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} | 1 | 2015-10-21T23:56:12Z | 2015-10-23T16:43:22Z | 2015-10-23T16:43:22Z | NONE | null | Since pandas-0.17 a merge on a datetime column fails if the datetime is tz-aware, see example below. Possibly related to #9663?
```
import pandas as pd
from datetime import datetime
from dateutil.tz import gettz
import sys, os
import traceback as tbm
# works
a = pd.DataFrame({'created' : [datetime(2015,10,10),
datetime(2015,10,20)],
'count' : [1,2]})
b = pd.DataFrame({'created' : [datetime(2015,10,10),
datetime(2015,10,20)],
'count' : [1,2]})
pd.merge(a, b, how='outer')
# doesn't work (used to work on pandas-0.16.2)
try:
utc = gettz('UTC')
a = pd.DataFrame({'created' : [datetime(2015,10,10, tzinfo=utc),
datetime(2015,10,20, tzinfo=utc)],
'count' : [1,2]})
b = pd.DataFrame({'created' : [datetime(2015,10,10, tzinfo=utc),
datetime(2015,10,20, tzinfo=utc)],
'count' : [1,2]})
pd.merge(a, b, how='outer')
except Exception as e:
print "Yeah, doesn't work: %s" % e
_, _, tb = sys.exc_info()
stack = lambda n : tbm.extract_tb(tb, 99)[n][0:]
print "called from", stack(0)
print "failing statement", stack(-1)
```
=>
```
Yeah, doesn't work: type object argument after * must be a sequence, not itertools.imap
called from ('<ipython-input-194-3c3669b26a55>', 23, '<module>', u"pd.merge(a, b, how='outer')")
failing statement ('/.../local/lib/python2.7/site-packages/pandas/tools/merge.py', 516, '_get_join_indexers', 'llab, rlab, shape = map(list, zip( * map(fkeys, left_keys, right_keys)))')
```
the culprit seems to be in the call to [`_factorize_keys`](https://github.com/pydata/pandas/blob/master/pandas/tools/merge.py#L513) though I couldn't quite figure out what goes wrong.
Version info
```
$ python --version
Python 2.7.6
$ pip freeze | grep pandas
pandas==0.17.0
```
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} | 4 | 2015-10-22T02:42:39Z | 2015-12-10T12:26:21Z | 2015-12-10T12:26:21Z | CONTRIBUTOR | null | xref #8435 (different issue though)
normally the semantic is to roll forward to the next period for `n=0`
``` python
In [15]: pd.Timestamp('2014-3-2') + pd.offsets.MonthBegin(n=0)
Out[15]: Timestamp('2014-04-01 00:00:00')
In [16]: pd.Timestamp('2014-3-2') + pd.offsets.QuarterBegin(n=0)
Out[16]: Timestamp('2014-03-01 00:00:00')
In [17]: #expected: Timestamp('2014-06-01 00:00:00')
```
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} | 7 | 2015-10-22T03:05:25Z | 2021-04-20T05:50:47Z | 2021-04-20T05:50:46Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/33272411/is-there-an-online-source-of-complete-documentation-on-pandas-objects-classes-b)
People just jump to the API w/o actually reading ANY Introduction where this is convered in the first sentence
so API docs should be more complete
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} | 8 | 2015-10-22T06:54:32Z | 2015-10-25T14:03:18Z | 2015-10-25T14:03:18Z | NONE | null | Hello all,
I want collect few tables into the one with pandas. I use the code presented below:
``` python
import pandas as pd
import itertools
import types
df = None
for frame in ['x', 'y']:
df_ = pd.read_excel(u'%s.xlsx' % frame)
df_ = df_.set_index([u'time'])
parameters = list(df_.columns)
tuples = []
for tup in itertools.product([frame,], parameters):
tuples.append(tup)
columns = pd.MultiIndex.from_tuples(tuples, names=[u'Frames',u'Parameters'])
df_new = pd.DataFrame(columns=columns, index=df_.index)
for par in parameters:
df_new[frame, par] = df_[par]
del df_
if df is None:
df = df_new
else:
df = pd.concat([df, df_new], axis=1)
df.to_excel('merged_xlsx.xlsx')
df.to_excel('merged_xls.xls')
```
Source data is `x.xlsx`
and `y.xlsx` files.
XLS engine works well (`merged_xls.xls`):
But something is wrong (cells merging) with XLSX engine (`merged_xlsx.xlsx`):
Manual cells unmerging works in Excel:
Is it bug in XLSX pandas engine (openpyxl)? Or what is wrong in my code?
Versions: `python-2.7.10`, `pandas-0.17.0`, `openpyxl-2.3.0`.
P.S. This issue is copy of [my question on stackoverflow](http://stackoverflow.com/questions/33274716/pandas-exporting-to-excel-xls-xlsx-with-multilevel-columns). It was suggested as bug and was adviced to post here.
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| null | 4 | 2015-10-22T12:19:33Z | 2015-10-22T15:10:45Z | 2015-10-22T12:35:04Z | CONTRIBUTOR | null | Hello,
I noticed a strange behavior involving important errors in my scripts.
``` python
a = pd.DataFrame(np.nan, index=[1,2], columns=[3,4]).astype(np.float64)
b = pd.DataFrame(np.nan, index=[1,2], columns=[3,4]).astype(np.float32)
c = pd.DataFrame(np.nan, index=[1,2], columns=[3,4])
d = pd.DataFrame(index=[1,2], columns=[3,4])
print(a.sum())
3 0
4 0
dtype: float64
print(b.sum())
3 NaN
4 NaN
dtype: float64
print(c.sum())
3 0
4 0
dtype: float64
print(d.sum())
3 NaN
4 NaN
dtype: float64
```
According to your doc, the right result should be the second and fourth output.
I guess it is due to Numpy changes 0.18. However I want to point out that numpy's choice is not good. A sum of NaN must not be zero.
First argument is about logic : Nothing + Nothing is not something but Nothing + object could be something. Two things which are not number can not be a number by arithmetic operation
Second argument : it's easy to fill the result with zeros if you don't want NaN values.
``` python
a = pd.DataFrame(np.nan, index=[1,2], columns=[3,4]).sum()
a.fillna(0, inplace=0)
```
Whereas you are not able to fill the result with NaN instead of zeros into NaN rows (or columns whatever..) if they have been filled with zeros before (easily). I hope you see
what I mean...
Thanks for your work ;)
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} | 4 | 2015-10-22T12:19:57Z | 2017-03-24T17:23:46Z | 2015-10-23T16:43:22Z | CONTRIBUTOR | null | closes #11405
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} | 6 | 2015-10-22T13:00:14Z | 2015-11-17T14:57:15Z | 2015-11-17T14:57:15Z | CONTRIBUTOR | null | Hello,
I noticed a bug about exporting DataFrame with columns containing localized datetime. I might be due to some another required libraries updated...
Example :
``` python
a = pd.to_datetime(['20120201', '20130201'])
b = pd.to_datetime(['20120201', '20130201'])
dfa = pd.DataFrame([a])
dfb = pd.DataFrame([b])
dfb[0] = dfb[0].dt.tz_localize('UTC')
dfa.to_hdf('tata.hdf', 'df') # success
dfb.to_hdf('toto.hdf', 'df') # fail
```
It works fine with Series
Thanks guys
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} | 3 | 2015-10-22T14:03:45Z | 2016-12-05T23:43:12Z | 2015-10-22T14:22:17Z | MEMBER | null | If I do
```
ddf = pd.DataFrame([['a', 'b', 1],
['a', 'b', 2]],
columns=['A', 'B', 'C']).set_index(['A', 'B'])
```
and then
```
ddf['C'].to_hdf('/tmp/store.hdf', 'test',
format="table",
min_itemsize={'A' : 3})
```
I get the following:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-46-66f05c11146d> in <module>()
1 ddf['C'].to_hdf('/tmp/store.hdf', 'test',
2 format="table",
----> 3 min_itemsize={'A' : 3})
/usr/lib/python2.7/dist-packages/pandas/core/generic.pyc in to_hdf(self, path_or_buf, key, **kwargs)
936
937 from pandas.io import pytables
--> 938 return pytables.to_hdf(path_or_buf, key, self, **kwargs)
939
940 def to_msgpack(self, path_or_buf=None, **kwargs):
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in to_hdf(path_or_buf, key, value, mode, complevel, complib, append, **kwargs)
268 with HDFStore(path_or_buf, mode=mode, complevel=complevel,
269 complib=complib) as store:
--> 270 f(store)
271 else:
272 f(path_or_buf)
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in <lambda>(store)
263 f = lambda store: store.append(key, value, **kwargs)
264 else:
--> 265 f = lambda store: store.put(key, value, **kwargs)
266
267 if isinstance(path_or_buf, string_types):
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in put(self, key, value, format, append, **kwargs)
825 format = get_option("io.hdf.default_format") or 'fixed'
826 kwargs = self._validate_format(format, kwargs)
--> 827 self._write_to_group(key, value, append=append, **kwargs)
828
829 def remove(self, key, where=None, start=None, stop=None):
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1263
1264 # write the object
-> 1265 s.write(obj=value, append=append, complib=complib, **kwargs)
1266
1267 if s.is_table and index:
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in write(self, obj, **kwargs)
4104 cols.append(name)
4105 obj.columns = cols
-> 4106 return super(AppendableMultiSeriesTable, self).write(obj=obj, **kwargs)
4107
4108
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in write(self, obj, data_columns, **kwargs)
4071 obj.columns = [name]
4072 return super(AppendableSeriesTable, self).write(
-> 4073 obj=obj, data_columns=obj.columns, **kwargs)
4074
4075 def read(self, columns=None, **kwargs):
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3769 self.create_axes(axes=axes, obj=obj, validate=append,
3770 min_itemsize=min_itemsize,
-> 3771 **kwargs)
3772
3773 for a in self.axes:
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3371 axis, axis_labels = self.non_index_axes[0]
3372 data_columns = self.validate_data_columns(
-> 3373 data_columns, min_itemsize)
3374 if len(data_columns):
3375 mgr = block_obj.reindex_axis(
/usr/lib/python2.7/dist-packages/pandas/io/pytables.pyc in validate_data_columns(self, data_columns, min_itemsize)
3247
3248 existing_data_columns = set(data_columns)
-> 3249 data_columns.extend([
3250 k for k in min_itemsize.keys()
3251 if k != 'values' and k not in existing_data_columns
AttributeError: 'Index' object has no attribute 'extend'
```
All goes smoothly instead if I don't specify "format=table", or if I don't specify the min_itemsize, or if I save as DataFrame (ddf[['C']]) rather than a as Series.
Tested with up to date pandas from git and pytables 3.2.2-1.
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} | 5 | 2015-10-22T15:35:51Z | 2017-08-12T20:10:57Z | 2016-03-22T14:32:31Z | NONE | null | Is there a reason why the compression options for read_csv and other IO functions don't include zip?
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| null | 6 | 2015-10-22T18:01:47Z | 2020-06-28T04:39:03Z | null | CONTRIBUTOR | null | I'm recoding multiple columns in a dataframe and have come across a strange result that I can't quite figure out. I'm probably not recoding in the most efficient manner possible, but it's mostly the error that I'm hoping someone can explain.
```
s1 = pd.DataFrame([np.nan, '1', '2', '3', '4', '5', columns='col1')
s2 = pd.DataFrame([np.nan, 1, 2, 3, 4, 5], columns='col1')
s1_dic = {np.nan: np.nan, '1': 1, '2':2, '3':3, '4':3, '5':3}
s2_dic = {np.nan: np.nan, 1: 1, 2:2, 3:3, 4:3, 5:3}
s1['col1'].apply(lambda x: s1_dic[x])
s2['col1'].apply(lambda x: s2_dic[x])
```
s1 works fine, but when I try to do the same thing with a list of integers and a np.nan, I get KeyError: nan which is confusing. Any help would be appreciated.
edit @hayd: http://stackoverflow.com/q/33286748/1240268
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| null | 3 | 2015-10-22T21:47:47Z | 2021-04-20T05:54:59Z | 2021-04-20T05:54:59Z | NONE | null | From http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_sql.html
> **chunksize** _: int, default None_
> If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once.
"this size"? Sounds like it could be the max size in bytes. How about "this number of rows"?
I think that's right given read_sql() says:
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql.html
> **chunksize** _: int, default None_
> If specified, return an iterator where chunksize is the number of rows to include in each chunk.
If that's not right please let me know. Thanks!
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} | 5 | 2015-10-23T00:44:21Z | 2015-10-24T01:11:48Z | 2015-10-23T20:30:33Z | CONTRIBUTOR | null | Resolves https://github.com/pydata/pandas/issues/11181
Is this testing OK? Or do we need to test with differing lengths of tuples etc?
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} | 1 | 2015-10-23T03:39:56Z | 2015-10-23T06:35:59Z | 2015-10-23T06:35:36Z | NONE | null | I have this data frame.
```
728 562 599 642 592 686 602 744 640 678
```
309 96 29 99 63 73 48 81 48 13 25
337 40 25 41 47 14 33 54 63 33 45
395 64 95 63 90 52 82 66 26 26 20
264 38 56 73 17 98 56 80 77 44 49
I want to sort it horizontally based on row index 264
I tried:
df.sort_values(by=264, axis=1)
I gives me: ValueError: When sorting by column, axis must be 0 (rows)
What is the usecase with axis=1
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| null | 3 | 2015-10-23T11:19:23Z | 2015-10-23T13:59:25Z | 2015-10-23T13:02:53Z | CONTRIBUTOR | null | I fired off a Travis-CI set of jobs, and all the jobs fail at this point:
> try:
> $ conda install -n root _license
> Fetching package metadata: .......Error: Could not find URL: http://conda.binstar.org/pandas/linux-64/
I have no clue what to do to fix this.
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| null | 5 | 2015-10-23T14:02:35Z | 2015-10-26T11:47:40Z | 2015-10-26T11:13:47Z | NONE | null | Today I decided to upgrade pandas from 0.16.2 to 0.17.0, and I started to get a segmentation fault when reading a big csv file with the `read_csv` method. If I simply type the following code below in the python terminal, loading an 160Mb csv file, my script crashes displaying a segmentation fault message. Although, if I use smaller files it works properly.
``` python
>>> import pandas as pd
>>> df = pd.read_csv("big_csv.csv")
```
When downgrading back to 0.16.2, it starts to work again. If I use `engine='python'` argument, it also works. So, that's probably revealing some problem in the C engine.
I'm running everything on Ubuntu 14.04 x86_64
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| null | 7 | 2015-10-23T17:57:44Z | 2015-10-23T18:16:52Z | 2015-10-23T18:13:40Z | NONE | null | I'm executing the following code:
```
import pandas as pd
df = pd.read_csv('2015-09-25.csv', header=None,\
names=['ID', 'ORIG', 'LAT', 'LONG', 'CITY', 'STATE', 'TIMESTAMP'], \
parse_dates=['TIMESTAMP'], \
usecols=['ORIG','LAT', 'LONG', 'CITY', 'STATE'])
df1 = df[df['ORIG']==1]
df1.drop(df1.columns[0], inplace=True)
```
However, the last line will give me this error in 0.17, but works fine with 0.16.2. The 0.17 version of `pandas` is being used with `python 3.5`, while `pandas 0.16.2` is being used with `python 3.4`.
```
Traceback (most recent call last):
File "C:/Users/Leb/Desktop/Python/test2.py", line 29, in <module>
df.drop(df.columns[0], inplace=True)
File "C:\Program Files\Python 3.5\lib\site-packages\pandas\core\generic.py", line 1616, in drop
new_axis = axis.drop(labels, errors=errors)
File "C:\Program Files\Python 3.5\lib\site-packages\pandas\core\index.py", line 2736, in drop
raise ValueError('labels %s not contained in axis' % labels[mask])
ValueError: labels ['ORIG'] not contained in axis
```
I've also tried `df1.drop('ORIG', inplace=True)` but the same problem arises. This presist ever with `df`.
Output of `df.columns` or `df1.columns` will show:
```
Index(['ORIG', 'LAT', 'LONG', 'CITY', 'STATE'], dtype='object')
Index(['ORIG', 'LAT', 'LONG', 'CITY', 'STATE'], dtype='object')
```
Adding `axis=1` will result in an additional error of:
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
```
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
df1.drop(df1.columns[0], axis=1, inplace=True)
```
These errors only show up with `python3.5` and `pandas 0.17`.
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| null | 3 | 2015-10-23T19:25:04Z | 2015-10-23T20:00:43Z | 2015-10-23T20:00:34Z | NONE | null | I have a use case where I want to select all the rows in a column that are not numbers:
`lc.loc[lc.id.str.isalpha()]`
Some rows in the `id` column are nan so I get a value error:
`ValueError: cannot index with vector containing NA / NaN values`
The work around is to use:
`lc[lc.id.str.contains('[A-z]', na=False)]`
I can provide a reproducible example if desired.
It would be nice if there were an na parameter in the string tests as well. E.g., `lc.loc[lc.id.str.islapha(na=False)]`.
On a related note, the the pattern `fillna(False)` to avoid this error doesn't seem to be documented well. My understanding is that this is just an arbitrary value and can easily have been True or 'apple'. Is this correct?
I'd be happy to add this to the documentation if my understanding is accurate.
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} | 3 | 2015-10-23T20:26:25Z | 2015-10-27T11:11:42Z | 2015-10-27T11:11:42Z | CONTRIBUTOR | null | closes #11402
Adds inplace sort_index() for Series.
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| null | 3 | 2015-10-24T00:44:08Z | 2015-10-24T02:12:11Z | 2015-10-24T02:10:45Z | NONE | null | calling pandas.io.pytables.HDFStore() function (or its alias pd.HDFStore) will generate the following warnings in pandas 0.17, which is incorrect since HDFStore is not part of pandas.io.data or pandas-datareader package.
The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.
After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import `from pandas.io import data, wb` to `from pandas_datareader import data, wb`.
FutureWarning)
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} | 1 | 2015-10-24T02:39:57Z | 2021-06-12T00:55:28Z | null | CONTRIBUTOR | null | I might have missed it, but I couldn't find this behavior documented anywhere. It is occasionally useful, if a bit magical.
``` python
In [1]: df1 = pd.DataFrame([1,2,3], columns=[['a'],['b']])
In [2]: df2 = pd.DataFrame([1,2,3], columns=[['a'],['']])
In [5]: df1['a'], type(df1['a'])
Out[5]:
( b
0 1
1 2
2 3, pandas.core.frame.DataFrame)
In [6]: df2['a'], type(df2['a'])
Out[6]:
(0 1
1 2
2 3
Name: a, dtype: int64, pandas.core.series.Series)
```
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} | 0 | 2015-10-24T10:03:17Z | 2015-10-25T14:06:38Z | 2015-10-25T14:06:38Z | MEMBER | null | I was wondering for a second in which direction the Index.difference worked, so thought some examples could clarify this
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} | 0 | 2015-10-24T10:51:23Z | 2015-11-01T21:09:58Z | 2015-11-01T21:09:58Z | MEMBER | null | Closes #6002
The signature of `apply_integrate_f_wrap` (http://pandas-docs.github.io/pandas-docs-travis/enhancingperf.html#more-advanced-techniques) used `Py_ssize_t` but on Windows that gives an error, as the dataframe is generated with numpy random arrays, which gives int32 on Windows, while `Py_ssize_t` is int64 on 64 bit Windows.
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} | 5 | 2015-10-24T21:09:45Z | 2016-01-15T01:03:40Z | 2015-12-13T20:42:04Z | CONTRIBUTOR | null | closes #11370
Also added a bit of documentation on the semantics for anchored offsets
currently blocked by #11406 - either need to change the vectorized or non-vectorized implementation of `QuarterBegin` As discussed in that issue, my opinion is the vectorized version is the "right" (or at least consistent) one.
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} | 5 | 2015-10-25T07:49:53Z | 2019-07-12T16:28:39Z | 2019-07-12T16:28:39Z | CONTRIBUTOR | null | Hi everyone, I recently upgraded from 0.16.2 to 0.17.0, and I am wondering if this change was intentional:
0.16.2
(numpy version 1.10)
```
In [15]: s
Out[15]:
0
2001-01-01 2
2001-01-02 5
2001-01-03 8
2001-01-04 11
In [16]: ddd
Out[16]: array(['2001-01-04', '2001-01-02', '2001-01-04', '2001-01-14'], dtype='datetime64[D]')
In [17]: s.loc[ddd]
Out[17]:
0
2001-01-04 11
2001-01-02 5
2001-01-04 11
2001-01-14 NaN
```
pd version 0.17.0
np version 1.10.1
```
There's a long message, but here is what happens at the end:
In [12]: ddd = np.array(['2001-01-04','2001-01-02','2001-01-04','2001-01-14'], dtype='datetime64')
In [13]: s = pd.DataFrame([2, 5, 8, 11], pd.date_range('2001-01-01', freq='D', periods=4))
In [14]: s.loc[ddd]
ValueError: Inferred frequency None from passed dates does not conform to passed frequency D
```
I realize that 2001-01-14 is not in the index of series s, and I was hoping that .loc would just fill it in as nan. I can understand if it threw a KeyError, but I am now getting a ValueError. Was this change intentional? I checked the docs, but I didn't see it there.
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} | 2 | 2015-10-25T15:58:35Z | 2015-10-27T02:08:54Z | 2015-10-25T19:59:49Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/11404#issuecomment-150928024
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| null | 19 | 2015-10-25T17:54:12Z | 2021-04-20T05:56:33Z | null | CONTRIBUTOR | null | I assume that this was officially supported before. Haven't narrowed it down any more than sometime between 0.16.2 and 0.17.0.
``` python
In [1]: pd.__version__
Out[1]: '0.16.2'
In [2]: pd.date_range("Jan 1", "March 31", name="date")
Out[2]:
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',
'2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',
'2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12',
...
```
``` python
In [1]: pd.__version__
Out[1]: '0.17.0'
In [2]: pd.date_range("Jan 1", "March 31", name="date")
---------------------------------------------------------------------------
OutOfBoundsDatetime Traceback (most recent call last)
<ipython-input-2-8eaca08051ac> in <module>()
----> 1 pd.date_range("Jan 1", "March 31", name="date")
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/tseries/index.py in date_range(start, end, periods, freq, tz, normalize, name, closed)
1912 return DatetimeIndex(start=start, end=end, periods=periods,
1913 freq=freq, tz=tz, normalize=normalize, name=name,
-> 1914 closed=closed)
1915
1916
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/util/decorators.py in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/tseries/index.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
234 return cls._generate(start, end, periods, name, freq,
235 tz=tz, normalize=normalize, closed=closed,
--> 236 ambiguous=ambiguous)
237
238 if not isinstance(data, (np.ndarray, Index, ABCSeries)):
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/tseries/index.py in _generate(cls, start, end, periods, name, offset, tz, normalize, ambiguous, closed)
383
384 if start is not None:
--> 385 start = Timestamp(start)
386
387 if end is not None:
pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:8967)()
pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:22303)()
pandas/tslib.pyx in pandas.tslib.convert_str_to_tsobject (pandas/tslib.c:24364)()
pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:23344)()
pandas/tslib.pyx in pandas.tslib._check_dts_bounds (pandas/tslib.c:26590)()
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1-01-01 00:00:00
```
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} | 1 | 2015-10-26T10:00:40Z | 2015-11-16T12:15:32Z | 2015-11-16T12:15:32Z | MEMBER | null | From SO: http://stackoverflow.com/questions/33337798/unicodeencodeerror-when-using-pandas-method-to-sql-on-a-dataframe-with-unicode-c
To reproduce:
```
from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
df = pd.DataFrame([[1,2],[3,4]], columns = [u'\xe9',u'b'])
df.to_sql('data', engine, if_exists='replace', index=False)
```
which gives `UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 0: ordinal not in range(128)`, because of this line: https://github.com/pydata/pandas/blob/master/pandas/io/sql.py#L856, where it stringifies the individual columns names with `str` (which fails on python 2 with unicode).
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} | 3 | 2015-10-26T10:09:32Z | 2015-11-16T12:15:32Z | 2015-11-16T12:15:32Z | MEMBER | null | Closes #11431
Still have to add tests
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} | 1 | 2015-10-26T13:42:56Z | 2015-11-15T16:55:16Z | 2015-11-15T16:55:16Z | CONTRIBUTOR | null | When instantiating a series with a datetimeindex but no data value (=None), a very slow codepath is used (involving Timestamp creation), compared to if a NaN value is supplied. The results are identical.
```
dr = pd.date_range(
start=datetime(2015,10,26),
end=datetime(2016,1,1),
freq='10s'
) # ~500k long
pd.Series(index=dr) # slow, 2.78 s
pd.Series(np.nan, index=dr) # fast, 0.01 s
```
pandas version 0.17.0
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} | 11 | 2015-10-26T15:45:14Z | 2021-04-20T05:58:55Z | null | NONE | null | In Pandas 0.162 I used:
```
df[df['var']==True].groupby('var2').plot(ax=ax[1],x='dfcolumnx',y='dfcolumy',legend=False,use_index=False ,kind='line',style='o-')
```
this plots df['dfcolumx'] as the x-axis and df['dfcolumy'] as the y-axis on ax[1].
This is not true for pandas 0.17:
This gives the wrong plots: instead of plotting in ax[1] it creates N new plots with un-understandable x and y axis and line plots.
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} | 5 | 2015-10-26T16:24:06Z | 2016-02-12T15:01:29Z | 2016-02-12T15:01:29Z | CONTRIBUTOR | null | I find this surprising as the rest of the pandas `Series.str.*` API ignores NaN values.
``` python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.__version__
Out[3]: u'0.17.0'
In [4]: s = pd.Series(['asdf','sdfg',np.nan,'qwer','wert'])
In [5]: s.str.cat(sep=' ')
Out[5]: nan
```
I think this should return
``` python
In [5]: s.str.cat(sep=' ')
Out[5]:'asdf sdfg qwer wert'
```
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| null | 3 | 2015-10-27T01:13:04Z | 2020-06-26T13:29:17Z | 2015-10-27T01:16:05Z | NONE | null | Hello,
I'm running into what I think is a bug in DataFrame.duplicated where it detects duplicates, but the data frame does not actually have any duplicated rows. It seems to only happen with integer columns, and somewhat large datasets (>600,000 rows).
I created a test data set to show the issue:
```
df = pd.read_table(
"https://www.dropbox.com/s/vkw8bzxp290jitz/test.tab?raw=1",
dtype = {"chrom" : "int64","pos" : "int64"}
)
```
If you ask for duplicates, it will detect them:
```
df.duplicated().any() # returns True
```
However, there are no duplicates:
```
In [5]: from collections import Counter
In [6]: counter = Counter(zip(df.chrom,df.pos))
In [7]: counter.most_common(5)
Out[7]:
[((0, 13704091), 1),
((0, 201539008), 1),
((0, 8573433), 1),
((0, 127434927), 1),
((0, 247829766), 1)]
```
If I convert one of the columns to float, and then ask for duplicates, it is correct:
```
df.loc[:,"pos"] = df.pos.astype("float64")
df.duplicated().any() # returns False
```
Strangely, converting the first column `chrom` to float or string does not seem to matter.
I had a difficult time in constructing this data frame to illustrate the example. It seems to only occur:
- With at least two columns
- One column (integer, string, float) that has a small number of unique values
- One column (must be integer) that has a wide range of values, but in many instances, the values are close to each other
- The dataset must be somewhat large, but it's hard to pin down. Somewhere on the order of > 500,000 rows, it seems.
From looking quickly at the `DataFrame.duplicated` code, it looks like it is using a hash table of some kind, and using integer columns differently than other columns - perhaps it's ending up with collisions?
Apologies if I'm missing something obvious here. Please let me know if I can be of any help in investigating further. My pandas version information is below.
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.22.1
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.7
pymysql: None
psycopg2: None
```
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} | 4 | 2015-10-27T09:12:16Z | 2018-05-15T03:11:17Z | 2015-11-01T16:21:42Z | NONE | null | i want use pandas2ri.py2ri(df) ro replace r_dataframe = com.convert_to_r_dataframe(df)
for my data,i find the old function can work well but the pyr2 report error
message like:
```
NotImplementedError
Traceback (most recent call last) <ipython-input-40-5278a5dfa2ad> in <module>()
18
19 # r_dataframe = com.convert_to_r_dataframe(data)
---> 20 r_datafrmae=pandas2ri.py2ri(data)
21
22 # type(r_dataframe)
/usr/local/lib/python2.7/dist-packages/singledispatch.pyc in wrapper(*args, **kw)
208
209 def wrapper(*args, **kw):
--> 210 return dispatch(args[0].__class__)(*args, **kw)
211
212 registry[object] = func
/usr/local/lib/python2.7/dist-packages/rpy2-2.7.2-py2.7-linux-x86_64.egg/rpy2/robjects/pandas2ri.pyc in py2ri_pandasdataframe(obj)
42 od[name] = StrVector(values)
43 else:
---> 44 od[name] = conversion.py2ri(values)
45 return DataFrame(od)
46
/usr/local/lib/python2.7/dist-packages/singledispatch.pyc in wrapper(*args, **kw)
208
209 def wrapper(*args, **kw):
--> 210 return dispatch(args[0].__class__)(*args, **kw)
211
212 registry[object] = func
/usr/local/lib/python2.7/dist-packages/rpy2-2.7.2-py2.7-linux-x86_64.egg/rpy2/robjects/conversion.pyc in _py2ri(obj)
58 (ri) objects.
59 """
---> 60 raise NotImplementedError("Conversion 'py2ri' not defined for objects of type '%s'" % str(type(obj)))
61
62 def _py2ro(obj):
NotImplementedError: Conversion 'py2ri' not defined for objects of type '<class 'pandas.core.series.Series'>'
```
the data type is pandas.core.frame.DataFrame,so i can not understand the error .
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https://api.github.com/repos/pandas-dev/pandas/issues/11438 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11438/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11438/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11438/events | https://github.com/pandas-dev/pandas/issues/11438 | 113,565,004 | MDU6SXNzdWUxMTM1NjUwMDQ= | 11,438 | Tilde expansion for paths (~/...) inconsistent between read_csv and to_csv | {
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| null | 6 | 2015-10-27T11:46:48Z | 2019-02-02T10:17:51Z | 2019-02-02T10:17:41Z | CONTRIBUTOR | null | When specifying a path in DataFrame.read_csv(), pandas has no problem with the tilde expansion:
``` python
In[1]:
import pandas as pd
test = pd.read_csv('~/Documents/test.csv', index_col=0)
test.head()
Out[1]:
Foo Bar
0 1 2
1 3 4
2 5 6
3 7 8
4 9 10
```
But DataFrame.to_csv() doesn't understand this:
``` python
In[2]:
test.to_csv('~/Documents/test1.csv')
Out[2]:
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-ef1160d6e930> in <module>()
----> 1 test.to_csv("~/Documents/test1.csv")
//anaconda/lib/python3.4/site-packages/pandas/core/frame.py in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal, **kwds)
1287 escapechar=escapechar,
1288 decimal=decimal)
-> 1289 formatter.save()
1290
1291 if path_or_buf is None:
//anaconda/lib/python3.4/site-packages/pandas/core/format.py in save(self)
1467 else:
1468 f = com._get_handle(self.path_or_buf, self.mode,
-> 1469 encoding=self.encoding)
1470 close = True
1471
//anaconda/lib/python3.4/site-packages/pandas/core/common.py in _get_handle(path, mode, encoding, compression)
2865 f = open(path, mode, encoding=encoding)
2866 else:
-> 2867 f = open(path, mode, errors='replace')
2868 else:
2869 f = open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: '~/Documents/test1.csv'
```
Instead, it needs the file name alone, or the full path: `test.to_csv('/Users/alexchase/Documents/test1.csv')`.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.22.1
numpy: 1.10.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
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} | 0 | 2015-10-27T12:38:40Z | 2015-10-27T13:18:42Z | 2015-10-27T13:18:42Z | CONTRIBUTOR | null | closes #11187
closes #11138
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| null | 15 | 2015-10-27T14:15:25Z | 2015-10-27T22:05:30Z | 2015-10-27T22:05:24Z | NONE | null | Hi,
I am trying to plot a set of values that are all uint64 with panda
The csv file is available here https://transfer.sh/Sy5DS/iperf-client-linux-2rtrs-f30b30-f30b30-w140k-lia-run1.csv and this is the command I used:
```
In [6]: df = pd.read_csv('/home/teto/ns3testing/iperf-client-linux_2rtrs_f30b30_f30b30_w140K_lia-run1.csv', sep='|', dtype={'dsn': np.uint64})
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-6-794bcaffa179> in <module>()
----> 1 df = pd.read_csv('/home/teto/ns3testing/iperf-client-linux_2rtrs_f30b30_f30b30_w140K_lia-run1.csv', sep='|', dtype={'dsn': np.uint64})
/usr/lib/python3/dist-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
461 skip_blank_lines=skip_blank_lines)
462
--> 463 return _read(filepath_or_buffer, kwds)
464
465 parser_f.__name__ = name
/usr/lib/python3/dist-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
247 return parser
248
--> 249 return parser.read()
250
251 _parser_defaults = {
/usr/lib/python3/dist-packages/pandas/io/parsers.py in read(self, nrows)
704 raise ValueError('skip_footer not supported for iteration')
705
--> 706 ret = self._engine.read(nrows)
707
708 if self.options.get('as_recarray'):
/usr/lib/python3/dist-packages/pandas/io/parsers.py in read(self, nrows)
1148
1149 try:
-> 1150 data = self._reader.read(nrows)
1151 except StopIteration:
1152 if nrows is None:
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader.read (pandas/parser.c:7287)()
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7511)()
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._read_rows (pandas/parser.c:8336)()
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._convert_column_data (pandas/parser.c:9544)()
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._convert_tokens (pandas/parser.c:10106)()
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser.TextReader._convert_with_dtype (pandas/parser.c:10503)()
/usr/lib/python3/dist-packages/pandas/parser.cpython-34m-x86_64-linux-gnu.so in pandas.parser._try_int64 (pandas/parser.c:18126)()
```
My version was the one with ubuntu's 15.04 repo
pd.version.version
Out[10]: '0.15.0'
I am trying to upgrade it, hoping it would solve this, would it ?
I believe the problem is related to:
https://github.com/pydata/pandas/issues/4471
I recently discovered panda and so far it's the best tool I found to plot/work with data (tried R/gnuplot etc...) thanks a lot for the work.
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| null | 19 | 2015-10-27T14:23:49Z | 2017-04-03T15:25:51Z | 2017-04-03T15:25:51Z | NONE | null | will make the following work
``` python
import numpy as np
import pandas as pd
d = {'one' : ['A', 'A', 'B', 'B', 'C'],
'two' : [4., 3., 2., 1., np.nan],
'three' : [10., 8., np.nan, 5., 7.]}
df = pd.DataFrame(d)
df.hist('two', by='one', weights='three', bins=range(0, 10))
# or
df.hist('two', by=df.one.values, weights='three', bins=range(0, 10))
```
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} | 6 | 2015-10-27T16:28:02Z | 2016-12-23T11:16:25Z | 2016-12-23T11:16:24Z | NONE | null | ```
i = pd.DatetimeIndex(pd.date_range('2015/01/01', periods=5), name='date')
d = pd.DataFrame({'A':[5,6,7,8,9], 'B':[1,2,3,4,5]}, index=i)
print i
print d
dg = d.groupby(level='date')
print dg.get_group('2015-01-01')
print dg.groups
```
**Output:**
```
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',
'2015-01-05'],
dtype='datetime64[ns]', name=u'date', freq='D', tz=None)
A B
date
2015-01-01 5 1
2015-01-02 6 2
2015-01-03 7 3
2015-01-04 8 4
2015-01-05 9 5
A B
date
2015-01-01 5 1
Traceback (most recent call last):
File "<ipython-input-2-f2b4d1146750>", line 12, in <module>
print dg.groups
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 405, in groups
return self.grouper.groups
File "pandas/src/properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas/lib.c:41917)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 1352, in groups
return self.groupings[0].groups
File "pandas/src/properties.pyx", line 34, in pandas.lib.cache_readonly.__get__ (pandas/lib.c:41917)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 2041, in groups
return self.index.groupby(self.grouper)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/tseries/base.py", line 60, in groupby
return _algos.groupby_object(objs, f)
TypeError: Argument 'labels' has incorrect type (expected numpy.ndarray, got DatetimeIndex)
```
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} | 8 | 2015-10-27T19:37:12Z | 2015-11-14T19:32:56Z | 2015-11-14T19:32:53Z | CONTRIBUTOR | null | Fix for https://github.com/pydata/pandas/issues/11223
@TomAugspurger should these two Ipython blocks be merged into one?
(this is what it looks like at the mo)
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} | 1 | 2015-10-27T20:22:04Z | 2017-02-27T19:52:14Z | 2015-10-27T22:03:56Z | NONE | null | ```
import pandas as pd
import datetime
u = [datetime.datetime(2015, x, 1) for x in range(12)]
v = list('aaabbbbbbccd')
df = pd.DataFrame('X':v, 'Y':u)
df.groupby('X')['Y'].agg(len)
## Returns the following:
X
a 1970-01-01 00:00:00.000000003
b 1970-01-01 00:00:00.000000006
c 1970-01-01 00:00:00.000000002
d 1970-01-01 00:00:00.000000001
```
You can fix the problem by casting the dates to strings before groupby/agg, but if you try to cast the returned datetimes to ints, errors go off in some versions of pandas. Either way, aggregating by length should always return an int. Also, this may be similar to #11442, which was just posted.
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| null | 6 | 2015-10-27T21:31:46Z | 2016-01-20T14:12:47Z | 2016-01-20T14:12:24Z | CONTRIBUTOR | null | closes #3570
closes #8251
This is a re-submission of PR #8395 and addresses issue #8251. My apologies for the duplicate PR, but despite rebasing to pydata/pandas master, the original PR would not update with my new commits and I couldn't get rid of the `LooseVersion()` errors in the Travis build.
This PR may need some fleshing out still, but I wanted to submit this to see what the thoughts are on this approach. For this PR I've created a new interpolation mechanism at the block level to implement filling across 3 or more dimensions. This avoids the fiddly problems of trying to implement filling of 3+ dimensions at the frame level. However, it isn't possible with this technique to fill across blocks of different dtypes, although that seems like that should be a rare occurrence.
Incidentally, this will also address #3570, which is one of the issues referenced in #9862.
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} | 2 | 2015-10-27T21:42:07Z | 2021-09-01T04:51:25Z | null | NONE | null | In sample code below rolling_apply takes an argument 'ix' which is a numpy array of dtype = 'int64' and by the time this array gets to get_type() function, its dtype has changed to 'float64'. I can make an explicit call in get_type() function to change this back: ix = ix.astype('int64'), but was curious why it gets changed.
Example below. I'm on version '0.17.0':
```
import numpy as np
import pandas as pd
def get_type(ix, df, hours):
# invoked by rolling_apply to illustrate the problem
# of rolling_apply changing the dtype of 'ix' array from
# int64 to float64
print ix.dtype
# need to convert index dtype back to int64
#ix = ix.astype('int64')
ixv = ix[ix > -1]
print ixv.dtype
# the data in ix must be int64 else following fails with
# IndexError: arrays used as indices must be of integer (or boolean) type
h = hours[ixv] - hours[ixv[0]]
df.iloc[ix[-1]] = h[0]
return 0.0
# we start out with ix.dtype = int64 but rolling_apply changes this to float64
ix = np.arange(0, 10)
hours = np.random.randint(0, 10, len(ix))
df = pd.DataFrame(np.random.randn(10, 1), columns=['h'])
pd.rolling_apply(ix, window=3, func=get_type, args=(df, hours,))
```
I also stepped through the code and believe I've identified the source of the problem. I thought I'd report it and see if others see this as an issue before trying to fix. Doing an explicit type change inside the get_type function as in this example also works.
The [_process_data_structure()](https://github.com/pydata/pandas/blob/99bf170ff326b716810f7012e3effdcb611b6e7e/pandas/stats/moments.py#L385) function turns this into a float.
Here's the [logic](https://github.com/pydata/pandas/blob/99bf170ff326b716810f7012e3effdcb611b6e7e/pandas/stats/moments.py#L436) that is explicitly changing the dtype to a float the first time. This can be omitted and the check updated to include 'float':
```
if kill_inf and values.dtype == float:
values = values.copy()
values[np.isinf(values)] = np.NaN
```
However, the cython code that I assume does the rolling window, also expects a [float64](https://github.com/pydata/pandas/blob/78fcce40c4e19f8e3a1b32d103c56c52124c284a/pandas/algos.pyx#L1836). In this case, maybe an option is to update the dtype after the [call_cython](https://github.com/pydata/pandas/blob/99bf170ff326b716810f7012e3effdcb611b6e7e/pandas/stats/moments.py#L752) function.
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} | 2 | 2015-10-27T22:57:49Z | 2015-12-01T20:38:19Z | 2015-12-01T20:38:19Z | NONE | null | Summary: read_excel is unable to read a file using the same S3 URL syntax as read_csv. read_excel should support accessing S3 data in the same manner as read_csv
read_excel fails with the following error:
``` python
>>> import pandas as pd
>>> df = pd.read_excel("s3://my-bucket/my_file.xlsx")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib64/python2.6/site-packages/pandas/io/excel.py", line 163, in read_excel
io = ExcelFile(io, engine=engine)
File "/usr/local/lib64/python2.6/site-packages/pandas/io/excel.py", line 206, in __init__
self.book = xlrd.open_workbook(io)
File "/usr/local/lib/python2.6/site-packages/xlrd/__init__.py", line 394, in open_workbook
f = open(filename, "rb")
IOError: [Errno 2] No such file or directory: 's3://my-bucket/my_file.xlsx'
>>>
```
read_csv on the other hand is able to successfully read a csv file in the same S3 bucket using the same URL syntax:
``` python
>>> import pandas as pd
>>> df = pd.read_csv("s3://my-bucket/my_file.csv")
>>> len(df.index)
1187
>>>
```
For the record, read_csv can also see the xlsx file but returns parse errors when attempting to tokenize the data.
``` python
>>> import pandas as pd
>>> df = pd.read_csv("s3://my-bucket/my_file.xlsx")
Exception pandas.parser.CParserError: CParserError('Error tokenizing data. C error: Expected 9 fields in line 210, saw 10\n',) in 'pandas.parser.TextReader._tokenize_rows' ignored
>>>
```
read_excel successfully reads and parses a local copy of the xlsx file
``` python
>>> import pandas as pd
>>> df = pd.read_excel("my_file.xlsx")
>>> len(df.index)
221
>>>
```
Pandas version string and dependencies:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.6.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.14.48-33.39.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.4
pip: 6.1.1
setuptools: 12.2
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
>>>
```
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| null | 1 | 2015-10-27T23:50:31Z | 2015-10-27T23:52:02Z | 2015-10-27T23:51:52Z | NONE | null | This below code segfaults every time
```
import pandas as pd
pd.DataFrame({'cat': ['a','b','a']})
df = df['cat'].astype('category')
df.to_json()
```
## INSTALLED VERSIONS
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.9-boot2docker
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.2
Cython: 0.23.2
numpy: 1.9.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.2
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: 0.99999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext lo64)
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} | 6 | 2015-10-28T00:04:37Z | 2015-11-07T17:25:24Z | 2015-11-07T14:54:27Z | CONTRIBUTOR | null | Not sure what the bar is for a PR here - this is a mild but meaningful improvement - in particular re the option to supply numbers & a tuple - by no means perfect though
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} | 7 | 2015-10-28T00:13:33Z | 2015-11-02T11:45:02Z | 2015-11-02T11:44:57Z | CONTRIBUTOR | null | removes some unused code
reverts this commit https://github.com/pydata/pandas/commit/a40226e5c4c3d29e5e2383433c5f0b94c3c0ecc1
performance consideration
```
import pandas
import numpy
arr = numpy.random.rand(1000000)
%timeit pandas.rolling_median(arr, 1000)
```
master
```
1 loops, best of 3: 4.94 s per loop
```
branch
```
1 loops, best of 3: 821 ms per loop
```
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} | 9 | 2015-10-28T02:29:38Z | 2018-07-06T20:21:08Z | 2017-07-10T21:07:59Z | CONTRIBUTOR | null | This used to work in 0.16.2:
``` python
In [78]: panel=pd.Panel(pd.np.random.rand(5,4,3))
In [80]: panel[0]
Out[80]:
0 1 2
0 0.191947 0.324781 0.070749
1 0.396628 0.966418 0.120066
2 0.551517 0.643907 0.230434
3 0.245471 0.485606 0.315219
In [79]: panel.where(panel>0.5, None)[0]
Out[79]:
0 1 2
0 None None None
1 None 0.9664181 None
2 0.5515173 0.6439069 None
3 None None None
```
But now in 0.17.0:
``` python
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-13-5edbb4ece05d> in <module>()
----> 1 bs_raw.where(bs_raw, 3)
/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in where(self, cond, other, inplace, axis, level, try_cast, raise_on_error)
3802
3803 if isinstance(cond, NDFrame):
-> 3804 cond, _ = cond.align(self, join='right', broadcast_axis=1)
3805 else:
3806 if not hasattr(cond, 'shape'):
/usr/local/lib/python2.7/dist-packages/pandas/core/panel.pyc in align(self, other, **kwargs)
630
631 def align(self, other, **kwargs):
--> 632 raise NotImplementedError
633
634 def dropna(self, axis=0, how='any', inplace=False):
NotImplementedError:
```
I think this is as a result of https://github.com/pydata/pandas/pull/11095 but @terrytangyuan / @jreback may know the status?
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} | 8 | 2015-10-28T04:45:23Z | 2020-03-31T05:25:27Z | 2020-03-31T05:25:17Z | NONE | null | If you have a dataframe with a multiindex, and use groupby and then shift, using the frequency argument, the shifting fails, resulting in an empty result. For example:
``` python
import numpy as np
import pandas as pd
dates1 = pd.date_range('2015-10-01', '2015-10-03')
df1 = pd.DataFrame(dict(grp='A', blah=[1,2,3]), index=dates1) .set_index('grp', append=True)
dates2 = pd.date_range('2015-10-02', '2015-10-04')
df2 = pd.DataFrame(dict(grp='B', blah=[10,np.nan,30]), index=dates2) .set_index('grp', append=True)
df = pd.concat([df1, df2])
In [ ]: df
Out[ ]:
blah
grp
2015-10-01 A 1
2015-10-02 A 2
2015-10-03 A 3
2015-10-02 B 10
2015-10-03 B NaN
2015-10-04 B 30
# This works:
In [ ]: df.groupby(level='grp').shift(1)
Out[ ]:
blah
grp
2015-10-01 A NaN
2015-10-02 A 1
2015-10-03 A 2
2015-10-02 B NaN
2015-10-03 B 10
2015-10-04 B NaN
# This fails:
In [ ]: df.groupby(level='grp').shift(1, freq='D')
Out[ ]:
Empty DataFrame
Columns: []
Index: []
```
I'm not sure why, however using 'grp' as a column, it works fine:
``` python
In [ ]: df.reset_index('grp').groupby('grp').shift(1, freq='D')
Out[ ]:
blah
grp
A 2015-10-02 1
2015-10-03 2
2015-10-04 3
B 2015-10-03 10
2015-10-04 NaN
2015-10-05 30
```
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| null | 2 | 2015-10-28T11:03:33Z | 2015-10-28T12:21:40Z | 2015-10-28T11:10:47Z | NONE | null | I am currently writing a script to take data from an API and save it to an Excel file for non-technical users who need to access the data. One of the requirements is to have separate date and time column, rather than a single datetime column.
I am using pandas 0.17.0.
``` python
import pandas as pd
# creating datetime series
dates = pd.date_range('2015-01-01 00:00', '2015-01-01 03:00', freq='H')
dates = pd.Series(dates)
# setting one record to NaT
dates.iloc[0] = pd.NaT
# converting to date
dates.apply(lambda x: x.date()) # ignores the NaT
# converting to time
dates.apply(lambda x: x.time()) # fails with error "NaTType does not support time"
```
I expected NaTType to behave in the same way for converting into a time as it did for a date i.e. returning NaT.
The full error is:
``` python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-98-b34fccfc4c06> in <module>()
9 dates.apply(lambda x: x.date()) # ignores the NaT
10 # converting to time
---> 11 dates.apply(lambda x: x.time()) # fails with error "NaTType does not support time"
C:\Users\Nathan\Anaconda\lib\site-packages\pandas\core\series.pyc in apply(self, func, convert_dtype, args, **kwds)
2158 values = lib.map_infer(values, lib.Timestamp)
2159
-> 2160 mapped = lib.map_infer(values, f, convert=convert_dtype)
2161 if len(mapped) and isinstance(mapped[0], Series):
2162 from pandas.core.frame import DataFrame
pandas\src\inference.pyx in pandas.lib.map_infer (pandas\lib.c:62187)()
```
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| null | 4 | 2015-10-28T11:29:31Z | 2015-10-28T12:35:00Z | 2015-10-28T11:46:54Z | NONE | null | Python 3.4.3/64bit, Pandas 0.16.2 or 0.17
The operation is a addition. The results of the first two rows are correct most of the time, on occasion a value 1.5-315 appears. Every test performed with same input. Fail rate is about 3-5% of the time.
df['v1']+=10.0
I copied the dataframe results to numpy and perform the same addition with same values and it works correctly every time.
npyarr=df.reset_index().values
npyarr[:,1]+=10.0
After a few weeks of noticing these symptoms it appears to only affect the first two rows every time, the data frame has thousands of rows.
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} | 1 | 2015-10-28T12:48:18Z | 2016-04-10T05:41:47Z | 2016-04-10T05:41:47Z | MEMBER | null | Splitted from #11351. `Series.append` with `DatetimeBlock` and `DatetimeTZBlock` raises `AttributeError`.
```
import pandas as pd
s = pd.Series([pd.Timestamp('2011-01-01'), pd.NaT])
s
#0 2011-01-01
#1 NaT
# dtype: datetime64[ns]
stz = pd.Series(pd.DatetimeIndex(['2012-01-01', '2012-01-02'], tz='Asia/Tokyo'))
stz
#0 2012-01-01 00:00:00+09:00
#1 2012-01-02 00:00:00+09:00
# dtype: datetime64[ns, Asia/Tokyo]
s.append(stz)
# AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize'
```
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| null | 1 | 2015-10-28T12:58:42Z | 2018-06-29T05:25:44Z | 2018-06-29T05:25:44Z | MEMBER | null | Followings should be changed to coerce to `object` dtype.
## Coerce to tz-naive / UTC
- [x] Slice (`__getitem__`) resets tz #11351
- [x] `.loc` assignment coerces to naive #11365
- [x] `GroupBy.first` coerces to naive #10668
- [x] `DataFrame.combine_first` #10567
- [x] `Index` creation #11488 -> changed to be Index(dtype=object) (#11696)
## Raises TypeError / ValueError
- [ ] `DatetimeIndex.union` raises `TypeError` #11351
## Bug
- [x] `Series.append` raises `AttributeError` #11455
- [x] `concat` raises `IndexError` #11693
- [x] `replace` #11792
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| null | 2 | 2015-10-28T15:10:10Z | 2015-10-28T17:07:51Z | 2015-10-28T15:13:31Z | CONTRIBUTOR | null | This code generates an error in Pandas 0.17.0, but works fine in pandas 0.16.2
``` python
import datetime
import pandas as pd
import numpy as np
print pd.show_versions()
SHOW_BUG=True
if SHOW_BUG:
df = pd.DataFrame({
'a': 1 * np.ones(10),
'b': [datetime.datetime.now() for nn in range(10)],
})
else:
df = pd.DataFrame({
'a': 1 * np.ones(10),
'b': range(10),
})
def _compute_length(batch):
return pd.Series({'c': 2})
dfg = df.groupby(by=['a']).apply(_compute_length)
```
Here is the version information:
```
print pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.22.1
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: 0.9.1
apiclient: 1.4.1
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
None
```
And here is the stack trace
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-3-072dccab0584> in <module>()
21 return pd.Series({'c': 2})
22
---> 23 dfg = df.groupby(by=['a']).apply(_compute_length)
/Users/rob/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py in apply(self, func, *args, **kwargs)
711 # ignore SettingWithCopy here in case the user mutates
712 with option_context('mode.chained_assignment',None):
--> 713 return self._python_apply_general(f)
714
715 def _python_apply_general(self, f):
/Users/rob/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py in _python_apply_general(self, f)
718
719 return self._wrap_applied_output(keys, values,
--> 720 not_indexed_same=mutated)
721
722 def aggregate(self, func, *args, **kwargs):
/Users/rob/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py in _wrap_applied_output(self, keys, values, not_indexed_same)
3125 date_cols = self._selected_obj.select_dtypes(
3126 include=list(_DATELIKE_DTYPES)).columns
-> 3127 result[date_cols] = (result[date_cols]
3128 ._convert(datetime=True,
3129 coerce=True))
/Users/rob/anaconda/lib/python2.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
1906 if isinstance(key, (Series, np.ndarray, Index, list)):
1907 # either boolean or fancy integer index
-> 1908 return self._getitem_array(key)
1909 elif isinstance(key, DataFrame):
1910 return self._getitem_frame(key)
/Users/rob/anaconda/lib/python2.7/site-packages/pandas/core/frame.py in _getitem_array(self, key)
1950 return self.take(indexer, axis=0, convert=False)
1951 else:
-> 1952 indexer = self.ix._convert_to_indexer(key, axis=1)
1953 return self.take(indexer, axis=1, convert=True)
1954
/Users/rob/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py in _convert_to_indexer(self, obj, axis, is_setter)
1119 mask = check == -1
1120 if mask.any():
-> 1121 raise KeyError('%s not in index' % objarr[mask])
1122
1123 return _values_from_object(indexer)
KeyError: "['b'] not in index"
```
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} | 7 | 2015-10-28T15:55:59Z | 2015-11-23T17:26:10Z | 2015-11-19T20:26:35Z | CONTRIBUTOR | null | closes #11438
Added calls to `_expand_user()` in multiple Formatter classes so that writer functions, such as `to_csv()` and `to_html()`, can perform tilde expansion on file paths beginning `~/`. This makes the writer functions consistent with the reader functions, such as `read_csv()`. I also changed the `__init__()` for `CSVFormatter` to conform to the pep8 width limit.
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| null | 1 | 2015-10-28T16:55:25Z | 2015-10-28T16:56:54Z | 2015-10-28T16:56:45Z | NONE | null | Under 0.17, numpy 1.10
```
In [32]: import pandas
In [33]: df = pandas.DataFrame(data={"a" : [0, 1, 3, 4], "b":[20, 16, 8, 4]})
In [34]: df
Out[34]:
a b
0 0 20
1 1 16
2 3 8
3 4 4
In [35]: df.drop_duplicates(['a','b'], keep='last')
Out[35]:
a b
3 4 4
```
0.16.2 has the correct behavior.
```
In [6]: import pandas
In [7]: df = pandas.DataFrame(data={"a" : [0, 1, 3, 4], "b":[20, 16, 8, 4]})
In [8]: df
Out[8]:
a b
0 0 20
1 1 16
2 3 8
3 4 4
In [9]: df.drop_duplicates(['a','b'])
Out[9]:
a b
0 0 20
1 1 16
2 3 8
3 4 4
```
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} | 3 | 2015-10-28T17:04:13Z | 2015-11-13T21:48:21Z | 2015-11-13T21:48:21Z | CONTRIBUTOR | null | This is a fix for issue #11324 in which grouping when datetime fields are involved raises an exceptoin
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| null | 12 | 2015-10-28T18:07:17Z | 2019-08-24T18:14:07Z | 2015-11-25T18:40:32Z | NONE | null | http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rename.html claims to work with a dict-like object, but according to https://github.com/jab/bidict/issues/18#issuecomment-147643550 it does not. This patch changes `isinstance(..., dict)` to `isinstance(..., collections.abc.Mapping)` as a first pass attempt to bring the code more in line with the documentation.
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} | 3 | 2015-10-28T18:54:11Z | 2015-10-28T21:56:56Z | 2015-10-28T21:49:00Z | CONTRIBUTOR | null | This PR moves the routines used for filling missing data into their own module as suggested in PR #11445.
Thoughts on re-naming `interpolate_1d()` and `interpolate_2d()`? They way they are currently named makes them sound like they're the same functions for arrays of differing dimensions. However, `interpolate_1d()` is used for interpolating with scipy interp methods whereas `interpolate_2d()` is used for forward- or back-filling.
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| null | 1 | 2015-10-28T19:40:02Z | 2015-10-28T19:49:50Z | 2015-10-28T19:49:41Z | NONE | null | I experienced a crash in 0.17.0 when using a pandas that's built from PyPI:
```
import pandas as pd
import StringIO
f=StringIO.StringIO("id\n2015091624765185258700105041865\n")
pd.read_csv(f)
==> Segmentation Fault
```
I traced it to a null pointer dereference in parser.c as found in the tarball that's
on PyPI. It does not appear in a parser.c if I rebuild from parser.pyx
(IOW, it's not reproducible in a build of 0.17.0 from GitHub).
Is it possible that invalid cython generated files snuck into the 0.17.0 release?
This is the valgrind output which points at parser.c line 20849:
```
==26809== Invalid read of size 1
==26809== at 0x4C2F122: strlen (in /usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so)
==26809== by 0x49C261: PyString_FromString (in /home/brian/bin/ipython-venv2/bin/python2)
==26809== by 0xC641706: __pyx_f_6pandas_6parser__try_int64 (parser.c:20849)
==26809== by 0xC658442: __pyx_f_6pandas_6parser_10TextReader__convert_with_dtype (parser.c:12372)
==26809== by 0xC64893A: __pyx_f_6pandas_6parser_10TextReader__convert_tokens (parser.c:11744)
```
If you look at that line, the __pyx_v_word variable will be NULL.
```
$ python -c 'from pandas.util.print_versions import show_versions; show_versions()'
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-32-lowlatency
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: None
pip: 1.5.6
setuptools: 12.2
Cython: 0.23.4
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 2 | 2015-10-28T22:25:24Z | 2015-11-13T17:17:58Z | 2015-11-13T17:17:58Z | CONTRIBUTOR | null | I was very confused about the purpose of the iterator and chunksize parameters when looking at the [help for read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html). It wasn't until I Google'd upon [the docs for IO Tools](http://pandas.pydata.org/pandas-docs/stable/io.html) that I began to understand.
I propose that links to the IO Tools docs be included in the docstrings for the iterator and chunksize parameters.
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} | 7 | 2015-10-29T03:45:05Z | 2021-01-18T15:35:32Z | null | NONE | null | I encountered this issue when plotting some vertical axes spans on the same axes as a bar plot. Some example code is below to reproduce the issue. When using either scatter or line plots, the spans show up where they are supposed to, but the bar plot is off.
Digging a little deeper, I found that the plot call is setting the xticks to a zero-indexed array with a step size of one while setting the tick labels to the correct values.
If you specify the x ticks in the call to the bar plot, the spans are plotted at the correct position, but the bars are plotted from the same 0-indexed, singly incrementing array.
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
spans = pd.DataFrame(np.array([[10,15]]), columns=['left','right'])
datdf = pd.DataFrame(zip(np.random.rand(15),np.random.rand(15)/10), columns=['foo','bar'], index=np.arange(5,20))
fig, axes = plt.subplots(nrows=3, ncols=1)
for a, ax in enumerate(axes):
for i, row in spans.iterrows():
ax.axvspan(row['left'],row['right'],color=(0,0,0,.5),zorder=0)
datdf.plot(ax=axes[0], kind='line') # all is good
datdf.plot(ax=axes[1], kind='bar', stacked=True) # span is wrong
datdf.plot(ax=axes[2], kind='bar', stacked=True, xticks=datdf.index.values) #bars are wrong
ticks = []
ticklabs = []
for a, ax in enumerate(axes):
print a,
print zip(ax.xaxis.get_ticklocs(), [lab.get_text() for lab in ax.xaxis.get_ticklabels()])
```
axes 0
[(5.0, u''), (10.0, u''), (15.0, u''), (20.0, u''), (25.0, u''), (30.0, u''), (35.0, u'')]
axes 1
[(0, u'5'), (1, u'7'), (2, u'9'), (3, u'11'), (4, u'13'), (5, u'15'), (6, u'17'), (7, u'19'), (8, u'21'), (9, u'23'), (10, u'25'), (11, u'27'), (12, u'29'), (13, u'31'), (14, u'33')]
axes 2
[(5, u'5'), (7, u'7'), (9, u'9'), (11, u'11'), (13, u'13'), (15, u'15'), (17, u'17'), (19, u'19'), (21, u'21'), (23, u'23'), (25, u'25'), (27, u'27'), (29, u'29'), (31, u'31'), (33, u'33')]
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| null | 2 | 2015-10-29T04:10:43Z | 2015-10-29T11:36:34Z | 2015-10-29T11:35:27Z | NONE | null | pd.read_csv("x.txt", lineseparator="\x00") is still separating based on carriage return.
I have a file which has lot of \0 which are to be treated as line separator.
But above code is not separating based on '\0'.
Any fix for this?
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} | 7 | 2015-10-29T04:12:28Z | 2015-10-29T21:08:48Z | null | NONE | null | current lineseparator for pd.read_csv() takes only string with 1 char.
Please provide the facility to be able to break the lines based on regex pattern
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} | 1 | 2015-10-29T10:47:04Z | 2015-12-23T14:51:17Z | 2015-12-23T14:50:41Z | NONE | null | http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html
does not mention the keyword "names"
``` python
import pandas as pd
df = pd.read_excel('yourfile.xls', header=None, names=['Attribut', 'Value'])
```
is equivalent but simpler to
``` python
df = pd.read_excel(file, sheetname='Metadata', header=None)
df.columns = ['Attribut', 'Value']
```
names keyword is defined as in read_csv
names: List of column names to use as column names. To replace header existing in file, explicitly pass header=0.
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} | 21 | 2015-10-29T10:57:32Z | 2015-11-10T09:54:56Z | 2015-11-09T22:22:10Z | CONTRIBUTOR | null | Also fixed a dead link to the Nose testing docs.
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} | 117 | 2015-10-29T11:20:00Z | 2017-03-28T20:45:49Z | 2017-03-28T20:45:42Z | NONE | null | Fixes #11276
Fixes #11745
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} | 9 | 2015-10-29T14:07:57Z | 2018-04-09T07:58:42Z | 2017-09-24T10:10:59Z | NONE | null | Compare these 2 plots below, this is not expected behavior:
```
import pandas as pd
from numpy.random import rand
testarr1=rand(100)
testarr2=rand(100)
pd.DataFrame(testarr2,index=testarr1).plot(style='.')
pd.DataFrame(testarr2,index=testarr1).sort().plot(style='.')
```
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| null | 9 | 2015-10-29T14:27:54Z | 2019-03-14T10:33:28Z | 2015-10-29T14:34:16Z | NONE | null | here is my problem.
What I have is a DataFrame as follows:
```
df:
2013-10-24 1
2013-10-25 2
2013-11-27 3
2013-11-28 4
2013-12-01 5
2013-12-02 6
```
What I want is a DataFrame such like this:
rolling_mean(df, window='1M'):
```
2013-10 1.5
2013-11 3.5
2013-12 5.5
```
rolling_mean(df, window='2M'):
```
2013-10 NAN
2013-11 2.5
2013-12 4.5
```
rolling_mean(df, window='3M'):
```
2013-10 NAN
2013-11 NAN
2013-12 3.5
```
rolling_mean(df, window='1Y'):
```
2013-10 NAN
2013-11 NAN
2013-12 NAN
```
where 1M is '1 month', 2M is '2 months'. The window is not a int value, but a time interval such as '1M', '3M', '1Y' and so on. The function could groupby the dataframe by the time unit such as 'M', 'Y', and then rolling the dataframe through the number before the time unit such as 1, 3...
I need a rolling function such like this?
Could anybody help me? Did I give a clear description?
Many thanks.
p.s.
The new dataframe's Freq is the time unit such as 'M' or 'Y', which is not related to the number.
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} | 4 | 2015-10-29T16:26:24Z | 2016-04-26T18:54:47Z | 2016-04-26T18:54:47Z | NONE | null | ```
>>> import pandas as pd; import pytz; import datetime; faulthandler
>>> faulthandler.enable()
>>> pd.DataFrame.from_records([{'ts': datetime.datetime(2015,1,1, tzinfo=pytz.UTC)}]).to_json()
Fatal Python error: Segmentation fault
Current thread 0x00007fff73afd300 (most recent call first):
File "<snip>lib/python2.7/site-packages/pandas/io/json.py", line 78 in write
File "<snip>lib/python2.7/site-packages/pandas/io/json.py", line 35 in to_json
File "<snip>lib/python2.7/site-packages/pandas/core/generic.py", line 893 in to_json
File "<stdin>", line 1 in <module>
Segmentation fault: 11
```
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| null | 3 | 2015-10-29T18:35:46Z | 2015-10-29T19:10:50Z | 2015-10-29T18:48:40Z | CONTRIBUTOR | null | I don't think this is related to #9732 or #10331, but maybe I'm missing something.
``` python
index = pd.MultiIndex.from_product([pd.date_range('2015-01-01', '2015-04-01', freq='8H'), ['A', 'B', 'C']], names=['level1', 'level2'])
s = pd.Series(np.random.randn(813), index=index)
```
Now I'm trying to select a slice from s as follows:
```
idx = pd.IndexSlice
s_small = s.loc[idx['2015-01-01':'2015-01-08', :]]
```
Now this is very strange, if I select a slice smaller than 8 days then it works fine, but for anything bigger the selection doesn't work:
```
>>> s.loc[idx['2015-01-01':'2015-01-08', :]].shape #(8 days times 3 8h periods times 3 levels)
(72,)
>>> s.loc[idx['2015-01-01':'2015-01-09', :]].shape #???
(569,)
>>>
```
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| null | 12 | 2015-10-29T18:49:44Z | 2015-10-30T17:10:38Z | 2015-10-29T21:07:56Z | NONE | null | I appear to have some code that is sometimes (inexplicably) running in pure Python (i.e. **not** Cython-optimized). Specifically, the problem is with `loc[]` indexing on a boolean Series:
_(Apologies for the redacted code and console output, but this project is not an open-source delivery)_
For context: `world.persons` returns a `DataFrame` with approximately 8 million records long and 15 columns wide. It uses a MultiIndex with 2 levels.
This `run()` method is being called twice in the software sequence. The first time `run()` is called, `persons.loc[filter_column]` takes **15 minutes** to execute, while the second time it only takes 5 seconds. What the heck is going on here?
Furthermore, this is NOT the first time in the program that `world.persons` is being indexed, so I am not sure how caching is being applied.
One other thing: prior to this `loc[]` call, my system starts to approach memory limits to the point that Windows begins paging.
My OS is Windows 7 x64, and I'm using Pandas version 0.17.0.
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| null | 2 | 2015-10-29T20:07:46Z | 2015-10-29T21:06:05Z | 2015-10-29T21:05:46Z | NONE | null | Is it possible to pass all columns to query? e.g.
```
df.query('substring in all_columns')
```
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} | 4 | 2015-10-29T20:32:09Z | 2015-11-14T14:57:23Z | 2015-11-14T14:57:23Z | NONE | null | When trying to confirm if a specific date is an observed holiday using the specific date for the start and end range, the attempt fails to confirm a known observed holiday. It works as expected if I change the end date. When I try to use the same start and end date again, it works every time.
In 2015, the July 4th holiday was observed on July 3rd. Since a BDay() offset ignores holidays, a confirmation of an observed holiday failed when using July 3, 2015 for the start and end dates. See code below:
``` python
from datetime import datetime
from pandas.tseries.holiday import get_calendar, HolidayCalendarFactory, GoodFriday
USFedCal = get_calendar('USFederalHolidayCalendar')
USFedCal.holidays(datetime(2015,7,3), datetime(2015,7,3)) # <-- same start and end dates
DatetimeIndex([], dtype='datetime64[ns]', freq=None)
USFedCal.holidays(datetime(2015,7,3), datetime(2015,7,6)) # <-- different start and end dates
DatetimeIndex(['2015-07-03'], dtype='datetime64[ns]', freq=None)
USFedCal.holidays(datetime(2015,7,3), datetime(2015,7,3)) # <-- same start and end dates
DatetimeIndex(['2015-07-03'], dtype='datetime64[ns]', freq=None)
```
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| null | 3 | 2015-10-30T02:21:58Z | 2015-10-30T21:14:41Z | 2015-10-30T13:13:39Z | NONE | null | Steps to reproduce,
- Create a string representation of a CSV file
- Convert it into a StringIO object
- Read it using pandas.read_csv
- Read it using pandas.DataFrame.from_csv
- The parsed headers will be different
```
# Testcase
import pandas
import cStringIO as StringIO
data = """col1|col2\ndata1|data2\ndata3|data4"""
file1 = StringIO.StringIO(data)
file2 = StringIO.StringIO(data)
r = pandas.read_csv(file1, delimiter="|")
r_ = pandas.DataFrame.from_csv(file2, sep="|")
print "Version: ", pandas.version.version
print " -- Data --"
print data
print "-- Headers --"
print r.columns.values
print r_.columns.values
```
```
# Output
Version: 0.16.0
-- Data --
col1|col2
data1|data2
data3|data4
-- Headers --
['col1' 'col2']
['col2']
```
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} | 5 | 2015-10-30T08:06:16Z | 2021-05-10T21:45:33Z | 2021-05-10T21:45:33Z | NONE | null | Not sure if it's know already, but couldn't find any open issues.
It's similar to this closed one:
https://github.com/pydata/pandas/issues/10388
using pandas 0.17 and numpy 1.10.1
code to reproduce the issues:
```
import pandas as pd
from pandas import DataFrame
import numpy as np
idx = np.arange(0, 10) # could have an NaN?
tstamp = pd.date_range('201507010000', freq='h', periods=10).values
df = DataFrame({'id': idx, 'tstamp': tstamp, 'a': list('abcdefghij')})
df.loc[3, 'tstamp'] = pd.NaT
print 'without timezone:'
try:
a = df.set_index(['id', 'tstamp']).reset_index('tstamp')
print 'a works'
except Exception as e:
print 'a fails: %s: %s' %(e.__class__.__name__, e)
try:
b = df.set_index(['id', 'tstamp']).reset_index('tstamp').reset_index('id')
print 'b works'
except Exception as e:
print 'b fails: %s: %s' %(e.__class__.__name__, e)
try:
c = df.set_index(['id', 'tstamp']).reset_index()
print 'c works'
except Exception as e:
print 'c fails: %s: %s' %(e.__class__.__name__, e)
try:
d = df.set_index(['id', 'tstamp']).reset_index('id')
print 'd works'
except Exception as e:
print 'd fails: %s: %s' %(e.__class__.__name__, e)
print 'with timezone:'
df['tstamp'] = pd.DatetimeIndex(df['tstamp']).tz_localize('Europe/Berlin')
try:
a = df.set_index(['id', 'tstamp']).reset_index('tstamp')
print 'a works'
except Exception as e:
print 'a fails: %s: %s' %(e.__class__.__name__, e)
try:
b = df.set_index(['id', 'tstamp']).reset_index('tstamp').reset_index('id')
print 'b works'
except Exception as e:
print 'b fails: %s: %s' %(e.__class__.__name__, e)
try:
c = df.set_index(['id', 'tstamp']).reset_index()
print 'c works'
except Exception as e:
print 'c fails: %s: %s' %(e.__class__.__name__, e)
try:
d = df.set_index(['id', 'tstamp']).reset_index('id')
print 'd works'
except Exception as e:
print 'd fails: %s: %s' %(e.__class__.__name__, e)
```
Output:
without timezone:
a works
b works
c works
d fails: ValueError: Could not convert object to NumPy datetime
with timezone:
a fails: TypeError: data type not understood
b fails: TypeError: data type not understood
c fails: TypeError: data type not understood
d fails: ValueError: Could not convert object to NumPy datetime
Can you suggest any workaround?
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| null | 1 | 2015-10-30T10:32:23Z | 2015-10-30T12:29:50Z | 2015-10-30T12:29:46Z | NONE | null | On Pandas 0.17.0, `read_csv` fails with a `TypeError` when csv file contain floats and `na_values` contains numeric value(s). Consider this example:
``` python
#!/usr/bin/env python2
import numpy as np
import pandas as pd
pd.show_versions()
# create a dataframe filled with integers 0 to 15
data = np.arange(16).reshape(4, 4)
df = pd.DataFrame(data, columns=list('ABCD'))
# save two csv files
df.to_csv('integers.csv') # contains integers 0 to 15
(df/10).to_csv('floats.csv') # contains floats 0.0 to 1.5
# this works
pd.read_csv('integers.csv', na_values='9999')
# this works
pd.read_csv('floats.csv', na_values='missing')
# this does not work
pd.read_csv('floats.csv', na_values='9999')
```
This raises the error below in my case:
``` python
Traceback (most recent call last):
File "./nabug.py", line 22, in <module>
pd.read_csv('floats.csv', na_values='9999')
File "/usr/lib/python2.7/site-packages/pandas/io/parsers.py", line 491, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/lib/python2.7/site-packages/pandas/io/parsers.py", line 278, in _read
return parser.read()
File "/usr/lib/python2.7/site-packages/pandas/io/parsers.py", line 740, in read
ret = self._engine.read(nrows)
File "/usr/lib/python2.7/site-packages/pandas/io/parsers.py", line 1187, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 766, in pandas.parser.TextReader.read (pandas/parser.c:8082)
File "pandas/parser.pyx", line 788, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:8338)
File "pandas/parser.pyx", line 868, in pandas.parser.TextReader._read_rows (pandas/parser.c:9465)
File "pandas/parser.pyx", line 975, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:10858)
File "pandas/parser.pyx", line 1035, in pandas.parser.TextReader._convert_tokens (pandas/parser.c:11744)
File "pandas/parser.pyx", line 1085, in pandas.parser.TextReader._convert_with_dtype (pandas/parser.c:12634)
File "pandas/parser.pyx", line 1499, in pandas.parser._try_double (pandas/parser.c:19996)
File "pandas/parser.pyx", line 1818, in pandas.parser.kset_float64_from_list (pandas/parser.c:22852)
TypeError: 'set' object does not support indexing
```
Version information:
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.11-1-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_CH.utf8
pandas: 0.17.0
nose: None
pip: None
setuptools: 18.4
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
I am otherwise just discovering this fantastic library (pandas). Thank you for the great work!
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} | 23 | 2015-10-30T11:01:22Z | 2017-04-08T21:59:06Z | 2017-04-08T21:59:06Z | NONE | null | xref #11708
In Europe DST ends by 2 am (this year on Sunday 2015-10-25), when clock is moved one hour back. However in Timestamp this transition is one hour earlier ( 1 am - which is hour, when transition happens in US I think). pytz works as expected.
See code ( tested in pandas 0.17.0 and 0.16.2):
``` ipython
In [1]: import pandas as pd
In [2]: import pytz
In [3]: from pandas import Timestamp
In [7]: pd.__version__
Out[7]: u'0.17.0'
In [8]: cz_tz=pytz.timezone('Europe/Prague')
In [9]: Timestamp('2015-10-25 00:00', tz=cz_tz)
Out[9]: Timestamp('2015-10-25 00:00:00+0200', tz='Europe/Prague')
In [10]: Timestamp('2015-10-25 01:00', tz=cz_tz)
Out[10]: Timestamp('2015-10-25 02:00:00+0200', tz='Europe/Prague')
In [11]: Timestamp('2015-10-25 02:00', tz=cz_tz)
Out[11]: Timestamp('2015-10-25 02:00:00+0100', tz='Europe/Prague')
In [13]: from pandas.tseries.offsets import Hour
In [14]: Timestamp('2015-10-25 01:00', tz=cz_tz) + Hour(1)
Out[14]: Timestamp('2015-10-25 02:00:00+0100', tz='Europe/Prague')
In [16]: from datetime import datetime
In [17]: cz_tz.dst(datetime(2015,10,25, 1, 0))
Out[17]: datetime.timedelta(0, 3600)
In [18]: cz_tz.dst(datetime(2015,10,25, 2, 0))
---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-18-4b6d0ae06c09> in <module>()
----> 1 cz_tz.dst(datetime(2015,10,25, 2, 0))
/home/ivan/tmp/pandas-test/local/lib/python2.7/site-packages/pytz/tzinfo.pyc in dst(self, dt, is_dst)
445 return None
446 elif dt.tzinfo is not self:
--> 447 dt = self.localize(dt, is_dst)
448 return dt.tzinfo._dst
449 else:
/home/ivan/tmp/pandas-test/local/lib/python2.7/site-packages/pytz/tzinfo.pyc in localize(self, dt, is_dst)
347 # ambiguous case
348 if is_dst is None:
--> 349 raise AmbiguousTimeError(dt)
350
351 # Filter out the possiblilities that don't match the requested
AmbiguousTimeError: 2015-10-25 02:00:00
In [19]: cz_tz.dst(datetime(2015,10,25, 2, 0), is_dst=True)
Out[19]: datetime.timedelta(0, 3600)
In [20]: cz_tz.dst(datetime(2015,10,25, 2, 0), is_dst=False)
Out[20]: datetime.timedelta(0)
In [21]: cz_tz.dst(datetime(2015,10,25, 3, 0))
Out[21]: datetime.timedelta(0)
```
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| null | 2 | 2015-10-30T11:04:43Z | 2015-10-30T13:43:08Z | 2015-10-30T13:43:08Z | NONE | null | I have a series with a lot of NA values. However, there are still some valid observations in between. This can be seen in this plot:
If I now to:
``` python
s.fillna(method='ffill').plot()
```
I end up with this result:
According to the documentation ffill should "propagate last valid observation forward to next valid". This is not what I see here.
The series I am testing with can be grabbed here (not allowed to upload on github :/):
http://wikisend.com/download/956820/series.msg
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| null | 4 | 2015-10-30T12:21:54Z | 2021-04-20T06:01:34Z | null | NONE | null | Found this when working on
https://github.com/pydata/pandas/pull/11441
``` python
import pandas as pd
d = {'one' : ['A', 'A', 'B', 'B', 'C'],
'two' : [4., 3., 2., 2, 1],
'three' : [10., 8., 3, 5, 7.]}
df = pd.DataFrame(d)
# this works
df.hist('two', by='one', bins=range(0, 10))
# this does not work (everything in one plot), also no way to specify column
df.plot.hist(by='one', bins=range(0, 10))
```
My idea was to make the df.plot.hist version similar to the df.hist. But the code is much more complex. Would it not be best to point the df.plot.hist to the df.hist version? Instead of having two separate logics for this?
Oh, and the by keyword does not seem to work for df.plot.box, have not found any it worked for. At least the way I expected it to work :)
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} | 4 | 2015-10-30T14:11:20Z | 2015-11-14T14:57:29Z | 2015-11-14T14:57:23Z | CONTRIBUTOR | null | Closes #11477
Closes #11533
There were some other bugs here that I added tests for. For example, `holiday.dates` for MLK day was returning all holidays from the holiday start date up to the end date rather than just between the range.
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} | 8 | 2015-10-30T18:47:42Z | 2016-04-29T17:05:49Z | 2016-04-29T17:05:49Z | CONTRIBUTOR | null | accept callables
- ~~`.query`~~ (not changed, see #12539)
- [x] `.where/.mask` (#12539)
- [x] `.loc` (and indexers) (#12539)
```
In [4]: df = DataFrame({'A' : [1,2,3,4], 'B' : ['a',np.nan,'b','a']})
In [5]: df
Out[5]:
A B
0 1 a
1 2 NaN
2 3 b
3 4 a
```
an operation that changes the shape of the DataFrame
```
In [9]: res = df.dropna()
In [10]: res[res.B=='a']
Out[10]:
A B
0 1 a
3 4 a
```
can be done like this
```
In [8]: df.dropna().pipe(lambda x: x[x.B=='a'])
Out[8]:
A B
0 1 a
3 4 a
```
SQL calls this `select`, which pandas has, but both `select/filter` are used for filtering LABELS (and not data).
I suppose making this work:
`df.dropna().loc[lambda x: x[x.B=='a']]` is _maybe_ a slight enhancement of this
any thoughts?
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} | 3 | 2015-10-30T19:25:38Z | 2015-10-31T04:22:11Z | 2015-10-30T22:08:05Z | CONTRIBUTOR | null | Fix https://github.com/pydata/pandas/issues/10770 by adding days_in_month to Time/Date Components. Am I right in thinking `days_in_month` (rather than `daysinmonth`) is the right name to use going forward? Is there anywhere else in the documentation that it should be added that I missed?
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https://api.github.com/repos/pandas-dev/pandas/issues/11487 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11487/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11487/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11487/events | https://github.com/pandas-dev/pandas/issues/11487 | 114,368,580 | MDU6SXNzdWUxMTQzNjg1ODA= | 11,487 | segfault with matplotlib 1.5.0 | {
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| null | 14 | 2015-10-30T22:45:08Z | 2015-11-20T19:59:36Z | 2015-11-20T19:59:36Z | NONE | null | The following example will lead to a segfault error (with pandas 0.17.0 and matplotlib 1.5.0)
`import pandas as pd`
`import matplotlib.pyplot as plt`
`df = pd.DataFrame({'data': range(10)})`
`df.plot()`
`plt.show()`
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https://api.github.com/repos/pandas-dev/pandas/issues/11488 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11488/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11488/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11488/events | https://github.com/pandas-dev/pandas/issues/11488 | 114,379,906 | MDU6SXNzdWUxMTQzNzk5MDY= | 11,488 | API: DatetimeIndex creation with mixed tz timestamps | {
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} | 6 | 2015-10-31T01:22:51Z | 2015-12-13T20:30:55Z | 2015-12-10T12:17:31Z | MEMBER | null | Related to #11456. Currently, `DatetimeIndex` handles mixed tz values like below. This behavior sometimes triggers coercion between tz-aware/tz-naive.
```
pd.Index([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02', tz='US/Eastern')])
# DatetimeIndex(['2010-12-31 19:00:00-05:00', '2011-01-02 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq=None)
# -> should be normal Index with object dtype?
pd.Index([pd.Timestamp('2011-01-01', tz='Asia/Tokyo'), pd.Timestamp('2011-01-02', tz='US/Eastern')])
# DatetimeIndex(['2010-12-31 15:00:00', '2011-01-02 05:00:00'], dtype='datetime64[ns]', freq=None)
# -> should be normal Index with object dtype?
pd.Index([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02', tz='US/Eastern')], tz='Asia/Tokyo')
# DatetimeIndex(['2011-01-01 09:00:00+09:00', '2011-01-02 14:00:00+09:00'], dtype='datetime64[ns, Asia/Tokyo]', freq=None)
# -> OK, localized to explicitly passed tz ('Asia/Tokyo')
```
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| null | 15 | 2015-10-31T06:53:19Z | 2016-01-30T15:47:59Z | 2016-01-30T15:47:59Z | CONTRIBUTOR | null | In accordance with https://github.com/pydata/pandas/issues/9606, I added `day_of_week` and `day_of_year` accessors to DatetimeIndex, Timestamp, and Period. Since https://github.com/pydata/pandas/issues/9606 didn't mention `weekofyear`, and there's already `week` as a replacement for that, I didn't add a `week_of_year`, but I'm happy to.
I updated the tests and docs, but this is my first PR to pandas involving actual code, so let me know if I missed anything.
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https://api.github.com/repos/pandas-dev/pandas/issues/11490 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11490/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11490/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11490/events | https://github.com/pandas-dev/pandas/issues/11490 | 114,404,227 | MDU6SXNzdWUxMTQ0MDQyMjc= | 11,490 | Empty preconstructed pandas results in error in Variable Explorer | {
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| null | 2 | 2015-10-31T11:13:40Z | 2015-10-31T11:16:45Z | 2015-10-31T11:15:22Z | NONE | null | If I construct a DF like this:
iv_bd = pd.DataFrame(columns=['Jan','Feb','Mar'])
It causes an internal error instead of showing something like e.g. "empty Dataframe"
```
Spyder Internal Console
This console is used to report application
internal errors and to inspect Spyder
internals with the following commands:
spy.app, spy.window, dir(spy)
Please don't use it to run your code
>>> /home/george/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated.
- use nbformat for read/write/validate public API
- use nbformat.vX directly to composing notebooks of a particular version
""")
Traceback (most recent call last):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dicteditor.py", line 503, in createEditor
if not editor.setup_and_check(value, title=key):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 494, in setup_and_check
self.dataModel = DataFrameModel(data, parent=self)
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 90, in __init__
self.max_min_col_update()
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 124, in max_min_col_update
mask = float_intran & (~ self.complex_intran)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 639, in __invert__
arr = operator.inv(_values_from_object(self))
TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
Traceback (most recent call last):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dicteditor.py", line 503, in createEditor
if not editor.setup_and_check(value, title=key):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 494, in setup_and_check
self.dataModel = DataFrameModel(data, parent=self)
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 90, in __init__
self.max_min_col_update()
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 124, in max_min_col_update
mask = float_intran & (~ self.complex_intran)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 639, in __invert__
arr = operator.inv(_values_from_object(self))
TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
Traceback (most recent call last):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dicteditor.py", line 503, in createEditor
if not editor.setup_and_check(value, title=key):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 494, in setup_and_check
self.dataModel = DataFrameModel(data, parent=self)
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 90, in __init__
self.max_min_col_update()
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 124, in max_min_col_update
mask = float_intran & (~ self.complex_intran)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 639, in __invert__
arr = operator.inv(_values_from_object(self))
TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
Traceback (most recent call last):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dicteditor.py", line 503, in createEditor
if not editor.setup_and_check(value, title=key):
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 494, in setup_and_check
self.dataModel = DataFrameModel(data, parent=self)
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 90, in __init__
self.max_min_col_update()
File "/home/george/anaconda/lib/python2.7/site-packages/spyderlib/widgets/dataframeeditor.py", line 124, in max_min_col_update
mask = float_intran & (~ self.complex_intran)
File "/home/george/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 639, in __invert__
arr = operator.inv(_values_from_object(self))
TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
Spyder 2.3.7
Python 2.7.10 64bits, Qt 4.8.7, PyQt4 (API v2) 4.11.4 on Linux
Ubuntu 14.04 LTS
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| null | 2 | 2015-10-31T17:21:38Z | 2015-11-07T14:55:16Z | 2015-11-07T14:55:16Z | NONE | null | fix #5888
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| null | 2 | 2015-10-31T19:46:07Z | 2015-11-18T12:12:59Z | 2015-11-18T12:12:59Z | NONE | null | Suppose we want to cap values in a dataframe of shape (6,5).
``` python
import pandas as pd,numpy as np
# WORKS WITH DF OF SHAPE (6,5)
ix=pd.date_range('1/1/2015',periods=6,freq='D')
df0=pd.DataFrame(np.random.normal(size=(len(ix),5)),index=ix)
print df0
df1=df0.quantile(q=.5,axis=1)
print df1
df2=df0.where(df0<df1,df1,axis=0)
print df2
```
The above code has capped values above the median. For example:
``` python
0 1 2 3 4
2015-01-01 1.209706 1.362493 -0.141184 -0.262279 0.108538
2015-01-02 -0.752241 0.554342 -0.053927 -1.150906 -0.993721
2015-01-03 0.049342 1.100449 0.616066 -1.453542 -0.023043
2015-01-04 -1.148023 -0.438199 -1.378162 0.678946 -0.224544
2015-01-05 0.395681 -1.237518 -0.606024 0.834100 0.545389
2015-01-06 -0.426188 -0.283056 -0.012219 0.510134 1.363700
2015-01-01 0.108538
2015-01-02 -0.752241
2015-01-03 0.049342
2015-01-04 -0.438199
2015-01-05 0.395681
2015-01-06 -0.012219
Freq: D, dtype: float64
0 1 2 3 4
2015-01-01 0.108538 0.108538 -0.141184 -0.262279 0.108538
2015-01-02 -0.752241 -0.752241 -0.752241 -1.150906 -0.993721
2015-01-03 0.049342 0.049342 0.049342 -1.453542 -0.023043
2015-01-04 -1.148023 -0.438199 -1.378162 -0.438199 -0.438199
2015-01-05 0.395681 -1.237518 -0.606024 0.395681 0.395681
2015-01-06 -0.426188 -0.283056 -0.012219 -0.012219 -0.012219
```
Now, if we do the same with a squared dataframe, the same code will fail.
``` python
# FAILS WITH DF OF SHAPE (5,5)
df0=df0.iloc[:5,:5]
df1=df1.iloc[:5]
df2=df0.where(df0<df1,df1,axis=0)
print df2
```
For some reason, broadcasting now is done through axis=1, although the argument explicitly asked for axis=0.
``` python
0 1 2 3 4
2015-01-01 0.108538 -0.752241 -0.141184 -0.262279 0.395681
2015-01-02 0.108538 -0.752241 0.049342 -1.150906 -0.993721
2015-01-03 0.108538 -0.752241 0.049342 -1.453542 -0.023043
2015-01-04 -1.148023 -0.752241 -1.378162 -0.438199 0.395681
2015-01-05 0.108538 -1.237518 -0.606024 -0.438199 0.395681
```
The same failure will take place is we use binary comparison functions.
``` python
# IT ALSO FAILS WITH BINARY COMPARISONS
# http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-compare
df2=df0.where(df0.lt(df1,axis=0),df1,axis=0)
print df2
```
``` python
0 1 2 3 4
2015-01-01 0.108538 -0.752241 -0.141184 -0.262279 0.395681
2015-01-02 0.108538 -0.752241 0.049342 -1.150906 -0.993721
2015-01-03 0.108538 -0.752241 0.049342 -1.453542 -0.023043
2015-01-04 -1.148023 -0.752241 -1.378162 -0.438199 0.395681
2015-01-05 0.108538 -1.237518 -0.606024 -0.438199 0.395681
```
pandas version 0.16.2 and numpy 1.9.2
Thanks!
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| null | 21 | 2015-10-31T19:59:08Z | 2021-06-18T11:32:25Z | 2015-10-31T23:23:41Z | NONE | resolved | I am having trouble with `read_csv` (Pandas 0.17.0) when trying to read a 380+ MB csv file. The file starts with 54 fields but some lines have 53 fields instead of 54. Running the below code gives me the following error:
```
parser = lambda x: datetime.strptime(x, '%y %m %d %H %M %S %f')
df = pd.read_csv(filename,
names=['YR', 'MO', 'DAY', 'HR', 'MIN', 'SEC', 'HUND',
'ERROR', 'RECTYPE', 'LANE', 'SPEED', 'CLASS',
'LENGTH', 'GVW', 'ESAL', 'W1', 'S1', 'W2', 'S2',
'W3', 'S3', 'W4', 'S4', 'W5', 'S5', 'W6', 'S6',
'W7', 'S7', 'W8', 'S8', 'W9', 'S9', 'W10', 'S10',
'W11', 'S11', 'W12', 'S12', 'W13', 'S13', 'W14'],
usecols=range(0, 42),
parse_dates={"TIMESTAMP": [0, 1, 2, 3, 4, 5, 6]},
date_parser=parser,
header=None)
```
Error:
```
CParserError: Error tokenizing data. C error: Expected 53 fields in line 1605634, saw 54
```
If I pass the `error_bad_lines=False` keyword, problematic lines are displayed similar to the example below:
```
Skipping line 1683401: expected 53 fields, saw 54
```
however I get the following error this time ( also the DataFrame does not get loaded):
```
CParserError: Too many columns specified: expected 54 and found 53
```
If I pass the `engine='python'` keyword, I do not get any errors, but it takes a really long time to parse the data. Please note that 53 and 54 are switched in the error messages depending on if `error_bad_lines=False` is used or not.
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| null | 6 | 2015-10-31T20:51:08Z | 2015-11-05T12:56:44Z | 2015-11-05T12:56:04Z | NONE | null | The following code throws AssertionError
df = pd.DataFrame({"x-1": X[:,0], "x-2": X[:,1], "label": np.where(y==1, "Driver's License", "Check")})
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| null | 2 | 2015-10-31T22:00:30Z | 2015-11-03T02:19:27Z | 2015-11-02T12:04:30Z | CONTRIBUTOR | null | ```
% nosetests -v pandas.tests.test_panel4d:TestPanel4d.test_sum
test_sum (pandas.tests.test_panel4d.TestPanel4d) ... FAIL
======================================================================
FAIL: test_sum (pandas.tests.test_panel4d.TestPanel4d)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/pandas/tests/test_panel4d.py", line 49, in test_sum
self._check_stat_op('sum', np.sum)
File "/usr/local/lib/python2.7/site-packages/pandas/tests/test_panel4d.py", line 148, in _check_stat_op
assert_panel_equal(result, obj.apply(skipna_wrapper, axis=i))
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1048, in assert_panelnd_equal
assert_func(litem, ritem, check_less_precise=check_less_precise)
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1028, in assert_frame_equal
obj='DataFrame.iloc[:, {0}]'.format(i))
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 925, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2685)
File "/usr/local/lib/python2.7/site-packages/pandas/util/testing.py", line 798, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.iloc[:, 1] are different
DataFrame.iloc[:, 1] values are different (12.5 %)
[left]: [0.0, 1.11252805895, -3.44337874224, 1.77230240341, -1.27472712412, -0.953662849233, -0.819950380871, 3.3111686889]
[right]: [nan, 1.11252805895, -3.44337874224, 1.77230240341, -1.27472712412, -0.953662849233, -0.819950380871, 3.3111686889]
----------------------------------------------------------------------
Ran 1 test in 0.169s
FAILED (failures=1)
```
```
$ python2.7 -m pandas.util.print_versions
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: FreeBSD
OS-release: 10.1-STABLE
machine: amd64
processor: amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: 1.3.7
pip: None
setuptools: 17.0
Cython: 0.23.4
numpy: 1.10.0
scipy: 0.16.0
statsmodels: None
IPython: 3.2.2
sphinx: None
patsy: None
dateutil: 2.3
pytz: 2015.7
blosc: None
bottleneck: 0.8.0
tables: 3.2.0
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.4.1
bs4: 4.3.2
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 0.7.10
pymysql: None
psycopg2: None
```
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| null | 3 | 2015-10-31T22:12:28Z | 2018-05-31T14:13:33Z | 2016-01-20T14:13:35Z | CONTRIBUTOR | null | I went hunting for some low-hanging fruit in the area of missing data and came across #7588. I had to implement it a little differently than krogh, pchip, etc. because there is no convenience function for Akima like there are for the already supported methods.
Once #11445 is merged I can rebase.
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} | 7 | 2015-11-01T12:05:05Z | 2015-11-29T19:42:00Z | 2015-11-29T18:01:15Z | MEMBER | null | `.loc` result with duplicated keys may have incorred `Index` dtype.
```
import pandas as pd
ser = pd.Series([0.1, 0.2], index=pd.Index([1, 2], name='idx'))
# OK
ser.loc[[2, 2, 1]].index
# Int64Index([2, 2, 1], dtype='int64', name=u'idx')
# NG, Int64Index(dtype=object)
ser.loc[[3, 2, 3]].index
# Int64Index([3, 2, 3], dtype='object', name=u'idx')
ser.loc[[3, 2, 3, 'x']].index
# Int64Index([3, 2, 3, u'x'], dtype='object', name=u'idx')
idx = pd.date_range('2011-01-01', '2011-01-02', freq='D', name='idx')
ser = pd.Series([0.1, 0.2], index=idx, name='s')
# OK
ser.loc[[pd.Timestamp('2011-01-02'), pd.Timestamp('2011-01-02'), pd.Timestamp('2011-01-01')]].index
# DatetimeIndex(['2011-01-02', '2011-01-02', '2011-01-01'], dtype='datetime64[ns]', name=u'idx', freq=None)
# NG, ValueError
ser.loc[[pd.Timestamp('2011-01-03'), pd.Timestamp('2011-01-02'), pd.Timestamp('2011-01-03')]].index
# ValueError: Inferred frequency None from passed dates does not conform to passed frequency D
```
## After the PR:
Above OK results are unchanged.
```
import pandas as pd
ser = pd.Series([0.1, 0.2], index=pd.Index([1, 2], name='idx'))
ser.loc[[3, 2, 3]].index
# Int64Index([3, 2, 3], dtype='int64', name=u'idx')
ser.loc[[3, 2, 3, 'x']].index
# Index([3, 2, 3, u'x'], dtype='object', name=u'idx')
idx = pd.date_range('2011-01-01', '2011-01-02', freq='D', name='idx')
ser = pd.Series([0.1, 0.2], index=idx, name='s')
ser.loc[[pd.Timestamp('2011-01-03'), pd.Timestamp('2011-01-02'), pd.Timestamp('2011-01-03')]].index
# DatetimeIndex(['2011-01-03', '2011-01-02', '2011-01-03'], dtype='datetime64[ns]', name=u'idx', freq=None)
```
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| null | 2 | 2015-11-01T13:54:36Z | 2015-11-01T14:46:53Z | 2015-11-01T14:46:53Z | NONE | null | I'm running pandas 0.17.0 and was surprised by this behavior, which seems inconsistent with the docs (I expected to get NaN, not zero, at index 2). If I substitute "None" for "np.nan", I get the same result, but I get the expected result--NaN--if I use "NaT". Wondering if I've stumbled on a bug . . .
```
In [136]: df = pd.DataFrame()
In [137]: df['a'] = [1,2,np.nan,3]
In [138]: df['b'] = [4,5,np.nan,6]
In [139]: df
Out[139]:
a b
0 1 4
1 2 5
2 NaN NaN
3 3 6
In [140]: df['total'] = df.sum(axis=1)
In [141]: df
Out[141]:
a b total
0 1 4 5
1 2 5 7
2 NaN NaN 0
3 3 6 9
```
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} | 13 | 2015-11-01T14:58:05Z | 2019-11-22T19:38:44Z | 2019-06-28T16:44:10Z | CONTRIBUTOR | null | I took a brief look at the existing code for importing files with xlrd but it looks tightly coupled with that library and not particularly straightforward to adapt.
I think that the read-only mode of openpyxl would be a good fit for Pandas and would like to work with you to provide the necessary API to go from rows to a dataframe. Data is stored by row so this is the only sensible approach. We will be adding read-support for NumPy types, ie. when passed them, but probably not when reading Excel files.
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| null | 20 | 2015-11-01T16:41:21Z | 2020-09-06T18:46:05Z | 2016-01-11T13:49:28Z | CONTRIBUTOR | null | Working model of copy-on-write. Aims to close #10954, alternative to #10973, extension of #11207.
## Copy-on-Write Behavior:
**Setting on child doesn't affect parent, but still uses views when can for efficiency**
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
child = parent.loc[0:0,]
child._is_view
Out[1]: True
child.loc[0:0] = -88
child
Out[2]:
col1 col2
0 -88 -88
parent
Out[3]:
col1 col2
0 1 3
1 2 4
```
**Setting on parent doesn't affect child**
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
child = parent.loc[0:0,]
child._is_view
Out[4]: True
parent.loc[0:0, 'col1'] = -88
child
Out[5]:
col1 col2
0 1 3
parent
Out[6]:
col1 col2
0 -88 3
1 2 4
```
**One exception is dictionary-like access, where views are preserved**
_(as suggested here: https://github.com/pydata/pandas/issues/10954#issuecomment-136521398 )_
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
child = parent['col1']
child._is_view
Out[7]: True
parent.loc[0:0, 'col1'] = -88
child
Out[8]:
0 -88
1 2
Name: col1, dtype: int64
parent
Out[9]:
col1 col2
0 -88 3
1 2 4
```
**Safe for views of views**
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
child = parent.loc[0:0,]
child._is_view
Out[20]: True
child_of_child = child.loc[0:0,'col1':'col1']
child_of_child._is_view
Out[21]: True
child_of_child.loc[0:0, 'col1'] = -88
child_of_child
Out[22]:
col1
0 -88
child
Out[23]:
col1 col2
0 1 3
parent
Out[24]:
col1 col2
0 1 3
1 2 4
```
## Chained indexing behavior now consistent
**Will always fail unless first class is a dictionary-like call for a single series (since that will always be a view)**
**Will fail if first call not dict-like**
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
parent.loc[0:0, 'col1']['col1'] = -88
parent
Out[10]:
col1 col2
0 1 3
1 2 4
```
**Will always succeed if first call dict-like**
```
parent = pd.DataFrame({'col1':[1,2], 'col2':[3,4]})
parent['col1'].loc[0:0] = -88
parent
Out[11]:
col1 col2
0 -88 3
1 2 4
```
To Do:
- Get feedback on implementation;
- if sound in behavior and principles, remove all SettingWithCopy machinery
- Add docs
@jreback
@TomAugspurger
@shoyer
@JanSchulz
cc @ellisonbg
cc @CarstVaartjes
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} | 9 | 2015-11-01T17:40:53Z | 2015-11-01T19:14:02Z | 2015-11-01T19:14:02Z | NONE | null | pandas: 0.17.0
python: 3.5.0.final.0
```
import pandas as pd
list1 = {"Date":["01/11/15"]} #1st November 2015
pd.set_option('display.date_dayfirst', True)
#pd.set_option('display.date_yearfirst', False)
df = pd.DataFrame(list1, columns=['Date'])
df['Date'] = pd.to_datetime(df['Date'], dayfirst = [True])
df1 = df.sort_values(['Date'], ascending=[True])
print(df1)
```
Prints:
```
Date
0 2015-11-01
```
Setting both options to `True` and `False` doesn't work nor does only setting one. Printing df and df1 have the same result. And not specifying a `dayfirst` argument in `pd.to_datetime` doesn't change anything.
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} | 7 | 2015-11-01T18:37:24Z | 2016-01-14T12:42:46Z | 2016-01-14T12:42:46Z | NONE | null | After upgrading to 0.17 (anaconda 2.7 + OSX), I'm finding that some previously working code is failing with `ValueError: buffer source array is read-only`. Driver code is below. The issue is basically the series version of #10043. I think the problem is that the patch for #10043 (#10070) only fixed the `take_2d` templates, leaving the `take_1d` (Series) versions with the old behavior. Based on my naive understanding, it might be straightforward to extend #10070 for the 1D cases.
```
import pandas as pd
import numpy as np
array = np.arange(10)
array.setflags(write=False)
X = pd.Series(array)
key = 0
keys = [key]
X.iloc[keys]
# OK
X.loc[keys]
# Fail
X.ix[keys]
# Fail
X.iloc[key]
# OK
X.loc[key]
# OK
X.ix[key]
# OK
```
Stacktrace is below.
```
ValueError Traceback (most recent call last)
<ipython-input-53-928f0ea51c58> in <module>()
----> 1 X.loc[keys]
~/opt/lib/python2.7/site-packages/pandas/core/indexing.py in __getitem__(self, key)
1196 return self._getitem_tuple(key)
1197 else:
-> 1198 return self._getitem_axis(key, axis=0)
1199
1200 def _getitem_axis(self, key, axis=0):
~/opt/lib/python2.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1330 raise ValueError('Cannot index with multidimensional key')
1331
-> 1332 return self._getitem_iterable(key, axis=axis)
1333
1334 # nested tuple slicing
~/opt/lib/python2.7/site-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
973
974 try:
--> 975 result = self.obj.reindex_axis(keyarr, axis=axis, level=level)
976
977 # this is an error as we are trying to find
~/opt/lib/python2.7/site-packages/pandas/core/series.py in reindex_axis(self, labels, axis, **kwargs)
2276 if axis != 0:
2277 raise ValueError("cannot reindex series on non-zero axis!")
-> 2278 return self.reindex(index=labels, **kwargs)
2279
2280 def take(self, indices, axis=0, convert=True, is_copy=False):
~/opt/lib/python2.7/site-packages/pandas/core/series.py in reindex(self, index, **kwargs)
2257 @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
2258 def reindex(self, index=None, **kwargs):
-> 2259 return super(Series, self).reindex(index=index, **kwargs)
2260
2261 @Appender(generic._shared_docs['fillna'] % _shared_doc_kwargs)
~/opt/lib/python2.7/site-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
1846 # perform the reindex on the axes
1847 return self._reindex_axes(axes, level, limit, tolerance,
-> 1848 method, fill_value, copy).__finalize__(self)
1849
1850 def _reindex_axes(self, axes, level, limit, tolerance, method,
~/opt/lib/python2.7/site-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
1865 obj = obj._reindex_with_indexers(
1866 {axis: [new_index, indexer]},
-> 1867 fill_value=fill_value, copy=copy, allow_dups=False)
1868
1869 return obj
~/opt/lib/python2.7/site-packages/pandas/core/generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
1957 fill_value=fill_value,
1958 allow_dups=allow_dups,
-> 1959 copy=copy)
1960
1961 if copy and new_data is self._data:
~/opt/lib/python2.7/site-packages/pandas/core/internals.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy)
3397 if axis == 0:
3398 new_blocks = self._slice_take_blocks_ax0(
-> 3399 indexer, fill_tuple=(fill_value,))
3400 else:
3401 new_blocks = [blk.take_nd(indexer, axis=axis,
~/opt/lib/python2.7/site-packages/pandas/core/internals.py in _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple)
3438 return [blk.take_nd(slobj, axis=0,
3439 new_mgr_locs=slice(0, sllen),
-> 3440 fill_tuple=fill_tuple)]
3441
3442 if sl_type in ('slice', 'mask'):
~/opt/lib/python2.7/site-packages/pandas/core/internals.py in take_nd(self, indexer, axis, new_mgr_locs, fill_tuple)
933 fill_value = fill_tuple[0]
934 new_values = com.take_nd(values, indexer, axis=axis,
--> 935 allow_fill=True, fill_value=fill_value)
936
937 if new_mgr_locs is None:
~/opt/lib/python2.7/site-packages/pandas/core/common.py in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill)
782 axis=axis, mask_info=mask_info)
783 indexer = _ensure_int64(indexer)
--> 784 func(arr, indexer, out, fill_value)
785
786 if flip_order:
pandas/src/generated.pyx in pandas.algos.take_1d_int64_int64 (pandas/algos.c:87226)()
~/opt/lib/python2.7/site-packages/pandas/algos.so in View.MemoryView.memoryview_cwrapper (pandas/algos.c:176760)()
~/opt/lib/python2.7/site-packages/pandas/algos.so in View.MemoryView.memoryview.__cinit__ (pandas/algos.c:173129)()
ValueError: buffer source array is read-only
```
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