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} | 7 | "2012-10-22T13:14:30Z" | "2012-11-03T00:23:14Z" | "2012-11-03T00:22:54Z" | CONTRIBUTOR | null | I'm not sure if the bug is in the drop or that it shouldn't have let me set this MultiIndex since it's non-unique. var1 is just a combination of var2 and var3. In any event the result given back by this is garbage. I wanted to drop all the rows where the count of var1 == 1. Since there's not to my knowledge a way to drop variables without setting them to an index, I tried to do this without thinking
```
df = pandas.DataFrame([["x-a", "x", "a", 1.5],["x-a", "x", "a", 1.2],
["z-c", "z", "c", 3.1], ["x-a", "x", "a", 4.1],
["x-b", "x", "b", 5.1],["x-b", "x", "b", 4.1],
["x-b", "x", "b", 2.2],
["y-a", "y", "a", 1.2],["z-b", "z", "b", 2.1]],
columns=["var1", "var2", "var3", "var4"])
grp_size = df.groupby("var1").size()
drop_idx = grp_size.ix[grp_size == 1]
df.set_index(["var1", "var2", "var3"]).drop(drop_idx.index, level=0).reset_index()
```
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} | 2 | "2012-10-22T14:04:40Z" | "2012-12-14T02:52:07Z" | "2012-12-14T02:52:07Z" | CONTRIBUTOR | null | Tags: Communit, Docs
Show how to implement:
- if condition in column 1 is true: mark data in column 2 as missing / invalid
- what numpy procedure shall be used to execute the mask?
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} | 1 | "2012-10-22T15:24:38Z" | "2012-11-02T16:40:07Z" | "2012-11-02T16:40:07Z" | CONTRIBUTOR | null | Consider this. It seems to work properly after sorting but not otherwise?
```
df = pandas.DataFrame([["A","a", 1.2],["A","b", 2.2],["B", "a", 1.1],["C", "a", 1.2]], columns=["var1","var2", "var3"])
df2 = pandas.DataFrame([["A","c", 1.2],["B", "c", 4.1],["C", "c", 2.2]], columns=["var1","var2", "var3"])
df3 = pandas.concat((df, df2))
df3.set_index(["var1","var2"])
df3.sort("var1").set_index(["var1","var2"])
```
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} | 17 | "2012-10-22T18:27:53Z" | "2014-06-23T04:22:12Z" | "2012-10-28T10:42:42Z" | NONE | null | This is the 3rd installment in the unicode saga, and once again,
there's a lot of issues to point out. fair warning given.
#1994 cleaned up things to use basestring and unicode() over str() where needed
#2005 consolidated and implemented unicode-friendly pretty-printing
The attempt here is to ensure that the internal representation of strings is
always a unicode object, as is the case by default in python3.
**It's not finished**, but I'd like to get feedback.
## A.Implementation
1. core.encoding implements the functionality for traversing sequences and decoding
strings into unicode objects inplace. this includes key/values of dicts (one
of the many supported input structures for constructing pandas data objects).
2. pure ascii strings are left untouched.
3. For strings containing unicode characters, we do actually change the input,
but since getters are symmetricly handled, the result should be seamless (if you
set a key using an encoded bytestrings,invoking a getter with the same key should
get you back the data, even though internally, the data is stored under the equiv.
unicode key).
4. this decoding step is placed in "choke-points" such as constructors, getters and
setters. I've probably missed a few at this point.
5. the encoding to be used for decoding is specified in a configurable (which will
be settable by the user, possibly via the mechanism in #2097.
6. if decoding fails, the user gets a message explaining what happend. if the `chardet`
package is available, the code tries to suggest an encoding to the user based
on the data. `chardet` has enough false-positives to make automatic-detection
a bad idea.
7. as part of the unicode-or-bust theme, the csv reader now uses utf-8 by default,
which forces the use of UnicodeReader, so you either get unicode or a decode error,
forcing the user to specify an encoding.
## B. Benefits
1. Making internal representations unicode should help reduce the whack-a-mole
which has been going on.
2. Once things are guranteed unicode, lib code can stop checking for and handling
corner-cases.
3. Mixing unicode and encoded byte-strings, or bytestrings encoded with
different encodings can create insoluable situations.
A related example is `fmt._put_lines()`, which was altered to fix #891 so
it would handle the case of mixing pure ascii with unicode.However, it now fails
when unicode is mixed with encoded byte-strings (which makes `repr_html()` fail,
yielding sometimes-html-sometimes-text repr confusion). This PR fixes that problem
amongst other things.
4. since python encodes input from the console using the console encoding, we can
convert things to unicode transparently for the user, so the behaviour becomes
closer to that of python3 - enter a string at the console, and you get unicode by
default. (no need for unicode literal).
## C. Disadvantages
1. It's hackish.
2. There's a performance hit (@wesm, what benchmarks would you like to see?)
3. Immutable sequences are currently not handled. not just because of copying, but
also because an experiment converting tuples raised all sorts of mysterious
problems, in cython code amongst other things.
4. Probably difficult to cover every possible entry-point for bytes-strings.
5. Not sure about c-parser compat. issues.
## D. Points to consider (feedback welcome)
1. there will be a configurable to turn all of this on or off. Not sure what the
default should be though.
2. Should the encoding used for decoding be specifiable per-object? or just
a global configurable available to the user.
3. what's the best way to deal with Immutable sequences?
The configurables will be sorted out later (Pending #2097?).
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} | 1 | "2012-10-23T19:41:30Z" | "2016-10-12T23:03:16Z" | "2012-11-03T15:23:50Z" | NONE | null | I have the following DataFrame:
```
market_caps
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 409938 entries, (20111230, '00036110') to (20121019, 'Y8564W10')
> Data columns:
> market_cap 409938 non-null values
> dtypes: float64(1)
```
market_caps.index.names
```
> ['date', 'security_id']
I want to get a cross-section by date. The following works just fine, implicitly slicing on the first level of the MultiIndex:
```
market_caps.xs(20111230)
```
> <class 'pandas.core.frame.DataFrame'>
> Index: 1993 entries, 00036110 to Y8564W10
> Data columns:
> market_cap 1993 non-null values
> dtypes: float64(1)
However, if I try to explicitly declare the first level, I get an exception:
```
market_caps.xs(20111230, level='date')
```
> ---
>
> Exception Traceback (most recent call last)
> <ipython-input-60-c2c77fa1057f> in <module>()
> ----> 1 market_caps.xs(20111230, level='date')
>
> C:\Python27\lib\site-packages\pandas\core\frame.pyc in xs(self, key, axis, level, copy)
> 1983
> 1984 result = self.ix[indexer]
> -> 1985 setattr(result, result._get_axis_name(axis), new_ax)
> 1986 return result
> 1987
>
> C:\Python27\lib\site-packages\pandas\core\frame.pyc in **setattr**(self, name, value)
> 1780 existing = getattr(self, name)
> 1781 if isinstance(existing, Index):
> -> 1782 super(DataFrame, self).**setattr**(name, value)
> 1783 elif name in self.columns:
> 1784 self[name] = value
>
> C:\Python27\lib\site-packages\pandas\lib.pyd in pandas.lib.AxisProperty.**set** (pandas\src\tseries.c:97451)()
>
> C:\Python27\lib\site-packages\pandas\core\generic.pyc in _set_axis(self, axis, labels)
> 461
> 462 def _set_axis(self, axis, labels):
> --> 463 self._data.set_axis(axis, labels)
> 464 self._clear_item_cache()
> 465
>
> C:\Python27\lib\site-packages\pandas\core\internals.pyc in set_axis(self, axis, value)
> 516 if len(value) != len(cur_axis):
> 517 raise Exception('Length mismatch (%d vs %d)'
> --> 518 % (len(value), len(cur_axis)))
> 519 self.axes[axis] = _ensure_index(value)
> 520
>
> Exception: Length mismatch (1993 vs 0)
Slicing on the second level works just fine:
```
market_caps.xs('00036110', level='security_id')
```
> <class 'pandas.core.frame.DataFrame'>
> Int64Index: 204 entries, 20111230 to 20121019
> Data columns:
> market_cap 204 non-null values
> dtypes: float64(1)
Is it supposed to be like this?
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} | 5 | "2012-10-23T21:22:32Z" | "2012-11-07T13:41:28Z" | "2012-10-24T15:49:36Z" | NONE | null | Discovered matplotlib/axis convert_units raised AttributeError
pandas/tseries/converter.py", line 157, in convert
values = np._asarray_tuplesafe(values)
AttributeError: 'module' object has no attribute '_asarray_tuplesafe'
It can be easily fixed by adding:
from pandas.core.common import _asarray_tuple
and change line 157 to
values = _asarray_tuplesafe(values)
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} | 0 | "2012-10-24T11:54:48Z" | "2012-11-02T18:09:02Z" | "2012-11-02T18:09:02Z" | CONTRIBUTOR | null | ```
import pandas
import numpy as np
```
define the mask function
```
def mask(self,condition):
new_self = self.copy()
new_self[~condition.values] = np.nan
return new_self
pandas.DataFrame.mask = mask
index = pandas.date_range('1/1/2000',periods=8,)
columns = ['A','B','C','D']
df = pandas.DataFrame(np.random.randn(len(index),len(columns)),index=index
printdf
```
```
A B C D
2000-01-01 0.752832 0.083465-0.273210 1.128781
2000-01-02 0.895254 0.401056 1.473770 1.998924
2000-01-03 2.318820 0.384354-1.056422-1.280257
2000-01-04 0.981042 0.717762-1.015285-1.146636
2000-01-05-0.979061-1.765188 0.025436-0.815622
2000-01-06-0.166251 1.887524-0.131171-0.802795
2000-01-07 0.025936 0.122587 0.517295 0.589679
2000-01-08 0.691059 0.458683-0.856201-0.412374
```
pandas supports boolean indexing for Series
```
s = pandas.Series(np.random.randn(len(index)),index=index)
prints
prints[s<0]
```
```
2000-01-01 -0.182340
2000-01-02 0.031729
2000-01-03 0.616713
2000-01-04 -0.329961
2000-01-05 -1.220345
2000-01-06 -1.323948
2000-01-07 1.182522
2000-01-08 -0.622332
Freq:D
2000-01-01 -0.182340
2000-01-04 -0.329961
2000-01-05 -1.220345
2000-01-06 -1.323948
2000-01-08 -0.622332
```
but not directly in DataFrame
the mask function will enable a convenient operation
`df[df < 0]` currently returns a numpy array which is correct but not that useful
```
print df.mask(df<0)
A B C D
2000-01-01 NaN-1.518799-0.574630 NaN
2000-01-02-1.023108 NaN NaN-0.009226
2000-01-03 NaN-0.623582 NaN-1.801656
2000-01-04-0.984583 NaN-1.082821 NaN
2000-01-05-0.709460-1.202316 NaN-0.484609
2000-01-06-0.775715 NaN-0.415970 NaN
2000-01-07-1.395435 NaN-0.293588 NaN
2000-01-08-0.377900-0.526218-0.660083 NaN
```
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} | 0 | "2012-10-24T12:07:09Z" | "2012-11-01T19:21:01Z" | "2012-11-01T19:21:01Z" | CONTRIBUTOR | null | ```
import pandas
import numpy as np
index = pandas.date_range('1/1/2000',periods=8,)
columns = ['A','B','C','D']
df = pandas.DataFrame(np.random.randn(len(index),len(columns)),index=index
printdf
```
```
A B C D
2000-01-01 0.752832 0.083465-0.273210 1.128781
2000-01-02 0.895254 0.401056 1.473770 1.998924
2000-01-03 2.318820 0.384354-1.056422-1.280257
2000-01-04 0.981042 0.717762-1.015285-1.146636
2000-01-05-0.979061-1.765188 0.025436-0.815622
2000-01-06-0.166251 1.887524-0.131171-0.802795
2000-01-07 0.025936 0.122587 0.517295 0.589679
2000-01-08 0.691059 0.458683-0.856201-0.412374
s = pandas.Series(np.random.randn(len(index)),index=index)
print s
2000-01-01 -0.182340
2000-01-02 0.031729
2000-01-03 0.616713
2000-01-04 -0.329961
2000-01-05 -1.220345
2000-01-06 -1.323948
2000-01-07 1.182522
2000-01-08 -0.622332
```
`__invert__` works in Series (the ~ operator)
but not in DataFrame
```
print s[~(s<0)]
print ~(df<0)
2000-01-02 0.031729
2000-01-03 0.616713
2000-01-07 1.182522
TypeError: bad operand type for unary~: 'DataFrame'
```
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} | 1 | "2012-10-24T16:21:33Z" | "2021-01-10T14:15:08Z" | "2021-01-10T14:15:08Z" | MEMBER | null | Possible to simplify?
xref http://stackoverflow.com/questions/12389898/python-pandas-how-to-add-a-totally-new-column-to-a-data-frame-inside-of-a-group
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} | 2 | "2012-10-24T17:41:03Z" | "2012-10-25T12:21:58Z" | "2012-10-24T18:16:29Z" | NONE | null | I came across this seg fault when I was trying to figure out the syntax to get column means. The below example triggers the seg fault, but strangely only about 30% of the time. About 10% of the time I get a reasonable error. The randomness of the result is very strange to me because the same command was run every time!
I am using the latest pandas and numpy from git. I have ECC memory and have never seen any hardware triggered issues.
```
$ python
Python 3.2.2 (default, Mar 9 2012, 14:43:22)
[GCC 4.4.4 20100726 (Red Hat 4.4.4-13)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> pandas.DataFrame({'x': [1,2,3], 'y': [3,4,5]}).groupby(level=0, axis='columns').mean()
Segmentation fault
$ python
Python 3.2.2 (default, Mar 9 2012, 14:43:22)
[GCC 4.4.4 20100726 (Red Hat 4.4.4-13)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> pandas.DataFrame({'x': [1,2,3], 'y': [3,4,5]}).groupby(level=0, axis='columns').mean()
0 1 2
x 1 2 3
y 3 4 5
>>> pandas.DataFrame({'x': [1,2,3], 'y': [3,4,5]}).groupby(level=0, axis='columns').mean()
0 1 2
x 1 2 3
y 3 4 5
>>> pandas.DataFrame({'x': [1,2,3], 'y': [3,4,5]}).groupby(level=0, axis='columns').mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.2/site-packages/pandas-0.9.1.dev_ebcbd33-py3.2-linux-x86_64.egg/pandas/core/generic.py", line 134, in groupby
sort=sort, group_keys=group_keys)
File "/usr/local/lib/python3.2/site-packages/pandas-0.9.1.dev_ebcbd33-py3.2-linux-x86_64.egg/pandas/core/groupby.py", line 481, in groupby
return klass(obj, by, **kwds)
File "/usr/local/lib/python3.2/site-packages/pandas-0.9.1.dev_ebcbd33-py3.2-linux-x86_64.egg/pandas/core/groupby.py", line 146, in __init__
grouper, exclusions = _get_grouper(obj, keys, axis=axis,
IndexError: tuple index out of range
>>> pandas.DataFrame({'x': [1,2,3], 'y': [3,4,5]}).groupby(level=0, axis='columns').mean()
Segmentation fault
```
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} | 2 | "2012-10-24T18:53:20Z" | "2012-11-04T22:04:13Z" | "2012-11-04T22:04:13Z" | NONE | null | I have an array of non-negative numbers, that when used with `rolling_sum` or `rolling_mean` produce an output array that has a small negative number in it.
The test looks like this:
``` python
import numpy as np
import pandas
data = np.load('data.npy')
assert all(data >= 0)
sums = pandas.rolling_sum(data, 2, min_periods=1)
zero = np.where(sums < 0)[0]
assert len(zero) == 0, zero
mean = pandas.rolling_mean(data, 2, min_periods=1)
zero = np.where(mean < 0)[0]
assert len(zero) == 0, zero
```
It requires a small binary array to reproduce, because of the floating point numbers (so I created a gist: https://gist.github.com/3948013).
You can run the test case:
```
$ git clone git://gist.github.com/3948013.git
$ cd 3948013
$ python test.py
```
I made sure this bug affects the most current version of Pandas:
``` python
>>> import pandas
>>> pandas.__version__
'0.9.1.dev-8cd93d3'
>>> import numpy
>>> numpy.__version__
'1.7.0b2'
```
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} | 7 | "2012-10-25T00:22:45Z" | "2013-09-28T17:23:24Z" | "2013-09-28T17:23:24Z" | MEMBER | null | xref http://stackoverflow.com/questions/12590131/how-to-slice-multindex-columns-in-pandas-dataframes
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} | 6 | "2012-10-25T11:11:06Z" | "2016-10-12T23:03:17Z" | "2012-11-02T23:29:31Z" | CONTRIBUTOR | null | The following was encountered when using Pandas 0.9.0 (specifically 0.9.0~ppa6~revno-3341~quantal1 from pythonxy PPA):
``` python
In [50]: df = pandas.read_csv("pandas_test.csv", parse_dates={'tstamp':[0,1]}, index_col='tstamp')
In [51]: df
Out[51]:
flow
tstamp
2008-01-01 00:15:00 0.103906
2008-01-01 00:30:00 0.103906
2008-01-01 00:45:00 0.103906
2008-01-01 01:00:00 0.103906
2008-01-01 01:15:00 0.103906
2008-01-01 01:30:00 0.103906
2008-01-01 01:45:00 0.103906
2008-01-01 02:00:00 0.103906
2008-01-01 02:15:00 0.103594
In [5]: df.index
Out[5]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2008-01-01 00:15:00, ..., 2008-01-01 02:15:00]
Length: 9, Freq: None, Timezone: None
In [52]: df.plot()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-52-4f76bcc16520> in <module>()
----> 1 df.plot()
/usr/lib/pymodules/python2.7/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
1249 sort_columns=sort_columns, secondary_y=secondary_y,
1250 **kwds)
-> 1251 plot_obj.generate()
1252 plot_obj.draw()
1253 if subplots:
/usr/lib/pymodules/python2.7/pandas/tools/plotting.pyc in generate(self)
573 self._compute_plot_data()
574 self._setup_subplots()
--> 575 self._make_plot()
576 self._post_plot_logic()
577 self._adorn_subplots()
/usr/lib/pymodules/python2.7/pandas/tools/plotting.pyc in _make_plot(self)
880 # this is slightly deceptive
881 if self.use_index and self._use_dynamic_x():
--> 882 data = self._maybe_convert_index(self.data)
883 self._make_ts_plot(data, **self.kwds)
884 else:
/usr/lib/pymodules/python2.7/pandas/tools/plotting.pyc in _maybe_convert_index(self, data)
1022
1023 if freq is None:
-> 1024 raise ValueError('Could not get frequency alias for plotting')
1025
1026 data = DataFrame(data.values,
ValueError: Could not get frequency alias for plotting
```
However:
``` python
In [7]: df2 = df.resample("15min")
In [8]: df2.plot()
Out[8]: <matplotlib.axes.AxesSubplot at 0x4be91d0>
```
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} | 2 | "2012-10-25T13:06:57Z" | "2012-11-03T19:11:45Z" | "2012-11-03T19:11:45Z" | CONTRIBUTOR | null | from [stackoverflow](http://stackoverflow.com/questions/13068551/pandas-slice-on-hierarchical-index-without-a-copy/)
``` python
In [25]: cpaste
Pasting code; enter '--' alone on the line to stop or use Ctrl-D.
:>>> index = pd.MultiIndex.from_arrays([['a','a', 'b', 'b'], [1,2,1,2]],
:... names=['first', 'second'])
:>>> data = pd.DataFrame(np.random.rand(len(index)), index=index, columns=['A'])
:>>> print data
:--
A
first second
a 1 0.026915
2 0.777457
b 1 0.445726
2 0.243053
In [26]: selected = data.xs(2, level='second', copy=False)
In [27]: selected
Out[27]:
A
first
a 0.777457
b 0.243053
In [28]: selected['A'][0] = 100
In [29]: selected
Out[29]:
A
first
a 100.000000
b 0.243053
In [30]: data
Out[30]:
A
first second
a 1 0.026915
2 0.777457
b 1 0.445726
2 0.243053
```
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} | 2 | "2012-10-25T13:46:14Z" | "2020-05-11T18:37:51Z" | "2020-05-11T18:37:51Z" | CONTRIBUTOR | null | so don't have to reindex twice.
http://stackoverflow.com/questions/13064400/resample-to-datetimeindex-raises-an-error
or furthermore, arbitrary non-reg freq resampling?
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} | 0 | "2012-10-25T13:49:27Z" | "2012-11-01T00:42:14Z" | "2012-11-01T00:42:14Z" | MEMBER | null | Look at Series with strings like 'A|B|C'
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} | 1 | "2012-10-25T14:00:58Z" | "2012-10-31T20:23:16Z" | "2012-10-31T20:23:03Z" | NONE | null | When printing a Series (I haven't checked if the same applies to a DataFrame), the output is truncated in such a way as to change the meaning.
``` python
import pandas as pd
vals = [2.08430917305e+10, 3.52205017305e+10, 2.30674817305e+10, \
2.03954217305e+10, 5.59897817305e+10]
s = pd.Series(vals)
print s.values == vals # True, ie the numbers stored are unaffected
print s # Should be 'e+10' rather than 'e+1'
```
Oddly, changing a single power to 9 rather than 10 makes the whole thing display properly:
``` python
s[0] = 2.08430917305e+9
print s # Now shows the correct exponent values.
```
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} | 6 | "2012-10-25T16:38:10Z" | "2014-06-06T21:50:03Z" | "2013-03-25T21:25:04Z" | NONE | null | I came across a strange slowness in GroupBy transform() function. I put together a simple function to avoid using apply() because it can be REALLY slow:
```
def apply_by_group(grouped, f):
"""
Applies a function to each DataFrame in a DataFrameGroupBy object, concatenates the results
and returns the resulting DataFrame.
Parameters
----------
grouped: DataFrameGroupBy
The grouped DataFrame that contains column(s) to be ranked and, potentially, a column with weights.
f: callable
Function to apply to each DataFrame.
Returns
-------
DataFrame that results from applying the function to each DataFrame in the DataFrameGroupBy object and
concatenating the results.
"""
assert isinstance(grouped, DataFrameGroupBy)
assert hasattr(f, '__call__')
data_frames = []
for key, data_frame in grouped:
data_frames.append(f(data_frame))
return pd.concat(data_frames)
```
Now I observe the following for the two equivalent ways of doing the same thing:
```
%timeit data.groupby(level=field_security_id).transform(lambda x: x.fillna())
```
> 1 loops, best of 3: 24.3 s per loop
```
%timeit apply_by_group(data.groupby(level=field_security_id), lambda x: x.fillna())
```
> 1 loops, best of 3: 2.72 s per loop
That was unexpected. Am I doing something wrong in using transform()?
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} | 6 | "2012-10-25T17:32:59Z" | "2012-11-02T22:06:26Z" | "2012-10-31T20:23:52Z" | CONTRIBUTOR | null | #2080
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} | 2 | "2012-10-25T19:47:38Z" | "2017-01-25T21:01:44Z" | "2017-01-25T15:51:36Z" | CONTRIBUTOR | null | The end is exclusive for integer indexing but inclusive using dates.
I guess if the data is irregular it's more weird to find the first data point after what you want to include, so we shouldn't change the current API. However it is still a little confusing so just wanted to note that here.
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} | 0 | "2012-10-25T20:02:35Z" | "2016-10-12T23:03:17Z" | "2012-11-04T21:18:36Z" | NONE | null | For example:
> > > p = pandas.Period('2012-10-23', 'D')
> > > p.start_time
> > > <Timestamp: 2012-10-23 00:00:00>
> > > p.end_time
> > > <Timestamp: 2012-10-23 00:00:00>
There are tests, but they fail to detect the problem because they are comparing the end_time to a constant which is the start_time (so they pass).
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} | 1 | "2012-10-25T20:03:33Z" | "2016-10-12T23:03:17Z" | "2012-11-04T21:18:58Z" | NONE | null | For example:
> > > p = pandas.Period('2012', freq='M')
> > > p.end_time
> > > <Timestamp: 2012-01-31 00:00:00>
This is the _start_ of the last day of the month. So if one were using this in code, you could erroneously find that any datetime on January 31st is after the end of the month of January, for example:
> > > datetime.datetime(2012, 1, 31, 12) > p.end_time
> > > True
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```
dlopen(/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so, 2): Symbol not found: _assign_value_1d
Referenced from: /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
Expected in: flat namespace
in /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/__init__.py", line 10, in <module>
import pandas.lib as lib
ImportError: dlopen(/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so, 2): Symbol not found: _assign_value_1d
Referenced from: /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
Expected in: flat namespace
in /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.9.1.dev_c252129-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
[144697 refs]
```
Thoughts? My Python is 2.7.3 from homebrew, OS is 10.8.2
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} | 0 | "2012-10-25T22:25:14Z" | "2012-11-04T21:24:45Z" | "2012-11-04T21:24:45Z" | NONE | null | Aligning two DataFrames with timezone-aware DatetimeIndex indexes returns a tuple (DataFrame, DataFrame) in which both DataFrames are timezone naive.
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} | 0 | "2012-10-26T02:22:14Z" | "2012-11-04T22:47:06Z" | "2012-11-04T22:47:05Z" | NONE | null | series = pandas.Series([1., numpy.NaN, numpy.NaN, 1.], index=pandas.DatetimeIndex(start='2000-01-01', periods=4, freq='T'))
pandas.ewma(series, com=5)
2000-01-01 00:00:00 1.000000
2000-01-01 00:01:00 0.545455
2000-01-01 00:02:00 0.545455
2000-01-01 00:03:00 1.000000
Almost right - but clearly the adjust factor is ticked in the wrong spot.
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} | 3 | "2012-10-26T02:24:21Z" | "2012-11-08T07:50:47Z" | "2012-11-02T16:39:22Z" | NONE | null | index = pandas.DatetimeIndex(start='2000-01-01', periods=4, freq='T')
index[0:0].intersection(index)
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "C:\dev\Python27\Lib\site-packages\pandas\tseries\index.py", line 845, in intersection
if self[0] <= other[0]:
File "C:\dev\Python27\Lib\site-packages\pandas\tseries\index.py", line 976, in **getitem**
val = arr_idx[key]
IndexError: index out of bounds
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} | 7 | "2012-10-26T08:54:52Z" | "2013-04-18T08:20:10Z" | "2013-04-18T08:20:10Z" | NONE | null | Continuing here from #2104, because this is related but indep.
I altered c-parser so thatparser.pyx decode all strings into unicode objects with utf-8 (b16b24b).
Since any source encoding can be transcoded into utf-8, this is a general solution for
getting c-parser to always return unicode. tested on py2.
**Data Used**:
`1M-latin.csv` was generated from `unicode_series.csv` (in tests/data), by replicating it to yield 1 million lines (roughly 32MB), each row contains a number and a longish string, so the overhead of encoding is most pronounced in this case.
`zeros.csv`(10MB) and `matrix.csv`(150MB) are from the recent [blog post](http://wesmckinney.com/blog/?p=543) benchmarking c-parser, They contain just integer/float numbers.
**Code versions tested**:
- c-parser : current c-parser branch [380f6e6]
- c-parser-t: same, but the file is routed through `codecs` and encoded into utf-8
as part of the test (1M-latin using latin-1->utf-8, the other two ascii->utf-8, just for uniformity) .
- c-parser-u-from-ascii: with b16b24b - tested against utf-8/ascii files, so no transcoding needed,all strings (actually none for the files tested) are decoded with utf-8 to yield unicode objects.
- c-parser-u: with b16b24b - the files are transcoded, and the parser decodes all string using
utf-8 to yield unicode objects.
**How**:
using IPython's %timeit, which gave best of 3 runs.
**Results**:
<table>
<tr>
<th> </td>
<th>c-parser</td>
<th>c-parser-t</td>
<th>c-parser-u-from-ascii</td>
<th>c-parser-u</td>
</tr>
<tr>
<th>zeros.csv</td>
<td>717 ms</td>
<td>768 ms</td>
<td>724ms</td>
<td>777 ms</td>
</tr>
<tr>
<th>matrix.csv</td>
<td>2.36 sec</td>
<td>3.17 sec</td>
<td>2.37 sec</td>
<td>3.21 sec</td>
</tr>
<tr>
<th>1M-latin.csv</td>
<td>427 ms</td>
<td>558 ms</td>
<td>N/A</td>
<td>570 ms</td>
</tr>
</table>
**Conclusions**:
- There's a performance hit, but the result is still very respectable,
and much better then the 10X hit in #2104, which is forced to traverse the
entire dataset, checking the type of each element.
- Most of the performance hit is due to the transcoding process, not
the decoding into utf-8. Since that step is unnecessary when the data file
is already encoded in utf-8 (and pure ascii fits into that catagory),
That extra work is somewhat justified, and performance is still very competitive even
with large files.
- The cost of returning unicode by default is virtually nil when transcoding isn't needed,
even when the file contains mostly strings.
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} | 0 | "2012-10-26T15:17:15Z" | "2012-11-02T18:17:38Z" | "2012-11-02T18:17:38Z" | CONTRIBUTOR | null | {
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] | closed | false | null | [] | null | 1 | "2012-10-26T15:49:20Z" | "2014-04-29T11:17:04Z" | "2012-11-03T18:00:45Z" | NONE | null | To reproduce the bug, do the following:
``` python
df = pandas.DataFrame([['a','b'] for i in range(1,1000)])
store = pandas.HDFStore('test.h5')
store['x'] = df
store.close()
```
The file test.h5 is now about 1.1M (seems like a lot, but OK)
now let's reopen the file
``` python
store = pandas.HDFStore('test.h5') #open it again
store['x'] = df #do the same thing as before!
store.close()
```
if you look at store['x'] they represent the exact same dataframe.
And yet, the file test.h5 is now 2.1M!
I've had h5 files which were initially a couple Mb grow to over 5.5Gb through repeated loads and save.
I'm using version 9.0
EDIT:
[stackoverflow reference](http://stackoverflow.com/questions/13089359/mystery-when-storing-a-dataframe-containing-strings-in-hdf-with-pandas)
**EDIT: this seems to be a limitation of HDF5. The assignment is a delete and a write, but the delete part doesn't free any space in the file... http://www.hdfgroup.org/hdf5-quest.html#del**
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} | 6 | "2012-10-26T15:56:21Z" | "2012-11-24T23:06:35Z" | "2012-11-24T23:06:35Z" | NONE | null | ```
import numpy as np
import pandas as pd
df = pd.DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)])
df[1] = df[1].view('M8[ns]')
print df[1].dtype
print df.groupby(0).first()[1].dtype
print type(df.groupby(0).first()[1][1])
datetime64[ns]
object
<type 'long'>
```
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} | 3 | "2012-10-26T18:21:16Z" | "2013-12-18T20:05:22Z" | "2013-12-18T20:05:22Z" | CONTRIBUTOR | null | ``` python
In [71]: data = """\
....: this, that, 0, 1
....: 10, 20, 30, 40
....: 50, 60, 70, 80
....: """
In [72]: df = pd.read_csv(StringIO(data), index_col=[0, 1])
In [73]: df
Out[73]:
0 1
this that
10 20 30 40
50 60 70 80
In [74]: df.index # <-- ok -->
Out[74]:
MultiIndex
[(10L, 20L) (50L, 60L)]
In [75]: df.columns # <-- Int64 can be used -->>
Out[75]: Index([ 0, 1], dtype=object)
In [76]: data = """\
this, that, 0, 1
foo, 20, 30, 40
50, 60, 70, 80
"""
In [77]: df = pd.read_csv(StringIO(data), index_col=[0, 1])
In [78]: df
Out[78]:
0 1
this that
foo 20 30 40
50 60 70 80
In [79]: df.index # <-- 50 is string -->
Out[79]:
MultiIndex
[('foo', 20L) ('50', 60L)]
In [80]: df.columns
Out[80]: Index([ 0, 1], dtype=object)
```
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} | 1 | "2012-10-26T19:54:30Z" | "2012-10-31T20:23:11Z" | "2012-10-31T20:23:11Z" | CONTRIBUTOR | null | ...0
implement + test proposed fix for #2120
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asfreq('D', normalize=True)
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} | 0 | "2012-10-27T15:31:13Z" | "2012-11-03T16:19:38Z" | "2012-11-03T16:19:38Z" | MEMBER | null | This needs to be implemented (hopefully) in the frequency inference code
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} | 7 | "2012-10-27T18:21:40Z" | "2019-10-21T19:52:14Z" | null | MEMBER | null | http://stackoverflow.com/questions/13030245/how-to-shift-a-pandas-multiindex-series
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} | 3 | "2012-10-28T04:00:34Z" | "2014-05-29T11:40:00Z" | "2014-05-29T05:37:22Z" | NONE | null | Series.unique returns an numpy array but np.unique(series) returns a series. Not sure if it is intentional but it might be better to keep both the calls consistent.
In [173]: a[:10]
Out[173]:
0 4
1 210
2 210
3 210
4 3197
5 3197
6 3197
7 3457
8 3713
9 3741
In [174]: a[:10].unique()
Out[174]: array([ 4, 210, 3197, 3457, 3713, 3741])
In [176]: np.unique(a[:10])
Out[176]:
0 4
1 210
4 3197
7 3457
8 3713
9 3741
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} | 6 | "2012-10-28T18:08:45Z" | "2021-03-23T17:53:04Z" | "2016-09-29T22:38:05Z" | NONE | null | I am writing here because I didn't find something on documentation. Sorry for spam.
Is there triple exponential smoothing availiable from pandas?
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} | 0 | "2012-10-29T16:16:42Z" | "2012-11-01T00:56:44Z" | "2012-11-01T00:56:44Z" | MEMBER | null | ```
In [14]: DataFrame([data])['name']
Out[14]:
0 [full, last, ascii_first, ascii_last, first]
Name: name
In [15]: DataFrame([data])['name'][0]
Out[15]:
{u'ascii_first': u'Paul',
u'ascii_last': u'Millsap',
u'first': u'Paul',
u'full': u'Paul Millsap',
u'last': u'Millsap'}
```
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} | 12 | "2012-10-29T22:43:36Z" | "2013-03-14T04:08:41Z" | "2013-03-14T04:08:02Z" | MEMBER | null | I'm totally willing to implement this myself, but I'm not exactly sure where to begin. I tried adding a microsecond period, but it seems like there are many low level changes required (plib.pyx, period.py (obviously), period.c, period.h, datetime.pyx, datetime.pxd and others that I can't remember off the top of my head).
Is there some documentation or a list of files and functions that need to be changed to add a period interval? If not, I can try and assemble one.
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} | 0 | "2012-10-30T12:17:49Z" | "2013-12-04T00:57:52Z" | "2012-11-02T18:20:02Z" | NONE | null | ```
>>> import pandas as pd
>>> import numpy as np
>>> print pd.Series(np.array([1100, 20], dtype='timedelta64[s]'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 911, in __str__
return repr(self)
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 847, in __repr__
name=True)
File "C:\Python27_64\lib\site-packages\pandas\core\series.py", line 908, in _get_repr
return formatter.to_string()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 126, in to_string
fmt_values = self._get_formatted_values()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 117, in _get_formatted_values
na_rep=self.na_rep)
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 691, in format_array
return fmt_obj.get_result()
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 814, in get_result
fmt_values = [formatter(x) for x in self.values]
File "C:\Python27_64\lib\site-packages\pandas\core\format.py", line 812, in <lambda>
formatter = lambda x: '% d' % x
TypeError: %d format: a number is required, not numpy.timedelta64
```
This is because timedelta64 is an int type (see format.format_array) but can't be formatted like that.
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} | 4 | "2012-10-31T11:44:31Z" | "2012-11-02T20:55:12Z" | "2012-11-02T20:55:12Z" | CONTRIBUTOR | null | I'm reading 0.9 PDF documentation, about DataFrame object.
I make empty DataFrame:
``` python
>>> from pandas import DataFrame
>>> df = DataFrame()
>>> df
Empty DataFrame
Columns: array([], dtype=object)
Index: array([], dtype=object)
>>> df['A'] = [1, 2, 3]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1804, in __setitem__
self._set_item(key, value)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1843, in _set_item
value = self._sanitize_column(key, value)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 1872, in _sanitize_column
assert(len(value) == len(self.index))
AssertionError
```
Error w/o any description.
If `df` has already columns, then I can add another:
``` python
>>> from pandas import DataFrame
>>> df = DataFrame.from_items([('A', [1, 2, 3])])
>>> df['B'] = [3, 2, 1]
>>> df
A B
0 1 3
1 2 2
2 3 1
```
I'm not sure if this is some kind of feature or it is a bug.
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} | 0 | "2012-11-01T15:03:23Z" | "2014-06-17T14:25:08Z" | "2012-11-02T18:17:32Z" | CONTRIBUTOR | null | pushed up to generic, raises TypeError if index doesn't have indexer_at_time
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} | 1 | "2012-11-01T15:22:20Z" | "2012-11-02T16:23:05Z" | "2012-11-02T16:22:54Z" | CONTRIBUTOR | null | In [26]: pd.offsets.Minute().isAnchored()
Out[26]: True
In [27]: pd.offsets.Day().isAnchored()
Out[27]: False
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} | 1 | "2012-11-01T16:38:32Z" | "2014-06-13T08:10:02Z" | "2012-11-02T18:08:57Z" | CONTRIBUTOR | null | #2109
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} | 8 | "2012-11-01T17:31:51Z" | "2013-12-04T00:57:52Z" | "2012-11-05T23:25:14Z" | CONTRIBUTOR | null | Now I figured out.
Here is the PR for #2153
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} | 0 | "2012-11-01T19:30:39Z" | "2012-11-02T21:29:46Z" | "2012-11-02T21:29:46Z" | MEMBER | null | ```
from datetime import datetime
import numpy as np
from pandas import DataFrame, Timestamp, DatetimeIndex
import pandas.core.datetools as datetools
columns = DatetimeIndex(start='1/1/2012', end='2/1/2012', freq=datetools.bday)
index = range(10)
data = DataFrame(columns=columns, index=index)
t = datetime(2012, 11, 1)
ts = Timestamp(t)
This raises an exception:
data[ts] = np.nan
while this works:
data[t] = np.nan
```
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} | 0 | "2012-11-01T19:39:08Z" | "2012-11-09T23:53:31Z" | "2012-11-01T20:53:26Z" | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/dev/io.html?highlight=to_csv
in the To_csv section:
```
sep : Field delimiter for the output file (default “’”)
```
should be
```
sep : Field delimiter for the output file (default “, ”)
```
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} | 1 | "2012-11-01T20:22:14Z" | "2012-11-04T02:35:25Z" | "2012-11-04T02:35:25Z" | CONTRIBUTOR | null | from [mailing list](https://groups.google.com/forum/#!searchin/pydata/Spacing$20error$20in$20bar$20graphs/pydata/KkIz8evL4As/ft8Puy8UwCwJ)
``` python
df = DataFrame({'A' : [3] * 5, 'B' : range(5)}, index = range(5))
df.plot(kind='bar', stacked='True', grid=True)
```
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} | 4 | "2012-11-01T20:50:42Z" | "2014-10-30T06:32:39Z" | "2014-05-29T05:43:42Z" | CONTRIBUTOR | null | I am not sure if this is a feature or a bug but when I use the default to_csv() and read_csv() functions on dataframes that have lists as members, I lose the lists.
This is a copy of [this stack overflow post.](http://stackoverflow.com/questions/13184553/list-to-string-in-df-to-csv#13184793)
```
df1 = DataFrame({'a':[['john quincy', 'tom jones', 'jerry rice'],['bob smith','sally ride','little wayne'],['seven','eight','nine'],['ten','eleven','twelve']],'b':[9,2,4,5], 'c': [7,3,0,9]})
df1.to_csv('temp.csv')
df2 = read_csv('temp.csv')
#note the list(df1) is now a string(df2)
df1['a'][0]
['john quincy', 'tom jones', 'jerry rice']
df2['a'][0]
"['john quincy', 'tom jones', 'jerry rice']"
```
as an answer in that post showed, the list can be recovered using ast:
```
import ast
df2['a']=df2['a'].apply(lambda x: ast.literal_eval(x))
df2['a'][1][1]
'sally ride'
```
Can the pandas read/write parsers do this automatically without needing this ast step?
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} | 1 | "2012-11-02T02:04:40Z" | "2014-06-12T04:55:31Z" | "2012-11-02T16:55:47Z" | CONTRIBUTOR | null | #1508
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} | 3 | "2012-11-02T02:06:43Z" | "2017-06-05T09:18:04Z" | "2017-06-05T09:18:04Z" | CONTRIBUTOR | null | when there are ties, whether the next rank will be the nearest integer to the current rank or tie-count ranks away.
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} | 2 | "2012-11-02T14:49:11Z" | "2013-09-21T17:10:39Z" | "2013-09-21T17:10:39Z" | MEMBER | null | ```
ids = np.arange(48000)
lens = np.maximum(np.round(15+9.5*np.random.randn(48000)), 1.0).astype(int)
id_vec = np.repeat(ids, lens)
lens_shift = np.concatenate(([0], lens[:-1]))
mon_vec = np.arange(lens.sum()) - np.repeat(np.cumsum(lens_shift), lens)
n = len(mon_vec)
df = pd.DataFrame.from_items([('pool', id_vec), ('month', mon_vec)] + [(c, np.random.rand(n)) for c in 'abcde'])
df = df.set_index(['pool', 'month'])
%time df_shift = df.groupby(level=0).transform(lambda x: x.shift(-1))
```
xref http://stackoverflow.com/questions/13180499/most-efficient-way-to-shift-multiindex-time-series
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} | 1 | "2012-11-02T17:39:08Z" | "2014-06-25T18:03:15Z" | "2012-11-02T18:20:09Z" | CONTRIBUTOR | null | com.is_integer_dtype now returns False for dtype timedelta64 #2146
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} | 0 | "2012-11-03T02:21:51Z" | "2012-11-03T15:23:42Z" | "2012-11-03T15:23:42Z" | CONTRIBUTOR | null | purely positional indexing would solve this problem better
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} | 1 | "2012-11-03T03:56:34Z" | "2012-11-03T19:23:06Z" | "2012-11-03T19:23:06Z" | CONTRIBUTOR | null | It looks like printing a DataFrame's representation gives a strange result when one column is datetime64. Details in this SO post:
http://stackoverflow.com/questions/13202326/pandas-to-datetime-produces-confusing-result
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} | 0 | "2012-11-03T15:38:56Z" | "2012-11-03T16:22:45Z" | "2012-11-03T16:22:45Z" | CONTRIBUTOR | null | Deprecate 'colors' parameter in LinePlot. Otherwise frame.plot(color='rgb') fails but frame.plot(kind='bar', color='rgb') works.
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https://api.github.com/repos/pandas-dev/pandas/issues/2170 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2170/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2170/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2170/events | https://github.com/pandas-dev/pandas/pull/2170 | 8,082,884 | MDExOlB1bGxSZXF1ZXN0Mjg3NDEzNw== | 2,170 | BUG: start_time end_time to_timestamp bugs #2124 #2125 #1764 | {
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} | 1 | "2012-11-03T20:49:25Z" | "2014-07-13T09:26:33Z" | "2012-11-04T21:18:12Z" | CONTRIBUTOR | null | @wesm can you review this plz?
The main change is I have to_timestamp default to second frequency now to get the first and last second of the Period. This doesn't **quite** solve #2125 since Timestamp resolution goes down to Nanos. So the question is is it appropriate to add 999999999ns to the end_time when ns doesn't exist asa period freq?
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https://api.github.com/repos/pandas-dev/pandas/issues/2171 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2171/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2171/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2171/events | https://github.com/pandas-dev/pandas/issues/2171 | 8,091,325 | MDU6SXNzdWU4MDkxMzI1 | 2,171 | GroupBy transform() throws unexpected exception when sorting each DataFrame | {
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} | 6 | "2012-11-04T19:33:25Z" | "2013-04-10T07:24:52Z" | "2013-04-10T07:24:52Z" | NONE | null | I have the following DataFrame:
```
data
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 410322 entries, (20111230, '00036110') to (20121019, 'Y8564W10')
> Data columns:
> market_cap 410117 non-null values
> average_volume 410322 non-null values
> return_std_daily 410322 non-null values
> return_std_monthly 410322 non-null values
> dtypes: float64(4)
```
data.index.names
```
> ['date', 'security_id']
I try to do this:
```
data.groupby(level='date').transform(lambda x: x.sort_index(by='average_volume'))
```
> Traceback (most recent call last):
> File "C:\Python27\lib\site-packages\IPython\core\interactiveshell.py", line 2721, in run_code
> exec code_obj in self.user_global_ns, self.user_ns
> File "<ipython-input-49-27a4db14f71f>", line 1, in <module>
> data.groupby(level='date').transform(lambda x: x.sort_index(by='average_volume'))
> 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
At the same time the following works
```
def apply_by_group(grouped, f):
"""
Applies a function to each Series or DataFrame in a GroupBy object, concatenates the results
and returns the resulting Series or DataFrame.
Parameters
----------
grouped: SeriesGroupBy or DataFrameGroupBy
f: callable
Function to apply to each Series or DataFrame in the grouped object.
Returns
-------
Series or DataFrame that results from applying the function to each Series or DataFrame in the
GroupBy object and concatenating the results.
"""
assert isinstance(grouped, (SeriesGroupBy, DataFrameGroupBy))
assert hasattr(f, '__call__')
groups = []
for key, group in grouped:
groups.append(f(group))
return pd.concat(groups)
```
```
apply_by_group(data.groupby(level='date'), lambda x: x.sort_index(by='average_volume'))
```
> <class 'pandas.core.frame.DataFrame'>
> MultiIndex: 410322 entries, (20111230, '31340030') to (20121019, '03783310')
> Data columns:
> market_cap 410117 non-null values
> average_volume 410322 non-null values
> return_std_daily 410322 non-null values
> return_std_monthly 410322 non-null values
> dtypes: float64(4)
Is this strange or am I doing something wrong with transform()?
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} | 3 | "2012-11-04T20:17:59Z" | "2013-09-21T13:24:41Z" | "2013-09-21T13:24:35Z" | CONTRIBUTOR | null | help with bar and line plot types
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} | 7 | "2012-11-05T04:15:01Z" | "2012-12-13T14:05:30Z" | "2012-12-13T14:05:30Z" | NONE | null | When using read_table to read a "|" delimited text file, an error stating "Exception: Reindexing only valid with uniquely valued Index objects" came up. It was not obvious at first glance what the problem was, so I set the option "header" to None and then parsing the file was fine. Here's the link to text file,
http://d.pr/f/t3Xe
and the error output
http://d.pr/f/Roal
It may be better to clarify the error or have have an assert command catch it?
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} | 9 | "2012-11-05T12:58:16Z" | "2012-11-24T18:49:32Z" | "2012-11-24T18:49:32Z" | NONE | null | - the `columns` argument gets clobbered:
In [13]: pd.DataFrame.from_records({1:["foo"],2:["bar"]},columns=['a','b']).columns
Out[13]: Index([1, 2], dtype=int64)
- if `index` is specified, and `result_index` computed, it will get clobbered
later on.
- if `index` is specified as a list of labels, with the first few matching columns names
and the others not, then sdict will get mutilated, because the removal of columns
occurs within the try clause rather then after success.
**EDIT** - ignore, I misread the code.
- There's duplication against the main Dataframe ctor which also accepts dicts and arrays,
and The exclusion and float coercsion which are unique to `from_records()` would be useful
to have generally available in the main ctor anyway.
- It's unclear if the `columns` argument should be specified relative to the original
data, or relative to the data modulo excluded columns
- Doesn't support duplicate column names (Although that's checked with
a warning)
- the docstring specifies the datatypes for `data` , which do not include a dict.
But the code specifically checks and handles dict input, This is a minor thing, but
using `columns` along with dict is not well defined because of key (non-)ordering,
so the original docstring spec seems more sane.
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} | 9 | "2012-11-05T21:27:28Z" | "2012-11-07T15:06:38Z" | "2012-11-05T22:28:08Z" | NONE | null | I'm using pandas 0.9.0. I have a function that returns a DataFrame. The resulting DataFrame is 'unstable': many operations I try on it, interactively in IPython, lead to a complete python.exe crash. I get no exception or indication on what's going wrong which is very frustrating.
Some operations do work however, like df.index or df.values.
In the screenshot below, r is a custom-built class with method to_dataframe() that generates the dataframe.
In [6]: df=r.to_dataframe()
In [7]: df.index
Out[7]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2010-01-01 00:00:00, ..., 2011-01-01 00:00:00]
Length: 108355, Freq: None, Timezone: None
In [9]: df.values
Out[9]:
array([[-0.],
[-0.],
[-0.],
...,
[-0.],
[-0.],
[-0.]], dtype=float32)
In [10]: df
Out[10]:
HERE IS THE CRASH
I also get the crash when doing print df, df.resample('D'), etc.
What can I do to get more information, or solve this bug?
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} | 1 | "2012-11-07T00:38:20Z" | "2012-11-07T01:03:56Z" | "2012-11-07T01:03:56Z" | MEMBER | null | ```
In [1314]: data = """\
......: A,B,C
......: 00001,001,5
......: 00002,002,6
......: """
......:
In [1315]: from cStringIO import StringIO
In [1316]: read_csv(StringIO(data), converters={'A' : lambda x: x.strip()})
Out[1316]:
A B C
0 00001 1 5
1 00002 2 6
2 NaN NaN
```
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} | 31 | "2012-11-07T08:27:53Z" | "2014-09-13T20:32:32Z" | "2012-11-25T21:08:16Z" | CONTRIBUTOR | null | After updating pandas I can't import it any more because the symbol _floatify is undefined in pandas/lib.so:
```
$ python -c 'import pandas'
dlopen(/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so, 2): Symbol not found: _floatify
Referenced from: /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
Expected in: flat namespace
in /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/__init__.py", line 10, in <module>
import pandas.lib as lib
ImportError: dlopen(/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so, 2): Symbol not found: _floatify
Referenced from: /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
Expected in: flat namespace
in /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
$ otool -L /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so
/Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so:
/usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 169.3.0)
$ nm /Users/deil/Library/Python/2.7/lib/python/site-packages/pandas-0.9.1.dev_7156920-py2.7-macosx-10.8-x86_64.egg/pandas/lib.so | grep floatify
U _floatify
```
The pandas build log doesn't show any problem with `lib.so` or `floatify`:
https://gist.github.com/4030153
My numpy install seems OK:
https://gist.github.com/4030177
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} | 4 | "2012-11-07T10:32:38Z" | "2012-11-09T17:18:15Z" | "2012-11-09T17:18:15Z" | NONE | null | Hi all,
I have a strange issue with pandas 0.9, I think it's a bug. I'm trying to use dataframe.join() and it works well on a random dataframe, but not on a dataframe created from my simulation result. The code below shows that join() on the second dataframe blows up the index and the result is completely wrong.
To run this code you need this file: https://dl.dropbox.com/u/6200325/mydf.dataframe in your work folder. The script below can also be downloaded here: https://dl.dropbox.com/u/6200325/BugJoin.py
This is the result I get:
In [17]: run -i 'C:\Workspace\Python\Tests\BugJoin.py'
Before:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100000 entries, 2012-01-01 00:00:00 to 2023-05-29 15:00:00
Freq: H
Empty DataFrame
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100000 entries, 2012-01-01 00:00:00 to 2023-05-29 15:00:00
Freq: H
Data columns:
0 100000 non-null values
dtypes: float64(1)
After:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100000 entries, 2012-01-01 00:00:00 to 2023-05-29 15:00:00
Freq: H
Data columns:
0 100000 non-null values
dtypes: float64(1)
Before:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 108355 entries, 2010-01-01 00:00:00 to 2011-01-01 00:00:00
Empty DataFrame
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 108355 entries, 2010-01-01 00:00:00 to 2011-01-01 00:00:00
Data columns:
SID0000 108355 non-null values
dtypes: float64(1)
After:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4054807 entries, 2010-01-09 16:00:00 to 2010-05-17 15:55:42
Data columns:
SID0000 4054807 non-null values
dtypes: float64(1)
This is the code from the script:
```
import numpy as np
import pandas as pd
from scipy.integrate import cumtrapz
df1=pd.DataFrame(np.random.rand(1e5),
index=pd.date_range('2012-01-01', freq='H', periods=1e5))
df2=pd.load('mydf.dataframe')
for dataframe in [df1, df2]:
cum = pd.DataFrame(index=dataframe.index)
for c in dataframe.columns:
# we need to remove the empty values for the cumtrapz function to work
ts = dataframe[c].dropna()
tscum = pd.DataFrame(data=cumtrapz(ts.values, ts.index.asi8/1e9, initial=0),
index=ts.index,
columns=[c])
print '\nBefore:'
print cum, '\n'
print tscum, '\n'
cum=cum.join(tscum, how='left')
print 'After:'
print cum
```
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} | 1 | "2012-11-07T16:16:20Z" | "2012-11-09T18:05:36Z" | "2012-11-09T18:05:36Z" | CONTRIBUTOR | null | Currently if I exponentiate a DataFrame with a Series, the indexing joining logic is performed on the columns. This means the following:
``` python
>>> data = pandas.DataFrame({"a": [1, 2, 3, np.nan], "b": [23, 4, 5, np.nan]}, index=["ax", "bx", "cx", "dx"])
>>> series = pandas.Series([2,2,3, 1], index=["ax", "bx", "cx", "dx"])
>>> data ** series
a ax b bx cx dx
ax 1 NaN NaN NaN NaN NaN
bx NaN NaN NaN NaN NaN NaN
cx NaN NaN NaN NaN NaN NaN
dx NaN NaN NaN NaN NaN NaN
```
IOW, the joining happened on columns and not on rows. The only way I know to do this is to do `(data.T ** series).T`, which is kind of ugly. The addition of a pow() or exponentiate() function would allow me to use a specific axis to exponentiate and avoid this problem.
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} | 3 | "2012-11-07T19:58:11Z" | "2012-11-09T03:22:03Z" | "2012-11-09T03:21:53Z" | MEMBER | null | Very, very surprised this bug isn't caught by the test suite.
df1 = pandas.DataFrame({'a':{True:1,False:5},'b':{True:7,False:8}})
df2 = pandas.DataFrame({'a':{True:1,False:5},'c':{True:7,False:8}})
```
In [15]: df1.add(df2, fill_value=0)
Out[15]:
a b c
False 10 NaN NaN
True 2 NaN NaN
```
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} | 1 | "2012-11-07T21:40:31Z" | "2014-02-18T01:38:24Z" | "2014-02-18T01:38:24Z" | CONTRIBUTOR | null | maybe have option to not do cross join. instead just join on order of appearance for the non-unique elements (and fill-in NAs for the rest)?
Related: #2189
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} | 26 | "2012-11-07T22:21:44Z" | "2018-07-26T13:00:55Z" | "2018-07-26T13:00:55Z" | NONE | null | Hi there,
I have a codebase that (so far) consumes data series in the form of dict-yielding iterators, similar in form to how DataFrame's constructor accepts a list of such. Unfortunately it seems from current implementation of that constructor, there is no straightforward way to consume from an iterator straight into a DataFrame.
For example, I have a row factory for sqlite3 written in C that produces dicts. If pandas would accept an iterator, it should be possible to consume a series directly from speedy storage code into speedy data structure code. :)
Is there an inherent limitation preventing this? Otherwise with a few pointers I would be willing to try contribute the patch myself.
Thanks,
David
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} | 3 | "2012-11-07T23:04:40Z" | "2013-05-02T14:53:28Z" | "2013-05-02T14:53:28Z" | CONTRIBUTOR | null | right now it fails in list of lists but the error isn't informative.
xref: commit 5a152bdfdd044c6f53c76b361065b1784dc73b61
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} | 1 | "2012-11-08T04:30:52Z" | "2012-11-09T23:53:16Z" | "2012-11-08T14:23:06Z" | CONTRIBUTOR | null | description for parameter 'kind' was given twice.
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} | 1 | "2012-11-08T11:26:40Z" | "2012-11-09T17:58:02Z" | "2012-11-09T17:58:02Z" | NONE | null | from [StackOverflow](http://stackoverflow.com/questions/13279690/unexpected-behavior-when-combining-two-dataframes-in-pandas):
While index.union is not guaranteed to sort results, it does not try to sort the result for monotonic indexes, but does try to sort non-monotonic indexes.
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} | 0 | "2012-11-08T15:48:53Z" | "2012-11-09T16:43:07Z" | "2012-11-09T16:43:07Z" | CONTRIBUTOR | null | use the int64 factorizer
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} | 9 | "2012-11-08T22:31:47Z" | "2013-02-05T22:28:02Z" | "2012-11-08T22:52:10Z" | NONE | null | ae63ebf77b355591413f15bf77d30943e9f957b
FAIL: testWLS (pandas.stats.tests.test_ols.TestOLS)
Traceback (most recent call last):
File "/home/user1/src/pandas/pandas/stats/tests/test_ols.py", line 92, in testWLS
self._check_wls(X, Y, weights)
File "/home/user1/src/pandas/pandas/stats/tests/test_ols.py", line 115, in _check_wls
self.checkMovingOLS('rolling', x, y, weights=weights)
File "/home/user1/src/pandas/pandas/stats/tests/test_ols.py", line 189, in checkMovingOLS
result_index=n)
File "/home/user1/src/pandas/pandas/stats/tests/test_ols.py", line 228, in compare
assert_almost_equal(ref, res)
File "/home/user1/src/pandas/pandas/util/testing.py", line 104, in assert_almost_equal
assert_almost_equal(a[i], b[i])
File "/home/user1/src/pandas/pandas/util/testing.py", line 120, in assert_almost_equal
1, a / b, decimal=5, err_msg=err_msg(a, b), verbose=False)
File "/usr/local/lib/python2.7/dist-packages/numpy/testing/utils.py", line 468, in assert_almost_equal
raise AssertionError(msg)
AssertionError:
Arrays are not almost equal to 5 decimals expected 1.79945 but got 1.50389
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https://api.github.com/repos/pandas-dev/pandas/issues/2199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2199/events | https://github.com/pandas-dev/pandas/issues/2199 | 8,221,534 | MDU6SXNzdWU4MjIxNTM0 | 2,199 | Curious: df.ix[False] returns the first row | {
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} | 6 | "2012-11-08T22:38:32Z" | "2012-11-09T18:18:21Z" | "2012-11-09T17:00:35Z" | NONE | null | ``` python
In [21]: df=pd.DataFrame({"a":[1,2,3]})
...: df.ix[False]
Out[21]:
a 1
Name: 0
```
Don't know if this is a bug or intentiional.
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} | 0 | "2012-11-08T23:13:36Z" | "2012-11-09T17:40:06Z" | "2012-11-09T17:40:06Z" | NONE | null | ``` python
import pandas as pd
df=pd.DataFrame({"a":[1,2,3],"b":[4,5,6],"c":[7,8,9]}).set_index(["a","b"])
l=list(df.index)
l[0]=("faz","boo")
df.index=l
print df
# all good
l[0]=["faz","boo"]
df.index=l
print df
# badness
```
If this acceptable usage and index entries must be immutable, failing early would be good.
**Note:** If you get an `UnboundLocalError` Exception, That's an un-related issue, PR fix as soon
as travis is green.
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