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https://api.github.com/repos/pandas-dev/pandas/issues/6301 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6301/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6301/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6301/events | https://github.com/pandas-dev/pandas/pull/6301 | 27,188,158 | MDExOlB1bGxSZXF1ZXN0MTIzMzc3NTY= | 6,301 | ENH: per axis and per level indexing (orig GH6134) | {
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} | 15 | 2014-02-08T02:00:53Z | 2014-06-16T19:17:40Z | 2014-02-13T23:12:32Z | CONTRIBUTOR | null | This is a reprise of #6134, with tests, and multi-axis support; it is dependent on #6299
closes #4036
closes #4116
closes #3057
closes #2598
closes #5641
closes #3738
- [x] docs
- [x] v0.14.0 example
- [x] release notes
- [x] setting #5641, #3738
This is the whatsnew/docs
MultiIndexing Using Slicers
In 0.14.0 we added a new way to slice multi-indexed objects. You can slice a multi-index by providing multiple indexers. You can use slice(None) to select all the contents of that level. You do not need to specify all the deeper levels, they will be implied as slice(None). See the docs
```
Warning
You should specify all axes in the .loc specifier, meaning the indexer for the index and for the columns. Their are some ambiguous cases where the passed indexer could be mis-interpreted as indexing both axes, rather than into say the MuliIndex for the rows.
You should do this:
df.loc[(slice('A1','A3'),.....,:]
rather than this:
df.loc[(slice('A1','A3'),.....]
```
```
Warning
You will need to make sure that the selection axes are fully lexsorted!
```
```
In [7]: def mklbl(prefix,n):
...: return ["%s%s" % (prefix,i) for i in range(n)]
...:
In [8]: index = MultiIndex.from_product([mklbl('A',4),
...: mklbl('B',2),
...: mklbl('C',4),
...: mklbl('D',2)])
...:
In [9]: columns = MultiIndex.from_tuples([('a','foo'),('a','bar'),
...: ('b','foo'),('b','bah')],
...: names=['lvl0', 'lvl1'])
...:
In [10]: df = DataFrame(np.arange(len(index)*len(columns)).reshape((len(index),len(columns))),
....: index=index,
....: columns=columns).sortlevel().sortlevel(axis=1)
....:
In [11]: df
Out[11]:
lvl0 a b
lvl1 bar foo bah foo
A0 B0 C0 D0 1 0 3 2
D1 5 4 7 6
C1 D0 9 8 11 10
D1 13 12 15 14
C2 D0 17 16 19 18
D1 21 20 23 22
C3 D0 25 24 27 26
D1 29 28 31 30
B1 C0 D0 33 32 35 34
D1 37 36 39 38
C1 D0 41 40 43 42
D1 45 44 47 46
C2 D0 49 48 51 50
D1 53 52 55 54
C3 D0 57 56 59 58
... ... ... ...
[64 rows x 4 columns]
```
```
In [12]: df.loc[(slice('A1','A3'),slice(None), ['C1','C3']),:]
Out[12]:
lvl0 a b
lvl1 bar foo bah foo
A1 B0 C1 D0 73 72 75 74
D1 77 76 79 78
C3 D0 89 88 91 90
D1 93 92 95 94
B1 C1 D0 105 104 107 106
D1 109 108 111 110
C3 D0 121 120 123 122
D1 125 124 127 126
A2 B0 C1 D0 137 136 139 138
D1 141 140 143 142
C3 D0 153 152 155 154
D1 157 156 159 158
B1 C1 D0 169 168 171 170
D1 173 172 175 174
C3 D0 185 184 187 186
... ... ... ...
[16 rows x 4 columns]
In [13]: df.loc[(slice(None),slice(None), ['C1','C3']),:]
Out[13]:
lvl0 a b
lvl1 bar foo bah foo
A0 B0 C1 D0 9 8 11 10
D1 13 12 15 14
C3 D0 25 24 27 26
D1 29 28 31 30
B1 C1 D0 41 40 43 42
D1 45 44 47 46
C3 D0 57 56 59 58
D1 61 60 63 62
A1 B0 C1 D0 73 72 75 74
D1 77 76 79 78
C3 D0 89 88 91 90
D1 93 92 95 94
B1 C1 D0 105 104 107 106
D1 109 108 111 110
C3 D0 121 120 123 122
... ... ... ...
[32 rows x 4 columns]
```
It is possible to perform quite complicated selections using this method on multiple axes at the same time.
```
In [14]: df.loc['A1',(slice(None),'foo')]
Out[14]:
lvl0 a b
lvl1 foo foo
B0 C0 D0 64 66
D1 68 70
C1 D0 72 74
D1 76 78
C2 D0 80 82
D1 84 86
C3 D0 88 90
D1 92 94
B1 C0 D0 96 98
D1 100 102
C1 D0 104 106
D1 108 110
C2 D0 112 114
D1 116 118
C3 D0 120 122
... ...
[16 rows x 2 columns]
In [15]: df.loc[(slice(None),slice(None), ['C1','C3']),(slice(None),'foo')]
Out[15]:
lvl0 a b
lvl1 foo foo
A0 B0 C1 D0 8 10
D1 12 14
C3 D0 24 26
D1 28 30
B1 C1 D0 40 42
D1 44 46
C3 D0 56 58
D1 60 62
A1 B0 C1 D0 72 74
D1 76 78
C3 D0 88 90
D1 92 94
B1 C1 D0 104 106
D1 108 110
C3 D0 120 122
... ...
[32 rows x 2 columns]
```
Furthermore you can set the values using these methods
```
In [16]: df2 = df.copy()
In [17]: df2.loc[(slice(None),slice(None), ['C1','C3']),:] = -10
In [18]: df2
Out[18]:
lvl0 a b
lvl1 bar foo bah foo
A0 B0 C0 D0 1 0 3 2
D1 5 4 7 6
C1 D0 -10 -10 -10 -10
D1 -10 -10 -10 -10
C2 D0 17 16 19 18
D1 21 20 23 22
C3 D0 -10 -10 -10 -10
D1 -10 -10 -10 -10
B1 C0 D0 33 32 35 34
D1 37 36 39 38
C1 D0 -10 -10 -10 -10
D1 -10 -10 -10 -10
C2 D0 49 48 51 50
D1 53 52 55 54
C3 D0 -10 -10 -10 -10
... ... ... ...
[64 rows x 4 columns]
```
You use a right-hand-side of an alignable object as well.
```
In [19]: df2 = df.copy()
In [20]: df2.loc[(slice(None),slice(None), ['C1','C3']),:] = df2*1000
In [21]: df2
Out[21]:
lvl0 a b
lvl1 bar foo bah foo
A0 B0 C0 D0 1 0 3 2
D1 5 4 7 6
C1 D0 1000 0 3000 2000
D1 5000 4000 7000 6000
C2 D0 17 16 19 18
D1 21 20 23 22
C3 D0 9000 8000 11000 10000
D1 13000 12000 15000 14000
B1 C0 D0 33 32 35 34
D1 37 36 39 38
C1 D0 17000 16000 19000 18000
D1 21000 20000 23000 22000
C2 D0 49 48 51 50
D1 53 52 55 54
C3 D0 25000 24000 27000 26000
... ... ... ...
[64 rows x 4 columns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6302/events | https://github.com/pandas-dev/pandas/pull/6302 | 27,196,214 | MDExOlB1bGxSZXF1ZXN0MTIzNDAxMTQ= | 6,302 | DOC: clarified to_excel and read_excel (GH6260) | {
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} | [] | closed | false | null | [] | null | 3 | 2014-02-08T08:07:37Z | 2014-06-21T04:42:54Z | 2014-02-09T20:46:53Z | CONTRIBUTOR | null | as discussed in issue #6260
- io.rst: added a paragraph explaining the difference in behaviour from 0.12
- excel.py: added docstring for has_index_names
thanks to jmcnamara
ps: sorry about the pollution with multiple commits, I'm still learning how to rebase/squash
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https://api.github.com/repos/pandas-dev/pandas/issues/6303 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6303/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6303/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6303/events | https://github.com/pandas-dev/pandas/issues/6303 | 27,196,860 | MDU6SXNzdWUyNzE5Njg2MA== | 6,303 | error with groupby and transform while using datetime64[ns] as part of a key | {
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] | closed | false | null | [] | null | 2 | 2014-02-08T09:01:50Z | 2014-02-08T15:09:17Z | 2014-02-08T15:08:23Z | NONE | null | from pandas.util.testing import choice
from pandas import DataFrame, date_range
from numpy.random import randn
n = 8
dates = date_range('1/1/2000', periods=n, freq='1T')
colors = choice(['red', 'green'], size=n)
df = DataFrame({'value': randn(n), 'date': dates, 'color': colors})
zscore = lambda x: (x - x.mean()) / x.std()
df
Out[2]:
color date value
0 red 2000-01-01 00:00:00 -0.382902
1 green 2000-01-01 00:01:00 0.991723
2 green 2000-01-01 00:02:00 -0.442476
3 green 2000-01-01 00:03:00 -0.776372
4 red 2000-01-01 00:04:00 -1.372511
5 red 2000-01-01 00:05:00 -0.276006
6 red 2000-01-01 00:06:00 -0.182162
7 red 2000-01-01 00:07:00 -0.232967
[8 rows x 3 columns]
df.dtypes
Out[3]:
color object
date datetime64[ns]
value float64
dtype: object
df.groupby(['color','date'])['value'].transform(zscore)
---
KeyError Traceback (most recent call last)
<ipython-input-4-97e715db56f2> in <module>()
----> 1 df.groupby(['color','date'])['value'].transform(zscore)
C:\Users\MD\Anaconda\lib\site-packages\pandas-0.13.1.dev-py2.7-win-amd64.egg\pandas\core\groupby.pyc in transform(self, func, _args, *_kwargs)
1845 # this needs to be an ndarray
1846 result = Series(result)
-> 1847 result.iloc[self._get_index(name)] = res
1848 result = result.values
1849
C:\Users\MD\Anaconda\lib\site-packages\pandas-0.13.1.dev-py2.7-win-amd64.egg\pandas\core\groupby.pyc in _get_index(self, name)
265 """ safe get index """
266 try:
--> 267 return self.indices[name]
268 except:
269 if isinstance(name, Timestamp):
KeyError: ('green', Timestamp('2000-01-01 00:01:00', tz=None))
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https://api.github.com/repos/pandas-dev/pandas/issues/6304 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6304/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6304/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6304/events | https://github.com/pandas-dev/pandas/pull/6304 | 27,196,973 | MDExOlB1bGxSZXF1ZXN0MTIzNDA0NjY= | 6,304 | Add example section to groupby.rst and one example | {
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} | [] | closed | false | null | [] | null | 1 | 2014-02-08T09:11:57Z | 2014-06-27T15:12:59Z | 2014-02-15T19:42:56Z | CONTRIBUTOR | null | following this question on [StackOverFlow](https://stackoverflow.com/questions/21584434/group-by-value-of-sum-of-columns-with-pandas) and [this issue](https://github.com/pydata/pandas/issues/6288).
I opened an "Examples" section in the doc of `groupby()` and add an example about how a way to regroup columns of a DataFrame according to their sum, and to sum the aggregated ones.
closes #6288
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https://api.github.com/repos/pandas-dev/pandas/issues/6305 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6305/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6305/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6305/events | https://github.com/pandas-dev/pandas/issues/6305 | 27,203,418 | MDU6SXNzdWUyNzIwMzQxOA== | 6,305 | Add pd.read_bin() to read data of binary file to dataframe. | {
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} | [] | closed | false | null | [] | null | 10 | 2014-02-08T16:32:42Z | 2017-12-21T12:24:59Z | 2014-02-17T23:42:43Z | NONE | null | Create `pd.read_bin(binary_file, record_fmt)` to wrap Python `struct.unpack()`, facilitate the data exchange with C binary data file.
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https://api.github.com/repos/pandas-dev/pandas/issues/6306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6306/events | https://github.com/pandas-dev/pandas/issues/6306 | 27,204,919 | MDU6SXNzdWUyNzIwNDkxOQ== | 6,306 | ENH: Allow subplots axes to be passed into DataFrame.plot() | {
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] | closed | false | null | [] | null | 4 | 2014-02-08T17:50:12Z | 2014-05-16T17:55:28Z | 2014-05-16T17:55:23Z | NONE | null | This works nicely:
```
Python 2.7.5+ (default, Sep 19 2013, 13:48:49)
Type "copyright", "credits" or "license" for more information.
IPython 1.1.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import pandas, numpy
In [2]: import matplotlib.pyplot as plt
In [3]: df1 = pandas.DataFrame(numpy.random.random((5, 4)))
In [4]: df2 = pandas.DataFrame(numpy.random.random((5, 4)))
In [5]: ax = df1.plot()
In [6]: df2.plot(ax=ax)
Out[6]: <matplotlib.axes.AxesSubplot at 0x3f4f910>
In [7]: plt.show()
```
But this doesn't:
```
In [8]: ax = df1.plot(subplots=True)
In [9]: df2.plot(subplots=True, ax=ax)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-70822353a5d1> in <module>()
----> 1 df2.plot(subplots=True, ax=ax)
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
1634 logy=logy, sort_columns=sort_columns,
1635 secondary_y=secondary_y, **kwds)
-> 1636 plot_obj.generate()
1637 plot_obj.draw()
1638 if subplots:
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in generate(self)
853 self._args_adjust()
854 self._compute_plot_data()
--> 855 self._setup_subplots()
856 self._make_plot()
857 self._post_plot_logic()
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in _setup_subplots(self)
909 ax = self._maybe_right_yaxis(ax)
910 else:
--> 911 fig = self.ax.get_figure()
912 if self.figsize is not None:
913 fig.set_size_inches(self.figsize)
AttributeError: 'numpy.ndarray' object has no attribute 'get_figure'
```
It would be nice if you could use the `subplots` flag, because it makes it easier to compare two data frames visually.
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} | 7 | 2014-02-08T20:12:48Z | 2017-12-19T23:04:26Z | 2017-12-19T23:04:26Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/21643407/define-a-pandas-tseries-index-datetimeindex-using-2-datetimes-dt-start-and-dt-s/21651263#21651263
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https://api.github.com/repos/pandas-dev/pandas/issues/6308 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6308/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6308/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6308/events | https://github.com/pandas-dev/pandas/pull/6308 | 27,211,136 | MDExOlB1bGxSZXF1ZXN0MTIzNDYyNzM= | 6,308 | CLN: Convert last of assert_(x == y) to assertEqual | {
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] | closed | false | null | [] | null | 3 | 2014-02-08T23:02:55Z | 2014-06-17T12:11:47Z | 2014-02-12T00:40:52Z | CONTRIBUTOR | null | Work on #6175. Fixes last of vanilla assert_(x == y) conversions in pandas/tests and pandas/*/tests. Tests still pass locally.
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https://api.github.com/repos/pandas-dev/pandas/issues/6309 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6309/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6309/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6309/events | https://github.com/pandas-dev/pandas/issues/6309 | 27,217,286 | MDU6SXNzdWUyNzIxNzI4Ng== | 6,309 | BUG: str.extract alignment problem on i/o dataframes. | {
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} | [] | closed | false | null | [] | null | 6 | 2014-02-09T05:59:06Z | 2014-02-23T22:10:10Z | 2014-02-17T14:16:50Z | NONE | null | Tried the code from here: http://stackoverflow.com/questions/21641857/pandas-valueerror-convert-float-nan-to-integer-i-o-csv-file
Not sure if this is fixed in the latest build but I had a similar problem as the one described in the Stackoverflow when I upgraded to the latest stable version of `pandas`
Seem like it shifts 1 cell up when you load the csv:
```
import pandas as pd
import numpy as np
data = pd.read_csv('dffile.csv', index_col=0)
df=data[['AreaNo.','ID']]
is_even = df['ID'].str.extract('([0-9]+).*')
```
Results (Notice the `Nan` and the values shifted up one cell from dataframe df)
```
>>> print (is_even)
Data
1 378
2 4740
3 3445
4 2801
5 4726
6 6239
7 6239
8 558
9 584
10 557
11 571
12 524
13 524
14 258
15 740
16 645
17 684
18 NaN
Name: ID, dtype: object
>>> print (df.ID)
Data
1 676
2 378
3 4740
4 3445
5 2801A
6 4726
7 6239B
8 6239.5
9 558
10 584
11 557
12 571 1/2
13 524
14 524
15 258
16 740
17 645
18 684
Name: ID, dtype: object
```
But when ran from a non loaded dataframes it is Not shifted:
```
import pandas as pd
df=pd.DataFrame({'ID': ['10A','6.5', '4 A', '3 1/2'], 'Name': ['J','K','L','M']})
def ExtractU(df):
is_even = df['ID'].str.extract('(\d+).*')
>>> is_even = df['ID'].str.extract('(\d+).*')
>>> is_even
0 10
1 6
2 4
3 3
Name: ID, dtype: object
```
Saw this in the most recent pandas update it gives a ValueError because the loaded data includes a Nan.
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} | [] | closed | false | null | [] | null | 5 | 2014-02-09T10:28:42Z | 2014-02-09T17:18:22Z | 2014-02-09T12:27:35Z | CONTRIBUTOR | null | Hello,
After visiting
https://www.google.fr/search?q=python+pandas+timeseries
I get the following url
http://pandas.pydata.org/pandas-docs/dev/timeseries.html
Is it really a good idea to point to dev version of pandas-doc ?
(maybe a link to stable version should be present on top of this webpage)
http://pandas.pydata.org/pandas-docs/stable/timeseries.html
Moreover there is a CSS problem.
Kind regards
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] | closed | false | null | [] | null | 4 | 2014-02-09T11:22:15Z | 2014-02-09T15:02:00Z | 2014-02-09T15:02:00Z | NONE | null | You need to setup a new iron.io account (they have a free tier)
and setup the appropriate IRON_TOKEN and IRON_PROJECT_ID secure
envars in `.travis.yml` for ci/prep_ccache and ci/submit_ccache to work.
The changes need to be made [here](https://github.com/pydata/pandas/blob/master/.travis.yml#L8), encrypted (using the travis gem) for the `pydata/pandas` account.
Until then, build times are back to where they were before #6218
cc @jreback
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} | 9 | 2014-02-09T15:44:29Z | 2016-11-11T12:55:24Z | 2016-11-11T12:45:53Z | CONTRIBUTOR | null | Allow to use `'size'` in groupby's aggregate, so you can do:
```
df.groupby(..).agg('size')
df.groupny(..).agg(['mean', 'size'])
```
---
http://stackoverflow.com/questions/21660686/pandas-groupby-straight-forward-counting-the-number-of-elements-in-each-group-i
- ~~`count` should directly implement `size` (enh)~~
- `count`/`size` should be allowed in an aggregation list (the bug)
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} | 13 | 2014-02-09T23:35:45Z | 2014-06-20T14:12:59Z | 2014-02-17T20:39:18Z | NONE | null | ```
FIX: hdfstore queries of the form where=[('date', '>=', datetime(2013,1,1)), ('date', '<=', datetime(2014,1,1))] were broken
- modified Expr.parse_back_compat to check for tuples, in w, and unpack into w, op, value
- modified Expr.__init__ to modify the where list/tuple with the parsed result
```
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> > > t=pd.DataFrame({'A':['x','y','z']})
> > > t.sort(['A'], ascending=[True])
> > > A
> > > 0 x
> > > 1 y
> > > 2 z
> > > t.sort(['A'], ascending=[False])
> > > A
> > > 0 x
> > > 1 y
> > > 2 z
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@jreback I now added it to requirements-2.7.txt. Should I add it to all test environments? Or if only one, is this the correct one?
@mangecoeur Is it possible that there is a `pymysql` missing in the requirement files to run the MySQL tests (they are all skipped on travis)?
At the moment, there are two tests failing with postgresql (see https://travis-ci.org/jorisvandenbossche/pandas/jobs/18576746).
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https://api.github.com/repos/pandas-dev/pandas/issues/6317 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6317/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6317/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6317/events | https://github.com/pandas-dev/pandas/issues/6317 | 27,283,911 | MDU6SXNzdWUyNzI4MzkxMQ== | 6,317 | TestClipboard test failure on osx-64 | {
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} | 9 | 2014-02-10T17:23:48Z | 2014-02-17T21:38:38Z | 2014-02-16T18:23:26Z | NONE | null | I see the following:
```
======================================================================
ERROR: test_round_trip_frame (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/tests/test_clipboard.py", line 67, in test_round_trip_frame
self.check_round_trip_frame(dt)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/tests/test_clipboard.py", line 54, in check_round_trip_frame
result = read_clipboard()
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/clipboard.py", line 32, in read_clipboard
return read_table(StringIO(text), **kwargs)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 420, in parser_f
return _read(filepath_or_buffer, kwds)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 218, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 502, in __init__
self._make_engine(self.engine)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 616, in _make_engine
self._engine = klass(self.f, **self.options)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 1303, in __init__
self.columns, self.num_original_columns = self._infer_columns()
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 1516, in _infer_columns
line = self._buffered_line()
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 1643, in _buffered_line
return self._next_line()
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 1659, in _next_line
line = next(self.data)
File "/Users/vagrant/src/master-env/lib/python2.7/site-packages/pandas/io/parsers.py", line 1420, in _read
line = next(f)
StopIteration
```
With pandas 0.13.1 on os x (python 2.7.3, numpy 1.8.0, cython 0.19.2).
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} | 30 | 2014-02-10T21:06:32Z | 2015-01-18T21:40:33Z | 2015-01-18T21:40:33Z | CONTRIBUTOR | null | fixes #6307
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https://api.github.com/repos/pandas-dev/pandas/issues/6319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6319/events | https://github.com/pandas-dev/pandas/issues/6319 | 27,303,661 | MDU6SXNzdWUyNzMwMzY2MQ== | 6,319 | KeyError for crosstab on Series with same name. | {
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} | 17 | 2014-02-10T21:52:04Z | 2018-07-06T15:07:35Z | 2018-01-01T00:44:04Z | CONTRIBUTOR | null | Doing a crosstab on two Series with the same name throws an error. This is due to a dictionary (indexed by the series name) in the crosstab function being used to store the data. Not sure if this is a feature or a bug, but a default similar to the behavior when Series without name are compared would be desirable to me.
``` python
In [56]:
s1 = pd.Series([1,1,2,2,3,3], name='s')
s2 = pd.Series([1,1,1,2,2,2], name='s')
pd.crosstab(s1, s2)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-56-9d16b2abac9f> in <module>()
2 s2 = pd.Series([1,1,1,2,2,2], name='s')
3
----> 4 pd.crosstab(s1, s2)
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/tools/pivot.pyc in crosstab(rows, cols, values, rownames, colnames, aggfunc, margins, dropna)
368 df['__dummy__'] = 0
369 table = df.pivot_table('__dummy__', rows=rownames, cols=colnames,
--> 370 aggfunc=len, margins=margins, dropna=dropna)
371 return table.fillna(0).astype(np.int64)
372 else:
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/tools/pivot.pyc in pivot_table(data, values, rows, cols, aggfunc, fill_value, margins, dropna)
108 to_unstack = [agged.index.names[i]
109 for i in range(len(rows), len(keys))]
--> 110 table = agged.unstack(to_unstack)
111
112 if not dropna:
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in unstack(self, level)
3339 """
3340 from pandas.core.reshape import unstack
-> 3341 return unstack(self, level)
3342
3343 #----------------------------------------------------------------------
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/core/reshape.pyc in unstack(obj, level)
416 def unstack(obj, level):
417 if isinstance(level, (tuple, list)):
--> 418 return _unstack_multiple(obj, level)
419
420 if isinstance(obj, DataFrame):
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/core/reshape.pyc in _unstack_multiple(data, clocs)
275 index = data.index
276
--> 277 clocs = [index._get_level_number(i) for i in clocs]
278
279 rlocs = [i for i in range(index.nlevels) if i not in clocs]
/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/core/index.pyc in _get_level_number(self, level)
2197 except ValueError:
2198 if not isinstance(level, int):
-> 2199 raise KeyError('Level %s not found' % str(level))
2200 elif level < 0:
2201 level += self.nlevels
KeyError: 'Level s not found'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6320/events | https://github.com/pandas-dev/pandas/issues/6320 | 27,318,789 | MDU6SXNzdWUyNzMxODc4OQ== | 6,320 | s390x (big endian, 64bit) + python 3.3.4 make pandas very unhappy | {
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} | [] | closed | false | null | [] | null | 6 | 2014-02-11T01:25:42Z | 2014-05-16T17:46:16Z | 2014-05-16T17:46:16Z | CONTRIBUTOR | null | http://www.onerussian.com/tmp/build-pandas-0.13.1-s390x.log
```
FAILED (SKIP=350, errors=87, failures=47)
```
Just FYI and may be anything there hints you on some possible issues
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https://api.github.com/repos/pandas-dev/pandas/issues/6321 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6321/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6321/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6321/events | https://github.com/pandas-dev/pandas/pull/6321 | 27,320,256 | MDExOlB1bGxSZXF1ZXN0MTI0MDAzNzM= | 6,321 | BUG: #6175 removed assert_ statements | {
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} | 7 | 2014-02-11T14:54:49Z | 2017-02-21T13:39:14Z | 2017-02-21T13:38:57Z | NONE | null | This is related to the following commit.
https://github.com/jreback/pandas/commit/625ee94a31e4011b677785f12dfd204591cadfcf
When one level in the MultiIndex is all NaN, line 2819 in DataFrame.reset_index() and subfunction _maybe_cast, will have a problem, because values is empty.
2819: values = values.take(labels)
IndexError: cannot do a non-empty take from an empty axes
A possible fix is to add the following lines before 2819:
if mask.all():
values = labels \* np.nan
return values
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} | [] | closed | false | null | [] | null | 3 | 2014-02-11T16:14:15Z | 2014-02-13T20:31:51Z | 2014-02-11T20:11:56Z | CONTRIBUTOR | null | It's often useful to have some sort of info associated with a table. It would be handy if pandas Panel/DataFrame/Series objects had an empty `metadata` field that could be set to arbitrary values by a user, and for this metadata to be preserved across operations - especially ones which return copies of pandas objects, since if you just dynamically add metadata to an object it will not be preserved if you do an operation that returns copies or slices of a dataframe.
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```
c:\Program Files\Microsoft Visual Studio 9.0\VC\BIN\cl.exe /c /nologo /Ox /MD /W3 /GS- /DNDEBUG -Ipandas/src/klib -Ipandas/src -IC:\Python27
\lib\site-packages\numpy\core\include -IC:\Python27\include -IC:\Python27\PC /Tcpandas/src/datetime/np_datetime.c /Fobuild\temp.win32-2.7\Release\pandas/src/datetime/np_datetime.obj
np_datetime.c
c:\python27\lib\site-packages\numpy\core\include\numpy\npy_1_7_deprecated_api.h(12) : Warning Msg: Using deprecated NumPy API, disable it by
#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
pandas/src/datetime/np_datetime.c(229) : error C2143: syntax error : missing ')' before '('
pandas/src/datetime/np_datetime.c(229) : error C2143: syntax error : missing ')' before '('
pandas/src/datetime/np_datetime.c(229) : error C2091: function returns function
pandas/src/datetime/np_datetime.c(229) : error C2143: syntax error : missing ')' before 'string'
pandas/src/datetime/np_datetime.c(229) : error C2091: function returns function
pandas/src/datetime/np_datetime.c(229) : error C2143: syntax error : missing '{' before 'string'
pandas/src/datetime/np_datetime.c(229) : error C2059: syntax error : '<Unknown>'
pandas/src/datetime/np_datetime.c(229) : error C2059: syntax error : ')'
pandas/src/datetime/np_datetime.c(229) : error C2059: syntax error : ')'
pandas/src/datetime/np_datetime.c(229) : error C2059: syntax error : ')'
pandas/src/datetime/np_datetime.c(267) : warning C4244: '=' : conversion from 'npy_int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(852) : warning C4244: '=' : conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(866) : warning C4244: '=' : conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(881) : warning C4244: '=' : conversion from 'npy_datetime' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(897) : warning C4244: '=' : conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(914) : warning C4244: '=' : conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(931) : warning C4244: '=' : conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(949) : warning C4244: '=' : conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(959) : warning C4244: '=' : conversion from '__int64' to 'npy_int32', possible loss of data
pandas/src/datetime/np_datetime.c(976) : warning C4244: 'function' : conversion from 'npy_datetime' to 'int', possible loss of data
pandas/src/datetime/np_datetime.c(1002) : warning C4013: 'add_seconds_to_datetimestruct' undefined; assuming extern returning int
error: command '"c:\Program Files\Microsoft Visual Studio 9.0\VC\BIN\cl.exe"' failed with exit status 2
```
with mingw, it builds but cannot import:
```
DLL load failed: The specified module could not be found.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas-0.13.1.dev-py2.7-win32.egg\pandas\__init__.py", line 6, in <module>
from . import hashtable, tslib, lib
ImportError: DLL load failed: The specified module could not be found.
```
dependency versions are:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: XP
machine: x86
processor: x86 Family 6 Model 15 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.13.1.dev
Cython: 0.20
numpy: 1.8.0
scipy: 0.13.2
statsmodels: None
IPython: 1.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3
matplotlib: 1.3.1
openpyxl: 1.6.1
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
```
mingw build output files are here:
https://github.com/ruidc/files/blob/master/build_output.txt
https://github.com/ruidc/files/blob/master/install_output.txt
run using:
```
python setup.py build --compiler=mingw32 > build_output.txt 2>&1
python setup.py install > install_output.txt 2>&1
```
and directory listing here:
https://github.com/ruidc/files/blob/master/dir.txt
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https://api.github.com/repos/pandas-dev/pandas/issues/6325 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6325/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6325/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6325/events | https://github.com/pandas-dev/pandas/issues/6325 | 27,391,153 | MDU6SXNzdWUyNzM5MTE1Mw== | 6,325 | BUG: pandas.io.sql.frame_query truncates column aliases (col names too?) | {
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} | [] | closed | false | null | [] | null | 14 | 2014-02-11T22:05:10Z | 2017-08-17T22:23:59Z | 2014-02-13T14:41:04Z | NONE | null | To reproduce: Connect to DB (SQL Server 2008 R2) using pyodbc (unixODBC drivers via FreeTDS, Mac OSX). SELECT blah as some_alias, call in to the DB (w/ write frame). If "some_alias" is longer than 30 characters (it looks like), the column names are truncated.
Code: Depending on the DB configuration...
``` python
>>> import pandas as pd
>>> import pyodbc as db
>>> query = """SELECT PartNumber as this_is_some_ridiculously_long_alias_that_will_be_truncated FROM clean.Parts"""
>>> conn = db.connect('DSN=PartsAutoUpdate;UID=suser;PWD=This_Is_A_Really_Secure_Passw0rd!!')
>>> df = pd.io.sql.frame_query(query, conn)
>>> df.columns
Index([u'this_is_some_ridiculously_long'], dtype='object')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6326/events | https://github.com/pandas-dev/pandas/issues/6326 | 27,425,359 | MDU6SXNzdWUyNzQyNTM1OQ== | 6,326 | DataFrame/Series.tz_convert with copy=False modifies original data | {
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} | 7 | 2014-02-12T11:02:53Z | 2019-01-08T00:27:58Z | 2019-01-08T00:27:58Z | NONE | null | Hi. Not sure if it is a bug, or something which needs to be clarified.
Consider the code
```
s = pd.Series(np.arange(0,5), index=pd.date_range('20131027', periods=5, freq='1H', tz='Europe/Berlin'))
s.tz_convert("UTC", copy=False)
```
`s` index is still the same as before. If i do the same for frames.
```
d = pd.DataFrame(s)
d.tz_convert("UTC", copy=False)
```
This time index of `d` has changed. From the code it is not clear if `DataFrame` is doing the right thing either.
So is it a bug or is it just inconsistent, or is it an intention?
Update:
```
In [1]: s = pd.Series(np.arange(0,5), index=pd.date_range('20131027', periods=5, freq='1H', tz='Europe/Berlin'))
s.tz_convert("UTC", copy=False)
Out[1]:
2013-10-26 22:00:00+00:00 0
2013-10-26 23:00:00+00:00 1
2013-10-27 00:00:00+00:00 2
2013-10-27 01:00:00+00:00 3
2013-10-27 02:00:00+00:00 4
Freq: H, dtype: int32
In [2]: s
Out[2]:
2013-10-27 00:00:00+02:00 0
2013-10-27 01:00:00+02:00 1
2013-10-27 02:00:00+02:00 2
2013-10-27 02:00:00+01:00 3
2013-10-27 03:00:00+01:00 4
Freq: H, dtype: int32
In [3]: d = pd.DataFrame(s)
d.tz_convert("UTC", copy=False)
Out[3]:
2013-10-26 22:00:00+00:00 0
2013-10-26 23:00:00+00:00 1
2013-10-27 00:00:00+00:00 2
2013-10-27 01:00:00+00:00 3
2013-10-27 02:00:00+00:00 4
In [214]: d
Out[214]:
2013-10-26 22:00:00+00:00 0
2013-10-26 23:00:00+00:00 1
2013-10-27 00:00:00+00:00 2
2013-10-27 01:00:00+00:00 3
2013-10-27 02:00:00+00:00 4
```
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} | 7 | 2014-02-12T12:41:01Z | 2014-03-05T01:12:44Z | 2014-03-05T01:12:44Z | CONTRIBUTOR | null | The relevant code is
``` python
self.DTYPE_MAP = \
dict(
lzip(range(1, 245), ['a' + str(i) for i in range(1, 245)]) +
[
(251, np.int16),
(252, np.int32),
(253, np.int64),
(254, np.float32),
(255, np.float64)
]
)
```
and
``` python
self.TYPE_MAP = lrange(251) + list('bhlfd')
```
which maps `h` to int32 and `l` to int64.
http://docs.python.org/2/library/struct.html#format-characters
shows that `h` is 2 bytes and `l` is 4, and so trying to run
``` python
struct.pack('<l',2**40)
```
produces an error.
The obvious fix is to use
``` python
self.TYPE_MAP = lrange(251) + list('blqfd')
```
but this will probably produce errors on 32-bit platforms.
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https://api.github.com/repos/pandas-dev/pandas/issues/6328 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6328/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6328/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6328/events | https://github.com/pandas-dev/pandas/issues/6328 | 27,430,670 | MDU6SXNzdWUyNzQzMDY3MA== | 6,328 | ENH: fastpath indexer API proposal (draft) | {
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] | closed | false | null | [] | null | 7 | 2014-02-12T12:46:34Z | 2015-10-20T22:29:13Z | 2015-10-20T22:29:13Z | CONTRIBUTOR | null | The discussion in #6134 has inspired an idea that I'm writing down for
discussion. The idea is pretty obvious so it should've been considered before,
but I still think pandas as it is right now can benefit from it.
My main complaint about pandas when using it in non-interactive way is that
lookups are significantly slower than with `ndarray` containers. I do realize
that this happens because of many ways the indexing may be done, but at some
point I've really started thinking about ditching pandas in some
performance-critical paths of my project and replacing them with the dreadful
`dict/ndarray` combo. Not only doing `arr = df.values[df.idx.get_loc[key]]`
gets old pretty fast but it's also slower when the frame contains different
dtypes and then you _need to go deeper_ to fix that.
Now I thought what if this slowdown can be reduced by creating _fastpath
indexers_ that look like the `IndexSlice` from #6134 and would convey a
message to `pandas` indexing facilities, like "trust me, I've done all the
preprocessing, just look it up already". I'm talking about something like that
(the names are arbitrary and chosen for illustrative purposes only):
``` python
masked_rows = df.fastloc[pd.bool_slice[bool_array]]
# or
masked_rows = df.fastloc[pd.bool_series_slice[bool_series]]
# or
rows_3_and_10 = df.fastloc[pd.pos_slice[3, 10]]
# or
rows_3_through_10 = df.fastloc[pd.range_slice[3:10]]
# or
rows_for_two_days = df.fastloc[pd.tpos_slice['2014-01-01', '2014-01-08']]
```
Given the actual slice objects will have a common base class, the
implementation could be as easy as:
``` python
class FastLocAttribute(object):
def __init__(self, container):
self._container = container
def __getitem__(self, smth):
if not isinstance(smth, FastpathIndexer):
raise TypeError("Indexing object is not a FastpathIndexer")
# open to custom FastpathIndexer implementations
return smth.getitem(self._container)
# or a better encapsulated, but not so open
return self._container._index_method[type(smth)](smth)
```
Cons:
- a change in public API
- one more lookup type
- inconvenient to use interactively
Pros:
- adheres to the Zen of Python (explicit is better than implicit)
- when used in programs, most of the time you know what will the indexing
object look like and how do you want to use its contents (e.g. no guessing if
np.array([0,1,0,1]) is a boolean mask or a series of "takeable" indices)
- lengthier than existing lookup schemes but still shorter than jumping through
the hoops of `NDFrame` and `Index` internals to avoid premature
pessimization (also, more reliable w.r.t. new releases)
- fastpath indexing API could be used in `pandas` internally for the speed (and
clarity, as in "interesting, what does this function pass to df.loc[...],
let's find this out")
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} | 4 | 2014-02-12T12:51:45Z | 2014-02-12T21:41:08Z | 2014-02-12T21:41:08Z | CONTRIBUTOR | null | The following snippit runs fine in pandas 0.12 on a machine with 8GB of RAM but throws a `MemoryError` on a machine with 16GB of RAM when using pandas 0.13
``` python
date_index = pd.date_range('01-Jan-2012', '23-Jan-2013', freq='T')
daily_dates = date_index.to_period('D').to_timestamp('S','S')
fracofday = date_index.view(np.ndarray) - daily_dates.view(np.ndarray)
fracofday = fracofday.astype('timedelta64[ns]').astype(np.float64)/864e11
fracofday = pd.TimeSeries(fracofday, daily_dates)
index = pd.date_range(date_index.min().to_period('A').to_timestamp('D','S'),
date_index.max().to_period('A').to_timestamp('D','E'),
freq='D')
temp = pd.TimeSeries(1.0, index)
fracofday *= temp[fracofday.index]
```
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} | 0 | 2014-02-12T14:35:03Z | 2014-07-02T15:55:41Z | 2014-02-12T21:41:08Z | CONTRIBUTOR | null | closes #6329
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https://api.github.com/repos/pandas-dev/pandas/issues/6331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6331/events | https://github.com/pandas-dev/pandas/issues/6331 | 27,447,910 | MDU6SXNzdWUyNzQ0NzkxMA== | 6,331 | Add option for to_csv() to split DataFrame in several files | {
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For example, df.to_csv(str_file,n_parts=3)
And then we get:
myfile.part1.csv
myfile.part2.csv
myfile.part3.csv
And then the csv readinf should ask which parts one wants to load.
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https://api.github.com/repos/pandas-dev/pandas/issues/6332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6332/events | https://github.com/pandas-dev/pandas/issues/6332 | 27,449,693 | MDU6SXNzdWUyNzQ0OTY5Mw== | 6,332 | BUG: replace should handle Bool blocks correctly | {
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} | 20 | 2014-02-12T17:04:26Z | 2016-10-12T23:04:38Z | 2014-02-13T12:36:24Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/21733802/pandas-series-replace-on-boolean-series
by definition they don't have missing values
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} | 3 | 2014-02-12T17:21:47Z | 2014-03-01T15:41:37Z | 2014-03-01T15:41:37Z | CONTRIBUTOR | null | `scipy.stats.rankdata` has five ranking methods: `average`, `min`, and `max`, equivalent to the same options in `Series.rank`; `ordinal`, corresponding to our `first`; but it also has `dense`:
```
'dense':
Like 'min', but the rank of the next highest element is assigned
the rank immediately after those assigned to the tied elements.
```
For example,
```
>>> scipy.stats.rankdata([3,1,2,1,3], "min")
array([ 4., 1., 3., 1., 4.])
>>> scipy.stats.rankdata([3,1,2,1,3], "dense")
array([ 3., 1., 2., 1., 3.])
```
I've wanted this in the past, and it just showed up on [SO](http://stackoverflow.com/questions/21724122/how-to-get-ranked-values-of-a-pandas-column-without-gaps).
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} | 6 | 2014-02-12T18:27:25Z | 2019-07-23T05:20:05Z | 2019-07-23T05:20:04Z | CONTRIBUTOR | null | Using:
```
from cStringIO import StringIO
df1 = pd.read_csv(StringIO("""s,date,v\nQ,2013-11-21 00:00:00,1\nQ,2013-11-22 00:00:00,2\nQ,2013-11-22 00:00:00,3"""),
index_col=[0,1])
df2 = pd.read_csv(StringIO("""s,date,v\nQ,2013-11-21 00:00:00,1\nQ,2013-11-22 00:00:00,2\nQ,2013-11-23 00:00:00,3"""),
index_col=[0,1])
```
Observer how slicing performs very different and non intuitively in these cases:
```
In [4]: df1.ix[["Q"]]
Out[4]:
v
Q NaN
[1 rows x 1 columns]
In [5]: df2.ix[["Q"]]
Out[5]:
v
s date
Q 2013-11-21 00:00:00 1
2013-11-22 00:00:00 2
2013-11-23 00:00:00 3
[3 rows x 1 columns]
```
I feel that a warning like this: http://pandas.pydata.org/pandas-docs/stable/indexing.html?highlight=sort#the-need-for-sortedness-with-multiindex should happen when there are duplicates in the index.
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Added test for this case.
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} | 2 | 2014-02-13T10:02:52Z | 2014-06-14T13:15:12Z | 2014-06-14T13:15:12Z | CONTRIBUTOR | null | should be simple to lower the bar to an older version
change recommended min version on install page as well
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} | 11 | 2014-02-13T13:59:25Z | 2014-06-12T11:41:49Z | 2014-02-14T12:01:37Z | CONTRIBUTOR | null | While working on #6328 I've stumbled upon this piece that deserves optimization.
Current implementation `Index.is_all_dates` invokes `lib.infer_dtype` which does a full-scan dtype inference which is not not always necessary. If I read cython sources correctly, new code is equivalent to the old one and the performance increase comes from short-circuiting the check on the first non-datetime element.
This change cuts roughly 50% of runtime of the following snippet:
``` python
import numpy as np
import pandas as pd
NUM_ELEMENTS = 1e7
s = pd.Series(np.arange(NUM_ELEMENTS),
index=['a%s' % i for i in np.arange(NUM_ELEMENTS)])
print("pd.version", pd.__version__)
%timeit s[:int(NUM_ELEMENTS/2)]
```
On recent master:
```
$ ipython test_index_is_all_dates.ipy
pd.version 0.13.1-108-g87b4308
10 loops, best of 3: 55.5 ms per loop
```
On this branch:
```
$ ipython test_index_is_all_dates.ipy
pd.version 0.13.1-109-g3b43e2b
10 loops, best of 3: 28 ms per loop
```
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} | 3 | 2014-02-13T15:15:44Z | 2016-10-12T23:04:38Z | 2014-02-16T21:08:52Z | NONE | null | ```
In [47]: import pandas as pd
In [48]: df = pd.DataFrame({'col': [1,2,3,4]})
In [49]: df.replace({'col': {1: 'a', 4: 'b'}})
Out[49]:
col
0 a
1 2
2 3
3 b
In [50]: df.replace({'col': {-1: '-', 1: 'a', 4: 'b'}})
Out[50]:
col
0 a
1 2
2 3
3 a
^^^^ Value 4 must be mapped to "b" instead of "a"
In [51]: pd.__version__
Out[51]: '0.12.0'
```
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} | 4 | 2014-02-13T18:15:00Z | 2014-07-11T12:27:44Z | 2014-02-14T06:36:23Z | CONTRIBUTOR | null | This adds a `method='dense'` option to `rank` which does the same thing as the one in `scipy.stats.rankdata`: the first ranked group gets 1, the second 2, etc. Basically it ports the approach used in `scipy.stats._rankdata_fused` to the pandas rankdata implementation.
It also adds a few tests that the various methods do what they're supposed to, which I couldn't find a test for before.
closes #6333
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} | 4 | 2014-02-13T21:48:33Z | 2014-04-06T18:16:27Z | 2014-04-06T18:16:27Z | CONTRIBUTOR | null | I believe that this worked on v0.13:
```
df = pd.DataFrame({
'Date':[pd.NaT, pd.Timestamp("2014-1-1")],
'Date2':[ pd.Timestamp("2013-1-1"),pd.NaT]
})
In [8]: df.fillna(value={'Date':df['Date2']})
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-d5273c4f5a7f> in <module>()
----> 1 df.fillna(value={'Date':df['Date2']})
/usr/lib64/python2.7/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
2172 continue
2173 obj = result[k]
-> 2174 obj.fillna(v, inplace=True)
2175 return result
2176 else:
/usr/lib64/python2.7/site-packages/pandas/core/generic.py in fillna(self, value, method, axis, inplace, limit, downcast)
2159
2160 new_data = self._data.fillna(value, inplace=inplace,
-> 2161 downcast=downcast)
2162
2163 elif isinstance(value, (dict, com.ABCSeries)):
/usr/lib64/python2.7/site-packages/pandas/core/internals.py in fillna(self, *args, **kwargs)
2408
2409 def fillna(self, *args, **kwargs):
-> 2410 return self.apply('fillna', *args, **kwargs)
2411
2412 def downcast(self, *args, **kwargs):
/usr/lib64/python2.7/site-packages/pandas/core/internals.py in apply(self, f, *args, **kwargs)
2373
2374 else:
-> 2375 applied = getattr(blk, f)(*args, **kwargs)
2376
2377 if isinstance(applied, list):
/usr/lib64/python2.7/site-packages/pandas/core/internals.py in fillna(self, value, inplace, downcast)
1633 values = self.values if inplace else self.values.copy()
1634 mask = com.isnull(self.values)
-> 1635 value = self._try_fill(value)
1636 np.putmask(values, mask, value)
1637 return [self if inplace else
/usr/lib64/python2.7/site-packages/pandas/core/internals.py in _try_fill(self, value)
1625 def _try_fill(self, value):
1626 """ if we are a NaT, return the actual fill value """
-> 1627 if isinstance(value, type(tslib.NaT)) or isnull(value):
1628 value = tslib.iNaT
1629 return value
/usr/lib64/python2.7/site-packages/pandas/core/generic.py in __nonzero__(self)
674 raise ValueError("The truth value of a {0} is ambiguous. "
675 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 676 .format(self.__class__.__name__))
677
678 __bool__ = __nonzero__
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
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} | 5 | 2014-02-13T22:58:36Z | 2016-10-12T23:04:38Z | 2014-06-08T03:02:20Z | CONTRIBUTOR | null | Using:
```
df =pd.read_csv(StringIO(""",Date,app,File
2013-04-23,2013-04-23 00:00:00,,log080001.log
2013-05-06,2013-05-06 00:00:00,,log.log
2013-05-07,2013-05-07 00:00:00,OE,xlsx"""), parse_dates=[0])
```
This does not work:
```
In [8]: df.groupby("Date")[["File"]].max()
Out[8]:
File
Date
2013-04-23 00:00:00 NaN
2013-05-06 00:00:00 NaN
2013-05-07 00:00:00 xlsx
[3 rows x 1 columns]
```
but this does:
```
In [9]: df.groupby("Date")["File"].max()
Out[9]:
Date
2013-04-23 00:00:00 log080001.log
2013-05-06 00:00:00 log.log
2013-05-07 00:00:00 xlsx
Name: File, dtype: object
```
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} | 0 | 2014-02-13T23:00:52Z | 2014-07-16T08:53:29Z | 2014-02-14T00:24:28Z | CONTRIBUTOR | null | closes #6345
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} | 6 | 2014-02-14T01:38:00Z | 2014-05-18T14:13:45Z | 2014-02-23T22:09:45Z | CONTRIBUTOR | null | series.str.extract does not work for time-series because core.strings.str_extract does not preserve the index. I am submitting a unittest and patch that demonstrates and hopefully fixes the issue.
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} | 15 | 2014-02-14T01:45:30Z | 2014-06-27T23:38:23Z | 2014-02-23T22:09:34Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/6348
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} | 0 | 2014-02-14T03:02:34Z | 2014-07-15T21:00:55Z | 2014-02-14T03:24:56Z | CONTRIBUTOR | null | BUG: Bug in TimeGrouper/resample when presented with a non-monotonic DatetimeIndex would return invalid results,
BUG: Bug in index name propogation in TimeGrouper/resample
closes #4161
API: TimeGrouper has a more compatible API to the rest of the groupers (e.g. `groups` was missing)
closes #3881
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} | 0 | 2014-02-14T03:20:42Z | 2016-10-12T23:04:38Z | 2014-02-14T12:24:29Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/21769406/pandas-and-hdf5-querying-a-table-string-containing-character
```
>>> from pandas import HDFStore, DataFrame
>>> df = DataFrame({'a': ['a', 'a', 'c', 'b', 'test & test', 'c' , 'b', 'e'],
'b': [1, 2, 3, 4, 5, 6, 7, 8]})
>>> store = HDFStore('test.h5')
>>> store.append('test', df, format='table', data_columns=True)
>>> df[df.a == 'test & test']
a b
4 test & test 5
>>> store.select('test', 'a="test & test"')
```
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} | 13 | 2014-02-14T15:48:13Z | 2014-02-17T23:09:10Z | 2014-02-17T23:09:10Z | NONE | null | I'm running into a problem with using `dropna` with a list of columns specified by `subset`. I wish to remove rows with missing values:
The dataset looks like this:
However, when I try to call `dropna` using these columns, it complains about a duplicate axis:
```
variables.dropna(subset=['cigarette_smokers', 'birth_wt_child',
'oxygen', 'length_of_stay', 'hospitalized_vitamin_d', 'breastfed'])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-168-96be0b0b00e8> in <module>()
1 variables.dropna(subset=['cigarette_smokers', 'birth_wt_child',
----> 2 'oxygen', 'length_of_stay', 'hospitalized_vitamin_d', 'breastfed'])
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/frame.pyc in dropna(self, axis, how, thresh, subset, inplace)
2407 if subset is not None:
2408 agg_axis_name = self._get_axis_name(agg_axis)
-> 2409 agg_obj = self.reindex(**{agg_axis_name: subset})
2410
2411 count = agg_obj.count(axis=agg_axis)
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/frame.pyc in reindex(self, index, columns, **kwargs)
2160 def reindex(self, index=None, columns=None, **kwargs):
2161 return super(DataFrame, self).reindex(index=index, columns=columns,
-> 2162 **kwargs)
2163
2164 @Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs)
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/generic.pyc in reindex(self, *args, **kwargs)
1561 return self._reindex_axes(axes, level, limit,
1562 method, fill_value, copy,
-> 1563 takeable=takeable).__finalize__(self)
1564
1565 def _reindex_axes(self, axes, level, limit, method, fill_value, copy,
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/frame.pyc in _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable)
2110 if columns is not None:
2111 frame = frame._reindex_columns(columns, copy, level, fill_value,
-> 2112 limit, takeable=takeable)
2113
2114 index = axes['index']
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/frame.pyc in _reindex_columns(self, new_columns, copy, level, fill_value, limit, takeable)
2137 return self._reindex_with_indexers({1: [new_columns, indexer]},
2138 copy=copy, fill_value=fill_value,
-> 2139 allow_dups=takeable)
2140
2141 def _reindex_multi(self, axes, copy, fill_value):
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/generic.pyc in _reindex_with_indexers(self, reindexers, method, fill_value, limit, copy, allow_dups)
1687 new_data = new_data.reindex_indexer(index, indexer, axis=baxis,
1688 fill_value=fill_value,
-> 1689 allow_dups=allow_dups)
1690
1691 elif (baxis == 0 and index is not None and
/Library/Python/2.7/site-packages/pandas-0.13.0_395_gef55e60-py2.7-macosx-10.9-intel.egg/pandas/core/internals.pyc in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups)
3227 # trying to reindex on an axis with duplicates
3228 if not allow_dups and not self.axes[axis].is_unique:
-> 3229 raise ValueError("cannot reindex from a duplicate axis")
3230
3231 if not self.is_consolidated():
ValueError: cannot reindex from a duplicate axis
```
Running a current build from master in Python 2.7.5 on OS X 10.9.1.
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] | closed | false | null | [] | {
"closed_at": "2014-05-30T12:17:57Z",
"closed_issues": 755,
"created_at": "2014-01-03T20:19:04Z",
"creator": null,
"description": "2014-02-3: previous version released",
"due_on": "2014-05-31T07:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/24",
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} | 0 | 2014-02-14T16:42:40Z | 2014-07-07T14:23:48Z | 2014-02-15T19:32:32Z | CONTRIBUTOR | null | WIP on #3662
```
In [4]: household.join(portfolio, how='inner')
Out[4]:
male wealth name share
household_id asset_id
1 nl0000301109 0 196087.3 ABN Amro 1.00
2 nl0000289783 1 316478.7 Robeco 0.40
gb00b03mlx29 1 316478.7 Royal Dutch Shell 0.60
3 gb00b03mlx29 0 294750.0 Royal Dutch Shell 0.15
lu0197800237 0 294750.0 AAB Eastern Europe Equity Fund 0.60
nl0000289965 0 294750.0 Postbank BioTech Fonds 0.25
[6 rows x 4 columns]
In [5]: household.join(portfolio, how='outer')
Out[5]:
male wealth name share
household_id asset_id
1 nl0000301109 0 196087.3 ABN Amro 1.00
2 nl0000289783 1 316478.7 Robeco 0.40
gb00b03mlx29 1 316478.7 Royal Dutch Shell 0.60
3 gb00b03mlx29 0 294750.0 Royal Dutch Shell 0.15
lu0197800237 0 294750.0 AAB Eastern Europe Equity Fund 0.60
nl0000289965 0 294750.0 Postbank BioTech Fonds 0.25
4 NaN NaN NaN NaN 1.00
[7 rows x 4 columns]
In [8]: household.index.name='foo'
In [9]: household.join(portfolio, how='outer')
ValueError: cannot join with no level specified and no overlapping names
```
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} | [] | closed | false | null | [] | null | 9 | 2014-02-14T18:07:43Z | 2014-02-16T22:26:41Z | 2014-02-16T21:20:08Z | CONTRIBUTOR | null | Currently the CSV loader has support for something like this:
```
pd.read_csv(StringIO("Date,Time\n2013-1-1,17:30"),
parse_dates={'Datetime':["Date","Time"]})
```
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} | [] | closed | false | null | [] | null | 3 | 2014-02-14T18:53:57Z | 2014-02-16T02:46:58Z | 2014-02-16T02:46:58Z | NONE | null | In pandas 0.12 and previous, I could construct a new DataFrame from a Series and use the "columns=" specifier to set the column name. This is particularly efficient to use when creating new DataFrames from groupby aggregations without requiring a second step of renaming the columns in the new DataFrame. It seems that in pandas 0.13.1 this has changed behavior to instead only select those columns from the source data/Series.
In 0.12
``` python
In [9]: S = Series([1,2,3],name='blah')
In [10]: DataFrame(S, columns=['asdf'])
Out[10]:
asdf
0 1
1 2
2 3
```
But in 0.13.1 this behavior has changed:
``` python
In [4]: S = Series([1,2,3],name='blah')
In [5]: DataFrame(S, columns=['asdf'])
Out[5]:
Empty DataFrame
Columns: [asdf]
Index: []
[0 rows x 1 columns]
```
the applicable versions for 0.13.1 are:
``` python
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.13.0
statsmodels: 0.5.0
IPython: 1.2.0
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.6.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: 0.8.3
lxml: 3.2.3
bs4: 4.3.1
html5lib: None
bq: None
apiclient: 1.2
```
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} | 25 | 2014-02-15T18:45:21Z | 2018-11-15T13:08:29Z | 2018-11-15T13:08:29Z | CONTRIBUTOR | null | #3662 is about merging a single-level index with a mi
This is is about a multi-multi merge
```
# example described there:
In [11]: df = pd.DataFrame(np.random.randn(10,5))
...: df1 = df[range(0, 3)].set_index([0, 2])
...: df2 = df[range(2, 5)].set_index([2, 4])
...: res = df1.join(df2, how='inner') # empty. Do I need to specify something here?
...: exp = pd.DataFrame({1: df[1], 3: df[3]})
...: exp.index = df[2]
```
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} | 2 | 2014-02-15T19:43:51Z | 2018-07-06T22:02:34Z | 2018-07-06T22:02:28Z | CONTRIBUTOR | null | related to #6288
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- add note about the result set at right edge by default
- clarified freq keyword a little bit
Closes #5504
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} | 0 | 2014-02-15T22:07:11Z | 2014-07-16T08:53:44Z | 2014-02-15T22:08:44Z | CONTRIBUTOR | null | closes #3662
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} | 0 | 2014-02-16T00:16:58Z | 2014-06-23T12:10:05Z | 2014-02-16T00:41:29Z | CONTRIBUTOR | null | closes #5567
```
In [1]: mi = MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
In [2]: s = Series(np.random.randn(1000000), index=mi)
In [5]: %time x=s.ix[999]
CPU times: user 4 ms, sys: 0 ns, total: 4 ms
Wall time: 3.74 ms
In [6]: mi = MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
In [7]: s = Series(np.random.randn(1000000), index=mi)
In [8]: df = DataFrame(s)
In [9]: %time x=df.ix[999]
CPU times: user 4 ms, sys: 0 ns, total: 4 ms
Wall time: 4.7 ms
```
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} | 29 | 2014-02-16T01:18:50Z | 2014-06-13T08:36:17Z | 2014-02-20T14:24:06Z | MEMBER | null | closes #5987
closes #5087
Relevant user-facing changes:
```
In [9]: df = DataFrame(randn(5, 2), columns=list('ab'))
In [10]: a, b = 1, 2
In [11]: # column names take precedence
In [12]: df.query('a < b')
Out[12]:
a b
0 -0.805549 -0.090572
1 -1.782325 -1.594079
2 -0.984364 0.934457
3 -1.963798 1.122112
[4 rows x 2 columns]
In [13]: # we must use @ whenever we want a local variable
In [14]: df.query('@a < b')
Out[14]:
a b
3 -1.963798 1.122112
[1 rows x 2 columns]
In [15]: # we cannot use @ in eval calls
In [16]: pd.eval('@a + b')
File "<string>", line unknown
SyntaxError: The '@' prefix is not allowed in top-level eval calls, please refer to your variables by name without the '@' prefix
In [17]: pd.eval('@a + b', parser='python')
File "<string>", line unknown
SyntaxError: The '@' prefix is only supported by the pandas parser
```
- [x] update query/eval docstrings/indexing/perfenhancing
- [x] make sure docs build
- [x] release notes
- [x] pytables
- [x] make the `repr` of `Scope` objects work or revert to previous version
- [x] more tests for new local variable scoping API
- [x] disallow (and provide a useful error message for) locals in expressions like `pd.eval('@a + b')`
- [x] Raise when your variables have the same name as the [builtin math functions that `numexpr` supports](https://code.google.com/p/numexpr/wiki/UsersGuide#Supported_functions), since you cannot override them in `numexpr.evaluate`, even when explicitly passing them. For example
``` python
import numexpr as ne
sin = randn(10)
d = {'sin': sin}
result = ne.evaluate('sin > 1', local_dict=d, global_dict=d)
result == array(True)
```
For reference, after this PR local variables are given lower precedence than column names. For example
``` python
a, b = 1, 2
df = DataFrame(randn(10, 2), columns=list('ab'))
res = df.query('a > b')
```
will no longer raise an exception about overlapping variable names. If you want the local `a` (as opposed to the column `a`) you must do
``` python
df.query('@a > b')
```
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} | 4 | 2014-02-16T03:05:41Z | 2014-06-19T15:12:11Z | 2014-02-16T16:31:49Z | CONTRIBUTOR | null | Work on #6175. Work in progress. One more commit needed to finish; splitting to keep reasonably sized.
- Changes assert_numpy_array_equals to assert_numpy_array_equal, for consistency with other methods. (API change of recently-added function)
- Changes instances of assert_(np.array_equal(A,B)) to assert_numpy_array_equal(A,B)
- Update release.rst to reflect work on #6175
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} | 2 | 2014-02-16T04:06:43Z | 2019-12-11T21:50:17Z | null | NONE | null | Assume we have a file `test.txt` which uses `§` (double-S) as a separator and has a header row in the first line followed by 1 line of data:
```
Name§Age
Jane Jones§33
```
Using pandas to read the file...
```
import pandas as pd
records = pd.read_table('test.txt', sep='§')
```
... the following is displayed:
```
OverflowError: value too large to convert to char.
```
I'd like to make a request for pandas to be able to handle such delimiters.
Thank you.
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} | 2 | 2014-02-16T14:33:39Z | 2014-05-19T08:15:00Z | 2014-02-28T15:21:09Z | CONTRIBUTOR | null | Once again, caused by #6328 investigation.
There's something very strange with how `Index` objects handle slices:
``` python
In [1]: import pandas.util.testing as tm
In [2]: idx = tm.makeStringIndex(1000000)
In [3]: timeit idx[:-1]
100000 loops, best of 3: 2 µs per loop
In [4]: timeit idx[slice(None,-1)]
100 loops, best of 3: 6.5 ms per loop
```
Obviously, this happens because `Index` doesn't override `__getslice__` provided by `ndarray`, hence `idx[:-1]` is executed via `ndarray.__getslice__` -> `Index.__array_finalize__` and `idx[slice(None, -1)]` goes via `Index.__getitem__` -> `Index.__new__`.
`__getitem__` is made 1000x slower trying to infer slice data type and convert it to a different subclass. The problem is that interactive invocation `idx[:-1]`, which is when that milliseconds-vs-microseconds issue doesn't matter, is likely to miss this feature, because it's dispatched via `__getslice__` . But for programmatic invocation `idx[slice(None, -1)]` which hits this soft spot, I'd argue that this type conversion magic is not at all necessary.
Is there a rationale behind this?
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} | 1 | 2014-02-16T15:11:28Z | 2014-02-16T18:00:50Z | 2014-02-16T18:00:40Z | CONTRIBUTOR | null | I was playing with axis 0 and 1 in DataFrame shift. I get the following error:
```
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10,5))
df.shift(1,axis=1)
```
This gives me an error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\WinPython-32bit-2.7.6.3\python-2.7.6\lib\site-packages\pandas\core\generic.py", line 3175, in shift
new_data = self._data.shift(indexer, periods, axis=block_axis)
File "c:\WinPython-32bit-2.7.6.3\python-2.7.6\lib\site-packages\pandas\core\internals.py", line 2407, in shift
return self.apply('shift', *args, **kwargs)
File "c:\WinPython-32bit-2.7.6.3\python-2.7.6\lib\site-packages\pandas\core\internals.py", line 2375, in apply
applied = getattr(blk, f)(*args, **kwargs)
File "c:\WinPython-32bit-2.7.6.3\python-2.7.6\lib\site-packages\pandas\core\internals.py", line 918, in shift
new_values = self.values.take(indexer, axis=axis)
IndexError: index 5 is out of bounds for size 5
```
I am using pandas 0.13.1
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} | 4 | 2014-02-16T15:33:02Z | 2014-06-12T11:41:49Z | 2014-02-16T18:59:15Z | CONTRIBUTOR | null | This is a continuation of #6341 and is related to #6370. The latter unfortunately concerns public API and may be not easy to fix gracefully.
So, regardless of the decision to be made in other ticket, this patch will optimize series slicing right away and remove some redundant code (no need to override `__getslice__` because `Series` doesn't inherit `ndarray` anymore).
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} | 0 | 2014-02-16T17:16:03Z | 2014-06-16T05:12:11Z | 2014-02-16T18:00:40Z | CONTRIBUTOR | null | closes #6371
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} | 2 | 2014-02-16T17:19:34Z | 2014-07-16T08:54:09Z | 2014-02-16T18:59:37Z | CONTRIBUTOR | null | Work on #6175. This is the second half of the work started in #6368.
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https://api.github.com/repos/pandas-dev/pandas/issues/6376 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6376/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6376/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6376/events | https://github.com/pandas-dev/pandas/pull/6376 | 27,706,207 | MDExOlB1bGxSZXF1ZXN0MTI2MDY1Mjk= | 6,376 | ENH: Add new specialized asserts for tests | {
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https://api.github.com/repos/pandas-dev/pandas/issues/6378 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6378/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6378/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6378/events | https://github.com/pandas-dev/pandas/pull/6378 | 27,735,180 | MDExOlB1bGxSZXF1ZXN0MTI2MTk5NjE= | 6,378 | BUG: interpolate should preserve dtypes | {
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Replaces PR https://github.com/pydata/pandas/pull/6291 due to github / git issues.
@jreback I think opening a new PR will be easier. I'm was having trouble getting that branch and the PR in #6291 to sync up.
I noticed that you got the missing comma in the join_merge vbench added so I've removed that commit.
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https://api.github.com/repos/pandas-dev/pandas/issues/6379 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6379/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6379/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6379/events | https://github.com/pandas-dev/pandas/issues/6379 | 27,736,107 | MDU6SXNzdWUyNzczNjEwNw== | 6,379 | DataFrame Memory Usage after HDF5 retrieve | {
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} | [] | closed | false | null | [] | null | 6 | 2014-02-17T17:18:13Z | 2014-02-17T20:34:08Z | 2014-02-17T20:34:08Z | NONE | null | Hi,
Pandas seems to be using way too much memory after retrieving data from a HDF5 store. In general memory peaks while reading (up to 30 times of the final data step) and then (even after calling the garbage collector) it uses around 10x the memory.
If I then get the numpy arrays out, delete the dataframe and create a new one, the new dataframe is memory efficient and what you would expect.
Please find an example underneath; the memory usage is what I see in "top" being used by ipython.
Create test data first:
```
#Create Testdata
import pandas as pd
import numpy as np
test_df = pd.DataFrame(np.random.randn(5000000, 26), columns=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'])
test_df[['a', 'b']] = test_df[['a', 'b']].astype(int)
with pd.get_store(path='test.h5', mode='w', complib='blosc', complevel=9) as store_hdf:
store_hdf.append('store_0', test_df, data_columns=['a', 'b'])
```
Now leave (i)python and go back into it and run the test:
```
import pandas as pd
import numpy as np
import gc
#44MB
path_name = '/srv/www/li/test.h5'
with pd.get_store(path_name, mode='r') as fact_hdf:
test_df = fact_hdf.select('store_0', columns=['a', 'c'])
# peaks at 3.6GB, ends at 3.1GB (drops to 2.1GB a little later)
print test_df.values.nbytes / (1024*1024)
# 76MB
print test_df.index.nbytes / (1024*1024)
# 38MB
gc.collect()
#1.2GB
a = test_df['a'].values
c = test_df['c'].values
# No impact on memory usage
del test_df
gc.collect()
# drops to 154MB
test_df = pd.DataFrame({'a': a, 'c': c})
# total memory usage goes up to 268MB
print test_df.values.nbytes / (1024*1024)
# 76MB
print test_df.index.nbytes / (1024*1024)
# 38MB
# now delete the numpy arrays
del a
del c
gc.collect()
# 229MB
del test_df
gc.collect()
# 115MB
```
Machine Info:
- Ubuntu 12.04LTS 64bit
- Pandas 0.13.1
- Pytables 3.1.0
Any ideas on what is happening?
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https://api.github.com/repos/pandas-dev/pandas/issues/6380 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6380/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6380/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6380/events | https://github.com/pandas-dev/pandas/pull/6380 | 27,737,098 | MDExOlB1bGxSZXF1ZXN0MTI2MjEwNDk= | 6,380 | API/CLN: add in common operations to Series/Index, refactored as a OpsMixin | {
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} | 9 | 2014-02-17T17:33:09Z | 2014-06-25T21:38:26Z | 2014-02-18T01:35:30Z | CONTRIBUTOR | null | closes #4551
closes #4056
closes #5519
allow a Series to utilize index methods for its index type, e.g. `Series.year` is now defined
for a Series with a `DatetimeIndex` or a `PeriodIndex`; trying this on an Index type will
now raise a `TypeError`. The following properties are affected:
`date,time,year,month,day,hour,minute,second,weekofyear`
`week,dayofweek,dayofyear,quarter,microsecond,nanosecond,qyear`
and methods: `min(),max()`,
```
In [1]: s = Series(np.random.randn(5),index=tm.makeDateIndex(5))
In [2]: s
Out[2]:
2000-01-03 -0.301523
2000-01-04 1.104868
2000-01-05 0.321592
2000-01-06 -0.565114
2000-01-07 0.888334
Freq: B, dtype: float64
In [3]: s.year
Out[3]:
2000-01-03 2000
2000-01-04 2000
2000-01-05 2000
2000-01-06 2000
2000-01-07 2000
Freq: B, dtype: int64
In [4]: s.index.year
Out[4]: Int64Index([2000, 2000, 2000, 2000, 2000], dtype='int64')
In [5]: Series(np.random.randn(5)).year
TypeError: cannot perform an year operations on this type <class 'pandas.core.index.Int64Index'>
```
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} | 1 | 2014-02-17T18:18:17Z | 2020-01-01T16:02:30Z | 2020-01-01T15:55:18Z | CONTRIBUTOR | null | after #6380, ability to pretty easily move ops over to the mix-in
- [x] `date/time` (added in #6380)
- [ ] more periods ops? #5202
- [x] additional field accessor (`is_beg_year`), #4823
- [x] validate `argmin/max`, (PR #7904)
- [x] StringMethods, e.g. `str` for `Index` (not for sub-classes though), #9068
(this turns out to be quite tricky, as sometimes construction depends on the existence of a `tz` attribute)
- [ ] `freq,frestr,inferred_freq`
- [x] `is_monotonic` #13336
- [x] `pd.infer_freq()`, related #6407,#6408
- [ ] `is_unique,lexsort_depth,nlevels`
- [x] `nunique()`,`unique()`,`value_counts()`, #6734
- [x] `factorize()` #7090
- [ ] `get_loc/find` #4616
- [ ] `__invert__` #8875
These are related in that they are generic.py functions as they have to compute something then set the axis with the result.
- [ ] `normalize` #5502
- [ ] `tz_convert/tz_localize` (though this requires a bit of wrapping as they have an axis parm,
but should be possible), see also here: https://github.com/pydata/pandas/pull/7915 (currently implemented only on `DatetimeIndex`, `PeriodIndex` has these as not implemented (and delegating from `NDFrame` is in `core/generic`)
- [x] `duplicated/drop_duplicates` #4060, (PR #7979)
- [ ] `has_duplicates` #9077
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} | 1 | 2014-02-17T19:22:43Z | 2014-02-17T20:34:48Z | 2014-02-17T20:34:48Z | CONTRIBUTOR | null | This showed up in the test suite for a PR for statsmodels. I have very little idea where this Series came from or why it's using get like this, but here it is anyway.
```
df = pd.Series(np.array([43, 48, 60, 48, 50, 51, 50, 45, 57, 48, 56,
45, 51, 39, 55, 43, 54, 52, 51, 54]),
index=pd.Float64Index([25.0, 36.0, 49.0, 64.0, 81.0, 100.0,
121.0, 144.0, 169.0, 196.0, 1225.0,
1296.0, 1369.0, 1444.0, 1521.0, 1600.0,
1681.0, 1764.0, 1849.0, 1936.0],
dtype='object'))
df.get(25, 0)
```
Traceback:
```
----> 1 df.get(25, 0)
/home/skipper/src/pandas-skipper/pandas/core/series.pyc in get(self, label, default)
756 """
757 try:
--> 758 return self.get_value(label)
759 except KeyError:
760 return default
/home/skipper/src/pandas-skipper/pandas/core/series.pyc in get_value(self, label)
776 value : scalar value
777 """
--> 778 return self.index.get_value(self.values, label)
779
780 def set_value(self, label, value):
/home/skipper/src/pandas-skipper/pandas/core/index.pyc in get_value(self, series, key)
1821 k = _values_from_object(key)
1822 loc = self.get_loc(k)
-> 1823 new_values = series.values[loc]
1824 if np.isscalar(new_values):
1825 return new_values
AttributeError: 'numpy.ndarray' object has no attribute 'values'
```
Version
```
[~/]
[11]: pd.version.version
[11]: '0.13.1-105-g8119991'
```
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} | 1 | 2014-02-17T19:44:17Z | 2017-11-24T10:39:16Z | 2017-11-24T10:39:12Z | CONTRIBUTOR | null | {
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} | 0 | 2014-02-17T20:18:46Z | 2014-06-14T14:30:48Z | 2014-02-17T20:34:48Z | CONTRIBUTOR | null | closes #6383
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```
#!/usr/bin/env python
import numpy as np
import pandas as pd
from datetime import datetime
"""
Notice that the two nonmissing data points are 8 days apart, not 7.
Therefore, any attempt to calculate a 7-day (week-over-week) percent change
should result in NaN, not an actual numeric value.
The bug is that pct_change does calculate an (erroneous) actual numeric value.
(The value it calculates is 414.53/430.30 - 1 = -0.036649.)
"""
idx = pd.DatetimeIndex(start=datetime(2013, 9, 12),
end=datetime(2013, 9, 21),
freq='D')
df = pd.DataFrame(data={'Series': [430.30,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
414.53,
np.NaN]}, index=idx)
pd.show_versions()
print('\n\nInput data:')
print(df)
print("\n\nThe bug is the pct_change for 09-20 should be NaN, but it isn't:\n")
print(df.pct_change(periods=7))
```
Here is the output:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 12.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1
Cython: 0.19.2
numpy: 1.8.0
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 1.1.0
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2013b
bottleneck: None
tables: 3.0.0
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.6.2
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
sqlalchemy: 0.8.3
lxml: 3.2.3
bs4: 4.3.1
html5lib: None
bq: None
apiclient: None
Input data:
Series
2013-09-12 430.30
2013-09-13 NaN
2013-09-14 NaN
2013-09-15 NaN
2013-09-16 NaN
2013-09-17 NaN
2013-09-18 NaN
2013-09-19 NaN
2013-09-20 414.53
2013-09-21 NaN
[10 rows x 1 columns]
The bug is the pct_change for 09-20 should be NaN, but it isn't:
Series
2013-09-12 NaN
2013-09-13 NaN
2013-09-14 NaN
2013-09-15 NaN
2013-09-16 NaN
2013-09-17 NaN
2013-09-18 NaN
2013-09-19 NaN
2013-09-20 -0.036649
2013-09-21 NaN
[10 rows x 1 columns]
```
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} | 5 | 2014-02-18T00:53:19Z | 2014-06-21T12:31:07Z | 2014-02-18T19:27:13Z | CONTRIBUTOR | null | Currently, `pandas.tseries.frequencies.to_offset` erroneously returns a zero time offset when the first part of its argument has a numerical value of zero, even if later parts have nonzero values. For instance,
```
In [123]: pandas.tseries.frequencies.to_offset('00H 00T 01S')
Out[123]: <0 * Days>
```
This change checks the sign of the numeric part of the argument before converting to `int` in order to support offsets like `'-00H 00T 01S'`.
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} | [] | closed | false | null | [] | null | 3 | 2014-02-18T05:11:58Z | 2016-10-12T23:04:38Z | 2014-02-18T05:44:34Z | CONTRIBUTOR | null | Trying to scrape data from the following URL: http://www.sochi2014.com/en/medal-standings
Clearly it contains a table as can be viewed from source.
``` python
In [1]: import pandas as pd
In [2]: from bs4 import BeautifulSoup
In [3]: url = "http://www.sochi2014.com/en/medal-standings"
In [4]: df = pd.read_html(url)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-4-24a7a592e948> in <module>()
----> 1 df = pd.read_html(url)
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in read_html(io, match, flavor, header, index_col, skiprows, infer_types, attrs)
904 'data (you passed a negative value)')
905 return _parse(flavor, io, match, header, index_col, skiprows, infer_types,
--> 906 attrs)
/usr/local/lib/python2.7/dist-packages/pandas/io/html.pyc in _parse(flavor, io, match, header, index_col, skiprows, infer_types, attrs)
773 break
774 else:
--> 775 raise retained
776
777 return [_data_to_frame(table, header, index_col, infer_types, skiprows)
AssertionError: No tables found
```
BeautifulSoup is able to find the table from this page.
``` python
In [6]: soup = BeautifulSoup(requests.get(url).text)
In [10]: soup.findAll("table")[0].findAll("tr")[1]
Out[10]:
<tr>
<td>1</td>
<td class="country ger">
<a href="/en/team-germany">Germany</a>
</td>
<td>8</td>
<td>3</td>
<td>2</td>
<td>13</td>
</tr>
```
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} | 6 | 2014-02-18T11:46:19Z | 2014-02-21T02:11:39Z | 2014-02-18T14:41:25Z | CONTRIBUTOR | null | In pandas 0.12 the order you indexed a DataFrame didn't matter, which I think is the correct behaviour:
``` python
In [6]: df = pd.DataFrame({'A': 5*[np.zeros(3)], 'B':5*[np.ones(3)]})
In [7]: df
Out[7]:
A B
0 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
1 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
2 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
3 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
4 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
In [8]: df['A'].iloc[2]
Out[8]: array([ 0., 0., 0.])
In [9]: df.iloc[2]['A']
Out[9]: array([ 0., 0., 0.])
In [10]: pd.__version__
Out[10]: '0.12.0'
In [11]: assert type(df.ix[2, 'A']) == type(df['A'].iloc[2]) == type(df.iloc[2]['A'])
In [12]:
```
In pandas 0.13 if you index in a different order you can get a different type out which can be problematic for code expecting an array, especially because of the difference between array indexing and label indexing.
``` python
In [1]: df = pd.DataFrame({'A': 5*[np.zeros(3)], 'B':5*[np.ones(3)]})
In [2]: df
Out[2]:
A B
0 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
1 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
2 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
3 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
4 [0.0, 0.0, 0.0] [1.0, 1.0, 1.0]
5 rows × 2 columns
In [3]: df['A'].iloc[2]
Out[3]: array([ 0., 0., 0.])
In [4]: df.iloc[2]['A']
Out[4]:
A 0
A 0
A 0
Name: 2, dtype: float64
In [5]: pd.__version__
Out[5]: '0.13.1'
In [6]: assert type(df.ix[2, 'A']) == type(df['A'].iloc[2]) == type(df.iloc[2]['A'])
Traceback (most recent call last):
File "<ipython-input-11-946e15564ee1>", line 1, in <module>
assert type(df.ix[2, 'A']) == type(df['A'].iloc[2]) == type(df.iloc[2]['A'])
AssertionError
```
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} | 3 | 2014-02-18T15:18:06Z | 2014-02-25T21:29:11Z | 2014-02-25T21:29:11Z | NONE | null | I encountered a similar issue to (closed) #3592 .
When trying to resample a dataframe with a localized Datetime Index using the how method:
``` python
rng = pd.date_range('1/1/2011', periods=20000, freq='H')
rng = rng.tz_localize('EST')
ts = pd.DataFrame(index=rng)
ts['first']=np.random.randn(len(rng))
ts['second']=np.cumsum(np.random.randn(len(rng)))
ts2=ts.resample('A', how={'first':np.sum, 'second':np.mean})
*File "tslib.pyx", line 465, in pandas.tslib._Timestamp.__richcmp__ (pandas\tslib.c:9313)
*TypeError: can't compare offset-naive and offset-aware datetimes
```
For other (,lower) frequencies like "M" the same code works fine and if I just use
```
how=np.mean
```
or just use 1 column in the dict
```
how={'first':np.mean}
```
it works too.
python 2.7.5
pandas 0.13.1
EDIT: Changed to include full example
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} | 0 | 2014-02-18T16:19:44Z | 2014-07-02T22:16:33Z | 2014-02-19T14:30:24Z | CONTRIBUTOR | null | closes #6032
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https://api.github.com/repos/pandas-dev/pandas/issues/6399 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6399/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6399/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6399/events | https://github.com/pandas-dev/pandas/issues/6399 | 27,818,224 | MDU6SXNzdWUyNzgxODIyNA== | 6,399 | BUG: Mergesort is unstable when ascending=False | {
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} | 2 | 2014-02-18T18:59:08Z | 2014-03-10T13:29:59Z | 2014-02-18T20:41:55Z | CONTRIBUTOR | null | ### The Issue
When using `DataFrame.sort_by(kind='mergesort')`, sorting is supposed to be stable. Unfortunately, that is not the case when `ascending=False`.
``` python
# Create a DataFrame where the first column is already in descending order.
In [96]: df = pd.DataFrame([[2, 'first'], [2, 'second'], [1, 'a'], [1, 'b']], columns=['sort_col', 'order'])
In [97]: df
Out[97]:
sort_col order
0 2 first
1 2 second
2 1 a
3 1 b
[4 rows x 2 columns]
# Look at the 'order' column. Clearly not stable.
In [98]: df.sort_index(by='sort_col', kind='mergesort', ascending=False)
Out[98]:
sort_col order
1 2 second
0 2 first
3 1 b
2 1 a
[4 rows x 2 columns]
```
### More Info
Inside the `sort_by()` source code, `argsort()` is called on the sorted column. Then, `ascending` is checked and if it is False, the indexes are simply reversed. Here is the relevant code snippet in `pandas/core/frame.py`:
``` python
k = self[by].values
...
indexer = k.argsort(kind=kind)
...
if not ascending:
indexer = indexer[::-1]
```
Since numpy always sorts in ascending order, this actually guarantees sorting is always _unstable_! Check this out:
``` python
In [110]: df['sort_col'].values.argsort(kind='mergesort')[::-1]
Out[110]: array([1, 0, 3, 2])
```
Clearly, simply reversing the indices doesn't work. We need to sort a reversed `k`, reverse the indices, and then subtract the indices from the highest index so they correspond to the original `k`:
``` python
In [112]: 3 - df['sort_col'].values[::-1].argsort(kind='mergesort')[::-1]
Out[112]: array([0, 1, 2, 3])
```
The workaround in my code is to stable sort descending is reverse the DataFrame, sort ascending, and reverse again.
What is the best way to fix this? This is probably an easy fix, but I've never contributed to pandas, so I need to set up my fork and make sure I can run tests before working on a pull request.
### My Versions
Here are my versions:
``` python
In [75]: show_versions()
INSTALLED VERSIONS
------------------
Python: 3.3.3.final.0
OS: Linux
Release: 3.12.9-2-ARCH
Processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: 0.20
Numpy: 1.8.0
Scipy: 0.13.0
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.2
pytz: 2013.9
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: 1.3.1
openpyxl: 1.8.2
xlrd: 0.9.2
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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https://api.github.com/repos/pandas-dev/pandas/issues/6400 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/6400/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/6400/comments | https://api.github.com/repos/pandas-dev/pandas/issues/6400/events | https://github.com/pandas-dev/pandas/pull/6400 | 27,823,966 | MDExOlB1bGxSZXF1ZXN0MTI2Njg2NTY= | 6,400 | BUG: Fix mergesort unstable when ascending=False (GH6399) | {
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} | 2 | 2014-02-18T20:15:01Z | 2014-07-17T17:35:09Z | 2014-02-18T20:41:44Z | CONTRIBUTOR | null | closes #6399. I hope I followed all of the correct procedures.
I ran the full test suite (test.sh), and there were no errors.
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