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} | 1 | 2015-08-12T07:04:36Z | 2015-08-12T09:31:13Z | 2015-08-12T09:31:13Z | CONTRIBUTOR | null | Solves #10750
Follow-up from #10771
'read_sql_table' will now allow reading from views.
Added also a note in whatsnew and a unit test to check the behaviour.
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} | 0 | 2015-08-12T11:20:35Z | 2015-08-22T20:03:01Z | 2015-08-22T20:03:01Z | CONTRIBUTOR | null | The internal impl of `Categorical.value_counts` should just do this. I think it is factorizing multiple times when it is not necessary.
```
In [32]: np.random.seed(1234)
In [33]: n = 500000
In [34]: u = int(0.1*n)
In [35]: arr = [ "s%04d" % i for i in np.random.randint(0,u,size=n) ]
In [36]: c = pd.Series(arr).astype('category')
In [37]: result1 = Series(np.arange(len(c.cat.categories)),c.cat.categories).map(c.cat.codes.value_counts()).order(ascending=False)
In [38]: result2 = c.value_counts()
In [39]: %timeit Series(np.arange(len(c.cat.categories)),c.cat.categories).map(c.cat.codes.value_counts()).order(ascending=False)
100 loops, best of 3: 17.2 ms per loop
In [40]: %timeit c.value_counts()
10 loops, best of 3: 62.3 ms per loop
In [41]: result1.equals(result2)
Out[41]: True
```
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| null | 2 | 2015-08-12T15:08:54Z | 2015-08-12T15:32:02Z | 2015-08-12T15:15:10Z | CONTRIBUTOR | null | I believe this is a bug in the definition of memorial day in `pandas.tseries.holiday`:
```
USMemorialDay = Holiday('MemorialDay', month=5, day=24, offset=DateOffset(weekday=MO(1)))
```
should be
```
USMemorialDay = Holiday('MemorialDay', month=5, day=31, offset=DateOffset(weekday=MO(-1)))
```
as memorial day is always the last monday in may. Tested against list from here http://www.timeanddate.com/holidays/us/memorial-day .
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} | 1 | 2015-08-12T15:21:36Z | 2016-07-21T15:05:26Z | 2016-07-21T15:05:26Z | CONTRIBUTOR | null | xref #10726
This is possible, just not implemented
```
In [3]: df = DataFrame(np.arange(16).reshape(4, 4), index=[1, 2, 3, 4], columns=['A', 'B', 'C', 'D'])
In [4]: df
Out[4]:
A B C D
1 0 1 2 3
2 4 5 6 7
3 8 9 10 11
4 12 13 14 15
In [5]: df.sort_values(by=1,axis=1)
ValueError: When sorting by column, axis must be 0 (rows)
```
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| null | 1 | 2015-08-12T15:27:40Z | 2015-08-12T15:33:35Z | 2015-08-12T15:33:35Z | NONE | null | for example:
```
import tables as tb
import pandas as pd
class TestTable(tb.IsDescription):
field1 = tb.UInt32Col()
field2 = tb.UInt32Col()
field3 = tb.UInt32Col()
h5file = tb.open_file('test.h5', mode = "w", title = "Test File")
table = h5file.create_table('/', 'test_table', TestTable, "Test Table")
h5file.close()
store = pd.HDFStore(h5file.filename, mode='r+', complib='zlib', complevel=5)
data = [{'field1': 1, 'field2': 2, 'field3': 3},
{'field1': 2, 'field2': 3, 'field3': 4},
{'field1': 5, 'field2': 6, 'field3': 7}]
df = pd.DataFrame(data)
store.append('test_table', df, mode='r+', append=True, format='table')
```
This code gave me an error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-5b74794d5ad5> in <module>()
----> 1 store.append('test_table', df, mode='r+', append=True, format='table', data_columns=['field1','field2','field3'])
/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py in append(self, key, value, format, append, columns, dropna, **kwargs)
910 kwargs = self._validate_format(format, kwargs)
911 self._write_to_group(key, value, append=append, dropna=dropna,
--> 912 **kwargs)
913
914 def append_to_multiple(self, d, value, selector, data_columns=None,
/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1266
1267 # write the object
-> 1268 s.write(obj=value, append=append, complib=complib, **kwargs)
1269
1270 if s.is_table and index:
/usr/local/lib/python3.4/dist-packages/pandas/io/pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3790
3791 # create the table
-> 3792 table = self._handle.create_table(self.group, **options)
3793
3794 else:
/usr/local/lib/python3.4/dist-packages/tables/file.py in create_table(self, where, name, description, title, filters, expectedrows, chunkshape, byteorder, createparents, obj)
1058 description=description, title=title,
1059 filters=filters, expectedrows=expectedrows,
-> 1060 chunkshape=chunkshape, byteorder=byteorder)
1061
1062 if obj is not None:
/usr/local/lib/python3.4/dist-packages/tables/table.py in __init__(self, parentnode, name, description, title, filters, expectedrows, chunkshape, byteorder, _log)
869
870 super(Table, self).__init__(parentnode, name, new, filters,
--> 871 byteorder, _log)
872
873 def _g_post_init_hook(self):
/usr/local/lib/python3.4/dist-packages/tables/leaf.py in __init__(self, parentnode, name, new, filters, byteorder, _log)
260 # is a lazy property that automatically handles their loading.
261
--> 262 super(Leaf, self).__init__(parentnode, name, _log)
263
264 def __len__(self):
/usr/local/lib/python3.4/dist-packages/tables/node.py in __init__(self, parentnode, name, _log)
244
245 # Is the parent node a group? Is it open?
--> 246 self._g_check_group(parentnode)
247 parentnode._g_check_open()
248 file_ = parentnode._v_file
/usr/local/lib/python3.4/dist-packages/tables/node.py in _g_check_group(self, node)
853 if not isinstance(node, class_name_dict['Group']):
854 raise TypeError("new parent node ``%s`` is not a group"
--> 855 % node._v_pathname)
856
857 _g_checkGroup = previous_api(_g_check_group)
TypeError: new parent node ``/test_table`` is not a group
```
is there a way to append dataframe into table who was created using pytables?
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} | 3 | 2015-08-12T20:47:31Z | 2015-08-15T12:46:01Z | 2015-08-14T18:40:52Z | CONTRIBUTOR | null | This fixes the issues raised in [GH 10779](https://github.com/pydata/pandas/issues/10779).
closes #10547
Previously, negative integers used in `iloc` were not stopped from going beyond the bounds of the Series. For instance, if `s = pd.Series([1,2,3])` then `s.iloc[-4]` returned bytes from memory outside `s`. This will now raise an IndexError instead.
Additionally, it was not possible to access the first row (index 0) of a Series or DataFrame with a negative integer in a list. For example, `s.iloc[[-3]]` raised an IndexError instead of returning a Series containing the first row of `s`. The behaviour of `iloc` has been changed to allow this.
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| null | 3 | 2015-08-12T20:56:15Z | 2015-08-13T19:36:44Z | 2015-08-12T22:23:06Z | NONE | null | Note how the string 'M15' becomes somehow 8/15/2015!
> > > pandas.Series([datetime.datetime(2015,1,1), 'M15'])
> > > 0 2015-01-01
> > > 1 2015-08-15
> > > dtype: datetime64[ns]
Compare other arbitrary strings that are left alone:
> > > pandas.Series([datetime.datetime(2015,1,1), 'MM15'])
> > > 0 2015-01-01 00:00:00
> > > 1 MM15
> > > dtype: object
Thank you for your prompt answers.
1) your dtype solution does not work for me:
> > > pd.Series([datetime.datetime(2015,1,1), 'M15'], dtype=object)
> > > 0 2015-01-01
> > > 1 2015-08-15
> > > dtype: datetime64[ns]
2) there are further inconsistencies when initializing a dataframe with the string 'M15'. printing the dataframe shows the string M15, but examining the first irow (or doing a loop over iterrows()) shows the data instead. this is in my opinion dangerous behaviour.
> > > pd.DataFrame( dict(start=[ pd.datetime(2015,1,1) ], end=['M15'] ) )
> > > end start
> > > 0 M15 2015-01-01
> > >
> > > pd.DataFrame( dict(start=[ pd.datetime(2015,1,1) ], end=['M15'] ) )['end']
> > > 0 M15
> > > Name: end, dtype: object
> > >
> > > pd.DataFrame( dict(start=[ pd.datetime(2015,1,1) ], end=['M15'] ) ).irow(0)
> > > end 2015-08-15
> > > start 2015-01-01
> > > Name: 0, dtype: datetime64[ns]
In case it matters:
> > > pd.**version**
> > > '0.15.2'
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} | 2 | 2015-08-12T21:36:16Z | 2015-08-18T11:23:04Z | 2015-08-18T10:48:47Z | MEMBER | null | Closes #2825.
CC @patricktokeeffe @bilderbuchi
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| null | 12 | 2015-08-13T00:12:36Z | 2015-08-14T16:13:06Z | 2015-08-14T16:13:06Z | NONE | null | something along the lines:
```
df.reindex(df.index.tolist()+new_values).sort().interpolate().ix[new_values]
```
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} | 1 | 2015-08-13T08:39:32Z | 2015-08-16T08:28:53Z | 2015-08-16T08:28:53Z | MEMBER | null | Closes #10735
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} | 11 | 2015-08-13T11:50:25Z | 2019-07-30T03:22:55Z | 2019-07-30T03:22:49Z | NONE | null | If I have a dataframe with cells containing lists of strings (or unicode strings), then these lists are broken when I use `to_csv()` with the `encoding` parameter set. The error does not occur if the `encoding` is not set.
Here is an example (using pandas version 0.16.2):
```
df = pd.DataFrame.from_records(
[('Mary S.',['Detroit, MI','New York, NY']),
('John U.',[u'Atlanta, GA',u'Paris, France'])],
columns=['name','residences'])
df.to_csv('ascii.csv')
df.to_csv('utf8.csv',encoding='utf-8')
```
The ascii-encoded CSV file is fine. (contents of 'ascii.csv' below)
```
,name,residences
0,Mary S.,"['Detroit, MI', 'New York, NY']"
1,John U.,"[u'Atlanta, GA', u'Paris, France']"
```
But the unicode CSV file fails to quote the strings within the lists. (contents of 'utf8.csv' below)
```
,name,residences
0,Mary S.,"[Detroit, MI, New York, NY]"
1,John U.,"[Atlanta, GA, Paris, France]"
```
This results in the data being impossible to recover. For example, if I load this file using `read_csv()`, the relevant cells are treated as strings, and cannot be accurately recast as lists.
The behavior is the same using `encoding='utf-16'` but I didn't check any other encodings.
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| null | 3 | 2015-08-13T15:25:58Z | 2015-08-13T15:43:59Z | 2015-08-13T15:32:39Z | NONE | null | In the 0.16.2 version, transform returns original values, while in the 0.15.2 version it properly transforms the values into group counts. The problem seems to be with how pandas deal with datetime objects.
The only difference is the pandas version, and everything else is the same.
---
```
import pandas as pd
import datetime
df = pd.DataFrame([[pd.to_datetime(datetime.date(2015,8,10)), 'A', 10],
[pd.to_datetime(datetime.date(2015,8,10)), 'A', 20],
[pd.to_datetime(datetime.date(2015,8,11)), 'B', 30],
[pd.to_datetime(datetime.date(2015,8,10)), 'B', 40],
], columns=['timestamp', 'name', 'value'])
grp = df.groupby(['timestamp', 'name'])['value']
grp.transform(lambda x: x.count())
```
---
## 0.15.2 version
0 2
1 2
2 1
3 1
Name: value, dtype: int64
---
## 0.16.2 version
0 10
1 20
2 30
3 40
Name: value, dtype: int64
---
Pandas versions
## INSTALLED VERSIONS
commit: None
python: 3.3.5.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.1
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.6
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: 2.5.5 (dt dec pq3 ext)
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} | 10 | 2015-08-13T15:29:06Z | 2015-09-03T14:19:27Z | 2015-09-03T14:19:26Z | CONTRIBUTOR | null | compat with `numpy` >= 1.8.2 and `bottleneck` >= 1.0, #9422
note that passing skipna=False will still return a NaN
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} | 8 | 2015-08-13T19:53:42Z | 2021-04-18T23:27:20Z | null | MEMBER | null | Say you have a multi-index:
```
In [34]: idx = pd.MultiIndex.from_product([['a', 'b', 'c'], [1, 2, 3], ['f', 'g'
]], names=['lev0', 'lev1', 'lev2'])
In [35]: df = pd.DataFrame(range(len(idx)), index=idx)
In [36]: df
Out[36]:
0
lev0 lev1 lev2
a 1 f 0
g 1
2 f 2
g 3
3 f 4
g 5
b 1 f 6
g 7
2 f 8
g 9
3 f 10
g 11
c 1 f 12
g 13
2 f 14
g 15
3 f 16
g 17
```
and you want to select certain levels of the Index (like you select columns of a frame, I want to select levels of an index and get a subset of the index).
At the moment, some possibilities:
```
In [37]: pd.MultiIndex.from_arrays([df.index.get_level_values(0), df.index.get_level_values(1)])
Out[37]:
MultiIndex(levels=[[u'a', u'b', u'c'], [1, 2, 3]],
labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], [0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2]],
names=[u'lev0', u'lev1'])
In [38]: idx.droplevel(-1) # if you know the ones to drop
Out[38]:
MultiIndex(levels=[[u'a', u'b', u'c'], [1, 2, 3]],
labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], [0, 0
, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2]],
names=[u'lev0', u'lev1'])
In [39]: df.reset_index().set_index(['lev0','lev1']).index
Out[39]:
MultiIndex(levels=[[u'a', u'b', u'c'], [1, 2, 3]],
labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], [0, 0
, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2]],
names=[u'lev0', u'lev1'])
In [40]: df.reset_index(-1).index # if you know the ones to drop
Out[40]:
MultiIndex(levels=[[u'a', u'b', u'c'], [1, 2, 3]],
labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], [0, 0
, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2, 0, 0, 1, 1, 2, 2]],
names=[u'lev0', u'lev1'])
```
Am I missing an easy way to do this?
And if not, I think we should have a better way to do this.
Note: triggerd by this SO question: http://stackoverflow.com/questions/31991388/combinations-of-multiindex-levels-which-occur-in-a-dataframe (but had already encountered this multiple times)
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} | 6 | 2015-08-13T21:34:40Z | 2015-09-02T15:59:34Z | 2015-09-02T11:52:39Z | CONTRIBUTOR | null | Background: I am trying to build a horizonal bar graph that shows me when various builds on a server started and ended, so that I can see visually which builds overlapped in time. But the idea that a horizontal bar graph's bar-width would be a timedelta seems completely foreign to all of the plotting libraries that I have tried today (matplotlib, bokeh, ggplot), so I want to punt and instead reduce my timedelta64's to a floating-point number of days. I can then fudge the axes to display dates anyway.
Problem: there is no easy way to move from a timedelta64 to the standard Python idea of an ordinal date. I would have to extract all of the components manually (days, seconds, fractions of a second) and then multiply them each by an appropriate factor and then add them back together.
The Python `timedelta` class has an alternative that I like very much: a `total_seconds()` method that expresses then entire value of the `timedelta` as a single scalar number of seconds.
This is not a perfect solution, since the factors it uses internally neglect the fact that a day might have included a leap second, but it works pretty well for non-astronomical use.
Could a `Series.dt.total_seconds()` method be added that returns the floating point value `days * 86400 + seconds + microseconds / 1e6`? It would be a great convenience for moving from date-land to scalar-land before plotting. Thanks!
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} | 5 | 2015-08-14T00:11:59Z | 2016-04-03T07:45:23Z | 2016-04-03T07:45:23Z | NONE | null | There appears to be an issue with the .from_coo() sparse function. If the frame is viewed, it gives a "non-NDFFrame error". It could potentially be due to overlapping entries on the same index (this is handled in sparse.coo_matrix by adding the entries by default).
More details here:
http://stackoverflow.com/questions/31970070/non-ndfframe-object-error-using-pandas-sparseseries-from-coo-function
Example:
```
import pandas as pd
import scipy.sparse as ss
import numpy as np
row = (np.random.random(100)*100).astype(int)
col = (np.random.random(100)*100).astype(int)
val = np.random.random(100)*100
sparse = ss.coo_matrix((val,(row,col)),shape=(100,100))
pd.SparseSeries.from_coo(sparse)
TypeError: cannot concatenate a non-NDFrame object
```
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} | 1 | 2015-08-14T01:54:26Z | 2015-09-01T12:06:24Z | 2015-09-01T12:06:24Z | NONE | null | I came across the same error described in [this stack post.](https://stackoverflow.com/questions/30623162/plotting-with-gridspec-index-is-out-of-bounds) I didn't experience the error until upgrading to 0.16.2. Like others who commented, downgrading to 0.15.2 fixed the issue. I couldn't find it reported anywhere else so I wanted to submit an issue.
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} | 8 | 2015-08-14T10:53:45Z | 2015-08-24T18:33:56Z | 2015-08-24T18:33:56Z | CONTRIBUTOR | null | It looks like `len(set)` beats both `len(np.unique)` and `pd.Series.nunique` if done naively -- here's an example with a large number of groups where we try to compute unique counts of a column when grouping by another column:
``` python
>>> df = pd.DataFrame({'a': np.random.randint(10000, size=100000),
'b': np.random.randint(10, size=100000)})
>>> g = df.groupby('a')
>>> %timeit g.b.nunique()
1 loops, best of 3: 1 s per loop
>>> %timeit g.b.apply(pd.Series.nunique)
1 loops, best of 3: 992 ms per loop
>>> %timeit g.b.apply(lambda x: np.unique(x.values).size)
1 loops, best of 3: 652 ms per loop
>>> %timeit g.b.apply(lambda x: len(set(x.values)))
1 loops, best of 3: 469 ms per loop
```
The fastest way I know to accomplish the same thing is this:
``` python
>>> g = df.groupby(['a', 'b'])
>>> %timeit g.b.first().groupby(level=0).size()
100 loops, best of 3: 6.2 ms per loop
```
... which is a _LOT_ faster apparently.
Wonder if something similar could be done in `GroupBy.nunique` since it's quite a common use case?
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} | 1 | 2015-08-14T16:40:56Z | 2015-08-18T23:37:27Z | 2015-08-18T23:37:27Z | CONTRIBUTOR | null | int64/object are implemented. This is slightly tricky as have to count `nan` but not too difficult.
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} | 3 | 2015-08-14T16:55:40Z | 2015-08-20T18:26:23Z | 2015-08-20T18:26:23Z | CONTRIBUTOR | null | xref #7599
@sinhrks
This fails on windows (actually doesn't on a windows vm, you need an actual windows machine....weird)
```
======================================================================
ERROR: test_invalid_index_types (pandas.tseries.tests.test_frequencies.TestFrequencyInference)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_frequencies.py", line 505, in test_invalid_index_types
self.assertRaises(ValueError, lambda : frequencies.infer_freq(i))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\util\testing.py", line 1831, in assertRaises
_callable(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tests\test_frequencies.py", line 505, in <lambda>
self.assertRaises(ValueError, lambda : frequencies.infer_freq(i))
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\frequencies.py", line 813, in infer_freq
index = pd.DatetimeIndex(index)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\util\decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\index.py", line 302, in __new__
subarr = tools.to_datetime(data, box=False)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\util\decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tools.py", line 267, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tools.py", line 376, in _to_datetime
return _convert_listlike(arg, box, format)
File "c:\Users\Jeff Reback\Documents\GitHub\pandas\build\lib.win32-2.7\pandas\tseries\tools.py", line 355, in _convert_listlike
require_iso8601=require_iso8601)
File "tslib.pyx", line 1809, in pandas.tslib.array_to_datetime (pandas\tslib.c:31227)
File "tslib.pyx", line 1995, in pandas.tslib.array_to_datetime (pandas\tslib.c:30864)
File "tslib.pyx", line 1989, in pandas.tslib.array_to_datetime (pandas\tslib.c:30754)
File "tslib.pyx", line 1596, in pandas.tslib.parse_datetime_string (pandas\tslib.c:24749)
File "C:\python27-32\lib\site-packages\python_dateutil-2.2-py2.7.egg\dateutil\parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "C:\python27-32\lib\site-packages\python_dateutil-2.2-py2.7.egg\dateutil\parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
```
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} | 5 | 2015-08-14T19:51:00Z | 2015-08-18T10:47:43Z | 2015-08-18T10:47:43Z | CONTRIBUTOR | null | I'm not sure if this is a bug or a typo in the documentation but for the `min_periods` parameter in MovingOLS the documentation claims
```
min_periods : int
Threshold of non-null data points to require.
If None, defaults to size of window.
```
However this does not seem to be the case? From line 640-648 we have
```
if self._is_rolling:
if window is None:
raise AssertionError("Must specify window.")
if min_periods is None:
min_periods = window
else:
window = len(self._x)
if min_periods is None:
min_periods = 1
```
So this is not true for expanding window MovingOLS? Would it be appropriate to update the documentation to reflect this?
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} | 0 | 2015-08-15T08:57:49Z | 2015-08-20T12:54:55Z | 2015-08-20T12:54:55Z | MEMBER | null | ```
import numpy as np
import pandas as pd
df1 = pd.DataFrame([], columns=['a', 'b', 'c'])
df2 = pd.DataFrame(np.random.randn(3, 3), columns=['x', 'y', 'z'])
# OK
pd.merge(df1, df2, how='right', left_index=True, right_index=True)
# a b c x y z
#0 NaN NaN NaN 0.665359 0.087728 -0.608138
#1 NaN NaN NaN -0.730847 0.882151 0.175648
#2 NaN NaN NaN 2.370834 -1.347337 -0.478547
pd.merge(df1, df2, how='outer', left_index=True, right_index=True)
# a b c x y z
#0 NaN NaN NaN 0.665359 0.087728 -0.608138
#1 NaN NaN NaN -0.730847 0.882151 0.175648
#2 NaN NaN NaN 2.370834 -1.347337 -0.478547
# NG, this should be the same as above
pd.merge(df1, df2, how='right', left_on='a', right_index=True)
# IndexError: cannot do a non-empty take from an empty axes.
pd.merge(df1, df2, how='outer', left_on='a', right_index=True)
# IndexError: cannot do a non-empty take from an empty axes.
```
This works if both `DataFrame` are empty.
```
df1 = pd.DataFrame([], columns=['a', 'b', 'c'])
df2 = pd.DataFrame([], columns=['x', 'y', 'z'])
pd.merge(df1, df2, how='right', left_on='a', right_index=True)
# Empty DataFrame
# Columns: [a, b, c, x, y, z]
# Index: []
pd.merge(df1, df2, how='outer', left_on='a', right_index=True)
# Empty DataFrame
# Columns: [a, b, c, x, y, z]
# Index: []
```
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} | 6 | 2015-08-15T14:23:49Z | 2015-09-19T00:38:10Z | 2015-08-17T11:09:50Z | CONTRIBUTOR | null | Fixes GH #10728
fixes #10548.
Also fixes:
- '\r' followed by EOF should be considered a blank line
- Escape character followed by EOF should produce an exception
- Line containing only whitespace should be skipped if `skip_blank_lines` and `delim_whitespace` are both `True`
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} | 1 | 2015-08-15T14:25:10Z | 2015-08-20T13:48:48Z | 2015-08-20T12:54:55Z | MEMBER | null | Closes #10824.
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} | 3 | 2015-08-15T16:45:00Z | 2015-09-19T00:38:10Z | 2015-08-15T22:22:36Z | CONTRIBUTOR | null | Fixes GH #9535
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} | 1 | 2015-08-15T22:26:41Z | 2015-08-20T17:39:31Z | 2015-08-20T17:39:31Z | CONTRIBUTOR | null | e.g. something in `test_common.py` like
```
p = pd.Period(....)
self.assertTrue(isinstance(p, ABCPeriod))
```
for all of the ABC types defined.
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| null | 2 | 2015-08-17T04:42:31Z | 2015-08-18T10:21:01Z | 2015-08-18T10:21:01Z | NONE | null | - `pip3 install --upgrade pandas` # installed 0.16.2
- Download 0.16.2 source, unzip, use `pip3` to install Sphinx (1.3.1, and dependencies such as sphinx-rtd-theme (0.1.8))
- run `make.py html` in `doc` folder
```
C:\Python34\pandas-0.16.2\doc>c:\Python34\python.exe make.py html
Traceback (most recent call last):
File "C:\Python34\Scripts\sphinx-build-script.py", line 9, in <module>
load_entry_point('Sphinx==1.3.1', 'console_scripts', 'sphinx-build')()
File "C:\Python34\lib\site-packages\sphinx\__init__.py", line 51, in main
sys.exit(build_main(argv))
File "C:\Python34\lib\site-packages\sphinx\__init__.py", line 61, in build_main
from sphinx import cmdline
File "C:\Python34\lib\site-packages\sphinx\cmdline.py", line 23, in <module>
from sphinx.application import Sphinx
File "C:\Python34\lib\site-packages\sphinx\application.py", line 37, in <module>
from sphinx.builders import BUILTIN_BUILDERS
File "C:\Python34\lib\site-packages\sphinx\builders\__init__.py", line 23, in <module>
from sphinx.util import i18n, path_stabilize
File "C:\Python34\lib\site-packages\sphinx\util\i18n.py", line 15, in <module>
from babel.messages.pofile import read_po
File "C:\Python34\lib\site-packages\babel\messages\__init__.py", line 12, in <module>
from babel.messages.catalog import *
File "C:\Python34\lib\site-packages\babel\messages\catalog.py", line 23, in <module>
from babel.dates import format_datetime
File "C:\Python34\lib\site-packages\babel\dates.py", line 28, in <module>
from babel.util import UTC, LOCALTZ
File "C:\Python34\lib\site-packages\babel\util.py", line 278, in <module>
from babel import localtime
File "C:\Python34\lib\site-packages\babel\localtime\__init__.py", line 21, in <module>
from babel.localtime._win32 import _get_localzone
File "C:\Python34\lib\site-packages\babel\localtime\_win32.py", line 18, in <module>
tz_names = get_global('windows_zone_mapping')
File "C:\Python34\lib\site-packages\babel\core.py", line 58, in get_global
_global_data = pickle.load(fileobj)
TypeError: an integer is required (got type str)
Building HTML failed.
```
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} | 2 | 2015-08-17T10:01:34Z | 2015-08-17T11:01:12Z | 2015-08-17T11:00:22Z | CONTRIBUTOR | null | ```
df1 = pd.DataFrame({"a": list("ABCD"), "b": [1,2,3,4]})
df2 = pd.DataFrame({"a": list("BCDF"), "b": [1,2,3,4]})
df1["c"] = df1["a"].astype("category")
df2["c"] = df2["a"].astype("category")
pd.merge(df1, df2, on=["c"], how="outer")
```
Results in:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-97-42d4959051b3> in <module>()
3 df1["c"] = df1["a"].astype("category")
4 df2["c"] = df2["a"].astype("category")
----> 5 pd.merge(df1, df2, on=["c"], how="outer")
C:\portabel\miniconda\envs\sc\lib\site-packages\pandas\tools\merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy)
36 right_index=right_index, sort=sort, suffixes=suffixes,
37 copy=copy)
---> 38 return op.get_result()
39 if __debug__:
40 merge.__doc__ = _merge_doc % '\nleft : DataFrame'
C:\portabel\miniconda\envs\sc\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
203 result = typ(result_data).__finalize__(self, method='merge')
204
--> 205 self._maybe_add_join_keys(result, left_indexer, right_indexer)
206
207 return result
C:\portabel\miniconda\envs\sc\lib\site-packages\pandas\tools\merge.pyc in _maybe_add_join_keys(self, result, left_indexer, right_indexer)
228 key_col.put(
229 na_indexer, com.take_1d(self.right_join_keys[i],
--> 230 right_na_indexer))
231 elif name in self.right:
232 na_indexer = (right_indexer == -1).nonzero()[0]
C:\portabel\miniconda\envs\sc\lib\site-packages\pandas\core\common.pyc in take_nd(arr, indexer, axis, out, fill_value, mask_info, allow_fill)
829 out_shape[axis] = len(indexer)
830 out_shape = tuple(out_shape)
--> 831 if arr.flags.f_contiguous and axis == arr.ndim - 1:
832 # minor tweak that can make an order-of-magnitude difference
833 # for dataframes initialized directly from 2-d ndarrays
AttributeError: 'Categorical' object has no attribute 'flags'
```
```
pd.__version__
'0.16.2'
```
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} | 4 | 2015-08-17T15:45:54Z | 2015-08-21T14:10:24Z | 2015-08-21T14:10:24Z | CONTRIBUTOR | null | I'm trying to save pd.DataFrame using `.to_csv` method.
If DataFrame has TimedeltaIndex the error is risen:
```
>>> dt = pd.Timedelta(seconds=10)
>>> timestamps = [dt, 2*dt, 3*dt]
>>> df = pd.DataFrame({'obs': [11,22,33]}, index=timestamps)
>>> df
obs
00:00:10 11
00:00:20 22
00:00:30 33
>>> df.to_csv('test')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27-9b2e5ea53beb> in <module>()
----> 1 df.to_csv('test')
C:\Python34\lib\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, doub
lequote, escapechar, decimal, **kwds)
1187 escapechar=escapechar,
1188 decimal=decimal)
-> 1189 formatter.save()
1190
1191 if path_or_buf is None:
C:\Python34\lib\site-packages\pandas\core\format.py in save(self)
1465
1466 else:
-> 1467 self._save()
1468
1469 finally:
C:\Python34\lib\site-packages\pandas\core\format.py in _save(self)
1565 break
1566
-> 1567 self._save_chunk(start_i, end_i)
1568
1569 def _save_chunk(self, start_i, end_i):
C:\Python34\lib\site-packages\pandas\core\format.py in _save_chunk(self, start_i
, end_i)
1592 quoting=self.quoting)
1593
-> 1594 lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.
writer)
1595
1596 # from collections import namedtuple
TypeError: Argument 'data_index' has incorrect type (expected numpy.ndarray, got
list)
```
But if Timedelta is not an Index, but a regular column, everything is OK:
```
>>> df['timedelta'] = df.index
>>> df.reset_index(drop=True, inplace=True)
>>> df.to_csv('test')
```
Python 3.4.2 x64 Windows version.
pandas (0.16.2), numpy (1.9.2)
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https://api.github.com/repos/pandas-dev/pandas/issues/10834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10834/events | https://github.com/pandas-dev/pandas/issues/10834 | 101,463,695 | MDU6SXNzdWUxMDE0NjM2OTU= | 10,834 | passing of `test_to_datetime_with_non_exact` depends on OS language. | {
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} | 2 | 2015-08-17T16:52:16Z | 2015-08-17T20:50:10Z | 2015-08-17T20:50:10Z | CONTRIBUTOR | null | The abbreviations of the names of months in `format_regex` are language specific. Thus the test fails on non-english systems:
`format_regex` on this machine is
`re.compile('(?P<d>3[0-1]|[1-2]\\d|0[1-9]|[1-9]| [1-9])(?P<b>jan|feb|m\\är|apr|mai|jun|jul|aug|sep|okt|nov|dez)(?P<y>\\d\\d)', re.IGNORECASE)`
```
ERROR: test_to_datetime_with_non_exact (pandas.tseries.tests.test_timeseries.TimeConversionFormats)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tseries/tools.py", line 363, in _convert_listlike
values, tz = tslib.datetime_to_datetime64(arg)
File "tslib.pyx", line 1451, in pandas.tslib.datetime_to_datetime64 (pandas/tslib.c:22987)
TypeError: Unrecognized value type: <class 'str'>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tseries/tests/test_timeseries.py", line 4335, in test_to_datetime_with_non_exact
result = to_datetime(s,format='%d%b%y',exact=False)
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tseries/tools.py", line 267, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tseries/tools.py", line 373, in _to_datetime
values = _convert_listlike(arg.values, False, format)
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tseries/tools.py", line 366, in _convert_listlike
raise e
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tseries/tools.py", line 336, in _convert_listlike
arg, format, exact=exact, errors=errors)
File "tslib.pyx", line 3018, in pandas.tslib.array_strptime (pandas/tslib.c:44557)
ValueError: time data '19MAY11' does not match format '%d%b%y' (search)
```
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} | 5 | 2015-08-17T17:30:03Z | 2015-08-22T20:12:18Z | 2015-08-22T20:12:18Z | CONTRIBUTOR | null | `test_none_comparison` fails for me for all versions of `numpy>=1.9.0`. I don't see this issue in any `numpy-1.8.x` version.
```
ERROR: test_none_comparison (pandas.tests.test_base.TestIndexOps)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/tests/test_base.py", line 303, in test_none_comparison
result = None != o
File "/Users/ch/repo/pandas/.tox/py34/lib/python3.4/site-packages/pandas/core/generic.py", line 723, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
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} | 5 | 2015-08-17T18:12:32Z | 2015-08-19T10:53:27Z | 2015-08-19T10:53:27Z | CONTRIBUTOR | null | ```
FAIL: testArrayNumpyLabelled (pandas.io.tests.test_json.test_ujson.NumpyJSONTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/ch/repo/pandas/.tox/py27/lib/python2.7/site-packages/pandas/io/tests/test_json/test_ujson.py", line 1141, in testArrayNumpyLabelled
self.assertTrue((expectedvals == output[0]).all())
AssertionError: False is not true
-------------------- >> begin captured stdout << ---------------------
[[42 31]
[24 99]
[ 2 78]]
[[31 42]
[99 24]
[78 2]]
--------------------- >> end captured stdout << ----------------------
```
Order of dict elements seems to be non-deterministic for python2.7 versions as well.
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} | 1 | 2015-08-18T00:03:35Z | 2019-08-10T19:45:05Z | 2015-08-18T10:44:03Z | CONTRIBUTOR | null | partial on #10360
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} | 6 | 2015-08-18T00:03:58Z | 2018-07-06T20:21:07Z | 2017-07-10T21:07:33Z | CONTRIBUTOR | null | xref #10360 / #10838
takes care of full slices, e.g. `p.iloc[0,0,:] = [1,2,3]`, but the following is still failing
(whether mixed blocks or not)
```
In [8]: axes = dict(items = pd.MultiIndex.from_tuples([('A','a'), ('B','b')]), major_axis = pd.MultiIndex.from_tuples([('A',1),('A',2)]), minor_axis=pd.MultiIndex.from_product([['X'],range(3)]))
In [9]: p = Panel(0., **axes)
In [11]: p
Out[11]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 3 (minor_axis)
Items axis: (A, a) to (B, b)
Major_axis axis: (A, 1) to (A, 2)
Minor_axis axis: (X, 0) to (X, 2)
In [13]: p.iloc[0,0,1:3]
Out[13]:
X 1 0
2 0
Name: (A, 1), dtype: float64
In [14]: p.iloc[0,0,1:3] = [1,2]
IndexError: tuple index out of range
```
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} | 23 | 2015-08-18T02:13:15Z | 2015-08-19T00:53:03Z | 2015-08-18T23:37:09Z | CONTRIBUTOR | null | Adresses #10821 - I get a doubling of performance for `value_counts` for a `Series` with a float dtype. Couldn't think of any new tests needed not covered by existing [here](https://github.com/pydata/pandas/blob/master/pandas/tests/test_algos.py#L416)
```
In [1]: f = np.hstack([np.hstack(np.linspace(0,1,1000) for _ in range(1000)), np.array([np.nan] * 1000)])
...: i = range(1000) * 1001
...: df = pd.DataFrame({'f':f, 'i':i})
```
### master
```
In [2]: %timeit df['i'].value_counts()
10 loops, best of 3: 21.1 ms per loop
In [3]: %timeit df['f'].value_counts()
10 loops, best of 3: 114 ms per loop
```
### pr
```
In [3]: %timeit df['i'].value_counts()
10 loops, best of 3: 21 ms per loop
In [2]: %timeit df['f'].value_counts()
10 loops, best of 3: 56 ms per loop
```
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} | 1 | 2015-08-18T02:15:46Z | 2015-08-18T10:47:48Z | 2015-08-18T10:47:43Z | CONTRIBUTOR | null | closes #10823
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| null | 8 | 2015-08-18T08:32:31Z | 2017-06-07T07:19:54Z | 2015-08-18T10:41:10Z | NONE | null | Hi
```
import pandas as pd
X = pd.DataFrame(dict(g=['a','a','b','b','b'],a=range(5)))
```
For some reason this loses the grouping variable 'g':
```
X.groupby(['g'],as_index=False).quantile(0.5)
a
0 0.5
1 3.0
```
I was expecting this output:
```
X.groupby(['g']).quantile(0.5).reset_index()
g a
0 a 0.5
1 b 3.0
```
Maybe related to the above this throws an error (this is useful if I have tons of columns but need result only for one).
```
X.groupby(['g'],as_index=False)['a'].quantile(0.5)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 40, in quantile
File "C:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 579, in wrapper
return self._aggregate_item_by_item(name, *args, **kwargs)
File "C:\Anaconda\lib\site-packages\pandas\core\groupby.py", line 2789, in _aggregate_item_by_item
raise errors
TypeError: quantile() got an unexpected keyword argument 'numeric_only'
```
If I replace `.quantile()` with other aggregate like mean or sum it works just fine.
---
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: FI
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
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: 1.0.6
pymysql: None
psycopg2: None
```
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| null | 9 | 2015-08-18T09:49:49Z | 2019-10-08T09:24:58Z | 2015-08-18T10:37:54Z | NONE | null | The simplest way to access underlying data (ndarray) for dataframe column is `df['column_name'].values`. But it is so slow, that special cache is used for it:
``` python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(np.zeroes((1000, 5)), columns=list('abcde'))
>>> %timeit df['a'].values; df._item_cache = {}
10000 loops, best of 3: 73.1 µs per loop
```
For regular non-sparse array this should approach:
``` python
>>> %cpaste
def get_col_values(df, name):
i = df.columns.get_loc(name)
bm = df._data
return bm.blocks[bm._blknos[i]].iget(bm._blklocs[i])
>>> %timeit get_col_values(df, 'a')
100000 loops, best of 3: 4.67 µs per loop
```
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} | 4 | 2015-08-18T18:38:21Z | 2015-08-21T14:21:21Z | 2015-08-21T14:21:21Z | CONTRIBUTOR | null | The method `DataFrame.to_dict` didn't have an option for index orientation unlike its cousin `to_json`, which does have.
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- [x] Potentially change `FutureWarning` to `DeprecationWarning` for the changed in sorting, xref https://github.com/pydata/pandas/pull/10726
- [x] go thru enhancements make sure that we are using versionadded tags
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- [x] add docs on how to use / add tests (in contributing.md / contributing.rst)
- [x] add/update vbenches to current
- [x] whatsnew note (put in highlites that this is added)
- [ ] test on Windows (basicly works, but had to do some small adjustments)
- [x] get it working with python 3
- [ ] clean up autogenerated tests
- [ ] fix failing tests
- [ ] remove redundancy (by putting things in classes and reusing setup, .
cc @qwhelan
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} | 2 | 2015-08-19T08:55:31Z | 2015-08-19T09:47:11Z | 2015-08-19T09:21:38Z | CONTRIBUTOR | null | Just took this from https://github.com/pydata/pandas/blob/master/doc/source/dsintro.rst#panel4d-experimental
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| null | 0 | 2015-08-19T10:17:06Z | 2015-08-20T12:34:11Z | 2015-08-20T12:34:11Z | CONTRIBUTOR | null | closes #10828
I created tests for this issue here: https://github.com/pydata/pandas/issues/10828. This is my first time working on Pandas, so please let me know if there is anything wrong with the way I have coded these tests. I haven't updated the documentation, I was unsure of what version I should update and whether I should update at all (it seems that the documentation is for additional features and bug fixes rather than increasing test coverage). I'd love any guidance as to what I can do to fully resolve this issue and how I can best help in the future!
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} | 1 | 2015-08-19T11:06:12Z | 2015-09-02T11:51:18Z | 2015-09-02T11:51:18Z | CONTRIBUTOR | null | xref #9456
[39] should raise.
```
In [38]: pd.DataFrame({'a':0.1})
ValueError: If using all scalar values, you must pass an index
In [39]: pd.DataFrame({'a':0.1}, columns=['b'])
Out[39]:
Empty DataFrame
Columns: [b]
Index: []
```
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} | 37 | 2015-08-19T21:29:11Z | 2015-09-14T03:28:03Z | 2015-09-13T20:20:52Z | NONE | null | closes #8325
closes #10652
- Added ability to automatically create a table using the gbq.to_gbq function if destination table does not exist
- Added `gbq.gbq_table_exists()` function to the gbq module
- Added `gbq.create_gbq_table()` function to the gbq module
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} | 1 | 2015-08-20T00:10:29Z | 2015-08-20T23:32:06Z | 2015-08-20T12:48:09Z | CONTRIBUTOR | null | `categories` and `ordered` are properties not attributes ([index.py#L3085-L3091](https://github.com/pydata/pandas/blob/3e35c842f37265ac7339908869d4cc7bdef784d9/pandas/core/index.py#L3085-L3091)). The side effect is that the categories are rehashed too often:
on master:
``` ipython
In [1]: from pandas.core.index import CategoricalIndex
In [2]: np.random.seed(2718281)
In [3]: n = 100000
In [4]: val = map('{:->4}'.format, np.random.randint(0, 10000, n))
In [5]: idx = CategoricalIndex(list(val))
In [6]: %timeit idx[::-1]
100 loops, best of 3: 14.1 ms per loop
In [7]: %timeit idx[idx.argsort()]
10 loops, best of 3: 22 ms per loop
In [8]: cProfile.run("idx[::-1]", sort=1)
174 function calls (171 primitive calls) in 0.025 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.014 0.014 0.014 0.014 {method 'lookup' of 'pandas.hashtable.PyObjectHashTable' objects}
1 0.004 0.004 0.004 0.004 {pandas.algos.take_1d_object_object}
1 0.003 0.003 0.003 0.003 {method 'map_locations' of 'pandas.hashtable.PyObjectHashTable' objects}
```
on branch:
``` ipython
In [6]: %timeit idx[::-1]
The slowest run took 6.78 times longer than the fastest. This could mean that an intermediate result is being cached
10000 loops, best of 3: 29 µs per loop
In [7]: %timeit idx[idx.argsort()]
100 loops, best of 3: 9.38 ms per loop
```
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} | 7 | 2015-08-20T07:22:33Z | 2015-08-20T17:39:19Z | 2015-08-20T17:39:19Z | CONTRIBUTOR | null | closes #10828
I used self.assertIsInstance(obj, type) and inlined as many of the expressions as I could while staying PEP8 compliant. I added in the two tests that I took out, that was an accident. Let me know if this looks okay!
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| null | 1 | 2015-08-20T13:32:56Z | 2015-08-20T13:56:27Z | 2015-08-20T13:56:19Z | NONE | null | The [docstring of this method](https://github.com/pydata/pandas/blob/master/pandas/io/wb.py#L112) states that the DataFrame contains the iso_code, which is not the case. The column is [dropped](https://github.com/pydata/pandas/blob/master/pandas/io/wb.py#L156) from the DataFrame before it is returned.
I commented out the above line, but then most values for iso_code are NAN, because of the call to [convert_objects](https://github.com/pydata/pandas/blob/master/pandas/io/wb.py#L158). I think it would be useful to include the iso_code in the result, but if the conversion is to be kept, it would need to become part of the index.
Not sure, what would be the best solution here. In case the current behaviour is to be kept, the docstring should be corrected.
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} | 1 | 2015-08-20T13:55:43Z | 2015-08-23T10:27:03Z | 2015-08-23T10:27:03Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/10061
closes #8961
point to [pandas-datareader package](https://github.com/pydata/pandas-datareader)
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} | 0 | 2015-08-20T14:08:34Z | 2016-03-25T13:30:15Z | 2016-03-25T13:30:15Z | CONTRIBUTOR | null | using https://github.com/pydata/pandas/blob/master/pandas/io/tests/generate_legacy_storage_files.py
its quite easy to produce a library of pickles/msgpacks from prior versions that we then store to test for back-compat with the current version (IOW, these have generated tests which run).
Ideally like to fill this out and regenerate all of the older ones (you generate using that released version). May have to add some skips for features which didn't exist in particular versions.
- if we can do this for multi-platforms is good.
- updating the contributing docs would be good (on how to do this)
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} | 4 | 2015-08-20T15:22:31Z | 2020-01-03T23:23:43Z | 2020-01-03T23:23:43Z | CONTRIBUTOR | null | ```
In [1]: import pandas;import numpy;import datetime;
In [2]: v = datetime.date.today()
In [3]: pandas.DataFrame({v : pandas.Series(range(3),index=range(3))}, columns=[v])
Out[3]:
2015-08-20
0 0
1 1
2 2
In [4]: v = v, v
In [5]: pandas.DataFrame({v : pandas.Series(range(3),index=range(3))}, columns=[v])
Out[5]:
(2015-08-20, 2015-08-20)
0 NaN
1 NaN
2 NaN
```
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| null | 2 | 2015-08-20T15:29:38Z | 2015-08-20T15:36:46Z | 2015-08-20T15:36:29Z | MEMBER | null | There are lots of codes like: `toolz.merge(dask, [x.dask for x in seq])`.
How about adding `_merge` function which can handle `dict`, `Base` and nested list for internal use? It accepts `_merge(dask, seq)`.
```
def _merge(*dasks, **kwargs):
""" Merge a collection of dask instances and dicts """
force = kwargs.pop('force', False)
result = dict()
for dask in dasks:
if isinstance(dask, Base):
dask = dask.dask
elif isinstance(dask, list):
# for nested list
dask = merge(*dask)
result.update(dask)
return result
```
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| null | 3 | 2015-08-20T15:34:01Z | 2015-08-20T15:56:22Z | 2015-08-20T15:37:04Z | MEMBER | null | Add function to return `graphviz` instance created from dask graph for below reasons:
- When using IPython, `.visualize` outputs unnecessary image file
- Sometimes we want to modify graphviz instance directly
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} | 0 | 2015-08-20T16:23:48Z | 2015-08-20T17:32:21Z | 2015-08-20T17:32:21Z | CONTRIBUTOR | null | closes #10439
closes #10441
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} | 4 | 2015-08-20T20:18:52Z | 2015-08-21T07:31:13Z | 2015-08-21T07:31:13Z | MEMBER | null | So this is visible to the users (already long time deprecated in the docs)
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| null | 7 | 2015-08-20T21:24:14Z | 2015-08-21T15:53:10Z | 2015-08-20T21:38:22Z | CONTRIBUTOR | null | Just stumbled about a very nasty subtle dtype inconsistency after join operation that crippled by out-of-core-join-many-result-files pipeline for unfitting index sizes for the HDF append operation.
See the example code here:
https://nbviewer.jupyter.org/gist/michaelaye/1c34cda54872046b5989
or here as screenshot:
<img width="849" alt="screenshot 2015-08-20 15 23 01" src="https://cloud.githubusercontent.com/assets/69774/9396054/5df0dbc6-474f-11e5-9282-eff823d51b18.png">
What happens that the dtypes of the to-be-joined dataframe depend on if the joining dataframe has either 1 or 2 columns.
Any hint how to avoid that would be appreciated.
Version: '0.16.2+148.gc740bb0'
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} | 10 | 2015-08-20T22:05:05Z | 2015-08-23T22:15:01Z | 2015-08-23T10:26:43Z | CONTRIBUTOR | null | closed #10861
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} | 2 | 2015-08-20T22:15:38Z | 2015-08-21T08:33:52Z | 2015-08-20T22:18:50Z | CONTRIBUTOR | null | I was looking to replace all `np.nan` values in a dataframe with `None`, I was trying to do this using `fillna`, but it seems like this is not supported (through `fillna`, though you can use `where`):
``` python
In [1]: import pandas as pd
i
In [2]: import numpy as np
In [3]: df = pd.DataFrame([[np.nan, 1], [1, np.nan]])
In [4]: df.fillna(value=None)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-f459dbd09698> in <module>()
----> 1 df.fillna(value=None)
/Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/pandas/core/frame.pyc in fillna(self, value, method, axis, inplace, limit, downcast, **kwargs)
2530 axis=axis, inplace=inplace,
2531 limit=limit, downcast=downcast,
-> 2532 **kwargs)
2533
2534 @Appender(_shared_docs['shift'] % _shared_doc_kwargs)
/Users/yoshikivazquezbaeza/.virtualenvs/ipy/lib/python2.7/site-packages/pandas/core/generic.pyc in fillna(self, value, method, axis, inplace, limit, downcast)
2522 if value is None:
2523 if method is None:
-> 2524 raise ValueError('must specify a fill method or value')
2525 if self._is_mixed_type and axis == 1:
2526 if inplace:
ValueError: must specify a fill method or value
```
This is what I really was expecting to see:
``` python
In [6]: df.where((pd.notnull(df)), None)
Out[6]:
0 1
0 None 1
1 1 None
```
---
Info about my system:
```
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: 0.14.0
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.2
patsy: None
dateutil: 2.4.2
pytz: 2015.4
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: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 3 | 2015-08-20T23:35:29Z | 2015-09-01T11:49:25Z | 2015-09-01T11:49:25Z | CONTRIBUTOR | null | closes #10610
I fixed the import statement and added a test to check for proper behavior when accessing a mixed-integer index with a list of values.
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} | 14 | 2015-08-21T00:34:30Z | 2017-05-17T06:10:21Z | 2015-10-18T14:04:01Z | CONTRIBUTOR | null | closes #10343 as discussed in #10345 , changed `Series.asof` to use `reindex` internally, and remove the unused leftovers from index classes. All existing tests in test_series.py:TestSeries.test_asof pass with the new implementation.
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} | 9 | 2015-08-21T00:40:06Z | 2015-08-22T20:28:02Z | 2015-08-22T20:03:01Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/10804
``` ipython
In [1]: np.random.seed(2718281)
In [2]: n = 500000
In [3]: u = int(0.1*n)
In [4]: arr = ["s%04d" % i for i in np.random.randint(0, u, size=n)]
In [5]: ts = pd.Series(arr).astype('category')
In [6]: %timeit ts.value_counts()
10 loops, best of 3: 82.7 ms per loop
```
on branch:
``` ipython
In [6]: %timeit ts.value_counts()
10 loops, best of 3: 31.3 ms per loop
```
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} | 2 | 2015-08-21T03:23:08Z | 2015-08-31T12:19:06Z | 2015-08-31T12:19:06Z | CONTRIBUTOR | null | The issue is existing name of `Index` is dropped during conversion to either `TimedeltaIndex` or `DatetimeIndex`.
```
In [42]: vec = ['01:02:03', '01:02:04']
In [43]: i = pd.Index(vec, name='label')
In [44]: i
Out[44]: Index(['01:02:03', '01:02:04'], dtype='object', name='label')
In [45]: pd.to_datetime(i)
Out[45]: DatetimeIndex(['2015-08-21 01:02:03', '2015-08-21 01:02:04'], dtype='datetime64[ns]', freq=None, tz=None)
In [46]: pd.to_timedelta(i)
Out[46]: TimedeltaIndex(['01:02:03', '01:02:04'], dtype='timedelta64[ns]', freq=None)
```
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} | 3 | 2015-08-21T08:33:29Z | 2015-08-24T11:36:30Z | 2015-08-24T11:36:30Z | CONTRIBUTOR | null | addresses second point of #10747
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| null | 4 | 2015-08-21T08:51:02Z | 2015-10-23T19:19:16Z | 2015-10-23T19:19:16Z | MEMBER | null | There will be a pandas sprint at EuroScipy (https://www.euroscipy.org/2015/sprints/) on Sunday the 30th of August.
The sprints will take place at the Seminar Centre of the Hauser Forum (very near to the conference venue) from 10am to 5pm.
This issue will be the landing page for contributors.
[pandas](http://pandas.pydata.org) is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
See our shiny new [Contributing Guidelines & Instructions](http://pandas-docs.github.io/pandas-docs-travis/contributing.html)
Here are various issues that are open and would like your attention:
- [Difficulty Novice](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Novice%22)
- [Difficulty Intermediate](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Intermediate%22)
- [Difficulty Advanced](https://github.com/pydata/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22Difficulty+Advanced%22)
Please comment on an issue if you are working on it.
Here is a [link](https://gitter.im/pydata/pandas) to the gitter dev chatroom.
#### How to prepare?
If you want to prepare the sprint, the most important is to _set up a development environment_: forking the pandas repo, cloning your fork locally, installing all development dependencies in an environment and testing that you can build pandas locally, ...
You can find some information about this in our contributing docs: http://pandas-docs.github.io/pandas-docs-travis/contributing.html
#### What to work on?
There is no plan to work on something specific, but the idea is that everybody can choose on something they would like to work, and that there is some guidance to do that (but of course, if some of the attendants want to work together on a specific topic, this is certainly possible!).
You can work on whatever you like, but to facilitate finding an appropriate issue, we have the 'Difficulty Novice / Intermediate / Advanced' labels.
I will also put some ideas of issues I would like to see tackled during the sprints here in the coming days.
Please comment on an issue if you are working on it.
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} | 0 | 2015-08-21T15:54:02Z | 2015-09-01T11:05:56Z | 2015-09-01T11:05:56Z | CONTRIBUTOR | null | http://pandas.pydata.org/pandas-docs/version/0.16.2/timeseries.html#legacy-aliases
discussed at bit in #7832
Leave these common aliases.
| Legacy Time Rule | Offset Alias |
| --- | --- |
| min | T |
| ms | L |
| us | U |
Deprecate These
| Legacy Time Rule | Offset Alias |
| --- | --- |
| WEEKDAY | B |
| EOM | BM |
| W@MON | W-MON |
| W@TUE | W-TUE |
| W@WED | W-WED |
| W@THU | W-THU |
| W@FRI | W-FRI |
| W@SAT | W-SAT |
| W@SUN | W-SUN |
| Q@JAN | BQ-JAN |
| Q@FEB | BQ-FEB |
| Q@MAR | BQ-MAR |
| A@JAN | BA-JAN |
| A@FEB | BA-FEB |
| A@MAR | BA-MAR |
| A@APR | BA-APR |
| A@MAY | BA-MAY |
| A@JUN | BA-JUN |
| A@JUL | BA-JUL |
| A@AUG | BA-AUG |
| A@SEP | BA-SEP |
| A@OCT | BA-OCT |
| A@NOV | BA-NOV |
| A@DEC | BA-DEC |
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} | 1 | 2015-08-21T15:54:55Z | 2015-09-01T12:16:17Z | 2015-09-01T12:06:24Z | MEMBER | null | Closes #10657. Closes #10819. Both are related to the logic how `pandas` detects axes layout. Use current logic as much to support cases like #7457, and added error handing logic for gridspec.
Following is an example notebook how the fix works.
- https://gist.github.com/sinhrks/2d29e2e8ef26f2757e92
CC @TomAugspurger @JanSchulz @heelancd @williamsmj
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} | 1 | 2015-08-21T16:18:12Z | 2015-08-21T20:27:39Z | 2015-08-21T20:27:39Z | CONTRIBUTOR | null | closed #10633
This might be able to be merged quicker than the other one.
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} | 2 | 2015-08-21T23:32:35Z | 2015-08-23T21:31:56Z | 2015-08-23T13:28:33Z | MEMBER | null | Currently, error is raised from `numpy` and incorrect for `pandas`
```
s = pd.Series([1, 2, 3])
s.quantile(2)
# File ".../numpy/lib/function_base.py", line 3078, in _percentile
# raise ValueError("Percentiles must be in the range [0,100]")
# ValueError: Percentiles must be in the range [0,100]
```
`describe` using `percentiles` option outputs better error message, but not check lower limit.
```
# OK
s.describe(percentiles=[2])
# Traceback (most recent call last):
# File "pandas/core/generic.py", line 4167, in describe
# raise ValueError(msg.format(list(percentiles)))
# ValueError: percentiles should all be in the interval [0, 1]. Try [0.02] instead.
# NG
s.describe(percentiles=[-2])
# File ".../numpy/lib/function_base.py", line 3078, in _percentile
# raise ValueError("Percentiles must be in the range [0,100]")
# ValueError: Percentiles must be in the range [0,100]
```
This PR fix both error messages. Also, remove deprecated `percentile_width` option in `describe`.
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} | 18 | 2015-08-22T02:24:27Z | 2017-01-04T22:28:28Z | 2017-01-04T22:28:13Z | NONE | null | I'd like to read a set of csv files but exclude specific columns. `read_csv` currently has a `usecols` keyword, but it requires writing a list of all the columns present. This is a bit tedious and more importantly, not all files have the same columns, so `usecols` would not work in general cases, whereas a complimentary function would work. Can a `skipcols` keyword be added to 0.17 that accepts a list of column names and reads all but those columns into a DataFrame? Thanks.
xref #4749
xref #8985
xref #6710
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| null | 9 | 2015-08-22T09:29:37Z | 2015-08-30T09:05:33Z | 2015-08-30T09:05:26Z | CONTRIBUTOR | null | closes #10720
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| null | 1 | 2015-08-22T09:43:52Z | 2015-08-22T13:00:15Z | 2015-08-22T13:00:15Z | CONTRIBUTOR | null | Hi all. This might take a bit more work to get the error messages right, but it's a start.
Let me know what you think.
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} | 2 | 2015-08-22T11:05:50Z | 2015-08-24T11:37:58Z | 2015-08-24T11:37:58Z | NONE | null | Hi Pandas devs!
This call produces 3 timestamps when it has to produce only two:
pd.date_range('2005-01-12 10:00', '2005-01-12 16:00', freq='345min')
this gives:
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-01-12 10:00:00, ..., 2005-01-12 **21:30:00**]
Length: 3, Freq: 345T, Timezone: None
strangely, for another date function works as expected:
pd.date_range('2005-01-13 10:00', '2005-01-13 16:00', freq='345min')
this gives:
<class 'pandas.tseries.index.DatetimeIndex'>
[2005-01-13 10:00:00, 2005-01-13 **15:45:00**]
Length: 2, Freq: 345T, Timezone: None
Could you please take a look? Thanks!
Pandas versions:
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.15.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
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} | 2 | 2015-08-22T12:15:06Z | 2015-08-31T01:03:13Z | 2015-08-31T01:03:13Z | MEMBER | null | Follow-up for #10726.
There, the specific section on sorting in basics.rst is adapted, but the rest of the docs should be updated as well.
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} | 4 | 2015-08-22T14:48:11Z | 2015-08-24T13:59:27Z | 2015-08-24T11:37:59Z | CONTRIBUTOR | null | closes #10885
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} | 2 | 2015-08-22T15:07:09Z | 2015-09-09T14:56:11Z | 2015-09-09T14:56:11Z | NONE | null | To reproduce the errors:
```
import pandas as pd
names = ['Bob','Jessica','Mary','John','Mel']
births = [968, 155, 77, 578, 973]
BabyDataSet = zip(names, births)
df = pd.DataFrame(data=BabyDataSet, columns=['Names', 'Births'])
```
Any of these will yield a `TypeError`:
```
print df.to_latex(frame=None)
print df.to_latex(force_unicode=False)
print df.to_latex(justify='left')
```
I assume this is a problem with documentation and that these arguments are deprecated
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} | 3 | 2015-08-22T20:20:30Z | 2015-08-28T02:29:10Z | 2015-08-28T02:29:04Z | CONTRIBUTOR | null | closes #10366
replaces #10454
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} | 1 | 2015-08-22T21:50:42Z | 2015-08-24T18:22:38Z | 2015-08-24T18:22:38Z | CONTRIBUTOR | null | xref #6581
remove `colSpace`
remove auto time series broadcasting, xref #2304
deprecate `TimeSeries` (forgot was not actually deprecated)
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| null | 7 | 2015-08-23T05:49:25Z | 2021-04-18T23:25:20Z | 2021-04-18T23:25:20Z | CONTRIBUTOR | null | I'm doing this:
``` python
df[df.CHANNEL=='MUV'].loc['2015'].DET_TEMP.plot()
```
with the index being a DatetimeIndex and if I don't plot it all works fine:
``` python
df[df.CHANNEL=='MUV'].loc['2015'][['CHANNEL','DET_TEMP']].head()
```
but when I try the plotting I get this error:
``` python
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-84-28500c6c30a0> in <module>()
----> 1 df[df.CHANNEL=='MUV'].loc['2015'].DET_TEMP.plot()
/usr/local/python3/miniconda/lib/python3.4/site-packages/pandas-0.16.2+409.g73dcb95-py3.4-linux-x86_64.egg/pandas/tools/plotting.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2526 yerr=yerr, xerr=xerr,
2527 label=label, secondary_y=secondary_y,
-> 2528 **kwds)
2529
2530
/usr/local/python3/miniconda/lib/python3.4/site-packages/pandas-0.16.2+409.g73dcb95-py3.4-linux-x86_64.egg/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2331 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2332
-> 2333 plot_obj.generate()
2334 plot_obj.draw()
2335 return plot_obj.result
/usr/local/python3/miniconda/lib/python3.4/site-packages/pandas-0.16.2+409.g73dcb95-py3.4-linux-x86_64.egg/pandas/tools/plotting.py in generate(self)
940 self._make_legend()
941 self._post_plot_logic()
--> 942 self._adorn_subplots()
943
944 def _args_adjust(self):
/usr/local/python3/miniconda/lib/python3.4/site-packages/pandas-0.16.2+409.g73dcb95-py3.4-linux-x86_64.egg/pandas/tools/plotting.py in _adorn_subplots(self)
1068 naxes=nrows * ncols, nrows=nrows,
1069 ncols=ncols, sharex=self.sharex,
-> 1070 sharey=self.sharey)
1071
1072 for ax in to_adorn:
/usr/local/python3/miniconda/lib/python3.4/site-packages/pandas-0.16.2+409.g73dcb95-py3.4-linux-x86_64.egg/pandas/tools/plotting.py in _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
3333 layout = np.zeros((nrows+1,ncols+1), dtype=np.bool)
3334 for ax in axarr:
-> 3335 layout[ax.rowNum, ax.colNum] = ax.get_visible()
3336
3337 if sharex and nrows > 1:
IndexError: index 5 is out of bounds for axis 1 with size 3
```
System: Conda, all updates, Pandas: '0.16.2+409.g73dcb95' (73dcb953efd1646f)
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} | 1 | 2015-08-23T12:44:39Z | 2015-08-24T18:24:19Z | 2015-08-24T18:24:19Z | CONTRIBUTOR | null | - Remove the table keyword in HDFStore.put/append, in favor of using format= #4645
- Remove unused keyword `kind` in `read_excel/ExcelFile` #4712
- Remove `infer_type` keyword from `pd.read_html` as its unused, #4770, #7032
- Remove `offset` and `timeRule` keywords from `Series.tshift/shift`, in favor of `freq`, #4853, #4864
- Remove `pd.load/pd.save` aliases in favor of `pd.to_pickle/pd.read_pickle`, #3787
- Deprecate `WidePanel/LongPanel`
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| null | 1 | 2015-08-23T18:39:50Z | 2015-08-23T21:19:29Z | 2015-08-23T21:19:09Z | CONTRIBUTOR | null | Dupe of #3324
---
So far it just says this:
```
freq : string or DateOffset, default ‘D’ (calendar daily)
Frequency strings can have multiples, e.g. ‘5H’
```
but it should also either refer to a doc where possible frequencies are being discussed or list them here.
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} | 6 | 2015-08-24T00:42:38Z | 2015-11-11T16:27:48Z | 2015-08-24T18:33:56Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/10820
on master:
``` ipython
In [2]: df = pd.DataFrame({'a': np.random.randint(10000, size=100000),
...: 'b': np.random.randint(10, size=100000)})
In [3]: %timeit df.groupby('a')['b'].nunique()
1 loops, best of 3: 1.66 s per loop
In [4]: %timeit df.groupby(['a', 'b'])['b'].first().groupby(level=0).size()
10 loops, best of 3: 36.3 ms per loop
```
on branch:
``` ipython
In [2]: %timeit df.groupby('a')['b'].nunique()
10 loops, best of 3: 29.2 ms per loop
```
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} | 4 | 2015-08-24T13:15:44Z | 2016-11-11T11:20:38Z | 2016-11-11T11:20:08Z | NONE | null | Data Packages is a project that offers a standardized and extensible way of packaging up data. There is already a growing community around Data Packages and an increasing number of [core datasets](http://data.okfn.org/roadmap/core-datasets) which can easily be grabbed from a URL.
It would be great to see easy import and export to and from a Tabular Data Package - [see spec here](http://data.okfn.org/doc/tabular-data-package) - into a Pandas DataFrame (e.g. pd.from_datapackage(...) & pd.to_datapackage(...)).
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| null | 6 | 2015-08-24T17:45:42Z | 2015-09-01T14:42:32Z | 2015-09-01T14:42:32Z | NONE | null | Right now using between_time on an empty series will raise an TypeError: Index must be DatetimeIndex. I would think that it would make more sense to return an empty list (or at least return a more helpful Error).
``` python
import datetime as dt
import pandas as pd
s = pd.Series([])
s.between_time(dt.time(0, 0), dt.time(23, 0))
```
I would assume that expanding the Error Handler of the method should do the trick:
``` python
[...]
except AttributeError:
if self.empty:
return self
else:
raise TypeError('Index must be DatetimeIndex')
```
The Error persists on this configuration:
INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.3
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.1.0
sphinx: 1.2.2
patsy: 0.2.1
dateutil: 2.2
pytz: 2014.3
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.0
openpyxl: 2.0.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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| null | 5 | 2015-08-24T18:08:28Z | 2015-08-26T14:28:48Z | 2015-08-26T01:13:13Z | CONTRIBUTOR | null | Here's my file:
```
;;;;
;;;;
;;;;
;;;;
;;;;
;;;;
;;;;
;;;;
;;;;
;;;;
date;Param1;Param2;Param4;Param5
;m²;°C;m²;A
;;;;
01.01.1990 00:00;1;1;2;3
01.01.1990 01:00;5;3;4;5
01.01.1990 02:00;9;5;6;7
01.01.1990 03:00;13;7;8;9
01.01.1990 04:00;17;9;10;11
01.01.1990 05:00;21;11;12;13
```
If I read it with the following line:
```
pd.read_csv(file, sep=';', header=10, parse_dates=True, skip_blank_lines=True, skiprows=0)
```
It gets the headers but then takes the two rows between header and data values.
```
date Param1 Param2 Param4 Param5
0 m² °C m² A
1 NaN NaN NaN NaN NaN
2 01.01.1990 00:00 1 1 2 3
3 01.01.1990 01:00 5 3 4 5
4 01.01.1990 02:00 9 5 6 7
```
If I use
```
pd.read_csv(file, sep=';', header=10, parse_dates=True, skip_blank_lines=True, skiprows=2)
```
It simply moves the header down, i.e. same effect as saying
```
pd.read_csv(file, sep=';', header=12, parse_dates=True, skip_blank_lines=True, skiprows=0)
```
I think the skiprows needs to skip rows either before or after header.
But how do I skip after the header, only?
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| null | 10 | 2015-08-24T20:23:42Z | 2017-12-15T01:27:15Z | null | CONTRIBUTOR | null | Hello,
I wonder if current use of `read_sql` couldn't lead to SQL injection.
I read in https://docs.python.org/2/library/sqlite3.html
```
# Never do this -- insecure!
symbol = 'RHAT'
c.execute("SELECT * FROM stocks WHERE symbol = '%s'" % symbol)
# Do this instead
t = ('RHAT',)
c.execute('SELECT * FROM stocks WHERE symbol=?', t)
print c.fetchone()
# Larger example that inserts many records at a time
purchases = [('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
('2006-04-05', 'BUY', 'MSFT', 1000, 72.00),
('2006-04-06', 'SELL', 'IBM', 500, 53.00),
]
c.executemany('INSERT INTO stocks VALUES (?,?,?,?,?)', purchases)
```
Most of people will use
```
"SELECT * FROM stocks WHERE symbol = '%s'" % symbol
```
(or `.format(...)`)
with `read_sql`
if `symbol` is an unsafe input it could lead some problems
http://xkcd.com/327/
Is it safe to do it here ?
Kind regards
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| null | 5 | 2015-08-24T23:33:32Z | 2015-08-26T01:09:52Z | 2015-08-26T00:57:14Z | CONTRIBUTOR | null | I am quite perplexed about this:
A function applied to rows of dataframes that returns a tuple throws an error if the result is stored into a new column, but does not if the column already exists? This does not happen if I return just a number.
Get the example notebook here:
https://gist.github.com/a83ed19c43fe2eaac6a4
or see it here:
https://nbviewer.jupyter.org/gist/michaelaye/a83ed19c43fe2eaac6a4
Using master pandas. ('0.16.2+442.g24c147a', 24c147a369d4)
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} | 17 | 2015-08-25T03:46:14Z | 2015-12-04T02:32:26Z | 2015-11-16T12:37:55Z | CONTRIBUTOR | null | closes - #4095 - adding Cythonized versions of `shift`, `cumprod` and `cumsum`, along with a helper `GroupBy._cython_transform` that other cythonized transform-ops could be added to.
I'm using fused types here instead of adding to `generated.pyx` - as far as I can tell that is working well, looking back through the old issues (#255) it looks like that wasn't done originally because of older versions of Cython?
### 0.16.2
```
In [17]: df = pd.DataFrame({'i':range(1000) * 1000,
'f':np.linspace(0, 2000, 1000000),
'f2':np.linspace(1, 2, 1000000)})
In [18]: %timeit df.groupby('i').cumsum()
1 loops, best of 3: 614 ms per loop
In [19]: %timeit df.groupby('i').shift()
1 loops, best of 3: 499 ms per loop
```
### PR
```
In [2]: %timeit df.groupby('i').cumsum()
10 loops, best of 3: 58.6 ms per loop
In [3]: %timeit df.groupby('i').shift()
10 loops, best of 3: 73.4 ms per loop
```
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} | 6 | 2015-08-25T09:37:43Z | 2019-10-21T19:52:15Z | null | NONE | null | It is convenient to be able to add one DateOffset to another one:
``` python
from pandas.tseries.offsets import DateOffset as DO
DO(hour=2) + DO(hour=3) # raises ValueError
```
Currently it is not supported. Note that `DateOffset`s basically replicates the functionality of `relativedelta`s from `dateutil` module which support addition
``` python
from dateutil.relativedelta import relativedelta as RD
RD(hour=2) + RD(hour=3) # yields relativedelta(hour=3)
```
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