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| null | 1 | 2015-06-21T20:01:44Z | 2015-06-22T08:20:09Z | 2015-06-22T08:19:56Z | NONE | null | Hi,
I am trying to initialize a sparseDataFrame into block of memory and assign the value row-by-row, which ends up with:
```
a = np.random.rand(4,5)
b = pd.DataFrame(a)
c = sparse.csr_matrix(a)
d = pd.SparseDataFrame(index=b.index, columns=b.columns)
elem = pd.SparseSeries(c[2].toarray().ravel())
d.loc[[2]] = [ elem ] # Got a NotImplementedError.
elem = pd.Series(c[2].toarray().ravel())
b.loc[[2]] = [ elem ] # Yes.
```
Seems SparseDataFrame can't be assigned row-by-row, in the manner of assigning DataFrame by DataSeries. Any other method to implement it?
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} | 3 | 2015-06-22T08:38:48Z | 2018-08-29T01:24:52Z | 2018-08-29T01:24:52Z | CONTRIBUTOR | null | follows #6755 / #10396
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} | 5 | 2015-06-22T13:21:28Z | 2015-06-23T14:12:14Z | 2015-06-23T14:12:07Z | MEMBER | null | Follow-up of #10397. Fix some ugly unicode tests. Shall I fix followings also?
- Remove `compat.u()` completely, because the escaped literal is different from normal unicode literals internally.
- Remove `compat.callable` brought back in v3.2 ([some code](https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L2307) doesn't use `compat.callable` already)
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} | 5 | 2015-06-22T16:56:39Z | 2019-03-26T09:56:16Z | 2019-03-26T06:26:21Z | NONE | null | When slicing data time - indexed dataframe with date in string format, it works:
```
df.ix['2014-11-12'].head()
Out[55]:
calories gsr heart-rate skin-temp steps
date
2014-11-12 00:00:00 2.1 7.04640 NaN 85.7750 30
2014-11-12 00:01:00 1.5 7.40759 NaN 85.7375 0
2014-11-12 00:02:00 1.4 7.46220 49 85.5500 0
2014-11-12 00:03:00 1.4 7.55800 NaN 85.5500 0
2014-11-12 00:04:00 1.5 7.52163 NaN 85.5125 0
```
But when i try it with a date object, of datetime class , it slices only for time 00:00:00.
```
d1=datetime.date(2014,11,12)
df.ix[d1]
Out[57]:
calories 2.1000
gsr 7.0464
heart-rate NaN
skin-temp 85.7750
steps 30.0000
Name: 2014-11-12 00:00:00, dtype: float64
```
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} | 6 | 2015-06-22T19:30:44Z | 2015-11-27T12:35:05Z | 2015-11-15T21:05:24Z | NONE | null | Hy,
in older versions pandas was able to plot onto inset-axis of matplotlib. In new versions this support seems to be broken. I was able to execute the following code without any exceptions using an old pandas version. However, the current pandas version (0.16.2) throws an exception. Since the last update of matplotlib is a while back, I think the bug is caused by a recent pandas update. (As far as i remember, I was able to plot onto inset-axis using pandas 0.16.0.)
``` python
import matplotlib.pylab as plt
import pandas as pd
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
df = pd.DataFrame(range(10))
ax = df.plot() # OK
axins = zoomed_inset_axes(ax, 2)
df.plot(ax=axins) # Throws exception
```
The thrown exception:
```
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
3324 layout = np.zeros((nrows+1,ncols+1), dtype=np.bool)
3325 for ax in axarr:
-> 3326 layout[ax.rowNum, ax.colNum] = ax.get_visible()
3327
3328 if sharex and nrows > 1:
AttributeError: 'Axes' object has no attribute 'rowNum'
```
Output of pd.show_version():
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-53-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.0.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.3.2
html5lib: 0.99999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: 0.6.2.None
psycopg2: None
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} | 13 | 2015-06-22T21:49:54Z | 2015-07-24T13:50:18Z | 2015-07-24T13:50:18Z | NONE | null | ```
import pandas as pd
import numpy as np
import datetime as dt
z=dt.date(2010,11,1)
zs=[z+dt.timedelta(days=r) for r in range(5)]
df=pd.DataFrame({'obj':zs, 'b':pd.Timestamp('2010-10-01'),'c':pd.Timestamp('2010-10-01')})
df.dtypes
#df.loc[0:2,'c']=dt.date(2010,10,12) # causes error: long() argument must be a string or a number, not 'datetime.date
df.loc[0:2,'c']=np.datetime64('2010-10-12') # sets to 1970...
df.at[4,'c']=np.datetime64('2010-10-12') # works
df.loc[0:2,'obj']=np.datetime64('2010-10-12') #works
df.loc[0:2,'obj']=dt.date(2010,10,12)
df
```
| ind | b | c | obj |
| --- | --- | --- | --- |
| 0 | 2010-10-01 | 1970-01-01 | 2010-10-12 |
| 1 | 2010-10-01 | 1970-01-01 | 2010-10-12 |
| 2 | 2010-10-01 | 1970-01-01 | 2010-10-12 |
| 3 | 2010-10-01 | 2010-10-01 | 2010-11-04 |
| 4 | 2010-10-01 | 2010-10-12 | 2010-11-05 |
I am using Pandas 0.16.2
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} | 8 | 2015-06-23T00:18:46Z | 2017-03-10T23:06:03Z | 2017-03-10T23:06:03Z | CONTRIBUTOR | null | xref #14351
None of the following merge operations retain the `category` types. Is this expected? How can I keep them?
#### Merging on a `category` type:
Consider the following:
```
A = pd.DataFrame({'X': np.random.choice(['foo', 'bar'],size=(10,)),
'Y': np.random.choice(['one', 'two', 'three'], size=(10,))})
A['X'] = A['X'].astype('category')
B = pd.DataFrame({'X': np.random.choice(['foo', 'bar'],size=(10,)),
'Z': np.random.choice(['jjj', 'kkk', 'sss'], size=(10,))})
B['X'] = B['X'].astype('category')
```
if I do the merge, we end up with:
```
> pd.merge(A, B, on='X').dtypes
X object
Y object
Z object
dtype: object
```
#### Merging on a `non-category` type:
```
A = pd.DataFrame({'X': np.random.choice(['foo', 'bar'],size=(10,)),
'Y': np.random.choice(['one', 'two', 'three'], size=(10,))})
A['Y'] = A['Y'].astype('category')
B = pd.DataFrame({'X': np.random.choice(['foo', 'bar'],size=(10,)),
'Z': np.random.choice(['jjj', 'kkk', 'sss'], size=(10,))})
B['Z'] = B['Z'].astype('category')
```
if I do the merge, we end up with:
```
pd.merge(A, B, on='X').dtypes
X object
Y object
Z object
dtype: object
```
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| null | 2 | 2015-06-23T04:50:32Z | 2015-06-23T10:35:42Z | 2015-06-23T10:35:32Z | NONE | null | I get this error "NotImplementedError: Writing as Excel with a MultiIndex is not yet implemented.
when running the example below(as described in your site):
"Generating Excel Reports from a Pandas Pivot Table. "
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} | 23 | 2015-06-23T08:21:54Z | 2015-08-20T00:47:38Z | 2015-08-18T23:05:06Z | MEMBER | null | xref #9817
This does not entirely solve the floating point precision issues, but gets us part of the way there -- we can explicitly lookup data with a fixed tolerance for nearest neighbor matches.
It is also is useful in its own right, mostly as a simple sanity check to verify that labels are not entirely misaligned.
Example usage:
```
In [2]: df = pd.DataFrame({'x': range(5)})
In [3]: df.reindex([0.1, 1.9, 3.5], method='nearest', max_distance=0.2)
Out[3]:
x
0.1 0
1.9 2
3.5 NaN
```
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} | 1 | 2015-06-23T11:40:02Z | 2015-07-24T13:50:18Z | 2015-07-24T13:50:18Z | MEMBER | null | Resulting in 1970 datetimes when using eg `datetime64[D]` resolution (splitted from #10408):
```
In [46]: df=pd.DataFrame({'c':pd.Timestamp('2010-10-01')}, index=range(5))
In [47]: df
Out[47]:
c
0 2010-10-01
1 2010-10-01
2 2010-10-01
3 2010-10-01
4 2010-10-01
In [48]: df.loc[0:2,'c'] = np.datetime64('2010-10-12')
In [49]: df
Out[49]:
c
0 1970-01-01 00:00:00.000014894
1 1970-01-01 00:00:00.000014894
2 1970-01-01 00:00:00.000014894
3 2010-10-01 00:00:00.000000000
4 2010-10-01 00:00:00.000000000
```
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} | 1 | 2015-06-23T14:55:38Z | 2015-09-17T10:43:12Z | 2015-07-07T09:30:45Z | CONTRIBUTOR | null | after this fix https://github.com/pydata/pandas/pull/10302
The following is failing (as `index_col` is `None`)
```
In [9]: data
Out[9]: 'header1,header2\n'
In [10]: read_csv(StringIO(data),iterator=True,index_col=False).read()
TypeError: object of type 'bool' has no len()
```
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} | 4 | 2015-06-23T15:48:02Z | 2015-07-01T10:58:05Z | 2015-07-01T10:58:05Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/stable/missing_data.html#na-values-in-groupby
```
NA groups in GroupBy are automatically excluded. This behavior is consistent with R, for example.
```
I can take this up.
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} | 1 | 2015-06-23T17:23:30Z | 2021-04-18T07:02:59Z | null | CONTRIBUTOR | null | Right now pandas can read fwf but there is no `to_fwf`.
From [SO](http://stackoverflow.com/questions/16490261/python-pandas-write-dataframe-to-fixed-width-file-to-fwf).
This output format seems similar to what [`numpy.savetxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html) handles.
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| null | 2 | 2015-06-23T17:29:11Z | 2015-06-24T14:26:13Z | 2015-06-23T21:17:38Z | NONE | null | Hello,
If I try to unstack using multiIndex I got some groups divided. However, if I tried to do it by successive unstacking they are correctly grouped. Please see image below
I believe this is due to the fact that the first rows don't have n=3 for "actor" and the corresponding column is added only when it appears in a posterior row. Please note this issue is more related with presentation than performance as both operations yield the same results in subsequent processing (e.g. plotting).
Best!
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} | 1 | 2015-06-23T19:10:49Z | 2015-08-08T12:18:12Z | 2015-08-08T12:18:12Z | NONE | null | ```
x = pd.DataFrame({'x':[1,1,3,3],'y':[3,3,5,5]},index=[11,11,12,12])
y = x.stack()
print(y)
print(y.groupby(level=[0,1]).sum())
```
Ouput
```
11 x 1
y 3
x 1
y 3
12 x 3
y 5
x 3
y 5
dtype: int64
11 x 1
y 3
x 1
y 3
12 x 3
y 5
x 3
y 5
dtype: int64
```
The stack and group by sum are just the same.
Expected output:
```
11 x 2
11 y 6
12 x 6
12 y 10
```
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} | 3 | 2015-06-23T20:33:59Z | 2015-08-07T10:54:08Z | 2015-08-07T10:54:07Z | CONTRIBUTOR | null | Close #10362 . Specify information on the use of index in pandas.Series.interpolate documentation.
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} | 2 | 2015-06-23T21:27:25Z | 2015-07-04T12:52:08Z | 2015-06-27T03:51:56Z | MEMBER | null | Closes #10150. Also, made `test_base` have name attributes.
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} | 5 | 2015-06-23T22:55:14Z | 2020-11-12T23:44:11Z | 2015-09-01T12:04:25Z | CONTRIBUTOR | null | [This code](https://github.com/pydata/pandas/blob/7d6fb510c1dfdbe7342f32f05ca5fd69b7854081/pandas/core/common.py#L1646) means the interpolator won't handle extrapolating to the front of the Series even though the underlying implementations may have no problem with the extrapolation.
See for example [`UnivariateSpline`](http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.interpolate.UnivariateSpline.html) whose default behavior is extrapolation.
Interpolation works fine at the end of the Series.
For example:
``` python
s = pd.Series([1, 2, 3, 4, np.nan, 6, np.nan])
s.interpolate(method='spline', order=1)
0 1
1 2
2 3
3 4
4 5
5 6
6 7
dtype: float64
```
but:
``` python
s = pd.Series([np.nan, 2, 3, 4, np.nan, 6, 7])
s.interpolate(method='spline', order=1)
0 NaN
1 2
2 3
3 4
4 5
5 6
6 7
dtype: float64
```
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} | 5 | 2015-06-23T23:15:39Z | 2015-08-20T15:26:07Z | 2015-08-20T15:26:07Z | CONTRIBUTOR | null | Added explanations for `axes` and `shape`
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} | 4 | 2015-06-24T07:05:36Z | 2020-08-17T18:59:43Z | 2020-08-17T18:59:43Z | NONE | null | ### Problem
Here is a problem that we had with a colleague, working on data available on a ftp (or http) server (internal network, we're sorry we can't have a proper example file to point to).
Reading a csv file (with csv_read) encoded with non utf8 (like latin-1), with special character in header, fails to properly unicode the header when file is accessed through an URL (http or ftp), but **not** when file is local, nor when it's utf-8 (local or distant) file.
The result looks like the file was decoded twice.
An example shoud be clearer.
Let's say we have 2 CSV files (on a distant server), _data.latin1.csv_ and _data.utf8.csv_, encoded in latin-1 and utf-8, and both containing :
```
a,b°
1.1,2.2
```
Then following code :
``` python
import sys
import os.path as op
import pandas as pd
path = "ftp://sorry/I/cant/supply/such/a/path/for/the/example/data.encoding.csv"
for enc in ('latin1', 'utf8') :
f = path.replace('encoding', enc)
data = pd.read_csv(f, encoding=enc)
print("encoding {0} : non-ascii={1} , length={2}".format(enc, data.columns[1].encode('utf8'), len(data.columns[1])))
```
will give :
```
encoding latin1 : non-ascii=b° , length=3
encoding utf8 : non-ascii=b° , length=2
```
This was tested with _Python 2.7.6 + Pandas 0.13.1_ and _Python 3.4.0 + Pandas 0.15.2_ with same result.
Same action on local files will give appropriate result, i.e. like previous 'utf8' encoding output (this REALLY IS a matter of URL+latin1 or anything but utf-8). It looks like data was decoded twice, as we can see in output length as latin1 escape code for '°' is considered as a "normal" character being converted to utf-8.
This test will raise an error ("UnicodeEncodeError: 'ascii' codec can't encode character u'\xb0' in position 3: ordinal not in range(128)") when python engine is used for read_csv() .
### in pandas code
Now, having a look at Pandas' code, I would focus on 2 points in pandas.io.parsers :
- when file is an url, data is opened through urllib (or urllib2), then read, decoded (according to requested encoding) and result is fed into a StringIO stream (Cf. _pandas.io.common.maybe_read_encoded_stream()_ ) ,
- as far as I could trace it, file seems to be decoded later, especially for 'c'-engine in _pandas.io.parsers.CParserWrapper.read()_ method (in fact by __parser.read()_ at the end, which is C-parser)
This would explain the twice decoding scheme when file is url, and normal decoding when file is local.
Furthermore, in pandas.io.common, when replacing (in _maybe_read_encoded_stream()_ function) :
``` python
from pandas.compat import StringIO
...
reader = StringIO(reader.read().decode(encoding, errors))
```
by :
``` python
from pandas.compat import StringIO, BytesIO
...
reader = BytesIO(reader.read())
```
this problem seems to be solved (which is logical when we look at which StringIO/ByteIO functions are pointing to (depending on Python version) and which data they're handling).
So it seems to me that the problem is located at that point, and it would then be a bug.
However, it could be a feature ;-) as I don't know whether there could be side-effects for other cases than the one discussed here, especially if StringIO was intentionally used for a purpose I can't figure out.
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} | 7 | 2015-06-24T08:49:32Z | 2015-06-26T23:12:06Z | 2015-06-26T23:12:06Z | CONTRIBUTOR | null | Hello,
Package pandas is failing to build from source on Debian big-endian builds with following message:
<pre>
======================================================================
FAIL: test_to_records (pandas.tests.test_categorical.TestCategoricalAsBlock)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«BUILDDIR»/pandas-0.15.1+git125-ge463818/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_categorical.py", line 2393, in test_to_records
tm.assert_almost_equal(result,expected)
File "das/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2745)
File "das/src/testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1830)
File "das/src/testing.pyx", line 143, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2618)
AssertionError: (0L, 'a') != (0L, 'a')
----------------------------------------------------------------------
</pre>
https://buildd.debian.org/status/package.php?p=pandas&suite=sid
Looking at a upstream source and testing it on big-endian build I found that this problem stops occurring after commit 7eb5668
Difference between this commit and Debian version is big.
I am new to pandas and I did not had much luck in identifying minimal change set needed to patch Debian version of package to work with big-endian.
I have seen TST: better testing for test_where (GH8997) #9015 and that did not help much.
Could someone please take a look at this?
Help is welcomed.
Thank you!
Regards,
Jurica
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} | 0 | 2015-06-24T12:39:34Z | 2015-06-24T20:46:26Z | 2015-06-24T20:46:26Z | CONTRIBUTOR | null | These are currently accepted, but parsed incorrectly. These don't have units, nor are of the standard format, so should raise as unparseable.
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} | 4 | 2015-06-24T12:43:06Z | 2015-07-28T21:45:17Z | 2015-07-28T21:45:17Z | MEMBER | null | Related to #10373. `pandas` `assert_array_equal` needs to be updated to perform `nan` comparison properly.
```
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import numpy.testing.utils as npt
npt.assert_array_equal(np.array([1, np.nan, 3]), np.array([1, np.nan, 3]))
# no AssertionError
pdt.assert_numpy_array_equal(np.array([1, np.nan, 3]), np.array([1, np.nan, 3]))
# AssertionError: [ 1. nan 3.] is not equal to [ 1. nan 3.].
```
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} | 0 | 2015-06-24T13:14:21Z | 2015-06-24T20:46:26Z | 2015-06-24T20:46:26Z | CONTRIBUTOR | null | closes #10426
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} | 0 | 2015-06-24T20:58:26Z | 2015-06-25T01:10:34Z | 2015-06-25T01:10:34Z | CONTRIBUTOR | null | seems a recent change in numpy master: https://github.com/numpy/numpy/issues/6014
I think might be a bug on numpy's part.
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} | 0 | 2015-06-25T00:10:49Z | 2015-06-25T01:10:34Z | 2015-06-25T01:10:34Z | CONTRIBUTOR | null | numpy 1.10 makes this an error for 1-d on axis != 0
closes #10430
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} | 0 | 2015-06-25T00:37:03Z | 2015-08-21T08:27:34Z | 2015-08-21T08:27:34Z | CONTRIBUTOR | null | @jreback, @shoyer, and @cpcloud will be at SciPy this year, and are going to host a sprint on July 11 (saturday).
SciPy sprint pages is [here](http://scipy2015.scipy.org/ehome/115969/292867/?&)
This issue will be the landing page for contributors.
[pandas](http://pandas.pydata.org) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
See our shiny new [Contributing Guidelines & Instructions](http://pandas-docs.github.io/pandas-docs-travis/contributing.html)
Here are various issues that are open and would like your attention:
- [Difficulty Novice](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22)
- [Difficulty Intermediate](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Intermediate%22)
- [Difficulty Advanced](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Advanced%22)
Please comment on an issue if you are working on it.
Here is a [link](https://gitter.im/pydata/pandas) to the gitter chatroom.
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} | 13 | 2015-06-25T02:08:32Z | 2015-08-30T02:30:09Z | 2015-08-08T12:18:12Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/10417
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} | 2 | 2015-06-25T02:37:20Z | 2015-08-20T22:03:24Z | 2015-08-20T22:03:14Z | NONE | null | `<type 'exceptions.UnicodeDecodeError'> at /token
'ascii' codec can't decode byte 0xd7 in position 0: ordinal not in range(128)`
This is hard to understand. I finally figured out the issue, and it was that strings and floats are being compared. I suppose now the error message makes sense. This is an issue related to an unclear error message.
Here is the line of code it points to, where `cc` is a string. `df = df[df['my_col'] > cc)]`.
I am assuming this is the right place for this error, but please close this issue if that is not the case.
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} | 5 | 2015-06-25T06:14:43Z | 2015-08-21T01:37:30Z | 2015-08-21T01:37:30Z | CONTRIBUTOR | null | Starting point for closing #10365.
I only have the trivial case. I think this method (and perhaps other methods as well) needs #10383 for it to be useful.
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| null | 2 | 2015-06-25T07:53:50Z | 2018-10-21T01:26:07Z | 2018-10-21T01:26:07Z | NONE | null | not_empty = pd.DataFrame([[None,None]],columns=['a', 'b']).squeeze()
type(not_empty)
empty_dim_1 = pd.DataFrame(columns=['a', 'b']).squeeze()
type(empty_dim_1)
empty_dim_0 = pd.DataFrame(index=['c', 'd']).squeeze()
type(empty_dim_0)
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| null | 1 | 2015-06-25T08:46:59Z | 2015-06-25T19:25:09Z | 2015-06-25T19:24:09Z | NONE | null | I was trying to reindex a Panel with a MultiIndex. The ultimate goal is to be able to do slicing like this:
```
wp.loc[(1,slice(None)),:,:] #Access the panel fixing the first index of the multiIndex
```
But whenever I try to reindex, like in this example I got this strange behaviour.
```
wp = pd.Panel(pd.randn(4, 5, 4), items=['Item_1_1', 'Item_1_2','Item_2_1','Item_2_2'],major_axis=['a','b','c','d','e'],minor_axis=['A', 'B', 'C', 'D'])
a=[tuple([int(i) for i in item.split('_')[1:]]) for item in wp.items]
swp=wp.reindex(items=x)
```
But when I try to access an item of the panel I got;
```
In [101]: swp[1,1]
Out[101]:
A B C D
a NaN NaN NaN NaN
b NaN NaN NaN NaN
c NaN NaN NaN NaN
d NaN NaN NaN NaN
e NaN NaN NaN NaN
```
The current workaround I have found is the following:
```
def sp(string):
s=string.split('_')
return int(s[-3]),int(s[-1])
wwp=wp.rename(items=sp)
w=wwp.items.sortlevel(level=0)
swwp=wwp.reindex(items=w[0])
```
I don't think that the first is something that is excepted, and the second way is quite complex for nothing, but I might be wrong.
And now it is working at it should.
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} | 3 | 2015-06-25T09:07:31Z | 2015-06-26T23:11:50Z | 2015-06-26T23:11:50Z | CONTRIBUTOR | null | BUG: Changing test_categorical to compare with native byte ordering instead of comparing with little-endian one
closes #10425
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} | 1 | 2015-06-25T13:21:46Z | 2015-08-20T17:32:21Z | 2015-08-20T17:32:21Z | NONE | null | I can pickle dataframes with periods but after import Period format becomes corrupted. After pickle Period stride appears to be none instead of 'Q' in the following example.
<pre><code>
import pandas as pd
df = pd.DataFrame({'period': [pd.Period('2014Q4')] * 5})
msk = df['period'] == pd.Period('2014Q4')
print msk
df.to_pickle('tmp.pkl')
df = pd.read_pickle('tmp.pkl')
msk = df['period'] == pd.Period('2014Q4')
</code></pre>
<pre><code>
Traceback (most recent call last):
File "/Users/scarrucciu/Downloads/period_pickle_test.py", line 11, in <module>
msk = df['period'] == pd.Period('2014Q4')
File "/Users/scarrucciu/anaconda/lib/python2.7/site-packages/pandas/core/ops.py", line 614, in wrapper
res = na_op(values, other)
File "/Users/scarrucciu/anaconda/lib/python2.7/site-packages/pandas/core/ops.py", line 562, in na_op
result = lib.scalar_compare(x, y, op)
File "pandas/lib.pyx", line 696, in pandas.lib.scalar_compare (pandas/lib.c:12434)
File "das/src/period.pyx", line 743, in pandas._period.Period.__richcmp__ (pandas/src/period.c:11121)
ValueError: Cannot compare non-conforming periods
</code></pre>
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
pandas: 0.16.2
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} | 8 | 2015-06-25T17:11:59Z | 2021-08-31T14:14:07Z | null | NONE | null | Best shown with an example.
``` python
import numpy as np, pandas as pd
timestamps = map(pd.Timestamp, ['2014-01-01', '2014-02-01'])
categories = ['A', 'B', 'C', 'D']
df = pd.DataFrame(index=pd.MultiIndex.from_product([timestamps, categories], names=['ts', 'cat']),
columns=['Col1', 'Col2'])
>>> df
Col1 Col2
ts cat
2014-01-01 A NaN NaN
B NaN NaN
C NaN NaN
D NaN NaN
2014-02-01 A NaN NaN
B NaN NaN
C NaN NaN
D NaN NaN
```
I want to set the values for all categories in a single month. These examples work just fine.
``` python
df.loc['2014-01-01', 'Col1'] = 5
df.loc['2014-01-01', 'Col2'] = [1,2,3,4]
>>> df
Col1 Col2
ts cat
2014-01-01 A 5 1
B 5 2
C 5 3
D 5 4
2014-02-01 A NaN NaN
B NaN NaN
C NaN NaN
D NaN NaN
```
These examples don't work.
``` python
df.loc['2014-01-01', 'Col1'] += 1
df.loc['2014-02-01', 'Col2'] = df.loc['2014-01-01', 'Col2']
>>> df
Col1 Col2
ts cat
2014-01-01 A NaN 1
B NaN 2
C NaN 3
D NaN 4
2014-02-01 A NaN NaN
B NaN NaN
C NaN NaN
D NaN NaN
```
It doesn't seem to be a "setting a value on a copy" issue. Instead, Pandas is writing the NaNs.
My current workaround is to unstack each column into a DataFrame with simple indexes. This works, but I have lots of columns to work with. One dataframe is much easier to work with than a pile of dataframes.
The computations for each month depend on the values computed in the previous month, hence why it can't be done fully vectorized on an entire column.
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} | 8 | 2015-06-25T17:56:15Z | 2015-08-20T16:27:25Z | 2015-08-20T16:24:10Z | NONE | null | Fix Period pickle issue https://github.com/pydata/pandas/issues/10439
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} | 3 | 2015-06-25T19:00:18Z | 2015-10-16T22:07:39Z | 2015-10-16T22:07:39Z | MEMBER | null | xref #10110
- [x] Fix `.astype(str)` on `DatetimeIndex` to use call `.format()` (Index method)
~~\- [ ] Add `Series.dt.format()` accessor to call `.format()`~~ (#10110 does this with `.strftime`)
- [x] Change `Series.astype(str)` to call equiv of `Series.dt.format()` for `datetime64[ns]` dtype
Recently the ability to convert a datetimelike series to strings with `astype(str)` was introduced (#9758 by @evanpw):
```
In [27]: pd.Series(pd.date_range('2012-01-01', periods=3))
Out[27]:
0 2012-01-01
1 2012-01-02
2 2012-01-03
dtype: datetime64[ns]
In [28]: pd.Series(pd.date_range('2012-01-01', periods=3)).astype(str)
Out[28]:
0 2012-01-01T01:00:00.000000000+0100
1 2012-01-02T01:00:00.000000000+0100
2 2012-01-03T01:00:00.000000000+0100
dtype: object
```
it would be nice if this also possible on a index (now you still get the same error as previously for a series):
```
In [29]: pd.date_range('2012-01-01', periods=3).astype(str)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-29-68a28f79c8e9> in <module>()
----> 1 pd.date_range('2012-01-01', periods=3).astype(str)
C:\Users\vdbosscj\Scipy\pandas-joris\pandas\tseries\index.py in astype(self, dty
pe)
692 return self.asi8.copy()
693 else: # pragma: no cover
--> 694 raise ValueError('Cannot cast DatetimeIndex to dtype %s' % d
type)
695
696 def _get_time_micros(self):
ValueError: Cannot cast DatetimeIndex to dtype |S0
```
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} | 4 | 2015-06-25T19:53:00Z | 2015-07-13T13:22:12Z | 2015-07-13T10:56:10Z | CONTRIBUTOR | null | Allow read_hdf to be used without a key when a single pandas object
is stored in a HDF file. Raises if multiple pandas objects found.
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} | 12 | 2015-06-25T23:08:57Z | 2019-05-24T15:47:02Z | 2019-05-24T15:47:02Z | NONE | null | pandas version: 0.16.2
matplotlib version 1.4.3 (and produced different error message on older version)
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10,2))
df_o = df.astype(np.object)
df_o.hist()
```
```
ValueError Traceback (most recent call last)
<ipython-input-1-26253737011d> in <module>()
4 df = pd.DataFrame(np.random.rand(10,2))
5 df_o = df.astype(np.object)
----> 6 df_o.hist()
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in hist_frame(data, column, by, grid, xlabelsize, xrot, ylabelsize, yrot, ax, sharex, sharey, figsize, layout, bins, **kwds)
2764 fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False,
2765 sharex=sharex, sharey=sharey, figsize=figsize,
-> 2766 layout=layout)
2767 _axes = _flatten(axes)
2768
/usr/local/lib/python2.7/dist-packages/pandas/tools/plotting.pyc in _subplots(naxes, sharex, sharey, squeeze, subplot_kw, ax, layout, layout_type, **fig_kw)
3244
3245 # Create first subplot separately, so we can share it if requested
-> 3246 ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
3247
3248 if sharex:
/usr/local/lib/python2.7/dist-packages/matplotlib/figure.pyc in add_subplot(self, *args, **kwargs)
962 self._axstack.remove(ax)
963
--> 964 a = subplot_class_factory(projection_class)(self, *args, **kwargs)
965
966 self._axstack.add(key, a)
/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_subplots.pyc in __init__(self, fig, *args, **kwargs)
62 raise ValueError(
63 "num must be 0 <= num <= {maxn}, not {num}".format(
---> 64 maxn=rows*cols, num=num))
65 if num == 0:
66 warnings.warn("The use of 0 (which ends up being the "
ValueError: num must be 0 <= num <= 0, not 1
```
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} | 2 | 2015-06-26T10:52:48Z | 2018-07-06T20:21:07Z | 2017-07-10T21:07:10Z | NONE | null | I'm not sure this is a bug, but it bit me recently.
I can fill-in an empty `DataFrame` with a `Series`:
```
In [1]: import pandas as pd
In [2]: d = pd.DataFrame()
In [3]: d[0] = pd.Series([1,2,3])
In [4]: d
Out[4]:
0
0 1
1 2
2 3
```
However, I cannot do the same for a `Panel`, even though it fails silently:
```
In [5]: p = pd.Panel()
In [6]: p[0] = d
In [7]: p
Out[7]:
<class 'pandas.core.panel.Panel'>
Dimensions: 1 (items) x 0 (major_axis) x 0 (minor_axis)
Items axis: 0 to 0
Major_axis axis: None
Minor_axis axis: None
In [8]: p.values
Out[8]: array([], shape=(1, 0, 0), dtype=float64)
```
I did not find a way to fill-in the empty panel once created empty.
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} | 1 | 2015-06-26T12:55:57Z | 2019-10-21T20:06:20Z | null | MEMBER | null | The documentation on the extensive pandas IO tools is currently one big page: http://pandas.pydata.org/pandas-docs/stable/io.html.
I propose to split this in several files: a page with a general introduction to the IO tools, and then one page per format (writer/reader combo).
(this could be seen as a first start for #6000)
Splitting this shouldn't be that hard (I was just a bit experimenting if it would be possible to keep git blame working for all splitted files, but not that important probably ..), but an issue I would like to get some feedback for: **What do we do with all existing links? How do we keep them working?**
Anybody who has some experience with such issues when restructuring a website what the best way is to deal with this?
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} | 2 | 2015-06-26T14:33:41Z | 2015-07-13T12:42:41Z | 2015-07-13T12:42:41Z | CONTRIBUTOR | null | Attempting to store a Panel4D of complext128 results in an error. This is true if I use table or fixed formats (different errors) [I am showing the last call from pandas as well as the actual error]
``` python
pd.Panel4D(np.tile(np.array([1+1j,2+2j]),[2,2,2,1])).to_hdf('complex.h5','complex',format='t')
Traceback (most recent call last):
...
File "/miniconda/envs/py34/lib/python3.4/site-packages/pandas/io/pytables.py", line 1930, in get_atom_data
return self.get_atom_coltype(kind=kind)(shape=block.shape[0])
...
File "/miniconda/envs/py34/lib/python3.4/site-packages/tables/atom.py", line 740, in __init__
"to avoid confusions with PyTables 1.X complex atom names, "
TypeError: to avoid confusions with PyTables 1.X complex atom names, please use ``ComplexAtom(itemsize=N)``, where N=8 for single precision complex atoms, and N=16 for double precision complex atoms
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-504.16.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: 3.1.0
sphinx: None
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: None
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
If you try fixed, you get
```
TypeError: cannot properly create the storer for: [_STORER_MAP] [group->/complex (Group) '',value-><class 'pandas.core.panelnd.Panel4D'>,format->fixed,append->False,kwargs->{'encoding': None}]
```
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} | 2 | 2015-06-26T16:14:25Z | 2015-07-08T14:19:57Z | 2015-06-26T18:07:27Z | CONTRIBUTOR | null | For certain well-chosen inputs, `group_var_float64` may return small negative values due to roundoff error. This then interferes with e.g. computing aggregate standard deviations:
``` python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({'a': (0, 0, 0), 'b': 0.832845131556193 * np.ones(3)})
>>> df.groupby('a').std()
b
a
0 NaN
```
To see the cause of this in isolation, consider
``` python
import numpy as np
from pandas.algos import group_var_float64
out = np.array([[0.0]], dtype=np.float64)
counts = np.array([0])
values = 0.832845131556193 * np.ones((3, 1), dtype=np.float64)
labels = np.zeros(3, dtype=np.int)
group_var_float64(out, counts, values, labels)
print out # This prints [[ -1.48029737e-16]] on my machine.
```
The fix for this should be easy (round up negative values to zero). I can provide a fix (+ tests) if needed.
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 14.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: nl_BE.UTF-8
pandas: 0.16.1
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.1.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.4
pymysql: None
psycopg2: None
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} | 10 | 2015-06-26T18:21:09Z | 2020-05-11T02:05:55Z | null | CONTRIBUTOR | null | Consider the following:
```
index = pd.date_range("2015-01-01", "2015-01-01 02:00", freq="1H", closed="left")
s = pd.Series(1, index=index)
s.resample("15T", fill_method="ffill", closed="left")
```
The result looks like this
<pre>2015-01-01 00:00:00 1
2015-01-01 00:15:00 1
2015-01-01 00:30:00 1
2015-01-01 00:45:00 1
2015-01-01 01:00:00 1
Freq: 15T, dtype: int64</pre>
What I actually want is
<pre>2015-01-01 00:00:00 1
2015-01-01 00:15:00 1
2015-01-01 00:30:00 1
2015-01-01 00:45:00 1
2015-01-01 01:00:00 1
2015-01-01 01:15:00 1
2015-01-01 01:30:00 1
2015-01-01 01:45:00 1
Freq: 15T, dtype: int64</pre>
Currently it seems that you always have to do the resampling yourself by creating a new index of the new frequency from the same `begin` and `end` values, reindexing, and forward filling.
Actually, I would have expected `closed` to work like this. Any hints on a reasonable parameter so I can try to prepare a PR for this?
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} | 5 | 2015-06-26T20:04:35Z | 2015-07-01T10:58:05Z | 2015-07-01T10:58:05Z | CONTRIBUTOR | null | closes #10414
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} | 9 | 2015-06-27T12:39:39Z | 2015-08-02T21:26:30Z | 2015-08-02T21:26:30Z | CONTRIBUTOR | null | I may very well be wrong on this, given how common an option it seems, but I am surprised that the "display.precision" option seems to limit the digits after the decimal to one less than specified.
``` python
In [1]: x = pd.Series(np.random.randn(5))
In [2]: x
Out[2]:
0 -0.163960
1 1.016273
2 0.861317
3 -0.521916
4 -0.069322
dtype: float64
In [3]: pd.set_option('display.precision', 3)
In [4]: x
Out[4]:
0 -0.16
1 1.02
2 0.86
3 -0.52
4 -0.07
dtype: float64
```
I can't see where in the code this is happening. At first glance, this looks like what the code is doing:
```
In [13]: fmt_str = '%% .%dg' % 3
In [14]: fmt_str % x[0]
Out[14]: '-0.164'
```
but clearly something else is happening.
numpy's precision seems fine/meets my expectations:
``` python
In [10]: np.set_printoptions(precision=3)
In [11]: np.random.randn(5)
Out[11]: array([ 0.569, -2.638, 0.707, 0.675, 1.191])
```
So is this a bug? (if so it's been around for a long time) Or are my expectations off?
This was tested on current pandas master (as of writing) with numpy 1.9.2 and python 3.4.
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} | 3 | 2015-06-27T13:43:35Z | 2016-03-30T12:39:02Z | 2016-03-30T12:39:02Z | MEMBER | null | `SparseSeries.shape` seems to return incorrect result ignoring shapes filled by `fill_value`.
```
s = pd.SparseSeries([0, 0, 1, 0], fill_value=0)
s
#0 0
#1 0
#2 1
#3 0
# dtype: int64
# BlockIndex
# Block locations: array([2], dtype=int32)
# Block lengths: array([1], dtype=int32)
len(s)
#4
# must be (4, )?
s.shape
# (1,)
# OK
s._data.shape
# (4,)
```
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} | 1 | 2015-06-27T14:36:26Z | 2015-06-27T14:40:08Z | 2015-06-27T14:39:07Z | CONTRIBUTOR | null | Very minor fix but `str.ljust()` and `str.rjust()` have broken links in the docstrings.
You can see in http://pandas.pydata.org/pandas-docs/version/0.16.2/generated/pandas.Series.str.ljust.html#pandas.Series.str.ljust that it should be linking to `str.ljust()` instead of `str.right()` (which doesn't exist)
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} | 9 | 2015-06-27T14:47:44Z | 2015-08-22T20:21:50Z | 2015-08-22T20:21:50Z | CONTRIBUTOR | null | closes #10366 .
Probably not quite the right approach but want to run travis. Tests are a bit weak at this point (just testing for non-exceptions). Encoding issues might be relevant to other types besides categoricals (but categoricals raise exceptions when strings to mangled to non-uniqueness).
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} | 4 | 2015-06-27T15:53:09Z | 2015-06-27T19:04:43Z | 2015-06-27T19:04:42Z | MEMBER | null | closes ContinuumIO/odo#244
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} | 9 | 2015-06-27T22:17:06Z | 2015-07-07T17:40:57Z | 2015-07-07T16:36:14Z | CONTRIBUTOR | null | @jreback, here is the proposed changed we talked about in [this PR](https://github.com/pydata/pandas/pull/10337#issuecomment-111822584) for other fellow newbs that make the same mistake I did.
You said "and more importantly prob a note in `internals.rst` about how/what to change", but I didn't understand that. `internals.rst` is not auto-generated, right? So, why would I add a note there? Perhaps you wanted a note to appear in `internals.html`? That seems unnecessary since if the user has that file on their machine, they must have intentionally generated it...
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| null | 14 | 2015-06-27T22:53:48Z | 2018-09-01T02:23:51Z | 2015-06-28T00:46:52Z | NONE | null | Typed in
pd.read_csv('Department_of_Recreation_and_Parks_Facility_and_Park_Information.csv')
and get this back:
-bash: syntax error near unexpected token `'Department_of_Recreation_and_Parks__Facility_and_Park_Information.csv''
The file exists in the folder
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} | 1 | 2015-06-28T00:04:07Z | 2015-06-30T13:03:04Z | 2015-06-30T10:46:13Z | MEMBER | null | `testing.py` has `assert_isinstance` and `assertIsInstance` duplicately, and each test case uses either of them by choice.
Changed to use `assertIsInstance` in all cases, and make `assert_isinstance` show warning.
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} | 4 | 2015-06-28T00:32:29Z | 2020-01-01T16:02:30Z | 2020-01-01T15:57:28Z | NONE | null | Thanks to the wonders of duck typing, `pandas.Timestamp` happily accepts `pywintypes.TimeType` objects (otherwise known as `pywintypes.datetime`), including those that employ the `win32timezone.TimeZoneInfo` object. And most methods work.
For some reason, `to_datetime` does not, and indeed it crashes:
```
import pandas as pd
from win32timezone import TimeZoneInfo
from pywintypes import TimeType
x = TimeType(2012,12,1,0,0,tzinfo=TimeZoneInfo('GMT Standard TIme'))
y = pd.Timestamp(x)
print(y)
y.tzinfo
y.utcoffset()
y.to_datetime64()
y.to_datetime()
```
This occurs with Anaconda installed win64 Python 3.4.3 and Pandas 0.16.2.
I'm going to see if I can't dig down to find a key source of failure. Stay tuned
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} | 1 | 2015-06-28T00:58:28Z | 2015-07-01T13:46:10Z | 2015-07-01T13:46:10Z | MEMBER | null | ```
import pandas as pd
import numpy as np
levels = ['foo', 'bar', 'baz', 'qux']
codes = np.random.randint(0, 4, size=100)
cats = pd.Categorical.from_codes(codes, levels, name='myfactor', ordered=True)
data = pd.DataFrame(np.random.randn(100, 4))
grouped = data.groupby(cats)
desc_result = grouped.describe()
desc_result.index.get_level_values(0)
# AttributeError: 'Categorical' object has no attribute 'flags'
```
Changing below line to use `.get_values()` looks fix the issue.
- https://github.com/pydata/pandas/blob/master/pandas/core/index.py#L4241
### After the above change
```
desc_result.index.get_level_values(0)
CategoricalIndex([u'foo', u'foo', u'foo', u'foo', u'foo', u'foo', u'foo',
u'foo', u'bar', u'bar', u'bar', u'bar', u'bar', u'bar',
u'bar', u'bar', u'baz', u'baz', u'baz', u'baz', u'baz',
u'baz', u'baz', u'baz', u'qux', u'qux', u'qux', u'qux',
u'qux', u'qux', u'qux', u'qux'],
categories=[u'bar', u'baz', u'foo', u'qux'], ordered=False, name=u'myfactor', dtype='category')
```
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} | 9 | 2015-06-28T01:04:10Z | 2021-04-18T07:09:14Z | null | MEMBER | null | Currently, `MultiIndex.get_level_values` handles both names and levels, it return different levels depending on its name (like `.ix`). Adding an option (or separate method) like `.loc` and `.iloc` to avoid any unexpected results?
```
idx = pd.MultiIndex.from_tuples([(1, 'A'), (2, 'B')])
idx.get_level_values(0)
# Int64Index([1, 2], dtype='int64')
idx = idx.set_names([1, 0])
idx.get_level_values(0)
# Index([u'A', u'B'], dtype='object', name=0)
```
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} | 10 | 2015-06-28T06:11:29Z | 2015-10-12T01:32:14Z | 2015-10-11T16:00:58Z | CONTRIBUTOR | null | closes #10445
I based this bugfix off how DataFrame handles the analogous situation ‒ being intialized to empty and then having a series added (cf. [DataFrame._ensure_valid_index](https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L2167))
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| null | 13 | 2015-06-28T10:10:14Z | 2015-06-28T11:57:45Z | 2015-06-28T10:57:04Z | CONTRIBUTOR | null | `numpy` has a very useful `vectorize` decorator: http://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html that allows writing a function for scalars and then pass in arrays transparently. Under the hood, the function is called multiple times on subsets of the data.
I'm currently writing code that operates on a `Series` object. Some of those functions can also be passed in DataFrames and still work the same while some would require special logic. I think a pandas vectorize decorator could be very useful for these cases. In my specific case, I can just write that myself easily but I thought I'd bring it up. Perhaps this already exists and I just don't know about it.
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} | 8 | 2015-06-28T11:03:24Z | 2015-07-01T13:47:52Z | 2015-07-01T13:46:10Z | MEMBER | null | Closes #10324. Closes #10460.
Based on #9848, using `.get_values` should be avoided?
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} | 1 | 2015-06-28T11:22:03Z | 2015-06-28T13:22:06Z | 2015-06-28T13:18:30Z | MEMBER | null | Closes #10460.
xref: #10464.
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} | 21 | 2015-06-28T19:51:19Z | 2015-12-06T19:09:55Z | 2015-12-06T19:09:55Z | NONE | null | closes #10353, extends the "pipe" method to a GroupBy object to allow one to chain it with NDFrame.pipe calls
- Moves the functionality of "pipe" from NDFrame into generics._pipe to avoid code duplication
- Leverages this in GroupBy object
- Adds unit test
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} | 3 | 2015-06-29T10:28:49Z | 2015-07-07T09:30:45Z | 2015-07-07T09:30:45Z | CONTRIBUTOR | null | ``` Python
import pandas as pd
import cStringIO as stringio
df = pd.read_csv(stringio.StringIO("x,y,z"), index_col=["x", "y"])
print df.columns
print df.index.names
```
Output:
```
$ python x.py
Index([u'y'], dtype='object')
[u'x', u'y']
```
Expected column name is "z".
## INSTALLED VERSIONS
commit: 01995b2c2c759c66b208dc0bd8a35f5f23c39b9f
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-400.1.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.16.2-58-g01995b2
nose: 1.3.0
Cython: 0.22
numpy: 1.9.2
scipy: 0.9.0
statsmodels: None
IPython: 3.2.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: 0.6.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.90
httplib2: $Rev$
apiclient: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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| null | 8 | 2015-06-29T10:35:38Z | 2017-12-19T20:06:47Z | 2015-06-29T11:07:26Z | CONTRIBUTOR | null | Today I discovered a strange behaviour when iterating over groups where the group name contains a nan. Take the following data frame and group it. As expected there are two groups:
``` python
In [1]: df = pd.DataFrame([['a', 1, 1], ['b', sp.nan, 2]], columns = ['a', 'b', 'c'])
In [2]: grouped = df.groupby(['a', 'b'])
In [3]: print grouped.groups
{('a', 1.0): [0], ('b', nan): [1]}
```
Now, if I iterate over the groups the second group is missing:
``` python
In [4]: for name, group in grouped:
...: print name
('a', 1.0)
```
I would expect that the iteration works like this:
``` python
In [5]: for name, group in grouped.groups.items():
...: print name
('a', 1.0)
('b', nan)
```
pandas version:
``` python
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-55-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.1
Cython: 0.20.1post0
numpy: 1.9.1
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 3.2.0
sphinx: None
patsy: 0.3.0
dateutil: 2.4.0
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
httplib2: 0.8
apiclient: None
rpy2: 2.3.9
sqlalchemy: 0.8.4
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
```
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} | 6 | 2015-06-29T13:45:00Z | 2015-07-07T09:30:45Z | 2015-07-07T09:30:45Z | CONTRIBUTOR | null | closes #10413
closes #10467
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} | 8 | 2015-06-29T14:56:39Z | 2019-04-05T06:54:16Z | 2019-04-05T06:54:05Z | CONTRIBUTOR | null | It looks like `io` is available from 2.6 onward. Can I move the importing out of `compat` (and replaces any `cStringIO` with `StringIO`)? `StringIO.StringIO` doesn't always do the same thing as `io.StringIO`.
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| null | 1 | 2015-06-29T17:19:03Z | 2021-04-18T07:13:19Z | 2021-04-18T07:13:19Z | CONTRIBUTOR | null | ```
~/pandas-0.16.2+git58-g01995b2# LC_ALL=C.UTF-8 nosetests3 -s -v /tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python3/dist-packages/pandas/tools/tests/test_util.py
test_datetimeindex (pandas.tools.tests.test_util.TestCartesianProduct) ... ok
test_simple (pandas.tools.tests.test_util.TestCartesianProduct) ... ok
test_get_locales (pandas.tools.tests.test_util.TestLocaleUtils) ... ok
test_get_locales_prefix (pandas.tools.tests.test_util.TestLocaleUtils) ... ok
test_set_locale (pandas.tools.tests.test_util.TestLocaleUtils) ... FAIL
======================================================================
FAIL: test_set_locale (pandas.tools.tests.test_util.TestLocaleUtils)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python3/dist-packages/pandas/tools/tests/test_util.py", line 90, in test_set_locale
self.assertEqual(current_locale, CURRENT_LOCALE)
AssertionError: Tuples differ: (None, None) != ('en_US', 'UTF-8')
First differing element 0:
None
en_US
- (None, None)
+ ('en_US', 'UTF-8')
----------------------------------------------------------------------
Ran 5 tests in 0.047s
FAILED (failures=1)
~/pandas-0.16.2+git58-g01995b2# nosetests3 -s -v /tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python3/dist-packages/pandas/tools/tests/test_util.py
test_datetimeindex (pandas.tools.tests.test_util.TestCartesianProduct) ... ok
test_simple (pandas.tools.tests.test_util.TestCartesianProduct) ... ok
test_get_locales (pandas.tools.tests.test_util.TestLocaleUtils) ... ok
test_get_locales_prefix (pandas.tools.tests.test_util.TestLocaleUtils) ... ok
test_set_locale (pandas.tools.tests.test_util.TestLocaleUtils) ... ok
----------------------------------------------------------------------
Ran 5 tests in 0.039s
OK
```
and I need to set LC_ALL so that tests like read_html pass while decoding unicode correctly
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} | 6 | 2015-06-29T17:23:27Z | 2015-07-08T21:19:54Z | 2015-07-08T14:19:41Z | CONTRIBUTOR | null | closes #10448
This PR reimplements the Cython functions `group_var_float64` and `group_var_float32` to use Welford's algorithm, rather than the sum-of-squares method, which is numerically unstable. This came up in #10448; see also #10242 for more discussion.
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} | 5 | 2015-06-29T19:27:56Z | 2015-07-13T13:22:09Z | 2015-07-13T12:42:41Z | CONTRIBUTOR | null | Enable table format to be used to store complex values
in DataFrames, Panels and Panel4Ds.
Add tests for both fixed and panel.
Add exception when attempting to write Series with complex values.
closes #10447
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} | 5 | 2015-06-29T21:44:36Z | 2017-02-26T20:22:29Z | 2017-02-26T20:11:08Z | NONE | null | gbq.py currently returns an error if the result of a query is what Google considers to be 'Large'. The google api allows jobs to be sent with a flag to allow large results. It would be very beneficial to provide this as an option in the BigQuery connector.
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} | 3 | 2015-06-30T13:14:38Z | 2020-03-31T04:55:31Z | 2020-03-31T04:55:31Z | NONE | null | Series.shift() is slow when freq parameter is supplied
```
ts = pd.util.testing.makeTimeSeries(10000)
ts.shift(5, 'D') # takes 0.5 sec
ts.shift(5, np.timedelta64(1, 'D')) # takes 0.16 sec
ts2 = ts.copy()
ts2.index = ts.index + np.timedelta64(5, 'D') # takes < 0.001 sec
```
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} | 4 | 2015-06-30T15:40:42Z | 2016-05-25T12:09:25Z | 2016-05-25T12:09:25Z | CONTRIBUTOR | null | Only thing I changed from my usually working reduction pipeline is to try `engine="python"` (because I wanted to use `nrows` for a smaller test-read, but that fails as well, and I thought maybe the python engine is buggy currently):
``` python
$ python reduction.py ~/data/planet4/2015-06-21_planet_four_classifications.csv
INFO:Starting reduction.
Traceback (most recent call last):
File "reduction.py", line 258, in <module>
args.test_n_rows, args.remove_duplicates)
File "reduction.py", line 182, in main
data = [chunk for chunk in reader]
File "reduction.py", line 182, in <listcomp>
data = [chunk for chunk in reader]
File "/Users/klay6683/miniconda3/lib/python3.4/site-packages/pandas-0.16.2_58_g01995b2-py3.4-macosx-10.5-x86_64.egg/pandas/io/parsers.py", line 697, in __iter__
yield self.read(self.chunksize)
File "/Users/klay6683/miniconda3/lib/python3.4/site-packages/pandas-0.16.2_58_g01995b2-py3.4-macosx-10.5-x86_64.egg/pandas/io/parsers.py", line 721, in read
ret = self._engine.read(nrows)
File "/Users/klay6683/miniconda3/lib/python3.4/site-packages/pandas-0.16.2_58_g01995b2-py3.4-macosx-10.5-x86_64.egg/pandas/io/parsers.py", line 1556, in read
content = self._get_lines(rows)
File "/Users/klay6683/miniconda3/lib/python3.4/site-packages/pandas-0.16.2_58_g01995b2-py3.4-macosx-10.5-x86_64.egg/pandas/io/parsers.py", line 2007, in _get_lines
for _ in range(rows):
TypeError: 'float' object cannot be interpreted as an integer
```
My function call is this:
``` python
# as chunksize and nrows cannot be used together yet, i switch chunksize
# to None if I want test_n_rows for a small test database:
if test_n_rows:
chunks = None
else:
chunks = 1e6
# creating reader object with pandas interface for csv parsing
# doing this in chunks as its faster. Also, later will do a split
# into multiple processes to do this.
reader = pd.read_csv(fname, chunksize=chunks, na_values=['null'],
usecols=analysis_cols, nrows=test_n_rows,
engine='c')
```
Using pandas-0.16.2_58_g01995b2-py3.4
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} | 78 | 2015-06-30T15:44:09Z | 2015-09-05T16:17:23Z | 2015-09-05T16:17:23Z | CONTRIBUTOR | null | closes #8260
closes #10763
ToDos:
- [x] doc updates
- [x] test with `Series.dt.*`
- [x] test with csv/HDF5
- [x] nat setting borked ATM
- [x] HDF5 example from 0.16.2
~~\- [ ] get_values/values - make consistent~~
~~\- [ ] maybe move `DatetimeTZBlock.shift` mostly to `DatetimeIndex.shift`~~
Also
- This cleans up the internal blocks calling conventions a bit
- Fixes a bug in `DatetimeIndex` and localizing when NaT's are present
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timestamp_tz_ops_diff1 | 0.1804 | 159.4527 | 0.0011 | # note these are 10k element
timestamp_tz_ops_diff2 | 2.5350 | 156.4047 | 0.0162 | # note these are 10k elements
timeseries_timestamp_downsample_mean | 3.1467 | 3.3040 | 0.9524 |
timestamp_series_compare | 9.0797 | 9.1290 | 0.9946 |
timestamp_ops_diff2 | 19.7570 | 19.6819 | 1.0038 | # this is 1M elements
series_timestamp_compare | 9.3430 | 9.0226 | 1.0355 |
timestamp_ops_diff1 | 9.7457 | 9.0450 | 1.0775 | # this is 1M elements
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [8502474] : API: add Block.make_block
API: add DatetimeBlockWithTZ #8260
Base [16a44ad] : Merge pull request #10199 from jreback/gil
PERF: releasing the GIL, #8882
```
Demo
```
In [1]: df = DataFrame({'A' : date_range('20130101',periods=3),
...: 'B' : date_range('20130101',periods=3,tz='US/Eastern'),
...: 'C' : date_range('20130101',periods=3,tz='CET')})
In [2]: df
Out[2]:
A B C
0 2013-01-01 2013-01-01 05:00:00 2012-12-31 23:00:00
1 2013-01-02 2013-01-02 05:00:00 2013-01-01 23:00:00
2 2013-01-03 2013-01-03 05:00:00 2013-01-02 23:00:00
In [3]: df.dtypes
Out[3]:
A datetime64[ns]
B datetime64[ns, US/Eastern]
C datetime64[ns, CET]
dtype: object
In [4]: df.B
Out[4]:
0 2013-01-01 00:00:00-05:00
1 2013-01-02 00:00:00-05:00
2 2013-01-03 00:00:00-05:00
Name: B, dtype: datetime64[ns, US/Eastern]
In [5]: df.B.dt.tz_localize(None)
Out[5]:
0 2013-01-01
1 2013-01-02
2 2013-01-03
dtype: datetime64[ns]
```
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} | 1 | 2015-06-30T19:55:38Z | 2015-06-30T21:21:14Z | 2015-06-30T21:21:01Z | CONTRIBUTOR | null | ``` ======================================================================
FAIL: test_concat_categorical (pandas.tests.test_categorical.TestCategoricalAsBlock)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_categorical.py", line 2985, in test_concat_categorical
tm.assert_frame_equal(df_expected, df_concat)
File "/tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 746, in assert_frame_equal
check_names=check_names)
File "/tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 675, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "/tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 552, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "/tmp/buildd/pandas-0.16.2+git58-g01995b2/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 533, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int64') != dtype('int32')
```
got while building 0.16.2+git58-g01995b2
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} | 4 | 2015-07-01T04:52:03Z | 2015-07-02T04:17:15Z | 2015-07-01T10:53:21Z | CONTRIBUTOR | null | I discovered entirely by accident that the `na_values` in `pd.read_*` can be a string. According to the docstring, it can only be a sequence or a dict. This PR fixes that.
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} | 11 | 2015-07-01T09:32:27Z | 2018-01-01T12:16:33Z | 2016-09-28T10:08:51Z | CONTRIBUTOR | null | ```
In [1]: import pandas as pd
In [2]: pd.Series([1,2,3]).sum(numeric_only=False)
Out[2]: 6
In [3]: pd.Series([1,2,3]).sum(numeric_only=True)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-3-2c46bd289e26> in <module>()
----> 1 pd.Series([1,2,3]).sum(numeric_only=True)
/users/is/whughes/pyenvs/research/lib/python2.7/site-packages/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/generic.pyc in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
4253 skipna=skipna)
4254 return self._reduce(f, name, axis=axis,
-> 4255 skipna=skipna, numeric_only=numeric_only)
4256 stat_func.__name__ = name
4257 return stat_func
/users/is/whughes/pyenvs/research/lib/python2.7/site-packages/pandas-0.16.2_ahl1-py2.7-linux-x86_64.egg/pandas/core/series.pyc in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
2081 if numeric_only:
2082 raise NotImplementedError(
-> 2083 'Series.{0} does not implement numeric_only.'.format(name))
2084 return op(delegate, skipna=skipna, **kwds)
2085
NotImplementedError: Series.sum does not implement numeric_only.
```
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} | 5 | 2015-07-01T14:34:48Z | 2015-07-20T01:46:53Z | 2015-07-20T01:46:53Z | MEMBER | null | `Series` can take over the `Categorical.name`.
```
import pandas as pd
c = pd.Categorical([1, 2, 3], name='xxx')
pd.Series(c)
#0 1
#1 2
#2 3
# Name: xxx, dtype: category
# Categories (3, int64): [1, 2, 3]
```
But `Index` doesn't.
```
pd.Index(c)
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category')
pd.CategoricalIndex(c)
# CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category')
```
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} | 4 | 2015-07-01T19:47:21Z | 2015-07-07T15:52:31Z | 2015-07-07T15:52:31Z | CONTRIBUTOR | null | Series.from_csv at the moment does not load the series.name and series.index.name when the header keyword argument is stated.
This was introduced in 6078fba9410918baa486ca008cc9e3ba066c03ec. The issue was that `Series.from_csv` uses `DataFrame.from_csv`, which automatically indexes columns (in practice, the index column gets labelled 0 and the values column gets labelled 1). Converting this to a series will then cause the `series.name` to be 1, and the `series.index.name` to be 0. The fix was to explicitly set the names in Series.from_csv to `None`.
This caused the headers to be deleted even if they were provided by setting the header kwarg.
The fix is simple, to check if the headers are provided, and only setting the names to None if they are not.
I included some tests, please let me know if this is enough as I am new to open-source and pandas.
Thanks!
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} | 1 | 2015-07-01T23:15:31Z | 2019-10-21T20:07:56Z | null | CONTRIBUTOR | null | from #10468
```
In [40]: df = DataFrame({'A' : [1,2,3]},index=pd.MultiIndex.from_tuples([(1,np.nan),(1,1),(2,1)],names=['first','second']))
In [41]: df
Out[41]:
A
first second
1 NaN 1
1 2
2 1 3
In [42]: df.groupby(level='second').sum()
Out[42]:
A
second
1 5
In [43]: df.groupby(level=['first','second']).sum()
Out[43]:
A
first second
1 1 2
2 1 3
```
I suppose that [43] is missing the `(1,nan)` group here.
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} | 5 | 2015-07-01T23:19:02Z | 2015-07-15T11:13:35Z | 2015-07-15T11:13:35Z | CONTRIBUTOR | null | ```
s = pd.Series(np.random.randn(100))
s.plot(kind='hist')
s.hist()
```
Running `s.plot(kind='hist')` labels the y axis with the word 'Degree'. A more informative label might be 'count', 'frequency', or 'density'.
Also, it seems strange that `s.plot(kind='hist')` and `s.hist()` produce plots with different labels / gridlines.
Looks like this came from PR #7809. cc @sinhrks, @jreback
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} | 1 | 2015-07-01T23:50:49Z | 2016-05-07T17:12:30Z | 2016-05-07T17:12:30Z | NONE | null | The following call of `df.query()` produces an error:
```
...
File "/Applications/anaconda/lib/python3.4/site-packages/numexpr/necompiler.py", line 629, in getType
raise ValueError("unkown type %s" % a.dtype.name)
ValueError: unkown type str128
```
in pandas 0.15.2
```
# -*- coding: utf-8 -*-
import re
import pandas as pd
def get_gene_site(tp):
site = re.sub('T.$', '',tp)
gene = site.split('%%')[0]
t = int(re.search(r'T(\d+)$',tp).group(1))
return gene, site, t
sub_sample_list = ['Actb%%qeqwT0',
'Actb%%qeqwT1',
'Actb%%qeqwT2',
'Actb%%qeqwT3',
'Actb%%tralalaT0',
'Actb%%tralalaT1',
'Actb%%tralalaT2',
'Actb%%tralalaT3',
'Dummy%%rrrrT0',
'Dummy%%rrrrT1',
'Dummy%%rrrrT2',
'Dummy%%rrrrT3',
]
dict_samples = {'sample':[], 'gene':[], 'site':[], 'timepoint':[]}
for tp in sub_sample_list:
gene, site, t = get_gene_site(tp)
dict_samples['sample'].append(tp)
dict_samples['gene'].append(str(gene))
dict_samples[ 'site'].append(site)
dict_samples['timepoint'].append(t)
df = pd.DataFrame(dict_samples ,
columns = ['sample', 'gene', 'site', 'timepoint'])
df.query('gene = "Actb"')
```
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| null | 1 | 2015-07-02T03:25:40Z | 2015-07-02T07:11:17Z | 2015-07-02T07:11:14Z | CONTRIBUTOR | null | Removed unneeded spaces in kwargs related to read_gbq()
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} | 1 | 2015-07-02T04:17:02Z | 2015-07-02T07:42:55Z | 2015-07-02T07:42:55Z | CONTRIBUTOR | null | According to the suggestion of @jorisvandenbossche from https://github.com/pydata/pandas/pull/10479, changing some wording in the docs.
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| null | 1 | 2015-07-02T06:30:27Z | 2015-07-02T07:15:40Z | 2015-07-02T07:15:40Z | NONE | null | I got two snippets code as follows.
> > numpy.std([766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346])
> >
> > 0
> >
> > pd.Series([766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346, 766897346]).std(ddof=0)
> >
> > 10.119288512538814
The two lists are identical but the result are quite different. I think the pandas' result must be wrong. I work on the latest version 0.16.2 with Python 3.4.
May I ask why? Is it a bug?
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} | 5 | 2015-07-02T12:44:12Z | 2015-07-03T11:58:57Z | 2015-07-03T11:58:52Z | CONTRIBUTOR | null | To close #10369
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} | 5 | 2015-07-02T16:25:38Z | 2018-11-28T21:03:51Z | null | NONE | null | When writing a DataFrame to hdf format ('to_hdf' method) you cannot pass a file handle instead of a string. i.e. you can't:
``` python
with open('test.hdf') as f:
df.to_hdf(f, "df")
```
This raises an error:
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "C:\Anaconda\lib\site-packages\pandas\core\generic.py", line 896, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "C:\Anaconda\lib\site-packages\pandas\io\pytables.py", line 286, in to_hdf
f(path_or_buf)
File "C:\Anaconda\lib\site-packages\pandas\io\pytables.py", line 279, in <lambda>
f = lambda store: store.put(key, value, **kwargs)
AttributeError: 'file' object has no attribute 'put'
```
This is inconsistent with the 'to_csv' method where you can do this. In the docs it states that you can pass a file buffer to both 'to_hdf' and 'to_csv'.
pd.show_versions(False)
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.14.1
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: 2.5.6
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
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} | 4 | 2015-07-02T17:00:45Z | 2020-10-08T01:23:14Z | 2020-10-08T01:23:14Z | CONTRIBUTOR | null | It's not clear from the docs that indexing with a boolean `ndarray` isn't the same as indexing with a boolean `Series`. It took me a while to realise that:
``` python
import pandas as pd
df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], list('abc'), ['one', 'two'])
sr = (df['one'] > 2)
df.loc[sr, 'two'] # This is ok
df.iloc[sr, 1] # But this is not
df.iloc[sr.values, 1] # The right way to use iloc
```
I've since read through #3631 and it makes sense to me. However, even though the docs emphasise that `.iloc` is **purely integer-based**, it didn't click at the time that indexability of `Series` was the problem I was facing.
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} | 6 | 2015-07-02T17:36:05Z | 2015-07-11T06:45:10Z | 2015-07-11T06:45:10Z | CONTRIBUTOR | null | More informative label for histogram.
Conversation in #10485
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| null | 1 | 2015-07-03T09:02:39Z | 2015-07-03T11:57:42Z | 2015-07-03T11:57:26Z | NONE | null | With an unconvertible value (like e.g. "2014-12-32"), `convert_objects(convert_dates='coerce')` is supposed to return `NaT`. When run on a series with a single unconvertible value it gives back the original string instead.
``` python
>>> from pandas import Series
>>> Series(['2014-12-32']).convert_objects(convert_dates='coerce')
0 2014-12-32
dtype: object
```
#### INSTALLED VERSIONS
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: sv_SE.UTF-8
pandas: 0.16.2
nose: 1.3.6
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.1.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
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lxml: None
bs4: None
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} | 1 | 2015-07-03T13:01:56Z | 2015-07-06T12:44:38Z | 2015-07-06T12:44:38Z | CONTRIBUTOR | null | Not sure if this is related to #10324 .
Running master with python 3.4.3.
``` python
import pandas
values = ['a', 'b', 'c', 'd']
a = pandas.Series(values)
b = pandas.Series(values, dtype='category')
print(a.shift()) # ok
print(b.shift()) # error
```
Error looks like this:
```
$ python f.py
0 NaN
1 a
2 b
3 c
dtype: object
Traceback (most recent call last):
File "f.py", line 8, in <module>
print(b.shift()) # error
File "/Users/davidcottrell/dev/pandas.git/pandas/core/series.py", line 2165, in shift
axis=axis, **kwargs)
File "/Users/davidcottrell/dev/pandas.git/pandas/core/generic.py", line 3688, in shift
new_data = self._data.shift(periods=periods, axis=block_axis)
File "/Users/davidcottrell/dev/pandas.git/pandas/core/internals.py", line 2498, in shift
return self.apply('shift', **kwargs)
File "/Users/davidcottrell/dev/pandas.git/pandas/core/internals.py", line 2462, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/davidcottrell/dev/pandas.git/pandas/core/internals.py", line 890, in shift
f_ordered = new_values.flags.f_contiguous
AttributeError: 'Categorical' object has no attribute 'flags'
```
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} | 8 | 2015-07-03T13:35:55Z | 2015-07-17T16:15:56Z | 2015-07-17T16:15:55Z | NONE | null | When reading a table while specifying duplicate column names - let's say two different names - pandas 0.16.1 will copy the last two columns of the data over and over again.
I opened a thread on this here:
http://stackoverflow.com/questions/31207560/pandas-read-table-with-duplicate-names
Is this a bug or an intended behavior?
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} | 4 | 2015-07-03T13:42:01Z | 2015-07-18T00:03:30Z | 2015-07-18T00:03:27Z | CONTRIBUTOR | null | Should resolve #9416.
CategoricalBlocks always seem to have ndim=1, even if multiple
categoricals are in a frame with the same categories. This simplifies
the axis shift logic somewhat.
Note that dataframe shift with axis=1 still doesn't work with multiple
categorical columns, since they are each a different block.
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} | 3 | 2015-07-03T13:42:13Z | 2015-07-06T12:45:10Z | 2015-07-06T12:45:10Z | CONTRIBUTOR | null | #10495
Waiting for Travis and will review further.
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} | 1 | 2015-07-03T13:50:30Z | 2015-07-03T19:36:00Z | 2015-07-03T19:35:54Z | NONE | null | Updated the docstring to make clear that a HDFStore is required for a buffer, if a path is not directly passed. See issue #10491
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} | 2 | 2015-07-03T15:04:30Z | 2015-07-04T12:49:42Z | 2015-07-04T11:29:41Z | MEMBER | null | The result of `DataFrame.quantile` should have `Float64Index` as below, but current test case doesn't check it.
```
import pandas as pd
pd.DataFrame([1, 2, 3, 4]).quantile([0.5, 0.75]).index
# Float64Index([0.5, 0.75], dtype='float64')
```
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} | 2 | 2015-07-03T15:15:40Z | 2015-07-06T12:59:19Z | 2015-07-06T12:54:05Z | MEMBER | null | Related to #10465, but different part.
Current `test_groupby_datetime_categorical` in `test_groupby.py` is incorrect, the actual result returns `CategoricalIndex` as level 0, otherwise expected result uses `DatetimeIndex` as level 0. Changed to use the same dtype and added explicit comparison.
### Actual Result (current test case)
```
levels = pd.date_range('2014-01-01', periods=4)
codes = np.random.randint(0, 4, size=100)
cats = pd.Categorical.from_codes(codes, levels, name='myfactor', ordered=True)
data = pd.DataFrame(np.random.randn(100, 4))
grouped = data.groupby(cats)
desc_result = grouped.describe()
desc_result.index.get_level_values(0)
# CategoricalIndex([2014-01-01T09:00:00.000000000+0900,
# ...
# 2014-01-04T09:00:00.000000000+0900],
# categories=[2014-01-01 00:00:00, 2014-01-02 00:00:00, 2014-01-03 00:00:00, 2014-01-04 00:00:00],
# ordered=True, name=u'myfactor', dtype='category')
```
### Expected Result (current test case)
It must be `CategoricalIndex`.
```
idx = cats.codes.argsort()
ord_labels = np.asarray(cats).take(idx)
ord_data = data.take(idx)
expected = ord_data.groupby(ord_labels, sort=False).describe()
expected.index.names = ['myfactor', None]
expected.index.get_level_values(0)
# DatetimeIndex(['2014-01-01', '2014-01-01', '2014-01-01', '2014-01-01',
# ...
# '2014-01-04', '2014-01-04', '2014-01-04', '2014-01-04'],
# dtype='datetime64[ns]', name=u'myfactor', freq=None, tz=None)
```
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} | 2 | 2015-07-03T15:41:16Z | 2015-07-13T13:14:25Z | 2015-07-13T13:14:25Z | MEMBER | null | Closes #10481
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