues/2120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2120/events | https://github.com/pandas-dev/pandas/issues/2120 | 7,861,957 | MDU6SXNzdWU3ODYxOTU3 | 2,120 | Series 'print' output truncation | {
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``` python
import pandas as pd
vals = [2.08430917305e+10, 3.52205017305e+10, 2.30674817305e+10, \
2.03954217305e+10, 5.59897817305e+10]
s = pd.Series(vals)
print s.va... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2121 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2121/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2121/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2121/events | https://github.com/pandas-dev/pandas/issues/2121 | 7,867,112 | MDU6SXNzdWU3ODY3MTEy | 2,121 | GroupBy transform() is surprisingly slow | {
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"closed_at": "2013-05-10T10:57:28Z",
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"followers_url": "https://api.github.com/users/wesm/... | 6 | 2012-10-25T16:38:10Z | 2014-06-06T21:50:03Z | 2013-03-25T21:25:04Z | NONE | null | I came across a strange slowness in GroupBy transform() function. I put together a simple function to avoid using apply() because it can be REALLY slow:
```
def apply_by_group(grouped, f):
"""
Applies a function to each DataFrame in a DataFrameGroupBy object, concatenates the results
and returns the result... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2122 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2122/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2122/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2122/events | https://github.com/pandas-dev/pandas/pull/2122 | 7,868,845 | MDExOlB1bGxSZXF1ZXN0Mjc3NTY3Mw== | 2,122 | BUG: fix unicode handling in plot label, close #2080 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2123 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2123/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2123/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2123/events | https://github.com/pandas-dev/pandas/issues/2123 | 7,872,641 | MDU6SXNzdWU3ODcyNjQx | 2,123 | Date indexing API | {
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"followers_url": "https://api.github.com/users/jorisva... | 2 | 2012-10-25T19:47:38Z | 2017-01-25T21:01:44Z | 2017-01-25T15:51:36Z | CONTRIBUTOR | null | The end is exclusive for integer indexing but inclusive using dates.
I guess if the data is irregular it's more weird to find the first data point after what you want to include, so we shouldn't change the current API. However it is still a little confusing so just wanted to note that here.
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> > > p = pandas.Period('2012-10-23', 'D')
> > > p.start_time
> > > <Timestamp: 2012-10-23 00:00:00>
> > > p.end_time
> > > <Timestamp: 2012-10-23 00:00:00>
There are tests, but they fail to detect the problem because they are comparing the end_time to a constant which is the start_time (so they pass).
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https://api.github.com/repos/pandas-dev/pandas/issues/2125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2125/events | https://github.com/pandas-dev/pandas/issues/2125 | 7,873,070 | MDU6SXNzdWU3ODczMDcw | 2,125 | Period.end_time is incorrect when period is W or longer | {
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> > > p = pandas.Period('2012', freq='M')
> > > p.end_time
> > > <Timestamp: 2012-01-31 00:00:00>
This is the _start_ of the last day of the month. So if one were using this in code, you could erroneously find that any datetime on January 31st is after the end of the month of January, for example:
> > ... | {
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```
dlopen(/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so, 2): Symbol... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2127 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2127/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2127/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2127/events | https://github.com/pandas-dev/pandas/issues/2127 | 7,877,129 | MDU6SXNzdWU3ODc3MTI5 | 2,127 | DataFrame.align() does not retain DatetimeIndex timezone awareness | {
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pandas.ewma(series, com=5)
2000-01-01 00:00:00 1.000000
2000-01-01 00:01:00 0.545455
2000-01-01 00:02:00 0.545455
2000-01-01 00:03:00 1.000000
Almost right - but clearly the adjust f... | {
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index[0:0].intersection(index)
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "C:\dev\Python27\Lib\site-packages\pandas\tseries\index.py", line 845, in intersection
if self[0] <= other[0]:
File "C:\dev\Python... | {
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"closed_at": "2013-12-30T19:53:14Z",
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I altered c-parser so thatparser.pyx decode all strings into unicode objects with utf-8 (b16b24b).
Since any source encoding can be transcoded into utf-8, this is a general solution for
getting c-parser to always return unicode. tested on py2.
**Data Used*... | {
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] | closed | false | null | [] | null | 1 | 2012-10-26T15:49:20Z | 2014-04-29T11:17:04Z | 2012-11-03T18:00:45Z | NONE | null | To reproduce the bug, do the following:
``` python
df = pandas.DataFrame([['a','b'] for i in range(1,1000)])
store = pandas.HDFStore('test.h5')
store['x'] = df
store.close()
```
The file test.h5 is now about 1.1M (seems like a lot, but OK)
now let's reopen the file
``` python
store = pandas.HDFStore('test.h5') #ope... | {
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import numpy as np
import pandas as pd
df = pd.DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)])
df[1] = df[1].view('M8[ns]')
print df[1].dtype
print df.groupby(0).first()[1].dtype
print type(df.groupby(0).first()[1][1])
datetime64[ns]
object
<type 'long'>
```
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"f... | 3 | 2012-10-26T18:21:16Z | 2013-12-18T20:05:22Z | 2013-12-18T20:05:22Z | CONTRIBUTOR | null | ``` python
In [71]: data = """\
....: this, that, 0, 1
....: 10, 20, 30, 40
....: 50, 60, 70, 80
....: """
In [72]: df = pd.read_csv(StringIO(data), index_col=[0, 1])
In [73]: df
Out[73]:
0 1
this that
10 20 30 40
50 60 70 80
In [74]: df.index # <-- ok -->
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implement + test proposed fix for #2120
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asfreq('D', normalize=True)
2011-1-1 10:00 -> 2011-1-1 00:00:00
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https://api.github.com/repos/pandas-dev/pandas/issues/2140 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2140/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2140/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2140/events | https://github.com/pandas-dev/pandas/issues/2140 | 7,911,819 | MDU6SXNzdWU3OTExODE5 | 2,140 | Time rule inference regression with WOM- frequencies | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2141 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2141/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2141/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2141/events | https://github.com/pandas-dev/pandas/issues/2141 | 7,913,009 | MDU6SXNzdWU3OTEzMDA5 | 2,141 | ENH: add level kw to .between_time, at_time, asfreq | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2142 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2142/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2142/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2142/events | https://github.com/pandas-dev/pandas/issues/2142 | 7,916,767 | MDU6SXNzdWU3OTE2NzY3 | 2,142 | Minor inconsistency? | {
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"f... | 3 | 2012-10-28T04:00:34Z | 2014-05-29T11:40:00Z | 2014-05-29T05:37:22Z | NONE | null | Series.unique returns an numpy array but np.unique(series) returns a series. Not sure if it is intentional but it might be better to keep both the calls consistent.
In [173]: a[:10]
Out[173]:
0 4
1 210
2 210
3 210
4 3197
5 3197
6 3197
7 3457
8 3713
9 3741
In [174]: a[:10].unique()... | {
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Is there triple exponential smoothing availiable from pandas?
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https://api.github.com/repos/pandas-dev/pandas/issues/2144 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2144/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2144/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2144/events | https://github.com/pandas-dev/pandas/issues/2144 | 7,942,264 | MDU6SXNzdWU3OTQyMjY0 | 2,144 | __repr__ display of dict elements misleading | {
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In [14]: DataFrame([data])['name']
Out[14]:
0 [full, last, ascii_first, ascii_last, first]
Name: name
In [15]: DataFrame([data])['name'][0]
Out[15]:
{u'ascii_first': u'Paul',
u'ascii_last': u'Millsap',
u'first': u'Paul',
u'full': u'Paul Millsap',
u'last': u'Millsap'}
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2146 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2146/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2146/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2146/events | https://github.com/pandas-dev/pandas/issues/2146 | 7,965,976 | MDU6SXNzdWU3OTY1OTc2 | 2,146 | Printing a Series with dtype timedelta64 | {
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>>> import pandas as pd
>>> import numpy as np
>>> print pd.Series(np.array([1100, 20], dtype='timedelta64[s]'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 911, in __str__
return repr(self)
File "C:\Python27_64... | {
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I make empty DataFrame:
``` python
>>> from pandas import DataFrame
>>> df = DataFrame()
>>> df
Empty DataFrame
Columns: array([], dtype=object)
Index: array([], dtype=object)
>>> df['A'] = [1, 2, 3]
Traceback (most recent call last):
File "<stdin>", line 1... | {
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Out[26]: True
In [27]: pd.offsets.Day().isAnchored()
Out[27]: False
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Here is the PR for #2153
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from datetime import datetime
import numpy as np
from pandas import DataFrame, Timestamp, DatetimeIndex
import pandas.core.datetools as datetools
columns = DatetimeIndex(start='1/1/2012', end='2/1/2012', freq=datetools.bday)
index = range(10)
data = DataFrame(columns=columns, index=index)
t = datetime(2012, 11, 1)... | {
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in the To_csv section:
```
sep : Field delimiter for the output file (default “’”)
```
should be
```
sep : Field delimiter for the output file (default “, ”)
```
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-01T20:22:14Z | 2012-11-04T02:35:25Z | 2012-11-04T02:35:25Z | CONTRIBUTOR | null | from [mailing list](https://groups.google.com/forum/#!searchin/pydata/Spacing$20error$20in$20bar$20graphs/pydata/KkIz8evL4As/ft8Puy8UwCwJ)
``` python
df = DataFrame({'A' : [3] * 5, 'B' : range(5)}, index = range(5))
df.plot(kind='bar', stacked='True', grid=True)
```
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"f... | 4 | 2012-11-01T20:50:42Z | 2014-10-30T06:32:39Z | 2014-05-29T05:43:42Z | CONTRIBUTOR | null | I am not sure if this is a feature or a bug but when I use the default to_csv() and read_csv() functions on dataframes that have lists as members, I lose the lists.
This is a copy of [this stack overflow post.](http://stackoverflow.com/questions/13184553/list-to-string-in-df-to-csv#13184793)
```
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ids = np.arange(48000)
lens = np.maximum(np.round(15+9.5*np.random.randn(48000)), 1.0).astype(int)
id_vec = np.repeat(ids, lens)
lens_shift = np.concatenate(([0], lens[:-1]))
mon_vec = np.arange(lens.sum()) - np.repeat(np.cumsum(lens_shift), lens)
n = len(mon_vec)
df = pd.DataFrame.from_items([('pool', id_vec), ('m... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2165 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2165/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2165/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2165/events | https://github.com/pandas-dev/pandas/pull/2165 | 8,065,663 | MDExOlB1bGxSZXF1ZXN0Mjg2NjU0OQ== | 2,165 | BUG: exclude timedelta64 from integer dtypes #2146 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2166 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2166/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2166/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2166/events | https://github.com/pandas-dev/pandas/pull/2166 | 8,076,096 | MDExOlB1bGxSZXF1ZXN0Mjg3MTgxNw== | 2,166 | BUG: xs MultiIndex integer level problem #2107 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2167 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2167/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2167/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2167/events | https://github.com/pandas-dev/pandas/issues/2167 | 8,076,546 | MDU6SXNzdWU4MDc2NTQ2 | 2,167 | Strange output in DataFrame representation of datetime64 | {
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http://stackoverflow.com/questions/13202326/pandas-to-datetime-produces-confusing-result
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https://api.github.com/repos/pandas-dev/pandas/issues/2169 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2169/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2169/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2169/events | https://github.com/pandas-dev/pandas/pull/2169 | 8,080,274 | MDExOlB1bGxSZXF1ZXN0Mjg3MzMzMg== | 2,169 | Plot color | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2170 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2170/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2170/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2170/events | https://github.com/pandas-dev/pandas/pull/2170 | 8,082,884 | MDExOlB1bGxSZXF1ZXN0Mjg3NDEzNw== | 2,170 | BUG: start_time end_time to_timestamp bugs #2124 #2125 #1764 | {
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The main change is I have to_timestamp default to second frequency now to get the first and last second of the Period. This doesn't **quite** solve #2125 since Timestamp resolution goes down to Nanos. So the question is is it appropriate to add 999999999ns to the end_time when ns doesn't... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2171/events | https://github.com/pandas-dev/pandas/issues/2171 | 8,091,325 | MDU6SXNzdWU4MDkxMzI1 | 2,171 | GroupBy transform() throws unexpected exception when sorting each DataFrame | {
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```
data
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 410322 entries, (20111230, '00036110') to (20121019, 'Y8564W10')
> Data columns:
> market_cap 410117 non-null values
> average_volume 410322 non-null values
> return_std_daily 410322 non-null v... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2172 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2172/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2172/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2172/events | https://github.com/pandas-dev/pandas/issues/2172 | 8,091,742 | MDU6SXNzdWU4MDkxNzQy | 2,172 | Use index option for bar plot | {
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"followers_url": "https://api.github.com/users/wesm... | 3 | 2012-11-04T20:17:59Z | 2013-09-21T13:24:41Z | 2013-09-21T13:24:35Z | CONTRIBUTOR | null | help with bar and line plot types
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https://api.github.com/repos/pandas-dev/pandas/issues/2173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2173/events | https://github.com/pandas-dev/pandas/pull/2173 | 8,091,836 | MDExOlB1bGxSZXF1ZXN0Mjg3NjgwNg== | 2,173 | BUG: stop overridding matplotlib unit registrations for datetime.datetim... | {
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http:/... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2178 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2178/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2178/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2178/events | https://github.com/pandas-dev/pandas/pull/2178 | 8,103,817 | MDExOlB1bGxSZXF1ZXN0Mjg4MTYxNw== | 2,178 | CLN: Typo fix in documentation | {
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In [13]: pd.DataFrame.from_records({1:["foo"],2:["bar"]},columns=['a','b']).columns
Out[13]: Index([1, 2], dtype=int64)
- if `index` is specified, and `result_index` computed, it will get clobbered
later on.
- if `index` is specified as a list of labels, with the first few... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2182/events | https://github.com/pandas-dev/pandas/issues/2182 | 8,119,737 | MDU6SXNzdWU4MTE5NzM3 | 2,182 | python crash without exception | {
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Some operations do work however, li... | {
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In [1314]: data = """\
......: A,B,C
......: 00001,001,5
......: 00002,002,6
......: """
......:
In [1315]: from cStringIO import StringIO
In [1316]: read_csv(StringIO(data), converters={'A' : lambda x: x.strip()})
Out[1316]:
A B C
0 00001 1 5
1 00002 2 6
2 NaN NaN
... | {
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"followers_url": "https://api.github.com/users/wesm/... | 31 | 2012-11-07T08:27:53Z | 2014-09-13T20:32:32Z | 2012-11-25T21:08:16Z | CONTRIBUTOR | null | After updating pandas I can't import it any more because the symbol _floatify is undefined in pandas/lib.so:
```
$ python -c 'import pandas'
dlopen(/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so, 2): Symbol not found: _floatify
Referenced f... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2189 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2189/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2189/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2189/events | https://github.com/pandas-dev/pandas/issues/2189 | 8,165,939 | MDU6SXNzdWU4MTY1OTM5 | 2,189 | bug in dataframe.join() | {
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I have a strange issue with pandas 0.9, I think it's a bug. I'm trying to use dataframe.join() and it works well on a random dataframe, but not on a dataframe created from my simulation result. The code below shows that join() on the second dataframe blows up the index and the result is completely wrong.
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https://api.github.com/repos/pandas-dev/pandas/issues/2190 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2190/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2190/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2190/events | https://github.com/pandas-dev/pandas/issues/2190 | 8,175,753 | MDU6SXNzdWU4MTc1NzUz | 2,190 | Add exponentiation function similar to add() or sub() | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-07T16:16:20Z | 2012-11-09T18:05:36Z | 2012-11-09T18:05:36Z | CONTRIBUTOR | null | Currently if I exponentiate a DataFrame with a Series, the indexing joining logic is performed on the columns. This means the following:
``` python
>>> data = pandas.DataFrame({"a": [1, 2, 3, np.nan], "b": [23, 4, 5, np.nan]}, index=["ax", "bx", "cx", "dx"])
>>> series = pandas.Series([2,2,3, 1], index=["ax", "bx", "c... | {
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"followers_url": "https://api.github.com/users/wesm/... | 3 | 2012-11-07T19:58:11Z | 2012-11-09T03:22:03Z | 2012-11-09T03:21:53Z | MEMBER | null | Very, very surprised this bug isn't caught by the test suite.
df1 = pandas.DataFrame({'a':{True:1,False:5},'b':{True:7,False:8}})
df2 = pandas.DataFrame({'a':{True:1,False:5},'c':{True:7,False:8}})
```
In [15]: df1.add(df2, fill_value=0)
Out[15]:
a b c
False 10 NaN NaN
True 2 NaN NaN
```
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"labels_url": "htt... | 1 | 2012-11-07T21:40:31Z | 2014-02-18T01:38:24Z | 2014-02-18T01:38:24Z | CONTRIBUTOR | null | maybe have option to not do cross join. instead just join on order of appearance for the non-unique elements (and fill-in NAs for the rest)?
Related: #2189
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"followers_url": "https://api.github... | 26 | 2012-11-07T22:21:44Z | 2018-07-26T13:00:55Z | 2018-07-26T13:00:55Z | NONE | null | Hi there,
I have a codebase that (so far) consumes data series in the form of dict-yielding iterators, similar in form to how DataFrame's constructor accepts a list of such. Unfortunately it seems from current implementation of that constructor, there is no straightforward way to consume from an iterator straight into... | {
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"i... | 3 | 2012-11-07T23:04:40Z | 2013-05-02T14:53:28Z | 2013-05-02T14:53:28Z | CONTRIBUTOR | null | right now it fails in list of lists but the error isn't informative.
xref: commit 5a152bdfdd044c6f53c76b361065b1784dc73b61
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https://api.github.com/repos/pandas-dev/pandas/issues/2195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2195/events | https://github.com/pandas-dev/pandas/pull/2195 | 8,194,662 | MDExOlB1bGxSZXF1ZXN0MjkyNDE4MA== | 2,195 | Updating help text for plot_series | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-08T04:30:52Z | 2012-11-09T23:53:16Z | 2012-11-08T14:23:06Z | CONTRIBUTOR | null | description for parameter 'kind' was given twice.
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https://api.github.com/repos/pandas-dev/pandas/issues/2196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2196/events | https://github.com/pandas-dev/pandas/issues/2196 | 8,201,289 | MDU6SXNzdWU4MjAxMjg5 | 2,196 | index.union does not sort monotonic indexes | {
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"followers_url": "https://api.github.com/users/wesm/... | 1 | 2012-11-08T11:26:40Z | 2012-11-09T17:58:02Z | 2012-11-09T17:58:02Z | NONE | null | from [StackOverflow](http://stackoverflow.com/questions/13279690/unexpected-behavior-when-combining-two-dataframes-in-pandas):
While index.union is not guaranteed to sort results, it does not try to sort the result for monotonic indexes, but does try to sort non-monotonic indexes.
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https://api.github.com/repos/pandas-dev/pandas/issues/2197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2197/events | https://github.com/pandas-dev/pandas/pull/2197 | 8,208,241 | MDExOlB1bGxSZXF1ZXN0MjkzMDQ3OQ== | 2,197 | BUG: join_non_unique doesn't sort properly for DatetimeIndex #2196 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2198 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2198/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2198/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2198/events | https://github.com/pandas-dev/pandas/issues/2198 | 8,221,365 | MDU6SXNzdWU4MjIxMzY1 | 2,198 | ae63ebf broke test suits | {
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FAIL: testWLS (pandas.stats.tests.test_ols.TestOLS)
Traceback (most recent call last):
File "/home/user1/src/pandas/pandas/stats/tests/test_ols.py", line 92, in testWLS
self._check_wls(X, Y, weights)
File "/home/user1/src/pandas/pandas/stats/tests/test_ols.py", line 115... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2199/events | https://github.com/pandas-dev/pandas/issues/2199 | 8,221,534 | MDU6SXNzdWU4MjIxNTM0 | 2,199 | Curious: df.ix[False] returns the first row | {
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"followers_url": "https://api.github.com/users/wesm/... | 6 | 2012-11-08T22:38:32Z | 2012-11-09T18:18:21Z | 2012-11-09T17:00:35Z | NONE | null | ``` python
In [21]: df=pd.DataFrame({"a":[1,2,3]})
...: df.ix[False]
Out[21]:
a 1
Name: 0
```
Don't know if this is a bug or intentiional.
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https://api.github.com/repos/pandas-dev/pandas/issues/2200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2200/events | https://github.com/pandas-dev/pandas/issues/2200 | 8,222,491 | MDU6SXNzdWU4MjIyNDkx | 2,200 | Breakage when setting MultiIndex with a mutable sequence | {
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"description": "__repr__ of pandas objec... | closed | false | null | [] | {
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"followers_url": "https://api.github.com/users/wesm/... | 0 | 2012-11-08T23:13:36Z | 2012-11-09T17:40:06Z | 2012-11-09T17:40:06Z | NONE | null | ``` python
import pandas as pd
df=pd.DataFrame({"a":[1,2,3],"b":[4,5,6],"c":[7,8,9]}).set_index(["a","b"])
l=list(df.index)
l[0]=("faz","boo")
df.index=l
print df
# all good
l[0]=["faz","boo"]
df.index=l
print df
# badness
```
If this acceptable usage and index entries must be immutable, failing early would be good.
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