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} | 57 | 2013-11-27T20:55:58Z | 2014-06-17T15:56:36Z | 2014-06-17T14:44:30Z | CONTRIBUTOR | null | ... a ticker.
Also added a few helper functions. These functions could be applied to refactor some of the other methods.
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} | 6 | 2013-11-28T00:44:35Z | 2014-05-16T19:44:16Z | 2014-05-16T19:44:16Z | CONTRIBUTOR | null | This came up in discussion on #5550 and #5594. The default plain & HTML repr now truncates DataFrames to the first max_rows rows and max_columns columns. At least for rows, truncating in the middle (so we show the first `max_rows//2` and the last `max_rows//2` rows) would be more consistent with the display of Series and numpy arrays.
This isn't a major change, but it's not completely trivial either, which is why I haven't done it already. I'll try to get round to it, but if someone else wants to work on it, go for it.
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closes #5480.
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} | 7 | 2013-11-28T01:18:28Z | 2013-11-28T05:29:28Z | 2013-11-28T03:19:24Z | CONTRIBUTOR | null | http://nbviewer.ipython.org/gist/michaelaye/7685834
Version: d057fc9 ('0.12.0-1178-gd057fc9')
I tried a sequence of "python setup.py clean && python setup.py install" without success.
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} | 0 | 2013-11-28T02:33:38Z | 2014-07-04T06:16:27Z | 2013-11-28T03:19:25Z | CONTRIBUTOR | null | closes #5605
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https://api.github.com/repos/pandas-dev/pandas/issues/5607 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5607/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5607/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5607/events | https://github.com/pandas-dev/pandas/issues/5607 | 23,436,767 | MDU6SXNzdWUyMzQzNjc2Nw== | 5,607 | set_index drop inconsistency | {
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} | [] | closed | false | null | [] | null | 3 | 2013-11-28T08:49:13Z | 2013-12-01T04:00:04Z | 2013-12-01T04:00:04Z | CONTRIBUTOR | null | I'm not sure if providing a Series of the dataframe as a key to drop is covered by the API (the docs don't mention `Series` as they do for df.groupby() ), but it's at least inconsistent that the set_index seems to work but then the default drop=True is being ignored.
See what I mean:
``` python
print df
a b c
0 0.454351 0.916299 0.272791
1 -0.249901 0.109531 -0.587856
2 1.166015 -1.833338 -1.253099
3 0.014515 -0.099151 -1.073973
4 -0.740403 -0.785952 -0.815626
5 rows ร 3 columns
print df.set_index(df.a)
a b c
a
0.454351 0.454351 0.916299 0.272791
-0.249901 -0.249901 0.109531 -0.587856
1.166015 1.166015 -1.833338 -1.253099
0.014515 0.014515 -0.099151 -1.073973
-0.740403 -0.740403 -0.785952 -0.815626
[5 rows x 3 columns]
```
When using a column name string, all works fine:
``` python
print df.set_index('a')
b c
a
0.454351 0.916299 0.272791
-0.249901 0.109531 -0.587856
1.166015 -1.833338 -1.253099
0.014515 -0.099151 -1.073973
-0.740403 -0.785952 -0.815626
[5 rows x 2 columns]
```
I thought I tracked it down to this relevant code in core/frame.py/set_index:
``` python
for col in keys:
if isinstance(col, Series):
level = col.values
names.append(col.name)
elif isinstance(col, (list, np.ndarray)):
level = col
names.append(None)
else:
level = frame[col].values
names.append(col)
if drop:
to_remove.append(col)
arrays.append(level)
```
To me, the lines:
``` python
if drop:
to_remove.append(col)
```
seem to be one scope too deep and should be one level higher so that independent of the type of `col`, the drop can be performed.
However, when trying my fix by reducing the indent, I get all sorts of iloc errors (again), not sure what's going on, so my naรฏve idea must be missing something.
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} | [] | closed | false | null | [] | null | 4 | 2013-11-28T09:52:55Z | 2013-11-29T07:55:00Z | 2013-11-28T23:49:46Z | MEMBER | null | @hayd
`DataFrameGroupBy.cumcount()` returns a Series while most others (the ones I tested, like count, cumsum, first, sum ) return a DataFrame. They are maybe not all comparable, but at least with `cumsum` it could be I think:
``` python
In [1]: data = pd.DataFrame({'name' : ['a', 'a', 'b', 'd'], 'counts' : [3,4,3,2]})
In [2]: data
Out[2]:
counts name
0 3 a
1 4 a
2 3 b
3 2 d
In [3]: g = data.groupby('name')
In [4]: g.cumcount()
Out[4]:
0 0
1 1
2 0
3 0
dtype: int64
In [5]: g.count()
Out[5]:
counts name
name
a 2 2
b 1 1
d 1 1
In [6]: g.first()
Out[6]:
counts
name
a 3
b 3
d 2
In [7]: g.sum()
Out[7]:
counts
name
a 7
b 3
d 2
In [8]: g.cumsum()
Out[8]:
counts name
0 3 a
1 7 aa
2 3 b
3 2 d
```
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} | 17 | 2013-11-28T10:59:48Z | 2014-03-20T20:04:35Z | 2014-03-20T20:04:35Z | NONE | null | Quick implementation of dataframe shift
```
def quick_shift(df, N=1):
'''Quick implementation of dataframe shift'''
shift_value = np.roll(df.values,N,axis=0)
shift_value[0:N] = np.NaN
return DataFrame(shift_value, index=df.index, columns=df.columns)
DataFrame.quick_shift = quick_shift
```
Perf benchmark:
```
df = DataFrame(np.random.rand(10000,500))
%timeit df1 = df.quick_shift(1)
10 loops, best of 3: 55.3 ms per loop
%timeit df2 = df.shift(1)
1 loops, best of 3: 238 ms per loop
```
Related to https://github.com/pydata/pandas/issues/4095
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} | 17 | 2013-11-28T11:52:46Z | 2016-08-26T21:13:28Z | 2014-04-29T20:12:46Z | MEMBER | null | `GroupBy.count()` (with the default `as_index=True`) return the grouping column both as index and as column, while other methods as `first` and `sum` keep it only as the index (which is most logical I think). This seems a minor inconsistency to me:
```
In [41]: data = pd.DataFrame({'name' : ['a', 'a', 'b', 'd'], 'counts' : [3,4,3,2]})
In [42]: data
Out[42]:
counts name
0 3 a
1 4 a
2 3 b
3 2 d
In [43]: g = data.groupby('name')
In [45]: g.count()
Out[45]:
counts name
name
a 2 2
b 1 1
d 1 1
In [46]: g.first()
Out[46]:
counts
name
a 3
b 3
d 2
In [47]: g.sum()
Out[47]:
counts
name
a 7
b 3
d 2
```
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} | 8 | 2013-11-28T13:25:02Z | 2016-11-11T10:09:36Z | 2016-11-11T10:09:32Z | NONE | null | #5013 is in the sandbox, the functionality would be useful as `df.inspect()`,
popping up a gui grid view of the frame.
In the future, it's possible to detect ipnb and render a js grid instead, if the coming ipython
js functionality is adequate, to cover the remote kernel use-case (less common).
cc @alefnula
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} | 4 | 2013-11-28T13:40:14Z | 2016-08-27T09:22:05Z | 2016-08-27T09:21:45Z | MEMBER | null | `Index.to_datetime` has no `format` keyword (and more generally, it lacks all keyword arguments from `pd.to_datetime` except for `dayfirst`).
Maybe it could be made more similar to `pd.datetime`? (or even just use it under the hood?)
Code: https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L278
Docs: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Index.to_datetime.html#pandas.Index.to_datetime
Thanks to @stijnvanhoey for reporting
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} | 15 | 2013-11-28T16:30:09Z | 2016-01-11T13:28:35Z | 2016-01-11T13:28:35Z | MEMBER | null | If your timeseries has a regularly spaced index, so if a frequency can be inferred, using `asfreq` or `resample` with this frequency does not set the frequency (it remains None):
```
In [1]: df = pd.DataFrame({'date':["2012-01-01", "2012-01-02", "2012-01-03"], 'col':[1,2,3]})
In [2]: df2 = df.set_index(pd.to_datetime(df.date))
In [3]: df2.index
Out[3]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-03 00:00:00]
Length: 3, Freq: None, Timezone: None
In [4]: df2.index.inferred_freq
Out[4]: 'D'
In [5]: df2.asfreq('D').index
Out[5]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-03 00:00:00]
Length: 3, Freq: None, Timezone: None
In [6]: df2.resample('D').index
Out[6]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-03 00:00:00]
Length: 3, Freq: None, Timezone: None
```
Note: this is the same with a Series, however in 0.12 it works for Series (as then it is still a TimeSeries)
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} | 16 | 2013-11-28T20:37:33Z | 2014-05-01T22:23:50Z | 2014-05-01T15:13:57Z | CONTRIBUTOR | null | It shouldn't sum the groupby'd col (in fact index col should be the index, if groupby as_index).
```
In [13]: df = pd.DataFrame([[1, 2, np.nan], [1, np.nan, 9], [3, 4, 9]], columns=['A', 'B', 'C'])
In [14]: g = df.groupby('A')
In [16]: g.cumsum()
Out[16]:
A B C
0 1 2 NaN
1 2 NaN 9
2 3 4 9
[3 rows x 3 columns]
```
Nature of it being dispatch. Should fix up for 0.14 possibly along with some other whitelisted groupby functions.
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} | 29 | 2013-11-29T06:15:33Z | 2014-06-17T19:04:21Z | 2013-12-07T00:40:20Z | CONTRIBUTOR | null | @jtratner I was a little slower moving on this than I'd hoped, but it's a pretty good start.
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} | 8 | 2013-11-29T16:50:12Z | 2013-12-10T13:19:30Z | 2013-12-10T13:19:30Z | CONTRIBUTOR | null | Example:
``` python
>>> df = pd.DataFrame({'a': [10, 20], 'b': [30, 40], 'c': [50, 60]},
... index=pd.MultiIndex.from_tuples([(70, 80), (90, 100)]))
>>> writer = pd.ExcelWriter('test.xls')
>>> df.to_excel(writer, sheet_name='1', index=False)
>>> writer.save()
>>> pd.read_excel('test.xls', '1')
a b c
10 30 50 NaN NaN
20 40 60 NaN NaN
```
The column names are obviously misplaced (erroneously shifted to the right by the number of levels in the MultiIndex). Or is it the data that is misplaced? Wonder why no one noticed this before.
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} | 11 | 2013-11-29T18:11:57Z | 2017-02-23T17:29:57Z | 2013-12-31T00:49:22Z | CONTRIBUTOR | null | It doesn't look like it's available yet as such, but it might be nice to have set_option usable as a context manager. E.g., I often find myself wanting to do something like
```
with pd.set_option('max_rows', 200):
print df
```
As simple as adding `__enter__` and `__exit__` methods and preserving state?
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} | [] | closed | false | null | [] | null | 3 | 2013-11-30T02:18:07Z | 2014-01-25T00:16:14Z | 2014-01-25T00:16:14Z | NONE | null | I'm dealing with some csv files which have hundreds of labeled columns. The first row in the files is the header row. I'd love to be able to load in only specific columns. The 'usecols' keyword argument to `read_csv` is useful but I currently have to read the first line of the file manually to get the header, parse the header into a list of strings, and then identify which column headers match a set of regular expressions to get the index of the header. These indexes can then be passed to 'usecol' so that I don't load those columns. So my feature request is to allow one to pass in a list of regular expressions to 'usecol' as an option to the indices. I'd be happy to contribute this code if you all think it is useful and could point me to the best place to implement it in the source tree.
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} | 10 | 2013-11-30T12:17:58Z | 2014-11-18T10:15:32Z | 2014-11-18T10:15:13Z | MEMBER | null | If you try following piece of code, my python process crashes:
```
df = pd.DataFrame({'A':[1,1,2], 'B':[1,2,1], 'C':[1,2,3]})
df = df.set_index(['A', 'B'])
df.index.groupby(df.index)
```
I know this code is as such probably bullshit (I also did it by accident, the first index shouldn't have been there :-)), but still it should then generate a Error Message instead of causing python to stop?
BTW `pd.Index.groupby` is a publicly available, but not really clear what is does or is meant for.
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} | 7 | 2013-11-30T16:10:11Z | 2014-06-15T18:49:12Z | 2014-02-18T20:05:24Z | CONTRIBUTOR | null | We added some notes on Index to the API Reference that probably
shouldn't be there at all, since they're not meant for public
consumption or not documented. I've slimmed down the methods to set
ops, copy, metadata (i.e. names) and a few of the DatetimeIndex methods.
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} | [] | closed | false | null | [] | null | 1 | 2013-11-30T18:05:58Z | 2013-11-30T19:53:53Z | 2013-11-30T19:53:53Z | NONE | null | I was trying to construct an index from datetime objects:
```
import pandas as pd, numpy, datetime
#idx = [datetime.date.fromordinal(n) for n in range(1,3)]
idx = [datetime.datetime.fromordinal(n) for n in range(1,3)]
df = pd.DataFrame(data=numpy.zeros(2),index=idx)
```
This code does not work. It seems that the line:
`converted, all_arrays = lib.clean_index_list(index_like)`
( from `pandas/core/index.py:3424`)
Will leave `converted` as an object ndarray rather than a native dtype.
I couldn't peek into lib.clean_index_list since I guess that is compiled code.
Things work fine with `date`. I think things should work both for date and datetimes?
Also, When I run `pd.Index` on the list it complains because the time part is 0, which I think is legal data.
```
idx = [datetime.datetime.fromordinal(n) for n in range(1,3)]
pd.Index(idx)
#--> OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1-01-01 00:00:00
```
Version -> `0.13.0rc1`
Thanks.
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} | 4 | 2013-11-30T20:15:08Z | 2014-06-15T07:05:39Z | 2014-01-15T15:28:37Z | CONTRIBUTOR | null | Unless there's a specific reason not to for this plot? All the others seem to accept them.
``` python
autocorrelation_plot(data, label='ACF')
```
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} | 32 | 2013-12-01T00:50:30Z | 2014-07-11T21:41:59Z | 2013-12-19T22:43:04Z | CONTRIBUTOR | null | Fixes #5618
cc @y-p and @jseabold
I went ahead and simplifed all the prefixing stuff to just change a single `prefix_key` function instead and handled the dynamic docstring by setting the `__doc__` attribute of functions/classes. set_option is now callable _and_ a contextmanager (whee!) so no need to add anything else to the global namespace (if you don't call `__exit__` it doesn't undo itself)
Had to add a metaclass to this because it's the only way to set docstrings on the _class_ level. Doesn't really add that much noise so I think it's fine.
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segmentation faults for me. This should fix the issue.
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$ nosetests
/usr/bin/xclip <<<< raise your hand if you've been ignoring this for 6 months
...........
```
`os.system` nastiness. The vbench package has similar problems.
https://github.com/pydata/pandas/pull/3848
:raised_hand:
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} | 17 | 2013-12-02T03:40:47Z | 2013-12-06T18:36:25Z | 2013-12-02T12:53:01Z | CONTRIBUTOR | null | In c239b06e I have the following weird behavior. I am even unsure how to describe it, but somehow the dropping of rows selected via a boolean leads to a failure of dropping the rows only for the Series of a DataFrame that met the condition in the first place.
Please have a look at the MWE I figured out after some hacking and trying out:
http://nbviewer.ipython.org/gist/michaelaye/7744592
Please tell me I'm not being dumb again?
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} | [] | closed | false | null | [] | null | 1 | 2013-12-02T06:10:48Z | 2013-12-02T15:52:50Z | 2013-12-02T11:03:53Z | CONTRIBUTOR | null | I have a quite large MultiIndex here that I accidentally tried to show with `__repr__`:
``` python
print len(counts.index)
print type(counts.index)
3982319
<class 'pandas.core.index.MultiIndex'>
```
whereupon my Macbook started to boil and the notebooks became unresponsive and I had to hard-kill the notebook server to get things back on track.
Is that avoidable somehow in a similar fashion you are checking the length of a dataframe before showing it?
Pandas version is 0.13.0rc1, with Canopy's python 2.7.3
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} | 2 | 2013-12-02T10:33:25Z | 2015-01-06T13:14:19Z | 2015-01-06T13:14:19Z | CONTRIBUTOR | null | Does it make sense to extend documentation with links to a 3rd party libraries that download financial data from different sources in the pandas format? E.g. interfaces for Quandl (http://www.quandl.com/help/packages/python) or for Thomson Reuters Datastream (https://github.com/vfilimonov/pydatastream)?
Also personally I would be happy if pydatastream was included into Pandas, though since TR Datastream itself is not a free data source, it may not fit well pandas structure.
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} | 0 | 2013-12-02T12:42:39Z | 2014-07-02T08:55:16Z | 2013-12-02T12:53:01Z | CONTRIBUTOR | null | closes #5628
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} | 6 | 2013-12-02T21:26:59Z | 2013-12-03T14:53:32Z | 2013-12-03T01:59:04Z | CONTRIBUTOR | null | This issue appears in 0.13.0-rc1 and did not appear in 0.12.0-854-gde63e00
Let us say we have an empty DataFrame. Then the following calls (which worked in pandas 0.12) no longer work:
``` python
df = pd.DataFrame()
df['foo'] = []
df = pd.DataFrame()
df['foo'] = df.index
df = pd.DataFrame()
df['foo'] = range(len(df))
```
They all throw an exception:
```
File "/usr/local/lib/python3.2/dist-packages/pandas-0.13.0rc1-py3.2-linux-x86_64.egg/pandas/core/frame.py", line 1855, in __setitem__
self._set_item(key, value)
File "/usr/local/lib/python3.2/dist-packages/pandas-0.13.0rc1-py3.2-linux-x86_64.egg/pandas/core/frame.py", line 1915, in _set_item
self._ensure_valid_index(value)
File "/usr/local/lib/python3.2/dist-packages/pandas-0.13.0rc1-py3.2-linux-x86_64.egg/pandas/core/frame.py", line 1899, in _ensure_valid_index
raise ValueError('Cannot set a frame with no defined index '
ValueError: Cannot set a frame with no defined index and a non-series
```
The following will work:
``` python
df = pd.DataFrame()
df['foo'] = pd.Series([])
df = pd.DataFrame()
df['foo'] = pd.Series(df.index)
df = pd.DataFrame()
df['foo'] = pd.Series(range(len(df)))
```
The issue appears to be on lines 1897-1899 of pandas.core.frame:
``` python
if not len(self.index):
if not isinstance(value, Series):
raise ValueError('Cannot set a frame with no defined index '
```
Perhaps it could be changed to:
``` python
if not len(self.index) and len(value) > 0:
if not isinstance(value, Series):
raise ValueError('Cannot set a frame with no defined index '
```
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} | 0 | 2013-12-02T22:19:33Z | 2014-06-18T03:40:04Z | 2013-12-03T01:59:04Z | CONTRIBUTOR | null | closes #5632
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https://api.github.com/repos/pandas-dev/pandas/issues/5634 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5634/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5634/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5634/events | https://github.com/pandas-dev/pandas/pull/5634 | 23,617,707 | MDExOlB1bGxSZXF1ZXN0MTA0NTc5ODk= | 5,634 | BUG: Validate levels in a multi-index before storing in a HDFStore (GH5527) | {
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} | 0 | 2013-12-03T02:41:10Z | 2014-06-22T06:01:57Z | 2013-12-03T03:00:46Z | CONTRIBUTOR | null | close #5527
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https://api.github.com/repos/pandas-dev/pandas/issues/5635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5635/events | https://github.com/pandas-dev/pandas/issues/5635 | 23,620,886 | MDU6SXNzdWUyMzYyMDg4Ng== | 5,635 | BUG: empty result for HDFStore where with leading NaNs | {
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} | 6 | 2013-12-03T04:36:41Z | 2014-02-15T21:00:15Z | 2014-02-15T21:00:15Z | CONTRIBUTOR | null | https://github.com/PyTables/PyTables/issues/282 is the culprit
I may be missing something here, but I think this is an odd result. When reading from a `table` HDFStore, if the first value is NaN then any `where`s based on that column will return an empty selection.
``` python
In [58]: sm
Out[58]:
HRHHID earnings
0 251395039600155 NaN
1 251396749500840 188461
2 251396749500840 57692
3 251408550479107 NaN
4 251465059600832 61500
In [63]: tst = pd.HDFStore('tst.h5')
# leading NaN, empty
In [64]: sm.to_hdf(tst, 'sm', data_columns=True, format='table')
In [65]: tst.select('sm', where="earnings>0")
Out[65]:
Empty DataFrame
Columns: [HRHHID, earnings]
Index: []
# leading isn't NaN, works
In [66]: sm.tail(4).to_hdf(tst, 'sm', data_columns=True, format='table')
In [67]: tst.select('sm', where="earnings>0")
Out[67]:
HRHHID earnings
1 251396749500840 188461
2 251396749500840 57692
4 251465059600832 61500
# Leading NaN again, empty
In [69]: sm.tail(2).to_hdf(tst, 'sm', data_columns=True, format='table')
In [70]: tst.select('sm', where="earnings>0")
Out[70]:
Empty DataFrame
Columns: [HRHHID, earnings]
Index: []
In [71]: pd.__version__
Out[71]: '0.12.0-1153-g476b6e3'
In [72]: import tables
In [73]: tables.__version__
Out[73]: '3.0.0'
```
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} | 3 | 2013-12-03T20:03:36Z | 2016-04-19T18:36:05Z | 2016-04-19T18:36:05Z | CONTRIBUTOR | null | With:
```
csv="""A,B,C
1,2,2003-11-1
"""
```
These all work as expected:
```
In [40]: pd.read_csv(StringIO(csv), parse_dates="C",index_col="C").index[0]
Out[40]: Timestamp('2003-11-01 00:00:00', tz=None)
In [41]: pd.read_csv(StringIO(csv), parse_dates=["C"],index_col="C").index[0]
Out[41]: Timestamp('2003-11-01 00:00:00', tz=None)
In [42]: pd.read_csv(StringIO(csv), parse_dates=["C"]).C[0]
Out[42]: Timestamp('2003-11-01 00:00:00', tz=None)
```
but this does not parse the string:
```
In [39]: pd.read_csv(StringIO(csv), parse_dates="C",).C[0]
Out[39]: '2003-11-1'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5637/events | https://github.com/pandas-dev/pandas/issues/5637 | 23,671,684 | MDU6SXNzdWUyMzY3MTY4NA== | 5,637 | datetools.BDay() does the wrong thing when given a Friday - Saturday range | {
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} | 1 | 2013-12-03T20:33:11Z | 2014-04-06T15:03:05Z | 2014-04-06T15:03:01Z | NONE | null | With a daily frequency, when you specify a range from a Friday morning UTC to Saturday morning UTC, you get back both days as expected:
```
(Pdb) pd.DatetimeIndex(start=datetime.datetime(2013,11,22,5,0,0,tzinfo=pytz.utc), end=datetime.datetime(2013,11,23,4,59,59,tzinfo=pytz.utc), freq=pd.datetools.Day())
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-11-22 05:00:00, 2013-11-23 05:00:00]
Length: 2, Freq: D, Timezone: UTC
```
With a BDay frequency, however, you get back _neither_ day, even though you should get back the Friday since it's a business dasy:
```
(Pdb) pd.DatetimeIndex(start=datetime.datetime(2013,11,22,5,0,0,tzinfo=pytz.utc), end=datetime.datetime(2013,11,23,4,59,59,tzinfo=pytz.utc), freq=pd.datetools.BDay())
<class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: B, Timezone: UTC
```
The correct thing happens if you shift both dates back by a day:
```
(Pdb) pd.DatetimeIndex(start=datetime.datetime(2013,11,21,5,0,0,tzinfo=pytz.utc), end=datetime.datetime(2013,11,22,4,59,59,tzinfo=pytz.utc), freq=pd.datetools.BDay())
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-11-21 05:00:00]
Length: 1, Freq: B, Timezone: UTC
```
You also get back the Friday if you extend the range to the next business day:
```
(Pdb) pd.DatetimeIndex(start=datetime.datetime(2013,11,22,5,0,0,tzinfo=pytz.utc), end=datetime.datetime(2013,11,25,4,59,59,tzinfo=pytz.utc), freq=pd.datetools.BDay())
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-11-22 05:00:00]
Length: 1, Freq: B, Timezone: UTC
```
Ref: https://github.com/quantopian/zipline/issues/243
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} | 31 | 2013-12-04T00:10:22Z | 2014-06-13T17:00:32Z | 2014-03-18T17:42:12Z | CONTRIBUTOR | null | close #3796
Addresses some of the concerns in issue #3796. New code allows the DataFrame and Series Line plots and Bar plot functions to include errorbars using `xerr` and `yerr` keyword arguments to `DataFrame/Series.plot()`. It supports specifying x and y errorbars as 1. a separate list/numpy/Series, 2. a DataFrame with the same column names as the plotting DataFrame. For example, using method 2 looks like this:
```
df = pd.DataFrame({'x':[1, 2, 3], 'y':[3, 2, 1]})
df_xerr = pd.DataFrame({'x':[0.6, 0.2, 0.3], 'y':[0.4, 0.5, 0.6]})
df_yerr = pd.DataFrame({'x':[0.5, 0.4, 0.6], 'y':[0.3, 0.7, 0.4]})
df.plot(xerr=df_xerr, yerr=df_yerr)
```
This is my first contribution. I tried to follow the contribution guidelines as best I could, but let me know if anything needs work!
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} | 1 | 2013-12-04T11:55:10Z | 2013-12-04T14:51:07Z | 2013-12-04T14:50:54Z | NONE | null | It works fine to select a column twice if the DataFrame does not consists of any columns of dtype bool, but works fine otherwise.
``` python
import pandas as pd
#Data with just bool columns
dbool = {'one' : pd.Series([True, True, False], index=['a', 'b', 'c']),
'two' : pd.Series([False, False, True, False], index=['a', 'b', 'c', 'd']),
'three': pd.Series([False, True, True, True], index=['a', 'b', 'c', 'd'])}
#No bool columns
dnum = {'four' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'five' : pd.Series([1., 2., 3., 0.4], index=['a', 'b', 'c', 'd']),
'six': pd.Series([9., 5., 4., -0.1], index=['a', 'b', 'c', 'd'])}
#data with both bool and non-bool columns
dmixed = {'one' : pd.Series([True, True, False], index=['a', 'b', 'c']),
'two' : pd.Series([False, False, True, False], index=['a', 'b', 'c', 'd']),
'three': pd.Series([False, True, True, True], index=['a', 'b', 'c', 'd']),
'four' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'five' : pd.Series([1., 2., 3., 0.4], index=['a', 'b', 'c', 'd']),
'six': pd.Series([9., 5., 4., -0.1], index=['a', 'b', 'c', 'd'])}
dfbool = pd.DataFrame(dbool)
dfnum = pd.DataFrame(dnum)
dfmixed = pd.DataFrame(dmixed)
#Works fine
dfnum[['four', 'five', 'four']]
#Does not work
dfbool[['one', 'three', 'one']]
#Does not work
dfmixed[['four', 'five', 'four']]
dfmixed[['one', 'three', 'one']]
dfmixed[['one', 'four', 'one']]
```
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} | 0 | 2013-12-04T14:16:30Z | 2014-06-19T11:13:37Z | 2013-12-04T14:50:54Z | CONTRIBUTOR | null | closes #5639
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} | 6 | 2013-12-04T18:04:05Z | 2014-02-13T23:12:32Z | 2014-02-13T23:12:32Z | NONE | null | In pandas version 0.81, this used to work:
```
import pandas as pd
import numpy as np
from pandas.tseries.offsets import *
from datetime import date
M_ASSETS=50
K_FACTORS=3
dates = pd.bdate_range(date(2013, 1, 1), date(2013, 1, 10))
factor_names=['F'+str(y) for y in xrange(1, K_FACTORS+1)]
asset_names = ['A'+str(y) for y in xrange(1, M_ASSETS+1)]
frames=[]
for dt in dates:
f=pd.DataFrame(np.random.randn(M_ASSETS, K_FACTORS),
index = pd.MultiIndex.from_tuples(zip([dt]*M_ASSETS, asset_names)),
columns = factor_names)
frames.append(f)
f=pd.concat(frames)
print "DataFrame for asset1\n", f.ix[:, "A1"]
print "DataFrame for factor1\n", f["F1"].unstack()
print "DataFrame for first date\n", f.ix[f.index.get_level_values(0)[0], :]
```
In pandas 0.11, however this same code gives the following error:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-09af79b5dd36> in <module>()
22 f=pd.concat(frames)
23
---> 24 print "DataFrame for asset1\n", f.ix[:, "A1"]
25 print "DataFrame for factor1\n", f["F1"].unstack()
26 print "DataFrame for first date\n", f.ix[f.index.get_level_values(0)[0], :]
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
32 pass
33
---> 34 return self._getitem_tuple(key)
35 else:
36 return self._getitem_axis(key, axis=0)
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
222 continue
223
--> 224 retval = retval.ix._getitem_axis(key, axis=i)
225
226 return retval
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
362 if com.is_integer(key) and not _is_integer_index(labels):
363 return self._get_loc(key, axis=axis)
--> 364 return self._get_label(lab, axis=axis)
365
366 def _getitem_iterable(self, key, axis=0):
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_label(self, label, axis)
46 return self.obj.xs(label, axis=axis, copy=False)
47 except Exception:
---> 48 return self.obj.xs(label, axis=axis, copy=True)
49
50 def _get_loc(self, key, axis=0):
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/frame.pyc in xs(self, key, axis, level, copy)
2285
2286 if axis == 1:
-> 2287 data = self[key]
2288 if copy:
2289 data = data.copy()
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
1969 raise ValueError('Cannot index using non-boolean DataFrame')
1970 else:
-> 1971 return self._get_item_cache(key)
1972
1973 def _getitem_array(self, key):
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
532 return cache[item]
533 except Exception:
--> 534 values = self._data.get(item)
535 res = self._box_item_values(item, values)
536 cache[item] = res
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item)
882
883 def get(self, item):
--> 884 _, block = self._find_block(item)
885 return block.get(item)
886
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/internals.pyc in _find_block(self, item)
1014
1015 def _find_block(self, item):
-> 1016 self._check_have(item)
1017 for i, block in enumerate(self.blocks):
1018 if item in block:
/mnt/bos-netapp01/epd/7.3-2_pandas0.10/lib/python2.7/site-packages/pandas/core/internals.pyc in _check_have(self, item)
1021 def _check_have(self, item):
1022 if item not in self.items:
-> 1023 raise KeyError('no item named %s' % com.pprint_thing(item))
1024
1025 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: u'no item named A1'
DataFrame for asset1
```
Now I understand that the slicing changed, so if I use:
```
print "DataFrame for asset1\n", f.xs("A1", level=1)
```
it works:
```
DataFrame for asset1
F1 F2 F3
2013-01-01 -0.327087 -0.898145 -1.726459
2013-01-02 1.276976 -0.410213 1.124507
2013-01-03 0.575263 1.132212 0.172895
2013-01-04 0.704949 -0.681410 0.384713
2013-01-07 -0.033519 -0.598248 0.629885
2013-01-08 -0.065769 0.245913 -0.941165
2013-01-09 0.337986 -0.715003 -0.093851
2013-01-10 0.303433 -0.870952 -1.397974
```
Questions:
1. Why does ix no longer work for this
2. However, how to I assign to a slice:
this does not work:
```
view = f.xs("A1", level=1, copy = False)
view = 10
```
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} | [] | closed | false | null | [] | null | 2 | 2013-12-04T21:14:47Z | 2013-12-07T06:34:26Z | 2013-12-07T06:34:26Z | NONE | null | python version: 2.7.5
pandas version: 0.12.0
numpy version: 1.7.1
psycopg2 version: 2.5.1
I observe this as I parse the result set from Postgres (my psycopg2 installation is configured to return mx.DateTimes). The ideal scenario is that these objects are converted to numpy.datetime64 along with appropriate time zone info as this will allow the user to follow all the pandas time series tutorials. Keeping the mx.DateTime objects as objects rather than string would be a second best option because we can build a wrapper to write out the string representation in a form easily parsable by numpy.datetime64. Alternatively, we may be able to construct datetime64 using the API bypassing strings altogether.
Converting to strings as it is currently implemented is particularly hellish because the numpy datetime64 object converts to UTC when parsing from the string format extracted by from_records.
For example, the string representation returned by from_records is:
2013-07-29 17:32:53-07
However, when I parse this value in datetime64, what is returned is:
2013-07-30 00:32:53
However, I see from the datetime64 tutorials that with other formats the datetime64 object will represent the time zone. I see no documentation on how to convert time zones once the datetime64 object is constructed.
Even if I wanted to parse the more stable datetime.datetime module for the purposes of converting over to datetime64 that "-07" is not part of the %z pattern which expects an extra two digits.
In summary, If the user has access to the original mx.DateTime objects returned by psycopg2 the user can most likely solve their own problems regarding the conversion to datetime64 or other formats. I wonder how other types returned by psycopg2 might be affected by the current implementation.
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} | [] | closed | false | null | [] | null | 2 | 2013-12-04T22:03:20Z | 2013-12-05T23:47:37Z | 2013-12-05T23:47:37Z | CONTRIBUTOR | null | ```
pd.Series([pd.NaT]).apply(lambda x:x.date())
```
```
0 0001-255-255
dtype: object
```
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Eliminate some more noise from tests, manifests only on system where `locale` isn't available.
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} | 16 | 2013-12-05T00:47:00Z | 2021-08-31T09:25:30Z | null | CONTRIBUTOR | null | related #6360
Based on [this SO question](http://stackoverflow.com/questions/20383972/binary-operation-broadcasting-across-multiindex?noredirect=1#comment30444368_20383972)
Consider the following two Series:
``` python
x = pd.DataFrame({'year':[1,1,1,1,2,2,2,2],
'country':['A','A','B','B','A','A','B','B'],
'prod':[1,2,1,2,1,2,1,2],
'val':[10,20,15,25,20,30,25,35]})
x = x.set_index(['year','country','prod']).squeeze()
y = pd.DataFrame({'year':[1,1,2,2],'prod':[1,2,1,2],
'mul':[10,0.1,20,0.2]})
y = y.set_index(['year','prod']).squeeze()
```
which look like:
``` python
year country prod
1 A 1 10
2 20
B 1 15
2 25
2 A 1 20
2 30
B 1 25
2 35
year prod
1 1 10.0
2 0.1
2 1 20.0
2 0.2
```
I find it to be an _extremely_ common task, to perform binary operations by distributing the values of `y` over a particular level of `x`. For example, I'd like to multiply all values of product 1 in year 1 by 10.0, regardless of `country`.
The required result is therefore as follows:
``` python
year country prod
1 A 1 100.0
2 2.0
B 1 150.0
2 2.5
2 A 1 400.0
2 6.0
B 1 500.0
2 7.0
```
The binary operation .mul() doesn't work as expected:
``` python
>>> x.mul(y, level=['year','prod'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 334, in f
return self._binop(other, op, level=level, fill_value=fill_value)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 2075, in _binop
this, other = self.align(other, level=level, join='outer')
File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 2570, in align
return_indexers=True)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/index.py", line 954, in join
return_indexers=return_indexers)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/index.py", line 1058, in _join_level
raise Exception('Join on level between two MultiIndex objects '
Exception: Join on level between two MultiIndex objects is ambiguous
```
To create the required result, the user currently has to do this:
``` python
x = x.reset_index('country').sort_index()
x.val = x.val * y
x = x.reset_index().set_index(['year',
'country',
'prod']).sortlevel()
```
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} | [] | closed | false | null | [] | null | 27 | 2013-12-05T06:30:52Z | 2014-06-20T18:35:28Z | 2013-12-22T20:25:09Z | NONE | null | Hello there,
I'm working on a clustered heatmap figure for pandas, like R's `heatmap.2` which I used I ton when I was still in R-land.
I'm having some trouble wrapping my mind around `pandas.tools.plotting` but I have a draft version of this `heatmap` function with some initial parameters in my fork: https://github.com/olgabot/pandas/commit/4759d31666ca476a07c09a450e42a847abdc2b3d But I'm unsure how to proceed with plumbing this function into the current `pandas` plotting tools.
Here's a notebook of a simple and complex example: http://nbviewer.ipython.org/gist/olgabot/7801024
Also, unlike other many plotting functions, `heatmap` creates a whole figure. I currently return the `fig` instance along with the dendrogram objects for both the rows and columns in case the user wants to do some stats on them. It seems like for this function, accepting an `ax` argument (or even a `fig`) doesn't make any sense because there's at least 4 (up to 6 if you label columns or rows with colors) `ax` instances to create.
FYI besides adding documentation and tests and such I'm still working on:
- reasonable dendrogram sizing for when the dimensions are really large. Right now the dendrogram axis size is a proportion of the figure size, but this should change for even medium (100+) size datasets because then the dendrogram takes over the figure.
- Allowing for multiple colors labels for each row/column
- Adding optimal leaf ordering (important for time-dependent data)
- vmin/vmax for `pcolormesh`
- maybe add some padding between the dendrogram, colorbars, and heatmap?
- Other suggestions welcome!
I'm also still working through all the developer FAQs and such so I'm probably making tons of n00b mistakes so please point them out to me, especially if there are some pandas/python conventions I'm totally not following.
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} | 14 | 2013-12-05T15:32:33Z | 2019-12-26T00:25:34Z | 2019-12-26T00:25:34Z | NONE | null | I encountered a very strange problem in 0.12 when a straightforward join fails while it works just fine in 0.11.
```
df1
```
> <class 'pandas.core.frame.DataFrame'>
> Int64Index: 71958 entries, 0 to 104014
> Data columns (total 3 columns):
> FactorDate 71958 non-null values
> FundSecNo 71958 non-null values
> SourceFundSecNo 71958 non-null values
> dtypes: datetime64[ns](1), int64(2)
```
df1.dtypes
```
> FactorDate datetime64[ns]
> FundSecNo int64
> SourceFundSecNo int64
> dtype: object
```
df2
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 94868 entries, (2011-10-18 00:00:00, 283) to (2011-10-19 00:00:00, 127419)
> Data columns (total 2 columns):
> DemeanGroupId 94868 non-null values
> NormalizationGroupId 94868 non-null values
> dtypes: float64(1), int64(1)
```
for ind in df2.index.levels:
print(type(ind))
```
> <class 'pandas.tseries.index.DatetimeIndex'>
> <class 'pandas.core.index.Int64Index'>
```
test = df1.join(df2, on=['FactorDate', 'SourceFundSecNo'])
test
```
> <class 'pandas.core.frame.DataFrame'>
> Int64Index: 71958 entries, 0 to 104014
> Data columns (total 5 columns):
> FactorDate 71958 non-null values
> FundSecNo 71958 non-null values
> SourceFundSecNo 71958 non-null values
> DemeanGroupId 0 non-null values
> NormalizationGroupId 0 non-null values
> dtypes: datetime64[ns](1), float64(2), int64(2)
```
date = datetime.datetime(2011, 10, 18)
sfsn = 283
test.loc[(test.FactorDate == date) & (test.SourceFundSecNo == sfsn)]
```
> ```
> FactorDate FundSecNo SourceFundSecNo DemeanGroupId NormalizationGroupId
> ```
>
> 1 2011-10-18 00:00:00 283 283 NaN NaN
> 17933 2011-10-18 00:00:00 65213 283 NaN NaN
```
df2.loc[(date, sfsn)]
```
> DemeanGroupId 350171
> NormalizationGroupId 35
> Name: (2011-10-18 00:00:00, 283), dtype: float64
I can attach the frames, but I forgot how to do it :-)
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} | [] | closed | false | null | [] | null | 7 | 2013-12-05T20:49:03Z | 2013-12-06T18:22:57Z | 2013-12-06T18:22:57Z | NONE | null | Don't know if this is a bug or my code...
python 2.7.5
anaconda 1.8.0
pandas 12.0
numpy 1.7.1
What seems to be occurring: DataFrame.idxmax/idxmin functions work or not depending on how the DataFrame object is created...
This works: **************************************
In [2]: df = pandas.DataFrame( {1:[1,2,3], 2:[4,5,6], 3:[7,8,9]}, index=[5,6,7])
In [3]: df
Out[3]:
1 2 3
5 1 4 7
6 2 5 8
7 3 6 9
In [4]: df.idxmax()
Out[4]:
1 7
2 7
3 7
dtype: int64
---
(the above also works for float values as well...)
This does not: ****************************************
def priors(self, tree): # most of code that creates object
priors = pandas.DataFrame(index=vps, columns=nps) # instantiate pandas.DataFrame...
for np in nps: # calculate its values...
for vp in vps:
np_prob = stats.beta.cdf(np / chs, self._sb_alpha, self._sb_beta) - \
stats.beta.cdf((np-1) / chs, self._sb_alpha, self._sb_beta)
vp_prob = stats.beta.cdf(vp / chs, self._vb_alpha, self._vb_beta) - \
stats.beta.cdf((vp-1) / chs, self._vb_alpha, self._vb_beta)
priors[np][vp] = np_prob \* vp_prob
norm = priors.sum().sum()
for np in nps:
for vp in vps:
priors[np][vp] = priors[np][vp] / norm
return priors # return it
In [15]: pr = st.priors(trees[1])
In [16]: pr # there is definitely a DataFrame object there
Out[16]:
1 2 7 11
3 0.4865759 0.2130292 0.06304571 0.0164135
8 0.1379886 0.0604132 0.0178792 0.004654722
In [18]: type(pr) # its type is correct
Out[18]: pandas.core.frame.DataFrame
In [20]: type(pr[1][3]) # the type of its values is correct
Out[20]: numpy.float64
In [29]: pr.max() # other related functions work
Out[29]:
1 0.486576
2 0.213029
7 0.063046
11 0.016413
dtype: float64
In [23]: pr.idxmax() # but idxmax (and idxmin) result in this error
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
---
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#5588
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} | 8 | 2013-12-06T05:40:56Z | 2013-12-07T00:18:10Z | 2013-12-07T00:18:10Z | CONTRIBUTOR | null | I am really happy about the SettingWithCopyWarning but is this situation the right place to pop up? Because what I want to do seems to work, maybe I don't see the danger of a possible case?
``` python
In [19]: df.convert_objects().dtypes
Out[19]:
hour float64
minute float64
second float64
tb float64
dtype: object
In [20]: df['hour'] = df.hour.astype('int')
-c:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
In [21]: df.dtypes
Out[21]:
hour int64
minute float64
second float64
tb float64
dtype: object
```
using master.
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https://api.github.com/repos/pandas-dev/pandas/issues/5651 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5651/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5651/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5651/events | https://github.com/pandas-dev/pandas/issues/5651 | 23,862,539 | MDU6SXNzdWUyMzg2MjUzOQ== | 5,651 | ValueError: zero-size array to reduction operation maximum | {
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} | 9 | 2013-12-06T14:56:18Z | 2013-12-06T21:04:42Z | 2013-12-06T18:36:25Z | NONE | null | I have recently incountered a strange issue which only happens on the dev branch. I've mostly encountered it when trying to display the dataframe, such as with print or wanting ipython output. Displaying a column/series of the dataframe works fine, just not displaying the whole frame. The dataframe is originally constructed with from_json(orient='values') from a json string retrieved from MongoDB. The index is then reset after the initial json load.
The reason I installed the dev branch was that I needed to switch to the latest in order to find a workaround to a slicing/selection issue regarding the latest dateutils version.
In anycase I ended up going back to 0.12.0 because I kept getting caught up on this. At first it looks like an error with IPython but it also complains in a regular python script if I try to print the dataframe.
Here is the traceback.
```
In [8]: e.spectrum.df
Out[8]: ---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-0462e2de8255> in <module>()
----> 1 e.spectrum.df
/usr/lib/python2.7/site-packages/IPython/core/displayhook.py in __call__(self, result)
245 self.start_displayhook()
246 self.write_output_prompt()
--> 247 format_dict, md_dict = self.compute_format_data(result)
248 self.write_format_data(format_dict, md_dict)
249 self.update_user_ns(result)
/usr/lib/python2.7/site-packages/IPython/core/displayhook.py in compute_format_data(self, result)
155
156 """
--> 157 return self.shell.display_formatter.format(result)
158
159 def write_format_data(self, format_dict, md_dict=None):
/usr/lib/python2.7/site-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
150 md = None
151 try:
--> 152 data = formatter(obj)
153 except:
154 # FIXME: log the exception
/usr/lib/python2.7/site-packages/IPython/core/formatters.py in __call__(self, obj)
479 type_pprinters=self.type_printers,
480 deferred_pprinters=self.deferred_printers)
--> 481 printer.pretty(obj)
482 printer.flush()
483 return stream.getvalue()
/usr/lib/python2.7/site-packages/IPython/lib/pretty.py in pretty(self, obj)
360 if callable(meth):
361 return meth(obj, self, cycle)
--> 362 return _default_pprint(obj, self, cycle)
363 finally:
364 self.end_group()
/usr/lib/python2.7/site-packages/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
480 if getattr(klass, '__repr__', None) not in _baseclass_reprs:
481 # A user-provided repr.
--> 482 p.text(repr(obj))
483 return
484 p.begin_group(1, '<')
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/base.pyc in __repr__(self)
53 Yields Bytestring in Py2, Unicode String in py3.
54 """
---> 55 return str(self)
56
57
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/base.pyc in __str__(self)
33 if compat.PY3:
34 return self.__unicode__()
---> 35 return self.__bytes__()
36
37 def __bytes__(self):
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/base.pyc in __bytes__(self)
45
46 encoding = get_option("display.encoding")
---> 47 return self.__unicode__().encode(encoding, 'replace')
48
49 def __repr__(self):
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in __unicode__(self)
455 width = None
456 self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
--> 457 line_width=width, show_dimensions=True)
458
459 return buf.getvalue()
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in to_string(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, nanRep, index_names, justify, force_unicode, line_width, max_rows, max_cols, show_dimensions)
1286 max_rows=max_rows, max_cols=max_cols,
1287 show_dimensions=show_dimensions)
-> 1288 formatter.to_string()
1289
1290 if buf is None:
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/format.pyc in to_string(self, force_unicode)
373 text = info_line
374 else:
--> 375 strcols = self._to_str_columns()
376 if self.line_width is None:
377 text = adjoin(1, *strcols)
/usr/lib/python2.7/site-packages/pandas-0.12.0_1168_gb1485a3-py2.7-linux-x86_64.egg/pandas/core/format.pyc in _to_str_columns(self)
332 minimum=max_colwidth, truncated=truncate_v)
333
--> 334 max_len = max(np.max([_strlen(x) for x in fmt_values]),
335 max_colwidth)
336 if self.justify == 'left':
/usr/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc in amax(a, axis, out, keepdims)
2123 except AttributeError:
2124 return _methods._amax(a, axis=axis,
-> 2125 out=out, keepdims=keepdims)
2126 # NOTE: Dropping the keepdims parameter
2127 return amax(axis=axis, out=out)
/usr/lib/python2.7/site-packages/numpy/core/_methods.pyc in _amax(a, axis, out, keepdims)
15 def _amax(a, axis=None, out=None, keepdims=False):
16 return um.maximum.reduce(a, axis=axis,
---> 17 out=out, keepdims=keepdims)
18
19 def _amin(a, axis=None, out=None, keepdims=False):
ValueError: zero-size array to reduction operation maximum which has no identity
```
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} | 10 | 2013-12-06T17:16:00Z | 2017-09-15T13:15:20Z | 2014-01-23T22:03:09Z | CONTRIBUTOR | null | This works with pandas 0.12:
``` python
from ggplot import meat
meat.get(None)
[returns None]
```
But throws an error in 0.13 (RC):
``` python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-86be6a7317b8> in <module>()
----> 1 meat.get(None)
C:\portabel\Python27\lib\site-packages\pandas\core\generic.pyc in get(self, key, default)
961 """
962 try:
--> 963 return self[key]
964 except KeyError:
965 return default
C:\portabel\Python27\lib\site-packages\pandas\core\frame.pyc in __getitem__(self, key)
1626 return self._getitem_multilevel(key)
1627 else:
-> 1628 return self._getitem_column(key)
1629
1630 def _getitem_column(self, key):
C:\portabel\Python27\lib\site-packages\pandas\core\frame.pyc in _getitem_column(self, key)
1633 # get column
1634 if self.columns.is_unique:
-> 1635 return self._get_item_cache(key)
1636
1637 # duplicate columns & possible reduce dimensionaility
C:\portabel\Python27\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
972 res = cache.get(item)
973 if res is None:
--> 974 values = self._data.get(item)
975 res = self._box_item_values(item, values)
976 cache[item] = res
C:\portabel\Python27\lib\site-packages\pandas\core\internals.pyc in get(self, item)
2737 if isnull(item):
2738 indexer = np.arange(len(self.items))[isnull(self.items)]
-> 2739 return self.get_for_nan_indexer(indexer)
2740
2741 _, block = self._find_block(item)
C:\portabel\Python27\lib\site-packages\pandas\core\internals.pyc in get_for_nan_indexer(self, indexer)
2789 indexer = indexer.item()
2790 else:
-> 2791 raise ValueError("cannot label index with a null key")
2792
2793 # take a nan indexer and return the values
ValueError: cannot label index with a null key
```
Not sure if that is intentional as this broke some code in the facet_wrap in ggplot (which now will get some update against this :-) )
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} | [] | closed | false | null | [] | null | 5 | 2013-12-06T17:42:55Z | 2013-12-06T18:55:49Z | 2013-12-06T17:49:16Z | CONTRIBUTOR | null | At least I think this is a bug. #4627 was meant to unify the Series and DataFrame.rename APIs right?
``` python
In [35]: idx = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3)])
In [36]: s = pd.Series(np.arange(6), index=idx)
In [37]: s
Out[37]:
1 1 0
2 1
3 2
2 1 3
2 4
3 5
dtype: int64
In [38]: s.index.names = ['outer', 'inner']
In [39]: s.rena
s.rename s.rename_axis
In [39]: s.rename({1: 'L1', 2: 'L2', 3: 'L3'}, level='inner')
Out[39]:
outer inner
L1 L1 0
L2 1
L3 2
L2 L1 3
L2 4
L3 5
dtype: int64
```
Edit: I guess I'm a bit confused because the docstring seems to indicate that Series.rename() does something a bit different:
```
Conform Series to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False
```
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} | 1 | 2013-12-06T17:51:35Z | 2014-01-21T15:16:06Z | 2013-12-06T18:53:50Z | CONTRIBUTOR | null | Here is a small example of a performance regression I've noticed between 0.12 and 0.13rc1:
``` python
s = pd.Series(np.arange(4096.))
df = pd.DataFrame({i:s for i in range(4096)})
# under 0.12
%timeit df.apply((lambda x: np.corrcoef(x, s)[0, 1]), axis=1)
1 loops, best of 3: 792 ms per loop
# under 0.13rc1
%timeit df.apply((lambda x: np.corrcoef(x, s)[0, 1]), axis=1)
1 loops, best of 3: 1.7 s per loop
```
These are run on the same machine, with the following pip requirements consistent between the two setups besides pandas 0.12.0 vs 0.13.0rc1:
Cython==0.19.2
Jinja2==2.7.1
MarkupSafe==0.18
Pygments==1.6
Sphinx==1.1.3
argparse==1.2.1
docutils==0.11
ipython==1.0.0
matplotlib==1.3.0
nose==1.3.0
numpy==1.7.1
pyparsing==2.0.1
python-dateutil==2.2
pytz==2013.8
pyzmq==14.0.1
scipy==0.12.0
six==1.4.1
tornado==3.1.1
wsgiref==0.1.2
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} | 8 | 2013-12-06T18:03:01Z | 2014-05-01T14:23:05Z | 2013-12-19T17:35:32Z | NONE | null | ``` python
In [12]: pd.read_json("No Such File")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-f479cae2f65a> in <module>()
----> 1 pd.read_json("No Such File")
/home/user1/src/pandas/pandas/io/json.pyc in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit)
188 obj = FrameParser(json, orient, dtype, convert_axes, convert_dates,
189 keep_default_dates, numpy, precise_float,
--> 190 date_unit).parse()
191
192 if typ == 'series' or obj is None:
/home/user1/src/pandas/pandas/io/json.pyc in parse(self)
256
257 else:
--> 258 self._parse_no_numpy()
259
260 if self.obj is None:
/home/user1/src/pandas/pandas/io/json.pyc in _parse_no_numpy(self)
473 if orient == "columns":
474 self.obj = DataFrame(
--> 475 loads(json, precise_float=self.precise_float), dtype=None)
476 elif orient == "split":
477 decoded = dict((str(k), v)
ValueError: Expected object or value
```
read_json interprets strings which are not filenames as json data, then fails to parse them
if the filename names a path that doesn't exist (due to typo, or being in wrong directory for example).
That overloading makes it <del>impossible</del> nasty to distinguish two distinct error cases, e.g.
missing file and malformed json. Dubious API choice to my tastes.
in any case, catch both errors and return a saner message "missing file or malformed input" etc.
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} | 1 | 2013-12-06T18:36:19Z | 2014-06-23T23:26:57Z | 2013-12-06T18:53:50Z | CONTRIBUTOR | null | closes #5654
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} | 2 | 2013-12-06T19:39:53Z | 2018-07-06T22:02:32Z | 2018-07-06T22:02:31Z | NONE | null | I have found that using the filter method to select columns that match a string pattern is ~3x slower than basic list comprehension on the df.columns list. Not sure how its implemented under the hood but for basic 'in' checks on lots of columns this could slow you down depending on how often you filter.
``` python
import pandas as pd
import numpy as np
# Generate Test DataFrame
NUM_ROWS = 2000
NUM_COLS = 1000
col_names = ['A'+num for num in map(str,np.arange(NUM_COLS).tolist())]
df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names)
df['TEST'] = 0
df['TEST2'] = 0
%timeit df.filter(like='TEST')
1000 loops, best of 3: 1.19 ms per loop
%timeit df[[col for col in df.columns if 'TEST' in col]]
1000 loops, best of 3: 400 ยตs per loop
%time df.filter(like='TEST')
Wall time: 1 ms
Out[4]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2000 entries, 0 to 1999
Data columns (total 2 columns):
TEST 2000 non-null values
TEST2 2000 non-null values
dtypes: int64(2)
%time df[[col for col in df.columns if 'TEST' in col]]
Wall time: 1 ms
Out[5]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2000 entries, 0 to 1999
Data columns (total 2 columns):
TEST 2000 non-null values
TEST2 2000 non-null values
dtypes: int64(2)
pd.__version__
Out[7]: '0.12.0'
np.__version__
Out[8]: '1.7.1'
```
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} | 0 | 2013-12-06T22:30:17Z | 2014-07-07T21:26:09Z | 2013-12-07T00:18:10Z | CONTRIBUTOR | null | closes #5650
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} | 2 | 2013-12-07T00:27:33Z | 2014-06-18T19:01:49Z | 2013-12-10T13:19:30Z | CONTRIBUTOR | null | closes #5616 caused by the updated Excel MultiIndex handling.
This change should go into v0.13.
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} | 28 | 2013-12-07T06:12:30Z | 2014-01-16T15:29:37Z | 2013-12-11T19:19:32Z | NONE | null | 30% slowdown and up.
```
melt_dataframe | 2.7920 | 2.1487 | 1.2994 |
concat_small_frames | 22.1807 | 17.0054 | 1.3043 |
dti_reset_index | 0.2950 | 0.2257 | 1.3070 |
frame_reindex_upcast | 28.8033 | 21.2270 | 1.3569 |
frame_fancy_lookup_all | 30.4507 | 22.1930 | 1.3721 |
timeseries_large_lookup_value | 0.0383 | 0.0277 | 1.3811 |
frame_boolean_row_select | 0.4419 | 0.3170 | 1.3941 |
panel_from_dict_equiv_indexes | 58.9810 | 41.6213 | 1.4171 |
panel_from_dict_same_index | 58.9364 | 41.5120 | 1.4197 |
frame_xs_row | 0.0637 | 0.0443 | 1.4355 |
groupby_frame_singlekey_integer | 4.3193 | 2.9940 | 1.4427 |
frame_ctor_nested_dict | 142.8297 | 98.7384 | 1.4465 |
frame_to_csv2 | 191.4937 | 130.7974 | 1.4640 |
series_ctor_from_dict | 5.1107 | 3.4617 | 1.4764 |
groupby_multi_python | 175.8397 | 117.9717 | 1.4905 |
frame_to_csv | 237.1720 | 154.0547 | 1.5395 |
series_string_vector_slice | 325.1186 | 208.9640 | 1.5559 |
indexing_panel_subset | 0.9351 | 0.5874 | 1.5919 |
frame_insert_500_columns_end | 198.1669 | 124.3424 | 1.5937 |
frame_ctor_nested_dict_int64 | 173.6054 | 106.8807 | 1.6243 |
frame_loc_dups | 1.2674 | 0.7770 | 1.6311 |
frame_get_dtype_counts | 0.1843 | 0.1113 | 1.6552 |
ctor_index_array_string | 0.0393 | 0.0236 | 1.6667 |
frame_get_numeric_data | 0.1736 | 0.0997 | 1.7424 |
frame_constructor_ndarray | 0.1047 | 0.0587 | 1.7846 |
frame_iteritems_cached | 1.1443 | 0.6117 | 1.8707 |
indexing_dataframe_boolean_rows_object | 0.9307 | 0.4853 | 1.9176 |
reshape_stack_simple | 2.6710 | 1.3684 | 1.9520 |
frame_iloc_dups | 0.5160 | 0.2420 | 2.1323 |
frame_xs_col | 0.0720 | 0.0323 | 2.2260 |
dataframe_getitem_scalar | 0.0199 | 0.0087 | 2.3028 |
datamatrix_getitem_scalar | 0.0193 | 0.0083 | 2.3143 |
indexing_dataframe_boolean_rows | 0.5593 | 0.2393 | 2.3374 |
series_constructor_ndarray | 0.0397 | 0.0167 | 2.3762 |
sparse_frame_constructor | 13.0450 | 4.7807 | 2.7287 |
frame_iteritems | 76.1917 | 27.6610 | 2.7545 |
indexing_frame_get_value_ix | 0.0123 | 0.0044 | 2.8182 |
series_getitem_scalar | 0.0199 | 0.0070 | 2.8523 |
frame_from_series | 0.1774 | 0.0534 | 3.3214 |
frame_fancy_lookup | 7.8727 | 1.7236 | 4.5675 |
series_get_value | 0.0123 | 0.0017 | 7.3810 |
```
**Ruled out**
```
frame_repr_tall | 49.2450 | 2.3817 | 20.6762 |
frame_repr_wide | 977.6090 | 1.1230 | 870.5710 |
```
#5550: truncated repr for large frames. legitimate change.
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} | 27 | 2013-12-07T19:17:52Z | 2014-06-24T11:20:04Z | 2013-12-08T15:17:30Z | CONTRIBUTOR | null | turns out was modifying the python pickle just by importing pandas
when sub classing have to copy a mutable property before modifying
http://stackoverflow.com/questions/20444593/pandas-compiled-from-source-default-pickle-behavior-changed
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} | 0 | 2013-12-07T21:46:47Z | 2015-03-31T21:06:09Z | 2015-03-31T21:06:09Z | NONE | null | The upper left y axis tick marks in a scatter_matrix appear to have units/values pulled from the (0, 0) histogram counts, instead of the data values that are used for all other axis tick values.
Example:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '0.12.0'
In [4]: df = pd.DataFrame(np.random.randn(100,3), columns=['a', 'b', 'c'])
In [5]: pd.tools.plotting.scatter_matrix(df)
Out[5]:
array([[<matplotlib.axes.AxesSubplot object at 0x1071d5d90>,
<matplotlib.axes.AxesSubplot object at 0x1089aa0d0>,
<matplotlib.axes.AxesSubplot object at 0x1071e36d0>],
[<matplotlib.axes.AxesSubplot object at 0x10a7d4f90>,
<matplotlib.axes.AxesSubplot object at 0x10a7fd450>,
<matplotlib.axes.AxesSubplot object at 0x10a8197d0>],
[<matplotlib.axes.AxesSubplot object at 0x10a83bf90>,
<matplotlib.axes.AxesSubplot object at 0x10a84fe50>,
<matplotlib.axes.AxesSubplot object at 0x10a886310>]], dtype=object)
```
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} | 15 | 2013-12-08T03:52:35Z | 2017-09-22T15:23:27Z | 2015-04-12T13:37:27Z | CONTRIBUTOR | null | Came across this when trying to parse a poorly-formatted csv file and got the number of columns wrong. Mostly it segfaults; sometimes it produces a broken last row.
```
>>> import pandas as pd
>>> from StringIO import StringIO
>>> pd.__version__
'0.13.0rc1-43-g4f9fefc'
>>>
>>> f = StringIO('1,1,1,1,0\n'*2 + '\n'*2)
>>> df = pd.read_csv(f,names=list("abcd"))
Segmentation fault (core dumped)
```
When it doesn't segfault it tends to produce
```
a b c d
1 1 1 1 0
1 1 1 1 0
NaN NaN NaN NaN NaN
NaN NaN NaN NaN [unicode box: 0 0 0 1]
```
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} | 16 | 2013-12-08T13:45:21Z | 2014-06-24T04:31:39Z | 2014-01-21T12:48:06Z | CONTRIBUTOR | null | close #5497
**Short story:**
In [this](https://github.com/pydata/pandas/issues/5497) issue I signaled that the x_lim and y_lim in scatter_matrix plots are different. In particular x/y_lim of diagonal hists and x/y_lim of scatter plots are different.
For instance, this causes peaks nonalignments in the 1-margin and 2-margin of the plotted multivariate pdf. See pictures in the issue.
This is a possible fix.
**Long Story:**
In a `scatter_matrix` plot we have a diagonal constituted by histograms and `scatter_plots` to show correlation among variables in the subplots away from the diagonal.
Let's consider this scatter matrix in which we have two variables `A` and `misfit`
The ranges of `A` and of `misfit` are shown by the axis thicks along the boundary subplots (i.e. plots in the first row and column (starting form the bottom left corner)).
As we can see there is a clear misalignment of the peak among plot 1,2 and plot 2,2.
This is due to the fact that the plot range for each plot (`xlim`) is determined automatically by matplotlib, hence there is a possible disagreement among different plots.
In this pull request, the `xlim`s and `ylim`s are forced to be consistent and are calculated as the real ranges of the variables (max - min) plus a given correction (here 5%).
i.e., for each plot
```
rdelta_ext = (rmax_ - rmin_)*xy_range_extension # xy_range_extension defaults 5%
ax.set_xlim( (rmin_ - rdelta_ext , rmax_+ rdelta_ext) )
```
This is an example of a `scatter_matrix` obtained with the correction (please compare e.g subplots 1,2 and 2,2 )
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} | 11 | 2013-12-09T04:31:29Z | 2014-06-13T06:54:13Z | 2013-12-09T20:19:46Z | CONTRIBUTOR | null | numpy 1.7.0 erroneously raises IndexError instead of ValueError
from ndarray.item() when the array is not of length 1. This can be
seen as a failure of
```
computation.tests.test_eval.TestScope.test_global_scope
```
for the cases that engine='numexpr'.
Absorb the splatter from this regression by explicitly catching the
erroneous IndexError.
closes #5535.
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} | 1 | 2013-12-09T16:02:33Z | 2021-04-11T03:58:01Z | null | CONTRIBUTOR | null | `read_csv` requires the multi-index column labels to be non-sparse in order to properly parse them. I am not sure if this can be done automatically, though maybe a keyword could trigger this.
```
In [40]: def unsparsify_labels(index):
....: new_labels = []
....: for label in index.values:
....: if label[0].startswith('Unnamed'):
....: label = list(label)
....: label[0] = ll
....: label = tuple(label)
....: else:
....: ll = label[0]
....: new_labels.append(label)
....: return MultiIndex.from_tuples(new_labels)
....:
In [41]: unsparsify_labels(x)
Out[41]:
MultiIndex(levels=[[u'A', u'B'], [u'1', u'2']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]])
In [42]: data = """A,,B,
....: 1,2,1,2
....: 1,2,3,4
....: 5,6,7,8
....: """
In [43]: df = read_csv(StringIO(data),header=[0,1],index_col=None)
In [44]: df
Out[44]:
A Unnamed: 1_level_0 B Unnamed: 3_level_0
1 2 1 2
0 1 2 3 4
1 5 6 7 8
[2 rows x 4 columns]
In [45]: df.columns = unsparsify_labels(df.columns)
In [46]: df
Out[46]:
A B
1 2 1 2
0 1 2 3 4
1 5 6 7 8
[2 rows x 4 columns]
```
http://stackoverflow.com/questions/20473271/importing-multilevel-indexed-cvs-data-with-pandas-0-13/20474982#20474982
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} | 9 | 2013-12-09T16:20:44Z | 2021-04-11T04:00:18Z | null | CONTRIBUTOR | null | reported here:
http://stackoverflow.com/questions/20459782/what-is-the-functionality-of-the-filling-method-when-reindexing
does not appear to be any tests for it, nor is fix that trivial
```
import pandas as pd
import numpy as np
hf_index = pd.date_range(start='2013-05-09 9:00', end='2013-05-13 23:59', freq='1min')
hf_prices = np.random.rand(len(hf_index))
hf = pd.DataFrame(hf_prices, index=hf_index)
hf.ix['2013-05-10 18:00':'2013-05-13 18:00',:]=np.nan
ind_daily = pd.date_range(start='2013-05-09 16:00', end='2013-05-13 16:00', freq='B')
daily1 = hf.reindex(index=ind_daily, method='ffill')
```
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```
>>> import numpy as np, pandas as pd
>>> pd.__version__
'0.13.0rc1-47-g9ac008c'
>>> df = pd.DataFrame(range(5))
>>> np.lexsort(df.values)
array([0])
>>> np.lexsort(df)
Segmentation fault
```
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} | 8 | 2013-12-09T19:29:45Z | 2014-01-28T17:09:24Z | 2014-01-28T16:49:31Z | CONTRIBUTOR | null | For the following example I see corrwith performing considerably more slowly than doing a "raw" apply of np.corrcoef, with the tip code as of 2013-12-08:
```
s = pd.Series(np.arange(4096.))
df = pd.DataFrame({i:i + np.arange(3360.) for i in range(4096)})
%timeit -n 3 df.apply((lambda x: np.corrcoef(x.values, s.values)[0, 1]), axis=1)
3 loops, best of 3: 942 ms per loop
%timeit -n 3 df.corrwith(s, axis=1)
3 loops, best of 3: 1.64 s per loop
```
From comments on #5654 and the forums I would not have expected this to be slower, but it seems corrwith is just turning around and doing an "apply" itself, with some extra conditioning that seems to add significant overhead.
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} | 15 | 2013-12-10T10:27:50Z | 2020-05-03T19:58:04Z | 2020-05-03T19:58:03Z | CONTRIBUTOR | null | Since the output of the string methods is primarily boolean, do we want to preserve NaNs? Use case. I load in some data, empty strings (i.e., missing data in a column that's string data) gets automatically converted to NaNs. I have to do fillna everywhere first, or I run into situations like this.
```
dta = pd.DataFrame([["blah"],[np.nan],["foobar"]], columns=['vari'])
dta.vari.str.startswith('f')
dta.ix[dta.vari.str.startswith('f')]
```
Since presumably the primary use case of a boolean series is indexing, do we want to just return False in place of NaNs? Just a thought.
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} | 3 | 2013-12-10T12:10:44Z | 2014-01-03T01:43:47Z | 2014-01-03T01:43:47Z | NONE | null | ``` python
In [83]: pd.options.display.max_rows=1
...: pd.options.display.max_columns=1
...: df=DataFrame(np.random.randn(1000,1000))
...: %prun -l 5 df._repr_html_()
ncalls tottime percall cumtime percall filename:lineno(function)
209 0.000 0.000 0.000 0.000 {isinstance}
In [84]: pd.options.display.max_rows=2
...: pd.options.display.max_columns=1
...: df=DataFrame(np.random.randn(1000,1000))
...: %prun -l 5 df._repr_html_()
ncalls tottime percall cumtime percall filename:lineno(function)
246 0.000 0.000 0.000 0.000 {isinstance}
In [85]: pd.options.display.max_rows=1
...: pd.options.display.max_columns=2
...: df=DataFrame(np.random.randn(1000,1000))
...: %prun -l 5 df._repr_html_()
ncalls tottime percall cumtime percall filename:lineno(function)
307 0.000 0.000 0.000 0.000 {isinstance}
In [86]: pd.options.display.max_rows=2
...: pd.options.display.max_columns=2
...: df=DataFrame(np.random.randn(1000,1000))
...: %prun -l 5 df._repr_html_()
ncalls tottime percall cumtime percall filename:lineno(function)
355 0.000 0.000 0.000 0.000 {isinstance}
```
I find it strange that outputting a single additional cell in a new column
requires an extra 100 calls to isinstance, or extra 50 if in a new row.
the defaults f
Not necesserily a bug, just puzzling. if this applies more generally,
such as in to_html, the perf could be a problem. as it stand repr
is plenty fast for interactive use, so not a problem.
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} | 0 | 2013-12-10T14:28:19Z | 2014-07-16T08:42:49Z | 2013-12-10T14:48:29Z | CONTRIBUTOR | null | surfaced in a doc example
http://stackoverflow.com/questions/20489519/how-come-pandas-select-results-in-the-examples-dont-match
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} | 0 | 2013-12-10T22:07:47Z | 2014-06-21T12:51:25Z | 2013-12-10T22:42:06Z | CONTRIBUTOR | null | related #5592
BUG: handle coercion of None for date/time like dtypes in a groupby result
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} | [] | closed | false | null | [] | null | 19 | 2013-12-11T00:30:23Z | 2014-01-29T21:28:06Z | 2013-12-14T22:21:11Z | NONE | null | Besides boxplots, another way to look at the distribution of data is by violin plots:
http://nbviewer.ipython.org/gist/olgabot/7902901
I'd like to add this to pandas. Is there interest in these plots? It currently depends on my `prettyplotlib` but that can be removed. My concern is that it depends on `scipy.stats.gaussian_kde` - is that fine?
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} | 17 | 2013-12-11T01:52:05Z | 2016-12-14T20:40:11Z | 2016-12-14T20:39:39Z | CONTRIBUTOR | null | Referenced briefly in the OP at #3275
``` python
In [11]: idx = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3)])
In [12]: idx.names = ['outer', 'inner']
In [13]: df = pd.DataFrame({"A": np.arange(6), 'B': ['one', 'one', 'two', 'two', 'one', 'one']}, index=idx)
```
So the idea is to be able to call
``` python
df.groupby('B', level='inner')
```
instead of
``` python
In [15]: df.reset_index().groupby(['B', 'inner']).mean()
Out[15]:
A
B inner
one 1 0.0
2 2.5
3 5.0
two 1 3.0
3 2.0
[5 rows x 1 columns]
```
Currently this raises `TypeError: 'numpy.ndarray' object is not callable`. Mostly just syntactic sugar, but I've been having to do a lot of this lately and all the `reset_index`es are getting annoying. Thoughts?
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} | 4 | 2013-12-11T13:10:33Z | 2013-12-11T18:05:38Z | 2013-12-11T15:26:47Z | NONE | null | See below for discussion
---
```
In [19]: import pandas as pd
In [20]: import numpy as np
In [21]: import random
In [22]: df = pd.DataFrame(np.random.random_sample((20,5)), index=[random.choice('ABCDE') for x in range(20)])
In [23]: df.loc[:,0].ix['A'].median()
Out[23]: 0.57704085832236685
In [24]: pd.version.version
Out[24]: '0.12.0'
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: import random
In [4]: df = pd.DataFrame(np.random.random_sample((20,5)), index=[random.choice('ABCDE') for x in range(20)])
In [5]: df.loc[:,0].ix['A'].median()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-57f9fc9d1583> in <module>()
----> 1 df.loc[:,0].ix['A'].median()
AttributeError: 'numpy.ndarray' object has no attribute 'median'
In [6]: pd.version.version
Out[6]: '0.13.0rc1-43-g4f9fefc'
```
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} | 3 | 2013-12-11T14:01:37Z | 2014-06-13T22:14:03Z | 2013-12-11T21:10:14Z | CONTRIBUTOR | null | related #5597
pretty common error that I have seen, originally from:
http://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug
```
In [1]: a = [12, 23]
In [2]: b = [123, None]
In [3]: c = [1234, 2345]
In [4]: d = [12345, 23456]
In [5]: tuples = [('eyes', 'left'), ('eyes', 'right'), ('ears', 'left'), ('ears', 'right')]
In [6]: events = {('eyes', 'left'): a, ('eyes', 'right'): b, ('ears', 'left'): c, ('ears', 'right'): d}
In [7]: multiind = pandas.MultiIndex.from_tuples(tuples, names=['part', 'side'])
In [8]: zed = pandas.DataFrame(events, index=['a', 'b'], columns=multiind)
```
Shows a warning (the message is comewhat generic)
```
In [9]: zed['eyes']['right'].fillna(value=555, inplace=True)
/usr/local/bin/ipython:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
#!/usr/local/bin/python
In [10]: zed
Out[10]:
part eyes ears
side left right left right
a 12 123 1234 12345
b 23 NaN 2345 23456
[2 rows x 4 columns]
```
Correct method
```
In [11]: zed.loc[:,('eyes','right')].fillna(value=555, inplace=True)
In [12]: zed
Out[12]:
part eyes ears
side left right left right
a 12 123 1234 12345
b 23 555 2345 23456
[2 rows x 4 columns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5680 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5680/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5680/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5680/events | https://github.com/pandas-dev/pandas/pull/5680 | 24,110,149 | MDExOlB1bGxSZXF1ZXN0MTA3Mjc2NzE= | 5,680 | BUG: Bug in repeated indexing of object with resultant non-unique index (GH5678) | {
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} | 0 | 2013-12-11T14:33:26Z | 2014-07-09T16:15:48Z | 2013-12-11T15:26:47Z | CONTRIBUTOR | null | closes #5678
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p = pandas.DataFrame({ 'x' : [1,2,3], 'y' : [1,2,3] })
sumOfP = p.sum() #Gives a Series of [6,6]. OK.
totals = pandas.DataFrame({ 'someOtherSeries' : [1,2])
totals['sumOfP'] = sumOfP #BAD! This is now [nan, nan]
I'd expect totals['sumOfP'] to be [6,6]. So why is it nan,nan?
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} | 20 | 2013-12-12T03:13:32Z | 2014-06-12T20:01:14Z | 2014-01-01T02:54:25Z | MEMBER | null | closes #3429.
Old:
```
In [39]: DataFrame(randn(10,2)).info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 2 columns):
0 10 non-null values
1 10 non-null values
dtypes: float64(2)
```
New:
```
In [39]: DataFrame(randn(10,2)).info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 2 columns):
0 10 non-null float64
1 10 non-null object
dtypes: float64(1), object(1)
```
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} | 1 | 2013-12-12T10:15:10Z | 2013-12-12T12:44:17Z | 2013-12-12T12:44:17Z | NONE | null | Hello,
```
#!/bin/env python
import pandas as pd
import numpy as np
import sys
print sys.version
print "NUMPY:", np.__version__
print "PANDAS:", pd.__version__
df = pd.DataFrame([[0, None], [4, 3]], columns=['a','b'])
print df
df["c"] = 1.0
#print df #uncomment and result change
df["c"][~df.all(axis=1)] = 2
print df
print df.c
```
gives
```
2.7.2 (default, Jul 30 2012, 17:32:47)
[GCC 4.1.2 20080704 (Red Hat 4.1.2-46)]
NUMPY: 1.7.1
PANDAS: 0.12.0
a b
0 0 NaN
1 4 3
a b c
0 0 NaN 1
1 4 3 1
0 2
1 1
Name: c, dtype: float64
```
As you can see, I try to change value of the c columns to "2" for the element where one of the column is null and it does not work.
The serie is however modified.
What is even stranger is that adding an intermediate print (uncomment above) changes the behavior.
I was not able to find any duplicate of this issue.
Thanks in advance for having a look.
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} | 4 | 2013-12-12T14:47:14Z | 2014-02-05T02:08:12Z | 2014-02-05T02:08:12Z | CONTRIBUTOR | null | _Edited to clarify the bug_
`Series.xs` slice fails with string index labels and MultiIndex:
``` python
In [1]: idx = pd.MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'), ('b', 'two')])
In [2]: df = pd.Series(np.random.randn(4), index=idx)
In [4]: df.index.set_names(['L1', 'L2'], inplace=True)
In [5]: df
Out[5]:
L1 L2
a one -0.136418
two -0.346941
b one -1.468534
two 1.217693
dtype: float64
In [6]: df.xs('one', level='L2')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-6-52601adf5184> in <module>()
----> 1 df.xs('one', level='L2')
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_27_g4d5ca5c-py2.7-macosx-10.8-x86_64.egg/pandas/core/series.pyc in _xs(self, key, axis, level, copy)
437
438 def _xs(self, key, axis=0, level=None, copy=True):
--> 439 return self.__getitem__(key)
440
441 xs = _xs
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_27_g4d5ca5c-py2.7-macosx-10.8-x86_64.egg/pandas/core/series.pyc in __getitem__(self, key)
482 def __getitem__(self, key):
483 try:
--> 484 return self.index.get_value(self, key)
485 except InvalidIndexError:
486 pass
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_27_g4d5ca5c-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.pyc in get_value(self, series, key)
2294 raise InvalidIndexError(key)
2295 else:
-> 2296 raise e1
2297 except Exception: # pragma: no cover
2298 raise e1
KeyError: 'one'
```
The same slice works on a DataFrame.
Previous post below:
``` python
In [12]: idx = pd.MultiIndex.from_tuples([('a', 0), ('a', 1), ('b', 0), ('b', 1)])
In [13]: df = pd.Series(np.random.randn(4), index=idx)
In [14]: df
Out[14]:
a 0 0.876121
1 0.638050
b 0 0.965934
1 1.061716
dtype: float64
In [15]: df.xs(0, level=1) # returns scaler
Out[15]: 0.87612104445620753
In [16]: df.index.names = ['L1', 'L2']
In [27]: df.xs(0, level='L2') # returns scaler
Out[27]: -0.98585685847339011
In [28]: df.xs(0, level='L1') # No key error
Out[28]: -0.98585685847339011
```
Works for DataFrames:
``` python
In [30]: df.xs(0, level='L2')
Out[30]:
0
L1
a -0.985857
b 0.648114
[2 rows x 1 columns]
```
`Series.xs` also seems to fail on string index labels?
``` python
In [50]: idx = pd.MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'), ('b', 'two')])
In [53]: df = pd.Series(np.random.randn(4), index=idx)
In [56]: df.xs('one', level='L2')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-56-52601adf5184> in <module>()
----> 1 df.xs('one', level='L2')
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_27_g4d5ca5c-py2.7-macosx-10.8-x86_64.egg/pandas/core/series.pyc in _xs(self, key, axis, level, copy)
437
438 def _xs(self, key, axis=0, level=None, copy=True):
--> 439 return self.__getitem__(key)
440
441 xs = _xs
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_27_g4d5ca5c-py2.7-macosx-10.8-x86_64.egg/pandas/core/series.pyc in __getitem__(self, key)
482 def __getitem__(self, key):
483 try:
--> 484 return self.index.get_value(self, key)
485 except InvalidIndexError:
486 pass
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.13.0rc1_27_g4d5ca5c-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.pyc in get_value(self, series, key)
2294 raise InvalidIndexError(key)
2295 else:
-> 2296 raise e1
2297 except Exception: # pragma: no cover
2298 raise e1
KeyError: 'one'
```
So I guess this is about 3 errors on `Series.xs` (possibly related?):
1. Returning scalers when it should return a Series when the label is an integer
2. Not raising key errors when the label is an integer
3. Failing on slices for `level>1` when the label is a string.
EDIT: Oh, and I know that `.loc` / `.ix` will work for these. I was just surprised by the results.
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} | [] | closed | false | null | [] | null | 3 | 2013-12-12T16:50:54Z | 2013-12-13T12:32:54Z | 2013-12-13T12:32:54Z | NONE | null | ```
In [80]: dat = pd.DataFrame({'a' : [1,2,3], 'b' : [4,5,6]})
In [81]: dat
Out[81]:
a b
0 1 4
1 2 5
2 3 6
[3 rows x 2 columns]
In [82]: dat.c = 1
In [83]: dat
Out[83]:
a b
0 1 4
1 2 5
2 3 6
[3 rows x 2 columns]
In [84]: dat['c'] = 2
In [85]: dat
Out[85]:
a b c
0 1 4 2
1 2 5 2
2 3 6 2
[3 rows x 3 columns]
In [86]: dat.c
Out[86]: 1
In [87]: pd.version.version
Out[87]: '0.13.0rc1-64-gceec8bf'
```
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} | 19 | 2013-12-12T17:16:47Z | 2021-10-16T17:40:03Z | null | NONE | null | CSV files can contains some errors, for example:
```
NAME,PAT
Peter,cat
really bad line
Fedor,cat
```
to skip really bad lines exist `error_bad_lines=False` parameter.
Another example without quotes and delimiters in field:
```
NAME,PAT
Peter,cat
Ira,cat,dog
Fedor,cat
```
Which with quotes will look like this:
```
NAME,PAT
Peter,cat
Ira,"cat,dog"
Fedor,cat
```
So it easy fix this line if know that first field not contain extra separators.
Also extra trailing delimiters issue: https://github.com/pydata/pandas/issues/2886
More real life example:
```
82,52,29,11,2,2013-08-02 00:00:00,,,gen,,FDP, employee,0,1,gen,,,0
55,69,36,19,2,2013-10-28 00:00:00,,,gen,,FDP employee,0,1,gen,,,0
```
There are difference for `FDP employee` and `FDP, employee`. So it will be grate to have ability process this bad lines with own handler.
My proposition add additional parameter `process_bad_lines` for `read_csv`.
For example, if I want fix line:
```
def bad_line_handler(items):
'''probably ugly example,
but lets imagine that `FDP, employee` is half of our data'''
fdp_index = items.index('FDP')
return items[:fdp_index] + ['FDP, employee'] + items[fdp_index + 2:]
pd.read_csv(file, process_bad_lines=bad_line_handler)
```
1. `error_bad_line` and `warn_bad_line` can work as before but at first once try replace bad string with `process_bad_lines` handler.
2. if `process_bad_lines` will return `None` when probably better just skip this line without exceptions (probably it more flexible), to store compatibility just return unchanged `items` parameter. Otherwise `None` can be equal to bad line and better raise exceptions from `process_bad_lines` handler.
3. not always I can already create CSV file with quotes, somebody already send me bad CSV.
4. I can pre-process file but it will take more time and work, see for example http://stackoverflow.com/questions/14550441/problems-reading-csv-file-with-commas-and-characters-in-pandas
Some additions:
For example I have no much string fields and can assume that one of strings contains separator:
```
int,int,int,int,int,datetime,,,str,,str,str,int,int,str,,,int
int,int,int,int,int,datetime,,,str,,str ,int,int,str,,,int
```
But it can work bad for many strings:
```
int,int,int,int,int,datetime,str,str,str,str,str,str,int,int,str,,,int
int,int,int,int,int,datetime, str,str,str,str,str ,int,int,str,,,int
```
However it also be grate have default methods to fix this strings with concatenating left strings:
```
int,int,int,int,int,datetime,str,str,str,str,str,str,int,int,str,,,int
int,int,int,int,int,datetime, STR ,str,str,str,str,int,int,str,,,int
# for example with next syntax
pd.read_csv(file, process_bad_lines='try_concat_left')
```
or right strings:
```
int,int,int,int,int,datetime,str,str,str,str,str,str,int,int,str,,,int
int,int,int,int,int,datetime,str,str,str,str, STR ,int,int,str,,,int
# for example with next syntax
pd.read_csv(file, process_bad_lines='try_concat_right')
```
and also removing extra trailing delimiters:
```
pd.read_csv(file, process_bad_lines='skip_right_delimiters')
```
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} | [] | closed | false | null | [] | null | 10 | 2013-12-12T20:19:40Z | 2013-12-13T11:38:27Z | 2013-12-12T21:36:13Z | NONE | null | pandas version: '0.12.0'
numpy version: '1.7.1'
erlport version: '1.0.0beta'
I am passing data between erlang and python using erlport, following the example here:
http://erlport.org/docs/python.html
the python file I'm calling only contains the line:
import pandas as pd
I am getting the error:
``` python
** exception error: {python,'exceptions.AttributeError',
"'function' object has no attribute 'lower'",
[{<<"/anaconda/lib/python2.7/site-packages/pandas/core/format.py">>,
1701,<<"detect_console_encoding">>,
<<"if not encoding or 'ascii' in encoding.lower(): # try again for something bette"...>>},
{<<"/anaconda/lib/python2.7/site-packages/pandas/core/config_init.py">>,
234,<<"<module>">>,
<<"cf.register_option('encoding', detect_console_encoding(), pc_encoding_doc,">>},
{<<"/anaconda/lib/python2.7/site-packages/pandas/__init__.py">>,
25,<<"<module>">>,<<"import pandas.core.config_init">>},
{<<"/Documents/data-algorithms/Alg"...>>,
3,<<"<module>">>,<<"import pandas as pd">>},
{<<"/Documents/testki"...>>,
237,<<"_incoming_call">>,
<<"f = __import__(module, {}, {}, [objects[0]])">>},
{<<"/Documents/te"...>>,
245,<<"_call_with_error_handler">>,<<"function(*args)">>}]}
in function erlport:call/3 (src/erlport.erl, line 234)
in call from algo_tester:start/0 (src/algo_tester.erl, line 27)
```
I can get rid of the error by commenting out the following two lines in /anaconda/lib/python2.7/site-packages/pandas/core/config_init.py:
``` python
234 cf.register_option('encoding', detect_console_encoding(), pc_encoding_doc,
235 validator=is_text)
```
but then `print` doesn't work any longer.
Has anyone encountered this before?
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} | 1 | 2013-12-12T21:27:31Z | 2013-12-12T21:59:22Z | 2013-12-12T21:59:22Z | CONTRIBUTOR | null | On pandas 0.12:
```
pd.DataFrame( pd.Series([datetime(2012,12,12).replace(tzinfo=pytz.utc)] + [pd.NaT]*100))
```
I would argue this should show 1 non null-value:
```
<class 'pandas.core.frame.DataFrame'>
Int64Index: 101 entries, 0 to 100
Data columns (total 1 columns):
0 101 non-null values
dtypes: object(1)
```
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} | 3 | 2013-12-12T23:09:22Z | 2014-02-04T15:53:19Z | 2014-01-20T23:37:17Z | CONTRIBUTOR | null | related #5690
This works:
```
pd.Series(np.array([np.timedelta64(300000000)]))
```
```
0 00:00:00.300000
dtype: timedelta64
```
but this does not:
```
pd.Series([np.timedelta64(300000000)])
```
on Numpy 1.7:
```
TypeError: don't know how to convert scalar number to float
```
and on Numpy 1.8:
```
0 300000000
dtype: int64
```
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} | 4 | 2013-12-12T23:50:07Z | 2013-12-13T15:20:00Z | 2013-12-13T15:20:00Z | CONTRIBUTOR | null | related #5689
On pandas v12:
I thought this was fixed in #2990, but:
both of these:
```
pd.DataFrame(pd.Series((np.array([np.timedelta64(3000000000),np.timedelta64(3000000000)])))).min()
```
and
```
pd.Series((np.array([np.timedelta64(3000000000),np.timedelta64(3000000000)]))).min()
```
produce:
```
/usr/lib64/python2.7/site-packages/pandas/core/series.py in min(self, axis, out, skipna, level)
1509 if level is not None:
1510 return self._agg_by_level('min', level=level, skipna=skipna)
-> 1511 return nanops.nanmin(self.values, skipna=skipna)
1512
1513 @Substitution(name='maximum', shortname='max',
/usr/lib64/python2.7/site-packages/pandas/core/nanops.py in f(values, axis, skipna, **kwds)
78 result = alt(values, axis=axis, skipna=skipna, **kwds)
79 except Exception:
---> 80 result = alt(values, axis=axis, skipna=skipna, **kwds)
81
82 return result
/usr/lib64/python2.7/site-packages/pandas/core/nanops.py in nanmin(values, axis, skipna)
279 @bottleneck_switch()
280 def nanmin(values, axis=None, skipna=True):
--> 281 values, mask, dtype = _get_values(values, skipna, fill_value_typ = '+inf')
282
283 # numpy 1.6.1 workaround in Python 3.x
/usr/lib64/python2.7/site-packages/pandas/core/nanops.py in _get_values(values, skipna, fill_value, fill_value_typ, isfinite, copy)
130 mask = _isfinite(values)
131 else:
--> 132 mask = isnull(values)
133
134 dtype = values.dtype
/usr/lib64/python2.7/site-packages/pandas/core/common.py in isnull(obj)
59 given which of the element is null.
60 """
---> 61 return _isnull(obj)
62
63
/usr/lib64/python2.7/site-packages/pandas/core/common.py in _isnull_new(obj)
68 from pandas.core.generic import PandasContainer
69 if isinstance(obj, np.ndarray):
---> 70 return _isnull_ndarraylike(obj)
71 elif isinstance(obj, PandasContainer):
72 # TODO: optimize for DataFrame, etc.
/usr/lib64/python2.7/site-packages/pandas/core/common.py in _isnull_ndarraylike(obj)
157 else:
158 # -np.isfinite(obj)
--> 159 result = np.isnan(obj)
160 return result
161
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
I get the same Error with max as well.
median appears to work, although the type is incorrect:
```
In [144]:
pd.DataFrame(pd.Series((np.array([np.timedelta64(3000000000),np.timedelta64(3000000000)])))).median()
Out[144]:
0 3000000000
dtype: float64
```
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} | [] | closed | false | null | [] | null | 3 | 2013-12-13T09:09:14Z | 2013-12-13T22:22:25Z | 2013-12-13T22:22:25Z | NONE | null | Hello,
I'm using Python 7.2.3 and Pandas 0.12.0.
When I run the code below, it works as expected, by creating new items and updating them.
```
quotes_premiums = quotes_cc_returns.transpose(2, 1, 0)
quotes_premiums['RiskPremium'] = quotes_premiums.CC_Returns.sub(ff_data_factors_subset.RF, axis=0)
quotes_premiums['MktRiskPremium'] = 0.0
quotes_premiums['MktRiskPremium'] = quotes_premiums.MktRiskPremium.add(ff_data_factors_subset.MktMinusRF, axis=0)
quotes_premiums.MktRiskPremium.iloc[0] = None
quotes_premiums = quotes_premiums.transpose(2, 1, 0)
quotes_premiums['VFINX'].head()
Open High Low Close Volume AdjClose CC_Returns RiskPremium MktRiskPremium
Date
2001-01-31 118.44 126.88 118.44 126.18 0 99.74 NaN NaN NaN
2001-02-28 126.88 126.88 114.65 114.65 0 90.62 -0.095892 -0.099792 -0.1008
2001-03-31 114.77 116.99 103.12 107.07 0 84.86 -0.065672 -0.070072 -0.0727
2001-04-30 105.74 115.75 101.83 115.39 0 91.45 0.074790 0.070890 0.0795
2001-05-31 116.95 121.36 115.11 116.14 0 92.05 0.006540 0.003340 0.0073
```
However, if I make a single change, using what I think is called Attribute access, it stops updating the MktRiskPremium item (column after transpose).
```
quotes_premiums.MktRiskPremium = quotes_premiums.MktRiskPremium.add(ff_data_factors_subset.MktMinusRF, axis=0)
quotes_premiums['VFINX'].head()
Open High Low Close Volume AdjClose CC_Returns RiskPremium MktRiskPremium
Date
2001-01-31 118.44 126.88 118.44 126.18 0 99.74 NaN NaN 0
2001-02-28 126.88 126.88 114.65 114.65 0 90.62 -0.095892 -0.099792 0
2001-03-31 114.77 116.99 103.12 107.07 0 84.86 -0.065672 -0.070072 0
2001-04-30 105.74 115.75 101.83 115.39 0 91.45 0.074790 0.070890 0
2001-05-31 116.95 121.36 115.11 116.14 0 92.05 0.006540 0.003340 0
```
Is this normal?
Thanks,
JM
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} | 4 | 2013-12-13T12:27:49Z | 2014-02-18T19:48:40Z | 2014-02-18T19:48:40Z | CONTRIBUTOR | null | In master, `NDFrame.fillna`'s `inplace` parameter's docs claim:
> If True, fill in place. Note: this will modify any
> other views on this object, (e.g. a no-copy slice for a column in a
> DataFrame). **Still returns the object.**
But this is not consistently true. If `inplace=True`, `self` is only returned on a couple of paths. In the typical case of `s = Series(); s.fillna(-1, inplace=True)`, `None` is returned.
In 0.12.0 (and judging by a warning in an earlier version, from 0.11), `Series.fillna` consistently (and very deliberately) returns `None` if `inplace=True`, despite its docs claiming something similar:
> If True, fill the Series in place. Note: this will modify any other
> views on this Series, for example a column in a DataFrame. Returns
> a reference to the filled object, **which is self if inplace=True**
So I believe the intended behaviour is to consistently return `None` if `inplace=True`, and the docs are simply out of date.
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] | closed | false | null | [] | null | 1 | 2013-12-13T16:30:20Z | 2013-12-13T16:33:33Z | 2013-12-13T16:33:19Z | NONE | null | The following code gives "IndexError: Out of bounds on buffer access (axis 0)"
(pandas version: 0.12.0, Python version: 3.3.2, OS: Linux):
pandas.Series([0], ["y"])[["x", "x", "y"]]
If "x" is replaced to 1 and "y" to 2, then the error disappears.
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} | 8 | 2013-12-13T16:30:20Z | 2014-11-06T12:42:10Z | 2014-11-06T12:42:10Z | NONE | null | related #5172
Given this DataFrame, with an index containing the moment when DST changes (October 27th in the case of the "Europe/Paris" timezone):
```
index = pandas.date_range('2013-09-30', '2013-11-02', freq = '30Min', tz = 'UTC').tz_convert('Europe/Paris')
column_a = pandas.np.random.random(index.size)
column_b = pandas.np.random.random(index.size)
df = pandas.DataFrame({ "a": column_a, "b": column_b }, index = index)
```
Let's say I want to find the "min" and "max" values for each month:
```
df.resample("MS", how = { "a": "min", "b": "max" })
```
Here's the incorrect result:
```
a b
2013-09-01 00:00:00+02:00 0.015856 0.979541
2013-10-01 00:00:00+02:00 0.002039 0.999960
2013-10-31 23:00:00+01:00 NaN NaN
```
Same problem with a "W-MON" frequency:
```
a b
2013-09-30 00:00:00+02:00 0.015856 0.979541
2013-10-07 00:00:00+02:00 0.007961 0.999734
2013-10-14 00:00:00+02:00 0.002614 0.993354
2013-10-21 00:00:00+02:00 0.005655 0.999960
2013-10-27 23:00:00+01:00 NaN NaN
2013-11-03 23:00:00+01:00 NaN NaN
```
Whereas it works fine with a "D" frequency.
```
a b
...
2013-10-26 00:00:00+02:00 0.004645 0.983281
2013-10-27 00:00:00+02:00 0.030151 0.986827
2013-10-28 00:00:00+01:00 0.015891 0.981455
2013-10-29 00:00:00+01:00 0.024176 0.999306
...
```
Should I resample only the "a" column, it also works fine:
```
df["a"].resample("MS", how = "min")
```
```
2013-09-01 00:00:00+02:00 0.015856
2013-10-01 00:00:00+02:00 0.002039
2013-11-01 00:00:00+01:00 0.000747
Freq: MS, dtype: float64
```
Tested with latest pandas from GIT master.
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} | 0 | 2013-12-13T16:31:35Z | 2014-01-20T23:37:17Z | 2014-01-20T23:37:17Z | CONTRIBUTOR | null | related example: http://stackoverflow.com/questions/20789976/python-pandas-dataframe-1st-line-issue-with-datetime-timedelta/20802902?noredirect=1#comment31195305_20802902
```
import datetime
import pandas as pd
parms = {'d': datetime.datetime(2013, 11, 5, 5, 56), 't':datetime.timedelta(0, 22500)}
df = pd.DataFrame(columns=list('dt'))
df = df.append(parms, ignore_index=True)
erroneous code:
>>> df.append(parms, ignore_index=True)
d t
0 2013-11-05 05:56:00 22500000000000
1 2013-11-05 05:56:00 6:15:00
```
The notion of nullness is not handled well for `timedelta64` columns when performing a left join:
```
In [194]:
pd.DataFrame(pd.Series([np.timedelta64(300000000),np.timedelta64(300000000)],dtype='m8[ns]',index=["A","B"])).join(
pd.DataFrame(pd.Series([np.timedelta64(300000000)],dtype='m8[ns]',index=["A"])),rsuffix='r', how="left").info()
Out [194]:
<class 'pandas.core.frame.DataFrame'>
Index: 2 entries, A to B
Data columns (total 2 columns):
0 2 non-null values
0r 1 non-null values
dtypes: float64(1), timedelta64[ns](1)
```
The column with a mix of timedelta64 and `nulls` gets cast to a `float64`.
This seems incorrect since `NaT` should be usable to indicate the null:
```
In [196]:
pd.Series([np.timedelta64(300000000), pd.NaT],dtype='m8[ns]')
Out[196]:
0 00:00:00.300000
1 NaT
dtype: timedelta64[ns]
```
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] | closed | false | null | [] | null | 3 | 2013-12-13T16:58:01Z | 2013-12-13T17:30:57Z | 2013-12-13T17:04:52Z | CONTRIBUTOR | null | I have a `Series` of "dates" (actually `datetime`s all at midnight), Is there any way to get this `Series` or `column` to render as just the date without the time string of "00:00:00"?
```
In [248]:
pd.Series([datetime(2013,1,1),datetime(2013,2,1)])
Out[248]:
0 2013-01-01 00:00:00
1 2013-02-01 00:00:00
dtype: datetime64[ns]
```
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} | 4 | 2013-12-13T17:09:12Z | 2017-05-04T13:41:27Z | 2017-05-04T13:41:27Z | NONE | null | The read_csv function gives strange results for certain input files
(pandas version: 0.12.0, Python version: 3.3.2, OS: Linux):
import io, pandas
f = io.StringIO("x\n\n1111111111111111111")
df = pandas.read_csv(f)
print(int(df.x[1])) # 1111111111111111168
We have data loss due to rounding! The correct behavior in my opinion would be to treat the column as string, if np.float64 is not enough to represent it exactly.
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} | 63 | 2013-12-13T23:22:57Z | 2014-09-21T00:55:42Z | 2014-01-24T15:24:19Z | CONTRIBUTOR | null | It seems that the changes to to Series break the data conversion to R: running this Notebook doesn't work anymore with some dev version from last week:
https://gist.github.com/kevindavenport/7771325/raw/87ab5603f406729c6a3866f95af9a1ebfedcf619/Mahalanobis_Outliers.ipynb
The resulting error is this:
``` python
#xydata=pandas.DataFrame(...)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-74fcaa767ca0> in <module>()
----> 1 get_ipython().run_cell_magic(u'R', u'-i xydata,xycols # list object to be transferred to python here', u'install.packages("ggplot2") # Had to add this for some reason, shouldn\'t be necessary\nlibrary(ggplot2)\ndf = data.frame(xydata)\nnames(df) <- c(xycols)\nprint(head(df))\nplot = ggplot(df, aes(x = X, y = Y)) + \ngeom_point(alpha = .8, color = \'dodgerblue\',size = 5) +\ngeom_point(data=subset(df, Y >= 6.7 | X >= 4), color = \'red\',size = 6) +\ntheme(axis.text.x = element_text(size= rel(1.5),angle=90, hjust=1)) +\nggtitle(\'Distance Pairs with outliers highlighted in red\')\nprint(plot)')
C:\portabel\Python27\lib\site-packages\IPython\core\interactiveshell.pyc in run_cell_magic(self, magic_name, line, cell)
2141 magic_arg_s = self.var_expand(line, stack_depth)
2142 with self.builtin_trap:
-> 2143 result = fn(magic_arg_s, cell)
2144 return result
2145
C:\portabel\Python27\lib\site-packages\IPython\extensions\rmagic.py in R(self, line, cell, local_ns)
C:\portabel\Python27\lib\site-packages\IPython\core\magic.pyc in <lambda>(f, *a, **k)
191 # but it's overkill for just that one bit of state.
192 def magic_deco(arg):
--> 193 call = lambda f, *a, **k: f(*a, **k)
194
195 if callable(arg):
C:\portabel\Python27\lib\site-packages\IPython\extensions\rmagic.py in R(self, line, cell, local_ns)
585 except KeyError:
586 raise NameError("name '%s' is not defined" % input)
--> 587 self.r.assign(input, self.pyconverter(val))
588
589 if getattr(args, 'units') is not None:
C:\portabel\Python27\lib\site-packages\rpy2\robjects\functions.pyc in __call__(self, *args, **kwargs)
84 v = kwargs.pop(k)
85 kwargs[r_k] = v
---> 86 return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
C:\portabel\Python27\lib\site-packages\rpy2\robjects\functions.pyc in __call__(self, *args, **kwargs)
29
30 def __call__(self, *args, **kwargs):
---> 31 new_args = [conversion.py2ri(a) for a in args]
32 new_kwargs = {}
33 for k, v in kwargs.iteritems():
C:\portabel\Python27\lib\site-packages\rpy2\robjects\pandas2ri.pyc in pandas2ri(obj)
26 od[name] = StrVector(values)
27 else:
---> 28 od[name] = ro.conversion.py2ri(values)
29 return DataFrame(od)
30 elif isinstance(obj, PandasIndex):
C:\portabel\Python27\lib\site-packages\rpy2\robjects\pandas2ri.pyc in pandas2ri(obj)
49 else:
50 # converted as a numpy array
---> 51 res = original_conversion(obj)
52 # "index" is equivalent to "names" in R
53 if obj.ndim == 1:
C:\portabel\Python27\lib\site-packages\rpy2\robjects\numpy2ri.pyc in numpy2ri(o)
56 raise(ValueError("Unknown numpy array type."))
57 else:
---> 58 res = ro.default_py2ri(o)
59 return res
60
C:\portabel\Python27\lib\site-packages\rpy2\robjects\__init__.pyc in default_py2ri(o)
146 res = rinterface.SexpVector([o, ], rinterface.CPLXSXP)
147 else:
--> 148 raise(ValueError("Nothing can be done for the type %s at the moment." %(type(o))))
149 return res
150
ValueError: Nothing can be done for the type <class 'pandas.core.series.Series'> at the moment.
```
I'm not sure if this is something pandas cares, but even if not it would be nice to mention it in the release notes.
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} | 15 | 2013-12-14T20:58:43Z | 2013-12-14T23:15:34Z | 2013-12-14T22:57:24Z | MEMBER | null | A unicode-indexed frame:
```
In [2]: df = mkdf(3, 2, r_idx_type='u')
In [3]: df
Out[3]:
C0 C_l0_g0 C_l0_g1
R0
7ืืฃืืืืืฆื4 R0C0 R0C1
ืืงืืื441ืืฅ R1C0 R1C1
35ืื5ืจ13ื2 R2C0 R2C1
[3 rows x 2 columns]
In [4]: <Ctrl-D>
sqlite3.ProgrammingError: You must not use 8-bit bytestrings unless you use a text_factory that can interpret 8-bit bytestrings (like text_factory = str). It is highly recommended that you instead just switch your application to Unicode strings.
```
If I don't `repr` it, things exit IPython smoothly.
A trivial subclass of `StringMixin` is fine
```
In [1]: class Blob(pandas.core.base.StringMixin):
...: def __unicode__(self):
...: return type(self).__name__
...:
In [2]: Blob()
Out[2]: Blob
In [3]: <Ctrl-D> # <- Fine
```
However, when I add unicode characters to the repr, mega-borkage follows:
```
In [1]: class Blob(pandas.core.base.StringMixin):
...: def __unicode__(self):
...: return pandas.compat.u('\u22ee')
...:
In [2]: Blob()
Out[2]: โฎ
In [3]: <Ctrl-D> # same traceback as above
```
Looks like could be IPython too. I'll see if I can't track it down.
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} | 4 | 2013-12-14T21:16:42Z | 2014-06-14T08:27:47Z | 2014-01-29T04:19:09Z | CONTRIBUTOR | null | WIP for now. Closes #2669
I'm using statsmodels' KDE implementation right now; it has a `cdf` method, but scipy's gaussian_kde doesn't. I need to check the math, but I think something like doing a cumsum on the density and normalizing by the sum should be the same.
I'm also going to add kwargs for things like the inverse cdf.
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