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} | 10 | 2015-04-15T02:26:52Z | 2016-04-29T15:59:34Z | 2016-04-29T15:59:08Z | CONTRIBUTOR | null | Minimal repro:
```
In [5]: pandas.__version__
Out[5]: '0.16.0-151-gcef3c85'
In [7]: DatetimeIndex([]).shift(3, 'H')
Out[7]: Index([], dtype='object')
```
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| null | 16 | 2015-04-15T04:00:15Z | 2015-11-10T01:27:17Z | 2015-11-10T01:27:17Z | CONTRIBUTOR | null | closes #9903
Fixes a bug where calling shift on an empty DatetimeIndex would result in the returned value being an Index rather than a DatetimeIndex.
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} | 2 | 2015-04-15T12:44:27Z | 2015-04-16T18:55:42Z | 2015-04-16T18:55:42Z | MEMBER | null | ```
s = pd.Series([0.1, 0.01, 0.001], index=['a', 'b', 'c'])
s.plot(kind='bar', log=True)
```
gives:
```
TypeError Traceback (most recent call last)
<ipython-input-13-75b989d29042> in <module>()
1 s = pd.Series([0.1, 0.01, 0.001], index=['a', 'b', 'c'])
----> 2 s.plot(kind='bar', log=True)
C:\Anaconda\lib\site-packages\pandas\tools\plotting.pyc in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2517 yerr=yerr, xerr=xerr,
2518 label=label, secondary_y=secondary_y,
-> 2519 **kwds)
2520
2521
C:\Anaconda\lib\site-packages\pandas\tools\plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
2323 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2324
-> 2325 plot_obj.generate()
2326 plot_obj.draw()
2327 return plot_obj.result
C:\Anaconda\lib\site-packages\pandas\tools\plotting.pyc in generate(self)
921 self._compute_plot_data()
922 self._setup_subplots()
--> 923 self._make_plot()
924 self._add_table()
925 self._make_legend()
C:\Anaconda\lib\site-packages\pandas\tools\plotting.pyc in _make_plot(self)
1884 w = self.bar_width / K
1885 rect = bar_f(ax, self.ax_pos + (i + 0.5) * w, y, w,
-> 1886 start=start, label=label, **kwds)
1887 self._add_legend_handle(rect, label, index=i)
1888
C:\Anaconda\lib\site-packages\pandas\tools\plotting.pyc in f(ax, x, y, w, start, **kwds)
1823 if self.kind == 'bar':
1824 def f(ax, x, y, w, start=None, **kwds):
-> 1825 start = start + self.bottom
1826 return ax.bar(x, y, w, bottom=start,log=self.log, **kwds)
1827 elif self.kind == 'barh':
TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'
```
This was working in 0.14.1
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} | 1 | 2015-04-15T15:32:42Z | 2015-04-16T08:57:28Z | 2015-04-15T20:10:09Z | CONTRIBUTOR | null | Fix for issue #9865 "Cannot convert tz-naive timestamps":
Docs for `tz_convert` and `tz_localize` were updated and "Raises" section were added.
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} | 1 | 2015-04-15T16:48:54Z | 2015-06-30T10:53:54Z | 2015-06-30T10:53:54Z | NONE | null | When creating a DatetimeIndex where start has a greater precision than end for a given frequency the output DatetimeIndex will be shorter than expected
``` python
>>>pd.__version__
0.15.2-318-g549b72f
>>>pd.DatetimeIndex(start='2015-04-15 00:00:03', end='2016-04-22 00:00:04', freq='Q')
2015-06-30 00:00:03
2015-09-30 00:00:03
2015-12-31 00:00:03
2016-03-31 00:00:03
>>>pd.DatetimeIndex(start='2015-04-15 00:00:03', end='2016-04-22 00:00', freq='Q')
2015-06-30 00:00:03
2015-09-30 00:00:03
2015-12-31 00:00:03
>>>pd.DatetimeIndex(start='2015-04-15 00:00:03', end='2015-6-22 00:00:04', freq='W')
2015-04-19 00:00:03
2015-04-26 00:00:03
2015-05-03 00:00:03
2015-05-10 00:00:03
2015-05-17 00:00:03
2015-05-24 00:00:03
2015-05-31 00:00:03
2015-06-07 00:00:03
2015-06-14 00:00:03
2015-06-21 00:00:03
>>>pd.DatetimeIndex(start='2015-04-15 00:00:03', end='2015-6-22 00:00', freq='W')
2015-04-19 00:00:03
2015-04-26 00:00:03
2015-05-03 00:00:03
2015-05-10 00:00:03
2015-05-17 00:00:03
2015-05-24 00:00:03
2015-05-31 00:00:03
2015-06-07 00:00:03
2015-06-14 00:00:03
```
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} | 10 | 2015-04-15T18:44:18Z | 2015-07-23T11:02:22Z | 2015-04-16T06:14:38Z | CONTRIBUTOR | null | Proposed fix for https://github.com/pydata/pandas/issues/9778
The formatting issue was caused by an incorrect number of elements in the (first) index columns of `strcols`. The length of reinserted columns was based on the number of elements per index level, but should have relied on the number of rows/occurrences of such elements.
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} | 3 | 2015-04-15T20:03:12Z | 2021-04-18T06:25:30Z | null | NONE | null | When a second scatter plot is added as ''secondary_y'', the outcome is that the primary is overridden.
``` python
import matplotlib.pyplot as plt
import pandas as pd,numpy as np
# secondary axis does not work on scatter plot
x=pd.Series(np.linspace(-10,10,201))
y,z=np.sin(x),np.cos(x)
df=pd.concat([x,np.sin(x),np.cos(x)],axis=1)
df.columns=['x','sin(x)','cos(x)']
ax=df.plot(kind='scatter',x='x',y='sin(x)',label='sin(x)',color='blue')
df.plot(kind='scatter',x='x',y='cos(x)',label='cos(x)',color='red', \
ax=ax,secondary_y=True)
plt.show()
```
The above code returns this plot.
This could be a workaround, however I think that the first snippet should have worked.
``` python
ax=df.plot(kind='scatter',x='x',y='sin(x)',label='sin(x)',color='blue')
ax2=ax.twinx()
df.plot(kind='scatter',x='x',y='cos(x)',label='cos(x)',color='red', \
ax=ax2,secondary_y=True)
plt.show()
```
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} | 12 | 2015-04-15T20:57:31Z | 2015-04-21T21:45:58Z | 2015-04-21T00:58:16Z | CONTRIBUTOR | null | @jreback this PR makes it easier to add/delete items from `__dir__`. The `.str/.dt/.cat` are now only present in `__dir__` when they are appropriate types.
However, the IPython tab completion does not seem to only source the list from `__dir__`, and therefore even if an item is removed from `__dir__` it is still showing up in tab completion for me.
@shoyer I'd probably need some help/feedback with this. Here's a related ticket https://github.com/pydata/pandas/pull/9617
Thanks.
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} | 4 | 2015-04-15T21:02:35Z | 2015-04-27T22:38:54Z | 2015-04-25T20:39:18Z | MEMBER | null | We still have a lot of API pages with 'old' namespace usage (eg `pandas.io.parsers.read_csv` instead of `pandas.read_csv` as how it should show up in the documentation). Now we build the pages as well, this PR changes this in adding redirect pages.
(The reason I care about this is that googling eg `read_csv` still often turns up the old page (although I don't know how fast this disappears when we would just remove it) and these are therefore used a lot on eg SO)
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| null | 3 | 2015-04-16T01:12:52Z | 2015-04-16T01:20:25Z | 2015-04-16T01:20:25Z | NONE | null | For some reason pandas isn't liking when I create a column named class and attempt to access with .{columnname} method
I also cannot run a query on the dataframe.
Code:
``` python
>>> a = pandas.DataFrame({'class':[0], 'clas1': [1]})
>>> a.clas1
0 1
Name: clas1, dtype: int64
>>> a.class
File "<stdin>", line 1
a.class
^
SyntaxError: invalid syntax
>>> a.query("clas1==1")
clas1 class
0 1 0
>>> a.query("class==0")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.4/site-packages/pandas/core/frame.py", line 1918, in query
res = self.eval(expr, **kwargs)
File "/usr/lib/python3.4/site-packages/pandas/core/frame.py", line 1970, in eval
return _eval(expr, **kwargs)
File "/usr/lib/python3.4/site-packages/pandas/computation/eval.py", line 230, in eval
truediv=truediv)
File "/usr/lib/python3.4/site-packages/pandas/computation/expr.py", line 635, in __init__
self.terms = self.parse()
File "/usr/lib/python3.4/site-packages/pandas/computation/expr.py", line 652, in parse
return self._visitor.visit(self.expr)
File "/usr/lib/python3.4/site-packages/pandas/computation/expr.py", line 310, in visit
node = ast.fix_missing_locations(ast.parse(clean))
File "/usr/lib/python3.4/ast.py", line 35, in parse
return compile(source, filename, mode, PyCF_ONLY_AST)
File "<unknown>", line 1
class ==0
^
SyntaxError: invalid syntax
```
Dependencies:
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.18.6-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.15.2
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.0.0
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2014.10
bottleneck: 1.0.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 2 | 2015-04-16T14:19:31Z | 2017-07-31T15:12:21Z | 2017-07-31T15:12:10Z | NONE | null | I experience these strange behavior of `unstack()` when its `level` parameter gets a list instead of string.
This changes `dtype` of some columns from numeric to object, which is obviously a worrying change.
I am wondering if this a bug or there is something I am missing about dealing with this.
I am on pandas 0.16.0, numpy 1.9.2.
Here is a code to illustrate the problem:
```
data = """a,b,c,d
2,101,A,11
3,101,A,12
1,201,B,21
2,201,B,22
3,201,B,23
1,301,C,33
2,301,C,32"""
df = pd.read_csv(pd.core.common.StringIO(data))
print df.dtypes
df_str = df.set_index(['a','b']).unstack(level='a') # Note that level has a str
print df_str.dtypes
df_list = df.set_index(['a','b']).unstack(level=['a']) # Note that level has a list
print df_list.dtypes
```
Which produces:
```
a int64
b int64
c object
d int64 <-- Note original dtype
dtype: object
a
c 1 object
2 object
3 object
d 1 float64 <-- expected change of dtype due to NaN addition
2 float64 <-- expected change of dtype due to NaN addition
3 float64 <-- expected change of dtype due to NaN addition
dtype: object
a
c 2 object
3 object
1 object
d 2 object <-- UNexpected change of dtype, when level got list
3 object <-- UNexpected change of dtype, when level got list
1 object <-- UNexpected change of dtype, when level got list
dtype: object
```
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} | 2 | 2015-04-16T14:55:15Z | 2015-04-18T10:43:50Z | 2015-04-16T18:55:42Z | MEMBER | null | Closes #9905. Also include a fix that `log` kw is ignored in horizontal bar plot. Test cases are based on standard mpl result:
```
plt.bar([0, 1, 2], [0.1, 0.01, 0.001], log=True)
plt.gca().get_ylim()
# (0.001, 0.10000000000000001)
plt.gca().yaxis.get_ticklocs()
# [ 1.00000000e-04 1.00000000e-03 1.00000000e-02 1.00000000e-01
# 1.00000000e+00 1.00000000e+01]
```
Maybe `log` in bar plot and `logx` and `logy` kw should be merged in future...
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} | 3 | 2015-04-16T17:36:44Z | 2016-04-17T20:37:34Z | 2016-04-17T20:37:34Z | NONE | null | I ran into a particular dataset that seems to cause Series.resample() to produce incorrect results. Each datapoint is almost but not exactly a minute apart. (This is trimmed down from a larger dataset, but this still reproduces the problem)
``` python
>>> import pandas; from pandas import Timestamp
>>> data1 = pandas.Series(
... {
... Timestamp('2015-03-31 21:48:52.672000'): 2,
... Timestamp('2015-03-31 21:49:52.739000'): 1,
... Timestamp('2015-03-31 21:50:52.806000'): 2}
... )
>>> data1
2015-03-31 21:48:52.672000 2
2015-03-31 21:49:52.739000 1
2015-03-31 21:50:52.806000 2
dtype: int64
>>> data1.resample("10S")
2015-03-31 21:48:50 NaN
2015-03-31 21:49:00 NaN
2015-03-31 21:49:10 NaN
2015-03-31 21:49:20 NaN
2015-03-31 21:49:30 NaN
2015-03-31 21:49:40 NaN
2015-03-31 21:49:50 NaN
2015-03-31 21:50:00 NaN
2015-03-31 21:50:10 NaN
2015-03-31 21:50:20 NaN
2015-03-31 21:50:30 NaN
2015-03-31 21:50:40 NaN
2015-03-31 21:50:50 NaN
Freq: 10S, dtype: float64
```
Expected output:
``` python
>>> data1.resample("10S")
2015-03-31 21:48:50 2
2015-03-31 21:49:00 NaN
2015-03-31 21:49:10 NaN
2015-03-31 21:49:20 NaN
2015-03-31 21:49:30 NaN
2015-03-31 21:49:40 NaN
2015-03-31 21:49:50 1
2015-03-31 21:50:00 NaN
2015-03-31 21:50:10 NaN
2015-03-31 21:50:20 NaN
2015-03-31 21:50:30 NaN
2015-03-31 21:50:40 NaN
2015-03-31 21:50:50 2
Freq: 10S, dtype: float64
```
Changing any one of the timestamps or taking one of the two datapoints out works correctly. Something about this particular dataset causes resample to fail.
I have more examples on this ipython notebook: http://nbviewer.ipython.org/gist/brownan/1000ba324ad7df917e32
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-123.20.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: 3.0.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
edit: I can also reproduce this with the latest version on master: 0.16.0-157-g161f38d
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| null | 1 | 2015-04-16T18:41:04Z | 2015-04-16T23:13:00Z | 2015-04-16T23:13:00Z | CONTRIBUTOR | null | this removes a check for `compat.PY3` where it is no longer needed
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} | 5 | 2015-04-16T22:35:57Z | 2015-07-28T21:53:19Z | 2015-07-28T21:53:19Z | NONE | null | Currently, converting to Excel a DataFrame containing `datetime.time` instances leads to unformated float values such as `0.666667` instead of `16:00:00`. This pull request fixes this by adding a default time format and adds an argument to specify your own time format.
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} | 2 | 2015-04-16T22:47:47Z | 2019-11-03T15:05:47Z | 2019-11-03T15:05:47Z | NONE | null | Sorting uint64 columns is incorrect when sorting on multiple columns:
``` python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({'a': pd.Series([18446637057563306014, 1162265347240853609]), 'b': pd.Series([1, 2])})
>>> df['a'] = df['a'].astype(np.uint64)
>>> df.sort(['a', 'b'])['a']
0 18446637057563306014
1 1162265347240853609
Name: a, dtype: uint64
>>> df.sort(['a'])['a']
1 1162265347240853609
0 18446637057563306014
Name: a, dtype: uint64
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-40.87-jump1.03
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: 0.15.1
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: None
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 5 | 2015-04-16T23:59:16Z | 2015-04-17T06:42:14Z | 2015-04-17T06:42:14Z | CONTRIBUTOR | null | All explained here:
http://nbviewer.ipython.org/gist/michaelaye/4af80037146dfb65b105
Not sure if I should have submitted it here or with matplotlib. Just let me know, then I can open one there.
Output of pd.show_versions():
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0-151-gcef3c85
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0-dev
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
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| null | 1 | 2015-04-17T00:00:15Z | 2015-04-17T17:25:41Z | null | CONTRIBUTOR | null | Bug shown at the end of this:
http://nbviewer.ipython.org/gist/michaelaye/4af80037146dfb65b105
Output of pd.show__versions():
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
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nose: 1.3.6
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scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0-dev
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
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sqlalchemy: 0.9.9
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} | 14 | 2015-04-17T15:24:21Z | 2015-04-29T10:32:15Z | 2015-04-29T10:32:15Z | CONTRIBUTOR | null | Starting from [this SO question](http://stackoverflow.com/questions/29699352/pandas-keyerror-on-nan-group-for-ints-but-not-floats), we can see that something is odd when doing a groupby using Series of category dtype. (Today's trunk, 0.16.0-163-gf9f88b2.)
```
>>> df = pd.DataFrame({"a": [5,15,25]})
>>> c = pd.cut(df.a, bins=[0,10,20,30,40])
>>> c
0 (0, 10]
1 (10, 20]
2 (20, 30]
Name: a, dtype: category
Categories (4, object): [(0, 10] < (10, 20] < (20, 30] < (30, 40]]
>>> df.groupby(c).sum()
a
a
(0, 10] 5
(10, 20] 15
(20, 30] 25
(30, 40] NaN
>>> df.groupby(c).transform(sum)
Traceback (most recent call last):
File "<ipython-input-81-1eb61986eff3>", line 1, in <module>
df.groupby(c).transform(sum)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.0_163_gf9f88b2-py2.7-linux-x86_64.egg/pandas/core/groupby.py", line 3017, in transform
indexer = indices[name]
KeyError: '(30, 40]'
>>> df.a.groupby(c).transform(max)
Traceback (most recent call last):
File "<ipython-input-87-9759e8c1e070>", line 1, in <module>
df.a.groupby(c).transform(max)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.0_163_gf9f88b2-py2.7-linux-x86_64.egg/pandas/core/groupby.py", line 2422, in transform
return self._transform_fast(cyfunc)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.0_163_gf9f88b2-py2.7-linux-x86_64.egg/pandas/core/groupby.py", line 2461, in _transform_fast
values = np.repeat(values, com._ensure_platform_int(counts))
File "/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py", line 393, in repeat
return repeat(repeats, axis)
ValueError: count < 0
```
It looks like we're somehow using the categories themselves, not the values. Not sure if this is a consequence of the special-casing of grouping on categories..
ISTM if we have a Series with categorical dtype passed to groupby, we should group on the values, and not return the missing elements-- if you want the missing elements, you should pass the Categorical object _itself_. Admittedly I haven't thought through all the consequences, but I was pretty surprised when I read the docs and saw this was the intended behaviour.
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} | 4 | 2015-04-17T17:29:02Z | 2015-04-20T19:00:32Z | 2015-04-20T19:00:32Z | CONTRIBUTOR | null | closes #9878
This PR adds a test for packaging and installing pandas from a tarball, inside of the 2.6 regression.
After regular nosetests complete, cython is removed from the venv, pandas is packaged with cdist command, and the package is installed via pip. Limited nosetests run once installed.
The travis log output is a little messy with the pip command output; ideally I wanted to 'fold'/collapse the output unless there was a failure but I don't think you can do that from within the 'script' portion of the regression, only the before and after scripts, see:
http://blog.travis-ci.com/2013-05-22-improving-build-visibility-log-folds/
If there is a way to do this maybe we can add it later, or move where this INSTALL_TEST is happening to a before or after script.
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| null | 3 | 2015-04-17T18:57:26Z | 2015-04-19T00:06:42Z | 2015-04-19T00:06:42Z | NONE | null | Here is a notebook I created with help at the pandas dev sprint in Montreal.
Its meant to help document the behavior in issue 9867: It's difficult to predict what DataFrame.groupby().apply() will return
https://github.com/pydata/pandas/issues/9867
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} | 3 | 2015-04-17T20:45:31Z | 2015-06-10T20:02:11Z | 2015-06-10T20:02:06Z | NONE | null | closes #9456.
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} | 0 | 2015-04-18T00:29:48Z | 2015-04-20T11:09:39Z | 2015-04-20T11:08:51Z | MEMBER | null | ```
import numpy as np
import pandas as pd
import datetime
df = pd.DataFrame({'A': [1, 1, 1, 2, 2, 2], 'B': [1, 2, 3, 4, 5, 6]})
grouped = df.groupby('A')
n = 20
data = np.random.randn(n, 4)
dt_df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])
dt_df['key'] = [datetime.datetime(2013, 1, 1), datetime.datetime(2013, 1, 2), pd.NaT,
datetime.datetime(2013, 1, 4), datetime.datetime(2013, 1, 5)] * 4
# OK, index is named as 'key'
grouped = dt_df.groupby('key')
grouped.size()
# key
#2013-01-01 4
#2013-01-02 4
#2013-01-04 4
#2013-01-05 4
# dtype: int64
# NG (no index name)
grouper = dt_df.groupby(pd.TimeGrouper(key='key', freq='D'))
grouper.size()
#2013-01-01 4
#2013-01-02 4
#2013-01-03 0
#2013-01-04 4
#2013-01-05 4
# dtype: int64
# other agg methods looks ok
grouper.count()
# A B C D
# key
#2013-01-01 4 4 4 4
#2013-01-02 4 4 4 4
#2013-01-03 0 0 0 0
#2013-01-04 4 4 4 4
#2013-01-05 4 4 4 4
```
ref: #9862
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} | 4 | 2015-04-18T00:36:11Z | 2015-06-02T19:25:19Z | 2015-05-18T11:59:45Z | MEMBER | null | ```
import pandas as pd
dtidx = pd.DatetimeIndex(['2011-01-01'], freq='D', name='dtidx')
(dtidx + 1).name
# dtidx
# NG
(dtidx + pd.Timedelta('1 day')).name
# None
tdidx = pd.TimedeltaIndex(['1 day'], freq='D', name='tdidx')
(tdidx + 1).name
# tdidx
# NG
(tdidx + pd.Timedelta('1 day')).name
# None
```
ref: #9862
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} | 7 | 2015-04-18T02:45:20Z | 2016-06-06T13:59:45Z | 2015-04-21T23:38:48Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/29709918/pandas-and-category-replacement/29712287#29712287)
- the `.add_categories` should be able to take an `Index`/ndarray, ATM it must be converted to a list
- should be a keyword for something like: add any additional categories that I am passing to you (even if the current ones are duplicates, just add these to the end). e.g.
```
In [147]: s = pd.Categorical(list('aabbcd'))
In [148]: s2 = list('aabbcdefg')
In [149]: s
Out[149]:
[a, a, b, b, c, d]
Categories (4, object): [a, b, c, d]
In [150]: s.add_categories(s.categories.sym_diff(Index(s2)).tolist())
Out[150]:
[a, a, b, b, c, d]
Categories (7, object): [a, b, c, d, e, f, g]
```
I would ideally just like to say:
```
s.add_categories(s2, take_new=True)
```
(maybe not the best keyword, but something like this)
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https://api.github.com/repos/pandas-dev/pandas/issues/9928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9928/events | https://github.com/pandas-dev/pandas/issues/9928 | 69,262,465 | MDU6SXNzdWU2OTI2MjQ2NQ== | 9,928 | Pandas gives 600 line long ValueError while runing sklearn GridSearchCV | {
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| null | 4 | 2015-04-18T03:43:50Z | 2015-04-28T12:11:32Z | 2015-04-18T05:59:42Z | NONE | null | I am using sklearn GridSearchCV to classify some data. When I run this code, pandas gives 600 line long error. If I set n_jobs=1, it runs fine without any error. Please kindly take a look at it. Thanks
```
rfc = RandomForestClassifier(random_state=1)
tuned_parameters = [{'max_features': ['sqrt', 'log2'],
'n_estimators': [20, 100, 200, 500, 1000]}]
clf = GridSearchCV(rfc, tuned_parameters, scoring='roc_auc', cv=3, n_jobs=-1, verbose=2)
clf.fit(X, y)
```
Error message
```
Fitting 3 folds for each of 10 candidates, totalling 30 fits
[CV] max_features=sqrt, n_estimators=20 ..............................
[CV] max_features=sqrt, n_estimators=20 ..............................
[CV] max_features=sqrt, n_estimators=20 ..............................
[CV] max_features=sqrt, n_estimators=100 .............................
An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (101150, 0))
An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (101150, 0))
An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (101150, 0))
An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (101150, 0))
[CV] max_features=sqrt, n_estimators=200 .............................[CV] max_features=sqrt, n_estimators=100 .............................[CV] max_features=sqrt, n_estimators=100 .............................[CV] max_features=sqrt, n_estimators=200 .............................
---------------------------------------------------------------------------
JoblibValueError Traceback (most recent call last)
<ipython-input-25-99a52d0f19db> in <module>()
4
5 clf = GridSearchCV(rfc, tuned_parameters, scoring='roc_auc', cv=3, n_jobs=-1, verbose=2)
----> 6 clf.fit(X, y)
/home/user/anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
730
731 """
--> 732 return self._fit(X, y, ParameterGrid(self.param_grid))
733
734
/home/user/anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
503 self.fit_params, return_parameters=True,
504 error_score=self.error_score)
--> 505 for parameters in parameter_iterable
506 for train, test in cv)
507
/home/user/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
664 # consumption.
665 self._iterating = False
--> 666 self.retrieve()
667 # Make sure that we get a last message telling us we are done
668 elapsed_time = time.time() - self._start_time
/home/user/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in retrieve(self)
547 # Convert this to a JoblibException
548 exception_type = _mk_exception(exception.etype)[0]
--> 549 raise exception_type(report)
550 raise exception
551 finally:
JoblibValueError: JoblibValueError
___________________________________________________________________________
Multiprocessing exception:
...........................................................................
/home/user/anaconda/lib/python2.7/runpy.py in _run_module_as_main(mod_name='IPython.kernel.__main__', alter_argv=1)
157 pkg_name = mod_name.rpartition('.')[0]
158 main_globals = sys.modules["__main__"].__dict__
159 if alter_argv:
160 sys.argv[0] = fname
161 return _run_code(code, main_globals, None,
--> 162 "__main__", fname, loader, pkg_name)
fname = '/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py'
loader = <pkgutil.ImpLoader instance>
pkg_name = 'IPython.kernel'
163
164 def run_module(mod_name, init_globals=None,
165 run_name=None, alter_sys=False):
166 """Execute a module's code without importing it
...........................................................................
/home/user/anaconda/lib/python2.7/runpy.py in _run_code(code=<code object <module> at 0x7f8c57a1c630, file "/...ite-packages/IPython/kernel/__main__.py", line 1>, run_globals={'__builtins__': <module '__builtin__' (built-in)>, '__doc__': None, '__file__': '/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py', '__loader__': <pkgutil.ImpLoader instance>, '__name__': '__main__', '__package__': 'IPython.kernel', 'app': <module 'IPython.kernel.zmq.kernelapp' from '/ho.../site-packages/IPython/kernel/zmq/kernelapp.pyc'>}, init_globals=None, mod_name='__main__', mod_fname='/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py', mod_loader=<pkgutil.ImpLoader instance>, pkg_name='IPython.kernel')
67 run_globals.update(init_globals)
68 run_globals.update(__name__ = mod_name,
69 __file__ = mod_fname,
70 __loader__ = mod_loader,
71 __package__ = pkg_name)
---> 72 exec code in run_globals
code = <code object <module> at 0x7f8c57a1c630, file "/...ite-packages/IPython/kernel/__main__.py", line 1>
run_globals = {'__builtins__': <module '__builtin__' (built-in)>, '__doc__': None, '__file__': '/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py', '__loader__': <pkgutil.ImpLoader instance>, '__name__': '__main__', '__package__': 'IPython.kernel', 'app': <module 'IPython.kernel.zmq.kernelapp' from '/ho.../site-packages/IPython/kernel/zmq/kernelapp.pyc'>}
73 return run_globals
74
75 def _run_module_code(code, init_globals=None,
76 mod_name=None, mod_fname=None,
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py in <module>()
1
2
----> 3
4 if __name__ == '__main__':
5 from IPython.kernel.zmq import kernelapp as app
6 app.launch_new_instance()
7
8
9
10
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/config/application.py in launch_instance(cls=<class 'IPython.kernel.zmq.kernelapp.IPKernelApp'>, argv=None, **kwargs={})
569
570 If a global instance already exists, this reinitializes and starts it
571 """
572 app = cls.instance(**kwargs)
573 app.initialize(argv)
--> 574 app.start()
app.start = <bound method IPKernelApp.start of <IPython.kernel.zmq.kernelapp.IPKernelApp object>>
575
576 #-----------------------------------------------------------------------------
577 # utility functions, for convenience
578 #-----------------------------------------------------------------------------
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelapp.py in start(self=<IPython.kernel.zmq.kernelapp.IPKernelApp object>)
368 def start(self):
369 if self.poller is not None:
370 self.poller.start()
371 self.kernel.start()
372 try:
--> 373 ioloop.IOLoop.instance().start()
374 except KeyboardInterrupt:
375 pass
376
377 launch_new_instance = IPKernelApp.launch_instance
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/zmq/eventloop/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)
146 PollIOLoop.configure(ZMQIOLoop)
147 return PollIOLoop.instance()
148
149 def start(self):
150 try:
--> 151 super(ZMQIOLoop, self).start()
self.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>
152 except ZMQError as e:
153 if e.errno == ETERM:
154 # quietly return on ETERM
155 pass
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/tornado/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)
835 self._events.update(event_pairs)
836 while self._events:
837 fd, events = self._events.popitem()
838 try:
839 fd_obj, handler_func = self._handlers[fd]
--> 840 handler_func(fd_obj, events)
handler_func = <function null_wrapper>
fd_obj = <zmq.sugar.socket.Socket object>
events = 5
841 except (OSError, IOError) as e:
842 if errno_from_exception(e) == errno.EPIPE:
843 # Happens when the client closes the connection
844 pass
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/tornado/stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 5), **kwargs={})
270 # Fast path when there are no active contexts.
271 def null_wrapper(*args, **kwargs):
272 try:
273 current_state = _state.contexts
274 _state.contexts = cap_contexts[0]
--> 275 return fn(*args, **kwargs)
args = (<zmq.sugar.socket.Socket object>, 5)
kwargs = {}
276 finally:
277 _state.contexts = current_state
278 null_wrapper._wrapped = True
279 return null_wrapper
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=5)
428 # dispatch events:
429 if events & IOLoop.ERROR:
430 gen_log.error("got POLLERR event on ZMQStream, which doesn't make sense")
431 return
432 if events & IOLoop.READ:
--> 433 self._handle_recv()
self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>
434 if not self.socket:
435 return
436 if events & IOLoop.WRITE:
437 self._handle_send()
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)
460 gen_log.error("RECV Error: %s"%zmq.strerror(e.errno))
461 else:
462 if self._recv_callback:
463 callback = self._recv_callback
464 # self._recv_callback = None
--> 465 self._run_callback(callback, msg)
self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>
callback = <function null_wrapper>
msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]
466
467 # self.update_state()
468
469
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py in _run_callback(self=<zmq.eventloop.zmqstream.ZMQStream object>, callback=<function null_wrapper>, *args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
402 close our socket."""
403 try:
404 # Use a NullContext to ensure that all StackContexts are run
405 # inside our blanket exception handler rather than outside.
406 with stack_context.NullContext():
--> 407 callback(*args, **kwargs)
callback = <function null_wrapper>
args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)
kwargs = {}
408 except:
409 gen_log.error("Uncaught exception, closing connection.",
410 exc_info=True)
411 # Close the socket on an uncaught exception from a user callback
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/tornado/stack_context.py in null_wrapper(*args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
270 # Fast path when there are no active contexts.
271 def null_wrapper(*args, **kwargs):
272 try:
273 current_state = _state.contexts
274 _state.contexts = cap_contexts[0]
--> 275 return fn(*args, **kwargs)
args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)
kwargs = {}
276 finally:
277 _state.contexts = current_state
278 null_wrapper._wrapped = True
279 return null_wrapper
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py in dispatcher(msg=[<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>])
247 if self.control_stream:
248 self.control_stream.on_recv(self.dispatch_control, copy=False)
249
250 def make_dispatcher(stream):
251 def dispatcher(msg):
--> 252 return self.dispatch_shell(stream, msg)
msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]
253 return dispatcher
254
255 for s in self.shell_streams:
256 s.on_recv(make_dispatcher(s), copy=False)
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py in dispatch_shell(self=<IPython.kernel.zmq.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, msg={'buffers': [], 'content': {u'allow_stdin': True, u'code': u"rfc = RandomForestClassifier(random_state=1)\n...auc', cv=3, n_jobs=-1, verbose=2)\nclf.fit(X, y)", u'silent': False, u'stop_on_error': True, u'store_history': True, u'user_expressions': {}}, 'header': {u'msg_id': u'122E08A036D8493A821D16CB2F4EE32A', u'msg_type': u'execute_request', u'session': u'D2F6FC266E4F453AB36721F7AF6CAD31', u'username': u'username', u'version': u'5.0'}, 'metadata': {}, 'msg_id': u'122E08A036D8493A821D16CB2F4EE32A', 'msg_type': u'execute_request', 'parent_header': {}})
208 else:
209 # ensure default_int_handler during handler call
210 sig = signal(SIGINT, default_int_handler)
211 self.log.debug("%s: %s", msg_type, msg)
212 try:
--> 213 handler(stream, idents, msg)
handler = <bound method IPythonKernel.execute_request of <IPython.kernel.zmq.ipkernel.IPythonKernel object>>
stream = <zmq.eventloop.zmqstream.ZMQStream object>
idents = ['D2F6FC266E4F453AB36721F7AF6CAD31']
msg = {'buffers': [], 'content': {u'allow_stdin': True, u'code': u"rfc = RandomForestClassifier(random_state=1)\n...auc', cv=3, n_jobs=-1, verbose=2)\nclf.fit(X, y)", u'silent': False, u'stop_on_error': True, u'store_history': True, u'user_expressions': {}}, 'header': {u'msg_id': u'122E08A036D8493A821D16CB2F4EE32A', u'msg_type': u'execute_request', u'session': u'D2F6FC266E4F453AB36721F7AF6CAD31', u'username': u'username', u'version': u'5.0'}, 'metadata': {}, 'msg_id': u'122E08A036D8493A821D16CB2F4EE32A', 'msg_type': u'execute_request', 'parent_header': {}}
214 except Exception:
215 self.log.error("Exception in message handler:", exc_info=True)
216 finally:
217 signal(SIGINT, sig)
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/kernelbase.py in execute_request(self=<IPython.kernel.zmq.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=['D2F6FC266E4F453AB36721F7AF6CAD31'], parent={'buffers': [], 'content': {u'allow_stdin': True, u'code': u"rfc = RandomForestClassifier(random_state=1)\n...auc', cv=3, n_jobs=-1, verbose=2)\nclf.fit(X, y)", u'silent': False, u'stop_on_error': True, u'store_history': True, u'user_expressions': {}}, 'header': {u'msg_id': u'122E08A036D8493A821D16CB2F4EE32A', u'msg_type': u'execute_request', u'session': u'D2F6FC266E4F453AB36721F7AF6CAD31', u'username': u'username', u'version': u'5.0'}, 'metadata': {}, 'msg_id': u'122E08A036D8493A821D16CB2F4EE32A', 'msg_type': u'execute_request', 'parent_header': {}})
357 if not silent:
358 self.execution_count += 1
359 self._publish_execute_input(code, parent, self.execution_count)
360
361 reply_content = self.do_execute(code, silent, store_history,
--> 362 user_expressions, allow_stdin)
user_expressions = {}
allow_stdin = True
363
364 # Flush output before sending the reply.
365 sys.stdout.flush()
366 sys.stderr.flush()
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/kernel/zmq/ipkernel.py in do_execute(self=<IPython.kernel.zmq.ipkernel.IPythonKernel object>, code=u"rfc = RandomForestClassifier(random_state=1)\n...auc', cv=3, n_jobs=-1, verbose=2)\nclf.fit(X, y)", silent=False, store_history=True, user_expressions={}, allow_stdin=True)
176
177 reply_content = {}
178 # FIXME: the shell calls the exception handler itself.
179 shell._reply_content = None
180 try:
--> 181 shell.run_cell(code, store_history=store_history, silent=silent)
shell.run_cell = <bound method ZMQInteractiveShell.run_cell of <I....kernel.zmq.zmqshell.ZMQInteractiveShell object>>
code = u"rfc = RandomForestClassifier(random_state=1)\n...auc', cv=3, n_jobs=-1, verbose=2)\nclf.fit(X, y)"
store_history = True
silent = False
182 except:
183 status = u'error'
184 # FIXME: this code right now isn't being used yet by default,
185 # because the run_cell() call above directly fires off exception
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py in run_cell(self=<IPython.kernel.zmq.zmqshell.ZMQInteractiveShell object>, raw_cell=u"rfc = RandomForestClassifier(random_state=1)\n...auc', cv=3, n_jobs=-1, verbose=2)\nclf.fit(X, y)", store_history=True, silent=False, shell_futures=True)
2866 self.displayhook.exec_result = result
2867
2868 # Execute the user code
2869 interactivity = "none" if silent else self.ast_node_interactivity
2870 self.run_ast_nodes(code_ast.body, cell_name,
-> 2871 interactivity=interactivity, compiler=compiler, result=result)
interactivity = 'last_expr'
compiler = <IPython.core.compilerop.CachingCompiler instance>
2872
2873 # Reset this so later displayed values do not modify the
2874 # ExecutionResult
2875 self.displayhook.exec_result = None
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py in run_ast_nodes(self=<IPython.kernel.zmq.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.Assign object>, <_ast.Assign object>, <_ast.Assign object>, <_ast.Expr object>], cell_name='<ipython-input-25-99a52d0f19db>', interactivity='last', compiler=<IPython.core.compilerop.CachingCompiler instance>, result=<IPython.core.interactiveshell.ExecutionResult object>)
2976 return True
2977
2978 for i, node in enumerate(to_run_interactive):
2979 mod = ast.Interactive([node])
2980 code = compiler(mod, cell_name, "single")
-> 2981 if self.run_code(code, result):
self.run_code = <bound method ZMQInteractiveShell.run_code of <I....kernel.zmq.zmqshell.ZMQInteractiveShell object>>
code = <code object <module> at 0x7f8c215a43b0, file "<ipython-input-25-99a52d0f19db>", line 6>
result = <IPython.core.interactiveshell.ExecutionResult object>
2982 return True
2983
2984 # Flush softspace
2985 if softspace(sys.stdout, 0):
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py in run_code(self=<IPython.kernel.zmq.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x7f8c215a43b0, file "<ipython-input-25-99a52d0f19db>", line 6>, result=<IPython.core.interactiveshell.ExecutionResult object>)
3030 outflag = 1 # happens in more places, so it's easier as default
3031 try:
3032 try:
3033 self.hooks.pre_run_code_hook()
3034 #rprint('Running code', repr(code_obj)) # dbg
-> 3035 exec(code_obj, self.user_global_ns, self.user_ns)
code_obj = <code object <module> at 0x7f8c215a43b0, file "<ipython-input-25-99a52d0f19db>", line 6>
self.user_global_ns = {'ALLOW_THREADS': 1, 'Annotation': <class 'matplotlib.text.Annotation'>, 'Arrow': <class 'matplotlib.patches.Arrow'>, 'Artist': <class 'matplotlib.artist.Artist'>, 'AutoLocator': <class 'matplotlib.ticker.AutoLocator'>, 'Axes': <class 'matplotlib.axes._axes.Axes'>, 'BUFSIZE': 8192, 'Button': <class 'matplotlib.widgets.Button'>, 'CLIP': 0, 'Circle': <class 'matplotlib.patches.Circle'>, ...}
self.user_ns = {'ALLOW_THREADS': 1, 'Annotation': <class 'matplotlib.text.Annotation'>, 'Arrow': <class 'matplotlib.patches.Arrow'>, 'Artist': <class 'matplotlib.artist.Artist'>, 'AutoLocator': <class 'matplotlib.ticker.AutoLocator'>, 'Axes': <class 'matplotlib.axes._axes.Axes'>, 'BUFSIZE': 8192, 'Button': <class 'matplotlib.widgets.Button'>, 'CLIP': 0, 'Circle': <class 'matplotlib.patches.Circle'>, ...}
3036 finally:
3037 # Reset our crash handler in place
3038 sys.excepthook = old_excepthook
3039 except SystemExit as e:
...........................................................................
/home/user/data-analysis/GA/project/final/<ipython-input-25-99a52d0f19db> in <module>()
1 rfc = RandomForestClassifier(random_state=1)
2 tuned_parameters = [{'max_features': ['sqrt', 'log2'],
3 'n_estimators': [20, 100, 200, 500, 1000]}]
4
5 clf = GridSearchCV(rfc, tuned_parameters, scoring='roc_auc', cv=3, n_jobs=-1, verbose=2)
----> 6 clf.fit(X, y)
7
8
9
10
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py in fit(self=GridSearchCV(cv=3, error_score='raise',
e...e_func=None,
scoring='roc_auc', verbose=2), X= 1516 Quinoa Weisse 18th Street / Against ...0 0.000000 0
[20000 rows x 5814 columns], y=0 False
0 True
1 False
1 ...lse
9999 True
Name: is_five_star, dtype: bool)
727 y : array-like, shape = [n_samples] or [n_samples, n_output], optional
728 Target relative to X for classification or regression;
729 None for unsupervised learning.
730
731 """
--> 732 return self._fit(X, y, ParameterGrid(self.param_grid))
self._fit = <bound method GridSearchCV._fit of GridSearchCV(..._func=None,
scoring='roc_auc', verbose=2)>
X = 1516 Quinoa Weisse 18th Street / Against ...0 0.000000 0
[20000 rows x 5814 columns]
y = 0 False
0 True
1 False
1 ...lse
9999 True
Name: is_five_star, dtype: bool
self.param_grid = [{'max_features': ['sqrt', 'log2'], 'n_estimators': [20, 100, 200, 500, 1000]}]
733
734
735 class RandomizedSearchCV(BaseSearchCV):
736 """Randomized search on hyper parameters.
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py in _fit(self=GridSearchCV(cv=3, error_score='raise',
e...e_func=None,
scoring='roc_auc', verbose=2), X= 1516 Quinoa Weisse 18th Street / Against ...0 0.000000 0
[20000 rows x 5814 columns], y=0 False
0 True
1 False
1 ...lse
9999 True
Name: is_five_star, dtype: bool, parameter_iterable=<sklearn.grid_search.ParameterGrid object>)
500 )(
501 delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_,
502 train, test, self.verbose, parameters,
503 self.fit_params, return_parameters=True,
504 error_score=self.error_score)
--> 505 for parameters in parameter_iterable
parameters = undefined
parameter_iterable = <sklearn.grid_search.ParameterGrid object>
506 for train, test in cv)
507
508 # Out is a list of triplet: score, estimator, n_test_samples
509 n_fits = len(out)
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=-1), iterable=<itertools.islice object>)
661 if pre_dispatch == "all" or n_jobs == 1:
662 # The iterable was consumed all at once by the above for loop.
663 # No need to wait for async callbacks to trigger to
664 # consumption.
665 self._iterating = False
--> 666 self.retrieve()
self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-1)>
667 # Make sure that we get a last message telling us we are done
668 elapsed_time = time.time() - self._start_time
669 self._print('Done %3i out of %3i | elapsed: %s finished',
670 (len(self._output),
---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
ValueError Fri Apr 17 23:31:01 2015
PID: 25128 Python 2.7.9: /home/user/anaconda/bin/python
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator=RandomForestClassifier(bootstrap=True, class_wei...lse, random_state=1, verbose=0, warm_start=False), X=<class 'pandas.core.frame.DataFrame'> instance, y=0 False
0 True
1 False
1 ...lse
9999 True
Name: is_five_star, dtype: bool, scorer=make_scorer(roc_auc_score, needs_threshold=True), train=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]), test=array([ 0, 1, 2, ..., 6723, 6725, 6729]), verbose=2, parameters={'max_features': 'sqrt', 'n_estimators': 20}, fit_params={}, return_train_score=False, return_parameters=True, error_score='raise')
1447 if parameters is not None:
1448 estimator.set_params(**parameters)
1449
1450 start_time = time.time()
1451
-> 1452 X_train, y_train = _safe_split(estimator, X, y, train)
1453 X_test, y_test = _safe_split(estimator, X, y, test, train)
1454
1455 try:
1456 if y_train is None:
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _safe_split(estimator=RandomForestClassifier(bootstrap=True, class_wei...lse, random_state=1, verbose=0, warm_start=False), X=<class 'pandas.core.frame.DataFrame'> instance, y=0 False
0 True
1 False
1 ...lse
9999 True
Name: is_five_star, dtype: bool, indices=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]), train_indices=None)
1514 if train_indices is None:
1515 X_subset = X[np.ix_(indices, indices)]
1516 else:
1517 X_subset = X[np.ix_(indices, train_indices)]
1518 else:
-> 1519 X_subset = safe_indexing(X, indices)
1520
1521 if y is not None:
1522 y_subset = safe_indexing(y, indices)
1523 else:
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/sklearn/utils/__init__.pyc in safe_indexing(X=<class 'pandas.core.frame.DataFrame'> instance, indices=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]))
147 indices : array-like, list
148 Indices according to which X will be subsampled.
149 """
150 if hasattr(X, "iloc"):
151 # Pandas Dataframes and Series
--> 152 return X.iloc[indices]
153 elif hasattr(X, "shape"):
154 if hasattr(X, 'take') and (hasattr(indices, 'dtype') and
155 indices.dtype.kind == 'i'):
156 # This is often substantially faster than X[indices]
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self=<pandas.core.indexing._iLocIndexer object>, key=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]))
1212
1213 def __getitem__(self, key):
1214 if type(key) is tuple:
1215 return self._getitem_tuple(key)
1216 else:
-> 1217 return self._getitem_axis(key, axis=0)
1218
1219 def _getitem_axis(self, key, axis=0):
1220 raise NotImplementedError()
1221
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self=<pandas.core.indexing._iLocIndexer object>, key=[6634, 6636, 6638, 6639, 6640, 6642, 6644, 6646, 6648, 6650, 6652, 6653, 6654, 6655, 6656, 6658, 6659, 6660, 6662, 6663, ...], axis=0)
1503 "non-integer key")
1504
1505 # validate the location
1506 self._is_valid_integer(key, axis)
1507
-> 1508 return self._get_loc(key, axis=axis)
1509
1510 def _convert_to_indexer(self, obj, axis=0, is_setter=False):
1511 """ much simpler as we only have to deal with our valid types """
1512
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_loc(self=<pandas.core.indexing._iLocIndexer object>, key=[6634, 6636, 6638, 6639, 6640, 6642, 6644, 6646, 6648, 6650, 6652, 6653, 6654, 6655, 6656, 6658, 6659, 6660, 6662, 6663, ...], axis=0)
87 raise IndexingError('no slices here, handle elsewhere')
88
89 return self.obj._xs(label, axis=axis)
90
91 def _get_loc(self, key, axis=0):
---> 92 return self.obj._ixs(key, axis=axis)
93
94 def _slice(self, obj, axis=0, kind=None):
95 return self.obj._slice(obj, axis=axis, kind=kind)
96
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _ixs(self=<class 'pandas.core.frame.DataFrame'> instance, i=[6634, 6636, 6638, 6639, 6640, 6642, 6644, 6646, 6648, 6650, 6652, 6653, 6654, 6655, 6656, 6658, 6659, 6660, 6662, 6663, ...], axis=0)
1709 return self[i]
1710 else:
1711 label = self.index[i]
1712 if isinstance(label, Index):
1713 # a location index by definition
-> 1714 result = self.take(i, axis=axis)
i = [6634, 6636, 6638, 6639, 6640, 6642, 6644, 6646, 6648, 6650, 6652, 6653, 6654, 6655, 6656, 6658, 6659, 6660, 6662, 6663, ...]
values = undefined
values.DataFrame.stack = undefined
axis = 0
axis.Return = undefined
result = undefined
1715 copy=True
1716 else:
1717 new_values = self._data.fast_xs(i)
1718
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in take(self=<class 'pandas.core.frame.DataFrame'> instance, indices=[6634, 6636, 6638, 6639, 6640, 6642, 6644, 6646, 6648, 6650, 6652, 6653, 6654, 6655, 6656, 6658, 6659, 6660, 6662, 6663, ...], axis=0, convert=True, is_copy=True)
1346 taken : type of caller
1347 """
1348
1349 new_data = self._data.take(indices,
1350 axis=self._get_block_manager_axis(axis),
-> 1351 convert=True, verify=True)
1352 result = self._constructor(new_data).__finalize__(self)
1353
1354 # maybe set copy if we didn't actually change the index
1355 if is_copy:
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in take(self=BlockManager
Items: Index([u'1516 Quinoa Weisse'...ock: slice(5239, 5240, 1), 1 x 20000, dtype: bool, indexer=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]), axis=1, verify=True, convert=True)
3264 raise Exception('Indices must be nonzero and less than '
3265 'the axis length')
3266
3267 new_labels = self.axes[axis].take(indexer)
3268 return self.reindex_indexer(new_axis=new_labels, indexer=indexer,
-> 3269 axis=axis, allow_dups=True)
3270
3271 def merge(self, other, lsuffix='', rsuffix=''):
3272 if not self._is_indexed_like(other):
3273 raise AssertionError('Must have same axes to merge managers')
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in reindex_indexer(self=BlockManager
Items: Index([u'1516 Quinoa Weisse'...ock: slice(5239, 5240, 1), 1 x 20000, dtype: bool, new_axis=Int64Index([3317, 3318, 3319, 3319, 3320, 3321, ...381, 3382, 3382, 3383, 3383, ...], dtype='int64'), indexer=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]), axis=1, fill_value=None, allow_dups=True, copy=True)
3151 indexer, fill_tuple=(fill_value,))
3152 else:
3153 new_blocks = [blk.take_nd(indexer, axis=axis,
3154 fill_tuple=(fill_value if fill_value is not None else
3155 blk.fill_value,))
-> 3156 for blk in self.blocks]
3157
3158 new_axes = list(self.axes)
3159 new_axes[axis] = new_axis
3160 return self.__class__(new_blocks, new_axes)
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in take_nd(self=FloatBlock: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1...6, 97, 98, 99, ...], 5813 x 20000, dtype: float64, indexer=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]), axis=1, new_mgr_locs=None, fill_tuple=(nan,))
846 new_values = com.take_nd(self.get_values(), indexer, axis=axis,
847 allow_fill=False)
848 else:
849 fill_value = fill_tuple[0]
850 new_values = com.take_nd(self.get_values(), indexer, axis=axis,
--> 851 allow_fill=True, fill_value=fill_value)
852
853 if new_mgr_locs is None:
854 if axis == 0:
855 slc = lib.indexer_as_slice(indexer)
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/core/common.pyc in take_nd(arr=memmap([[ 0. , 0. , 0. , ... 0. ,
0. , 0. ]]), indexer=array([ 6634, 6636, 6638, ..., 19997, 19998, 19999]), axis=1, out=array([[ 0., 0., 0., ..., 0., 0., 0.],
...0.],
[ 0., 0., 0., ..., 0., 0., 0.]]), fill_value=nan, mask_info=None, allow_fill=True)
818
819 func = _get_take_nd_function(arr.ndim, arr.dtype, out.dtype,
820 axis=axis, mask_info=mask_info)
821
822 indexer = _ensure_int64(indexer)
--> 823 func(arr, indexer, out, fill_value)
824
825 if flip_order:
826 out = out.T
827 return out
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/algos.so in pandas.algos.take_2d_axis1_float64_float64 (pandas/algos.c:95466)()
4000
4001
4002
4003
4004
-> 4005
4006
4007
4008
4009
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/algos.so in View.MemoryView.memoryview_cwrapper (pandas/algos.c:175798)()
609
610
611
612
613
--> 614
615
616
617
618
...........................................................................
/home/user/anaconda/lib/python2.7/site-packages/pandas/algos.so in View.MemoryView.memoryview.__cinit__ (pandas/algos.c:172387)()
316
317
318
319
320
--> 321
322
323
324
325
ValueError: buffer source array is read-only
___________________________________________________________________________
```
pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.1.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.7.2
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9929/events | https://github.com/pandas-dev/pandas/pull/9929 | 69,278,048 | MDExOlB1bGxSZXF1ZXN0MzM1OTc5MjE= | 9,929 | Allow add_categories() to accept Series/np.array and make raising on dup... | {
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} | 3 | 2015-04-18T07:01:35Z | 2015-04-22T00:08:07Z | 2015-04-21T23:38:48Z | CONTRIBUTOR | null | ...licates optional, fixes #9927
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} | 1 | 2015-04-18T15:23:16Z | 2016-11-11T13:02:04Z | 2016-11-11T10:00:32Z | CONTRIBUTOR | null | Hello,
I noticed in http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html#pandas.read_excel
that `read_excel` can have a parameter `sheetname`set to `None` or a to a `list`
In such a case a dictionary of `DataFrame` is returned.
Maybe it should output an `OrderedDict` of `DataFrame` to preserve sheets order.
Kind regards
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| null | 2 | 2015-04-18T15:35:35Z | 2015-04-19T00:17:02Z | 2015-04-19T00:16:58Z | CONTRIBUTOR | null | Travis build status badge should be clickable.
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| null | 1 | 2015-04-18T15:36:37Z | 2015-04-19T00:17:09Z | 2015-04-19T00:17:09Z | CONTRIBUTOR | null | Travis build status badge is not clickable
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| null | 4 | 2015-04-18T15:43:36Z | 2015-04-20T11:52:18Z | 2015-04-20T04:32:21Z | CONTRIBUTOR | null | It should fix https://github.com/pydata/pandas/issues/9930
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} | 3 | 2015-04-18T17:22:20Z | 2015-04-18T20:52:21Z | 2015-04-18T20:42:41Z | CONTRIBUTOR | null | Fixes #9596
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} | 2 | 2015-04-18T19:57:34Z | 2015-04-19T18:52:22Z | 2015-04-19T18:51:08Z | NONE | null | | Q | A |
| --- | --- |
| Bug fix? | no |
| New feature? | yes |
| BC breaks? | no |
| Deprecations? | no |
| Tests pass? | yes |
| Fixed tickets | - |
| License | BSD |
| Doc PR | - |
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| null | 1 | 2015-04-19T01:47:49Z | 2019-01-13T00:57:15Z | null | NONE | null | Hi,
I am new to Pandas and like it a lot. I noticed that while doing an outer merge if the dataframe on the left side is empty, then the key column in the merged dataframe is not the first.
First the summary and then my code, print output, and pd.show_versions is included below. Consider 2 dataframes left and right. The left data frame has 2 columns - key and lval. The right data frame has 2 columns - key and rval. Then consider 4 situations below.
1) Both left and right dataframes are non-empty
2) Left is non-empty and right is empty
3) Left is empty and right is non-empty
4) Both left and right dataframes are empty
In each of the situations, do an outer merge on the key column. The merged data frame has the columns in the following order:
In (1) and (2), the merged columns are key, lval, rval -> Good and as I expected
In (3) and (4), the merged columns are lval, key, rval -> Not as I expected. Is this expected? I'd expect the merged columns to be key, lval, rval. Is there a way to force this? I could do a workaround by checking the shape of the left data frame but I am wondering if I am doing something wrong or there's something wrong.
## Code
```
import pandas as pd
import numpy as np
def print_matrices():
print 'left'
print left
print 'right'
print right
print 'merged'
print merged
print 'pandas show_versions'
pd.show_versions(as_json=False)
print '\n\nAn outer join'
left = pd.DataFrame({'key': ['foo1', 'foo2'], 'lval': [1, 2]})
right = pd.DataFrame({'key': ['foo1', 'foo3'], 'rval': [4, 5]})
merged = pd.merge(left, right, on='key', how='outer')
print_matrices()
print '\n\nAn outer join with right empty dataframe'
left = pd.DataFrame({'key': ['foo1', 'foo2'], 'lval': [1, 2]})
right = pd.DataFrame({'key': [], 'rval': []})
merged = pd.merge(left, right, on='key', how='outer')
print_matrices()
print '\n\nAn outer join with left empty dataframe'
left = pd.DataFrame({'key': [], 'lval': []})
right = pd.DataFrame({'key': ['foo1', 'foo3'], 'rval': [4, 5]})
merged = pd.merge(left, right, on='key', how='outer')
print_matrices()
print '\n\nAn outer join with both left and right empty dataframe'
left = pd.DataFrame({'key': [], 'lval': []})
right = pd.DataFrame({'key': [], 'rval': []})
merged = pd.merge(left, right, on='key', how='outer')
print_matrices()
```
## Output:
An outer join
```
left
key lval
0 foo1 1
1 foo2 2
right
key rval
0 foo1 4
1 foo3 5
merged
key lval rval
0 foo1 1 4
1 foo2 2 NaN
2 foo3 NaN 5
```
An outer join with right empty dataframe
```
left
key lval
0 foo1 1
1 foo2 2
right
Empty DataFrame
Columns: [key, rval]
Index: []
merged
key lval rval
0 foo1 1 NaN
1 foo2 2 NaN
```
An outer join with left empty dataframe
```
left
Empty DataFrame
Columns: [key, lval]
Index: []
right
key rval
0 foo1 4
1 foo3 5
merged
lval key rval
0 NaN foo1 4
1 NaN foo3 5
```
An outer join with two empty dataframe
```
left
Empty DataFrame
Columns: [key, lval]
Index: []
right
Empty DataFrame
Columns: [key, rval]
Index: []
merged
Empty DataFrame
Columns: [lval, key, rval]
Index: []
```
pandas show_versions
## INSTALLED VERSIONS
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: None
Cython: None
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2014.10
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9
apiclient: 1.4.0
sqlalchemy: None
pymysql: None
psycopg2: None
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| null | 1 | 2015-04-19T10:38:43Z | 2015-04-19T17:54:34Z | 2015-04-19T17:54:28Z | MEMBER | null | Just a link in the cookbook, as suggested by https://github.com/pydata/pandas/issues/9641
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} | 3 | 2015-04-19T18:27:44Z | 2015-04-28T23:16:52Z | 2015-04-28T12:16:00Z | CONTRIBUTOR | null | From this StackOverflow question: http://stackoverflow.com/questions/29730488/setting-columns-for-an-empty-pandas-dataframe
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} | 0 | 2015-04-19T18:30:36Z | 2015-04-20T11:15:48Z | 2015-04-20T11:15:48Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_to_csv_mixed | 107.7774 | 668.5090 | 0.1612 |
frame_to_csv | 55.9403 | 140.1517 | 0.3991 |
write_csv_standard | 25.6400 | 49.6367 | 0.5166 |
packers_write_csv | 663.6736 | 1228.0974 | 0.5404 |
read_csv_standard | 10.2447 | 15.0284 | 0.6817 |
read_csv_skiprows | 12.1686 | 16.3487 | 0.7443 |
frame_to_csv_date_formatting | 10.2980 | 13.3820 | 0.7695 |
```
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} | 2 | 2015-04-19T21:24:18Z | 2016-12-18T20:24:12Z | 2016-12-18T20:24:12Z | CONTRIBUTOR | null | What's the expected behavior when grouping on a column containing `NaN` and then applying `transform`? For a `Series`, the current result is to throw an exception:
```
>>> df = pd.DataFrame({
... 'a' : range(10),
... 'b' : [1, 1, 2, 3, np.nan, 4, 4, 5, 5, 5]})
>>>
>>> df.groupby(df.b)['a'].transform(max)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/groupby.py", line 2422, in transform
return self._transform_fast(cyfunc)
File "pandas/core/groupby.py", line 2463, in _transform_fast
return self._set_result_index_ordered(Series(values))
File "pandas/core/groupby.py", line 498, in _set_result_index_ordered
result.index = self.obj.index
File "pandas/core/generic.py", line 1997, in __setattr__
return object.__setattr__(self, name, value)
File "pandas/src/properties.pyx", line 65, in pandas.lib.AxisProperty.__set__ (pandas/lib.c:41301)
obj._set_axis(self.axis, value)
File "pandas/core/series.py", line 273, in _set_axis
self._data.set_axis(axis, labels)
File "pandas/core/internals.py", line 2219, in set_axis
'new values have %d elements' % (old_len, new_len))
ValueError: Length mismatch: Expected axis has 9 elements, new values have 10 elements
```
For a `DataFrame`, the missing value gets filled in with what looks like an uninitialized value from `np.empty_like`:
```
>>> df.groupby(df.b).transform(max)
a
0 1
1 1
2 2
3 3
4 -1
5 6
6 6
7 9
8 9
9 9
```
It seems like either it should fill in the missing values with `NaN` (which might require a change of dtype), or just drop those rows from the result (which requires the shape to change). Either solution has the potential to surprise.
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} | 0 | 2015-04-19T22:14:36Z | 2015-04-20T01:04:01Z | 2015-04-20T01:04:01Z | CONTRIBUTOR | null | xref #9770
xref #9845
xref #9804
xref #9802
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} | 8 | 2015-04-20T03:15:12Z | 2021-04-18T06:31:58Z | 2021-04-18T06:31:58Z | NONE | null | Very subtle. Index name stays when using .ix [ list ], but gets lost when using .ix[ Int64Index ].
```
import pandas as pd
from pandas.util.testing import assert_frame_equal
import numpy as np
assert pd.__version__ == '0.16.0'
df = pd.DataFrame([np.nan, np.nan], columns = ['tags'], index=pd.Int64Index([4815961, 4815962], dtype='int64', name='id'))
assert str(df) == ' tags\nid \n4815961 NaN\n4815962 NaN'
# OK.
L = [4815962]
assert list(L) == list(df.index.intersection(L))
# succeeds. It's just a type difference
print df.ix[L].tags.index.name
#>>> 'id'
print df.ix[df.index.intersection(L)].tags.index.name
#>>>
assert df.ix[L].tags.index.name == df.ix[df.index.intersection(L)].tags.index.name
# assertion failure. Should really succeed.
assert_frame_equal(df.ix[L], df.ix[df.index.intersection(L)])
# assertion failure. Should really succeed.
# AssertionError: attr is not equal [names]: FrozenList([u'id']) != FrozenList([None])
```
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} | 13 | 2015-04-20T04:20:38Z | 2015-05-10T14:20:54Z | 2015-05-10T14:20:54Z | CONTRIBUTOR | null | Fix https://github.com/pydata/pandas/issues/9930
read_excel with sheetname=None or sheetname=[0,1,”Sheet5”] returns an OrderedDict instead of a dict
Follow-up of #9933
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} | 3 | 2015-04-20T05:54:25Z | 2021-04-16T04:35:38Z | 2021-04-16T04:35:29Z | NONE | null | Here is the test code:
```
import pandas as pd
from numpy.random import randint, rand, seed
seed(10)
N_Group = 20
N_Rows = 1000
df = pd.DataFrame({"g":randint(0, N_Group, N_Rows), "a":rand(N_Rows), "b":rand(N_Rows)})
def f(df):
df["a"] -= df["b"].mean()
return df
%time df1 = df.groupby("g").apply(f)
%time df2 = df.groupby("g").transform(f)
```
the output is:
```
Wall time: 17 ms
Wall time: 682 ms
```
before the for-loop for every group in apply(), it disable the 'mode.chained_assignment' opition:
```
# ignore SettingWithCopy here in case the user mutates
with option_context('mode.chained_assignment',None):
return self._python_apply_general(f)
```
but there is not such code for `transform()`.
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| null | 5 | 2015-04-20T06:46:32Z | 2018-07-07T20:15:35Z | 2018-07-07T20:15:27Z | NONE | null | ```
df = pd.DataFrame({"g":[1, 2, 2, 2], "a":[1, 2, 3, 4], "b":[5, 6, 7, 8]})
df.groupby("g").apply(lambda x:x.copy())
```
raise
```
ValueError: Shape of passed values is (3, 4), indices imply (3, 2)
```
but `lambda x:x` or `lambda x:x[:]` works.
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} | 26 | 2015-04-20T07:55:40Z | 2015-07-17T14:02:03Z | 2015-07-16T08:32:48Z | CONTRIBUTOR | null | currently on `master` (after the PR https://github.com/pydata/pandas/issues/9785), we have the following behavior when comparing two Indexes with `==`:
```
In [3]: index_a = Index(['a', 'b', 'c'])
In [4]: index_b = Index(['a', 'b', 'c', 'd'])
In [5]: index_a == index_b
Out[5]: False
```
Firstly, `==` should be element-wise comparison, so it really shouldn't return just a scalar `bool`. That should be the case for `Index.equals()` instead.
Secondly it is also not consistent with the behavior with `Series`
```
In [6]: series_a = Series(['a', 'b', 'c'])
In [7]: series_b = Series(['a', 'b', 'c', 'd'])
In [8]: series_a == series_b
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-97f84083a0cd> in <module>()
----> 1 series_a == series_b
/Users/mortada_mehyar/code/github/pandas/pandas/core/ops.py in wrapper(self, other)
576 name = _maybe_match_name(self, other)
577 if len(self) != len(other):
--> 578 raise ValueError('Series lengths must match to compare')
579 return self._constructor(na_op(self.values, other.values),
580 index=self.index, name=name)
ValueError: Series lengths must match to compare
```
This PR makes the behavior consistent by checking the Index lengths. Also fixes a few unit tests which seem to incorrectly assume `.equal()` should return the same output as `==`
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| null | 4 | 2015-04-20T08:34:32Z | 2015-04-20T10:58:44Z | 2015-04-20T10:58:38Z | CONTRIBUTOR | null | The following fails:
```
pd.DataFrame([], columns="a b".split())
```
I'd expect this to be equivalent to
```
pd.DataFrame(None, columns="a b".split())
```
Is there any reason not to do it like this?
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| null | 2 | 2015-04-20T09:37:31Z | 2019-10-02T07:34:03Z | 2015-04-20T10:38:42Z | NONE | null | When dealing with data sizes at the limit of the machine, pandas produces a memory error when one tries to assign a numpy array to a equally typed and sized pandas column. `__setitem__()` seems to force an unnecessary copy since assigning directly to the array backing the block is possible:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: M,N=int(1e7),10
In [4]: A1=np.zeros((M,N),'f', 'F')
In [5]: df_A1 = pd.DataFrame(index=xrange(M), columns=range(N), dtype='f')
In [6]: X=np.arange(M+0.0)
# assigning directly to column of array backing the DataFrame works:
In [7]: df_A1._data.blocks[0].values[0,:] = X
# assigning to column of the DataFrame fails:
In [8]: df_A1[0] = X
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
<ipython-input-9-c37d594d8c18> in <module>()
----> 1 df_A1[0]=X
C:\Anaconda\envs\finance1\lib\site-packages\pandas\core\frame.pyc in __setitem__
(self, key, value)
2115 else:
2116 # set column
-> 2117 self._set_item(key, value)
2118
2119 def _setitem_slice(self, key, value):
C:\Anaconda\envs\finance1\lib\site-packages\pandas\core\frame.pyc in _set_item(s
elf, key, value)
2193 self._ensure_valid_index(value)
2194 value = self._sanitize_column(key, value)
-> 2195 NDFrame._set_item(self, key, value)
2196
2197 # check if we are modifying a copy
C:\Anaconda\envs\finance1\lib\site-packages\pandas\core\generic.pyc in _set_item
(self, key, value)
1188
1189 def _set_item(self, key, value):
-> 1190 self._data.set(key, value)
1191 self._clear_item_cache()
1192
C:\Anaconda\envs\finance1\lib\site-packages\pandas\core\internals.pyc in set(sel
f, item, value, check)
3000 else:
3001 self._blklocs[blk.mgr_locs.indexer] = -1
-> 3002 blk.delete(blk_locs)
3003 self._blklocs[blk.mgr_locs.indexer] = np.arange(len(
blk))
3004
C:\Anaconda\envs\finance1\lib\site-packages\pandas\core\internals.pyc in delete(
self, loc)
274 Delete given loc(-s) from block in-place.
275 """
--> 276 self.values = np.delete(self.values, loc, 0)
277 self.mgr_locs = self.mgr_locs.delete(loc)
278
C:\Anaconda\envs\finance1\lib\site-packages\numpy\lib\function_base.pyc in delet
e(arr, obj, axis)
3615 keep[obj, ] = False
3616 slobj[axis] = keep
-> 3617 new = arr[slobj]
3618
3619 if wrap:
MemoryError:
```
Am I doing something wrong? Is there a better/correct way of assigning data to a column?
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} | 25 | 2015-04-20T09:57:19Z | 2016-10-28T07:49:53Z | 2015-06-03T11:36:51Z | NONE | null | When I create a pandas.Series or pandas.DataFrame having np.NaN values, I get the following warning.
Example:
```
>>> import pandas as pd, numpy as np
>>> pd.Series([np.nan,np.nan,np.nan],index=[1,2,3])
1 NaN
2 NaN
3 NaN
dtype: float64
C:\Users\domen\Anaconda\lib\site-packages\pandas\core\format.py:1995: RuntimeWarning: invalid value encountered in absolute
abs_vals = np.abs(self.values)
C:\Users\domen\Anaconda\lib\site-packages\pandas\core\format.py:1998: RuntimeWarning: invalid value encountered in greater
has_large_values = (abs_vals > 1e8).any()
C:\Users\domen\Anaconda\lib\site-packages\pandas\core\format.py:1999: RuntimeWarning: invalid value encountered in less
has_small_values = ((abs_vals < 10 ** (-self.digits)) &
C:\Users\domen\Anaconda\lib\site-packages\pandas\core\format.py:2000: RuntimeWarning: invalid value encountered in greater
(abs_vals > 0)).any()
```
I am using Anaconda-2.2.0-Windows-x86_64, with Python 2.7.9. and pandas 0.16.0.
I would ask you to advise.
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| null | 1 | 2015-04-20T10:17:49Z | 2015-04-20T10:45:54Z | 2015-04-20T10:45:34Z | NONE | null | Whilst answering this [SO question](http://stackoverflow.com/questions/29706825/pandas-version-0-16-0-after-changing-dataframe-index-all-values-become-nan) I discovered that the DataFrame constructor is ignoring the fact that I'm passing an index explicitly resulting in a df with all NaNs:
```
In [6]:
df = pd.DataFrame({'AAA':[4,5,6,7],'BBB':[10,20,30,40],'CCC':[100,50,-30,-50]})
df1 = pd.DataFrame(data=df,index=(['a','b','c','d']))
df1
Out[6]:
AAA BBB CCC
a NaN NaN NaN
b NaN NaN NaN
c NaN NaN NaN
d NaN NaN NaN
```
Stepping through the code I see that it reaches here in frame.py:
```
if isinstance(data, BlockManager):
mgr = self._init_mgr(data, axes=dict(index=index, columns=columns),
dtype=dtype, copy=copy)
```
and then ends up here in generic.py:
```
119 def _init_mgr(self, mgr, axes=None, dtype=None, copy=False):
120 """ passed a manager and a axes dict """
121 for a, axe in axes.items():
122 if axe is not None:
123 mgr = mgr.reindex_axis(
124 -> axe, axis=self._get_block_manager_axis(a), copy=False)
```
The behaviour can be reproduced simply like this:
```
In [46]:
data1 = pd.DataFrame({'AAA':[4,5,6,7],'BBB':[10,20,30,40],'CCC':[100,50,-30,-50]})
data1.reindex_axis(list('abcd'))
Out[46]:
AAA BBB CCC
a NaN NaN NaN
b NaN NaN NaN
c NaN NaN NaN
d NaN NaN NaN
```
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} | 6 | 2015-04-20T12:26:04Z | 2021-05-10T21:45:32Z | 2021-05-10T21:45:32Z | MEMBER | null | When trying to do a calculation with two dataframes with both multi-indexed rows, but with swapped levels, you get the error message `"NotImplementedError: merging with more than one level overlap on a multi-index is not implemented"`.
This is not very informative in this case.
```
In [11]: df = pd.DataFrame({'a':np.random.randn(6)}, index=pd.MultiIndex.from_product([['a', 'b'],[0,1,2]], names=['levA', 'levB']))
In [12]: df2 = df.copy()
In [13]: df2.index = df2.index.swaplevel(0,1)
In [14]: df
Out[14]:
a
levA levB
a 0 -0.248626
1 2.382662
2 1.349179
b 0 0.720629
1 0.205297
2 0.113340
In [15]: df2
Out[15]:
a
levB levA
0 a -0.248626
1 a 2.382662
2 a 1.349179
0 b 0.720629
1 b 0.205297
2 b 0.113340
In [16]:
In [16]: df - df2
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-16-ffb739d7f3d7> in <module>()
----> 1 df - df2
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\ops.pyc in f(self, other, axis,
level, fill_value)
813 def f(self, other, axis=default_axis, level=None, fill_value=None):
814 if isinstance(other, pd.DataFrame): # Another DataFrame
--> 815 return self._combine_frame(other, na_op, fill_value, level)
816 elif isinstance(other, pd.Series):
817 return self._combine_series(other, na_op, fill_value, axis,
level)
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\frame.pyc in _combine_frame(sel
f, other, func, fill_value, level)
3099
3100 def _combine_frame(self, other, func, fill_value=None, level=None):
-> 3101 this, other = self.align(other, join='outer', level=level, copy=
False)
3102 new_index, new_columns = this.index, this.columns
3103
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\generic.pyc in align(self, othe
r, join, axis, level, copy, fill_value, method, limit, fill_axis)
3146 copy=copy, fill_value=fill_value,
3147 method=method, limit=limit,
-> 3148 fill_axis=fill_axis)
3149 elif isinstance(other, Series):
3150 return self._align_series(other, join=join, axis=axis, level
=level,
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\generic.pyc in _align_frame(sel
f, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
3167 join_index, ilidx, iridx = \
3168 self.index.join(other.index, how=join, level=level,
-> 3169 return_indexers=True)
3170
3171 if axis is None or axis == 1:
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.pyc in join(self, other,
how, level, return_indexers)
1781 pass
1782 else:
-> 1783 return self._join_multi(other, how=how, return_indexers=
return_indexers)
1784
1785 # join on the level
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.pyc in _join_multi(self,
other, how, return_indexers)
1876 raise ValueError("cannot join with no level specified and no
overlapping names")
1877 if len(overlap) > 1:
-> 1878 raise NotImplementedError("merging with more than one level
overlap on a multi-index is not implemented")
1879 jl = overlap[0]
1880
NotImplementedError: merging with more than one level overlap on a multi-index i
s not implemented
```
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| null | 0 | 2015-04-20T12:26:18Z | 2017-04-05T02:06:24Z | 2015-04-20T18:41:21Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9158
Fixes up a git mistake in https://github.com/pydata/pandas/pull/9164
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| null | 0 | 2015-04-20T15:38:07Z | 2015-04-20T17:06:13Z | 2015-04-20T17:06:13Z | CONTRIBUTOR | null | documenation -> documentation
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} | 9 | 2015-04-20T16:39:32Z | 2015-04-21T14:14:33Z | 2015-04-21T14:10:18Z | NONE | null | with pandas 0.16 and sqlalchemy, the to_sql method of a dataframe creates duplicate redundant constraints on each boolean column, at least when exporting to Microsoft SQL Server, because for each column it creates 2 constraints checking the field must be either 0 or 1. This is redundant and slows down the database. A short example is below. Of course you must check the output in the sql database. Also, the fact that there is no control and no documentation over the constraints created by to_sql is extremely frustrating
import pandas as pd
import numpy as np
from sqlalchemy import create_engine, MetaData, Table, select
import sqlalchemy as sqlalchemy
from sqlalchemy import create_engine, MetaData, Table, select
import sqlalchemy as sqlalchemy
ServerName = "cdodb-wb"
Database = "DealAnalysis"
engine = create_engine('mssql+pyodbc://' + ServerName + '/' + Database, encoding='latin1')
conn = engine.connect()
metadata = MetaData(conn)
df=pd.DataFrame(np.random.rand(20)>0.5, columns=['random_boolean'])
df.to_sql('python_test_table',engine,if_exists='replace')
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} | 1 | 2015-04-20T19:08:27Z | 2015-04-21T23:41:25Z | 2015-04-21T23:41:18Z | CONTRIBUTOR | null | closes #9878
Adds a test for packaging and installing pandas from a tarball, inside of the 2.6 regression.
After regular nosetests complete, cython is removed from the venv, pandas is packaged with cdist command, and the package is installed via pip. Limited nosetests run once installed.
The output of this test in the log is now 'folded' via http://blog.travis-ci.com/2013-05-22-improving-build-visibility-log-folds/ by putting install_test.sh into the 'after_script' section of the travis config.
This is a new PR with squashed commits to replace the original closed PR: https://github.com/pydata/pandas/pull/9922
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| null | 2 | 2015-04-20T21:01:00Z | 2015-04-20T21:09:50Z | 2015-04-20T21:09:27Z | CONTRIBUTOR | null | I have an installation of Pandas of 0.16.0 that fails TestSeries.test_sum when using Numpy 1.9.2 compiled against MKL 11.2. Here is a minimal test:
```
import numpy as np
import pandas as pd
s = np.nan*pd.util.testing.makeStringSeries()
print s.sum()
```
For Pandas 0.15.2, Numpy 1.9.1 compiled against MKL 10.3, this prints nan, but for Numpy 1.9.2 against 11.2 it prints 0.0.
I don't know whether to blame Pandas version, blas or Numpy version, and will run additional tests to distinguish which is at fault. I have stepped through execution for both cases and they are identical up to the call to <built-in function nansum> at pandas/core/nanops.py:89
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} | 11 | 2015-04-20T22:05:38Z | 2020-03-28T22:30:48Z | 2016-06-12T21:04:40Z | NONE | null | When I left-join two data frames on an int64 column, this column is cast to a float if it is the index of one of the two data frames:
``` python
>>> id = [1, 143349558619761320]
>>> a = pd.DataFrame({'a': [1, 2]}, index=pd.Int64Index(id, name='id'))
>>> b = pd.DataFrame({'id': id[1], 'b': [2]})
>>> merge_index = pd.merge(a, b, left_index=True, right_on='id', how='left')
>>> print(merge_index.dtypes)
a int64
b float64
id float64
dtype: object
```
In this case this is a problem because casting back to an int changes the value of this column:
``` python
>>> int(float(143349558619761320))
143349558619761312
```
If the int64 column is not in the index, the column is not cast to a float:
``` python
>>> merge_column = pd.merge(a.reset_index(), b, on='id', how='left')
>>> print(merge_column.dtypes)
id int64
a int64
b float64
dtype: object
```
I understand that integer columns get cast to a float if NaNs are introduced (like column b here), but in this case the final column contains no missing values, so casting to a float can be avoided.
Output from pd.show_versions():
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-5-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: None
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 3 | 2015-04-21T00:56:18Z | 2020-08-31T22:31:08Z | 2020-08-31T22:31:08Z | NONE | null | ```
df = pd.DataFrame({"g":[1, 2, 1, 2, 1], "a":range(5), "b":range(1, 6), "c":range(2, 7)})
g = df.groupby("g", group_keys=False)
print g.group_keys #False
print g[["a", "b", "c"]].group_keys #True
```
So the `apply()` results are different for these two case:
```
print g.apply(lambda x:x[:2])
```
ouput:
```
a b c g
0 0 1 2 1
2 2 3 4 1
1 1 2 3 2
3 3 4 5 2
```
but
```
print g[["a", "b", "c"]].apply(lambda x:x[:2])
```
output:
```
a b c
g
1 0 0 1 2
2 2 3 4
2 1 1 2 3
3 3 4 5
```
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} | 19 | 2015-04-21T07:51:09Z | 2021-04-18T06:37:56Z | null | MEMBER | null | At this moment, `pd.io.sql.get_schema` is not documented (not in the API docs, and not in the io docs). But, it is a potential useful function, so I think it would be good to be more explicit about its status (by mentioning it in the docs).
However, there are some quirks about the function:
- The signature: `pd.io.sql.get_schema(frame, name, flavor='sqlite', keys=None, con=Non
e, dtype=None)` -> flavor keyword in the third place, while we want to deprecate it (and this means you cannot do `get_schema(df, 'name', engine)`, but always have to do `get_schema(df, 'name', con=engine)`.
Ideally this should have the same arguments (order) as `to_sql` (`pd.io.sql.to_sql(frame, name, con, flavor='sqlite', schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)`) (only chunksize is not relevant)
- It should have all options to modify the resulting sql table as `to_sql`
- Maybe also the option to return the sqlalchemy `Table` instead of the string itself?
That we maybe should first solve before making it more explicitely public?
Triggered by http://stackoverflow.com/questions/29749356/python-pandas-export-structure-only-no-rows-of-a-dataframe-to-sql/
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| null | 9 | 2015-04-21T14:05:03Z | 2015-04-24T11:22:25Z | 2015-04-24T11:22:10Z | NONE | null | `RangeIndex(1, 10, 2)` is a memory saving alternative to `Index(np.arange(1, 10,2))`: c.f. #939.
This re-implementation is compatible with the current `Index()` api and is a drop-in replacement for `Int64Index()`. It automatically converts to Int64Index() when required by operations.
At present only for a minimum number of operations the type is conserved (e.g. slicing, inner-, left- and right-joins). Most other operations trigger creation of an equivalent Int64Index (or at least an equivalent numpy array) and fall back to its implementation.
This PR also extends the functionality of the `Index()` constructor to allow creation of `RangeIndexes()` with
```
Index(20)
Index(2, 20)
Index(0, 20, 2)
```
in analogy to
```
range(20)
range(2, 20)
range(0, 20, 2)
```
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| null | 6 | 2015-04-21T15:11:24Z | 2015-04-28T19:32:57Z | 2015-04-28T19:32:57Z | CONTRIBUTOR | null | I would be pleased if pandaSDMx (http://pandasdmx.readthedocs.org) could be added to the pandas Ecosystem directory. The following text is just a proposal, feel free to change it as you see fit:
"pandaSDMX is a libraryaimed at becoming the most intuitive and versatile tool to retrieve and acquire statistical data and metadata disseminated in
SDMX
format. As of v0.2 it works well with the SDMX services of the European statistics office (Eurostat) and the European Central Bank (ECB) both of which provide hundreds of thousands social and economic indicators. While pandaSDMX is extensible
to cater any output format, it currently exports SDMX datasets with observations and/or attributes to multi-indexed pandas DataFrames and Series. Metadata such as datastructure definitions, dataflow definitions, and code lists are represented in terms of the SDMX-2.1 information model."
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} | 4 | 2015-04-21T15:56:08Z | 2016-07-12T07:07:41Z | 2016-07-12T07:07:41Z | NONE | null | Hi all,
I've noticed the following behaviour in .asfreq method when dealing with frames/series containing original NaN values. When calling for instance asfreq('W-FRI',method='ffill') only NaN generated by DateOffset are forward filled, while the already present NaN values are not treated. I'm trying to figure out whether this was an intentional feature or a bug. In the former case however I would say that the documentation is a bit misleading
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.asfreq.html
Indeed, the description of parameter 'method' is exactly equivalent to that present in fillna,
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html
so it comes natural to expect all invalid observations be filled.
Thanks for your help,
Umberto
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} | 2 | 2015-04-22T00:23:43Z | 2015-04-30T10:04:04Z | 2015-04-30T10:04:04Z | CONTRIBUTOR | null | `.iloc` returns `object` dtype for a non-object empty series:
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a':[1,2,3],'b':['b','b2','b3']})
In [3]: df.ix[[],:].loc[:,'a']
Out[3]: Series([], name: a, dtype: int64)
In [4]: df.ix[[],:].iloc[:,0]
Out[4]: Series([], name: a, dtype: object)
```
(came across this as I think it is related to the issue reported http://stackoverflow.com/questions/29749356/python-pandas-export-structure-only-no-rows-of-a-dataframe-to-sql/ , i.e. in sql code which is trying to infer series datatypes)
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.6
Cython: 0.21.1
numpy: 1.9.2
scipy: 0.14.0
statsmodels: 0.6.1
IPython: 3.1.0
sphinx: 1.2.3
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: 0.6.2.None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
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} | 16 | 2015-04-22T00:32:36Z | 2015-08-06T01:21:48Z | 2015-08-05T21:39:50Z | CONTRIBUTOR | null | had some time this morning and started working
closes #9943
for now I just uncommented the explicit setting to None of the index names.
let me know if I should remove it completely.
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} | 3 | 2015-04-22T11:12:43Z | 2015-09-06T16:31:07Z | 2015-09-06T16:31:07Z | NONE | null | I have stumbled onto problems with slicing a pandas DataFrame when an index that originally contained integers has been manipulated such that it contains floats. Consider the following example (somewhat contrived to make it stand on its own; in practice, data are loaded from file):
```
df = pd.DataFrame({'i': np.linspace(0, 5, 6).astype(np.int64),
'j': np.linspace(2, 3, 6)})
df.set_index('i', inplace=True)
print(df.index) # Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')
df.index = df.index / 10.
print(df.index) # Int64Index([0.0, 0.1, 0.2, 0.3, 0.4, 0.5], dtype='float64')
df[:0.3]
```
The final line raises this exception:
```
TypeError: the slice stop value [None] is not a proper indexer
for this index type (Int64Index)
```
While it was relatively easy to work around this using an explicit cast along the lines of
```
df.index = np.asarray(df.index, dtype=np.float64) / 10.
```
it took me quite a while to figure out what was going on. What I expected to happen in the original code was that the index would become a Float64Index or something similar. When I first noticed the Int64Index with dtype=float I thought that was wierd. Is it a good idea to change this behaviour so that there is not this mismatch between index type and dtype?
My pandas version is 0.15.2. User EdChum at StackOverflow[1] has kindly tested this on 0.16, finding the same behaviour with the code above. Slicing with .ix works on 0.16 but not on 0.15.2; slicing with .loc works for both versions.
[1] http://stackoverflow.com/questions/29795225/pandas-int64index-with-dtype-float
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} | 4 | 2015-04-22T13:33:11Z | 2016-12-22T11:25:52Z | 2016-12-22T11:25:52Z | NONE | null | I have stumbled across a curious issue when working with `pandas.concat`.
Let's consider a simple example:
``` python
import pandas as pd
from copy import deepcopy
example_multiindex1 = pd.MultiIndex.from_product([['a'], ['b']])
example_dataframe1 = pd.DataFrame([0], index=example_multiindex1)
example_multiindex2 = pd.MultiIndex.from_product([['a'], ['c']])
example_dataframe2 = pd.DataFrame([1], index=example_multiindex2)
example_dict = {'s1': example_dataframe1, 's2': example_dataframe2}
print pd.concat(example_dict, names=['testname'])
```
The output is as expected:
```
0
testname
s1 a b 0
s2 a c 1
```
Strange thing will happen if we pass to `concat` not an original dict but a deepcopy of it:
``` python
print pd.concat(deepcopy(example_dict), names=['testname'])
```
The output, surprisingly, will be:
```
0
testname
s1 a b 0
s2 a b 1
```
Thus meaning, that the multiindex of the first DataFrame was taken two times.
Contents of the copied dict though seems to be correct at first glance:
``` python
>>> print deepcopy(example_dict)
{'s2': 0
a c 1, 's1': 0
a b 0}
```
PS. Version information:
``` python
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.11.0-26-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.6
Cython: 0.20.2
numpy: 1.9.2
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 3.1.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
Issue is reproducible down to version 0.15.0. Version 0.14.1 is fine.
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| null | 2 | 2015-04-22T13:54:35Z | 2015-04-23T13:34:21Z | 2015-04-22T14:19:13Z | MEMBER | null | {
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} | 2 | 2015-04-22T17:11:48Z | 2015-04-24T00:43:43Z | null | NONE | null | A student came to me for help using PyData tools for processing data in many-column CSV files. The files have 2740 rows; the longer file has 50k columns of floats, the smaller has 2740 columns of 0/1 binary data (defining a graph).
She currently is using numpy.loadtxt to read the files; the float file takes ~10 - 15 min to read on various Macs we've benchmarked. I was aware of benchmarks such as these indicating poor performance of loadtxt vs. various alternatives, including Pandas:
Fastest Python library to read a CSV file
http://softwarerecs.stackexchange.com/questions/7463/fastest-python-library-to-read-a-csv-file
A new high performance, memory-efficient file parser engine for pandas
http://wesmckinney.com/blog/a-new-high-performance-memory-efficient-file-parser-engine-for-pandas/
I suggested she try Pandas; she reported it performed _worse_ than loadtxt. This puzzled me, so I did some benchmarks. I duplicated the benchmarks in the SO post linked above, showing Pandas greatly outperforming loadtxt on a many-row, few-column CSV file; this likely rules out a problem with our installations (we are both using current Anaconda Python 2.7). I also duplicated her poor Pandas performance on the large float file.
I've put a MWE online here:
https://dl.dropboxusercontent.com/u/40075366/BDA2015/CSV_MWE.zip
That unpacks to a benchmark script and two large data files; it's a 500 kB .zip archive but it unpacks to 59 MB (otherwise I would have just posted a Gist). The data files are truncated to 10% of full size. The results on my 2009 quad-core Mac Pro (48 GB RAM) are:
```
loadtxt, float file laps:
* CSV: 8.576581 s
* csr_matrix: 180.887938 ms
Total elapsed time = 8.757631 s
loadtxt, graph file laps:
* CSV: 386.301041 ms
* csr_matrix: 22.848845 ms
Total elapsed time = 409.296036 ms
pandas, float file elapsed time = 17.595026 s
iopro, float file elapsed time = 473.067179 s
```
Notably, IOPro performs abysmally with the float file.
Perhaps this is just not a use case Pandas was designed to address. Any advice on the fastest way to read such wide CSV files in Python would be appreciated.
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/33"
} | 7 | 2015-04-22T19:47:10Z | 2018-01-31T15:18:46Z | 2018-01-31T15:18:46Z | NONE | null | ```
s = pd.Series([1,1,0], index=[1,2,2])
s.groupby(s).fillna(method='bfill')
```
I have a dataframe with a column containing some strings and NaNs. I can do do `df.col.bfill()` but when I try to do `df.groupby('col2').col1.bfill()` I get a `ValueError`.
I can do `first` and `apply(lambda x: x is str)` without error.
Here is the full traceback:
```
Traceback (most recent call last):
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 558, in wrapper
return self.apply(curried_with_axis)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 662, in apply
return self._python_apply_general(f)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 669, in _python_apply_general
not_indexed_same=mutated)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2380, in _wrap_applied_output
not_indexed_same=not_indexed_same)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1142, in _concat_objects
result = result.reindex(ax)
File "C:\Anaconda3\lib\site-packages\pandas\core\series.py", line 2149, in reindex
return super(Series, self).reindex(index=index, **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1731, in reindex
method, fill_value, copy).__finalize__(self)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1749, in _reindex_axes
allow_dups=False)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1834, in _reindex_with_indexers
copy=copy)
File "C:\Anaconda3\lib\site-packages\pandas\core\internals.py", line 3150, in reindex_indexer
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 561, in wrapper
return self.apply(curried)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 662, in apply
return self._python_apply_general(f)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 669, in _python_apply_general
not_indexed_same=mutated)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 2380, in _wrap_applied_output
not_indexed_same=not_indexed_same)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 1142, in _concat_objects
result = result.reindex(ax)
File "C:\Anaconda3\lib\site-packages\pandas\core\series.py", line 2149, in reindex
return super(Series, self).reindex(index=index, **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1731, in reindex
method, fill_value, copy).__finalize__(self)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1749, in _reindex_axes
allow_dups=False)
File "C:\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1834, in _reindex_with_indexers
copy=copy)
File "C:\Anaconda3\lib\site-packages\pandas\core\internals.py", line 3150, in reindex_indexer
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 570, in wrapper
return self._aggregate_item_by_item(name, *args, **kwargs)
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 511, in __getattr__
(type(self).__name__, attr))
AttributeError: 'SeriesGroupBy' object has no attribute '_aggregate_item_by_item'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Program Files (x86)\JetBrains\PyCharm 3.4.1\helpers\pydev\pydevd.py", line 1733, in <module>
debugger.run(setup['file'], None, None)
File "C:\Program Files (x86)\JetBrains\PyCharm 3.4.1\helpers\pydev\pydevd.py", line 1226, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files (x86)\JetBrains\PyCharm 3.4.1\helpers\pydev\_pydev_execfile.py", line 38, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc) #execute the script
File "G:/Users/ari/Documents/Git/Misc/testbross.py", line 5, in <module>
brwbt = KevinsDB.KevinToWBT(brdb.both[brdb.both.SYMBOL=='VCLT'])
File "G:\Users\ari\Documents\Git\KevinsDB.py", line 95, in KevinToWBT
s = grp.OrderTime.ffill().bfill()
File "C:\Anaconda3\lib\site-packages\pandas\core\groupby.py", line 572, in wrapper
raise ValueError
ValueError
```
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} | 2 | 2015-04-22T22:11:27Z | 2015-04-22T23:55:18Z | 2015-04-22T23:46:26Z | CONTRIBUTOR | null | ``` python
In [1]: %paste
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'GOOG', 'IBM', 'YHOO', 'MSFT']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2013')
price = pd.DataFrame({tic: data['Adj Close']
for tic, data in all_data.iteritems()})
## -- End pasted text --
```
I took the snippet above from [vincent's quickstart](https://vincent.readthedocs.org/en/latest/quickstart.html#quick-data) and found that an `UnboundLocalError` is being raised (probably pointing to a bug).
``` python
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-1-c2d08f863001> in <module>()
2 all_data = {}
3 for ticker in ['AAPL', 'GOOG', 'IBM', 'YHOO', 'MSFT']:
----> 4 all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2013')
5 price = pd.DataFrame({tic: data['Adj Close']
6 for tic, data in all_data.iteritems()})
/Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/pandas/io/data.pyc in get_data_yahoo(symbols, start, end, retry_count, pause, adjust_price, ret_index, chunksize, interval)
418 raise ValueError("Invalid interval: valid values are 'd', 'w', 'm' and 'v'")
419 return _get_data_from(symbols, start, end, interval, retry_count, pause,
--> 420 adjust_price, ret_index, chunksize, 'yahoo')
421
422
/Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/pandas/io/data.pyc in _get_data_from(symbols, start, end, interval, retry_count, pause, adjust_price, ret_index, chunksize, source)
359 # If a single symbol, (e.g., 'GOOG')
360 if isinstance(symbols, (compat.string_types, int)):
--> 361 hist_data = src_fn(symbols, start, end, interval, retry_count, pause)
362 # Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])
363 elif isinstance(symbols, DataFrame):
/Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/pandas/io/data.pyc in _get_hist_yahoo(sym, start, end, interval, retry_count, pause)
206 '&g=%s' % interval +
207 '&ignore=.csv')
--> 208 return _retry_read_url(url, retry_count, pause, 'Yahoo!')
209
210
/Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/pandas/io/data.pyc in _retry_read_url(url, retry_count, pause, name)
175 #Get rid of unicode characters in index name.
176 try:
--> 177 rs.index.name = rs.index.name.decode('unicode_escape').encode('ascii', 'ignore')
178 except AttributeError:
179 #Python 3 string has no decode method.
UnboundLocalError: local variable 'rs' referenced before assignment
```
---
My installation:
```
In [5]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: 0.14.0
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.2
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 1 | 2015-04-23T14:17:06Z | 2015-04-29T01:22:19Z | 2015-04-28T11:10:00Z | MEMBER | null | Derived from #9862. Added `check_names` kw to followings testing functions:
- `assert_index_equal` (default=True).
- `assert_series_equal` (default=True). Maybe the same option can be used to check `Series.name` in the future, otherwise it may better to choose separate name such as `check_index_name`.
- `assert_panelnd_equal` (default=False). We have to check following ToDo to specify default=True.
- https://github.com/pydata/pandas/blob/master/pandas/util/testing.py#L771
- `assert_frame_equal` is untouched as it already has the kw. Not refactored the logic because it has a path which doesn't use `assert_index_equal` internally.
Looked through test cases which have to be `check_names=False`, and added name check tests.
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| null | 1 | 2015-04-23T17:18:27Z | 2015-04-24T10:47:19Z | 2015-04-24T10:47:01Z | NONE | null | In [1]: import pandas as pd; pd.Series ([-49.2, 0.0], index=[0,1]) / pd.Series([49,0], index=[0,1])
Out[1]:
0 -inf
1 inf
dtype: float64
This appears to be resolved in 0.15, and 0.16 - so not sure that this is worth reporting
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} | 6 | 2015-04-23T20:00:42Z | 2015-08-05T21:36:43Z | 2015-08-05T21:36:42Z | NONE | null | This PR should eventually address issue #9966. I will look at testing first to make sure I have understood how pandas ought to work in these situations.
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| null | 3 | 2015-04-23T21:45:35Z | 2015-04-24T22:15:44Z | 2015-04-23T23:11:19Z | CONTRIBUTOR | null | Makes the string operators available to the `Series.str` object
``` python
In [1]: a = pd.Series(['%s', '%s'])
In [2]: a.str % 2
Out[2]: array(['2', '2'], dtype=object)
In [3]: a = pd.Series(['a', 'b'])
In [4]: a.str * 2
Out[4]: array(['aa', 'bb'], dtype=object)
In [5]: a.str + 'test'
Out[5]: array(['atest', 'btest'], dtype=object)
```
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| null | 2 | 2015-04-24T10:33:18Z | 2015-04-24T10:59:59Z | 2015-04-24T10:43:30Z | NONE | null | Pandas (core.generic) gives a misleading error message for AttributeError with wrong attribute in attribute in version 0.16.0. Example:
```
import pandas as pd
class MyDataFrame(pd.DataFrame):
@property
def my_attribute(self):
self.a_wrong_attribute
return 'Hello, World'
```
Applying it yields:
```
>>> my_frame = MyDataFrame()
>>> my_frame.my_attribute
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 1978, in __getattr__ (type(self).__name__, name))
AttributeError: 'MyDataFrame' object has no attribute 'my_attribute'
```
The 'my_attribute' is of course there. I would have expected:
```
AttributeError: 'MyDataFrame' object has no attribute 'a_wrong_attribute'
```
It would be nice with a better error message directly pointing to the wrong attribute. The problem also appears for, e.g., the pandas.Panel class.
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} | 18 | 2015-04-24T11:21:15Z | 2015-12-30T15:39:31Z | 2015-12-30T15:39:31Z | NONE | null | closes #939
`RangeIndex(1, 10, 2)` is a memory saving alternative to `Index(np.arange(1, 10,2))`: c.f. #939.
This re-implementation is compatible with the current `Index()` api and is a drop-in replacement for `Int64Index()`. It automatically converts to Int64Index() when required by operations.
At present only for a minimum number of operations the type is conserved (e.g. slicing, inner-, left- and right-joins). Most other operations trigger creation of an equivalent Int64Index (or at least an equivalent numpy array) and fall back to its implementation.
This PR also extends the functionality of the `Index()` constructor to allow creation of `RangeIndex()` with
```
Index(20)
Index(2, 20)
Index(0, 20, 2)
```
in analogy to
```
range(20)
range(2, 20)
range(0, 20, 2)
```
---
TODO:
- [x] Cache a private `Int64Index` object the first time it or its values are required.
- [x] Restore `Index(5)` as error. Restore its test. Allow `Index(0, 5)` and `Index(0, 5, 1)`.
- [x] Make `RangeIndex` immutable. See `start`, `stop`, `step` properties.
- [x] In `test_constructor()`: check class, attributes (possibly including dtype).
- [x] In `test_copy()`: check that copy is not identical (but equal) to the existing.
- [ ] In `test_join_non_unique()`: test .append for ri.append(int64index) and int64index.append(ri).
- [x] In `test_duplicates()`: Assert `is_unique` and `has_duplicates` return correct values.
- [ ] After consensus on desired behaviour, adjust `test_view()`.
- [ ] After consensus on constructor, adjust `test_constructor()` and possible add further tests.
- [x] After consensus on `RangeIndex()` validity, adjust `test_pickle_compat_construction()`.
- [ ] After consensus on slice behaviour, test for correct return type in `test_slice_specialised()`.
- [ ] Accept `xrange` objects as input
- [x] Setting `RangeIndex` as the default index.
- [ ] Clean up `Index` constructor: use ABCs and named arguments
- [ ] In `__getitem__()`: replace `np.isscalar` to exclude singe-element numpy arrays
- [ ] Tests for `pandas.io.packers`
In need of consensus:
- [x] If we disallow `Index(10)` should we also disallow `RangeIndex(10)`? That would seem like an unfortunate api break with respect to built-in `range()`, `xrange()` and numpy `arange()`. _Decision: allow_
- [x] Should `RangeIndex()` be allowed as a constructor alternative to the kludgy `Index(np.empty(0, dtype='...'))`? _Decision: allow_
- [x] Should `RangeIndex` accept array inputs? If so, what should it do with them? Should it try to infer a corresponding `RangeIndex`? What should it do with inputs it cannot infer? _Decision: delegate all array inputs to `Int64Index`._
- [x] Should the `RangeIndex` constructor accept `dtype` as an argument? _Decision: accept, but only for array inputs._
- [x] If so, should it accept non-int64 dtypes as valid input? _Decision: Irrelevant due to delegation._
- [x] If so, does it need to check that the other inputs are within the range of the dtype? _Decision: Irrelevant due to delegation._
- [x] Related to constructor question, should slicing with a list return? Always `Int64Index` or `RangeIndex` when possible and `Int64Index` when not? _Decision: slicing with list will always return `Int64Index`._
- [ ] What should `RangeIndex(10).view('i8')` return? I believe `RangeIndex(10)`. _Decision: return `RangeIndex(10)`._
- [ ] What should `RangeIndex(10).view('i4')` return? I believe `Index(RangeIndex(10), dtype='i4')`. _Decision: return `Index(RangeIndex(10), dtype='i4')`._
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| null | 2 | 2015-04-24T15:28:28Z | 2015-07-28T21:53:30Z | 2015-07-28T21:53:30Z | NONE | null | [Pandas v0.16.0]
The Excel export (`data_frame.to_excel`) currently hard codes cell styles for all header frames. This pull request allows this style to be overridden by the user.
Don't override the default header style (Default)
`data_frame.to_excel(..... "override_header_style":None)`
Don't style the header at all
`data_frame.to_excel(..... "override_header_style": {} )`
Pass in a custom style
`data_frame.to_excel(..... "override_header_style": { "font" : {"font_color" : "#990000", "bold" : False }, "align" : {"horizontal" : "right"} } )`
There are currently no tests for the to_excel function mentioned here. (All the other, existing tests pass.) I'd be happy to write some tests, though it might mean refactoring some functionality out of constructors as **init** is notoriously difficult to mock. (mock.assert_called_with ?)
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https://api.github.com/repos/pandas-dev/pandas/issues/9979 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9979/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9979/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9979/events | https://github.com/pandas-dev/pandas/issues/9979 | 70,732,257 | MDU6SXNzdWU3MDczMjI1Nw== | 9,979 | AttributeError: Cannot use pandas from a script file | {
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| null | 3 | 2015-04-24T16:24:00Z | 2018-09-24T15:21:05Z | 2015-04-24T16:48:16Z | NONE | null | I am using the Anaconda distribution of Pandas on a Mac.
I am able to use the Pandas module from within the python console, but am unable to use it from within a script file. i.e. when I run a script that imports the pandas module, then I get an attribute error.
For example, this:
```
import pandas as pd
df = pd.DataFrame()
```
will give me this error:
```
Traceback (most recent call last):
File "/Path/To/File/file.py", line 1, in <module>
import pandas as pd
File "/Path/To/File/file.py", line 2, in <module>
df = pd.DataFrame()
AttributeError: 'module' object has no attribute 'DataFrame'
```
And similarly, this:
```
import pandas as pd
df = pd.Series()
```
will give me this error:
```
Traceback (most recent call last):
File "/Path/To/File/file.py", line 1, in <module>
import pandas as pd
File "/Path/To/File/file.py", line 2, in <module>
sr = pd.Series()
AttributeError: 'module' object has no attribute 'Series'
```
Yet both of these work fine from the console.
I've tried uninstalling and re-installing from anaconda, pip, and from the github source, to no avail.
Thanks.
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} | 3 | 2015-04-24T21:06:42Z | 2015-05-08T00:16:51Z | 2015-05-08T00:16:51Z | MEMBER | null | Follow-ups for #9667. Noticed 2 methods which can return `DataFrame`.
### 1. `Index.str.extract`
As shown in docstring, it returns `DataFrame` when the expression has 2 or more groups.
```
pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
#0 1
#1 2
#2 NaN
# dtype: object
pd.Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
# 0 1
#0 a 1
#1 b 2
#2 NaN NaN
```
Currently, `Index.str.extract` raises an error in both cases. I think 1st case should return `Index`, and 2nd case should raise understandable error.
```
pd.Index(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
# AttributeError: 'Index' object has no attribute 'index'
pd.Index(['a1', 'b2', 'c3']).str.extract('([ab])(\d)')
# AttributeError: 'Index' object has no attribute 'empty'
```
### 2. `Index.str.get_dummies`
Because it returns `DataFrame`, should raise an understandable error.
```
pd.Index(['a1', 'b2', 'c3']).str.get_dummies()
# AttributeError: 'Index' object has no attribute 'fillna'
```
CC: @mortada
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} | 1 | 2015-04-24T21:23:21Z | 2015-04-25T07:38:35Z | 2015-04-25T07:34:47Z | CONTRIBUTOR | null | I noticed some of the HTML links are broken in `text.rst`
@sinhrks @jorisvandenbossche this is related to https://github.com/pydata/pandas/pull/9843 but can be independently merged
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} | 0 | 2015-04-24T21:47:13Z | 2015-04-24T21:47:26Z | null | MEMBER | null | It may be nice to have a doc like "Comparison with R / SQL"?
- https://databricks.com/blog/2015/02/17/introducing-dataframes-in-spark-for-large-scale-data-science.html
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Notice that `assert_frame_equal` now fails for empty dataframes with different dtypes (as, I think, it should). However, this means some tests need to be patched now.
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Also, fixed broken section name for subclass document.
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} | 0 | 2015-04-25T20:45:37Z | 2015-05-02T19:08:45Z | 2015-05-02T19:08:45Z | MEMBER | null | Part 1: 10min and basics.rst
Addresses #3705 (for two files). I mainly replaced most ```method_name``` occurences with `:meth:`name``
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| null | 1 | 2015-04-25T22:27:15Z | 2015-04-25T22:40:01Z | 2015-04-25T22:39:32Z | NONE | null | If I create a dataframe with two arrays that have different endianness, it works fine if I access the whole DataFrame or individual arrays:
``` python
In [1]: import pandas
In [2]: d = pandas.DataFrame()
In [3]: import numpy as np
In [4]: d['a'] = np.array([1,2,3], dtype='>f4')
In [5]: d['b'] = np.array([1,2,3], dtype='<f4')
In [6]: d['a']
Out[6]:
0 1
1 2
2 3
Name: a, dtype: float32
In [7]: d['b']
Out[7]:
0 1
1 2
2 3
Name: b, dtype: float32
In [8]: d
Out[8]:
a b
0 1 1
1 2 2
2 3 3
```
However if I try and access multiple arrays in one go I get an error:
```
In [9]: d[['a','b']]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-63d4e2414242> in <module>()
----> 1 d[['a','b']]
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/frame.py in __getitem__(self, key)
1772 if isinstance(key, (Series, np.ndarray, Index, list)):
1773 # either boolean or fancy integer index
-> 1774 return self._getitem_array(key)
1775 elif isinstance(key, DataFrame):
1776 return self._getitem_frame(key)
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/frame.py in _getitem_array(self, key)
1817 else:
1818 indexer = self.ix._convert_to_indexer(key, axis=1)
-> 1819 return self.take(indexer, axis=1, convert=True)
1820
1821 def _getitem_multilevel(self, key):
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/generic.py in take(self, indices, axis, convert, is_copy)
1330 new_data = self._data.take(indices,
1331 axis=self._get_block_manager_axis(axis),
-> 1332 convert=True, verify=True)
1333 result = self._constructor(new_data).__finalize__(self)
1334
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/internals.py in take(self, indexer, axis, verify, convert)
3273 new_labels = self.axes[axis].take(indexer)
3274 return self.reindex_indexer(new_axis=new_labels, indexer=indexer,
-> 3275 axis=axis, allow_dups=True)
3276
3277 def merge(self, other, lsuffix='', rsuffix=''):
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/internals.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy)
3155 if axis == 0:
3156 new_blocks = self._slice_take_blocks_ax0(
-> 3157 indexer, fill_tuple=(fill_value,))
3158 else:
3159 new_blocks = [blk.take_nd(indexer, axis=axis,
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/internals.py in _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple)
3236 blocks.append(blk.take_nd(
3237 blklocs[mgr_locs.indexer], axis=0,
-> 3238 new_mgr_locs=mgr_locs, fill_tuple=None))
3239
3240 return blocks
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/internals.py in take_nd(self, indexer, axis, new_mgr_locs, fill_tuple)
844 fill_value = self.fill_value
845 new_values = com.take_nd(self.get_values(), indexer, axis=axis,
--> 846 allow_fill=False)
847 else:
848 fill_value = fill_tuple[0]
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/core/common.py in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill)
821
822 indexer = _ensure_int64(indexer)
--> 823 func(arr, indexer, out, fill_value)
824
825 if flip_order:
/Users/tom/miniconda3/envs/production/lib/python3.4/site-packages/pandas/algos.so in pandas.algos.take_2d_axis0_float32_float32 (pandas/algos.c:89026)()
ValueError: Big-endian buffer not supported on little-endian compiler
```
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| null | 1 | 2015-04-26T04:57:35Z | 2015-04-28T00:26:40Z | 2015-04-28T00:26:34Z | NONE | null | I have script fetched the data from yahoo in multithread, the script always got stuck.
It looks like we need add a timeout parameter somewhere when we call the urlopen.
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| null | 5 | 2015-04-26T07:07:50Z | 2015-04-28T00:24:00Z | 2015-04-28T00:23:53Z | NONE | null | Hope you can help with the following error (thanks):
(with any financial data source, after I upgraded to v0.16 because yahoo finance was not working 0.14)
$goog=web.DataReader('GOOG', data_source='google', start='3/14/2009', end = '4/14/2014')
anaconda/lib/python2.7/site-packages/pandas/io/data.pyc in _retry_read_url(url, retry_count, pause, name)
175 #Get rid of unicode characters in index name.
176 try:
--> 177 rs.index.name = rs.index.name.decode('unicode_escape').encode('ascii', 'ignore')
178 except AttributeError:
179 #Python 3 string has no decode method.
UnboundLocalError: local variable 'rs' referenced before assignment
In [33]: pd.**version**
Out[33]: '0.16.0'
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} | 5 | 2015-04-26T09:23:39Z | 2015-05-08T13:19:51Z | 2015-05-08T13:19:51Z | NONE | null | Hi there,
I recently upgraded pandas from 0.15.1 to 0.16.0. after running the nosetest i got the following error msgs. wondering if anything series (i tested HDF5 writing/reading. everything looks normal).
#
## ERROR: test_conv_read_write (pandas.io.tests.test_pytables.TestHDFStore)
Traceback (most recent call last):
File "/usr/local/lib/python3.4/site-packages/pandas/io/tests/test_pytables.py", line 205, in test_conv_read_write
assert_series_equal(o, roundtrip('series',o))
File "/usr/local/lib/python3.4/site-packages/pandas/io/tests/test_pytables.py", line 201, in roundtrip
obj.to_hdf(self.path, key,*_kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/core/generic.py", line 913, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 266, in to_hdf
complib=complib) as store:
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 396, in **init**
self.open(mode=mode, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 536, in open
self._handle = tables.open_file(self._path, 'r', *_kwargs)
File "/usr/local/lib/python3.4/site-packages/tables/file.py", line 318, in open_file
return File(filename, mode, title, root_uep, filters, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/tables/file.py", line 791, in **init**
self._g_new(filename, mode, *_params)
File "hdf5extension.pyx", line 359, in tables.hdf5extension.File._g_new (tables/hdf5extension.c:3756)
File "/usr/local/lib/python3.4/site-packages/tables/utils.py", line 157, in check_file_access
raise IOError("`%s` does not exist" % (filename,))
nose.proxy.OSError: ``tmp.__5JMnSD5gVu__.h5`` does not exist
-------------------- >> begin captured stdout << ---------------------
Opening tmp.**5JMnSD5gVu**.h5 in read-only mode
--------------------- >> end captured stdout << ----------------------
#
## ERROR: test_copy (pandas.io.tests.test_pytables.TestHDFStore)
Traceback (most recent call last):
File "/usr/local/lib/python3.4/site-packages/pandas/io/tests/test_pytables.py", line 4331, in test_copy
st = HDFStore(self.path)
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 396, in **init**
self.open(mode=mode, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 536, in open
self._handle = tables.open_file(self._path, 'r', *_kwargs)
File "/usr/local/lib/python3.4/site-packages/tables/file.py", line 318, in open_file
return File(filename, mode, title, root_uep, filters, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/tables/file.py", line 791, in **init**
self._g_new(filename, mode, *_params)
File "hdf5extension.pyx", line 359, in tables.hdf5extension.File._g_new (tables/hdf5extension.c:3756)
File "/usr/local/lib/python3.4/site-packages/tables/utils.py", line 157, in check_file_access
raise IOError("`%s` does not exist" % (filename,))
nose.proxy.OSError: ``tmp.__NZTYQk5IW2__.h5`` does not exist
-------------------- >> begin captured stdout << ---------------------
Opening tmp.**NZTYQk5IW2**.h5 in read-only mode
--------------------- >> end captured stdout << ----------------------
#
## ERROR: test_factory_fun (pandas.io.tests.test_pytables.TestHDFStore)
Traceback (most recent call last):
File "/usr/local/lib/python3.4/site-packages/pandas/io/tests/test_pytables.py", line 160, in test_factory_fun
with get_store(self.path) as tbl:
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 1273, in get_store
return HDFStore(path, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 396, in **init**
self.open(mode=mode, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/pandas/io/pytables.py", line 536, in open
self._handle = tables.open_file(self._path, 'r', *_kwargs)
File "/usr/local/lib/python3.4/site-packages/tables/file.py", line 318, in open_file
return File(filename, mode, title, root_uep, filters, *_kwargs)
File "/usr/local/lib/python3.4/site-packages/tables/file.py", line 791, in __init__
self._g_new(filename, mode, **params)
File "hdf5extension.pyx", line 359, in tables.hdf5extension.File._g_new (tables/hdf5extension.c:3756)
File "/usr/local/lib/python3.4/site-packages/tables/utils.py", line 157, in check_file_access
raise IOError("`%s` does not exist" % (filename,))
nose.proxy.OSError: ``tmp.__lL9hBPXiYa__.h5`` does not exist
-------------------- >> begin captured stdout << ---------------------
Opening tmp.**lL9hBPXiYa**.h5 in read-only mode
--------------------- >> end captured stdout << ----------------------
my python/pandas environment (Redhat server v6.5):
certifi (14.05.14)
Cython (0.21.1)
ipython (2.3.1)
jieba3k (0.35.1)
Jinja2 (2.7.3)
lxml (3.4.0)
MarkupSafe (0.23)
matplotlib (1.4.2)
mysql-connector-python (2.0.2)
nltk (3.0.1)
nose (1.3.4)
numexpr (2.4)
numpy (1.8.2)
openpyxl (1.6.1)
oursql (0.9.4)
pandas (0.16.0)
patsy (0.3.0)
pip (1.5.6)
pyparsing (2.0.3)
pyreadline (2.0)
python-dateutil (2.2)
pytz (2014.9)
pyzmq (14.4.1)
scipy (0.14.0)
setuptools (2.1)
six (1.8.0)
SQLAlchemy (0.9.8)
statsmodels (0.6.0)
tables (3.1.1)
tornado (4.0.2)
xlrd (0.9.3)
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| null | 2 | 2015-04-26T13:11:03Z | 2015-04-27T16:22:00Z | 2015-04-27T16:22:00Z | CONTRIBUTOR | null | Hello,
I don't find any method to plot a matrix (such as confusion matrix) using "pure" Pandas.
Having a more convenient approach than
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
http://matplotlib.org/examples/pylab_examples/matshow.html
will be great.
```
import pandas as pd
y_actu = pd.Series([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2])
y_pred = pd.Series([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])
df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'])
```
Something like
```
df_confusion.plot(kind='matrix')
```
It should display matrix with colormap (and a legend of this colormap)
A normalized matrix plot will be great too.
```
def plot_confusion_matrix(df_confusion, title='Confusion matrix', cmap=plt.cm.gray_r):
plt.matshow(df_confusion, cmap=cmap) # imshow
#plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(df_confusion.columns))
plt.xticks(tick_marks, df_confusion.columns, rotation=45)
plt.yticks(tick_marks, df_confusion.index)
#plt.tight_layout()
plt.ylabel(df_confusion.index.name)
plt.xlabel(df_confusion.columns.name)
plot_confusion_matrix(df_confusion)
```
A Pandas class to manage confusion matrix (and also binary confusion matrix) will be great.
Kind regards
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} | 4 | 2015-04-26T19:04:19Z | 2015-09-19T00:38:08Z | 2015-04-29T10:31:53Z | CONTRIBUTOR | null | Fixes GH #9921
closes #9700
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| null | 2 | 2015-04-26T23:21:25Z | 2015-04-27T06:03:54Z | 2015-04-27T06:03:54Z | CONTRIBUTOR | null | BUG DESCRIPTION:
length_of_indexer() (defined in pandas/core/indexing.py) returns incorrect result
if (stop-start) is not divisible by step. As a consequence some slice assignments
throw exceptions. Affected panda versions: 0.16.x, 0.15.x, current git master.
HOW TO REPRODUCE:
import pandas as pd
sr= pd.Series([-1]*6) # series with 6 elements indexed from 0 to 5.
sr[0::2]= [0,2,4] # setting even elements works fine!
sr[1::2]= [1,3,5] # setting odd elements results in error:
.../pandas/core/internals.pyc in setitem(self, indexer, value)
568 if is_list_like(value) and l:
569 if len(value) != length_of_indexer(indexer, values):
--> 570 raise ValueError("cannot set using a slice indexer with a "
571 "different length than the value")
572
ValueError: cannot set using a slice indexer with a different length than the value
BUG FIX: I fixed the bug (and created a new test case for it) in my private repo
and will submit a pull request shortly.
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| null | 2 | 2015-04-26T23:24:36Z | 2016-08-08T08:45:04Z | 2015-04-27T06:03:18Z | CONTRIBUTOR | null | ... assignments.
BUG DESCRIPTION:
length_of_indexer() (defined in pandas/core/indexing.py) returns incorrect result
if (stop-start) is not divisible by step. As a consequence some slice assignments
throw exceptions. Affected panda versions: 0.16.x, 0.15.x, current git master.
HOW TO REPRODUCE:
import pandas as pd
sr= pd.Series([-1]*6) # series with 6 elements indexed from 0 to 5.
sr[0::2]= [0,2,4] # setting even elements works fine!
sr[1::2]= [1,3,5] # setting odd elements results in error:
.../pandas/core/internals.pyc in setitem(self, indexer, value)
568 if is_list_like(value) and l:
569 if len(value) != length_of_indexer(indexer, values):
--> 570 raise ValueError("cannot set using a slice indexer with a "
571 "different length than the value")
572
ValueError: cannot set using a slice indexer with a different length than the value
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| null | 1 | 2015-04-27T05:48:15Z | 2015-04-27T07:55:41Z | 2015-04-27T07:55:41Z | NONE | null | I added an example of how to compare two sets of data through measures of center and spread and created methods which print out statements and create tables to illustrate this.
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| null | 1 | 2015-04-27T08:22:57Z | 2015-04-27T12:47:46Z | 2015-04-27T12:47:46Z | NONE | null | Changes have been made to the correct place in the project this time
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| null | 6 | 2015-04-27T10:33:10Z | 2015-05-02T13:51:27Z | 2015-05-02T13:51:10Z | NONE | null | WIP on making RangeIndex (#9977) the default index
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} | 41 | 2015-04-27T12:05:35Z | 2020-04-03T03:51:45Z | 2020-04-03T03:51:44Z | MEMBER | null | Here's our roadmap document: https://docs.google.com/document/d/151ct8jcZWwh7XStptjbLsda6h2b3C0IuiH_hfZnUA58/edit#
Just because it is a nice round number :-)
Or maybe we can use it to discuss how we imagine a possible pandas 1.0 ..
---
Some clarification (from @shoyer): This is not the place to make new feature requests -- please continue to make separate GitHub issues for those. Almost every new feature can be added without a 1.0 release. If there is a change you think would be _necessary_ to do in pandas 1.0, feel free to reference issues where it is described in more detail.
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} | 11 | 2015-04-27T19:58:18Z | 2019-12-12T11:58:03Z | 2016-05-06T12:10:40Z | NONE | null | I am trying to read an Excel file which someone else created and the wrongly formatted a column as "date" when it is not. It has a large integer in it, which triggers an error
OverflowError: normalized days too large to fit in a C int
But I have "parse_dates=False" so I thought pandas.ExcelFile would not try to parse the dates and return a string instead. Is this a bug?
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} | 1 | 2015-04-27T22:12:26Z | 2015-04-29T22:20:10Z | 2015-04-29T22:20:10Z | MEMBER | null | From SO: http://stackoverflow.com/questions/29906512/pandas-variation-on-filling-nan-whats-an-elegant-and-pythonic-way-to-do-thi/29906743?noredirect=1#29906743
When doing `df.fillna(10, limit=2)` what is `limit=2` doing?
The [docstring](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html) says:
> limit : int, default None
> Maximum size gap to forward or backward fill
But this only explains for filling with a method, not for filling with a value.
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