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} | 1 | "2012-10-01T12:47:03Z" | "2012-11-24T21:35:06Z" | "2012-11-24T21:35:05Z" | CONTRIBUTOR | null | add an argument, min_i (minimum intersection) to nancorr in pandas.src.moments.pyx
this will nan the element if the nobs is < min_i (which should default to 1 to replicate current operations)
need to also propogate argument to: core.frame.corr/cov
possibly add to: core.series.corr/cov
```
@cython.boundscheck(False)
@cython.wraparound(False)
def nancorr(ndarray[float64_t, ndim=2] mat, cov=False, min_i=1):
cdef:
Py_ssize_t i, j, xi, yi, N, K
ndarray[float64_t, ndim=2] result
ndarray[uint8_t, ndim=2] mask
int64_t nobs = 0
float64_t vx, vy, sumx, sumy, sumxx, sumyy, meanx, meany, divisor
N, K = (<object> mat).shape
result = np.empty((K, K), dtype=np.float64)
mask = np.isfinite(mat).view(np.uint8)
for xi in range(K):
for yi in range(xi + 1):
nobs = sumxx = sumyy = sumx = sumy = 0
for i in range(N):
if mask[i, xi] and mask[i, yi]:
vx = mat[i, xi]
vy = mat[i, yi]
nobs += 1
sumx += vx
sumy += vy
#### change from nobs == 0 ####
if nobs < min_i:
result[xi, yi] = result[yi, xi] = np.NaN
else:
meanx = sumx / nobs
meany = sumy / nobs
# now the cov numerator
sumx = 0
for i in range(N):
if mask[i, xi] and mask[i, yi]:
vx = mat[i, xi] - meanx
vy = mat[i, yi] - meany
sumx += vx * vy
sumxx += vx * vx
sumyy += vy * vy
divisor = (nobs - 1.0) if cov else sqrt(sumxx * sumyy)
if divisor != 0:
result[xi, yi] = result[yi, xi] = sumx / divisor
else:
result[xi, yi] = result[yi, xi] = np.NaN
return result
```
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} | 2 | "2012-10-01T15:21:16Z" | "2012-11-18T22:08:33Z" | "2012-11-18T22:08:33Z" | NONE | null | Unless I misunderstood what the documentation says, this is not the intended behavior:
df1 = pandas.DataFrame({'a':np.arange(5),'b':np.arange(5)},index=3+np.arange(5))
df2 = pandas.DataFrame({'c':np.arange(5),'d':np.arange(5)})
pandas.concat([df1,df2],axis=1,ignore_index=False)
a b c d
0 NaN NaN 0 0
1 NaN NaN 1 1
2 NaN NaN 2 2
3 0 0 3 3
4 1 1 4 4
5 2 2 NaN NaN
6 3 3 NaN NaN
7 4 4 NaN NaN
The following function does a work-around for this bug by explicitly re-setting the index:
def myConcat(df1,df2):
if df1.shape[0] != df2.shape[0]:
return False
else:
df2.index = df1.index
return pandas.concat((df1,df2),axis=1)
Pandas version: commit de6cce5dde22870e279d495c25013a5696bc31df
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```
y = pandas.Series(np.random.random(10))
type(y[:,None])
```
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} | 8 | "2012-10-02T00:39:30Z" | "2012-10-12T23:36:15Z" | "2012-10-12T23:36:03Z" | NONE | null | addresses the same issue in 72ed09d applied to other occurences in the tree,
factored out the pprint code to a helper.
now supports pprinting of heterogeneous nested-nested-nested tuples with unicode strings,
FTW.
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} | 8 | "2012-10-02T12:00:33Z" | "2016-10-12T23:03:14Z" | "2012-10-05T00:56:46Z" | CONTRIBUTOR | null | ```
import datetime
import sys
import numpy
from matplotlib import pyplot
import pandas;print(pandas.__version__); #comment out this line to have it work
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
dates = [datetime.date(2008, 12, 31), datetime.date(2009, 1, 31)]
values1 = numpy.arange(10.0, 11.0, 0.5)
values2 = numpy.arange(11.0, 12.0, 0.5)
kw = dict(fmt='-', lw=4)
ax.plot_date([x.toordinal() for x in dates], values1, **kw)
ax.plot_date([x.toordinal() for x in dates], values2, **kw)
fig.show()
raw_input("press 'enter' to end")
```
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} | 4 | "2012-10-02T15:16:42Z" | "2012-11-05T01:09:51Z" | "2012-11-05T01:09:51Z" | NONE | null | I get a very strange behaviour with pandas current dev version (tagged as '0.9.0rc2') :
Calling set_index() on a DataFrame breaks depending on the columns name I use !!!
This code fails :
``` python
df3 = DataFrame({
'0001-INTERNAL_PRODUCT_CODE': {('A', 'A0006000', 'nuit'): 'A0006000'},
'0004-PACKAGING_LANGUAGE_CODE': {('A', 'A0006000', 'nuit'): nan},
'1050-LOCALISED_DESCRIPTION': {('A', 'A0006000', 'nuit'): nan},
'0024-PRODUCT_BAR_CODED': {('A', 'A0006000', 'nuit'): nan},
'Source system': {('A', 'A0006000', 'nuit'): 'A'},
'0027-LOCAL_COMMERCIAL_AUTHORIZATION': {('A', 'A0006000', 'nuit'): nan},
})
df3.set_index(['0001-INTERNAL_PRODUCT_CODE', '1050-LOCALISED_DESCRIPTION']) # fails !
```
But If I change the indexed column names to 'a' and 'x', It works :
``` python
df3 = DataFrame({
'a': {('A', 'A0006000', 'nuit'): 'A0006000'},
'0004-PACKAGING_LANGUAGE_CODE': {('A', 'A0006000', 'nuit'): nan},
'x': {('A', 'A0006000', 'nuit'): nan},
'0024-PRODUCT_BAR_CODED': {('A', 'A0006000', 'nuit'): nan},
'Source system': {('A', 'A0006000', 'nuit'): 'A'},
'0027-LOCAL_COMMERCIAL_AUTHORIZATION': {('A', 'A0006000', 'nuit'): nan},
})
df3.set_index(['a', 'x']) # works !
```
At first I thought It was related to column name .... but If I keep my exotic column name and remove one column (one not used by set_index), it works !! :
``` python
df3 = DataFrame({
'0001-INTERNAL_PRODUCT_CODE': {('A', 'A0006000', 'nuit'): 'A0006000'},
'1050-LOCALISED_DESCRIPTION': {('A', 'A0006000', 'nuit'): nan},
'0024-PRODUCT_BAR_CODED': {('A', 'A0006000', 'nuit'): nan},
'Source system': {('A', 'A0006000', 'nuit'): 'A'},
'0027-LOCAL_COMMERCIAL_AUTHORIZATION': {('A', 'A0006000', 'nuit'): nan},
})
df3.set_index(['0001-INTERNAL_PRODUCT_CODE', '1050-LOCALISED_DESCRIPTION']) # works !
```
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} | 1 | "2012-10-02T19:07:00Z" | "2014-07-29T05:06:43Z" | "2012-10-05T00:20:42Z" | CONTRIBUTOR | null | Go ahead and use the given name for the series name instead of the generic VALUE. Makes grabbing and joining multiple series a bit easier, though I suspect there's a way to do this through the API.
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} | 6 | "2012-10-02T19:09:13Z" | "2016-09-22T17:21:30Z" | "2014-03-09T15:21:07Z" | CONTRIBUTOR | null | from [mailing list](https://groups.google.com/forum/?fromgroups=#!searchin/pystatsmodels/pandas$20plot$20with$20DatetimeIndex$20ignore$20x$20axis$20margin/pystatsmodels/9ZQkzoSibSE/jvU35qVeasUJ)
Hello,
I noticed that setting the x margin in a plot of a data frame with a DatetimeIndex is ignored.
Example plot:
``` python
#!/usr/bin/python
from matplotlib.pyplot import *
from pandas import *
from pylab import show
fig, axes = subplots(2,1)
df1 = DataFrame([0.5,1,1.5,2,2.5,3,3.5])
df1.plot(ax=axes[0],drawstyle='steps',style='g')
# this x axis margin is honored
axes[0].margins(0.05,0.05)
df2 = DataFrame([0.5,1.5,2.5,3.5],index=date_range('1-1-2000',periods=4))
df2.plot(ax=axes[1],drawstyle='steps',style='r')
# this x axis margin is ignored
axes[1].margins(0.05,0.05)
show()
```
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} | 1 | "2012-10-02T21:35:48Z" | "2012-10-05T00:55:29Z" | "2012-10-05T00:55:19Z" | CONTRIBUTOR | null | Docs say default start for DataReader should be 2010-1-1, but it returns 1 year ago from today. I prefer the docs. Thoughts?
```
>>> from pandas.io.data import _sanitize_dates
>>> _sanitize_dates(None, None)
(datetime.datetime(2011, 10, 3, 17, 32, 52, 969119),
datetime.datetime(2012, 10, 2, 17, 32, 52, 969163))
```
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} | 3 | "2012-10-03T14:43:25Z" | "2014-07-22T13:35:56Z" | "2012-10-04T23:59:52Z" | NONE | null | fixes #2001
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} | 11 | "2012-10-03T22:28:06Z" | "2014-06-27T22:56:10Z" | "2013-04-10T06:43:27Z" | CONTRIBUTOR | null | The examples in documentation did not render very nicely.
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https://api.github.com/repos/pandas-dev/pandas/issues/2016 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2016/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2016/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2016/events | https://github.com/pandas-dev/pandas/pull/2016 | 7,359,863 | MDExOlB1bGxSZXF1ZXN0MjU0NzI4OQ== | 2,016 | DOC: update parsers header, names args doc | {
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} | 4 | "2012-10-04T19:39:27Z" | "2012-10-05T07:14:46Z" | "2012-10-05T00:41:37Z" | NONE | null | ```
import pandas as pd
import numpy as np
df = pd.DataFrame([(1,2,3), (4,5,6)], columns = ['a','b','c'])
df.groupby(['a']).agg({'b': [np.mean, np.sum]}).reset_index()
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-1-a362e367701d> in <module>()
2 import numpy as np
3 df = pd.DataFrame([(1,2,3), (4,5,6)], columns = ['a','b','c'])
----> 4 df.groupby(['a']).agg({'b': [np.mean, np.sum]}).reset_index()
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in reset_index(self, level, drop, inplace)
2522 if name is None or name == 'index':
2523 name = 'index' if 'index' not in self else 'level_0'
-> 2524 new_obj.insert(0, name, _maybe_cast(self.index.values))
2525
2526 new_obj.index = new_index
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in insert(self, loc, column, value)
1857 """
1858 value = self._sanitize_column(column, value)
-> 1859 self._data.insert(loc, column, value)
1860
1861 def _sanitize_column(self, key, value):
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in insert(self, loc, item, value)
899 raise Exception('cannot insert %s, already exists' % item)
900
--> 901 new_items = self.items.insert(loc, item)
902 self.set_items_norename(new_items)
903
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/index.pyc in insert(self, loc, item)
2340 if not isinstance(item, tuple) or len(item) != self.nlevels:
2341 raise Exception("%s cannot be inserted in this MultiIndex"
-> 2342 % str(item))
2343
2344 new_levels = []
Exception: a cannot be inserted in this MultiIndex
```
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} | 5 | "2012-10-04T19:44:28Z" | "2014-08-07T10:32:22Z" | "2012-11-03T19:21:36Z" | NONE | null | The docstring says
## Parameters
arg : string, datetime, array of strings (with possible NAs)
Without any date format option.
I do get some weird output though:
```
In [4]:
pd.to_datetime('121001')
Out[4]:
datetime.datetime(2012, 10, 1, 0, 0)
In [5]:
pd.to_datetime('121001', dayfirst=True)
Out[5]:
datetime.datetime(2012, 10, 1, 0, 0)
In [6]:
pd.to_datetime(['121001'])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-e09fc721e662> in <module>()
----> 1 pd.to_datetime(['121001'])
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, utc, box)
106
107 try:
--> 108 return _convert_f(arg)
109 except ValueError:
110 raise
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/tseries/tools.pyc in _convert_f(arg)
80 return DatetimeIndex._simple_new(values, None, tz=tz)
81 except (ValueError, TypeError):
---> 82 raise e
83
84 if arg is None:
ValueError: Out of bounds nanosecond timestamp: 121001-01-01 00:00:00
```
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} | 1 | "2012-10-04T20:59:55Z" | "2012-11-24T08:21:52Z" | "2012-11-24T08:21:52Z" | CONTRIBUTOR | null | Without the extended message the user hardly understand why no integers are allowed.
Actually, I do not even understand it by now.
Can we not use _values.fill(np.nan)_ with integers?
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} | 2 | "2012-10-04T21:25:32Z" | "2016-12-31T11:59:08Z" | "2016-12-29T11:12:14Z" | CONTRIBUTOR | null | Please add to
https://github.com/pydata/pandas/blob/master/pandas/tseries/period.py
- hourofyear
BTW,
How is it possible for user to add further convenience functions like:
- minuteofday
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} | 2 | "2012-10-04T22:40:03Z" | "2012-10-05T01:53:14Z" | "2012-10-05T01:53:14Z" | NONE | null | I noticed this while doing some work where I'm filtering values based on day of year: The dayofyear method of the Timestamp class returns the day of the month, not the day of the year. I realize there are a number of ways to compute this, none of which is very fast, but it could be changed to return the difference, in days, between it's own value and 0000 01 January of the current year.
In pandas/src/datetime.pyx, the problematic code is, starting on line 147/148, in the definition of the function.
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} | 1 | "2012-10-05T00:23:19Z" | "2014-03-09T15:21:30Z" | "2014-03-09T15:21:30Z" | CONTRIBUTOR | null | 1. Returns DataFrame for SeriesGroupBy.transform
2. Returns DataFrame with MultiIndexed columns for DataFrameGroupBy.transform
3. `chain` parameter to control whether transforms are applied in sequence or not
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} | 2 | "2012-10-05T09:33:52Z" | "2012-11-28T03:10:37Z" | "2012-11-28T03:07:50Z" | NONE | null | ```
import pandas as pd
import numpy as np
df = pd.DataFrame([(1,2,3), (4,5,6)], columns = ['a','b','c'])
new_df = df.groupby(['a']).agg({'b': [np.mean, np.sum]})
other_df = df = pd.DataFrame([(1,2,3), (7,10,6)], columns = ['a','b','d'])
other_df.set_index('a', inplace=True)
print new_df
print other_df
pd.merge(new_df, other_df, left_index=True, right_index=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-4c9da13e85ff> in <module>()
7 print new_df
8 print other_df
----> 9 pd.merge(new_df, other_df, left_index=True, right_index=True)
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
31 right_index=right_index, sort=sort, suffixes=suffixes,
32 copy=copy)
---> 33 return op.get_result()
34 if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame'
35
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in get_result(self)
181
182 # this is a bit kludgy
--> 183 ldata, rdata = self._get_merge_data()
184
185 # TODO: more efficiently handle group keys to avoid extra consolidation!
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/tools/merge.pyc in _get_merge_data(self)
271 lsuf, rsuf = self.suffixes
272 ldata, rdata = ldata._maybe_rename_join(rdata, lsuf, rsuf,
--> 273 copydata=False)
274 return ldata, rdata
275
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in _maybe_rename_join(self, other, lsuffix, rsuffix, copydata)
1115
1116 def _maybe_rename_join(self, other, lsuffix, rsuffix, copydata=True):
-> 1117 to_rename = self.items.intersection(other.items)
1118 if len(to_rename) > 0:
1119 if not lsuffix and not rsuffix:
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/index.pyc in intersection(self, other)
2270 Index
2271 """
-> 2272 self._assert_can_do_setop(other)
2273
2274 if self.equals(other):
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0rc2-py2.7-linux-x86_64.egg/pandas/core/index.pyc in _assert_can_do_setop(self, other)
2319 if len(other) == 0:
2320 return True
-> 2321 raise TypeError('can only call with other hierarchical '
2322 'index objects')
2323
TypeError: can only call with other hierarchical index objects
b
mean sum
a
1 2 2
4 5 5
b d
a
1 2 3
7 10 6
```
also this fails
```
pd.merge(new_df.reset_index(), other_df.reset_index(), left_index=False, right_index=False, left_on =('a', ''), right_on ='a' )
```
and this
```
pd.merge(new_df.reset_index(), other_df.reset_index(), left_index=False, right_index=False, left_on =[('a', '')], right_on =['a'] )
```
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} | 2 | "2012-10-05T12:43:36Z" | "2012-10-06T17:49:10Z" | "2012-10-06T17:49:04Z" | CONTRIBUTOR | null | Please see the example below:
``` python
In [39]: df = pd.DataFrame({'a': np.arange(3)}, index=pd.date_range('2010-01-01', periods=3, freq='B'))
In [40]: print df
a
2010-01-01 0
2010-01-04 1
2010-01-05 2
In [41]: print df.to_html()
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>a</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>1970-01-15 48:00:00</strong></td>
<td> 0</td>
</tr>
<tr>
<td><strong>1970-01-15 120:00:00</strong></td>
<td> 1</td>
</tr>
<tr>
<td><strong>1970-01-15 144:00:00</strong></td>
<td> 2</td>
</tr>
</tbody>
</table>
In [42]: print pd.version.version
0.9.0.dev-a884949
In [43]: print np.version.version
1.6.1
In [44]: df.index.dtype
Out[44]: dtype('datetime64[ns]')
```
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} | 9 | "2012-10-05T14:29:33Z" | "2012-12-07T15:16:57Z" | "2012-12-02T16:55:23Z" | CONTRIBUTOR | null | I would like the DataFrame.to_csv method to have an option to store inf values as "inf" and nan values as "nan". This will be important for computations that use inf and nan differently.
Currently:
In [72]: df = pd.DataFrame({'A': [inf]})
In [73]: print isinf(df)
A
0 True
In [74]: df.to_csv('temp.csv')
In [75]: df2 = pd.read_csv('temp.csv', index_col=0)
In [76]: print isinf(df2)
A
0 False
In the new implementation (possibly with an option passed to df.to_csv to select it), the result of isinf(df2) would be True.
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} | 3 | "2012-10-05T14:51:19Z" | "2012-11-30T23:46:22Z" | "2012-11-30T23:46:22Z" | CONTRIBUTOR | null | ```
In [1]: import pandas
In [2]: import numpy as np
In [3]: s = pandas.Series(range(10),dtype=np.float64)
In [4]: s[3] = np.nan
In [5]: s[6] = np.nan
In [6]: s
Out[6]:
0 0
1 1
2 2
3 NaN
4 4
5 5
6 NaN
7 7
8 8
9 9
```
backfilling ok
```
In [8]: s.fillna(method='bfill')
Out[8]:
0 0
1 1
2 2
3 4
4 4
5 5
6 7
7 7
8 8
9 9
```
here the value argument takes precedence over the method argument (which is ignored)
I propose that this should raise an exception("incompatible arguments to fillna, method is ignored?")
this applies to Series,DataFrame,Panel
```
In [11]: s.fillna(value=0,method='bfill')
Out[11]:
0 0
1 1
2 2
3 0
4 4
5 5
6 0
7 7
8 8
9 9
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2028 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2028/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2028/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2028/events | https://github.com/pandas-dev/pandas/pull/2028 | 7,389,930 | MDExOlB1bGxSZXF1ZXN0MjU2MjI2Nw== | 2,028 | Fix for EOF issue on c-parser branch | {
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} | 1 | "2012-10-05T21:19:44Z" | "2014-07-21T16:38:13Z" | "2012-10-29T18:07:48Z" | CONTRIBUTOR | null | Fix EOF issue on c-parser branch due to parser_handle_eof() not explicitely returning a value if state is START_RECORD. This results in the return value sometimes incorrectly set to -1 on Mac OS (seems to work okay on linux though). As a result, a parser exception is raised even though the file is read correctly.
Patch returns 0 if parser state is START_RECORD, since that should mean that a line has just been finished being parsed correctly and it's at a valid end of the file. Also added a default return value of -1 if none of the other tests pass.
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https://api.github.com/repos/pandas-dev/pandas/issues/2029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2029/events | https://github.com/pandas-dev/pandas/pull/2029 | 7,394,863 | MDExOlB1bGxSZXF1ZXN0MjU2NDU2OQ== | 2,029 | (much) faster testing | {
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} | 1 | "2012-10-06T05:59:52Z" | "2012-10-12T23:41:02Z" | "2012-10-12T23:40:40Z" | NONE | null | after priming the cache, the extensions and cythoning will only recompile if files
change. otherwise they are retrieved from the cache.
the caching is disabled by default just in case it's buggy, but i've had no problems
so far.
with caching enabled and using tox_prll.shl it now takes 25 seconds to test all
2.x and about a minute and a half to test everything.
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https://api.github.com/repos/pandas-dev/pandas/issues/2030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2030/events | https://github.com/pandas-dev/pandas/pull/2030 | 7,399,324 | MDExOlB1bGxSZXF1ZXN0MjU2NjA0Mw== | 2,030 | Fix groupby.median documentation | {
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} | 1 | "2012-10-06T18:57:58Z" | "2014-07-19T02:19:40Z" | "2012-10-06T19:22:59Z" | CONTRIBUTOR | null | Minor oversight - looks like when the documentation was copied to median from mean the function name didn't get updated.
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https://api.github.com/repos/pandas-dev/pandas/issues/2031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2031/events | https://github.com/pandas-dev/pandas/issues/2031 | 7,400,645 | MDU6SXNzdWU3NDAwNjQ1 | 2,031 | pearson corr can throw IndexError | {
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} | 3 | "2012-10-06T21:33:59Z" | "2013-01-20T00:13:43Z" | "2013-01-20T00:13:43Z" | CONTRIBUTOR | null | I just got this from a ipython parallel run (so unfortunately no reproduceable case):
```
IndexError Traceback (most recent call last)<string>in <module>()
C:\data\phd\montecarlo-paper\functions.py in simulate(inputs)
C:\portabel\Python27\lib\site-packages\pandas\core\frame.pyc in corr(self, method)
3355 valid=mask[i]&mask[j]
3356 if not valid.all():
-> 3357 c=corrf(ac[valid],bc[valid])
3358 else:
3359 c=corrf(ac,bc)
C:\portabel\Python27\lib\site-packages\pandas\core\nanops.pyc in _pearson(a, b)
328
329 def _pearson(a,b):
--> 330 return np.corrcoef(a,b)[0,1]
331 def _kendall(a,b):
332 return kendalltau(a,b)[0]
IndexError: invalid index
IndexError(invalid index)
```
(i had to do some S&R to get the stacktrace into this form, so hopefully I didn't reformat/remove anything)
I'm not sure under which circumstance np.corrcoef does not return an array, but if so pandas should return "nan" in that case.
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https://api.github.com/repos/pandas-dev/pandas/issues/2032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2032/events | https://github.com/pandas-dev/pandas/pull/2032 | 7,403,066 | MDExOlB1bGxSZXF1ZXN0MjU2NzIxNQ== | 2,032 | fixed color keyword in time series plot - issue | {
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} | 2 | "2012-10-07T06:54:33Z" | "2014-07-08T12:28:44Z" | "2012-10-31T20:20:18Z" | CONTRIBUTOR | null | Fix 33bb84b was to series.plot not timeseries.plot. This is the same fix for timeseries.plot.
Not sure why the def _maybe_add_color(kwargs, style, i) occurs in both _make_plot and _make_ts_plot?
#1890
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} | 5 | "2012-10-07T11:40:41Z" | "2014-07-08T11:43:03Z" | "2013-12-18T20:09:34Z" | CONTRIBUTOR | null | ```
df3 = DataFrame({"a":[1,2,3,4], "b":[1,2,3,4]})
df3.ix[:,["b","c"]]
Out[1]:
b c
0 1 NaN
1 2 NaN
2 3 NaN
3 4 NaN
```
I would have (had...) expected that the `df3.ix[:,["b","c"]]` will throw an error :-(
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https://api.github.com/repos/pandas-dev/pandas/issues/2034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2034/events | https://github.com/pandas-dev/pandas/issues/2034 | 7,404,641 | MDU6SXNzdWU3NDA0NjQx | 2,034 | Default column names for `read_csv` and for data frames created from other data | {
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} | 8 | "2012-10-07T12:51:41Z" | "2012-12-09T19:13:19Z" | "2012-12-09T19:12:37Z" | NONE | null | If you read data from a file with `read_csv` the default column names of the resulting data frame are set to `X.1` to `X.N` (and to `X1` to `XN` for versions >= 0.9), which are strings.
If you create a data frame from exiting arrays or lists or something the column names default to `0` to `N` and are integers.
Should these default names be the same? I think this would be more consistent and would avoid confusion (Not sure how this is handled in `R` if this a part of this design decision).
See also [this question](http://stackoverflow.com/q/12747417/1301710) and the answers on stackoverflow.
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https://api.github.com/repos/pandas-dev/pandas/issues/2035 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2035/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2035/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2035/events | https://github.com/pandas-dev/pandas/issues/2035 | 7,405,937 | MDU6SXNzdWU3NDA1OTM3 | 2,035 | Need syntax sugar for dataframe row selection | {
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} | 4 | "2012-10-07T16:06:37Z" | "2013-12-16T15:10:28Z" | "2013-12-16T15:10:14Z" | NONE | null | Current dataframe row selection by columns is wordy , 'rowsl()' method is need to make it looks simple.
def _rowsl(df, selection_str):
#syntax sugar to simplify column selection:
# df.rowsl('col_1>0.5 & col_2<40') act as
# df[(df['col_1']>0.5) & (df['col_2']<40)]
```
#not_implement_yet
#rst = parsing_and_ filtering
return rst
```
DataFrame.rowsl = _rowsl
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https://api.github.com/repos/pandas-dev/pandas/issues/2036 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2036/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2036/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2036/events | https://github.com/pandas-dev/pandas/pull/2036 | 7,409,100 | MDExOlB1bGxSZXF1ZXN0MjU2OTAxNQ== | 2,036 | TST: DF should not error when 'columns' is a list that contains dupes | {
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> In [1]: DataFrame(data,columns=["a","a"])
>
> ...
> pandas/pandas/core/internals.pyc in _stack_dict(dct, ref_items, dtype)
> 1344 stacked = np.empty(shape, dtype=dtype)
> 1345 for i, item in enumerate(items):
> -> 1346 stacked[i] = _asarray_compat(dct[item])
> 1347
> 1348 # stacked = np.vstack([_asarray_compat(dct[k]) for k in items])
>
> IndexError: index out of bounds
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https://api.github.com/repos/pandas-dev/pandas/issues/2037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2037/events | https://github.com/pandas-dev/pandas/issues/2037 | 7,411,837 | MDU6SXNzdWU3NDExODM3 | 2,037 | Can speed of shift() improved ? | {
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} | 4 | "2012-10-08T04:53:20Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:36:55Z" | NONE | null | I cprofile my python code and found 'shift()' is one of the speed bottleneck.
Then I do below test :
In [27]: df = DataFrame(np.random.randn(50000, 10))
In [28]: timeit df.shift(1)
10 loops, best of 3: 19.7 ms per loop
In [29]: df = DataFrame(np.random.randn(50000, 100))
In [30]: timeit df.shift(1)
1 loops, best of 3: 208 ms per loop
I found the time spent on 'df.shift(1)' is in proportion to len(df.columns) . Can it be improved ? For all columns share same index, why not shift all columns in a batch (instead of one by one column ?)
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https://api.github.com/repos/pandas-dev/pandas/issues/2038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2038/events | https://github.com/pandas-dev/pandas/issues/2038 | 7,412,777 | MDU6SXNzdWU3NDEyNzc3 | 2,038 | DataFrame header is misaligned when column names contain tabs | {
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} | 5 | "2012-10-08T06:52:20Z" | "2012-11-17T03:53:27Z" | "2012-11-17T03:53:27Z" | NONE | null | Like it says in the title:
```
In [10]: pd.DataFrame([{"a\tb": 1, "c": 2}])
Out[10]:
a b c
0 1 2
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2039 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2039/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2039/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2039/events | https://github.com/pandas-dev/pandas/issues/2039 | 7,422,701 | MDU6SXNzdWU3NDIyNzAx | 2,039 | kde plots produce unexpected behavior with integers | {
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] | closed | false | null | [] | null | 2 | "2012-10-08T15:08:41Z" | "2012-10-09T14:09:21Z" | "2012-10-09T14:09:21Z" | NONE | null | If we feed a series of integers into the kde plot the results are a horizontal line:
```
newy = pandas.Series([10,26,36,64,69,73,76,79,80,81,81,82,83,86,89,93,98,126,136,152])
newy.plot(kind='kde')
```
However, if the series is floats, instead of ints, things work as expected:
```
newy = pandas.Series([10.0,26,36,64,69,73,76,79,80,81,81,82,83,86,89,93,98,126,136,152])
newy.plot(kind='kde')
```
I originally noticed the behavior using `scatter_matrix(..., diagonal='kde')` which uses the same plotting engine, I presume.
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https://api.github.com/repos/pandas-dev/pandas/issues/2040 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2040/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2040/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2040/events | https://github.com/pandas-dev/pandas/pull/2040 | 7,424,840 | MDExOlB1bGxSZXF1ZXN0MjU3NTcyNQ== | 2,040 | ENH: add arguments for scatter_plot | {
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} | 2 | "2012-10-08T16:28:49Z" | "2014-07-27T21:52:06Z" | "2013-07-29T05:09:11Z" | MEMBER | null | Modifies pandas/tools/plotting.py
- xlim, ylim, sharex, sharey, xlabelsize and ylabelsize arguments are added
- To handle the common arguments for axes decoration, _decorate_axes() function has been added (currently, only scatter_plot uses this function).
- kwds argument added which is passed to ax.scatter()
- Some test cases are added.
This pull request should be merged to following pull request, but I've mistaken to merget the latest master branch.
https://github.com/pydata/pandas/pull/1947
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} | 3 | "2012-10-08T19:19:54Z" | "2012-11-01T14:31:52Z" | "2012-11-01T14:10:01Z" | CONTRIBUTOR | null | from [mailing list](https://groups.google.com/forum/#!topic/pydata/Wy3eBEnoDrM), same discussion led to #2026
``` python
In [42]: data = """\
....: ,A
....: a,inf
....: b,-inf
....: """
In [43]: df = pd.read_csv(StringIO(data), index_col=0)
In [44]: df
Out[44]:
A
a -9223372036854775808
b -9223372036854775808
In [45]: df.dtypes
Out[45]: A int64
```
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} | 2 | "2012-10-08T21:41:52Z" | "2012-10-31T21:02:21Z" | "2012-10-31T21:02:21Z" | NONE | null | ```
In [93]: a = rand(1,5)
In [94]: b = rand(5,1)
In [95]: A = pd.DataFrame(a)
In [96]: B = pd.DataFrame(b)
In [97]: a.dot(b)
Out[97]: array([[ 1.50425267]])
In [98]: A.dot(B)
Out[98]:
0
0
1.504253
In [99]: a.dot(B)
Out[99]: array([[ 1.50425267]])
In [100]: A.dot(b)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
D:\Projects\moc\<ipython-input-100-2a3d67ed3e0f> in <module>()
----> 1 A.dot(b)
C:\Python27\lib\site-packages\pandas\core\frame.pyc in dot(self, other)
722 """
723 lvals = self.values
--> 724 rvals = other.values
725 result = np.dot(lvals, rvals)
726 return DataFrame(result, index=self.index, columns=other.columns)
AttributeError: 'numpy.ndarray' object has no attribute 'values'
```
I would guess replace `rvals = other.values` with
```
try:
rvals = other.values
except AttributeError:
pass
```
unless you specifically intended for this to fail?
Thanks,
Ludovic
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} | 0 | "2012-10-08T22:28:29Z" | "2012-11-02T14:22:33Z" | "2012-11-02T14:22:33Z" | CONTRIBUTOR | null | "Note that if using the index from either the left or right DataFrame (or both) using the left_index / right_index options, the join operation is no longer a many-to-many join by construction, as the index values are necessarily unique. There will be some examples of this below."
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@jseabold any opinions?
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} | 2 | "2012-10-08T23:36:03Z" | "2012-11-19T01:45:18Z" | "2012-11-19T00:20:51Z" | CONTRIBUTOR | null | ``` python
In [31]: df1
Out[31]:
date val
0 2012-01-01 00:00:00 -0.199984
1 2012-01-04 00:00:00 1.175552
2 2012-01-07 00:00:00 0.190748
3 2012-01-10 00:00:00 0.993547
4 2012-01-13 00:00:00 0.582836
In [32]: df2
Out[32]:
date val
0 2012-01-02 00:00:00 0.701435
1 2012-01-05 00:00:00 0.825832
2 2012-01-08 00:00:00 0.298673
3 2012-01-11 00:00:00 1.554882
4 2012-01-14 00:00:00 0.990432
In [33]: pd.ordered_merge(df1, df2)
Out[33]:
date val
0 2012-01-13 00:00:00 0.582836
1 2012-01-14 00:00:00 0.990432
2 2012-01-04 00:00:00 1.175552
3 2012-01-05 00:00:00 0.825832
4 2012-01-07 00:00:00 0.190748
5 2012-01-08 00:00:00 0.298673
6 2012-01-10 00:00:00 0.993547
7 2012-01-11 00:00:00 1.554882
8 2012-01-01 00:00:00 -0.199984
9 2012-01-02 00:00:00 0.701435
```
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} | 2 | "2012-10-09T12:36:43Z" | "2012-11-18T18:54:52Z" | "2012-11-18T18:54:52Z" | CONTRIBUTOR | null | have two choices:
1. iterate over unique columns and returns self[col]
2. iterate over each column entry and return self.icol(i)
#1 is better for a lot of current cases in using iteritems to reconstruct a dict. #2 seems more natural though.
@wesm @lodagro any opinions?
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https://api.github.com/repos/pandas-dev/pandas/issues/2048 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2048/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2048/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2048/events | https://github.com/pandas-dev/pandas/pull/2048 | 7,453,428 | MDExOlB1bGxSZXF1ZXN0MjU4ODY3MA== | 2,048 | fix sql keywords in columns name bug | {
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} | 1 | "2012-10-09T16:05:11Z" | "2014-07-25T22:13:15Z" | "2012-11-18T21:16:55Z" | CONTRIBUTOR | null | the function `pandas.io.sql.write_frame` failed if keywords were
used as column names in a DataFrame. i have added hard brackets
to the column names as generated by the `get_sqlite_schema`
function and created a test case in `test_sql.py`
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} | 1 | "2012-10-09T16:49:52Z" | "2012-11-02T20:51:55Z" | "2012-11-02T20:51:51Z" | NONE | null | https://github.com/blog/1184-contributing-guidelines
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} | 3 | "2012-10-09T17:58:45Z" | "2014-06-20T15:11:02Z" | "2012-12-02T03:14:36Z" | CONTRIBUTOR | null | This branch makes inf/-inf different from nan/None (GH #1919), and also fixes a few bugs with INF and NaN values (GH #2026, #2041).
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} | 0 | "2012-10-10T07:46:56Z" | "2012-10-12T23:38:13Z" | "2012-10-12T23:36:01Z" | CONTRIBUTOR | null | ``` python
In [140]: s = pd.Series(list('abcd'), name=('foo', 1))
In [141]: s
Out[141]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
.../pandas/core/format.pyc in _get_footer(self)
86 series_name = self.series.name
87 elif isinstance(self.series.name, tuple):
---> 88 series_name = "('%s')" % "', '".join(self.series.name)
89 else:
90 series_name = str(self.series.name)
TypeError: sequence item 1: expected string, int found
```
Found the issue when indexing a MultiIndexed frame.
``` python
In [142]: mi = pd.MultiIndex.from_tuples([('foo', 1), ('foo', 2)])
In [143]: df = pd.DataFrame(np.random.randn(2,2), index=mi)
In [144]: df.ix[('foo', 1)]
Out[144]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
```
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} | 9 | "2012-10-10T15:37:19Z" | "2014-06-19T21:48:24Z" | "2013-07-29T05:17:57Z" | CONTRIBUTOR | null | I do not know if this is possible. But very much needed.
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} | 3 | "2012-10-10T18:13:39Z" | "2013-02-19T22:02:18Z" | "2013-02-19T22:02:18Z" | CONTRIBUTOR | null | Don't have time to look into now. Not sure it really matters, but the docs state you can use the force_unicode option here but it's not an included keyword argument.
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} | 3 | "2012-10-10T20:25:12Z" | "2014-02-16T21:55:28Z" | "2014-02-16T21:55:28Z" | CONTRIBUTOR | null | @wesm can I get a second pair of eyes to skim over this PR real quick? All tests pass but touches a lot of core modules.
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```
$: git describe
v0.7.0rc1-1778-g9d4f557
```
```
$: python
Enthought Python Distribution -- www.enthought.com
Version: 7.1-1 (32-bit)
>>> import tables
>>> tables.__version__
'2.4.0'
>>>
```
```
$: nosetests -v pandas
...
test_verbose_import (pandas.io.tests.test_parsers.TestParsers) ... ok
test_xlsx_table (pandas.io.tests.test_parsers.TestParsers) ... ok
test_append (pandas.io.tests.test_pytables.TestHDFStore) ...
```
The tests then hang and the python process quits.
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} | 4 | "2012-10-11T17:20:52Z" | "2012-11-19T04:59:35Z" | "2012-11-19T04:59:35Z" | NONE | null | I have a script that works fine when run without any options but generates the following traceback when run with 'python -O':
File "C:\Python27\lib\site-packages\pandas\core\groupby.py", line 1745, in transform
return self._transform_item_by_item(obj, wrapper)
File "C:\Python27\lib\site-packages\pandas\core\groupby.py", line 1777, in _transform_item_by_item
raise TypeError('Transform function invalid for data types')
TypeError: Transform function invalid for data types
I have Python 2.7.3 and pandas 0.9.0:
Python 2.7.3 (default, Apr 10 2012, 23:24:47) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > pandas.**version**
> > > '0.9.0'
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} | 1 | "2012-10-11T18:07:28Z" | "2020-05-11T18:34:11Z" | "2020-05-11T18:34:10Z" | CONTRIBUTOR | null | if resampled index bin edges don't correspond to the original index, snap the starting data according to the specified convention
http://stackoverflow.com/questions/12844900/upsampling-to-weekly-data-in-pandas
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} | 6 | "2012-10-11T19:07:25Z" | "2012-10-12T23:37:59Z" | "2012-10-12T06:56:57Z" | CONTRIBUTOR | null | Not sure if this is in line with latest unicode work, @y-p?
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} | 0 | "2012-10-11T23:40:07Z" | "2012-11-01T14:19:31Z" | "2012-11-01T14:19:31Z" | MEMBER | null | CHAPTER 6
ESSENTIAL BASIC FUNCTIONALITY
As the reader moves from section 6.1 to 6.2 to 6.3.1
The df variable does not trace through as the tutorial would expect.
I think it needs to insert something like the following
```
In [14]:
d = {'one': Series(randn(3), index=['a', 'b', 'c']),'two': Series(randn(4),index=['a','b','c','d']),'three': Series(randn(3), index=['b','c','d'])}
df =DataFrame(d)
```
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} | 4 | "2012-10-12T14:38:04Z" | "2014-04-24T13:27:59Z" | "2014-04-24T12:01:25Z" | CONTRIBUTOR | null | I know how hard it can be to stick to conventions, but it might be nice to try to unify arguments that do similar things. For example, the argument to select/name columns is not consistent. I realize it's a bit difficult since columns usually means labels and there's an axis argument. I don't know to what extent it's feasible because of this, but maybe all of these should be column(s)? Or even name(s) since this doesn't imply an axis but can be inferred from context when there's no axis argument. The few that come to mind since I just used them.
dropna -- subset
drop_duplicates -- cols
DataFrame -- columns
Series -- name
Just a thought.
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} | 1 | "2012-10-12T17:08:38Z" | "2012-10-12T23:40:06Z" | "2012-10-12T23:39:58Z" | NONE | null | I always find it hard to get Packaging issues right, i'd appreciate if someone
would back me up on pandas/setup.py being superfluous.
I made sure that setup.py install/develop still works, and that introducing
a failed test is caught by tox as you would expect,
Also see #2029 for much faster tox runs.
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} | 6 | "2012-10-12T19:19:11Z" | "2017-10-17T11:06:11Z" | "2017-10-17T11:06:11Z" | CONTRIBUTOR | null | Maybe there's already a way to do this, and there's certainly a safer and more robust/full-featured way to do this, but I just monkey-patched my DataFrames to have a dropif method because I got tired of typing.
```
def dropif(self, col, cond):
return self.ix[~eval("self['%s']"%col +cond)]
```
In the spirit of Stata's drop and keep commands.
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https://api.github.com/repos/pandas-dev/pandas/issues/2066 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2066/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2066/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2066/events | https://github.com/pandas-dev/pandas/pull/2066 | 7,565,208 | MDExOlB1bGxSZXF1ZXN0MjYzOTc4Ng== | 2,066 | Fix for Python 3.3 | {
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} | 1 | "2012-10-13T19:13:35Z" | "2014-07-15T12:12:35Z" | "2012-10-13T19:28:21Z" | CONTRIBUTOR | null | There were two problems with a simple 'import pandas' on python 3.3, that have to do with slots.
For the first one, I converted the class initializer into an instance variable.
For the second one, there seems to be a property to compute 'ndim' so I simply removed it.
(I couldn't figure out quickly how to run the tests under Python 3; a number of problems are in the way.)
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} | 0 | "2012-10-14T14:48:53Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:37:12Z" | NONE | null | Since reindex and ix should behave the same way I guess since
"Some might conclude that ix and reindex are 100% equivalent based on this. This is indeed true except in the case of integer indexing."
The docstring also doesn't give any hint that one should use a unique index.
```
In [35]:
import pandas as pd
In [36]:
df = pd.DataFrame([('a',2), ('a',3), ('b',4)], columns = ['A', 'B'])
df2 = pd.DataFrame([('a',2), ('a',3), ('d',4)], columns = ['A', 'B'])
df.set_index('A', inplace=True)
df2.set_index('A', inplace=True)
Out[36]:
B
A
a 2
a 3
d 4
In [37]:
df.ix[df.index.intersection(df2.index)]
Out[37]:
B
A
a 2
a 3
In [38]:
df.reindex(df.index.intersection(df2.index))
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-38-ad4829c75d14> in <module>()
----> 1 df.reindex(df.index.intersection(df2.index))
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in reindex(self, index, columns, method, level, fill_value, limit, copy)
2244 if index is not None:
2245 frame = frame._reindex_index(index, method, copy, level,
-> 2246 fill_value, limit)
2247
2248 return frame
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in _reindex_index(self, new_index, method, copy, level, fill_value, limit)
2320 limit=None):
2321 new_index, indexer = self.index.reindex(new_index, method, level,
-> 2322 limit=limit)
2323 return self._reindex_with_indexers(new_index, indexer, None, None,
2324 copy, fill_value)
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/index.pyc in reindex(self, target, method, level, limit)
825 else:
826 indexer = self.get_indexer(target, method=method,
--> 827 limit=limit)
828 return target, indexer
829
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0-py2.7-linux-x86_64.egg/pandas/core/index.pyc in get_indexer(self, target, method, limit)
746
747 if not self.is_unique:
--> 748 raise Exception('Reindexing only valid with uniquely valued Index '
749 'objects')
750
Exception: Reindexing only valid with uniquely valued Index objects
```
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} | 13 | "2012-10-14T19:23:06Z" | "2014-09-12T10:05:04Z" | "2013-09-21T12:28:14Z" | MEMBER | null | http://stackoverflow.com/questions/12877189/float64-with-pandas-to-csv
What does R (or others) do?
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} | 3 | "2012-10-15T04:23:59Z" | "2013-01-21T17:17:58Z" | "2013-01-21T17:17:58Z" | NONE | null | When using a Series indexed by a PeriodIndex and downsampling, resampling fails when using how='mean' and where the series to be resampled does not span multiple lower-frequency bins.
For example:
```
ix = period_range(start="2012-01-01", end="2012-12-31", freq="M")
s = Series(np.random.randn(len(ix)), index=ix)
s.resample("A", how='mean')
```
Fails because the period range is entirely contained within a single year. I've been able to replicate this going from quarterly to annual, or monthly to quarterly, etc. As of 0.9.1-dev, crashes Python without an exception as Cython function group_mean_bin() attempts to index into an empty bins array.
```
if bins[len(bins) - 1] == len(values): # Crash
```
I don't know how fine-grained pandas is right now when aggregating partially-filled periods, but it could be nice to have an option to return a NaN when the higher-frequency window is only partially filled. For example, suppose we sum daily to monthly and take a percent change across months, and either the recording started partway through the first month or data is only available partway through the last month. Then the first or last period percent change will possibly show a dramatic swing, and the user may not realize its simply an artifact of the data availability, as opposed to a truly interesting move in the underlying process. When running alot of automated aggregations the user may wish to not aggregate any partially filled periods in order to protect themselves from reaching a false conclusion about the time-series trend at the beginning or end of the series.
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} | 8 | "2012-10-15T09:23:44Z" | "2012-11-15T16:07:15Z" | "2012-11-15T15:46:12Z" | CONTRIBUTOR | null | An example is better than words:
``` python
In [16]: cat test.txt
AAA
BBB
CCC
DDD
EEE
FFF
GGG
In [17]: with open("test.txt") as handle:
iterator = itertools.islice(handle, 3)
print list(iterator)
res = pandas.read_table(handle, squeeze=True, header=None)
....:
['AAA\n', 'BBB\n', 'CCC\n']
In [18]: res
Out[18]:
Empty DataFrame
Columns: array([], dtype=object)
Index: array([], dtype=object)
```
This works with the python engine. Notice that the handle is not really iterated through: when debugging I noticed that after iterator usage, the handle keeps on staying at the same file line (IOW the parser is not iterating on it at all).
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} | 13 | "2012-10-15T16:31:20Z" | "2013-03-22T20:06:10Z" | "2013-03-22T20:06:10Z" | CONTRIBUTOR | null | I recently was using **.ix** on a DataFrame with an index that is very close to the integer index and got some unexpected results:
http://nbviewer.ipython.org/3882591/wonky-indexes.ipynb
I'm not sure if this is a bug or if I'm misreading the docs.
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} | 0 | "2012-10-15T18:34:12Z" | "2014-06-04T19:47:32Z" | "2014-06-04T19:47:32Z" | CONTRIBUTOR | null | I'm up-sampling quarterly data to monthly periods and using a fill method which is working fine, but then when I pass in an argument for "how", it seems to disable the filling feature.
My understanding was that "how" doesn't mean anything in the context of up-sampling, in which case I would expect passing the parameter to have no effect on the output.
Is this a bug or am I missing something about how this is supposed to work?
See below (pandas 0.8.1):
```
In [18]: s = pandas.Series(range(9), index=pandas.date_range('2010-01-01', periods=9, freq='Q'))
In [19]: s
Out[19]:
2010-03-31 0
2010-06-30 1
2010-09-30 2
2010-12-31 3
2011-03-31 4
2011-06-30 5
2011-09-30 6
2011-12-31 7
2012-03-31 8
Freq: Q-DEC
In [20]: s.resample('M', fill_method='ffill')
Out[20]:
2010-03-31 0
2010-04-30 0
2010-05-31 0
2010-06-30 1
2010-07-31 1
2010-08-31 1
2010-09-30 2
2010-10-31 2
2010-11-30 2
2010-12-31 3
2011-01-31 3
2011-02-28 3
2011-03-31 4
2011-04-30 4
2011-05-31 4
2011-06-30 5
2011-07-31 5
2011-08-31 5
2011-09-30 6
2011-10-31 6
2011-11-30 6
2011-12-31 7
2012-01-31 7
2012-02-29 7
2012-03-31 8
Freq: M
In [21]: s.resample('M', fill_method='ffill', how='last')
Out[21]:
2010-03-31 0
2010-04-30 NaN
2010-05-31 NaN
2010-06-30 1
2010-07-31 NaN
2010-08-31 NaN
2010-09-30 2
2010-10-31 NaN
2010-11-30 NaN
2010-12-31 3
2011-01-31 NaN
2011-02-28 NaN
2011-03-31 4
2011-04-30 NaN
2011-05-31 NaN
2011-06-30 5
2011-07-31 NaN
2011-08-31 NaN
2011-09-30 6
2011-10-31 NaN
2011-11-30 NaN
2011-12-31 7
2012-01-31 NaN
2012-02-29 NaN
2012-03-31 8
Freq: M
```
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} | 9 | "2012-10-15T20:21:49Z" | "2014-05-29T07:42:03Z" | "2014-05-29T07:42:03Z" | CONTRIBUTOR | null | Not sure yet what's going on here. Doesn't appear to be a unicode issue
```
df = pandas.DataFrame([[u'A Confe\xdfion of the most auncient and true christen catholike olde belefe accordyng to the ordre of the .XII. Articles of our co[m]mon crede, set furthe in Englishe to the glory of almightye God, and to the confirmacion of Christes people in Christes catholike olde faith.']], columns=["title"])
df.title.str.match(".*[A|a]lmight")
# returns
#0 ()
#Name: title
re.match(".*[A|a]lmight", df.title.ix[0])
# expected output
#<_sre.SRE_Match at 0x4fa8100>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2075 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2075/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2075/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2075/events | https://github.com/pandas-dev/pandas/pull/2075 | 7,612,837 | MDExOlB1bGxSZXF1ZXN0MjY1ODY4NA== | 2,075 | Panel constructed with a None as a dict value throws an error | {
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} | 2 | "2012-10-16T02:42:36Z" | "2014-06-18T19:13:57Z" | "2012-11-02T20:48:38Z" | CONTRIBUTOR | null | Python 2.7.3 (default, Jun 21 2012, 07:50:29)
[GCC 4.4.5] on linux2
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > print pandas.**version**
> > > 0.8.1
> > > print pandas.Panel(dict(a = None, b = pandas.DataFrame(index=[1,2,3],columns=[1,2,3])))
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/panel.py", line 219, in **init**
> > > mgr = self._init_dict(data, passed_axes, dtype=dtype)
> > > File "/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/panel.py", line 256, in _init_dict
> > > major = _extract_axis(data, axis=0)
> > > File "/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/panel.py", line 1396, in _extract_axis
> > > raw_lengths.append(v.shape[axis])
> > > AttributeError: 'NoneType' object has no attribute 'shape'
same behavior exists in 0.9
added tests and fixes for this case (and the pathological case of constructing with dict(a=None))
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} | 8 | "2012-10-16T13:06:49Z" | "2012-12-12T15:53:39Z" | "2012-12-11T14:48:51Z" | NONE | null | Hi,
What's the plan for displaying head() cleanly on my dataframe when there are lots of columns?
R handle's this easily by wrapping the output across multiple lines.
Thanks,
Vishal
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} | 4 | "2012-10-16T17:05:12Z" | "2012-11-02T17:03:25Z" | "2012-11-02T16:47:12Z" | CONTRIBUTOR | null | ```
In [1]: import pandas
In [2]: import numpy as np
In [3]: index = pandas.date_range('1/1/2000', periods=8,)
In [4]: p = pandas.Panel(np.random.randn(2, len(index), 4), items=['Item1', 'Item2'],
major_axis=index, minor_axis=['A', 'B', 'C', 'D'])
In [5]: p
Out[5]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 8 (major) x 4 (minor)
Items: Item1 to Item2
Major axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00
Minor axis: A to D
In [7]: x = p.reindex(items = ['Item1','Item2'], copy = False)
In [8]: x.reindex(minor = ['A','B'])
Out[8]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 8 (major) x 2 (minor)
Items: Item1 to Item2
Major axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00
Minor axis: A to B
```
This fails on a comparison in the internals.Block integrity_check of:
block.ref_items is self.items
they are in fact equal (e.g. ==), but id(block.ref_items) != id(self.items) and hence the is fails
since I first reindex on an index which is considered equal, you just return the index directly (rather
than create a new one as in an reindex where its not equal), then on the second reindex the block
manager thinks items has changed (to what the block sees), but its actually the other index
I guess you could copy on an equals index, but not entirely sure this will solve the problem
This is an odd and probably pathological case where I pre-reindexed some panels (on items), then reindexed another
axis later - passing copy = False on the reindex where there is the potential for an indentical index being passed is a work-around
```
In [10]: x = p.reindex(items = ['Item1','Item2'])
In [11]: x.reindex(minor = ['A','B'])
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-11-5f7bfc09b93d> in <module>()
----> 1 x.reindex(minor = ['A','B'])
/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/panel.pyc in reindex(self, major, items, minor, method, major_axis, minor_axis, copy)
691
692 if minor is not None:
--> 693 result = result._reindex_axis(minor, method, 2, copy)
694
695 if items is not None:
/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in _reindex_axis(self, new_index, fill_method, axis, copy)
589 def _reindex_axis(self, new_index, fill_method, axis, copy):
590 new_data = self._data.reindex_axis(new_index, axis=axis,
--> 591 method=fill_method, copy=copy)
592
593 if new_data is self._data and not copy:
/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in reindex_axis(self, new_axis, method, axis, copy)
959
960 new_axis, indexer = cur_axis.reindex(new_axis, method)
--> 961 return self.reindex_indexer(new_axis, indexer, axis=axis)
962
963 def reindex_indexer(self, new_axis, indexer, axis=1, fill_value=np.nan):
/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in reindex_indexer(self, new_axis, indexer, axis, fill_value)
981 new_axes = list(self.axes)
982 new_axes[axis] = new_axis
--> 983 return BlockManager(new_blocks, new_axes)
984
985 def _reindex_indexer_items(self, new_items, indexer, fill_value):
/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in __init__(self, blocks, axes, do_integrity_check)
491
/usr/local/lib/python2.7/site-packages/pandas-0.8.1-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in _verify_integrity(self)
572 mgr_shape = self.shape
573 for block in self.blocks:
--> 574 assert(block.ref_items is self.items)
575 assert(block.values.shape[1:] == mgr_shape[1:])
576 tot_items = sum(len(x.items) for x in self.blocks)
AssertionError:
```
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} | 0 | "2012-10-16T21:15:38Z" | "2012-11-02T21:55:14Z" | "2012-11-02T21:55:14Z" | MEMBER | null | Using IPython:
``` python
In [1]: import pandas
In [2]: Micro = pandas.datetools.Micro
In [3]: 3 * Micro() > 2 * Micro()
Out[3]: False
In [4]: 2 * Micro() > 3 * Micro()
Out[4]: False
```
They can't both be `False`. Giving the arguments to `>` to `cmp` works as expected. Not sure where the issue lies since neither `Micro` nor any of its superclasses implements any of the comparison methods except `__eq__` and `__ne__`.
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} | 4 | "2012-10-17T05:41:41Z" | "2012-11-05T16:56:02Z" | "2012-11-05T16:56:02Z" | NONE | null | ```
In [1]: DataFrame(data,columns=["a","a"])
...
pandas/pandas/core/internals.pyc in _stack_dict(dct, ref_items, dtype)
1344 stacked = np.empty(shape, dtype=dtype)
1345 for i, item in enumerate(items):
-> 1346 stacked[i] = _asarray_compat(dct[item])
1347
1348 # stacked = np.vstack([_asarray_compat(dct[k]) for k in items])
IndexError: index out of bounds
```
5e6db32e is a failing test for this.
it looks like `_to_sdict` threads down to a call to `_convert_object_array` which builds a dict
keyed on column names, so dupe columns get squashed and you end up with a mismatch
between the length of the `columns` arg to `df.__init__` and the data.
_to_sdict is not used for ndarrays so this doesn't haoppen, I was able to reuse
`_init_ndarray` for the case of `columns` being a flat list and have things work as expected.
still, too much code touching this, better left to the core devs to decide how to handle this.
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} | 1 | "2012-10-17T13:21:27Z" | "2012-10-31T20:24:29Z" | "2012-10-31T20:24:29Z" | CONTRIBUTOR | null | When running nosetests on master i get one error.
This occurs when plotting a dataframe with multiindex holding unicode.
It must be related to my environment since the [travis build](https://travis-ci.org/#!/pydata/pandas/jobs/2795124) passes.
python 2.7
numpy 1.6.2
matplotlib 1.1.0 (backend: Qt4Agg)
``` python
======================================================================
ERROR: test_plot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "<...>/pandas/tests/test_graphics.py", line 207, in test_plot
_check_plot_works(df.plot, title=u'\u03A3')
<...>
<...>/lib/python2.7/site-packages/matplotlib/cbook.py", line 1832, in is_math_text
"matplotlib display text must have all code points < 128 or use Unicode strings")
ValueError: matplotlib display text must have all code points < 128 or use Unicode strings
```
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} | 2 | "2012-10-19T01:04:08Z" | "2012-12-11T05:57:05Z" | "2012-12-11T05:57:05Z" | NONE | null | I'm working on the next Unicode PR, which incorporates some global configurables,
and I can't seem to find a convention for handling that uniformly in pandas.
format.py has `_GlobalPrintConfig`/`print_config`, and core.api has the corresponding
`set_printoptions`, It looks to me like they need to be integrated into something more general.
Examples from other projects:
- `matplotlib` has rcParams
- `IPython` does things differently with [config.configurable](http://ipython.org/ipython-doc/stable/api/generated/IPython.config.configurable.html) , but still has a well defined way of
using/adding configurables.
I suggest a new `core.config` module to hold all configurables,
It will take over print_config (set_printoptions will keep working),
but will provide a more general namespacing scheme for configurables along with a
getter/setter to be added to core.api.
Once the getter/setter convention is put into place and used uniformly in the code,
the underlying implementation can change as necessary when pandas grows.
if this sounds reasonable, I can follow up with a PR. @wesm?
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} | 0 | "2012-10-19T13:22:18Z" | "2012-11-03T16:23:01Z" | "2012-11-03T16:23:01Z" | CONTRIBUTOR | null | for line graph `style` correctly sets color cycle
for bar/barh `color` does.
Need to make both work for both
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} | 0 | "2012-10-19T14:45:21Z" | "2012-11-01T00:10:29Z" | "2012-11-01T00:10:29Z" | MEMBER | null | xref: http://stackoverflow.com/questions/12974404/unexpected-results-of-min-and-max-methods-of-pandas-series-made-of-timestamp
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#72 hours starting with midnight Jan 1st, 2011
rng = date_range(’1/1/2011’, periods=72, freq=’H’)
but when I type this
rng = date_range('date.txt', periods=72, freq='H')
unable to parse date.txt
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} | 1 | "2012-10-19T17:33:03Z" | "2014-02-18T20:25:01Z" | "2014-02-18T20:25:01Z" | CONTRIBUTOR | null | http://stackoverflow.com/questions/12979062/using-a-custom-offset-alias-in-pandas
I'm thinking:
@offset_alias
class CustomOffset(DateOffset):
...
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} | 3 | "2012-10-19T21:57:12Z" | "2012-10-31T20:27:52Z" | "2012-10-31T20:27:52Z" | CONTRIBUTOR | null | Except for test_\* files i ran every file through pep8, and resolved most of the violations.
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} | 1 | "2012-10-20T12:33:17Z" | "2016-10-12T23:03:16Z" | "2012-11-04T23:03:17Z" | NONE | null | Series.diff() is not exact for huge numbers. This might be due to some quick approximation or just a bug/mistake.
In [40]: a = 10000000000000000
In [41]: log10(a)
Out[41]: 16.0
In [42]: b = a + 1
In [43]: s = Series([a,b])
In [44]: s.diff()
Out[44]:
0 NaN
1 0
numpy.diff() is not to blame:
In [45]: v = s.values
In [46]: v
Out[46]: array([10000000000000000, 10000000000000001], dtype=int64)
In [47]: diff(v)
Out[47]: array([1], dtype=int64)
One less digit is fine:
In [48]: a = 1000000000000000
In [48]: log10(a)
Out[48]: 15.0
In [49]: b = a + 1
In [50]: s = Series([a,b])
In [51]: s.diff()
Out[51]:
0 NaN
1 1
Why do I need these huge numbers? Certain timestamps (in my case VMS) have this many digits (tenth of microseconds since the year 1858 if I remember correctly).
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} | 1 | "2012-10-20T14:21:51Z" | "2019-12-23T04:12:12Z" | null | NONE | null | Related to matplotlib/matplotlib#1257.
This is a feature available in Excel and [OpenOffice 3.3](http://www.openoffice.org/dev_docs/features/3.3/#Hierarchical_Axis_Labels_for_Charts).
Example:
I think there may be a simple way to do it with matplotlib's [table](http://matplotlib.org/users/screenshots.html#table-demo) to construct the hierarchical axes labels. I'm not an expert in matplotlib or pandas, but I've cobbled together a small demo:
```
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
row_labels = itertools.product(['ASub', 'DSub'], ['LD', 'LS'], ['GD', 'GS'],
['Attn', 'Dist'])
midx = pd.MultiIndex.from_tuples([row for row in row_labels],
names=['Group', 'Local', 'Global', 'Attn'])
df = pd.DataFrame({'mean': np.random.rand(16)}, index=midx)
ax = df.plot(kind='bar')
ax.xaxis.set_visible(False)
cell_text = np.array(df.index.tolist()).T
ax.table(cellText=cell_text, rowLabels=df.index.names, cellLoc = 'center')
plt.subplots_adjust(bottom=0.2)
plt.show()
```
Produces the plot
There's probably a smarter way to populate the table in order to place the labels at the center of the merged cells as well as fix the spacing clashes.
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} | 0 | "2012-10-20T15:41:16Z" | "2012-11-17T21:31:20Z" | "2012-11-17T21:31:20Z" | MEMBER | null | This needs (problem and solution) to be converted to a unit test somewhere. Perhaps wait until c-parser branch has been merged into mainline to avoid merging unpleasantness.
http://stackoverflow.com/questions/12947521/python-pandas-mixed-boolean-yes-true-and-nan-columns
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} | 2 | "2012-10-20T16:24:24Z" | "2013-04-01T12:16:53Z" | "2013-04-01T12:16:53Z" | MEMBER | null | I've seen this too:
http://stackoverflow.com/questions/12910187/pandas-dataframe-slicing-by-day-hour-minute
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} | [] | closed | false | null | [] | null | 2 | "2012-10-20T17:35:57Z" | "2012-11-01T20:13:34Z" | "2012-11-01T20:13:34Z" | NONE | null | When I execute a io.sql.frame_query, and the one of the rows in a sql data column (just ints) is Null (SQL Server using FreeTDS), I get a type error when one of the frame functions tries to get the length of a NoneType object.
The reason I suspect it is due to a Null value is because when I change the query to be top X it works fine, until X includes a row that has a NULL Value
Traceback (most recent call last):
File "/Users/kbrandt/src/Orionpy/test_script.py", line 4, in <module>
ny_web01.applications.Socket_Server.components.SocketServer_exe.get_data()
File "/Users/kbrandt/src/Orionpy/orion.py", line 159, in get_data
Component.get_data(self, query_string, time_win)
File "/Users/kbrandt/src/Orionpy/orion.py", line 144, in get_data
self.data = psql.frame_query(query, self.node._connection)
File "/Library/Python/2.7/site-packages/pandas/io/sql.py", line 146, in read_frame
coerce_float=coerce_float)
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 846, in from_records
coerce_float=coerce_float)
File "/Library/Python/2.7/site-packages/pandas/core/frame.py", line 4957, in _to_sdict
if len(data) == 0 or not data:
TypeError: object of type 'NoneType' has no len()
[Finished in 2.8s with exit code 1]
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https://api.github.com/repos/pandas-dev/pandas/issues/2093 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2093/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2093/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2093/events | https://github.com/pandas-dev/pandas/issues/2093 | 7,740,464 | MDU6SXNzdWU3NzQwNDY0 | 2,093 | Look into bench script failure | {
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} | 0 | "2012-10-20T19:18:02Z" | "2012-11-03T18:50:15Z" | "2012-11-03T18:50:15Z" | MEMBER | null | per http://wesmckinney.com/blog/?p=414#comment-676452789 . Putting this here rather than sit in my inbox
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https://api.github.com/repos/pandas-dev/pandas/issues/2094 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2094/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2094/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2094/events | https://github.com/pandas-dev/pandas/issues/2094 | 7,740,710 | MDU6SXNzdWU3NzQwNzEw | 2,094 | Sorting by column, API improvements possible with SO example? | {
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} | 1 | "2012-10-20T19:44:34Z" | "2014-09-11T00:49:44Z" | "2014-01-29T21:22:10Z" | MEMBER | null | cookie crumbs, in case there's anything to learn here: http://stackoverflow.com/questions/12641606/pandas-sorting-numbers-within-text-by-column
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} | 8 | "2012-10-20T20:19:49Z" | "2012-12-06T23:39:11Z" | "2012-11-01T14:00:10Z" | MEMBER | null | see:
```
arr = np.array([ (datetime.datetime(2012, 9, 9, 0, 0), datetime.datetime(2012, 9, 8, 15, 10))],
dtype=[('Date', '<M8[us]'), ('Forecasting', '<M8[us]')])
DataFrame(arr)
```
cf http://stackoverflow.com/questions/12369546/pandas-parsing-datetime-column-from-sqlite-database
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https://api.github.com/repos/pandas-dev/pandas/issues/2096 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2096/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2096/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2096/events | https://github.com/pandas-dev/pandas/issues/2096 | 7,741,010 | MDU6SXNzdWU3NzQxMDEw | 2,096 | Bug with boolean indexing on empty DataFrame | {
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} | 1 | "2012-10-20T20:23:37Z" | "2012-11-02T23:06:06Z" | "2012-11-02T23:06:06Z" | MEMBER | null | xref http://stackoverflow.com/questions/12700251/pandas-edge-case-on-boolean-index-into-empty-dataframe
```
blah = pandas.DataFrame(numpy.empty([0, 1]), columns=['A'], index=pandas.DatetimeIndex([]))
blah[numpy.array([], bool)]
```
Empty DataFrame
```
blah[numpy.array([], bool)] = 0.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2097 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2097/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2097/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2097/events | https://github.com/pandas-dev/pandas/pull/2097 | 7,746,921 | MDExOlB1bGxSZXF1ZXN0MjcyMDU2Mw== | 2,097 | PR: adding a core.config module to hold package-wide configurables | {
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} | 15 | "2012-10-21T11:28:51Z" | "2014-06-13T09:21:50Z" | "2012-11-27T22:19:28Z" | NONE | null | PR for #2081
## Summary
- core.config is a new module which serves as a general mechanism for working with configurables.
- superceds fmt.print_config, while remaining backward-compatible (set_printoptions(), reset_printoptions() are still working).
- adds the following user API's: `get_option()`, `set_option`, `reset_option()`, `reset_options()` and `describe_option()` (all under the pandas top level module).
- adds the following for developer use: `register_option()` and `deprecate_option()`.
## TL;DR description
```
ENH: Add core.config module for managing package-wide configurables
The config module holds package-wide configurables and provides
a uniform API for working with them.
Overview
========
This module supports the following requirements:
- options are referenced using keys in dot.notation, e.g. "x.y.option - z".
- options can be registered by modules at import time.
- options can be registered at init-time (via core.config_init)
- options have a default value, and (optionally) a description and
validation function associated with them.
- options can be deprecated, in which case referencing them
should produce a warning.
- deprecated options can optionally be rerouted to a replacement
so that accessing a deprecated option reroutes to a differently
named option.
- options can be reset to their default value.
- all option can be reset to their default value at once.
- all options in a certain sub - namespace can be reset at once.
- the user can set / get / reset or ask for the description of an option.
- a developer can register and mark an option as deprecated.
Implementation
==============
- Data is stored using nested dictionaries, and should be accessed
through the provided API.
- "Registered options" and "Deprecated options" have metadata associcated
with them, which are stored in auxilary dictionaries keyed on the
fully-qualified key, e.g. "x.y.z.option".
- the config_init module is imported by the package's __init__.py file.
placing any register_option() calls there will ensure those options
are available as soon as pandas is loaded. If you use register_option
in a module, it will only be available after that module is imported,
which you should be aware of.
- `config_prefix` is a context_manager (for use with the `with` keyword)
which can save developers some typing, see the docstring.
```
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 4 | "2012-10-21T23:09:13Z" | "2013-09-21T13:03:39Z" | "2013-09-21T13:03:39Z" | CONTRIBUTOR | null | ```
import pandas
import numpy as np
from datetime import datetime
d = {"var1" : np.random.randint(0, 10, size=10), "var2" : np.random.randint(0, 10, size=10), "var3" : [datetime(2012, 1, 12), datetime(2011, 2, 4), datetime(2010, 2, 3), datetime(2012, 1, 12), datetime(2011, 2, 4), datetime(2012, 4, 3), datetime(2012, 3, 4), datetime(2008, 5, 1), datetime(2010, 2, 3), datetime(2012, 2, 3)]}
df = pandas.DataFrame.from_dict(d)
df.ix[:7]
var3 = df.var3.unique()
var3.sort()
new = pandas.DataFrame.from_dict({"var3" : var3, "var8" : np.random.random(7)})
df.merge(new, on="var3", sort=False).ix[:7]
```
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"description": "A milestone for closed or open issues for which there is no action needed (eg not a bug, just a question / discussion, nothing to resolve, wontfix, etc).\r\n\r\n",
"due_on": null,
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"number": 33,
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} | 2 | "2012-10-21T23:37:58Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:37:35Z" | CONTRIBUTOR | null | Also maybe auto-exclude non-numerical columns by default like groupby agg?
http://stackoverflow.com/questions/13003051/how-do-i-exclude-a-few-columns-from-a-dataframe-plot
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https://api.github.com/repos/pandas-dev/pandas/issues/2100 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2100/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2100/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2100/events | https://github.com/pandas-dev/pandas/issues/2100 | 7,754,338 | MDU6SXNzdWU3NzU0MzM4 | 2,100 | Odd unstack failure | {
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} | 0 | "2012-10-22T00:32:03Z" | "2012-11-01T00:20:38Z" | "2012-11-01T00:20:38Z" | MEMBER | null | Set day,time, smoker as index, unstack(2)
```
day,time,smoker,sum,len
Fri,Dinner,No,8.25,3
Fri,Dinner,Yes,27.03,9
Fri,Lunch,No,3.0,1
Fri,Lunch,Yes,13.68,6
Sat,Dinner,No,139.63,45
Sat,Dinner,Yes,120.77,42
Sun,Dinner,No,180.57,57
Sun,Dinner,Yes,66.82,19
Thur,Dinner,No,3.0,1
Thur,Lunch,No,117.32,44
Thur,Lunch,Yes,51.51,17
```
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Subsets and Splits