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.append drops MultiIndex, .join not supported for MultiIndex
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| 2 | 2011-10-06T18:57:04Z | 2011-10-06T21:32:22Z | 2011-10-06T21:24:33Z |
NONE
| null |
You are probably already aware, but .append and .join are not yet fully functional with MultiIndex.
1. .append turns the index into numbers
> > > s1=s[:4]
> > > s2=s[4:]
> > > s1
> > > bar one 0.229167564165
> > > two -1.12529475186
> > > baz one 1.03310069555
> > > two -0.859600695252
> > > s2
> > > foo one 1.75545035115
> > > two -1.44073899127
> > > qux one -0.239883854241
> > > two -1.22027857654
> > > s1.append(s2)
> > > 0 0.229167564165
> > > 1 -1.12529475186
> > > ...
> > > 6 -0.239883854241
> > > 7 -1.22027857654
> > >
> > > df1=df[:4]
> > > df2=df[4:]
> > > df1
> > > A B C
> > > bar one -1.165 0.6214 -0.4865
> > > two -0.8845 -0.3048 -0.1469
> > > baz one 1.533 -0.7665 1.133
> > > two -0.2581 2.067 -0.4839
> > >
> > > df2
> > > A B C
> > > foo one -1.321 -0.08075 -1.135
> > > two -0.3081 0.8832 0.09399
> > > qux one -0.7685 0.8034 -1.032
> > > two 0.3256 1.158 0.163
> > >
> > > df1.append(df2)
> > > A B C
> > > 0 -1.165 0.6214 -0.4865
> > > 1 -0.8845 -0.3048 -0.1469
> > > ...
> > > 6 -0.7685 0.8034 -1.032
> > > 7 0.3256 1.158 0.163
1. .join errors out
> > > df1t=df1.T
> > > df2t=df2.T
> > > df1t
> > > bar baz
> > > one two one two
> > > A -1.165 -0.8845 1.533 -0.2581
> > > B 0.6214 -0.3048 -0.7665 2.067
> > > C -0.4865 -0.1469 1.133 -0.4839
> > >
> > > df2t
> > > foo qux
> > > one two one two
> > > A -1.321 -0.3081 -0.7685 0.3256
> > > B -0.08075 0.8832 0.8034 1.158
> > > C -1.135 0.09399 -1.032 0.163
> > >
> > > df1t.join(df2t)
> > > ...
> > > IndexError: list index out of range
1. both .join and .append are fine when you are lining the data up with MultiIndex, rather than creating an expanded MultiIndex.
>>> dfa
A B
bar one 0.5341 0.5299
two -0.3412 0.8686
baz one 0.6352 1.835
two -0.4022 -0.2686
> > > dfb
> > > C D
> > > bar one -1.066 -1.029
> > > two -0.8317 -0.9012
> > > baz one -0.4223 0.9805
> > > two -1.348 1.204
> > >
> > > dfa.join(dfb)
> > > A B C D
> > > bar one 0.5341 0.5299 -1.066 -1.029
> > > two -0.3412 0.8686 -0.8317 -0.9012
> > > baz one 0.6352 1.835 -0.4223 0.9805
> > > two -0.4022 -0.2686 -1.348 1.204
> > >
> > > (dfa.T.append(dfb.T)).T
> > > A B C D
> > > bar one 0.5341 0.5299 -1.066 -1.029
> > > two -0.3412 0.8686 -0.8317 -0.9012
> > > baz one 0.6352 1.835 -0.4223 0.9805
> > > two -0.4022 -0.2686 -1.348 1.204
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| 202 |
.sortlevel does not retain .names in MultiIndex
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| 2 | 2011-10-06T20:26:31Z | 2011-10-06T20:49:08Z | 2011-10-06T20:46:52Z |
NONE
| null |
.sortlevel does not retain .names in MultiIndex. For example:
> > > options
> > > ForwardPrice Strike CallPut Premium
> > > 105169 57.2024993896 15.03 15.03 C 2.206
> > > -41.0596008301 15.03 15.03 P 2.231
> > > ...
> > >
> > > options.delevel()
> > > SecurityID Delta ForwardPrice Strike CallPut Premium
> > > 0 105169 57.2 15.03 15.03 C 2.206
> > > 1 105169 -41.06 15.03 15.03 P 2.231
> > > ...
> > >
> > > options = options.sortlevel(0)
> > > options.delevel()
> > > level_0 level_1 ForwardPrice Strike CallPut Premium
> > > 0 105169 -41.06 15.03 15.03 P 2.231
> > > 1 105169 57.2 15.03 15.03 C 2.206
> > > ...
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| 203 |
Expose isnull and notnull as methods
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| 0 | 2011-10-07T11:55:26Z | 2011-10-09T16:52:47Z | 2011-10-09T16:52:47Z |
CONTRIBUTOR
| null |
Feature suggestion:
I was using hackish solutions to select rows where a particular column is empty/non-empty, because I didn't know about isnull and notnull. I think these should be exposed as methods, particularly of `Series` (i.e. columns), so they're readily discoverable via tab completion.
Example use:
```
selection = df[df['col'].notnull()]
```
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HDFStore chokes on writing empty LongPanel
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| 1 | 2011-10-07T18:30:04Z | 2011-10-07T19:18:07Z | 2011-10-07T19:18:07Z |
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Sparse speed optimization
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| 0 | 2011-10-08T20:25:44Z | 2011-10-09T14:25:43Z | 2011-10-09T14:25:43Z |
MEMBER
| null |
Ops in intindex form are much faster than in block form, for some reason
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MDExOlB1bGxSZXF1ZXN0Mzg4MTE2
| 206 |
Use stdlib csv module to write csv
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[] | null | 2 | 2011-10-08T23:00:37Z | 2014-06-13T12:02:14Z | 2011-10-09T00:03:16Z |
CONTRIBUTOR
| null |
I noticed while using pandas that data with commas in the fields wasn't getting escaped properly when written to CSV (data in biology often has bits of text as well as numbers). This causes the data to spread out over extra columns. This commit uses the `csv` module from the standard library to write out CSV 'properly'.
I've also added a simple test for this case.
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Add inplace option to many DataFrame, etc. methods
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| 2 | 2011-10-08T23:47:24Z | 2016-10-12T23:02:36Z | 2012-05-21T18:12:22Z |
MEMBER
| null |
On larger datasets methods like
- sort_index
- rename
(add methods to list)
etc. would benefit from an inplace option, e.g.:
``` python
df.sort_index(by=col, inplace=True)
```
and this would be more memory efficient
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Datetime string parser integration with Index class
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| 0 | 2011-10-09T14:11:38Z | 2013-11-28T14:05:37Z | 2012-04-13T01:45:48Z |
MEMBER
| null |
`Index.to_datetime` or something like that to convert Index containing parsable strings to `DatetimeIndex`
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| 209 |
Series methods isnull and notnull
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[] | null | 1 | 2011-10-09T16:02:04Z | 2014-06-16T16:14:10Z | 2011-10-09T16:56:36Z |
CONTRIBUTOR
| null |
Fulfils enhancement #203: isnull and notnull methods for series (especially for use with columns in dataframes).
I've also slipped in a couple of other small tweaks - fixing a bug with writing series CSV, and adding trove classifiers to indicate Python 3 compatibility.
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| 210 |
Add some kind of describe-like / summary ability for non-numeric data
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| 0 | 2011-10-09T17:43:57Z | 2011-11-10T04:10:30Z | 2011-11-10T04:10:30Z |
MEMBER
| null |
Similar to R's summary on a data.frame with non-numeric data
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Refactor internals.join_managers to reduce overhead in homogeneous case
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| 1 | 2011-10-10T01:37:35Z | 2011-10-11T20:13:30Z | 2011-10-11T20:13:30Z |
MEMBER
| null |
Could achieve 15-20% savings over the current code, unnecessary computations are being performed in the float-only case
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Optimize nested dict -> DataFrame conversions
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| 0 | 2011-10-10T03:16:22Z | 2011-10-13T04:17:16Z | 2011-10-13T04:17:16Z |
MEMBER
| null |
With advances in the Cython code since 0.3 this should be relatively straightforward (maybe)
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Add simple attribute access to DataFrame columns
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CONTRIBUTOR
| null |
This one might need some discussion, but I think it makes sense.
If a column name in a dataframe is a valid Python name and doesn't conflict with any DataFrame attributes, this lets you type `df.MyColumn` than `df['MyColumn']`. That's much handier for interactive use, especially because IPython will tab complete `df.MyColumn.<tab>`, while it won't by default tab complete `df['MyColumn'].<tab>`.
This doesn't interfere with accessing columns through the dictionary interface.
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Add multi-key join capability
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| 1 | 2011-10-11T17:54:16Z | 2011-10-11T19:39:18Z | 2011-10-11T19:39:18Z |
MEMBER
| null |
API would look like
``` python
a.join(b, on=['foo', 'bar'])
```
where 'foo', and 'bar' are columns in a and b is indexed by a MultiIndex with 2 levels
I am already working on this...just here so I don't forget
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Speed enhancements in DataFrame.xs
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General xs function in internals.py is way too slow
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Add method argument to align functions for forward/backfilling
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Micro-optimizations in DataFrame for tracking state of internal consolidation
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Optimize BlockManager.join_on for special types
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Currently using the Cython function `merge_indexer_object`, could be much faster with compatible types
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See if possible to optimize multi-key joining beyond its current state
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Implement outer/inner/right join logic when joining on a key
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| 1 | 2011-10-11T21:09:08Z | 2011-12-31T00:16:27Z | 2011-12-31T00:16:27Z |
MEMBER
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Currently only left join is implemented. This will make the join-on more consistent with its SQL counterparts
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Groupby level name
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Plotting series (columns) fails
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| 6 | 2011-10-12T12:03:52Z | 2011-10-12T15:04:10Z | 2011-10-12T15:04:10Z |
CONTRIBUTOR
| null |
Calling plot from matplotlib with series (e.g. columns of a DataFrame) fails, because it tries to add an axis to the series objects. A traceback can be found here: https://gist.github.com/1281034 .
One way to solve this could be, if we try to index a series to multiple dimensions, simply return a plain numpy array of the data. Or maybe it makes more sense to make changes on the matplotlib side?
`Series.plot()` and `DataFrame.plot()` are designed to plot columns against the index, but don't appear to help with plotting one column against another.
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read_csv interprets index column as dates
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| 6 | 2011-10-12T14:19:49Z | 2011-10-12T20:21:48Z | 2011-10-12T18:54:16Z |
CONTRIBUTOR
| null |
I loaded a dataset with an index column going from 1 to 304. Then I try to get the repr of the dataframe, but it raises `ValueError: year=100 is before 1900; the datetime strftime() methods require year >= 1900`. Looking at `df.index` shows that the numeric indices have been transformed into datetimes (the first 31 to days of this month, 32-60 as years 20xx, 61-99 as years 19xx, and higher numbers as a raw year).
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read_csv should have better failure mode with spurious 'index' column
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| 2 | 2011-10-12T14:53:39Z | 2011-10-13T08:40:25Z | 2011-10-13T03:27:27Z |
CONTRIBUTOR
| null |
If I use `read_csv` to load a datafile with the default index_col, but the first column does not have unique values, it succeeds at first, but when I try to select rows later, I get `Exception: Index cannot contain duplicate values!`. This raises a couple of points:
1. Should the default be to use an index column? My data often doesn't have one. [Apparently](http://pinard.progiciels-bpi.ca/libR/library/base/html/read.table.html) R takes the first column as an index if it doesn't have a header, and otherwise does an integer index.
2. Non-unique values should be detected when we're creating the index, and either leave that column as a regular column (falling back to an integer index), or raise an exception at that point.
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Document recent updates to parser functions and API changes in sphinx docs
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Update docs on reading CSV files.
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CONTRIBUTOR
| null |
This does the essential reworking of the documentation for `read_csv` and `read_table` for #227. I'm happy to do more doc updates.
I also think the autosummary API documentation isn't particularly good - it doesn't seem to handle classes at all. I'll see if I can copy what IPython uses (example output is [here](http://ipython.org/ipython-doc/stable/api/generated/IPython.testing.tools.html#tempfilemixin)).
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Remove deprecations from 0.3-0.4 for 0.5 release
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| 230 |
Register the DataFrame type with `IPython.utils.generic.complete_object`
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Objects indexed by datetime.date can't be stored in HDFStore
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| 232 |
to_string() returning None
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| 6 | 2011-10-13T16:11:54Z | 2011-10-15T08:24:08Z | 2011-10-14T21:49:30Z |
NONE
| null |
DataFrame.to_string() is printing directly to console and not returning a string :
``` Python
>>> import pandas
>>> df = pandas.DataFrame(range(5))
>>> df
0
0 0
1 1
2 2
3 3
4 4
>>> view_repr = repr(df)
>>> view_to_string = df.to_string()
0
0 0
1 1
2 2
3 3
4 4
>>> view_repr == view_to_string
False
```
For large DataFrame, we have to use to_string() to get a tabular view.
But the return value is not set (None) and can't be retrieved :
``` Python
>>> view_repr
' 0\n0 0\n1 1\n2 2\n3 3\n4 4\n'
>>> view_to_string
>>>
```
It would be usefull to get a string (or StringIO) returned by DataFrame.to_string()
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MDExOlB1bGxSZXF1ZXN0NDAxNDY3
| 233 |
Add columns to IPython tab completions of DataFrame attributes
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[] | null | 11 | 2011-10-13T20:26:39Z | 2014-06-22T02:51:22Z | 2011-10-14T04:14:05Z |
CONTRIBUTOR
| null |
This addresses #230.
I'm not entirely happy about how we check whether IPython is imported, but it's the best option I could see, and I don't think we should unconditionally import IPython and dependent modules just because it's installed.
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Write nice user-facing pivot table creation function
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Index class ignores dtype parameter
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CONTRIBUTOR
| null |
Apparently Index always forces dtype to object, irrespective of what is specified as parameter.
Pandas 0.4.3
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Add Float64Index class?
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| 25 | 2011-10-14T12:40:38Z | 2013-09-25T20:08:12Z | 2013-09-25T20:08:12Z |
MEMBER
| null |
Idea from conversation with @CRP in #235
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Groupby apply mixed-type casting problem
|
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| 0 | 2011-10-14T21:01:49Z | 2011-10-14T21:15:38Z | 2011-10-14T21:15:38Z |
MEMBER
| null |
the following groupby transform results in things getting casted to object...
``` python
df = DataFrame({'d' : [1.,1.,1.,2.,2.,2.],
'c' : np.tile(['a','b','c'], 2),
'v' : np.arange(1., 7.)})
# in IPython
In [34]: df
Out[34]:
c d v
0 a 1 1
1 b 1 2
2 c 1 3
3 a 2 4
4 b 2 5
5 c 2 6
```
Now write a small transform function:
``` python
def f(group):
v = group['v']
group['v2'] = (v - v.min()) / (v.max() - v.min())
return group
```
Note that this also handles NAs since the v variable is a pandas Series object.
Now group by the d column and apply f:
``` python
In [36]: df.groupby('d').apply(f)
Out[36]:
c d v v2
0 a 1 1 0
1 b 1 2 0.5
2 c 1 3 1
3 a 2 4 0
4 b 2 5 0.5
5 c 2 6 1
```
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| 238 |
Joining with empty DataFrame on outer breaks
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| 4 | 2011-10-14T22:28:24Z | 2011-10-15T19:23:26Z | 2011-10-15T18:53:42Z |
NONE
| null |
In some problems it is convenient to create an empty dataframe, and then append to it in a loop. The following code now breaks:
import pandas as ps
x = ps.DataFrame()
x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
x.join(ps.DataFrame([3], index=[0], columns=['A']))
Pandas 0.4.1
> > > import pandas as ps
> > > x = ps.DataFrame()
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
> > > A
> > > 0 3
> > >
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']))
> > > Empty DataFrame
> > > Index([], dtype=object)
Pandas 0.4.3
> > > import pandas as ps
> > > x = ps.DataFrame()
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
Traceback (most recent call last):
File "<pyshell#1197>", line 1, in <module>
x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2168, in join
return self._join_index(other, how, lsuffix, rsuffix)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2193, in _join_index
merged_data = join_managers(thisdata, otherdata, axis=1, how=how)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 958, in join_managers
lblocks = _maybe_upcast_blocks(left.blocks, lneed_masking)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1029, in _maybe_upcast_blocks
return _consolidate(new_blocks, newb.ref_items)
UnboundLocalError: local variable 'newb' referenced before assignment
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']))
> > > Empty DataFrame
> > > Index([], dtype=object)
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MDU6SXNzdWUxOTE4ODky
| 239 |
Enable creation of empty Panel objects
|
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| 2 | 2011-10-15T19:36:07Z | 2011-10-16T01:11:43Z | 2011-10-15T19:52:37Z |
MEMBER
| null |
@creeson reported that this is not possible
|
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| 1,919,603 |
MDU6SXNzdWUxOTE5NjAz
| 240 |
Add unique value counter method to Series / DataFrame
|
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| 11 | 2011-10-15T22:40:50Z | 2011-10-20T22:12:38Z | 2011-10-19T22:22:19Z |
MEMBER
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| 1,922,618 |
MDExOlB1bGxSZXF1ZXN0NDA2NTUx
| 241 |
.describe() for series of objects
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[] | null | 5 | 2011-10-16T18:06:10Z | 2014-06-14T07:05:34Z | 2011-10-19T20:34:41Z |
CONTRIBUTOR
| null |
See #210.
I don't know if `series.dtype == object` is the best way to check for non-numeric data, but it appears to work.
At present, I've used fields 'count', 'unique', 'top' (i.e. most common) and 'freq' (i.e. count of the most common). These seem like obvious things for a summary.
I also noticed a problem doing `df.describe()` if df has integer columns, so I copied some code from series to fix that.
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Add nrows argument and chunking iterator to file parsers
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| 1 | 2011-10-16T22:55:48Z | 2011-10-21T04:32:49Z | 2011-10-21T04:32:49Z |
MEMBER
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This would be a very nice (and easy to implement) feature
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Add rename function on Panel
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| 0 | 2011-10-17T03:30:32Z | 2011-10-21T18:10:54Z | 2011-10-21T18:10:54Z |
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Support different delimiters for to_csv
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[] | null | 2 | 2011-10-17T12:33:49Z | 2014-06-14T10:26:54Z | 2011-10-19T20:23:20Z |
CONTRIBUTOR
| null |
This pull requests implements delimter support into to_csv. A number of analysis tools expects TSV files rather than CSV files, and this is a simple solution to the problem.
The delimiter parameter is by default ",", hence there are no side effects with existing code.
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Add ad-hoc indexing to DataFrame
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| 6 | 2011-10-17T16:13:36Z | 2016-12-31T00:32:21Z | 2016-12-31T00:32:21Z |
MEMBER
| null |
Not necessarily set row labels but enable fast selections by some criteria
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DataFrame.join on key with single key failure
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| 0 | 2011-10-18T18:22:23Z | 2011-10-18T18:31:50Z | 2011-10-18T18:31:50Z |
MEMBER
| null |
e.g.
```
df.join(df2, on=['key'])
```
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Add histogram / unique counter function to Series
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Add "inner join" option for DataFrame
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Add join / merge functionality for non-uniquely keyed data
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| 0 | 2011-10-18T18:43:08Z | 2011-12-31T00:16:45Z | 2011-12-31T00:16:45Z |
MEMBER
| null |
Save this for 0.6. Want to implement a proper SQL-type join. Maybe be a bit difficult since you have to do a cartesian product basically when the keys are not unique on the right / left
|
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Add helpful warning/error message when calling "import pandas" from source directory
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| 0 | 2011-10-18T18:58:47Z | 2011-10-19T21:21:07Z | 2011-10-19T21:21:07Z |
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Subplots error when DataFrame column contains all null values
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| 1 | 2011-10-18T19:12:20Z | 2011-10-18T20:59:19Z | 2011-10-18T20:59:19Z |
CONTRIBUTOR
| null |
This crashes the plot() command in matplotlib. Simple fix would be to just check that all only columns containing non-null values are plotted.
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Casting float column to int does not work
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| 0 | 2011-10-18T19:36:14Z | 2011-10-19T21:07:19Z | 2011-10-19T21:07:19Z |
MEMBER
| null |
```
In [1]: df = DataFrame(np.random.randint(0, 5, 10), dtype=float)
In [2]: df
Out[2]:
0
0 1
1 4
2 0
3 0
4 1
5 1
6 2
7 4
8 3
9 3
In [3]: df[0] = df[0].astype('i8')
In [4]: df
Out[4]:
0
0 1
1 4
2 0
3 0
4 1
5 1
6 2
7 4
8 3
9 3
In [5]: df.dtypes
Out[5]:
0 float64
Name: None, Length: 1
```
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MDU6SXNzdWUxOTY3NDgx
| 253 |
Enable select list of DataFrame columns from __getitem__
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| 1 | 2011-10-18T19:39:08Z | 2011-10-18T22:08:01Z | 2011-10-18T22:08:01Z |
MEMBER
| null |
This should be reasonably trivial to implement (without breaking hierarchical indexing) and be a fairly high impact since it's one of the first things that people try. Disambiguating from passing a boolean vector may not be the easiest thing in the world though.
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| 1,976,135 |
MDExOlB1bGxSZXF1ZXN0NDEzMTY1
| 254 |
fixed #251
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[] | null | 3 | 2011-10-18T21:38:22Z | 2014-06-20T19:27:37Z | 2011-10-19T20:28:53Z |
CONTRIBUTOR
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MDU6SXNzdWUxOTk0NTMz
| 255 |
Use Cython fused types
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| 3 | 2011-10-19T14:39:56Z | 2016-01-14T22:06:04Z | 2016-01-14T21:30:08Z |
MEMBER
| null |
Is this Cython 0.16? or 0.15.1?
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MDU6SXNzdWUxOTk0NTQz
| 256 |
Faster count function
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| 1 | 2011-10-19T14:40:34Z | 2011-10-20T22:44:23Z | 2011-10-20T22:44:23Z |
MEMBER
| null |
Should write faster Cython function for counting values-- speed up DataFrame.**repr**
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MDU6SXNzdWUxOTk1MTc5
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Specifying names should make header=None by default in read_table
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read_table will modify passed names if index_col is not None
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Counter class for Python < 2.7
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CONTRIBUTOR
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Copied verbatim from Python 2.7 collections module. Tested in Python 2.6, but not in 2.5, although I've checked the docs, and it shouldn't rely on anything that's not in 2.5.
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MultiIndex.diff not implemented and drops through to Index.diff
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NONE
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MultiIndex implements custom union() and intersection() behavior, but diff() is not implemented and drops through to Index.diff, giving incorrect behavior.
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Make & and | call intersection/union on Index objects
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| 0 | 2011-10-20T15:11:40Z | 2011-10-20T22:54:13Z | 2011-10-20T22:54:13Z |
MEMBER
| null |
```
MultiIndex names get lost in the returned dataframe after sort_index is used.
Here is an example:
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.4.3'
In [3]: index = pandas.MultiIndex.from_tuples([('x','y','z'), ('a','b','c'), ('p','q','r')], names=['one','two','three'])
In [4]: df = pandas.DataFrame(randn(3, 5), index=index)
In [5]: df
Out[5]:
0 1 2 3 4
x y z -1.098 -0.3314 -0.5526 0.3791 1.12
a b c 1.141 0.3309 -0.6509 -0.561 -1.086
p q r 0.3399 -0.3135 1.677 0.533 0.1158
In [6]: df.index.names
Out[6]: ['one', 'two', 'three']
In [7]: dfs = df.sort_index()
In [8]: dfs
Out[8]:
0 1 2 3 4
a b c 1.141 0.3309 -0.6509 -0.561 -1.086
p q r 0.3399 -0.3135 1.677 0.533 0.1158
x y z -1.098 -0.3314 -0.5526 0.3791 1.12
In [9]: dfs.index.names
Out[9]: [None, None, None]
```
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| 263 |
Cannot represent empty MultiIndex
|
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| 8 | 2011-10-20T15:49:22Z | 2011-10-21T16:10:38Z | 2011-10-21T04:48:45Z |
NONE
| null |
An empty MultiIndex cannot be constructed:
```
In [120]: pa.MultiIndex([],[])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
C:\Users\dfortunov\<ipython-input-120-ca5695a01949> in <module>()
----> 1 pa.MultiIndex([],[])
C:\VirtualEnvs\mss.dev\lib\site-packages\pandas-0.5.0.dev20111019_17461b7-py2.6-win32.egg\pandas\core\index.pyc in __new
__(cls, levels, labels, sortorder, names)
833 assert(len(levels) == len(labels))
834 if len(levels) == 0:
--> 835 raise Exception('Must pass non-zero number of levels/labels')
836
837 if len(levels) == 1:
Exception: Must pass non-zero number of levels/labels
```
This does not maintain parity with regular indices, where an empty index may be constructed:
```
In [119]: pa.Index([])
Out[119]: Index([], dtype=object)
```
And causes problems when doing set operations with MultiIndexes.
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Fix for IPython completion with non-string column names.
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[] | null | 1 | 2011-10-20T16:52:16Z | 2011-10-20T23:04:00Z | 2011-10-20T23:04:00Z |
CONTRIBUTOR
| null |
As discussed on #233.
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Rename Series.histogram -> value_counts and sort descending
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| 0 | 2011-10-20T18:11:03Z | 2011-10-20T22:44:05Z | 2011-10-20T22:44:05Z |
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Add convenience method for adding index from record-like DataFrame
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| 0 | 2011-10-20T21:29:41Z | 2011-10-24T18:28:54Z | 2011-10-24T18:28:54Z |
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| 267 |
Implement true SQL-style joins
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| 268 |
Add general load/save functions to pandas.* namespace, and update docs
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| 1 | 2011-10-21T04:42:01Z | 2011-10-22T01:58:56Z | 2011-10-22T01:58:56Z |
MEMBER
| null |
Preferable to using `DataFrame.load`
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Promote modules out of core?
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| 270 |
Series constructor fails on list of tuples
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| 0 | 2011-10-21T16:02:13Z | 2011-10-21T17:13:58Z | 2011-10-21T17:13:58Z |
MEMBER
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example:
```
Series([(1,2)])
```
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Add pyx dependencies for tseries.pyx
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MDU6SXNzdWUyMDE4NjEw
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Change Series.clip to work with numpy.clip
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| 1 | 2011-10-21T19:18:36Z | 2011-10-22T02:23:20Z | 2011-10-22T02:23:20Z |
MEMBER
| null |
from @bburan
```
I always find Series.clip() a bit confusing because the order of
arguments are reversed compared with Numpy's clip() method:
Series.clip(self, upper=None, lower=None)
numpy.clip(a, a_min, a_max, out=None)
Also, the following doesn't work well:
x = Series(data=np.random.uniform(size=10))
np.clip(x, 0.4, 0.6)
I think numpy.clip is checking for the existence of the clip() method
on the Series and attempting to pass the arguments to Series.clip().
Since Series.clip() doesn't support the axis argument this approach
fails. Ideally, I'd prefer to be able to use clip() without having to
check whether the data is a numpy.ndarray or pandas.Series object.
```
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Add optimized multiple-join / concatenation functions
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Decide what to do about DataFrame.from_csv default options
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nanRep argument deprecation + FutureWarning
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Make sure DataFrame.append is equivalent to rbind_fill for different columns
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Do something about NA values in Series.value_counts and describe on object arrays
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Update install docs now that pandas works on Python 3
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CONTRIBUTOR
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I realised I'd forgotten to update the docs when I did the Python 3 support. Feel free to tweak the wording, and let me know if there are other bits of the docs I should update.
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Add level names to Series.__repr__
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Check for duplicate index names or None in MultiIndex
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Refactor Series to_string / repr-related methods to be cleaner
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Speed up GroupBy Cython methods to not have to sort data
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Test Index.append when passing empty list
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| 284 |
Issues with mod_wsgi?
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[] | null | 7 | 2011-10-24T13:14:12Z | 2011-10-24T15:19:07Z | 2011-10-24T15:07:07Z |
NONE
| null |
Hi,
I keep getting a 'module' object has no attribute 'core' when using with mod_wsgi. Please see http://stackoverflow.com/questions/7766400/pandas-django-mod-wsgi-virtualenv for the full error description.
Evan
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Investigate erroneous int conversion in MultiIndex
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| 0 | 2011-10-24T19:00:45Z | 2011-10-25T01:35:04Z | 2011-10-25T01:35:04Z |
MEMBER
| null |
```
>> I was just using this feature and noticed a curious behaviour -- if you have dates in the ISO8601 YYYYMMDD format then importing from csv with a MultiIndex turns them into ints, whereas importing with a regular index parses them to dates.
>>
>> Looking at pandas/io/parsers.py (https://github.com/wesm/pandas/commit/1a5a252d97cb3f691a6bf93412b921b085f1be76#L1L113) it seems that the _maybe_convert_int_mindex() method you added prefers ints over dates -- is this intentional or were you just not expecting any overlap between ints and dates?
>>
>
> Most likely the latter.
>
>> You also no longer call _maybe_convert_int, which was previously being called but is now unused, and instead do the int parsing with a map().
>>
>> Why not just implement _maybe_convert_int_mindex() along the lines of what was originally there, something like this:
>> for i in range(len(index)):
>> if parse_dates:
>> index = _try_parse_dates(index[i], parser=date_parser)
>> index[i] = _maybe_convert_int(np.array(index[i], dtype=object))
>>
>> I would just do it and submit a patch but I just wanted to check with you as you're more familiar with this code since you wrote it.
```
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ENH: Adding argmin, argmax to Series and DataFrame.
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[] | null | 3 | 2011-10-25T11:46:37Z | 2014-06-13T12:03:28Z | 2011-11-14T07:22:04Z |
CONTRIBUTOR
| null |
Hi Wess,
Pull request to let you know that i added argmin and argmax methods to Series and DataFrame.
I also included unit tests. It`s not that much work/code, but anyway an interesting enhancement i think.
some examples:
In [23]: s
Out[23]:
a 0.80015881463
b 0.983432892599
c 0.965122593504
d 0.81910825442
e 0.726239458506
Name: None, Length: 5
In [24]: s.argmin()
Out[24]: 'e'
In [25]: s[s.argmax()] == s.max()
Out[25]: True
In [26]: df
Out[26]:
A B C D E
one two three
a b 10 -0.6155 1.237 1.371 1.24 -0.7725
p q 20 1.14 -0.3743 0.5251 0.2426 1.81
x y 30 0.6626 2.004 -0.01327 1.587 0.3524
In [27]: df.argmax(axis=0)
Out[27]:
A ('p', 'q', 20)
B ('x', 'y', 30)
C ('a', 'b', 10)
D ('x', 'y', 30)
E ('p', 'q', 20)
Name: None, Length: 5
In [28]: df.argmin(axis=1)
Out[28]:
a b 10 E
p q 20 B
x y 30 C
Name: None, Length: 3
What do you think?
Wouter
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ENH: Adding DataFrame.boxplot()
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[] | null | 4 | 2011-10-25T13:42:51Z | 2014-07-25T01:41:23Z | 2011-11-14T08:31:05Z |
CONTRIBUTOR
| null |
Added DataFrame.boxplot() in similar way as DataFrame.plot() and DataFrame.hist().
Some syntactic sugar ;-)
Thinking about adding DataFrameGroupBy.boxplot(), now this call is transfered to DataFrame.boxplot().
But i would prefer the behavior to be different. When groupby is used i`m interested to see in a single plot boxplots for one particular column for all groupby keys and not in one plot to have boxpots for all columns for one groupby key. An input argument can be used to so both ways of boxplotting can be used.
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| 288 |
R-like range() function
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[] | null | 6 | 2011-10-25T14:16:59Z | 2012-01-23T15:13:00Z | 2012-01-23T15:13:00Z |
CONTRIBUTOR
| null |
In R, if I supply range(dataframe), I get back a vector of the minimum value in all the dataframe and the maximum value in all the dataframe.
DataFrame's min() and max() functions operate only on single columns. The way to do it currently is
``` python
minimum, maximum = (min(df.min()), max(df.max())
```
It's not a critical limitation, but it is convenient to have at times.
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Add fast API function to filter using a set of values
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| 0 | 2011-10-25T18:08:19Z | 2011-11-02T05:48:37Z | 2011-11-02T05:48:37Z |
MEMBER
| null |
Call it `isin`?
|
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| 290 |
DataFrame.to_csv throws exception
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| 2 | 2011-10-26T00:56:05Z | 2011-10-26T10:24:27Z | 2011-10-26T01:14:10Z |
CONTRIBUTOR
| null |
Hi, i ran into a problem with the current master on github. Here's
import cStringIO as StringIO
import pandas
f1 = StringIO.StringIO('a,1.0\nb,2.0')
df = pandas.DataFrame.from_csv(f1,header=None)
newdf = pandas.DataFrame({'t': df[df.columns[0]]})
newdf.to_csv('/tmp/test.csv')
The last line gives me an exception like this:
Traceback (most recent call last):
File "/tmp/test.py", line 6, in <module>
newdf.to_csv('/tmp/test.csv')
File "/usr/lib/python2.7/dist-packages/pandas/core/frame.py", line 531, in to_csv
csvout.writerow(list(index_label) + list(cols))
TypeError: 'NoneType' object is not iterable
Code like this was working until quite recently. What i'm really trying to do is to read a bunch of csv files, each of which contains two columns of data, the first being the index, the second some values. The index is the same across files, so
i'm trying to combine these files into one DataFrame.
thanks
dieter
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Add skipfooter option to read_{csv,table}
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| 0 | 2011-10-26T01:39:36Z | 2011-11-05T00:53:26Z | 2011-11-05T00:53:26Z |
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Add support for ordered factors and groupby handling
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DataFrame constructor fails on list of tuples
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| 1 | 2011-10-26T13:52:34Z | 2011-11-04T20:04:21Z | 2011-11-04T20:04:21Z |
CONTRIBUTOR
| null |
In [93]: pandas.**version**
Out[0]: '0.5.0'
In [94]: df = pandas.DataFrame({'A': [(1, 2)]})
---
AssertionError Traceback (most recent call last)
< .. snip ... >
The latter fails, however the following works fine:
In [95]: s = pandas.Series([(1, 2)]) ---> this was fixed in issue #270
In [96]: df = pandas.DataFrame({'A': s})
In [97]: s
Out[0]:
0 (1, 2)
Name: None, Length: 1
In [98]: df
Out[0]:
A
0 (1, 2)
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Series.__setitem__ ValueError
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[] | null | 5 | 2011-10-26T14:44:22Z | 2011-10-27T13:47:29Z | 2011-10-27T13:47:29Z |
CONTRIBUTOR
| null |
Depending on Series.values.dtype `Series.__setitem__()` fails.
Two examples below. First one has no problem, second on shows issue.
pandas 0.5.0
python 2.7
# example1 - ok
``` python
In [15]: s = pandas.Series([1, 2.2352345, (3, 4), 'hello world'], index=['a', 'b', 'c', 'd'])
In [16]: s
Out[16]:
a 1
b 2.2352345
c (3, 4)
d hello world
Name: None, Length: 4
In [17]: s.values.dtype
Out[17]: dtype('object')
In [18]: s['a'] = 'cheese'
In [19]: s
Out[19]:
a cheese
b 2.2352345
c (3, 4)
d hello world
Name: None, Length: 4
```
# example2 - ValueError
``` python
In [20]: s = pandas.Series(data=np.random.rand(4), index=['a', 'b', 'c', 'd'])
In [21]: s
Out[21]:
a 0.056213774349
b 0.329577766125
c 0.463165873436
d 0.364501153967
Name: None, Length: 4
In [22]: s.values.dtype
Out[22]: dtype('float64')
In [23]: s['a'] = 'cheese'
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<..snip..>/lib/python2.7/site-packages/pandas/core/series.pyc in __setitem__(self, key, value)
325 try:
326 loc = self.index.get_loc(key)
--> 327 values[loc] = value
328 return
329 except KeyError:
ValueError: setting an array element with a sequence.
```
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| 295 |
add .head() and .tail() to Series
|
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NONE
| null |
These methods are convenient when working with DataFrame. For consistency and convenience, it would be nice to have them on Series as well.
|
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add .head() and .tail() to Series
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NONE
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These methods are convenient when working with DataFrame. For consistency and convenience, it would be nice to have them on Series as well.
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Add "count distinct" function, useful for groupby
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| 2 | 2011-10-26T16:25:32Z | 2011-11-16T09:04:32Z | 2011-11-16T09:04:32Z |
MEMBER
| null |
something like
```
def dist(x):
return len(np.unique(x))
```
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Raise more helpful Exception if dateutil.parser fails in DateRange
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Improve performance of groupby on MultiIndex'd data
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| 0 | 2011-10-26T23:49:10Z | 2011-11-04T20:35:35Z | 2011-11-04T20:35:35Z |
MEMBER
| null |
Calling `reindex` is very slow-- multiple issues to address
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Add function to read DataFrame from clipboard across platforms
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| 6 | 2011-10-27T04:01:48Z | 2012-09-16T14:21:49Z | 2011-11-14T18:57:51Z |
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