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MDExOlB1bGxSZXF1ZXN0MjMxNjAyNA==
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mention Ubuntu for NeuroDebian
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| 1 | 2012-09-12T09:44:43Z | 2013-09-30T16:17:12Z | 2012-09-12T14:44:37Z |
CONTRIBUTOR
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Allow setting quoting
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| 2 | 2012-09-12T11:23:10Z | 2014-06-24T20:28:27Z | 2012-09-12T15:17:04Z |
NONE
| null |
Allow setting the quoting type for to_csv. Adds "qouting" argument to the to_csv method of DataFrame and pass this on to the csv.writer. Can then use the defaults from the standard library to set the desired type of quoting, like so:
import csv
...
dataframe.to_csv(filepath, quoting=csv.QUOTE_NONNUMERIC)
...
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MDU6SXNzdWU2ODE4NTY5
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Think about Series item access via attribute (a la DataFrame columns)
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| 2 | 2012-09-12T13:45:44Z | 2013-08-23T15:54:38Z | 2013-08-23T15:54:38Z |
MEMBER
| null |
http://stackoverflow.com/questions/12383436/access-pandas-series-item-with-dot-notation-like-dataframe
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ENH: Series item access via attribute #1903
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| 8 | 2012-09-12T19:40:13Z | 2014-06-27T16:25:28Z | 2013-08-23T15:52:47Z |
NONE
| null |
Hack based on http://stackoverflow.com/questions/12383436/access-pandas-series-item-with-dot-notation-like-dataframe
Some tests included.
First contribution to this great project !
I hope this could help.
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MDU6SXNzdWU2ODMwMjg5
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read_csv regression
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| 1 | 2012-09-12T20:11:43Z | 2012-10-05T01:54:51Z | 2012-10-05T01:54:51Z |
CONTRIBUTOR
| null |
parses dates after type conversions now so can't parse dates like '010508'
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DataFrame formatting column name truncation problem
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| 0 | 2012-09-13T01:09:29Z | 2012-09-13T03:04:20Z | 2012-09-13T03:04:20Z |
MEMBER
| null |
```
In [43]: read_csv(StringIO(data2))
Out[43]:
Id StringC
0 7117434 Is it possible to modify drop plot code so that t
In [44]: read_csv(StringIO(data2)).columns
Out[44]: Index([Id, StringCol], dtype=object)
```
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| 1 | 2012-09-13T02:13:14Z | 2015-06-08T12:27:35Z | 2015-06-08T12:26:56Z |
MEMBER
| null |
Future wishful thinking (to keep the milestone open for now)
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DataFrame.to_records converts dates wrongly
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| 7 | 2012-09-13T09:36:40Z | 2012-12-26T03:04:37Z | 2012-12-18T00:34:06Z |
NONE
| null |
Possibly related to #1720:
When converting a DataFrame to a recarray using `df.to_records`, date indexes are incorrectly converted.
``` python
>>> import pandas
>>> df = pandas.DataFrame([["one", "two", "three"], ["four", "five", "six"]], index=pandas.date_range("2012-01-01", "2012-01-02"))
>>> df
0 1 2
2012-01-01 one two three
2012-01-02 four five six
>>> df.to_records()
rec.array([(datetime.datetime(1970, 1, 16, 224, 0), 'one', 'two', 'three'),
(datetime.datetime(1970, 1, 16, 248, 0), 'four', 'five', 'six')],
dtype=[('index', ('<M8[ns]', {})), ('0', '|O8'), ('1', '|O8'), ('2', '|O8')])
```
Notice the dates have been converted to 1970, even though the original dates were in 2012.
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ENH: indicate DataFrame repr truncation, close #1854
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| 6,860,019 |
MDU6SXNzdWU2ODYwMDE5
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DOC: clarify Series.apply docstring
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| 0 | 2012-09-13T20:51:59Z | 2012-11-19T00:48:50Z | 2012-11-19T00:48:50Z |
CONTRIBUTOR
| null |
see SO discussion: http://stackoverflow.com/questions/12393387/python-pandas-how-to-broadcast-an-operation-using-apply-without-writing-a-secon
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Output for small decimal numbers is misleading
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| 2 | 2012-09-14T02:35:46Z | 2012-09-17T23:30:44Z | 2012-09-17T23:30:28Z |
NONE
| null |
I think that [this](http://stackoverflow.com/questions/12417129/how-do-i-convert-a-column-from-a-pandas-dataframe-from-str-scientific-notation) question on stackoverflow sums up the problem fairly concisely.
In a nutshell, the `0` and `-0` output for very small decimal numbers is misleading and looks a lot like an error. It might be better just to show scientific notation directly.
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python-dateutil downgrade on 2.7.2
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| 2 | 2012-09-14T15:33:52Z | 2012-09-18T00:22:26Z | 2012-09-14T18:13:17Z |
NONE
| null |
```
pip install pandas
```
downgrades my python-dateutil from 2.1 to 1.5. I'm on python 2.7.2, OSX 10.8, distribute==0.6.28, numpy==1.6.2.
Is there any reason to believe that manually upgrading python-dateutil after installing pandas==0.8.1 will break pandas? How could I test the just-installed pandas?
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| 6,893,724 |
MDU6SXNzdWU2ODkzNzI0
| 1,913 |
Constructing a Series with a set returns a set and not a Series
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| 4 | 2012-09-15T08:18:33Z | 2012-09-17T17:52:57Z | 2012-09-17T17:52:37Z |
CONTRIBUTOR
| null |
With latest master:
``` python
In [1]: import pandas
In [2]: data = set([1,2,3,4])
In [3]: pandas.Series(data)
Out[3]: set([1, 2, 3, 4])
```
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Resample does not work on seconds
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[] | null | 1 | 2012-09-15T08:28:02Z | 2012-09-17T23:32:38Z | 2012-09-17T23:32:38Z |
NONE
| null |
Hello everybody,
Just found the resample functionality on a datetimeIndex series. Wonderful. My second attempt was on second base (attempted 15Sec), similar to 5Min and it did not work....
Is this not implemented ? Is my syntax wrong?
Thanks,
Luc
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Add support for Series argument in DataFrame.dot, align the arguments if needed
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| 1 | 2012-09-16T00:25:10Z | 2014-06-14T08:48:29Z | 2012-09-17T17:08:58Z |
CONTRIBUTOR
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Boxplot xlim does not match given x values for by
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[] | null | 1 | 2012-09-16T17:05:04Z | 2012-09-16T17:21:37Z | 2012-09-16T17:21:37Z |
CONTRIBUTOR
| null |
I have a boxplot by a boolean variable. It looks like DataFrame.boxplot changes the x values for the plotting however from 0 and 1. I'd like to add a jittered scatterplot on top of it, but I can't get them to line up because of this change. Still looking for where this actually happens.
Are there cases where using 0, 1, 2, 3, etc. values for the groups doesn't make sense? I can see a case for Categorical types but then you should also know the underlying integer representation right?
[_Edit_: It occurred to me that this may be matplotlib behavior and I'm just trying to do this the wrong way.]
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drop_duplicates regression with variable name
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| 2 | 2012-09-16T18:28:47Z | 2012-09-17T20:13:31Z | 2012-09-17T16:09:22Z |
CONTRIBUTOR
| null |
I just updated to master and now this doesn't work.
```
df = pandas.DataFrame([1,1,2,2,3,3,4,5], columns=['variable'])
df.drop_duplicates('variable')
```
I believe cfe674e is the offending commit. A string is iterable but we don't want to iterate over it.
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Using pandas.TimeSeries_DateFormatter in bar plot?
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| 12 | 2012-09-16T18:44:46Z | 2020-12-15T16:42:34Z | null |
MEMBER
| null |
xref #5774
When time series is set as index, line plot uses the TimeSeries_DateFormatter for recognizable date formatting. However, bar plot use the FixedFormatter which outputs the datetime as it is. Can bar plot also use the DateFormatter?
```
In [1]: from pandas import *
In [2]: from numpy.random import *
In [3]: ts = Series(randn(100), index=date_range('1/1/2000', periods=100))
In [4]: df = DataFrame(randn(100, 4), index=ts.index, columns=['A', 'B', 'C', 'D'])
In [5]: df = df.cumsum()
In [7]: a = df.plot(kind='bar')
In [9]: a.get_xaxis().get_major_formatter()
Out[9]: <matplotlib.ticker.FixedFormatter instance at 0x108700f38>
In [11]: a = df.plot()
In [12]: a.get_xaxis().get_major_formatter()
Out[12]: <pandas.tseries.converter.TimeSeries_DateFormatter instance at 0x10877ccf8>
```
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| 1,919 |
Stop treating inf/-inf as missing
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| 4 | 2012-09-16T22:45:54Z | 2012-12-02T03:16:16Z | 2012-12-02T03:16:16Z |
MEMBER
| null |
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| 6,908,173 |
MDU6SXNzdWU2OTA4MTcz
| 1,920 |
Add a fast cross-correlation method to Series and DataFrame objects
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[] | null | 1 | 2012-09-16T23:55:45Z | 2013-01-08T22:55:06Z | 2013-01-08T22:55:06Z |
MEMBER
| null |
For some reason there doesn't seem to be a built in cross-correlation method in NumPy that is fast for large input arrays. Hence this code (it computes the CCF using FFTs, I know there's one in statsmodels, but mine has more options :P, and this code was written somewhat as an exercise in understanding the cross correlation).
I adapted it to Pandas because Pandas totally rocks for organizing and munging, so I thought I would share this code. The functions not listed here are available upon request if there's interest in adding this to Pandas.
``` python
"""
Module for cross-correlation.
"""
import numpy as np
import pandas as pd
from span.utils import nextpow2, pad_larger, get_fft_funcs, detrend_mean
def autocorr(x, n):
"""Compute the autocorrelation of `x`
Parameters
----------
x : array_like
Input array
n : int
Number of fft points
Returns
-------
r : array_like
The autocorrelation of `x`.
"""
ifft, fft = get_fft_funcs(x)
return ifft(np.absolute(fft(x, n)) ** 2.0, n)
def crosscorr(x, y, n):
"""Compute the cross correlation of `x` and `y`
Parameters
----------
x, y : array_like
n : int
Returns
-------
c : array_like
Cross correlation of `x` and `y`
"""
ifft, fft = get_fft_funcs(x, y)
return ifft(fft(x, n) * fft(y, n).conj(), n)
def matrixcorr(x, nfft=None):
"""Cross-correlation of the columns in a matrix
Parameters
----------
x : array_like
The matrix from which to compute the cross correlations of each column
with the others
Returns
-------
c : array_like
"""
m, n = x.shape
if nfft is None:
nfft = int(2 ** nextpow2(2 * m - 1))
ifft, fft = get_fft_funcs(x)
X = fft(x.T, nfft)
Xc = X.conj()
mx, nx = X.shape
c = np.empty((mx ** 2, nx), dtype=X.dtype)
for i in xrange(n):
c[i * n:(i + 1) * n] = X[i] * Xc
return ifft(c, nfft).T, m
def xcorr(x, y=None, maxlags=None, detrend=detrend_mean, scale_type='normalize'):
"""Compute the cross correlation of `x` and `y`.
This function computes the cross correlation of `x` and `y`. It uses the
equivalence of the cross correlation with the negative convolution computed
using a FFT to achieve much faster cross correlation than is possible with
the traditional signal processing definition.
By default it computes the cross correlation at each of 1 - maxlags to maxlags,
scaled by the lag 0 cross correlation after mean centering the data.
Note that it is not necessary for `x` and `y` to be the same size.
Parameters
----------
x : array_like
y : array_like, optional
If not given or is equal to `x`, the autocorrelation is computed.
maxlags : int, optional
The highest lag at which to compute the cross correlation.
detrend : callable, optional
A callable to detrend the data.
scale_type : str, optional
Returns
-------
c : pandas.Series or pandas.DataFrame
"""
def unbiased(c, lsize):
"""
"""
return c / (lsize - np.abs(c.index))
def biased(c, lsize):
"""
"""
return c / lsize
def normalize(c, lsize):
"""
"""
assert c.ndim in (1, 2), 'invalid size of cross correlation array'
if c.ndim == 1:
cdiv = c.ix[0]
else:
mc, nc = c.shape
ncsqrt = int(np.sqrt(nc))
jkl = np.diag(np.r_[:nc].reshape((ncsqrt, ncsqrt)))
tmp = np.sqrt(c.ix[0, jkl])
cdiv = np.outer(tmp, tmp).ravel()
return c / cdiv
SCALE_FUNCTIONS = {
None: lambda c, lsize: c,
'none': lambda c, lsize: c,
'unbiased': unbiased,
'biased': biased,
'normalize': normalize
}
assert x.ndim in (1, 2), 'x must be a vector or matrix'
x = detrend(x)
if x.ndim == 2 and np.all(np.greater(x.shape, 1)):
assert y is None, 'y argument not allowed when x is a 2D array'
ctmp, lsize = matrixcorr(x)
elif y is None or y is x or np.array_equal(x, y):
lsize = x.size
ctmp = autocorr(x, int(2 ** nextpow2(2 * lsize - 1)))
else:
x, y, lsize = pad_larger(x, detrend(y))
ctmp = crosscorr(x, y, int(2 ** nextpow2(2 * lsize - 1)))
if maxlags is None:
maxlags = lsize
else:
assert maxlags <= lsize, 'max lags must be less than or equal to %i' % lsize
lags = np.r_[1 - maxlags:maxlags]
return_type = pd.DataFrame if ctmp.ndim == 2 else pd.Series
scaler = SCALE_FUNCTIONS[scale_type]
return scaler(return_type(ctmp[lags], index=lags), lsize)
```
Right now I'm using it like
``` python
class Series(pd.Series):
def __new__(cls, *args, **kwargs):
return pd.Series.__new__(cls, *args, **kwargs).view(Series)
def __init__(self, *args, **kwargs):
super(Series, self).__init__(*args, **kwargs)
def xcorr(self, other=None, maxlags=None, detrend=span.utils.detrend_mean,
scale_type='normalize'):
return xcorr(self, other, maxlags=maxlags, detrend=detrend,
scale_type=scale_type)
def asdfasdf(self):
return 1
class DataFrame(pd.DataFrame):
def __init__(self, *args, **kwargs):
super(DataFrame, self).__init__(*args, **kwargs)
def xcorr(self, other=None, maxlags=None, detrend=span.utils.detrend_mean,
scale_type='normalize'):
return xcorr(self, maxlags=maxlags, detrend=detrend,
scale_type=scale_type)
```
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MDU6SXNzdWU2OTA5MzE2
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AttributeError on import
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[] | null | 3 | 2012-09-17T02:53:02Z | 2012-09-17T14:15:42Z | 2012-09-17T14:15:41Z |
NONE
| null |
Just pulled and built 0.9.0 from the master branch. The resulting package yields an error on import:
```
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/__init__.py", line 25, in <module>
from pandas.core.api import *
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/core/api.py", line 14, in <module>
from pandas.core.series import Series, TimeSeries
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/core/series.py", line 23, in <module>
from pandas.tseries.index import DatetimeIndex
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/tseries/index.py", line 10, in <module>
from pandas.tseries.frequencies import infer_freq, to_offset, get_period_alias
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/tseries/frequencies.py", line 6, in <module>
from pandas.tseries.offsets import DateOffset
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/tseries/offsets.py", line 3, in <module>
from pandas.tseries.tools import to_datetime
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_e98a568_20120916-py2.7-macosx-10.8-x86_64.egg/pandas/tseries/tools.py", line 262, in <module>
normalize_date = lib.normalize_date
AttributeError: 'module' object has no attribute 'normalize_date'
```
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MDU6SXNzdWU2OTA5ODI5
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Handling for FixedOffsetTimezone in tseries/tools.py
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| 1 | 2012-09-17T04:00:11Z | 2013-12-04T00:57:52Z | 2012-09-17T23:36:27Z |
NONE
| null |
**Version:** 0.9.0.dev e98a568-py2.7-macosx-10.6-x86_64
When passing a string column with timestamps like `"2012-04-23 10:00:23.123000-07:00"` (fixed offset timezone) to DataFrame.set_index() , getting exception:
**AttributeError: 'FixedOffsetTimezone' object has no attribute 'zone'**
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-d8465aa13e80> in <module>()
----> 1 df.set_index('ts')
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/core/frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity)
2422 arrays.append(level)
2423
-> 2424 index = MultiIndex.from_arrays(arrays, names=names)
2425
2426 if verify_integrity and not index.is_unique:
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/core/index.pyc in from_arrays(cls, arrays, sortorder, names)
1551 if len(arrays) == 1:
1552 name = None if names is None else names[0]
-> 1553 return Index(arrays[0], name=name)
1554
1555 cats = [Categorical.from_array(arr) for arr in arrays]
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/core/index.pyc in __new__(cls, data, dtype, copy, name)
109 if _shouldbe_timestamp(subarr):
110 from pandas.tseries.index import DatetimeIndex
--> 111 return DatetimeIndex(subarr, copy=copy, name=name)
112
113 if lib.is_period_array(subarr):
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
228 else:
229 try:
--> 230 subarr = tools.to_datetime(data)
231 except ValueError:
232 # tz aware
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, utc, box)
90 if isinstance(arg, list):
91 arg = np.array(arg, dtype='O')
---> 92 result = _convert_f(arg)
93 return result
94 try:
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/tseries/tools.pyc in _convert_f(arg)
75 except ValueError, e:
76 try:
---> 77 values, tz = lib.datetime_to_datetime64(arg)
78 return DatetimeIndex._simple_new(values, None, tz=tz)
79 except (ValueError, TypeError):
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/lib.so in pandas.lib.datetime_to_datetime64 (pandas/src/tseries.c:38359)()
/Users/redacted/.pythonbrew/venvs/Python-2.7.3/redacted/lib/python2.7/site-packages/pandas-0.9.0.dev_e98a568-py2.7-macosx-10.6-x86_64.egg/pandas/lib.so in pandas.lib._get_zone (pandas/src/tseries.c:37262)()
AttributeError: 'FixedOffsetTimezone' object has no attribute 'zone'
```
As an aside, I believe this is a common tz-aware timestamp for Postgres, so encountering this with:
`df = pd.io.sql.frame_query(QRY, con=_engine.raw_connection(), index_col='ts')`
```
```
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Have the possibility for Series.unique() to return a Series rather than an array
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| 13 | 2012-09-17T09:06:36Z | 2019-07-26T14:00:46Z | 2012-11-28T03:26:04Z |
CONTRIBUTOR
| null |
I admit I haven't looked at the code so there may be reasons for this, but I've found myself in the need of squeezing out duplicates from a Series but keeping the results as a Series.
Series.unique() however returns an array, so in my code I have to construct a Series twice:
``` python
series = pandas.Series([1,2,3,3])
series = pandas.Series(series.unique())
```
Is this by design? If so, feel free to close this bug.
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Result of add/sub/mul/div Series and DataFrame depends on type of index
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| 2 | 2012-09-17T16:56:12Z | 2013-01-18T04:50:27Z | 2013-01-18T04:50:20Z |
CONTRIBUTOR
| null |
Hi,
We came across this bug while attempting to demean a DataFrame. Here's typical behavior:
```
aapl = DataReader('AAPL', data_source='yahoo')
aapl - aapl.mean(1)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 251 entries, 2011-09-19 00:00:00 to 2012-09-14 00:00:00
Data columns:
Open 251 non-null values
High 251 non-null values
Low 251 non-null values
Close 251 non-null values
Volume 251 non-null values
Adj Close 251 non-null values
dtypes: float64(6)
```
However, if we perform this same operation with an Int64Index rather than a DatetimeIndex, we get the following:
```
aapl = DataReader('AAPL', data_source='yahoo')
aapl.index = range(251)
aapl - aapl.mean(1)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 251 entries, 0 to 250
Columns: 257 entries, 0 to Volume
dtypes: float64(257)
```
In the first case we got a 251x6 array, but in the latter we get a 251x257 array. It seems there's a transpose in the logic for DatetimeIndices that is not present for other indices.
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Clean up the C code
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| 1 | 2012-09-17T20:40:34Z | 2012-09-17T23:00:48Z | 2012-09-17T23:00:48Z |
CONTRIBUTOR
| null |
Some patches to make `python setup.py build` produce less noise. Compiled with GCC 4.6.3 and Clang 3.0; all tests pass.
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Smoke Testing
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| 12 | 2012-09-17T20:49:58Z | 2014-06-20T00:00:30Z | 2013-02-04T19:36:39Z |
CONTRIBUTOR
| null |
Was thinking on writing smoke tests for Series and DataFrame. Idea is to have "it works"-style of tests on all methods of Series (and later also For DataFrame) for all combinations of index type, value type, and possible input argument combinations.
Current code is certainly not intended to be merged in, just want to introduce the idea.
Smoke tests are easy/quick to write and i think can avoid issues like "method x works fine for y-dtype but unexpectedly raises exception for z-dtype".
Of course it is better to have verification of the output also, and not only check "it works" for all possible cases, but that is much more work and (maybe) for a second phase.
For now, one of the tests _smoking_ `__eq__`, this came out:
``` python
In [17]: s1 = pandas.Series(['foo', 'bar', 'baz'])
In [18]: s2 = pandas.Series()
In [19]: s2 == s1
Out[19]: Series([], dtype=bool)
In [20]: s1 == s2
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
...
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'double'
```
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| 1 | 2012-09-18T02:07:28Z | 2012-09-18T14:25:21Z | 2012-09-18T14:25:21Z |
CONTRIBUTOR
| null |
Actually they can use skip_footer here.
I changed it slightly so it specifies the row at which to start skipping and can handle negative indexing.
Note that this does constitute an API change.
|
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| 6,941,792 |
MDU6SXNzdWU2OTQxNzky
| 1,928 |
One more "'tzoffset' object has no attribute 'zone' "
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| 2 | 2012-09-18T05:37:22Z | 2016-10-12T23:03:12Z | 2012-09-19T02:19:05Z |
NONE
| null |
**Version:** 0.9.0.dev_de31cbe (py2.7-macosx-10.6-x86_64)
Related to #1922 (closed)
I could be the only dummy with this kind of data so I might be able to find a few more of these.
Although I think these are typical Postgres tz-aware timestamps.
**Repro**
```
from StringIO import StringIO
table="""ts, d1
2012-05-23 10:00:01.123000-07:00, 3.1459265
2012-05-23 10:00:02.456000-07:00, 2.7182818
"""
df = pd.read_table(StringIO(table), parse_dates=True, index_col='ts', sep=',')
df.resample('t')
```
**Traceback**
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-90-a0ce8072eb5a> in <module>()
----> 1 df.resample('t')
pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
187 fill_method=fill_method, convention=convention,
188 limit=limit, base=base)
--> 189 return sampler.resample(self)
190
191 def first(self, offset):
pandas/tseries/resample.pyc in resample(self, obj)
66
67 if isinstance(axis, DatetimeIndex):
---> 68 rs = self._resample_timestamps(obj)
69 elif isinstance(axis, PeriodIndex):
70 offset = to_offset(self.freq)
pandas/tseries/resample.pyc in _resample_timestamps(self, obj)
180 axlabels = obj._get_axis(self.axis)
181
--> 182 binner, grouper = self._get_time_grouper(obj)
183
184 # Determine if we're downsampling
pandas/tseries/resample.pyc in _get_time_grouper(self, obj)
95
96 if self.kind is None or self.kind == 'timestamp':
---> 97 binner, bins, binlabels = self._get_time_bins(axis)
98 else:
99 binner, bins, binlabels = self._get_time_period_bins(axis)
pandas/tseries/resample.pyc in _get_time_bins(self, axis)
111 first, last = _get_range_edges(axis, self.freq, closed=self.closed,
112 base=self.base)
--> 113 binner = labels = DatetimeIndex(freq=self.freq, start=first, end=last)
114
115 # a little hack
pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
175 if data is None:
176 return cls._generate(start, end, periods, name, offset,
--> 177 tz=tz, normalize=normalize)
178
179 if not isinstance(data, np.ndarray):
pandas/tseries/index.pyc in _generate(cls, start, end, periods, name, offset, tz, normalize)
282 end = Timestamp(end)
283
--> 284 inferred_tz = tools._infer_tzinfo(start, end)
285
286 if tz is not None and inferred_tz is not None:
pandas/tseries/tools.pyc in _infer_tzinfo(start, end)
31 tz = None
32 if start is not None:
---> 33 tz = _infer(start, end)
34 elif end is not None:
35 tz = _infer(end, start)
pandas/tseries/tools.pyc in _infer(a, b)
27 tz = a.tzinfo
28 if b and b.tzinfo:
---> 29 assert(tz.zone == b.tzinfo.zone)
30 return tz
31 tz = None
AttributeError: 'tzoffset' object has no attribute 'zone'
```
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| 6,946,828 |
MDU6SXNzdWU2OTQ2ODI4
| 1,929 |
to_html: sparsify mutliindex keys in dataframe rows
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| 1 | 2012-09-18T09:46:35Z | 2012-09-18T19:47:31Z | 2012-09-18T19:43:31Z |
NONE
| null |
The sparsify option of DataFrame.to_html currently works only for columns. It would be useful to add this option also for rows, similarly to what is already done for data frame representation in console.
Example:
``` python
df = pd.DataFrame(np.arange(3), index=[['X', 'X', 'Y'], ['a','b','a']])
df.to_html(fid)
```
currently produces the following output:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>X</strong></td>
<td><strong>a</strong></td>
<td> 0</td>
</tr>
<tr>
<td><strong>X</strong></td>
<td><strong>b</strong></td>
<td> 1</td>
</tr>
<tr>
<td><strong>Y</strong></td>
<td><strong>a</strong></td>
<td> 2</td>
</tr>
</tbody>
</table>
This should be changed to:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>0</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2" valign="top"><strong>X</strong></td>
<td><strong>a</strong></td>
<td> 0</td>
</tr>
<tr>
<td><strong>b</strong></td>
<td> 1</td>
</tr>
<tr>
<td><strong>Y</strong></td>
<td><strong>a</strong></td>
<td> 2</td>
</tr>
</tbody>
</table>
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| 1,930 |
FDPanel object, a 4 dimensional pandas container (version 2)
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| 3 | 2012-09-18T17:40:13Z | 2012-11-14T04:29:42Z | 2012-11-14T04:29:42Z |
CONTRIBUTOR
| null |
FDPanel is like a Panel object, but provides 4 dimensions: labels, items, major_axis, minor_axis
(version 2: much of the code changes now occur in panel.py; fdpanel.py is a thin wrapper over an n-dim
panel like obj; supporting NDPanel like objects with named higher dimension will be very easy)
instead of using a dict of Panels to hold data, the FDPanel provides a convenient represenation in pandas space
with named dimensions to allow easy axis swapping and slicing
see examples/4D Example 2.pdf for creation and usage
## motivation
io/pytables currently provides a nice representation of a table to store a Panel object
directly (assume item x major (dates) x minor)
FDPanel allows many named tables to represent a collection of these tables in a single hdf5 file
## testing
tests/test_fdpanel.py provides a similar methodology to test_panel.py
FDPanel required an overhall of many methods in panel.py and one change in core/index.py (regarding multi-indexing)
almost all methods in a Panel are extended to FDPanel (with the exception in that Panel now allows a multi-axis on axis 0)
docstrings need to be refreshed a bit and made a bit more general
all tests that are not skipped pass
join works but merge testing code not complete yet (pandas.tools.merge)
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MDExOlB1bGxSZXF1ZXN0MjM3ODYzMw==
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Apply nonuq
|
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| 0 | 2012-09-18T19:53:54Z | 2012-09-18T19:56:53Z | 2012-09-18T19:56:52Z |
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https://api.github.com/repos/pandas-dev/pandas/issues/1932
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| 6,963,696 |
MDExOlB1bGxSZXF1ZXN0MjM3ODkyMg==
| 1,932 |
Better error message for mixed series/nonseries DataFrame construction
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| 1 | 2012-09-18T20:14:53Z | 2014-06-18T10:14:20Z | 2012-09-18T20:36:19Z |
CONTRIBUTOR
| null |
While trying the tutorial, I decided to see what happens when I break things. I got a bare `AssertionError`, which I don't like, so I decided to replace it with a `ValueError` holding an appropriate error message.
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Fixedoffset
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| 0 | 2012-09-18T21:34:48Z | 2014-08-06T13:12:19Z | 2012-09-19T02:18:45Z |
CONTRIBUTOR
| null |
exposed _get_zone in datetime.pyx as lib.get_timezone and used in tseries/index.py and tseries/tools.py
HDFStore:
Store total seconds if no name and added timezone parsing if integer
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BUG: DatetimeIndex localizes twice if input is localized DatetimeIndex #...
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[] | null | 1 | 2012-09-18T22:32:32Z | 2012-09-20T03:14:52Z | 2012-09-20T03:14:52Z |
CONTRIBUTOR
| null |
...1838
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Groupby last
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| 1 | 2012-09-19T03:02:41Z | 2014-07-12T08:38:15Z | 2012-09-20T03:12:15Z |
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ENH: Allow referencing of Excel columns by their Excel column names.
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| 3 | 2012-09-19T15:30:51Z | 2014-06-12T21:15:14Z | 2012-10-31T20:40:08Z |
CONTRIBUTOR
| null |
I find it more convenient to refer to the columns as they are in Excel by their string representations.
The excel2num() function I found on StackOverflow. Does that need to be referenced/acknowledged?
I'm happy to put in some unit tests but couldn't find a natural home for it. Most of the ExcelFile tests sit in test_frame.py but I think something like test_io.py or test_parsers.py would be a better fit.
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read_csv disable TextParser._get_index_name magic?
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[] | null | 1 | 2012-09-19T17:15:20Z | 2012-09-19T17:33:32Z | 2012-09-19T17:33:32Z |
NONE
| null |
There is a way to specify which columns to use for an Index, but there isn't a way to specify "DON'T auto-look for an index". I'm getting some weird behavior where a big file creates an integer Index, but a smaller subset will create a MultiIndex which appears to be messing up columns and joins later.
Tracing through the pandas code now, will see if I can get a non-proprietary example... But I'd like to be able to specify in the read_csv call to just create an integer index.
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| 1 | 2012-09-19T21:51:25Z | 2012-09-20T03:03:58Z | 2012-09-20T03:03:50Z |
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| 4 | 2012-09-20T04:09:18Z | 2012-09-21T03:48:04Z | 2012-09-21T03:48:04Z |
CONTRIBUTOR
| null |
I have a MultiIndexed DataFrame which, when pickled and unpickled, results in some of the data changing.
Specifically, some of its columns contain data which is a tuple of integers, and these integers are different each unpickling. Here's a DataFrame which shows this problem:
http://bec.physics.monash.edu/docs/dataframe.pickle
It was pickled with pandas 0.9.0.dev-667220a (current git-master) and Python 2.7.3 64 bit.
Try the following to reproduce:
``` python
import pickle
df = pickle.load(open('dataframe.pickle'))
print df['top','roi0']
```
Repeating this gives me different data each time.
Which integers I get seems slightly platform dependent. The tuples contain four integers, and on Linux most rows (but not all) in the DataFrame end up getting three zeros and one nonzero value, and all the nonzero values are somewhat similar, and in the tens of millions. On Windows the data seems to be random integers that are less regular, and without most of them being zeros. The data that was pickled should have four different values, between 0 and 2048 or so (these are definitions of region of interest rects on a CCD camera).
I am unable to reproduce the problem with non-herarchical index DataFrames.
Any clue as to why this might be, or any suggested workarounds? We use different serialisation formats for on-disk persistence, but we've been using pickle for slinging dataframes across networks.
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Regression in date formatting in IPython Notebook (v0.9-dev)
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| 1 | 2012-09-20T04:09:28Z | 2012-09-20T15:36:46Z | 2012-09-20T15:36:46Z |
NONE
| null |
This is a possible regression in Pandas v0.9.0-667220a versus v0.8.1:
The following works, in both v0.8.1 and v0.9.0-667220a:
```
>>> from pandas.io.data import DataReader
>>> ts = DataReader("AAPL", "yahoo", start="01/01/2000")
>>> ts.head()
Open High Low Close Volume Adj Close
Date
2000-01-03 104.87 112.50 101.69 111.94 19144400 27.87
2000-01-04 108.25 110.62 101.19 102.50 18310000 25.52
2000-01-05 103.75 110.56 103.00 104.00 27818000 25.89
2000-01-06 106.12 107.00 95.00 95.00 27443200 23.65
2000-01-07 96.50 101.00 95.50 99.50 16463200 24.77
```
However, with v0.9.0-667220a, the graphical IPython Notebook output of the dates is garbled. In it, the dates appear as follows:
```
Date
1970-01-11 232:00:00
1970-01-11 00:00:00
1970-01-11 24:00:00
1970-01-11 48:00:00
1970-01-11 72:00:00
```
(I can attach a screenshot if someone can tell me how to upload or attach a file to an issue using GitHub...)
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MDU6SXNzdWU3MDA0NzU5
| 1,941 |
nosetest errors when "xlrd" isn't installed
|
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| 0 | 2012-09-20T08:32:31Z | 2012-09-20T16:12:36Z | 2012-09-20T09:02:29Z |
NONE
| null |
Maybe this is because I have openpyxl but not xlrd installed.
Anyway, I guess the tests should be skipped instead of throwing an error.
```
======================================================================
ERROR: test_parse_cols_int (pandas.io.tests.test_parsers.TestParsers)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/arthur/python-packages/pandas/pandas/io/tests/test_parsers.py", line 795, in test_parse_cols_int
xls = ExcelFile(pth)
File "/home/arthur/python-packages/pandas/pandas/io/parsers.py", line 1257, in __init__
import xlrd
ImportError: No module named xlrd
======================================================================
ERROR: test_parse_cols_list (pandas.io.tests.test_parsers.TestParsers)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/arthur/python-packages/pandas/pandas/io/tests/test_parsers.py", line 813, in test_parse_cols_list
xlsx = ExcelFile(pth)
File "/home/arthur/python-packages/pandas/pandas/io/parsers.py", line 1257, in __init__
import xlrd
ImportError: No module named xlrd
======================================================================
ERROR: test_to_excel_float_format (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/arthur/python-packages/pandas/pandas/tests/test_frame.py", line 3663, in test_to_excel_float_format
reader = ExcelFile(filename)
File "/home/arthur/python-packages/pandas/pandas/io/parsers.py", line 1257, in __init__
import xlrd
ImportError: No module named xlrd
======================================================================
ERROR: test_to_excel_unicode_filename (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/arthur/python-packages/pandas/pandas/tests/test_frame.py", line 3693, in test_to_excel_unicode_filename
reader = ExcelFile(filename)
File "/home/arthur/python-packages/pandas/pandas/io/parsers.py", line 1257, in __init__
import xlrd
ImportError: No module named xlrd
----------------------------------------------------------------------
Ran 2372 tests in 140.055s
FAILED (SKIP=18, errors=4)
```
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| 7,010,839 |
MDU6SXNzdWU3MDEwODM5
| 1,942 |
assignment with index broken
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| 0 | 2012-09-20T12:56:09Z | 2012-09-20T19:15:21Z | 2012-09-20T19:15:21Z |
CONTRIBUTOR
| null |
``` python
a=DataFrame(randn(20,2),index=[chr(x+65) for x in range(20)])
a.ix[-1]=a.ix[-2]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-49-79d0c89159f2> in <module>()
----> 1 a.ix[-1]=a.ix[-2]
/usr/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
80 indexer = self._convert_to_indexer(key)
81
---> 82 self._setitem_with_indexer(indexer, value)
83
84 def _convert_tuple(self, key):
/usr/local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
134 value = self._align_frame(indexer, value)
135
--> 136 self.obj.values[indexer] = value
137
138 def _align_series(self, indexer, ser):
ValueError: operands could not be broadcast together with shapes (2) (20)
pandas.__version__
Out[51]: '0.9.0.dev-ba93669'
```
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| 7,013,605 |
MDU6SXNzdWU3MDEzNjA1
| 1,943 |
Selecting multiple columns from DataFrame with duplicate column labels failure.
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| 6 | 2012-09-20T14:30:31Z | 2016-10-12T23:03:13Z | 2013-04-30T17:49:54Z |
CONTRIBUTOR
| null |
``` python
In [25]: df = pandas.DataFrame(np.random.randn(4,4), columns=list('AABC'))
In [26]: df
Out[26]:
A A B C
0 -0.174905 0.332522 1.134984 -0.201270
1 1.730445 0.382556 -0.607761 1.221815
2 0.513049 0.196231 -1.746732 -0.252282
3 -0.297577 -1.000121 -0.090442 -2.129467
In [27]: df.ix[:,['A', 'B']]
Out[27]:
A A B
0 -0.174905 0.332522 1.134984
1 1.730445 0.382556 -0.607761
2 0.513049 0.196231 -1.746732
3 -0.297577 -1.000121 -0.090442
In [28]: df[['A', 'B']]
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
...
Exception: Reindexing only valid with uniquely valued Index objects
In [29]: df[['B', 'C']]
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
...
Exception: Reindexing only valid with uniquely valued Index objects
```
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MDU6SXNzdWU3MDE1NTgx
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read_csv allow scalar arg to na_values
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| 0 | 2012-09-20T15:19:39Z | 2012-09-20T15:21:23Z | 2012-09-20T15:21:23Z |
CONTRIBUTOR
| null |
make it so `na_values='abcdefg'` is not interpreted as `na_values=['a', 'b', 'c', 'd', 'e', 'f', 'g']`
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MDU6SXNzdWU3MDI5MTkw
| 1,945 |
Build error in tseries.c on OS X 10.8.2
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| 3 | 2012-09-21T00:41:48Z | 2012-09-21T03:13:04Z | 2012-09-21T02:24:11Z |
NONE
| null |
Just updated OS X to 10.8.2 and Xcode to 4.5. Building pandas (commit 1e68fd9) now fails with an undeclared variable error in some cython code.
```
gcc-4.2 -DNDEBUG -g -Os -Wall -Wstrict-prototypes -pipe -arch x86_64 -I/Library/Python/2.7/site-packages/numpy-1.8.0.dev_9597b1f_20120920-py2.7-macosx-10.8-x86_64.egg/numpy/core/include -I/System/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7 -c pandas/src/tseries.c -o build/temp.macosx-10.8-x86_64-2.7/pandas/src/tseries.o
In file included from /Library/Python/2.7/site-packages/numpy-1.8.0.dev_9597b1f_20120920-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/ndarraytypes.h:1722,
from /Library/Python/2.7/site-packages/numpy-1.8.0.dev_9597b1f_20120920-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/ndarrayobject.h:17,
from /Library/Python/2.7/site-packages/numpy-1.8.0.dev_9597b1f_20120920-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/arrayobject.h:15,
from pandas/src/tseries.c:253:
/Library/Python/2.7/site-packages/numpy-1.8.0.dev_9597b1f_20120920-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/npy_deprecated_api.h:11:2: warning: #warning "Using deprecated NumPy API, disable it by #defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
pandas/src/tseries.c: In function ‘__pyx_pf_6pandas_3lib_112monthrange’:
pandas/src/tseries.c:48311: error: ‘_days_per_month_table’ undeclared (first use in this function)
pandas/src/tseries.c:48311: error: (Each undeclared identifier is reported only once
pandas/src/tseries.c:48311: error: for each function it appears in.)
error: command 'gcc-4.2' failed with exit status 1
```
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MDU6SXNzdWU3MDU1MDU3
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hours are wrong for DatetimeIndex with timezone set
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| 3 | 2012-09-21T22:58:18Z | 2012-09-23T17:37:16Z | 2012-09-23T17:37:09Z |
CONTRIBUTOR
| null |
Using `hours` on a basic hourly `DatetimeIndex` with a time zone gives the incorrect hours.
```
import pandas
from pandas.io.parsers import read_csv
timezone = 'America/Atikokan'
filename = 'forecast.csv'
data = read_csv(filename, index_col=0, header=None, squeeze=True)
data.index = pandas.DatetimeIndex(data.index).tz_localize(timezone)
print data.index[:10]
print [str(t) for t in data.index[:10]]
print data.index.hour[:10]
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-10-02 00:00:00, ..., 2011-10-02 09:00:00]
Length: 10, Freq: None, Timezone: America/Atikokan
['2011-10-02 00:00:00-05:00', '2011-10-02 01:00:00-05:00', '2011-10-02 02:00:00-05:00', '2011-10-02 03:00:00-05:00', '2011-10-02 04:00:00-05:00', '2011-10-02 05:00:00-05:00', '2011-10-02 06:00:00-05:00', '2011-10-02 07:00:00-05:00', '2011-10-02 08:00:00-05:00', '2011-10-02 09:00:00-05:00']
[ 9 9 19 19 23 0 1 2 3 4]
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ENH: add arguments for scatter_plot
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| 1 | 2012-09-22T14:37:53Z | 2012-10-08T15:06:19Z | 2012-10-08T15:06:19Z |
MEMBER
| null |
Modifies pandas/tools/plotting.py
- xlim, ylim, sharex, sharey arguments are added
- kwds argument added which is passed to ax.scatter()
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Release notes don't warn/inform about skip_footer changes
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| 7 | 2012-09-22T17:06:40Z | 2014-04-27T18:51:58Z | 2012-09-26T02:07:43Z |
NONE
| null |
This change ( before, skipping the last row was skip_footer=1, now this is accomplished by skip_footer=-1, otherwise you will only receive one row) should be announced clearly in the release notes imho.
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Update to API reference documentation.
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| 1 | 2012-09-22T22:49:59Z | 2012-09-23T16:01:35Z | 2012-09-23T16:01:27Z |
CONTRIBUTOR
| null |
Added some of the missing functions from the API reference documentation and fixed a couple things in the time series guide.
|
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MDU6SXNzdWU3MDczNjc3
| 1,950 |
pivot_annual works only with float values
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| 1 | 2012-09-23T18:40:23Z | 2014-05-29T05:45:15Z | 2014-05-29T05:45:15Z |
CONTRIBUTOR
| null |
The following does not work:
```
import pandas as pd
import numpy as np
rng = pd.date_range('1/1/1994', periods=216, freq='M')
data = np.random.randint(100, high=350, size=216)
ts = pd.Series(data, index=rng)
from pandas.tseries.util import pivot_annual
ts_pivot = pivot_annual(ts)
```
Error:
```
ts_pivot = pivot_annual(ts)
------------------------------------------------------------
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\tseries\util.py", line 68, in pivot_annual
raise Exception('need to upcast')
Exception: need to upcast
```
While the following works:
```
ts_pivot_float = pivot_annual(ts.astype('float32'))
```
Why is this implemented in such way in the source?
I would also call for a more helpful exception than _Exception: need to upcast_.
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pivot_annual returns null values
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| 1 | 2012-09-23T18:56:36Z | 2013-09-16T03:51:38Z | 2013-09-16T03:51:38Z |
CONTRIBUTOR
| null |
The data info
```
ts_monthly.dtype
Out[41]: dtype('float64')
ts_monthly.shape
Out[43]: (216,)
ts_monthly.index
Out[44]:
<class 'pandas.tseries.index.DatetimeIndex'>
[1994-01-31 00:00:00, ..., 2011-12-31 00:00:00]
Length: 216, Freq: M, Timezone: None
```
The data returns when pivoted to annual null values:
```
from pandas.tseries.util import pivot_annual
ts_monthly_ann = pivot_annual(ts_monthly)
ts_monthly_ann
Out[40]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 18 entries, 1994 to 2011
Data columns:
1 18 non-null values
2 18 non-null values
3 18 non-null values
4 18 non-null values
5 18 non-null values
6 18 non-null values
7 18 non-null values
8 18 non-null values
9 18 non-null values
10 18 non-null values
11 18 non-null values
12 18 non-null values
dtypes: float64(12)
```
What could be wrong?
|
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pivot_annual returns is not part of namespace
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| 2 | 2012-09-23T18:58:30Z | 2013-03-21T13:44:25Z | 2013-03-21T13:44:25Z |
CONTRIBUTOR
| null |
```
In [1]: import pandas as pd
In [2]: pd.tseries.util.pivot_annual
------------------------------------------------------------
Traceback (most recent call last):
File "<ipython console>", line 1, in <module>
AttributeError: 'module' object has no attribute 'util'
```
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MDU6SXNzdWU3MDczODQ2
| 1,953 |
Expand the tseries/util.py to ease time series reshaping
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[] | null | 2 | 2012-09-23T19:00:53Z | 2012-09-23T20:54:50Z | 2012-09-23T20:54:50Z |
CONTRIBUTOR
| null |
The logic in the module
_tseries/util.py_
Currently, there's only one function that eases the reshaping of a (time) Series into a dataframe:
pivot_annual.
More functions in that range shall help processing of the series.
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MDU6SXNzdWU3MDc0OTAw
| 1,954 |
DOC: show alraeady completed features
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[] | null | 3 | 2012-09-23T21:02:39Z | 2012-10-06T20:46:18Z | 2012-10-06T20:45:35Z |
CONTRIBUTOR
| null |
Strike through all features already completed:
http://pandas.pydata.org/developers.html#roadmap-to-1-0
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| 7,075,704 |
MDU6SXNzdWU3MDc1NzA0
| 1,955 |
pip install pandas fails on Mageia
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| 13 | 2012-09-23T22:50:58Z | 2012-09-29T15:36:39Z | 2012-09-29T15:36:39Z |
NONE
| null |
As title says, I get an error trying to pip install pandas on Mageia. Unfortunately, there is no pre-built pandas package in the Mageia repositories. Don't know if I should post the issue here with pandas or if it is Mageia related. The error is:
``` python
pandas/src/generated.c:237:31: fatal error: numpy/arrayobject.h: No such file or directory
compilation terminated.
error: command 'gcc' failed with exit status 1
```
I have the necessary pandas dependencies, as shown in the pip.log:
```
Requirement already satisfied (use --upgrade to upgrade): python-dateutil<2 in /usr/lib/python2.7/site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): pytz in /usr/lib/python2.7/site-packages (from pandas)
Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6 in /usr/lib/python2.7/site-packages (from pandas)
```
Thanks,
Bob
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MDU6SXNzdWU3MDc2Nzg3
| 1,956 |
Shuffle words have crappy documentation and no examples
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[] | null | 2 | 2012-09-24T01:22:47Z | 2012-09-24T03:00:26Z | 2012-09-24T01:23:07Z |
NONE
| null |
They have this:
```
Shuffle word. Rearranges the top of the datastack as indicated in the stack effect pattern.
```
Not very helpful to new-comers who don't know what datastack means, or what exactly a stack effect pattern is.
At the very least it could link to help articles, better would be to have "shuffle-specific" documentation for the common words.
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| 7,078,191 |
MDU6SXNzdWU3MDc4MTkx
| 1,957 |
reset_index fails if level and drop are both specified
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| 0 | 2012-09-24T03:56:04Z | 2012-09-24T05:32:23Z | 2012-09-24T05:32:23Z |
CONTRIBUTOR
| null |
If `drop` is specified then `level` looks like it's ignored and all levels are reset
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| 7,078,217 |
MDExOlB1bGxSZXF1ZXN0MjQyNTg1Mg==
| 1,958 |
Fix for Issue #1798
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| 1 | 2012-09-24T03:58:59Z | 2014-07-13T10:22:35Z | 2012-09-26T23:10:56Z |
CONTRIBUTOR
| null |
Added comments to specify how variance is normalized.
#1798
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rework test for #1782, add simple mocking and a stdin_encoding context_manager
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| 0 | 2012-09-24T11:37:31Z | 2013-07-06T23:44:22Z | 2012-10-01T08:44:23Z |
NONE
| null |
I was unhappy with the way I left the test in #1782.
This is cleaner, and the test actually works, which is always nice.
The mocking helper is primitive, but at least no new dependency is added.
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MDU6SXNzdWU3MTA3Mzcx
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inplace dropna?
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| 6 | 2012-09-24T22:10:29Z | 2013-10-28T02:06:38Z | 2013-10-28T02:06:38Z |
CONTRIBUTOR
| null |
see related discussion in #2325
Possible and/or desirable to add an inplace keyword to dropna? Maybe an out keyword, so you can avoid typing the assignment? [**Edit:** I guess out doesn't really make sense like it does for numpy ufuncs.] Don't really mind if there's a copy even I just get tired of always doing assignment to the same name when I'm doing pre-, post-processing, etc.
```
dataframe = dataframe.action()
```
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DOC: add more details on selecting / indexing of time series
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| 2 | 2012-09-25T07:55:25Z | 2014-05-25T23:40:35Z | 2014-05-25T23:40:35Z |
CONTRIBUTOR
| null |
Add more details on selecting / indexing of time series based on dates or intervals.
to
https://github.com/pydata/pandas/blob/master/doc/source/indexing.rst
and
https://github.com/pydata/pandas/blob/master/doc/source/timeseries.rst
But I could not find any part showing how to E.g. getting all data where year =
2004 or all data from day 2012-09-24.
Reference:
https://groups.google.com/forum/?fromgroups=#!topic/pydata/Bt8jO2LkAdk
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| 1,962 |
DateOffset in seconds
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[] | null | 2 | 2012-09-25T09:19:29Z | 2012-09-25T14:23:24Z | 2012-09-25T11:40:14Z |
NONE
| null |
Hello,
I think I have encountered a bug (0.8).
When using
DateOffset(second = 10) the seconds in the time series are not added or substracted to the datetime object, the seconds in the datetime are setto 10 instead of calculating the offset.
Thanks,
Luc
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"dataframe.groupby(level = index_name)" is much slow than "dataframe.groupby(col_name)"
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[] | null | 2 | 2012-09-25T14:46:39Z | 2013-12-04T00:57:52Z | 2012-10-02T18:03:00Z |
NONE
| null |
I timeit, "groupby index" is almost 10 time slower than 'groupby column' , the index content is same as column for index is created by 'dataframe.set_index(col_name, drop=False)'. Can "dataframe.groupby(level=index)" be improved ?
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Can Pandas unify the function/syntax ?
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[] | null | 6 | 2012-09-25T14:59:42Z | 2012-10-08T05:53:12Z | 2012-10-06T20:35:55Z |
NONE
| null |
I might be wrong for I am a Pandas newbie and know little about the Pandas inside logic . But I will feel much better if Pandas can unify its syntax/function for below :
1. series & dataframe
series is a one-column dataframe, why not use dataframe's function to cover series ?
2. index & columns
can dataframe index be treated as a special column ? just like the SQL,which select/update the 'key_column' the same way as 'non_key_column'
That will reduce and simplify the function list , lower the learning curve.
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ENH: Add default_index method to DataFrame
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[] | null | 2 | 2012-09-25T15:08:44Z | 2014-06-24T09:08:48Z | 2012-09-25T19:47:26Z |
CONTRIBUTOR
| null |
This partially addresses #1894. Just a method to reset the index in the sense of give me the default np.range index.
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| 7,132,533 |
MDU6SXNzdWU3MTMyNTMz
| 1,966 |
Encoding non-ascii characters in to_csv with encodings
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| 5 | 2012-09-25T20:02:30Z | 2012-11-27T02:53:14Z | 2012-11-27T02:53:14Z |
CONTRIBUTOR
| null |
Right now if you want to use an encoding, you have to make sure that all fields that contain non-ascii characters are in that encoding. Maybe this is okay, but I'm working with a lot of data and it is a bit cumbersome for me to be doing these checks constantly. For example
```
from StringIO import StringIO
import pandas
df = pandas.read_table(StringIO('Ki\xc3\x9fwetter, Wolfgang;Ki\xc3\x9fwetter, Wolfgang'), sep=";", header=None)
df["X.1"] = df["X.1"].apply(lambda x : x.decode('utf-8'))
df.to_csv("blah.csv", encoding="utf-8")
```
The question is, is this something that the user should be worrying about or is there a "safe_encode" that could be used instead, similar idea to #1804?
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DOC: add import prefix to all pandas imports
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| 5 | 2012-09-25T21:18:25Z | 2015-05-05T10:40:55Z | 2015-05-05T10:40:55Z |
CONTRIBUTOR
| null |
in the docs, numpy is often imported with
```
from numpy import np
```
http://pandas.pydata.org/pandas-docs/dev/reshaping.html?highlight=curious
for an easier reuse, what about adopting
```
import pandas as pd
```
for the docs?
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MDU6SXNzdWU3MTM1Nzkw
| 1,968 |
Unicode, unique, and ndarrays
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[] | null | 4 | 2012-09-25T22:09:08Z | 2013-01-18T04:44:27Z | 2013-01-18T04:44:27Z |
CONTRIBUTOR
| null |
More unicode fun. I seem to recall seeing this before, but I can't find anything about it. Is this numpy, what pandas hands off to numpy, or user error?
``` python
df = pandas.DataFrame([u"\xe9"])
df[0].unique()
<snip>
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/numpy/core/numeric.py", line 1409, in array_repr
', ', "array(")
File "/usr/local/lib/python2.7/dist-packages/numpy/core/arrayprint.py", line 445, in array2string
separator, prefix, formatter=formatter)
File "/usr/local/lib/python2.7/dist-packages/numpy/core/arrayprint.py", line 320, in _array2string
_summaryEdgeItems, summary_insert)[:-1]
File "/usr/local/lib/python2.7/dist-packages/numpy/core/arrayprint.py", line 491, in _formatArray
word = format_function(a[-1])
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 0: ordinal not in range(128)
```
```
pandas.version.version
'0.9.0rc1'
np.version.full_version
'1.8.0.dev-578a419'
```
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np.delete fails when it could succeed
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| 1 | 2012-09-25T22:30:30Z | 2014-05-29T03:51:41Z | 2014-05-29T03:51:41Z |
NONE
| null |
Calling `np.delete` on a `pandas.DataFrame` fails like so:
``` python
>>> df = pandas.DataFrame([[1,2,3],[4,5,6],[7,8,9]])
>>> np.delete(df, [1], axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/numpy-1.6.2-py2.7-macosx-10.8-intel.egg/numpy/lib/function_base.py", line 3390, in delete
return wrap(new)
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/core/frame.py", line 1506, in __array_wrap__
columns=self.columns, copy=False)
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/core/frame.py", line 392, in __init__
copy=copy)
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/core/frame.py", line 504, in _init_ndarray
block = make_block(values.T, columns, columns)
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/core/internals.py", line 461, in make_block
do_integrity_check=do_integrity_check)
File "/Library/Python/2.7/site-packages/pandas-0.9.0.dev_1e1c922-py2.7-macosx-10.8-intel.egg/pandas/core/internals.py", line 26, in __init__
assert(len(items) == len(values))
AssertionError
```
But it could do this instead:
``` python
>>> np.delete(np.array(df), [1], axis=1)
array([[1, 3],
[4, 6],
[7, 9]])
```
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| 1 | 2012-09-26T02:17:22Z | 2012-09-26T21:04:39Z | 2012-09-26T21:04:39Z |
CONTRIBUTOR
| null |
In [33]: cols2 = pd.MultiIndex.from_tuples(list(itertools.product(['A', 'B', 'C'], ['X', 'Y', 'Z']))*2)
In [34]: df2 = DataFrame(np.random.randn(3, len(cols2)), columns=cols2)
In [35]: df2
Out[35]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-35-d4a59695a920> in <module>()
----> 1 df2
/Users/changshe/code/ipython/IPython/core/displayhook.pyc in **call**(self, result)
236 self.start_displayhook()
237 self.write_output_prompt()
--> 238 format_dict = self.compute_format_data(result)
239 self.write_format_data(format_dict)
240 self.update_user_ns(result)
...
/Users/changshe/code/pandas/pandas/core/format.pyc in _get_formatted_column_labels(self)
341 fmt_columns = self.columns.format(sparsify=False, adjoin=False)
342 fmt_columns = zip(_fmt_columns)
--> 343 dtypes = self.frame.dtypes.values
344 need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
345 str_columns = zip(_[[' ' + y
/Users/changshe/code/pandas/pandas/core/frame.pyc in **getattr**(self, name)
1764 return self[name]
1765 raise AttributeError("'%s' object has no attribute '%s'" %
-> 1766 (type(self).**name**, name))
1767
1768 def **setattr**(self, name, value):
AttributeError: 'DataFrame' object has no attribute 'dtypes'
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MDU6SXNzdWU3MTQ1NjA1
| 1,971 |
set_index breaks with multi keys but one empty
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| 3 | 2012-09-26T09:52:21Z | 2012-09-27T14:40:12Z | 2012-09-27T00:40:07Z |
CONTRIBUTOR
| null |
Migrated from StackOverflow: http://stackoverflow.com/questions/12598520/set-index-on-multiple-columns-with-one-empty-column
```
df = DataFrame([
dict(a=1, p=0),
dict(a=2, m=10),
dict(a=3, m=11, p=20),
dict(a=4, m=12, p=21)
], columns=('a', 'm', 'p', 'x'))
```
```
a m p x
0 1 NaN 0 NaN
1 2 10 NaN NaN
2 3 11 20 NaN
3 4 12 21 NaN
```
```
# single column index on an empty column works
df.set_index(['x'])
# two-columns index on non-empty columns works
df.set_index(['a', 'm'])
df.set_index(['a', 'p'])
df.set_index(['m', 'p'])
# but two-columns index including an empty column fails
df.set_index(['a', 'x'])
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas-0.8.2.dev_f5a74d4_20120725-py2.7-macosx-10.8-x86_64.egg/pandas/core/frame.py", line 2328, in set_index
if verify_integrity and not index.is_unique:
File "properties.pyx", line 27, in pandas.lib.cache_readonly.__get__ (pandas/src/tseries.c:95395)
File "/Library/Python/2.7/site-packages/pandas-0.8.2.dev_f5a74d4_20120725-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.py", line 227, in is_unique
return self._engine.is_unique
File "engines.pyx", line 186, in pandas.lib.IndexEngine.is_unique.__get__ (pandas/src/tseries.c:115047)
File "engines.pyx", line 215, in pandas.lib.IndexEngine._do_unique_check (pandas/src/tseries.c:115456)
File "engines.pyx", line 228, in pandas.lib.IndexEngine._ensure_mapping_populated (pandas/src/tseries.c:115629)
File "engines.pyx", line 231, in pandas.lib.IndexEngine.initialize (pandas/src/tseries.c:115678)
File "engines.pyx", line 212, in pandas.lib.IndexEngine._get_index_values (pandas/src/tseries.c:115414)
File "/Library/Python/2.7/site-packages/pandas-0.8.2.dev_f5a74d4_20120725-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.py", line 247, in <lambda>
return self._engine_type(lambda: self.values, len(self))
File "/Library/Python/2.7/site-packages/pandas-0.8.2.dev_f5a74d4_20120725-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.py", line 1363, in values
for lev, lab in zip(self.levels, self.labels)]
File "/Library/Python/2.7/site-packages/pandas-0.8.2.dev_f5a74d4_20120725-py2.7-macosx-10.8-x86_64.egg/pandas/core/common.py", line 348, in ndtake
return arr.take(_ensure_platform_int(indexer), axis=axis, out=out)
IndexError: index -1 is out of bounds for axis 0 with size 0
```
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| 7,146,257 |
MDU6SXNzdWU3MTQ2MjU3
| 1,972 |
fillna(None) inconsistent with fillna(1) or fillna(np.NaN)
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[] | null | 2 | 2012-09-26T10:23:51Z | 2013-07-11T07:45:04Z | 2012-09-26T21:09:43Z |
NONE
| null |
Migrated from StackOverflow comment : http://stackoverflow.com/questions/12598520/set-index-on-multiple-columns-with-one-empty-column
``` python
df = DataFrame([
dict(a=1, p=0),
dict(a=2, m=10),
dict(a=3, m=11, p=20),
dict(a=4, m=12, p=21)
], columns=('a', 'm', 'p', 'x'))
```
```
. a m p x
0 1 NaN 0 NaN
1 2 10 NaN NaN
2 3 11 20 NaN
3 4 12 21 NaN
```
Applying fillna(None) turns one value to 0 !
```
df.fillna(None)
. a m p x
0 1 NaN 0 NaN
1 2 10 0 NaN
2 3 11 20 NaN
3 4 12 21 NaN
```
Applying fillna(1) works as expected :
```
df.fillna(1)
. a m p x
0 1 1 0 1
1 2 10 1 1
2 3 11 20 1
3 4 12 21 1
```
Applying fillna(np.NaN) works as expected :
```
df.fillna(np.NaN)
. a m p x
0 1 NaN 0 NaN
1 2 10 NaN NaN
2 3 11 20 NaN
3 4 12 21 NaN
```
Could be related to #1971
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MDExOlB1bGxSZXF1ZXN0MjQ1NzMwMA==
| 1,973 |
DOC: Add line about resetting to default index
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| 1 | 2012-09-26T14:05:37Z | 2014-07-15T18:59:52Z | 2012-09-26T23:12:46Z |
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MDExOlB1bGxSZXF1ZXN0MjQ1NzM2NA==
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DOC: Specify default merge behavior for on = None
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| 1 | 2012-09-26T14:10:42Z | 2014-07-14T11:59:22Z | 2012-09-26T23:09:15Z |
CONTRIBUTOR
| null |
Had to look through the source for this unless I missed it.
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read_table and encoding
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| 3 | 2012-09-26T14:37:15Z | 2012-09-26T21:50:52Z | 2012-09-26T21:40:33Z |
CONTRIBUTOR
| null |
What in the world is going on here? Shouldn't these both give unicode?
``` python
import pandas
from StringIO import StringIO
fin = StringIO("Łaski, Jan;1")
df1 = pandas.read_table(fin, sep=";", encoding="utf-8", header=None)
fin.seek(0)
df2 = pandas.read_csv(fin, sep=";", encoding="utf-8", header=None)
type(df1["X.1"].values[0])
type(df2["X.1"].values[0])
```
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regex filter in DataFrame.filter
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| 0 | 2012-09-26T16:53:22Z | 2013-06-03T13:14:02Z | 2012-11-02T22:30:04Z |
NONE
| null |
Hi pandas folks -
Back to using pandas after having to take a hiatus in MATLAB. One minor problem I ran into was the use of re.match instead of re.search (as is documented) in the DataFrame.filter method.
I assumed there was a good reason for the change, but for most of my use cases the re.search way was less verbose.
We should update the method documentation anyway, but could you see supporting both re.match and re.search?
Excellent package and am using in on a daily basis for both research and production code.
Best,
Elliott
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MDU6SXNzdWU3MTYzNzQw
| 1,977 |
PeriodIndex slicing by datetime fails when either end out-of-bounds
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| 1 | 2012-09-26T21:32:15Z | 2012-09-26T23:08:28Z | 2012-09-26T23:08:28Z |
NONE
| null |
pi = pandas.PeriodIndex(start='2012-01-01', periods=10, freq='W-MON')
ts = pandas.TimeSeries(range(len(pi)), index=pi)
dt1 = datetime.datetime(2011, 10, 2)
dt2 = datetime.datetime(2012, 1, 2)
dt3 = datetime.datetime(2012, 2, 10)
dt4 = datetime.datetime(2012, 4, 20)
ts[dt2: dt3]
ts[dt1: dt3]
ts[dt2: dt4]
The first slice works, the second and third do not. It looks like the code that handles 'out-of-bounds' slices is failing in this case.
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| 5 | 2012-09-26T21:41:17Z | 2013-03-28T05:20:38Z | 2013-03-28T05:19:19Z |
MEMBER
| null |
from pystatsmodels mailing list
```
qcut in pandas 0.8.1 is failing for some quantile lists but not others (see below). Sorry if I'm missing something...
type(F.g)
Out[3]:
pandas.core.series.TimeSeries
chg = (F.g[20:]-F.g[20:].shift(1))
fac = qcut(chg, [0, .5, 1])
fac
Out[4]:
Categorical: g
array([nan, [-1005094.81, 0], [-1005094.81, 0], ..., (0, 1478547.3],
(0, 1478547.3], (0, 1478547.3]], dtype=object)
Levels (2): Index([[-1005094.81, 0], (0, 1478547.3]], dtype=object)
chg = (F.g[20:]-F.g[20:].shift(1))
fac = qcut(chg, [0, .5, 1])
fac
Out[5]:
Categorical: g
array([nan, [-1005094.81, 0], [-1005094.81, 0], ..., (0, 1478547.3],
(0, 1478547.3], (0, 1478547.3]], dtype=object)
Levels (2): Index([[-1005094.81, 0], (0, 1478547.3]], dtype=object)
fac = qcut(chg, [0, .5, .75, 1])
fac
Out[6]:
Categorical: g
array([nan, [-1005094.81, 0], [-1005094.81, 0], ..., (0, 1478547.3],
(0, 1478547.3], (0, 1478547.3]], dtype=object)
Levels (3): Index([[-1005094.81, 0], (0, 0], (0, 1478547.3]], dtype=object)
fac = qcut(chg, [0, .25, .5, .75, 1])
fac
Out[7]:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/birone/<ipython-input-9-3896feb36dcd> in <module>()
----> 1 fac = qcut(chg, [0, .25, .5, .75, 1])
2 fac
/usr/lib/pymodules/python2.7/pandas/tools/tile.pyc in qcut(x, q, labels, retbins, precision)
139 bins = algos.quantile(x, quantiles)
140 return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,
--> 141 precision=precision, include_lowest=True)
142
143
/usr/lib/pymodules/python2.7/pandas/tools/tile.pyc in _bins_to_cuts(x, bins, right, labels, retbins, precision, name, include_lowest)
177 levels = np.asarray(levels, dtype=object)
178 np.putmask(ids, na_mask, 0)
--> 179 fac = Categorical(ids - 1, levels, name=name)
180 else:
181 fac = ids - 1
/usr/lib/pymodules/python2.7/pandas/core/categorical.pyc in __init__(self, labels, levels, name)
43 def __init__(self, labels, levels, name=None):
44 self.labels = labels
---> 45 self.levels = levels
46 self.name = name
47
/usr/lib/pymodules/python2.7/pandas/core/categorical.pyc in _set_levels(self, levels)
62 levels = _ensure_index(levels)
63 if not levels.is_unique:
---> 64 raise ValueError('Categorical levels must be unique')
65 self._levels = levels
66
ValueError: Categorical levels must be unique
fac = qcut(chg, n) fails with the same error for n>3
```
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qcut bin formatting bugs
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| 6 | 2012-09-26T21:59:04Z | 2012-11-03T17:08:52Z | 2012-11-03T17:08:52Z |
NONE
| null |
I'm reading CSV tables and then use qcut to bin the continuous numbers. Given the number of bins, most of the time the qcut works fine. However, some times, it doesn't function correctly. The following are some weird cases I got in the result levels index. Sorry I can't give the experimental data now because I'm running it on a very large data collections and currently I'm not allowed to public the data
(1) When dealing with negative numbers, sometimes it gives [-117.-1, 1], This happens at the first bin index in my test case.
(2) when bin# is two, it gives results like [1, 2005], (2005, 2005]
(3)a bin result like this: please note the value 0.993, and value 0.1
array([[0.6, 0.991], (0.991, 0.992], (0.992, 0.993], (0.993, 0.993],
(0.993, 0.994], (0.994, 0.995], (0.995, 0.996], (0.996, 0.997],
(0.997, 0.998], (0.998, 0.1], (0.1, 1.2], (1.2, 1.5], (1.5, 2.1],
(2.1, 3.5], (3.5, 5.2], (5.2, 7.1], (7.1, 8.5], (8.5, 11],
(11, 13.7], (13.7, 65.8]], dtype=object)
(2) and (3) might be caused by the same reason
I'm using pandas 0.8.1 thanks.
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[] | null | 3 | 2012-09-27T04:58:00Z | 2012-09-27T08:54:20Z | 2012-09-27T08:28:28Z |
NONE
| null |
I'm new. :(
On advice I installed Python 3.2 on my Windows x64 system. All was going well until I got to Pandas (which seems like the answer to a couple of my prayers).
I can't find a version that fits. In case they worked I tried installing:
pandas-0.8.1.win-amd64-py3.1.exe
pandas-0.8.1.win32-py3.2.exe
but both advised that they couldn't find the python installation in registry.
So, I need help. As background I'm pretty competent (C++, windows, messed with linux). Is there a pandas version that I can install with Python 3.2 (or will there be one soon). Or should I drop back to Python 3.1?
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[] | null | 2 | 2012-09-27T06:56:40Z | 2013-12-20T17:54:27Z | 2012-10-05T01:52:31Z |
CONTRIBUTOR
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| 0 | 2012-09-27T15:22:38Z | 2012-10-01T01:53:53Z | 2012-10-01T01:53:53Z |
NONE
| null |
> idx=pd.Index([u'\u05d3\u05e4\u05e0\u05d4',u'\u05d3\u05e4\u05e0\u05d4'])
> idx.summary()
---
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-135-26bf122d0c51> in <module>()
1 idx=pd.Index([u'\u05d3\u05e4\u05e0\u05d4',u'\u05d3\u05e4\u05e0\u05d4'])
----> 2 idx.summary()
3
/usr/local/lib/python2.7/dist-packages/pandas-0.9.0.dev_37f4ded-py2.7-linux-x86_64.egg/pandas/core/index.pyc in summary(self, name)
197 def summary(self, name=None):
198 if len(self) > 0:
--> 199 index_summary = ', %s to %s' % (str(self[0]), str(self[-1]))
200 else:
201 index_summary = ''
UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-3: ordinal not in range(128)
|
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MDExOlB1bGxSZXF1ZXN0MjQ3MjM5Mw==
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Fix #1982
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| 1 | 2012-09-27T15:34:53Z | 2014-06-18T17:53:12Z | 2012-10-01T01:51:40Z |
NONE
| null |
fixes #1982
Had to hemstring another test so the fix will not break the tests, see
first patch in series I think it's the right thing to do.
There was another fix for a similar unicode problem in 703bbba7ff3bd,
there seems to be some Unicode whack-a-mole going on...
I'm willing to try my hand at fixing the unicode issues more seriously,
but that probably involves many changes throughout the codebase,
and some pain until things settle down.
would you consider such a big PR?
|
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MDExOlB1bGxSZXF1ZXN0MjQ3NTQzMw==
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BUG: fix drop_duplicates for tuple column label
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| 1 | 2012-09-27T19:14:08Z | 2014-07-12T21:35:49Z | 2012-09-29T21:37:57Z |
CONTRIBUTOR
| null |
related to #1917
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| 7,208,043 |
MDU6SXNzdWU3MjA4MDQz
| 1,985 |
HDFStore('file.ext') can't load HDF5 stores created by pytables
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| 12 | 2012-09-28T09:00:18Z | 2013-01-20T03:04:39Z | 2013-01-20T03:04:39Z |
NONE
| null |
Hello,
A store created outside of pandas can't be loaded into pandas with the HFDStore() method.
An error occurs : AttributeError: Attribute 'pandas_type' does not exist in node: '/'
To reproduce, create a store using pytables, and try to load it in pandas. It will raise this error.
In my opinion, if pandas is supposed to suppord HDF5, it should do so, without only supporting stores created in pandas. If nothing can be done, can you tell me what is is attribute so that I can create it when I create my store with pytables ?
Thanks !
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qtpandas should handle simple and hierarchical indexes/ hierarchical columns
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| 2 | 2012-09-28T14:16:50Z | 2015-03-08T16:26:25Z | 2015-03-08T16:26:22Z |
CONTRIBUTOR
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MDU6SXNzdWU3MjE4MzQ2
| 1,987 |
When a series contains all strings, converting to dtype "int" generates bogus int values
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| 3 | 2012-09-28T15:29:03Z | 2017-10-17T01:07:30Z | 2012-09-29T21:33:39Z |
CONTRIBUTOR
| null |
This works:
``` python
In [3]: series = pandas.Series(["car", "house", "tree", 1])
In [4]: series
Out[4]:
0 car
1 house
2 tree
3 1
In [5]: series.astype("int")
ValueError: invalid literal for long() with base 10: 'tree'
```
This doesn't:
``` python
In [7]: series = pandas.Series(["car", "house", "tree","1"])
In [8]: series.astype("int")
Out[8]:
0 0
1 44735616
2 139883221510192
3 1
```
This with RC2 state from git.
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Panel.update
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| 0 | 2012-09-28T19:46:02Z | 2012-10-05T01:51:51Z | 2012-10-05T01:51:51Z |
CONTRIBUTOR
| null |
Panel analogue to DataFrame.update
Should be able to take another Panel or dict of DataFrame
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Using numpy median with groupby aggregrate
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| 3 | 2012-09-29T11:39:09Z | 2012-11-28T21:29:35Z | 2012-11-28T21:29:35Z |
CONTRIBUTOR
| null |
Migrated from http://stackoverflow.com/questions/12651618/inconsisitency-in-results-of-aggregating-pandas-groupby-object-using-numpy-media
```
test = pd.DataFrame({'A' : [10,11,12,13,15,25,43,70],
'B' : [1,2,3,4,5,6,7,8],
'C' : [1,1,1,1,2,2,2,2]})
A B C
0 10 1 1
1 11 2 1
2 12 3 1
3 13 4 1
4 15 5 2
5 25 6 2
6 43 7 2
7 70 8 2
test_g = test.groupby('C')
```
Aggregating using `np.median` (unexpectedly) produces DataFrame-wise aggregation within groups:
```
test_g.aggregate(np.median)
A B
C
1 7.0 7.0
2 11.5 11.5
```
It works perfectly when `axis=0` is passed in:
```
test_g.aggregate(np.median, axis=0)
A B
C
1 11.5 2.5
2 34.0 6.5
```
For `np.mean` (also `sum`, `min`, `max`) this doesn't average over entire array:
```
test_g.aggregate(np.mean)
A B
C
1 11.50 2.5
2 38.25 6.5
```
Perhaps worth noting that when passing in as a list this behaviour is not seen:
```
test_g.agg([np.median])
A B
median median
C
1 11.5 2.5
2 34.0 6.5
```
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NaN comparison in merge
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| 2 | 2012-09-29T17:31:42Z | 2012-09-29T21:04:45Z | 2012-09-29T21:03:14Z |
CONTRIBUTOR
| null |
Parking this here so I don't forget about it. I assume that the NaNs here are not comparing equal, so we get "duplicate" rows on a merge, which I think is a bug.
```
data = [[1950, "A", 1.5],
[1950, "B", 1.5],
[1955, "B", 1.5],
[1960, "B", np.nan],
[1970, "B", 4.],
[1950, "C", 4.],
[1960, "C", np.nan],
[1965, "C", 3.],
[1970, "C", 4.],
]
frame = pandas.DataFrame(data, columns=["year", "panel", "data"])
other_data = [[1960, 'A', np.nan],
[1970, 'A', np.nan],
[1955, 'A', np.nan],
[1965, 'A', np.nan],
[1965, 'B', np.nan],
[1955, 'C', np.nan]]
other = pandas.DataFrame(other_data, columns=['year', 'panel', 'data'])
together = frame.merge(other, how="outer")
```
Worked around this by filling in the NaNs before the merge, but then I have to convert them back to NaNs later.
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ENH: autocov function for Series
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| 3 | 2012-09-29T17:55:43Z | 2014-07-19T02:19:29Z | 2013-09-21T12:58:04Z |
CONTRIBUTOR
| null |
Add an autocov function to Series.
Default is single lag w/ bias, but those are configurable.
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Series vs. TimeSeries methods
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| 2 | 2012-09-29T18:10:01Z | 2013-09-21T12:57:19Z | 2013-09-21T12:57:19Z |
CONTRIBUTOR
| null |
Just looking at a few of these after PR #1991. Should autocorr and autocov be methods of TimeSeries rather than Series since not all Series are going to be TimeSeries? Maybe at least head in this direction?
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MDU6SXNzdWU3MjQ0NjM1
| 1,993 |
date_range export to csv weird behavior
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| 2 | 2012-09-30T10:47:35Z | 2012-10-01T01:20:22Z | 2012-10-01T01:19:11Z |
NONE
| null |
Hello,
Working with 0.8.1
date_range works nicely, I am using the technique to reduce sparse GPS measurements to a fixed time step.
procedure:
step 1: create date_range ("10S"), all datetime within a single day (input from gpx)
step 2: outer join with original data
step 3: interpolate the locations
Everything works fine, .to_string() results in results as expected.
When I export the result to_csv(), the date_range object is exported with the correct datetime, but the date part is reduced to to january 1970. How is this possible?
Can somebody get me on the road again?
Luc
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More unicode fixes
|
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| 1 | 2012-09-30T11:32:27Z | 2014-07-17T06:08:21Z | 2012-10-01T01:44:27Z |
NONE
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| 1,995 |
coding style - assert vs self.assert* vs self.assert_ in tests, which is it?
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[] | null | 1 | 2012-09-30T11:53:14Z | 2012-10-12T17:25:32Z | 2012-10-12T17:25:32Z |
NONE
| null |
going through the code , i ran a few grep counts on the tree:
<table>
<tr>
<th>grep regexp</th><th>count</th>
</tr>
<tr><td>self.assertSomething()</td><td>1617</td> </tr>
<tr><td>self.assert_() </td><td>2122</td> </tr>
<tr><td>assert()</td><td>407</td> </tr>
</table>
This might be a non-issue, but consistent is often nicer.
also:
/usr/lib/python2.7/unittest/case.py:592
```
# Synonyms for assertion methods
# The plurals are undocumented. Keep them that way to discourage use.
# Do not add more. Do not remove.
# Going through a deprecation cycle on these would annoy many people.
[...]
assert_ = assertTrue
```
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PyTables enhancements for selection
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[] | null | 7 | 2012-09-30T15:21:29Z | 2013-05-19T14:35:12Z | 2012-11-24T22:24:57Z |
CONTRIBUTOR
| null |
# now
changes to pandas.io.pytables to support more natural selection (from tables):
1. rename column -> major, index -> minor ( to be more consistent with panel nomenclature)
2. provide parsable string selection methodology - pretty easy to do - and can be backwards compatible
`store.select('mypanel', where = [ 'major>=20120103', 'major<=20120401', dict(minor = ['A','B','C' ]))`
rather than existing
```
store.select('mypanel', where = [
dict(field = 'column', op = '>=', value = datetime.datetime(2012,1,3)),
dict(field = 'column', op = '<=', value = datetime.datetime(2012,4,1)),
dict(field = 'index', value = ['A','B','C']) ])
```
# future
not sure that pandas should get really fancy just yet with operations - (e.g. 'or' operations, and actual value selection)
```
where = [ ( 'major>20120901' & dict(minor = ['A','B','C']) | (minor = ['D']) ]
where = [ item['foo']>2.0 ]
```
but probably necessary once pandas support 'chunking' type operations on pytables
need to build a full-fledged selection parser to translate to the numexpr type operations (maybe with a patsy backend????)
BUT this may actually be useful to support generic operations in this way on in-memory panels/frames
not sure of use cases here though - I usually just read in 'about' what data I need and sub-select from there
unless you have hundreds of millions of rows I don't know if its necessary to optimize more (**in which case it is!**)
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| 1,997 |
ENH: Allow DataFrame.update to work on non DataFrame object if coercible
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| 1 | 2012-09-30T15:47:02Z | 2014-06-17T00:40:43Z | 2012-10-05T00:36:27Z |
CONTRIBUTOR
| null |
On issue #1988, a new Panel.update function should accept a dict of DataFrames. Right now, DataFrame.update can't accept a dict of Series (or a dict of arrays for example). To me, it wouldn't make much sense for Panel.update to have the functionality while a DataFrame does not.
Currently:
```
>>> df.update(s) #s is a dict of arrays or of Series
...
AttributeError: 'dict' object has no attribute 'reindex_like'
```
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TimeSeries.resample how='last' & 'first' broken?
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| 3 | 2012-09-30T21:07:36Z | 2012-11-24T03:57:17Z | 2012-11-24T03:57:17Z |
NONE
| null |
'Last' seems to be 'first', but what 'first' is doing I can't figure out...
pd.**version**
Out[79]: '0.8.1'
rng8h = pd.date_range('1/1/12', periods=10, freq='8H')
rng8h
Out[70]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01 00:00:00, ..., 2012-01-04 00:00:00]
Length: 10, Freq: 8H, Timezone: None
ts8h = Series(randn(len(rng8h)), index=rng8h)
ts8h
Out[75]:
2012-01-01 00:00:00 0.546799
2012-01-01 08:00:00 0.272687
2012-01-01 16:00:00 -0.730920
2012-01-02 00:00:00 -0.121706
2012-01-02 08:00:00 -1.670595
2012-01-02 16:00:00 1.415328
2012-01-03 00:00:00 -0.433233
2012-01-03 08:00:00 0.340239
2012-01-03 16:00:00 -0.186495
2012-01-04 00:00:00 0.689044
Freq: 8H
ts8h.resample('D', how='first')
Out[76]:
2012-01-01 0.546799
2012-01-02 0.272687
2012-01-03 -1.670595
2012-01-04 0.340239
Freq: D
ts8h.resample('D', how='last')
Out[77]:
2012-01-01 0.546799
2012-01-02 -0.121706
2012-01-03 -0.433233
2012-01-04 0.689044
Freq: D
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ENH: Add method Panel.update
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| 1 | 2012-09-30T23:15:42Z | 2014-06-20T22:05:44Z | 2012-10-05T00:25:57Z |
CONTRIBUTOR
| null |
```
panel_one.update(panel_two)
```
Addresses Issue #1988.
Delegates most the work to DataFrame.update. Aligns on items whereas DataFrame.update aligns on indices.
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Change default column names in read_* functions when header=False
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| 7 | 2012-10-01T00:14:26Z | 2012-10-01T18:59:29Z | 2012-10-01T18:59:29Z |
MEMBER
| null |
In retrospect I think using R's convention of `X.1`, ..., `X.N` was a bad move. Maybe `range(N)` would be better? Will cause API breakage but not the end of the world
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