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https://api.github.com/repos/pandas-dev/pandas/issues/11503 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11503/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11503/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11503/events | https://github.com/pandas-dev/pandas/issues/11503 | 114,515,313 | MDU6SXNzdWUxMTQ1MTUzMTM= | 11,503 | Pandas' read_excel, ExcelFile, failing to open some .xls files. | {
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| null | 25 | 2015-11-02T03:03:20Z | 2021-10-20T10:55:47Z | 2015-11-02T13:10:02Z | CONTRIBUTOR | null | I am trying to read in `.xls` files from [http://www.eia.gov/coal/data.cfm#production](http://www.eia.gov/coal/data.cfm#production) -- specifically the **Historical detailed coal production data (1983-2013)** `coalpublic2012.xls` file that's freely available via the dropdown. Pandas cannot read it.
In contrast, the file for the most recent year available, 2013, `coalpublic2013.xls` file, works without a problem:
```
import pandas as pd
df1 = pd.read_excel("coalpublic2013.xls")
```
but the next decade of `.xls` files (2004-2012) do not load. I have looked at these files with Excel, and they open, and are not corrupted.
The error that I get from pandas is:
`````` ---------------------------------------------------------------------------
XLRDError Traceback (most recent call last)
<ipython-input-28-0da33766e9d2> in <module>()
----> 1 df = pd.read_excel("coalpublic2012.xlsx")
/Users/jonathan/anaconda/lib/python2.7/site-packages/pandas/io/excel.pyc in read_excel(io, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, **kwds)
161
162 if not isinstance(io, ExcelFile):
--> 163 io = ExcelFile(io, engine=engine)
164
165 return io._parse_excel(
/Users/jonathan/anaconda/lib/python2.7/site-packages/pandas/io/excel.pyc in __init__(self, io, **kwds)
204 self.book = xlrd.open_workbook(file_contents=data)
205 else:
--> 206 self.book = xlrd.open_workbook(io)
207 elif engine == 'xlrd' and isinstance(io, xlrd.Book):
208 self.book = io
/Users/jonathan/anaconda/lib/python2.7/site-packages/xlrd/__init__.pyc in open_workbook(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)
433 formatting_info=formatting_info,
434 on_demand=on_demand,
--> 435 ragged_rows=ragged_rows,
436 )
437 return bk
/Users/jonathan/anaconda/lib/python2.7/site-packages/xlrd/book.pyc in open_workbook_xls(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)
89 t1 = time.clock()
90 bk.load_time_stage_1 = t1 - t0
---> 91 biff_version = bk.getbof(XL_WORKBOOK_GLOBALS)
92 if not biff_version:
93 raise XLRDError("Can't determine file's BIFF version")
/Users/jonathan/anaconda/lib/python2.7/site-packages/xlrd/book.pyc in getbof(self, rqd_stream)
1228 bof_error('Expected BOF record; met end of file')
1229 if opcode not in bofcodes:
-> 1230 bof_error('Expected BOF record; found %r' % self.mem[savpos:savpos+8])
1231 length = self.get2bytes()
1232 if length == MY_EOF:
/Users/jonathan/anaconda/lib/python2.7/site-packages/xlrd/book.pyc in bof_error(msg)
1222 if DEBUG: print("reqd: 0x%04x" % rqd_stream, file=self.logfile)
1223 def bof_error(msg):
-> 1224 raise XLRDError('Unsupported format, or corrupt file: ' + msg)
1225 savpos = self._position
1226 opcode = self.get2bytes()
XLRDError: Unsupported format, or corrupt file: Expected BOF record; found '<?xml ve'```
And I have tried various other things:
```df = pd.ExcelFile("coalpublic2012.xls", encoding_override='cp1252')
import xlrd
wb = xlrd.open_workbook("coalpublic2012.xls")
``````
to no avail. My pandas version: 0.17.0
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} | 2 | 2015-11-02T09:45:26Z | 2015-11-02T11:32:44Z | 2015-11-02T10:06:53Z | CONTRIBUTOR | null | Thanks to pypi.python.org/pypi/misspellings
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} | 1 | 2015-11-02T22:52:33Z | 2015-11-13T17:17:46Z | 2015-11-13T17:17:46Z | CONTRIBUTOR | null | Addressing issue #11464 - Provide links to help for iterator and chunksize usage (since there's no docstring help for TextFileReader).
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} | 1 | 2015-11-02T23:48:55Z | 2015-11-13T22:22:28Z | 2015-11-13T22:22:28Z | CONTRIBUTOR | null | this makes the conda builds use the proper numpy build
e.g.
`conda build conda.recipe --python 3.5 --numpy 1.9`
will then generate
`pandas-v0.17.0-np19py35` which is what we want
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https://api.github.com/repos/pandas-dev/pandas/issues/11507 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11507/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11507/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11507/events | https://github.com/pandas-dev/pandas/pull/11507 | 114,766,613 | MDExOlB1bGxSZXF1ZXN0NDk1Mjg1Njg= | 11,507 | BUG: GH10355 groupby std() doesnt sqrt grouping cols | {
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| null | 11 | 2015-11-03T08:17:46Z | 2019-02-14T05:24:07Z | 2016-01-06T17:20:51Z | NONE | null | New attempt at #10355 Hopefully should address the issues raised in #11300
Previously, grouping columns were square rooted when as_index=False
We now test whether the grouping keys are in the columns, and
if so don't square root those columns.
Note that we squash TypeError which occurs when self.keys is not
Hashable, and so we can't check for existence in columns.
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| null | 2 | 2015-11-03T09:01:24Z | 2019-04-03T17:46:44Z | 2015-11-03T09:03:54Z | NONE | null | Indexing is inconsistent in Pandas v.0.16.2 `read_csv`'s `skiprows` option. For example:
Given the csv `temp.txt`:
```
a,b,c
1,2,3
4,5,6
7,8,9
```
this happens:
```
In [76]: pd.read_csv('temp.txt', dtype=str, skiprows=[1,2])
Out[76]:
a b c
0 7 8 9
In [77]: pd.read_csv('temp.txt', dtype=str, skiprows=[0,1])
Out[77]:
4 5 6
0 7 8 9
```
whereas the documentation says:
```
skiprows : list-like or integer
Line numbers to skip (0-indexed) or number of lines to skip (int)
at the start of the file
```
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} | 2 | 2015-11-03T12:10:23Z | 2015-11-03T13:30:28Z | 2015-11-03T13:29:56Z | NONE | null | ## I write a short program like this:
# coding: utf-8
from pandas import DataFrame
from string import letters
df = DataFrame(
{'a': range(0, 10), 'b': range(10, 20), 'c': range(20, 30)},
index=list(letters[:10])
)
rec = df.ix['A']
rec['a'] = 1
print df
## When I run it with ipython, everything is fine!
## $ ipython test.py
a b c
A 1 10 20
B 1 11 21
C 2 12 22
D 3 13 23
E 4 14 24
F 5 15 25
G 6 16 26
H 7 17 27
I 8 18 28
J 9 19 29
## But when I run it just use python,I get this below:
## $ python test.py
Traceback (most recent call last):
File "test.py", line 9, in <module>
rec = df.ix['A']
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.2-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 70, in **getitem**
return self._getitem_axis(key, axis=0)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.2-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 929, in _getitem_axis
return self._get_label(key, axis=axis)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.2-py2.7-linux-x86_64.egg/pandas/core/indexing.py", line 86, in _get_label
return self.obj._xs(label, axis=axis)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.2-py2.7-linux-x86_64.egg/pandas/core/generic.py", line 1473, in xs
loc = self.index.get_loc(key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.16.2-py2.7-linux-x86_64.egg/pandas/core/index.py", line 1572, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas/index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)
File "pandas/index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)
File "pandas/hashtable.pyx", line 686, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)
File "pandas/hashtable.pyx", line 694, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)
KeyError: 'A'
## Why pandas act different when I use just python?
python version = 2.7.6
ipython version = 4.0.0
pandas version = 0.16.2
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| null | 2 | 2015-11-03T14:49:48Z | 2015-11-04T00:52:48Z | 2015-11-03T15:02:24Z | NONE | null | ## I try to make a DataFrame from a dict, and need to change it's value then. I find when dict as different data type value, It does't work.
# dict has same data type, It works.
from pandas import DataFrame
a = {'a': 1, 'b': 1}
df = DataFrame([a])
print df
rec = df.ix[0]
rec['a'] = 100
print df
out:
a b
0 1 1
a b
0 100 1
# dict has different data type, go wrong.
from pandas import DataFrame
a = {'a': 1, 'b': '1'}
df = DataFrame([a])
print df
rec = df.ix[0]
rec['a'] = 100
print df
out:
a b
0 1 1
a b
0 1 1
-c:6: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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| null | 3 | 2015-11-03T16:44:38Z | 2015-11-05T13:54:38Z | 2015-11-05T12:53:59Z | CONTRIBUTOR | null | I am seeing soem wrong-size argument when trying to write a large-sized DF to HDF.
``` python
import numpy as np
import pandas as pd
def generate_large_df(n,m):
cols = {}
for i in range(m):
if i % 3 == 0:
temp = np.random.randn(n)
temp *= 2**32
temp[n//2:] *= 2**32
cols['col_' + str(i)] = temp
elif i % 3 == 1:
temp = 2 * (np.random.rand(n) - 0.5)
temp *= 2**32
temp[n//2:] *= 2**32
cols['col_' + str(i)] = temp.astype(np.int64)
else:
str_len = int(np.ceil(10 * np.random.rand() + 1))
temp = np.tile(np.array('A' * str_len),(n,))
cols['col_' + str(i)] = temp
return pd.DataFrame(cols)
# Done to avoid memory errors
df = generate_large_df(8e5,25)
df = pd.concat([df]*10,0)
df = pd.concat([df]*10,0)
df.to_hdf('test.h5','test')
```
This code produces:
```
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-18-406bc2726c38> in <module>()
----> 1 df2.to_hdf('test-2.h5','test',mode='w')
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-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):
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-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)
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-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):
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-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):
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-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:
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in write(self, obj, **kwargs)
2810 # I have no idea why, but writing values before items fixed #2299
2811 blk_items = data.items.take(blk.mgr_locs)
-> 2812 self.write_array('block%d_values' % i, blk.values, items=blk_items)
2813 self.write_index('block%d_items' % i, blk_items)
2814
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in write_array(self, key, value, items)
2577 except:
2578 pass
-> 2579 vlarr.append(value)
2580 else:
2581 if empty_array:
/data/unixhome/ksheppard/anaconda/lib/python2.7/site-packages/tables/vlarray.pyc in append(self, sequence)
535 nparr = None
536
--> 537 self._append(nparr, nobjects)
538 self.nrows += 1
539
tables/hdf5extension.pyx in tables.hdf5extension.VLArray._append (tables/hdf5extension.c:20849)()
OverflowError: value too large to convert to int
```
The error occurs when writing the large string block.
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| null | 1 | 2015-11-03T18:17:58Z | 2015-11-03T18:49:09Z | 2015-11-03T18:48:53Z | NONE | null | When I upgraded from 0.16.2 to 0.17.0, I was met with a nasty surprise when dropping duplicates. It looks like DataFrame.drop_duplicates() is not working as I would expect it to based on the previous version. I have a dataframe
```
test_ids = df['test_id'].unique()
print('N test ids: {}'.format(test_ids.shape))
print('N tests: {}'.format(df[['test_id', <some other columns>]].drop_duplicates().shape))
```
the output is:
```
N test ids: (341334,)
N tests: (237426, 10)
```
when I run the same in 0.16.2 the output is:
```
N test ids: (341334,)
N tests: (341334, 10)
```
I don't think you should be able to get fewer rows than the number of unique entries in a single column.
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| null | 2 | 2015-11-03T20:08:57Z | 2015-11-05T12:55:03Z | 2015-11-05T12:54:46Z | NONE | null | I think we could improve bootstrap functionality because what we have [now](https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L577) is limited in terms of customization and functionality. Right now we only calculate mean, median and mid ranges and basically plot them all in the same figure.
It would be nice to be able to pass in specific stat functions, have customizable parameters such as number of samples and sample sizes and different calculation methods, such as this [library](https://github.com/cgevans/scikits-bootstrap/blob/master/scikits/bootstrap/bootstrap.py#L20).
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} | 1 | 2015-11-03T20:36:55Z | 2016-04-10T22:12:08Z | 2016-04-10T22:12:08Z | NONE | null | Is it currently possible to combine the CustomBusinessDay and BusinessHour classes in any way?
My use-case is that I want to be able to create a SubClass of BusinessHour that would skip over all time occurring in the 'holidays' or outside the 'weekmask' defined within a CustomBusinessDay instance. This would be incredibly useful for me, and, I suspect, many others.
I have currently tried subclassing BusinessHour and replacing the next_bday attribute with in an instance of a CustomBusinessDate. Unfortunately, it does not rollforward past days defined as holidays, and seems to function the same as a typical BusinessDay object.
If such functionality is not currently available, please consider this a feature request.
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} | 14 | 2015-11-04T01:19:49Z | 2017-07-19T16:58:36Z | 2017-07-19T16:58:36Z | NONE | null | What is the reasoning for defaulting "display.width" to 80? Pandas knows the width of the terminal so why not default "display.width" to 'None' and let Pandas wrap to the terminal width by default?
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| null | 2 | 2015-11-04T12:33:37Z | 2015-11-04T12:55:58Z | 2015-11-04T12:55:13Z | CONTRIBUTOR | null | While it's possible to find the `max` of a Series containing strings, it's not possible to find the `idxmax`:
```
>>> s = pd.Series(list('ABCDEFGHIJKLMNOPQRSTUVWXYZ'))
>>> s.max()
'Z'
>>> s.idxmax()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-292-28e0d79e56be> in <module>()
----> 1 s.idxmax()
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/core/series.py in idxmax(self, axis, out, skipna)
1218 numpy.ndarray.argmax
1219 """
-> 1220 i = nanops.nanargmax(_values_from_object(self), skipna=skipna)
1221 if i == -1:
1222 return np.nan
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/core/nanops.py in nanargmax(values, axis, skipna)
492 """
493 values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
--> 494 isfinite=True)
495 result = values.argmax(axis)
496 result = _maybe_arg_null_out(result, axis, mask, skipna)
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/core/nanops.py in _get_values(values, skipna, fill_value, fill_value_typ, isfinite, copy)
178 values = _values_from_object(values)
179 if isfinite:
--> 180 mask = _isfinite(values)
181 else:
182 mask = isnull(values)
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/core/nanops.py in _isfinite(values)
221 is_integer_dtype(values) or is_bool_dtype(values)):
222 return ~np.isfinite(values)
--> 223 return ~np.isfinite(values.astype('float64'))
224
225
ValueError: could not convert string to float: 'Z'
```
This surprised me because it works without a problem in numpy:
```
>>> arr = np.array(list('ABCDEFGHIJKLMNOPQRSTUVWXYZ'))
>>> arr.argmax()
25
```
It seems that pandas `idxmax` implementation implicitly assumes numerical types.
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} | 2 | 2015-11-04T17:37:52Z | 2015-11-10T02:27:58Z | 2015-11-10T02:27:58Z | NONE | null | I recently migrated some of my code to Pandas 0.17.0. I found that the df.describe() method is clobbering index names when used after a transpose. Here I'm just using transpose as an easy way to create multi-index column names.
Pandas 0.17.0
Numpy 1.9.2
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
# Create Multi Index For Rows
In [11]: df.groupby(['A', 'B']).mean().index.names
Out[11]: FrozenList([u'A', u'B'])
# Transposing the row index to column names works.
In [31]: df.groupby(['A', 'B']).mean().T.columns.names
Out[31]: FrozenList([u'A', u'B'])
# After the describe() method the column names are dropped.
In [23]: df.groupby(['A', 'B']).mean().T.describe().columns.names
Out[23]: FrozenList([None]) # In Pandas 0.13.1 the column names were preserved.
```
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| null | 3 | 2015-11-04T20:37:39Z | 2015-11-04T21:21:08Z | 2015-11-04T20:53:02Z | CONTRIBUTOR | null | _is_view previously only testing whether first block was a view, when should return `_is_view = True` if _any_ block is a view.
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} | 2 | 2015-11-04T22:52:03Z | 2015-11-11T03:23:53Z | 2015-11-11T03:23:52Z | NONE | null | I have found that 'right' joins on index values raise TypeEerror: Argument 'left' has incorrect type (expected numpy.ndarray, got Int64Index).
Both index types are the same Int64Index index. This works in pandas 0.13.1.
Pandas: 0.17.0
Numpy: 1.9.2
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
s = pd.Series(np.repeat(np.arange(8),2), index=np.repeat(np.arange(8),2), name='TEST')
In []: s.head()
Out[]:
0 0
0 0
1 1
1 1
2 2
dtype: int32
# The following all work as expected
df.join(s, how='inner')
df.join(s, how='outer')
df.join(s, how='left')
# Right Joins Type Error
df.join(s, how='right')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-80-26e8bf54fd8f> in <module>()
----> 1 df.join(s, how='right')
D:\Python27\lib\site-packages\pandas\core\frame.pyc in join(self, other, on, how, lsuffix, rsuffix, sort)
4218 # For SparseDataFrame's benefit
4219 return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
-> 4220 rsuffix=rsuffix, sort=sort)
4221
4222 def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
D:\Python27\lib\site-packages\pandas\core\frame.pyc in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)
4232 return merge(self, other, left_on=on, how=how,
4233 left_index=on is None, right_index=True,
-> 4234 suffixes=(lsuffix, rsuffix), sort=sort)
4235 else:
4236 if on is not None:
D:\Python27\lib\site-packages\pandas\tools\merge.pyc in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)
33 right_index=right_index, sort=sort, suffixes=suffixes,
34 copy=copy, indicator=indicator)
---> 35 return op.get_result()
36 if __debug__:
37 merge.__doc__ = _merge_doc % '\nleft : DataFrame'
D:\Python27\lib\site-packages\pandas\tools\merge.pyc in get_result(self)
194 self.left, self.right = self._indicator_pre_merge(self.left, self.right)
195
--> 196 join_index, left_indexer, right_indexer = self._get_join_info()
197
198 ldata, rdata = self.left._data, self.right._data
D:\Python27\lib\site-packages\pandas\tools\merge.pyc in _get_join_info(self)
309 if self.left_index and self.right_index:
310 join_index, left_indexer, right_indexer = \
--> 311 left_ax.join(right_ax, how=self.how, return_indexers=True)
312 elif self.right_index and self.how == 'left':
313 join_index, left_indexer, right_indexer = \
D:\Python27\lib\site-packages\pandas\core\index.pyc in join(self, other, how, level, return_indexers)
2212 if self.is_monotonic and other.is_monotonic:
2213 return self._join_monotonic(other, how=how,
-> 2214 return_indexers=return_indexers)
2215 else:
2216 return self._join_non_unique(other, how=how,
D:\Python27\lib\site-packages\pandas\core\index.pyc in _join_monotonic(self, other, how, return_indexers)
2463 join_index, lidx, ridx = self._left_indexer(sv, ov)
2464 elif how == 'right':
-> 2465 join_index, ridx, lidx = self._left_indexer(other, self)
2466 elif how == 'inner':
2467 join_index, lidx, ridx = self._inner_indexer(sv, ov)
TypeError: Argument 'left' has incorrect type (expected numpy.ndarray, got Int64Index)
```
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} | 11 | 2015-11-04T23:26:18Z | 2015-11-15T20:49:53Z | 2015-11-15T18:37:03Z | NONE | null | When attempting to use the Pandas 0.17.0 plot method to target plotting on an external axes: AttributeError: 'Axes' object has no attribute 'is_first_col'.
Pandas: 0.17.0
Numpy: 1.9.2
Matplotlib: 1.5.0
``` python
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
fig = plt.figure()
ax = fig.add_axes((0,0,1,1))
df.plot(kind='scatter', ax=ax, x='C', y='D', c=df.C, s=100, linewidth=0, colormap='hsv')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-b38f872a3fc3> in <module>()
----> 1 df.plot(kind='scatter', ax=ax, x='C', y='D', c=df.C, s=100, linewidth=0)
D:\Python27\lib\site-packages\pandas\tools\plotting.pyc in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
3665 fontsize=fontsize, colormap=colormap, table=table,
3666 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 3667 sort_columns=sort_columns, **kwds)
3668 __call__.__doc__ = plot_frame.__doc__
3669
D:\Python27\lib\site-packages\pandas\tools\plotting.pyc in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2550 yerr=yerr, xerr=xerr,
2551 secondary_y=secondary_y, sort_columns=sort_columns,
-> 2552 **kwds)
2553
2554
D:\Python27\lib\site-packages\pandas\tools\plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
2378 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2379
-> 2380 plot_obj.generate()
2381 plot_obj.draw()
2382 return plot_obj.result
D:\Python27\lib\site-packages\pandas\tools\plotting.pyc in generate(self)
990 self._post_plot_logic_common(ax, self.data)
991 self._post_plot_logic(ax, self.data)
--> 992 self._adorn_subplots()
993
994 def _args_adjust(self):
D:\Python27\lib\site-packages\pandas\tools\plotting.pyc in _adorn_subplots(self)
1141 naxes=nrows * ncols, nrows=nrows,
1142 ncols=ncols, sharex=self.sharex,
-> 1143 sharey=self.sharey)
1144
1145 for ax in self.axes:
D:\Python27\lib\site-packages\pandas\tools\plotting.pyc in _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
3403 # as we only have labels in teh first column and we always have a subplot there,
3404 # we can skip the layout test
-> 3405 if ax.is_first_col():
3406 continue
3407 if sharey or len(ax.get_shared_y_axes().get_siblings(ax)) > 1:
AttributeError: 'Axes' object has no attribute 'is_first_col'
```
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| null | 8 | 2015-11-05T01:40:15Z | 2020-05-16T03:44:09Z | null | NONE | null | repro:
(1) make a psycopg2 conn
(2) make a pandas df
(3) call df.read_sql_query("SELECT pg_sleep(1000);", conn)
(4) kill the terminal
actual: connection on backend is left hanging as idle in transaction
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} | 5 | 2015-11-05T07:49:19Z | 2015-12-19T12:58:19Z | 2015-12-19T12:58:09Z | CONTRIBUTOR | null | Hi,
I encountered a problem when using read_sql with a remote mySQL database.
After loading the modules,
``` python
import pymysql
import pandas as pd
import sqlalchemy
```
I create both a `pymysql` connector and a `sqlalchemy` engine with `pymysql` for the same database.
``` python
connector = pymysql.connect(...)
engine = sqlalchemy.create_engine('mysql+pymysql://...')
```
Everything is fine with the engine
``` python
df = pd.DataFrame()
query = " SELECT id, created_at FROM themes"
for chunk in pd.read_sql(query, engine, chunksize=100):
df = df.append(chunk)
```
but fails with the connector
``` python
df = pd.DataFrame()
query = " SELECT id, created_at FROM themes"
for chunk in pd.read_sql(query, connector, chunksize=100):
df = df.append(chunk)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-9a0ee2188a78> in <module>()
1 df = pd.DataFrame()
2 query = " SELECT id, created_at FROM themes"
----> 3 for chunk in pd.read_sql(query, connector_mySQL, chunksize=100):
4 df = df.append(chunk)
/usr/local/lib/python3.5/site-packages/pandas/io/sql.py in _query_iterator(cursor, chunksize, columns, index_col, coerce_float, parse_dates)
1563 yield _wrap_result(data, columns, index_col=index_col,
1564 coerce_float=coerce_float,
-> 1565 parse_dates=parse_dates)
1566
1567 def read_query(self, sql, index_col=None, coerce_float=True, params=None,
/usr/local/lib/python3.5/site-packages/pandas/io/sql.py in _wrap_result(data, columns, index_col, coerce_float, parse_dates)
135
136 frame = DataFrame.from_records(data, columns=columns,
--> 137 coerce_float=coerce_float)
138
139 _parse_date_columns(frame, parse_dates)
/usr/local/lib/python3.5/site-packages/pandas/core/frame.py in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
928 else:
929 arrays, arr_columns = _to_arrays(data, columns,
--> 930 coerce_float=coerce_float)
931
932 arr_columns = _ensure_index(arr_columns)
/usr/local/lib/python3.5/site-packages/pandas/core/frame.py in _to_arrays(data, columns, coerce_float, dtype)
5211 if isinstance(data[0], (list, tuple)):
5212 return _list_to_arrays(data, columns, coerce_float=coerce_float,
-> 5213 dtype=dtype)
5214 elif isinstance(data[0], collections.Mapping):
5215 return _list_of_dict_to_arrays(data, columns,
/usr/local/lib/python3.5/site-packages/pandas/core/frame.py in _list_to_arrays(data, columns, coerce_float, dtype)
5289 def _list_to_arrays(data, columns, coerce_float=False, dtype=None):
5290 if len(data) > 0 and isinstance(data[0], tuple):
-> 5291 content = list(lib.to_object_array_tuples(data).T)
5292 else:
5293 # list of lists
TypeError: Argument 'rows' has incorrect type (expected list, got tuple)
```
Here are the versions of the modules
``` python
print(pymysql.__version__)
print(sqlalchemy.__version__)
print(pd.__version__)
0.6.7.None
1.0.9
0.17.0
```
Best regards,
Flavien.
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| null | 1 | 2015-11-05T10:37:09Z | 2015-11-05T12:46:46Z | 2015-11-05T12:46:22Z | CONTRIBUTOR | null | I was trying to read a soft link to a csv file but failed.
Is this the expected behavior? Sometimes working with data it would be more convenient if I can use a soft link instead of the real file path.
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| null | 2 | 2015-11-05T11:03:46Z | 2015-11-05T12:43:09Z | 2015-11-05T12:43:03Z | NONE | null | I seem to have encountered a **bug** while using
_DataFrame.apply(np.std)_ or _DF.groupby.agg(np.std)_
**tested on:**
- debian 7.9 (wheezy), pandas version: 0.16.2.dev
- ubuntu 15.10, pandas version: 0.15.0
- _more version details_ for the tested snippets _below_
**test code:**
``` python
import numpy as np
import pandas as pd
print("pandas version: ", pd.__version__)
# why is there a difference ?
s = pd.Series([1.11] * 10)
print(np.std(s))
#1.88486436615e-08
print(np.std(s.values))
#2.22044604925e-16
# why is it significantly != 0 ?
# why is there a difference ?
print(np.std(pd.Series([53426.7756333882,] * 50)))
#0.0011048543456
print(np.std(pd.Series([53426.7756333882,] * 123)))
#0.000704429402084
# doing that with data frames
df = pd.DataFrame([538512.198638,] * 123)
print(df.apply(np.std))
#0 0.030867
# dtype: float64
print(np.std(df))
#0 0.030867
# dtype: float64
print(np.std(df.values))
#2.32830643654e-10
```
using the following data set even **NaNs appear as result for std**
funnily this **happens if you add enough digits after the comma**
``` python
import numpy as np
import pandas as pd
print("pandas version: ", pd.__version__)
df = pd.DataFrame([538512.1986379109,]*126)
df['uid']=15
df.set_index('uid', inplace=True)
# why do I occasionally even get NaN for identical values?
# NaN appears if there are enough decimal places after the comma
print(df.groupby(level=0).agg([np.mean, np.std]))
# 0
# mean std
#uid
#15 538512.198638 NaN
df['bla'] = 911.
print(df.groupby(level=0).agg([np.mean, np.std]))
# 0 bla
# mean std mean std
#uid
#15 538512.198638 NaN 911 0
df['bla'] = 911.12351171571542312243214
print(df.groupby(level=0).agg([np.mean, np.std]))
# 0 bla
# mean std mean std
#uid
#15 538512.198638 NaN 911 NaN
```
**bug hypotheses:**
this looks like a detail-problem regarding the way pandas applies functions
is the size of the mapped-on container calculated wrong? (e.g. not the single column series length is used, but the overall container's number of elements)
**version details:** (ubuntu 15.10)
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_AT.UTF-8
pandas: 0.15.0
nose: 1.3.6
Cython: 0.23.3
numpy: 1.8.2
scipy: 0.14.1
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.2
pytz: 2014.10
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: 2.3.0-b1
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: 2.6.2
sqlalchemy: 1.0.8
pymysql: 0.6.2.None
psycopg2: 2.6.1 (dt dec mx pq3 ext lo64)
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| null | 1 | 2015-11-05T15:07:58Z | 2015-11-05T15:20:58Z | 2015-11-05T15:20:37Z | NONE | null | I am getting an error when indexing by comparing tuples:
``` python
In [13]: df = pd.DataFrame([[('A', 1), ('B', 0)], [('A', 0), ('B', 1)], [('A', 1), ('B', 0)]], columns=['A', 'B'])
In [14]: df
Out[14]:
A B
0 (A, 1) (B, 0)
1 (A, 0) (B, 1)
2 (A, 1) (B, 0)
In [15]: df[df.A == ('A', 1)]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-15-2663526e3e28> in <module>()
----> 1 df[df.A == ('A', 1)]
/home/ankur/anaconda3/envs/py3/lib/python3.4/site-packages/pandas/core/ops.py in wrapper(self, other, axis)
724 op_desc = _op_descriptions[op_name]
725 if op_desc['reversed']:
--> 726 equiv = 'other ' + op_desc['op'] + ' series'
727 else:
728 equiv = 'series ' + op_desc['op'] + ' other'
/home/ankur/anaconda3/envs/py3/lib/python3.4/site-packages/pandas/core/ops.py in na_op(x, y)
642 if isinstance(y, list):
643 y = lib.list_to_object_array(y)
--> 644
645 if isinstance(y, (np.ndarray, pd.Series)):
646 if (x.dtype == np.bool_ and
pandas/lib.pyx in pandas.lib.vec_compare (pandas/lib.c:14361)()
ValueError: Arrays were different lengths: 3 vs 2
```
But this used to work in 0.16.2, so not sure if this is expected or a bug.
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} | 11 | 2015-11-05T16:00:38Z | 2016-09-11T13:55:39Z | 2016-09-11T13:55:39Z | CONTRIBUTOR | null | I have the following error when reading a bunch of stata file
```
ValueError: Version of given Stata file is not 104, 105, 108, 113 (Stata 8/9), 114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), or 118 (Stata 14)
```
I do not know the way to check the stata version of the file but I suspect it is fairly recent.
Opening the file with stata 12 and saving it solves the problem.
But I do not have stata installed on my machine and I cannot do that everytime.
How can I check the version of the file ?
Is there anybody kind enough to have a look at the problematic file (personnal email please) ?
I suspect encoding problem. data is a sample of a french survey with many string with accent etc.
Thanks for help
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| null | 2 | 2015-11-06T06:03:41Z | 2015-11-07T15:11:45Z | 2015-11-06T06:22:27Z | NONE | null | When I run this code in OSX, I get 0's for rows that only have NaN in them:
```
import pandas as pd
import numpy as np
df = pd.DataFrame.from_dict({'AAPL': [1,2,3,4,5,6,np.nan,np.nan], 'Date': [1,2,3,4,5,6,7,8]})
df = df.set_index('Date')
df.sum(axis=1)
```
```
Date
1 1
2 2
3 3
4 4
5 5
6 6
7 0
8 0
dtype: float64
```
When running in Ubuntu for example I get the expected output:
```
Date
1 1
2 2
3 3
4 4
5 5
6 6
7 NaN
8 NaN
dtype: float64
```
The only difference I have been able to attribute this to is the OS. They are both using 0.17 pandas and numpy 1.10. Can anyone replicate this error?
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} | 1 | 2015-11-06T07:21:39Z | 2015-11-09T21:11:09Z | 2015-11-09T21:11:09Z | NONE | null | The origin post with pictures is on: http://stackoverflow.com/questions/33559772/is-this-a-bug-of-series-plot-in-pandas-0-17
I always use `ser.plot(xticks=range(0, len(ser), step), ..)` to plot figures with specified xticks. But it doesn't work anymore in pandas 0.17 when the Series with labels. Here are the codes:
``` python
In [1]: from numpy import random as rnd
In [2]: import pandas as pd
In [3]: pd.__version__
Out[3]: '0.17.0'
In [4]: %matplotlib inline
In [5]: rnd.seed(123)
In [6]: ser = pd.Series(rnd.randn(73).cumsum(), index=['P%02d' % i for i in range(73)])
In [7]: ser.plot(figsize=(9, 6), rot=60, title='Figure without xticks')
Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0x8370198
In [8]: ser.plot(figsize=(9, 6), xticks=list(range(0, 73, 6)), rot=60, title='Figure with xticks')
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x83e9940>
```
The figure with xticks specified doesn't display xticklabes correctly.
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| null | 5 | 2015-11-06T10:05:34Z | 2015-11-08T19:49:14Z | 2015-11-07T15:18:11Z | NONE | null | For me it would be extremely helpful if the read_csv function would raise a recognizable exception when it is called with an empty stream. Currently I'm checking the type and message of the exception raised to guess if this situation has occurred. It would be less fragile it an exception of a specific type would be raised.
Examples of the exception raised in different version of pandas
> >>> import pandas
> >>> from StringIO import StringIO
> >>> pandas.read_csv(StringIO())
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "<some path to>\pandas\io\parsers.py", line 474, in parser_f
> return _read(filepath_or_buffer, kwds)
> File "<some path to>\pandas\io\parsers.py", line 250, in _read
> parser = TextFileReader(filepath_or_buffer, **kwds)
> File "<some path to>\pandas\io\parsers.py", line 566, in __init__
> self._make_engine(self.engine)
> File "<some path to>\pandas\io\parsers.py", line 705, in _make_engine
> self._engine = CParserWrapper(self.f, **self.options)
> File "<some path to>\pandas\io\parsers.py", line 1072, in __init__
> self._reader = _parser.TextReader(src, **kwds)
> File "pandas\parser.pyx", line 512, in pandas.parser.TextReader.__cinit__ (pandas\parser.c:4830)
> ValueError: No columns to parse from file
> >>> pandas.version.version
> '0.16.2'
> >>>
and
> >>> import pandas
> >>> from StringIO import StringIO
> >>> pandas.read_csv(StringIO())
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "<some path to>\pandas\io\parsers.py", line 420, in parser_f
> return _read(filepath_or_buffer, kwds)
> File "<some path to>\pandas\io\parsers.py", line 218, in _read
> parser = TextFileReader(filepath_or_buffer, **kwds)
> File "<some path to>\pandas\io\parsers.py", line 502, in __init__
> self._make_engine(self.engine)
> File "<some path to>\pandas\io\parsers.py", line 610, in _make_engine
> self._engine = CParserWrapper(self.f, **self.options)
> File "<some path to>\pandas\io\parsers.py", line 972, in __init__
> self._reader = _parser.TextReader(src, **kwds)
> File "parser.pyx", line 483, in pandas.parser.TextReader.__cinit__ (pandas\parser.c:4616)
> File "parser.pyx", line 605, in pandas.parser.TextReader._get_header (pandas\parser.c:6056)
> pandas.parser.CParserError: Passed header=0 but only 0 lines in file
> >>> pandas.version.version
> '0.13.1'
> >>>
Of course I could try to find out if this situation occurs before calling read_csv but when working with streams this is not trivial. If the situation is easily flagged within pandas then that might be a more sane location to implement this.
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https://api.github.com/repos/pandas-dev/pandas/issues/11531 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11531/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11531/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11531/events | https://github.com/pandas-dev/pandas/pull/11531 | 115,481,643 | MDExOlB1bGxSZXF1ZXN0NDk5Mzk1OTk= | 11,531 | BUG: fix incorrect xticklabels when specifying xticks (GH11529) | {
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} | 1 | 2015-11-06T10:50:01Z | 2015-11-09T21:11:09Z | 2015-11-09T21:11:09Z | MEMBER | null | Fixes #11529
@sinhrks in PR #10717 you moved the setting of the ticklabels from `_adorn_subplots` to its own function `_post_plot_logic_common`. But this is called before `_adorn_subplots`, and the specified `xticks` are only set in `_adorn_subplots`, so now it does not use the new xticks to determine the xticklabels.
I just switched the order of the two functions. At first sight this seems OK, but do you know if there could be any side effects of switching the order?
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| null | 1 | 2015-11-06T11:30:45Z | 2015-11-06T14:02:30Z | 2015-11-06T14:02:19Z | NONE | null | ``` python
>>> import pandas
>>> import StringIO
>>> pandas.read_csv(StringIO.StringIO('test\n13324234234324234322332432432'))
```
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 1.5.4
setuptools: 3.3
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.1
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: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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https://api.github.com/repos/pandas-dev/pandas/issues/11533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11533/events | https://github.com/pandas-dev/pandas/issues/11533 | 115,546,095 | MDU6SXNzdWUxMTU1NDYwOTU= | 11,533 | AbstractHolidayCalendar: suggestion to update documentation with correct usage | {
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} | 5 | 2015-11-06T17:00:14Z | 2015-11-14T14:57:23Z | 2015-11-14T14:57:23Z | NONE | null | ``` python
from pandas.tseries.holiday import Holiday, nearest_workday, get_calendar, HolidayCalendarFactory, USPresidentsDay, \
USMemorialDay, USLaborDay, USThanksgivingDay, AbstractHolidayCalendar, USFederalHolidayCalendar
from pandas.util.print_versions import show_versions
>>> show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.4
Cython: None
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2013.9
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.3.1
openpyxl: 2.1.4
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.4.6 (dt dec pq3 ext)
>>> class MyCalendar(AbstractHolidayCalendar):
rules = [
Holiday("New Years Day", month=1, day=1, observance=nearest_workday),
USPresidentsDay,
USMemorialDay,
Holiday("Independence Day", month=7, day=4, observance=nearest_workday),
USLaborDay,
Holiday("Veterans Day", month=11, day=11, observance=nearest_workday),
USThanksgivingDay,
Holiday("Christmas", month=12, day=25, observance=nearest_workday)
]
>>> cal = MyCalendar()
>>> cal.rules
[Holiday: New Years Day (month=1, day=1, observance=<function nearest_workday at 0x7f6dbbfd5bf8>),
Holiday: Presidents Day (month=2, day=1, offset=<DateOffset: kwds={'weekday': MO(+3)}>),
Holiday: MemorialDay (month=5, day=24, offset=<DateOffset: kwds={'weekday': MO(+1)}>),
Holiday: Independence Day (month=7, day=4, observance=<function nearest_workday at 0x7f6dbbfd5bf8>),
Holiday: Labor Day (month=9, day=1, offset=<DateOffset: kwds={'weekday': MO(+1)}>),
Holiday: Veterans Day (month=11, day=11, observance=<function nearest_workday at 0x7f6dbbfd5bf8>),
Holiday: Thanksgiving (month=11, day=1, offset=<DateOffset: kwds={'weekday': TH(+4)}>),
Holiday: Christmas (month=12, day=25, observance=<function nearest_workday at 0x7f6dbbfd5bf8>)]
#this works as expected, but isn't in the documentation:
>>> cal.holidays(start='11/5/2014', end='11/4/2015')
DatetimeIndex(['2014-11-11', '2014-11-27', '2014-12-25', '2015-01-01',
'2015-02-16', '2015-05-25', '2015-07-03', '2015-09-07'],
dtype='datetime64[ns]', freq=None, tz=None)
#this is what the documentation says to do, and the results make no sense:
from pandas.tseries.offsets import CDay #docs don't actually say where CDay comes from, I had to dig that up myself
>>> oneyear = pd.DatetimeIndex(start='11/5/2014', end='11/4/2015', freq=CDay(calendar=cal)).to_pydatetime()
>>> oneyear
array([datetime.datetime(2014, 11, 5, 0, 0),
datetime.datetime(2014, 11, 6, 0, 0),
datetime.datetime(2014, 11, 7, 0, 0),
datetime.datetime(2014, 11, 10, 0, 0),
datetime.datetime(2014, 11, 11, 0, 0),
datetime.datetime(2014, 11, 12, 0, 0),
datetime.datetime(2014, 11, 13, 0, 0),
datetime.datetime(2014, 11, 14, 0, 0),
datetime.datetime(2014, 11, 17, 0, 0),
datetime.datetime(2014, 11, 18, 0, 0),
datetime.datetime(2014, 11, 19, 0, 0),
datetime.datetime(2014, 11, 20, 0, 0),
datetime.datetime(2014, 11, 21, 0, 0),
datetime.datetime(2014, 11, 24, 0, 0),
datetime.datetime(2014, 11, 25, 0, 0),
datetime.datetime(2014, 11, 26, 0, 0),
datetime.datetime(2014, 11, 27, 0, 0),
datetime.datetime(2014, 11, 28, 0, 0), etc.
```
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} | 14 | 2015-11-06T17:32:41Z | 2015-11-24T14:55:23Z | 2015-11-24T13:11:20Z | CONTRIBUTOR | null | Hello,
I hope I've followed the contribution guidelines correctly, but am happy to change things if necessary.
I noticed that andrews_curves doesn't make use of numpy arrays in what I thought was a sensible use case: for generating its samples.
I added a test which uses variable length random data, so that I could check the timing changes between the numpy and non-numpy versions and found the following (rough data):
| Length of data | Time / s (non-numpy) | Time / s (numpy) |
| --- | --- | --- |
| Without test | 3.43 | 3.84 |
| 10 | 4.69 | 4.21 |
| 100 | 6.58 | 5.20 |
| 1000 | 20.67 | 16.41 |
| 10000 | 162.17 | 125.37 |
The test adds some overhead (though it is decorated with `@slow`), so I'm happy to amend the commit and remove it. Otherwise, the changes seem to have resulted in a small speed up, which becomes more important for larger data (my original motivation, since I was trying to do it with a 100k x 5 dataframe).
Thanks,
Kyle
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} | 1 | 2015-11-06T17:39:29Z | 2015-11-07T09:40:44Z | 2015-11-07T09:40:13Z | CONTRIBUTOR | null | As discussed in _gitter_, I added a couple of cross-links to the _Computational tools_ section in the docs.
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| null | 2 | 2015-11-07T00:13:49Z | 2015-11-07T20:23:48Z | 2015-11-07T00:25:06Z | NONE | null | ``` python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(6, 4), index=pd.date_range('20150101', periods=6), columns=list('ABCD'))
plotters = [
"df.plot()",
"df.plot(legend=None)",
"df.plot(legend=None, subplots=True, layout=(2, 2))",
"df['A'].plot(legend=None)",
"df['A'].plot(legend=None, subplots=True)"
]
for i, plotter in enumerate(plotters, 1):
eval(plotter)
plt.title(plotter)
plt.savefig('%d.png' % i)
plt.close('all')
```
Also, notice how the title is broken in the third image.
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} | 3 | 2015-11-07T02:47:02Z | 2021-04-20T06:02:50Z | null | MEMBER | null | From #11497. Found inconsistencyr regarding `fillna` downcasting.
I understand `fillna(downcast=None)` should downcast values appropriately. It doesn't work on `float` dtype.
```
# NG
pd.Series([1, 2, np.nan, 4]).fillna(3)
#0 1
#1 2
#2 3
#3 4
# dtype: float64
# OK
pd.Series([1, 2, np.nan, 4], dtype=object).fillna(3)
#0 1
#1 2
#2 3
#3 4
# dtype: int64
```
Based on the internal logic, `downcast` can accept dtype. It works on `float`, but not on `object`.
- https://github.com/pydata/pandas/blob/master/pandas/core/internals.py#L366
```
# OK
pd.Series([1, 2, np.nan, 4]).fillna(3, downcast='int')
#0 1
#1 2
#2 3
#3 4
# dtype: int64
# NG
pd.Series([1, 2, np.nan, 4], dtype=object).fillna(3, downcast='int')
#0 1
#1 2
#2 3
#3 4
# dtype: object
```
I understood the expected behavior as below:
- all dtypes should be downcast by default (downcast=None)
- if any dtype is passed via `downcast` kw, downcast to the specified dtype if possible.
- `downcast=False` will not perform downcast.
- [ ] Add `Index.fillna` to API (follow-up of #11343)
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| null | 4 | 2015-11-07T09:40:31Z | 2015-11-20T18:42:15Z | 2015-11-20T18:42:15Z | CONTRIBUTOR | null | {
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} | 11 | 2015-11-07T10:32:26Z | 2019-08-01T17:19:50Z | 2017-10-31T19:58:48Z | CONTRIBUTOR | null | pandas: 0.17.0
``` python
import pandas
s = pandas.Series([1,2,3])
s.sort_values(by=["a"])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-4a7ccaa074a5> in <module>()
1 import pandas
2 s = pandas.Series([1,2,3])
----> 3 s.sort_values(by=["a"])
TypeError: sort_values() got an unexpected keyword argument 'by'
```
https://github.com/pydata/pandas/blob/1135ce34fc2fb995e239a7d36aee1b687ef69dd9/pandas/core/series.py#L1609 does indeed not support `by`, but [the docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_values.html) list it as a supported keyword argument.
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| null | 1 | 2015-11-07T10:49:42Z | 2015-11-07T14:49:56Z | 2015-11-07T14:49:52Z | CONTRIBUTOR | null | Turn RST's :: and other variants like ': :' or '<word> :' into simple
colons.
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| null | 2 | 2015-11-07T14:08:35Z | 2015-11-07T14:17:21Z | 2015-11-07T14:12:28Z | NONE | null | It seems that drop_duplicates() and duplicated() methods are not working properly for large integer columns. Here is my example data frame http://pastebin.com/KVHxUpgz
```
import pandas as pd
pd.read_clipboard(delimiter=',')
r = x.duplicated(keep=False)
print(x[r])
```
This gives me:
x1 x2
8 16000010001 8470207
95 16000010009 8470039
Clearly these are not duplicates but seems like pandas thinks they are!
Also drop_duplicates() seems to fail:
```
print(len(x),len(x.drop_duplicates()))
```
gives: 101 100
When I convert my columns to string they are not duplicates anymore:
```
r1 = x.apply(lambda x: '%d-%d' % tuple(x),axis=1).duplicated()
print(r1.sum())
```
is 0 as it should.
Here is the versions:
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fi_FI
pandas: 0.17.0
nose: None
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
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} | 3 | 2015-11-07T14:47:00Z | 2017-03-28T12:49:58Z | 2017-03-28T12:49:58Z | CONTRIBUTOR | null | xref #11527
xref #10001
currently we pass thru keywords to the `TextReader` but some of these don't do anything. Should catch and raise on these.
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} | 7 | 2015-11-07T22:14:36Z | 2015-11-16T09:31:54Z | 2015-11-11T03:23:40Z | CONTRIBUTOR | null | Issue #11519
Somehow right joins had been forgotten in a previous bugfix. There were tests already written that should have seen the problem, but they had been commented out because the expected results were wrong, because of a subtlety in the way non-unique index are handled.
Thanks for the labels "difficulty novice" and "effort low": I'm using pandas every day, and I'm glad I could contribute that easily.
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} | 12 | 2015-11-07T22:30:01Z | 2015-11-10T04:52:18Z | 2015-11-10T02:27:23Z | CONTRIBUTOR | null | Hi,
This fixes the bug mentioned in https://github.com/pydata/pandas/issues/11517
I have added column names after the describe operation.
Request you to review the change and merge it.
Thanks
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} | 15 | 2015-11-08T00:06:08Z | 2016-02-24T12:54:28Z | 2016-02-24T12:53:12Z | CONTRIBUTOR | null | I'm proposing a solution to #8341
As @jorisvandenbossche suggested, I use a sqlite3 adapter to transform `datetime.time` objects into string (`hh:mm:ss.ffffff`, as this what sqlalchemy... I think?)
I added a test in the class `_TestSQLApi`, so that the solution is tested with both sqlalchemy and the sqlite3 fallback. Thus, the result should be consistent.
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| null | 2 | 2015-11-08T04:00:58Z | 2015-11-08T16:13:44Z | 2015-11-08T16:13:44Z | NONE | null | ```
df=pd.DataFrame(
[[75,1,'2013-07-15',],
[44,3,'2014-04-02',],
[15,2,'2013-05-23',],
[93,1,'2014-04-04',]] ,columns=['Sales','Store','Date'])
df.Date=pd.to_datetime(df.Date)
gb=df.groupby('Store')
gb.apply(lambda df: pd.Series([1,2,3]))
```
**KeyError: "['Date'] not in index"**
If I comment out `to_datetime(df.Date)` to use string type for date, then it works fine.
---
### more details follows:
INSTALLED VERSIONS
python: 3.4.3.final.0
pandas: 0.17.0
---
KeyError Traceback (most recent call last)
<ipython-input-109-45118450dd9c> in <module>()
8 df.Date=pd.to_datetime(df.Date)
9 gb=df.groupby('Store')
---> 10 gb.apply(lambda df: pd.Series([1,2,3]))
......I omit the intermediate traces for succinct.
/Users/apple/miniconda3/lib/python3.4/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: "['Date'] not in index"
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https://api.github.com/repos/pandas-dev/pandas/issues/11549 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11549/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11549/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11549/events | https://github.com/pandas-dev/pandas/issues/11549 | 115,742,302 | MDU6SXNzdWUxMTU3NDIzMDI= | 11,549 | Python3.4 and pandas 0.17, core dump on loading a csv | {
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| null | 2 | 2015-11-08T16:22:07Z | 2015-11-08T16:53:28Z | 2015-11-08T16:25:03Z | NONE | null | Hi,
I get a core dump while only loading a csv with d = pd.read_csv('roudrobin.csv')
Here is the csv:
https://transfer.sh/EYNLT/roudrobin.csv
pd.**version**
Out[3]: '0.17.0'
```
gdb python3 core
GNU gdb (Ubuntu 7.10-1ubuntu2) 7.10
Copyright (C) 2015 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type "show copying"
and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
<http://www.gnu.org/software/gdb/documentation/>.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
warning: bad breakpoint number at or near 'pretty-printers'
Source directories searched: /home/teto/iperf3/src:/home/teto/ns3off/src:/home/teto/iperf2/src:$:$cdir:$cwd
Reading symbols from python3...(no debugging symbols found)...done.
warning: core file may not match specified executable file.
[New LWP 24251]
warning: platform-specific solib_create_inferior_hook did not load initial shared libraries.
Reading symbols from /lib/x86_64-linux-gnu/libpthread.so.0...Reading symbols from /usr/lib/debug//lib/x86_64-linux-gnu/libpthread-2.21.so...done.
done.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
Reading symbols from /lib/x86_64-linux-gnu/libc.so.6...Reading symbols from /usr/lib/debug//lib/x86_64-linux-gnu/libc-2.21.so...done.
done.
Reading symbols from /lib/x86_64-linux-gnu/libdl.so.2...Reading symbols from /usr/lib/debug//lib/x86_64-linux-gnu/libdl-2.21.so...done.
done.
Reading symbols from /lib/x86_64-linux-gnu/libutil.so.1...Reading symbols from /usr/lib/debug//lib/x86_64-linux-gnu/libutil-2.21.so...done.
done.
Reading symbols from /lib/x86_64-linux-gnu/libexpat.so.1...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libz.so.1...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libm.so.6...Reading symbols from /usr/lib/debug//lib/x86_64-linux-gnu/libm-2.21.so...done.
done.
Reading symbols from /lib64/ld-linux-x86-64.so.2...Reading symbols from /usr/lib/debug//lib/x86_64-linux-gnu/ld-2.21.so...done.
done.
Reading symbols from /usr/lib/python3.4/lib-dynload/readline.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libreadline.so.6...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libtinfo.so.5...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/hashtable.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/numpy/core/multiarray.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/numpy/core/umath.cpython-34m.so...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_ctypes.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /usr/lib/x86_64-linux-gnu/libffi.so.6...(no debugging symbols found)...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_bz2.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libbz2.so.1.0...(no debugging symbols found)...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_hashlib.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libcrypto.so.1.0.0...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/numpy/linalg/lapack_lite.cpython-34m.so...(no debugging symbols found)...done.
Reading symbols from /usr/lib/liblapack.so.3...(no debugging symbols found)...done.
Reading symbols from /usr/lib/libblas.so.3...(no debugging symbols found)...done.
Reading symbols from /usr/lib/x86_64-linux-gnu/libgfortran.so.3...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libgcc_s.so.1...(no debugging symbols found)...done.
Reading symbols from /usr/lib/x86_64-linux-gnu/libquadmath.so.0...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/numpy/linalg/_umath_linalg.cpython-34m.so...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/numpy/fft/fftpack_lite.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/numpy/random/mtrand.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/tslib.cpython-34m.so...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_ssl.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libssl.so.1.0.0...(no debugging symbols found)...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_lzma.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/liblzma.so.5...(no debugging symbols found)...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/parser.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/lib.cpython-34m.so...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_csv.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/algos.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/index.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/_period.cpython-34m.so...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_decimal.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /usr/lib/x86_64-linux-gnu/libmpdec.so.2...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/_sparse.cpython-34m.so...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_opcode.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/parser.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/json.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/msgpack/_packer.cpython-34m.so...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/msgpack/_unpacker.cpython-34m.so...done.
Reading symbols from /usr/lib/python3.4/lib-dynload/_json.cpython-34m-x86_64-linux-gnu.so...(no debugging symbols found)...done.
Reading symbols from /lib/x86_64-linux-gnu/libuuid.so.1...(no debugging symbols found)...done.
Reading symbols from /home/teto/.local/lib/python3.4/site-packages/pandas/_testing.cpython-34m.so...done.
Reading symbols from system-supplied DSO at 0x7ffec77b7000...(no debugging symbols found)...done.
Core was generated by `python3'.
Program terminated with signal SIGSEGV, Segmentation fault.
Reading in symbols for ../sysdeps/x86_64/strlen.S...done.
#0 strlen () at ../sysdeps/x86_64/strlen.S:106
106 ../sysdeps/x86_64/strlen.S: Aucun fichier ou dossier de ce type.
(gdb) bt
Reading in symbols for pandas/parser.c...done.
#0 strlen () at ../sysdeps/x86_64/strlen.S:106
#1 0x00000000005a8f1c in PyBytes_FromString ()
#2 0x00007f58e4a7a50c in __pyx_f_6pandas_6parser__try_int64 (__pyx_v_parser=0x1979bf0, __pyx_v_col=__pyx_v_col@entry=23,
__pyx_v_line_start=__pyx_v_line_start@entry=1, __pyx_v_line_end=__pyx_v_line_end@entry=3522,
__pyx_v_na_filter=__pyx_v_na_filter@entry=1, __pyx_v_na_hashset=__pyx_v_na_hashset@entry=0x187a680) at pandas/parser.c:20849
#3 0x00007f58e4a87c47 in __pyx_f_6pandas_6parser_10TextReader__convert_with_dtype (__pyx_v_self=0x7f58e47a39e8,
__pyx_v_dtype=0x7f58e4cb5bc8, __pyx_v_i=23, __pyx_v_start=1, __pyx_v_end=3522, __pyx_v_na_filter=1, __pyx_v_user_dtype=0,
__pyx_v_na_hashset=0x187a680, __pyx_v_na_flist=0x7f58ece2d9e8) at pandas/parser.c:12372
#4 0x00007f58e4a8beaf in __pyx_f_6pandas_6parser_10TextReader__convert_tokens (__pyx_v_self=__pyx_v_self@entry=0x7f58e47a39e8,
__pyx_v_i=__pyx_v_i@entry=23, __pyx_v_start=__pyx_v_start@entry=1, __pyx_v_end=__pyx_v_end@entry=3522,
__pyx_v_name=__pyx_v_name@entry=0x7f58e3ee93f0, __pyx_v_na_filter=__pyx_v_na_filter@entry=1, __pyx_v_na_hashset=0x187a680,
__pyx_v_na_flist=0x7f58ece2d9e8) at pandas/parser.c:11744
#5 0x00007f58e4a8ee50 in __pyx_pf_6pandas_6parser_10TextReader_16_convert_column_data (__pyx_v_footer=<optimized out>,
__pyx_v_upcast_na=<optimized out>, __pyx_v_rows=<optimized out>, __pyx_v_self=0x7f58e47a39e8) at pandas/parser.c:10858
#6 __pyx_pw_6pandas_6parser_10TextReader_17_convert_column_data (__pyx_v_self=0x7f58e47a39e8, __pyx_args=<optimized out>,
__pyx_kwds=<optimized out>) at pandas/parser.c:10148
#7 0x00007f58e4a78b80 in __Pyx_PyObject_Call (kw=0x7f58eb91b9c8, arg=<optimized out>, func=0x7f58e4760548) at pandas/parser.c:32928
#8 __pyx_f_6pandas_6parser_10TextReader__read_rows (__pyx_v_self=0x7f58e47a39e8, __pyx_v_rows=<optimized out>, __pyx_v_trim=0)
at pandas/parser.c:9465
#9 0x00007f58e4a67910 in __pyx_f_6pandas_6parser_10TextReader__read_low_memory (__pyx_v_self=0x7f58e47a39e8,
__pyx_v_rows=0x9ef5a0 <_Py_NoneStruct>) at pandas/parser.c:8338
#10 0x00007f58e4a6e3b4 in __pyx_pf_6pandas_6parser_10TextReader_8read (__pyx_v_rows=<optimized out>, __pyx_v_self=0x7f58e47a39e8)
at pandas/parser.c:8082
#11 __pyx_pw_6pandas_6parser_10TextReader_9read (__pyx_v_self=0x7f58e47a39e8, __pyx_args=<optimized out>, __pyx_kwds=<optimized out>)
at pandas/parser.c:8042
#12 0x0000000000511a1a in PyEval_EvalFrameEx ()
#13 0x0000000000519757 in PyEval_EvalCodeEx ()
#14 0x0000000000516213 in PyEval_EvalFrameEx ()
#15 0x0000000000519757 in PyEval_EvalCodeEx ()
#16 0x0000000000516213 in PyEval_EvalFrameEx ()
#17 0x0000000000515c2f in PyEval_EvalFrameEx ()
```
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} | 6 | 2015-11-08T17:14:36Z | 2016-07-21T15:44:46Z | 2015-11-13T13:55:10Z | CONTRIBUTOR | null | closes #6703
I would have like to have a solution also working for panels, but I've never used them, so I'm not sure how it should behave.
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} | 7 | 2015-11-08T17:37:41Z | 2015-11-16T09:31:41Z | 2015-11-11T03:20:12Z | CONTRIBUTOR | null | closes #7791
xref #6797
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} | 0 | 2015-11-08T19:16:28Z | 2015-11-08T19:16:28Z | null | CONTRIBUTOR | null | I could get this only to show up in the notebook, but the idea is that the attributes (e.g. `_format_attrs()`)
should be wrapped like we do data so that they don't get cut off.
```
In [14]: pd.options.display.width=20
In [15]: pd.CategoricalIndex(['foo','bar','baz','a really long string']*100,name='a long name')
Out[15]:
CategoricalIndex([u'foo',
u'bar',
u'baz',
u'a really long string',
u'foo',
u'bar',
u'baz',
u'a really long string',
u'foo',
u'bar',
...
u'baz',
u'a really long string',
u'foo',
u'bar',
u'baz',
u'a really long string',
u'foo',
u'bar',
u'baz',
u'a really long string'],
categories=[u'a really long string', u'bar', u'baz', u'foo'], ordered=False, name=u'a long name', dtype='category', length=400)
```
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} | 3 | 2015-11-08T20:38:56Z | 2015-12-28T19:19:39Z | 2015-12-28T19:19:39Z | CONTRIBUTOR | null | xref #11551
Parameter `float_format` and `decimal` options are ignored in an Index, but work in the data itself.
```
In [2]: pd.DataFrame({'a': [0.1,1.1], 'b': [2, 3]}).to_csv(float_format='%.2f', index=False)
Out[2]: 'a,b\n0.10,2\n1.10,3\n
In [3]: pd.DataFrame({'a': [0.1,1.1], 'b': [2, 3]}).set_index('a').to_csv(float_format='%.2f')
Out[3]: 'a,b\n0.1,2\n1.1,3\n'
```
and
```
In [4]: pd.DataFrame({'a': [0.1,1.1], 'b': [2, 3]}).to_csv(decimal='^', index=False)
Out[4]: 'a,b\n0^1,2\n1^1,3\n'
In [4]: pd.DataFrame({'a': [0.1,1.1], 'b': [2, 3]}).set_index('a').to_csv(decimal='^')
Out[4]: 'a,b\n0.1,2\n1.1,3\n'
```
I'll do a PR, soon I hope :)
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} | 3 | 2015-11-08T21:50:18Z | 2016-03-06T08:22:09Z | 2016-03-06T08:21:12Z | CONTRIBUTOR | null | Following on from discussion in #11534, this uses a list comprehension in the following function:
```
def function(amplitudes):
def f(t):
x1 = amplitudes[0]
result = x1 / sqrt(2.0)
harmonic = 1.0
for x_even, x_odd in zip(amplitudes[1::2], amplitudes[2::2]):
result += (x_even * np.sin(harmonic * t) +
x_odd * np.cos(harmonic * t))
harmonic += 1.0
if len(amplitudes) % 2 != 0:
result += amplitudes[-1] * np.sin(harmonic * t)
return result
return f
```
As both `amplitudes` and `t` are `numpy ndarrays`, there is probably some performance gain to be had from vectorising the loop.
My `numpy` knowledge and available time aren't sufficient to make this entirely trivial. I came across the following issues in particular:
- Basic slices of `numpy ndarrays` return views rather than copies, which then can't be subject to many `numpy` operations (i.e. `np.resize`).
- Addressing `amplitudes` in the inner scope assigns the name in the inner scope (see [here](http://stackoverflow.com/questions/5218895/python-nested-functions-variable-scoping) for details). This causes an `amplitudes is not defined` exception.
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} | 0 | 2015-11-09T00:24:42Z | 2016-02-12T03:07:36Z | 2016-02-12T03:07:36Z | CONTRIBUTOR | null | need to update the docs for `read_csv` to match the doc-string. Let's make sure they have the same text / examples as much as possible (and are in the same order and such).
In particular, the `header` option doesn't have `header='infer'` which is the default in the docs, but it exists in the doc-string.
Further, should show what this does:
```
if kwds.get('header', 'infer') == 'infer':
kwds['header'] = 0 if kwds.get('names') is None else None
```
meaning if the the `header` kw is not specified, this it is set to the first line if no `names` are specified, else `None`.
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} | 6 | 2015-11-09T00:28:00Z | 2015-11-15T18:36:51Z | 2015-11-15T18:36:51Z | CONTRIBUTOR | null | The following code raises an error (tested on pandas 0.17.0, matplotlib 1.4.3, and Python 3.4.2)
```
>>> fig, ax = plt.subplots()
>>> fig.add_axes([0.2, 0.2, 0.2, 0.2])
>>> pd.Series(np.random.rand(100)).plot(ax=ax)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-13-80531efe48c4> in <module>()
8 fig, ax = plt.subplots()
9 inset = fig.add_axes([0.2, 0.2, 0.2, 0.2])
---> 10 data.plot(ax=ax)
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/tools/plotting.py in __call__(self, 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)
3491 colormap=colormap, table=table, yerr=yerr,
3492 xerr=xerr, label=label, secondary_y=secondary_y,
-> 3493 **kwds)
3494 __call__.__doc__ = plot_series.__doc__
3495
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/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)
2581 yerr=yerr, xerr=xerr,
2582 label=label, secondary_y=secondary_y,
-> 2583 **kwds)
2584
2585
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2378 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2379
-> 2380 plot_obj.generate()
2381 plot_obj.draw()
2382 return plot_obj.result
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/tools/plotting.py in generate(self)
990 self._post_plot_logic_common(ax, self.data)
991 self._post_plot_logic(ax, self.data)
--> 992 self._adorn_subplots()
993
994 def _args_adjust(self):
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/tools/plotting.py in _adorn_subplots(self)
1141 naxes=nrows * ncols, nrows=nrows,
1142 ncols=ncols, sharex=self.sharex,
-> 1143 sharey=self.sharey)
1144
1145 for ax in self.axes:
/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/pandas/tools/plotting.py in _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
3378 layout = np.zeros((nrows+1,ncols+1), dtype=np.bool)
3379 for ax in axarr:
-> 3380 layout[ax.rowNum, ax.colNum] = ax.get_visible()
3381
3382 for ax in axarr:
AttributeError: 'Axes' object has no attribute 'rowNum'
```
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| null | 0 | 2015-11-09T01:35:53Z | 2015-11-12T01:08:46Z | 2015-11-09T13:18:51Z | CONTRIBUTOR | null | {
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} | 3 | 2015-11-09T04:32:58Z | 2016-03-29T20:09:45Z | 2016-03-29T20:09:45Z | NONE | null | Replicating example
```
In [1]: df = pd.DataFrame([[1,2],[3,4]],columns=pd.CategoricalIndex(list('AB')))
In [2]: df.describe()
AttributeError: 'DataFrame' object has no attribute 'value_counts'
```
The behaviour in [this notebook](https://gist.github.com/williamsmj/c6cbabcb25f27f08407f) seems like a bug to me. This is pandas 0.17.0.
In it, `g` and `gcat` are the results of two `df.groupby(['medium', 'artist']).count().unstack()` operations. The only difference is that one of those operations is on `df` where one of the columns that the `groupby` operates over has been converted to Categorical.
`g` and `gcat` behave very differently. I've tried to pin this down to the exact operation in the split-apply-combine that causes the problem without much luck.
Slicing a column out of `g` returns a Series as expected, while slicing a column out of `gcat` returns a DataFrame (see cells 4 and 5).
`g.describe()` works as expected, but `gcat.describe()` raises the exception
```
AttributeError: 'DataFrame' object has no attribute 'value_counts'
```
and `g['painting'] + g['sculpture']` works as expected but `g['painting'] + g['sculpture']` raises
```
Exception: Data must be 1-dimensional
```
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} | 3 | 2015-11-09T08:21:53Z | 2017-08-10T21:59:47Z | 2016-04-17T13:54:53Z | NONE | null | Running the code:
``` python
import pandas
class SubclassedSeries(pandas.Series):
@property
def _constructor(self):
return SubclassedSeries
@property
def _constructor_expanddim(self):
return SubclassedDataFrame
def c(self):
return 1
class SubclassedDataFrame(pandas.DataFrame):
@property
def _constructor(self):
return SubclassedDataFrame
@property
def _constructor_sliced(self):
return SubclassedSeries
b = SubclassedDataFrame([[1,2,3,4],[2,3,4,5]])
print(b[0].c())
```
works as expected.
However running:
``` python
print(b.ix[1].c())
```
throws an exception as:
``` python
b.ix[1]
```
returns a Series instance instead of an SublcassedSeries instance as expected.
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} | 5 | 2015-11-09T12:04:53Z | 2015-11-13T15:16:48Z | 2015-11-13T15:06:26Z | CONTRIBUTOR | null | Hi,
1. When DataFrame contain Numerics and Booleans, than numpy will have type object,
so `np.isfinite(mat)` will raise Exception.
I've fixed this by using `com._ensure_float64` like for other correlation.
1. I've skipped half of computation, because correlation is symmetrical
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} | 6 | 2015-11-09T14:48:37Z | 2015-11-15T18:36:39Z | 2015-11-15T18:36:39Z | MEMBER | null | closes #11556
closes #11520
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} | 6 | 2015-11-09T16:15:52Z | 2021-04-21T04:53:08Z | null | NONE | null | This works:
```
df = pd.DataFrame({'A': [1, 2, 1, 2, 1, 2, 3], 'B': [1, 1, 1, 2, 2, 2, 2]})
df.groupby('B').agg(pd.Series.mode)
```
but this doesn't:
```
df.groupby('B').agg('mode')
...
AttributeError: Cannot access callable attribute 'mode' of 'DataFrameGroupBy' objects, try using the 'apply' method
```
I thought all the series aggregate methods propagated automatically to groupby, but I've probably misunderstood?
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} | 6 | 2015-11-10T01:16:49Z | 2016-08-31T08:07:31Z | 2016-08-31T08:07:31Z | NONE | null | Hi,
https://github.com/pydata/pandas/blob/e4cb0f8a6cbb5f0c89b24783baa44326e4b2cccb/pandas/core/format.py#L893
Currently, a `border="0"` seems to be hard coded in the pandas `to_html()` table output. I think it'd be more convenient to have that as a CSS property on `dataframe` than put directly like that.
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} | 29 | 2015-11-10T05:53:32Z | 2016-01-05T18:44:24Z | 2015-12-31T13:46:31Z | CONTRIBUTOR | null | closes #11349
closes #11925
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} | 3 | 2015-11-10T06:02:54Z | 2019-11-12T23:29:28Z | 2019-11-12T23:29:28Z | CONTRIBUTOR | null | I noticed this quirk today: When you do a DataFrame-wide comparison (excluding `==`) using a number, it doesn't raise a ValueError (which you'd expect in Python 3); instead it always returns True.
``` python
>>> from pandas import DataFrame
>>> df = DataFrame(x: {'x': 'foo', 'y': 'bar', 'z': 'baz'} for x in ['a', 'b', 'c']})
>>> df
a b c
x foo foo foo
y bar bar bar
z baz baz baz
>>> df < 0
a b c
x True True True
y True True True
z True True True
>>> df > 0
a b c
x True True True
y True True True
z True True True
```
However, when you compare a Series of strings to a number, you get the expected ValueError:
``` python
>>> df.a < 0
TypeError: unorderable types: str() < int()
```
Is this a bug or a feature?
Python: 3.4.3
Pandas: 0.17.0
OS: Mac OSX 10.11
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} | 18 | 2015-11-10T12:13:58Z | 2015-11-21T15:41:20Z | 2015-11-21T13:46:57Z | CONTRIBUTOR | null | looking to do this on around Nov 20th (next friday)
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| null | 1 | 2015-11-10T20:56:39Z | 2015-11-11T01:39:24Z | 2015-11-11T01:39:17Z | NONE | null | At least for large datasets `DataFrame.duplicated` returns incorrect results.
Consider MovieLens10M data (this code will automatically download the data from grouplens website):
``` python
import pandas as pd
from requests import get
from StringIO import StringIO
zip_file_url = 'http://files.grouplens.org/datasets/movielens/ml-10m.zip'
zip_response = get(zip_file_url)
zip_contents = StringIO(zip_response.content)
with ZipFile(zip_contents) as zfile:
zdata = zfile.read('ml-10M100K/ratings.dat')
delimiter = ';'
zdata = zdata.replace('::', delimiter) # makes data compatible with pandas c-engine
mldata = pd.read_csv(StringIO(zdata), sep=delimiter, header=None, engine='c',
names=['userid', 'movieid', 'rating', 'timestamp'],
usecols=['userid', 'movieid', 'rating'])
```
The data (`mldata` variable) contains no duplicates, which can be verified:
``` python
(mldata.groupby(['userid', 'movieid']).size()>1).any()
False
mldata.set_index(['userid', 'movieid']).index.is_unique
True
```
However, `DataFrame.duplicated` gives:
``` python
dups = mldata.duplicated(['userid', 'movieid'], keep=False)
print dups.any()
print dups.sum()
True
12127
```
Expected:
``` python
False
0
```
`pd.show_versions()`:
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.3.2
Cython: 0.23.3
numpy: 1.10.0
scipy: 0.16.0
statsmodels: None
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
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pymysql: None
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| null | 6 | 2015-11-10T21:06:27Z | 2015-11-11T01:38:57Z | 2015-11-11T01:38:50Z | NONE | null | Getting some really funny functionality with pd.DataFrame.fillna(). After running fillna() on a non-null dataframe (or series), it converts items that should be objects (as I typecasted them) back to floats (as they originally were).
``` python
import pandas as pd
import numpy as np
test = pd.DataFrame(np.random.randn(1,10),columns=['a','b','c','d','e','f','g','h','i','k'])
test.dtypes # all floats
test = test.astype('object')
test.dtypes # all objects
test['j'] = '0' # this is a string containing the number zero
test.dtypes # all objects
test = test.fillna('-1')
test.dtypes # test is now all floats, except test['j']
```
Any help would be appreciated.
pd.show_versions()
``` python
In [14]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.2
nose: 1.3.4
Cython: None
numpy: 1.10.0.post2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.6
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.1 (dt dec pq3 ext)
```
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} | 1 | 2015-11-10T23:19:50Z | 2021-04-21T04:54:41Z | null | NONE | null | I stumbled upon this today while working with the chunking feature. I was trying to implement a CSV cleaner that would set aside error rows (or a chunk that has an error) for future review.
Given input table as 'test.csv':
```
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4,5,6,7,8
1,2,3,4
1,2,3,4
```
And reading it with chunking...
```
df = pd.read_csv(
'test.csv',
header=None,
chunksize=1,
error_bad_lines=True
)
for chunk in df:
print(chunk)
```
...results in:
```
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
0 1 2 3
0 1 2 3 4
```
...silently truncating row 7.
It does the expected if the error row is in the first chunk. But error rows in subsequent chunks are silently truncated. I assume this is not expected behavior -- correct me if I'm wrong.
I'd love to tackle this. But I've never contributed to this codebase. I figured I'd raise it first before diving in headfirst.
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| null | 1 | 2015-11-11T10:45:39Z | 2015-11-11T16:21:42Z | 2015-11-11T16:21:42Z | NONE | null | I'm attempting to install pandas via pip into a virtualenv. I have the latest version of numpy, `virtualenv/bin/pip3 install numpy --upgrade` confirms that it is the latest version. However, the pandas installation fails and gives me the following traceback:
``` bash
../virtualenv/bin/pip3 install pandas
Collecting pandas
Using cached pandas-0.17.0.tar.gz
Collecting python-dateutil>=2 (from pandas)
Using cached python_dateutil-2.4.2-py2.py3-none-any.whl
Collecting pytz>=2011k (from pandas)
Downloading pytz-2015.7-py2.py3-none-any.whl (476kB)
100% |████████████████████████████████| 479kB 1.0MB/s
Requirement already satisfied (use --upgrade to upgrade): numpy>=1.7.0 in /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages (from pandas)
Collecting six>=1.5 (from python-dateutil>=2->pandas)
Downloading six-1.10.0-py2.py3-none-any.whl
Building wheels for collected packages: pandas
Running setup.py bdist_wheel for pandas
Complete output from command /home/rowan/sites/dash.mysite.com/virtualenv/bin/python3 -c "import setuptools;__file__='/tmp/pip-build-9f88lmba/pandas/setup.py';exec(compile(open(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" bdist_wheel -d /tmp/tmpbhka8g3ppip-wheel-:
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-x86_64-3.4
creating build/lib.linux-x86_64-3.4/pandas
copying pandas/__init__.py -> build/lib.linux-x86_64-3.4/pandas
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copying pandas/rpy/__init__.py -> build/lib.linux-x86_64-3.4/pandas/rpy
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copying pandas/sparse/panel.py -> build/lib.linux-x86_64-3.4/pandas/sparse
copying pandas/sparse/__init__.py -> build/lib.linux-x86_64-3.4/pandas/sparse
copying pandas/sparse/frame.py -> build/lib.linux-x86_64-3.4/pandas/sparse
copying pandas/sparse/series.py -> build/lib.linux-x86_64-3.4/pandas/sparse
creating build/lib.linux-x86_64-3.4/pandas/sparse/tests
copying pandas/sparse/tests/test_libsparse.py -> build/lib.linux-x86_64-3.4/pandas/sparse/tests
copying pandas/sparse/tests/test_list.py -> build/lib.linux-x86_64-3.4/pandas/sparse/tests
copying pandas/sparse/tests/test_array.py -> build/lib.linux-x86_64-3.4/pandas/sparse/tests
copying pandas/sparse/tests/__init__.py -> build/lib.linux-x86_64-3.4/pandas/sparse/tests
copying pandas/sparse/tests/test_sparse.py -> build/lib.linux-x86_64-3.4/pandas/sparse/tests
creating build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/ols.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/moments.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/api.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/interface.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/misc.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/math.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/plm.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/__init__.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/common.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/var.py -> build/lib.linux-x86_64-3.4/pandas/stats
copying pandas/stats/fama_macbeth.py -> build/lib.linux-x86_64-3.4/pandas/stats
creating build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/clipboard.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/testing.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/print_versions.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/decorators.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/misc.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/doctools.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/__init__.py -> build/lib.linux-x86_64-3.4/pandas/util
copying pandas/util/terminal.py -> build/lib.linux-x86_64-3.4/pandas/util
creating build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_util.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_compat.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_frame.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_reshape.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_tseries.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_format.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_algos.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_graphics.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_base.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_graphics_others.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_panel4d.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_panel.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_config.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_generic.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_groupby.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_categorical.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_index.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/__init__.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_strings.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_indexing.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_multilevel.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_rplot.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_series.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_expressions.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_dtypes.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_panelnd.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_stats.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_lib.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_common.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_testing.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_nanops.py -> build/lib.linux-x86_64-3.4/pandas/tests
copying pandas/tests/test_internals.py -> build/lib.linux-x86_64-3.4/pandas/tests
creating build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_case.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_buffer.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_read_size.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_format.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_unpack_raw.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_subtype.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_obj.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_seq.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/__init__.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_newspec.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_sequnpack.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_pack.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_except.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_limits.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_unpack.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
copying pandas/tests/test_msgpack/test_extension.py -> build/lib.linux-x86_64-3.4/pandas/tests/test_msgpack
creating build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/plotting.py -> build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/rplot.py -> build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/pivot.py -> build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/tile.py -> build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/__init__.py -> build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/merge.py -> build/lib.linux-x86_64-3.4/pandas/tools
copying pandas/tools/util.py -> build/lib.linux-x86_64-3.4/pandas/tools
creating build/lib.linux-x86_64-3.4/pandas/tools/tests
copying pandas/tools/tests/test_util.py -> build/lib.linux-x86_64-3.4/pandas/tools/tests
copying pandas/tools/tests/test_merge.py -> build/lib.linux-x86_64-3.4/pandas/tools/tests
copying pandas/tools/tests/test_tile.py -> build/lib.linux-x86_64-3.4/pandas/tools/tests
copying pandas/tools/tests/test_pivot.py -> build/lib.linux-x86_64-3.4/pandas/tools/tests
copying pandas/tools/tests/__init__.py -> build/lib.linux-x86_64-3.4/pandas/tools/tests
creating build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/timedeltas.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/period.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/interval.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/plotting.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/frequencies.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/api.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/offsets.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/holiday.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/__init__.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/tdi.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/common.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/base.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/index.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/resample.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/util.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/converter.py -> build/lib.linux-x86_64-3.4/pandas/tseries
copying pandas/tseries/tools.py -> build/lib.linux-x86_64-3.4/pandas/tseries
creating build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_util.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_plotting.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_daterange.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_base.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_converter.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_timezones.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_holiday.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_offsets.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_tslib.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/__init__.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_frequencies.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_timeseries.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_timedeltas.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_resample.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_period.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
copying pandas/tseries/tests/test_timeseries_legacy.py -> build/lib.linux-x86_64-3.4/pandas/tseries/tests
creating build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_date_converters.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_wb.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_parsers.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_stata.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_packers.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_clipboard.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_cparser.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_gbq.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_json_norm.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_sql.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/generate_legacy_storage_files.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_pytables.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_ga.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/__init__.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_sas.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_html.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_pickle.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_data.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_common.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
copying pandas/io/tests/test_excel.py -> build/lib.linux-x86_64-3.4/pandas/io/tests
creating build/lib.linux-x86_64-3.4/pandas/io/tests/test_json
copying pandas/io/tests/test_json/test_pandas.py -> build/lib.linux-x86_64-3.4/pandas/io/tests/test_json
copying pandas/io/tests/test_json/test_ujson.py -> build/lib.linux-x86_64-3.4/pandas/io/tests/test_json
copying pandas/io/tests/test_json/__init__.py -> build/lib.linux-x86_64-3.4/pandas/io/tests/test_json
creating build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/test_var.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/__init__.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/test_math.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/test_fama_macbeth.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/common.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/test_moments.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
copying pandas/stats/tests/test_ols.py -> build/lib.linux-x86_64-3.4/pandas/stats/tests
creating build/lib.linux-x86_64-3.4/pandas/msgpack
copying pandas/msgpack/exceptions.py -> build/lib.linux-x86_64-3.4/pandas/msgpack
copying pandas/msgpack/__init__.py -> build/lib.linux-x86_64-3.4/pandas/msgpack
copying pandas/msgpack/_version.py -> build/lib.linux-x86_64-3.4/pandas/msgpack
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/legacy_0.10.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/pytables_native2.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/legacy.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/datetimetz_object.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/legacy_table.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/pytables_native.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
copying pandas/io/tests/data/legacy_hdf/legacy_table_0.11.h5 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_hdf
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.10.1
copying pandas/io/tests/data/legacy_pickle/0.10.1/x86_64_linux_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.10.1
copying pandas/io/tests/data/legacy_pickle/0.10.1/AMD64_windows_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.10.1
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.15.2
copying pandas/io/tests/data/legacy_pickle/0.15.2/0.15.2_x86_64_darwin_2.7.9.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.15.2
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.0
copying pandas/io/tests/data/legacy_pickle/0.16.0/0.16.0_x86_64_darwin_2.7.9.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.11.0
copying pandas/io/tests/data/legacy_pickle/0.11.0/x86_64_linux_3.3.0.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.11.0
copying pandas/io/tests/data/legacy_pickle/0.11.0/0.11.0_x86_64_linux_3.3.0.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.11.0
copying pandas/io/tests/data/legacy_pickle/0.11.0/x86_64_linux_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.11.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.14.0
copying pandas/io/tests/data/legacy_pickle/0.14.0/0.14.0_x86_64_linux_2.7.8.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.14.0
copying pandas/io/tests/data/legacy_pickle/0.14.0/0.14.0_x86_64_darwin_2.7.6.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.14.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.15.0
copying pandas/io/tests/data/legacy_pickle/0.15.0/0.15.0_x86_64_linux_2.7.8.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.15.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.12.0
copying pandas/io/tests/data/legacy_pickle/0.12.0/0.12.0_x86_64_linux_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.12.0
copying pandas/io/tests/data/legacy_pickle/0.12.0/0.12.0_AMD64_windows_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.12.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.14.1
copying pandas/io/tests/data/legacy_pickle/0.14.1/0.14.1_x86_64_linux_2.7.8.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.14.1
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_AMD64_windows_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_x86_64_darwin_2.7.6.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_i686_linux_3.2.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_x86_64_linux_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_i686_linux_2.7.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_i686_linux_2.6.5.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_x86_64_darwin_2.7.5.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_x86_64_linux_3.3.0.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
copying pandas/io/tests/data/legacy_pickle/0.13.0/0.13.0_x86_64_linux_2.7.8.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.13.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_x86_64_darwin_2.7.10.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_x86_64_linux_3.4.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_AMD64_windows_2.7.10.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_x86_64_darwin_2.7.9.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_x86_64_darwin_3.4.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_x86_64_linux_2.7.10.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
copying pandas/io/tests/data/legacy_pickle/0.16.2/0.16.2_AMD64_windows_3.4.3.pickle -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_pickle/0.16.2
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.0
copying pandas/io/tests/data/legacy_msgpack/0.16.0/0.16.0_x86_64_darwin_2.7.9.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.0
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_x86_64_linux_3.4.3.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_x86_64_darwin_3.4.3.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_AMD64_windows_2.7.10.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_x86_64_darwin_2.7.9.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_x86_64_linux_2.7.10.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_x86_64_darwin_2.7.10.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/legacy_msgpack/0.16.2/0.16.2_AMD64_windows_3.4.3.msgpack -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/legacy_msgpack/0.16.2
copying pandas/io/tests/data/stata6.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/DRXFCD_G.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata5.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/iris.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/banklist.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test2.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/SSHSV1_A.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/unicode_series.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata3.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/DEMO_G.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test1.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test1.csv.bz2 -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/tips.csv -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test1.csv.gz -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/DEMO_G.XPT -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/DRXFCD_G.XPT -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/SSHSV1_A.XPT -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata4_114.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata2_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata6_113.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata9_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata5_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata3_113.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata14_118.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata2_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata10_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata4_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata10_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata7_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata2_114.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata3_114.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata9_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata6_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata1_114.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata2_113.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata1_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata4_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata6_114.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata1_encoding.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata8_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata5_114.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata3_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata8_113.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata12_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata6_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata7_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata11_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata3_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata8_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata4_113.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata5_115.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata11_117.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/stata5_113.dta -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/utf16_ex.txt -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/gbq_fake_job.txt -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/times_1904.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/times_1900.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test2.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test3.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test1.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_index_name_pre17.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/blank.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_types.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_converters.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/blank_with_header.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test4.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_multisheet.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/testmultiindex.xls -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test2.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/testmultiindex.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/times_1900.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test1.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/times_1904.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test3.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/blank.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test4.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/blank_with_header.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_converters.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_types.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_multisheet.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_index_name_pre17.xlsx -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/blank_with_header.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test2.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test3.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_converters.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_index_name_pre17.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test4.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/times_1900.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/times_1904.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/testmultiindex.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_multisheet.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test1.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/blank.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/test_types.xlsm -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/salary.table -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/computer_sales_page.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/yahoo_options3.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/wikipedia_states.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/valid_markup.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/macau.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/yahoo_options2.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/spam.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/yahoo_options1.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/banklist.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
copying pandas/io/tests/data/nyse_wsj.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data
creating build/lib.linux-x86_64-3.4/pandas/io/tests/data/html_encoding
copying pandas/io/tests/data/html_encoding/letz_latin1.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/html_encoding
copying pandas/io/tests/data/html_encoding/chinese_utf-16.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/html_encoding
copying pandas/io/tests/data/html_encoding/chinese_utf-32.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/html_encoding
copying pandas/io/tests/data/html_encoding/chinese_utf-8.html -> build/lib.linux-x86_64-3.4/pandas/io/tests/data/html_encoding
creating build/lib.linux-x86_64-3.4/pandas/io/tests/test_json/data
copying pandas/io/tests/test_json/data/tsframe_iso_v012.json -> build/lib.linux-x86_64-3.4/pandas/io/tests/test_json/data
copying pandas/io/tests/test_json/data/tsframe_v012.json -> build/lib.linux-x86_64-3.4/pandas/io/tests/test_json/data
creating build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/s1-0.12.0.pickle -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/categorical_0_15_2.pickle -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/categorical_0_14_1.pickle -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/s2-0.12.0.pickle -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/multiindex_v1.pickle -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/mindex_073.pickle -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/iris.csv -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/unicode_series.csv -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tests/data/tips.csv -> build/lib.linux-x86_64-3.4/pandas/tests/data
copying pandas/tools/tests/cut_data.csv -> build/lib.linux-x86_64-3.4/pandas/tools/tests
creating build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
copying pandas/tseries/tests/data/series_daterange0.pickle -> build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
copying pandas/tseries/tests/data/frame.pickle -> build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
copying pandas/tseries/tests/data/dateoffset_0_15_2.pickle -> build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
copying pandas/tseries/tests/data/daterange_073.pickle -> build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
copying pandas/tseries/tests/data/series.pickle -> build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
copying pandas/tseries/tests/data/cday-0.14.1.pickle -> build/lib.linux-x86_64-3.4/pandas/tseries/tests/data
UPDATING build/lib.linux-x86_64-3.4/pandas/_version.py
set build/lib.linux-x86_64-3.4/pandas/_version.py to '0.17.0'
running build_ext
building 'pandas._period' extension
creating build/temp.linux-x86_64-3.4
creating build/temp.linux-x86_64-3.4/pandas
creating build/temp.linux-x86_64-3.4/pandas/src
creating build/temp.linux-x86_64-3.4/pandas/src/datetime
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/period.c -o build/temp.linux-x86_64-3.4/pandas/src/period.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/period.c:243:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/period.c:245:0:
pandas/src/datetime_helper.h:7:0: warning: "PyInt_AS_LONG" redefined [enabled by default]
#define PyInt_AS_LONG PyLong_AsLong
^
pandas/src/period.c:141:0: note: this is the location of the previous definition
#define PyInt_AS_LONG PyLong_AS_LONG
^
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:27:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/period.c:243:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/__multiarray_api.h:1634:1: warning: ‘_import_array’ defined but not used [-Wunused-function]
_import_array(void)
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/datetime/np_datetime.c -o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime.c:18:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/datetime/np_datetime.c:10:0:
/usr/include/python3.4m/datetime.h:191:25: warning: ‘PyDateTimeAPI’ defined but not used [-Wunused-variable]
static PyDateTime_CAPI *PyDateTimeAPI = NULL;
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/datetime/np_datetime_strings.c -o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime_strings.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime_strings.c:17:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/period_helper.c -o build/temp.linux-x86_64-3.4/pandas/src/period_helper.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from pandas/src/period_helper.h:12,
from pandas/src/period_helper.c:1:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
pandas/src/period_helper.c:303:12: warning: function declaration isn’t a prototype [-Wstrict-prototypes]
static int calc_conversion_factors_matrix_size() {
^
pandas/src/period_helper.c:352:13: warning: function declaration isn’t a prototype [-Wstrict-prototypes]
static void populate_conversion_factors_matrix() {
^
pandas/src/period_helper.c:33:20: warning: ‘NULL_AF_INFO’ defined but not used [-Wunused-variable]
static asfreq_info NULL_AF_INFO;
^
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.4/pandas/src/period.o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime.o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime_strings.o build/temp.linux-x86_64-3.4/pandas/src/period_helper.o -o build/lib.linux-x86_64-3.4/pandas/_period.cpython-34m.so
building 'pandas.algos' extension
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/algos.c -o build/temp.linux-x86_64-3.4/pandas/algos.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/algos.c:242:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/algos.c:252:0:
pandas/src/datetime_helper.h:7:0: warning: "PyInt_AS_LONG" redefined [enabled by default]
#define PyInt_AS_LONG PyLong_AsLong
^
pandas/algos.c:141:0: note: this is the location of the previous definition
#define PyInt_AS_LONG PyLong_AS_LONG
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/algos.c:257:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
x86_64-linux-gnu-gcc: internal compiler error: Killed (program cc1)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-4.8/README.Bugs> for instructions.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 4
----------------------------------------
Failed building wheel for pandas
Failed to build pandas
Installing collected packages: six, python-dateutil, pytz, pandas
Running setup.py install for pandas
Complete output from command /home/rowan/sites/dash.mysite.com/virtualenv/bin/python3 -c "import setuptools, tokenize;__file__='/tmp/pip-build-9f88lmba/pandas/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-9nux__c1-record/install-record.txt --single-version-externally-managed --compile --install-headers /home/rowan/sites/dash.mysite.com/virtualenv/include/site/python3.4/pandas:
running install
running build
running build_py
UPDATING build/lib.linux-x86_64-3.4/pandas/_version.py
set build/lib.linux-x86_64-3.4/pandas/_version.py to '0.17.0'
running build_ext
building 'pandas.hashtable' extension
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/hashtable.c -o build/temp.linux-x86_64-3.4/pandas/hashtable.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/hashtable.c:243:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/hashtable.c:245:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.4/pandas/hashtable.o -o build/lib.linux-x86_64-3.4/pandas/hashtable.cpython-34m.so
building 'pandas.parser' extension
creating build/temp.linux-x86_64-3.4/pandas/src/parser
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/parser.c -o build/temp.linux-x86_64-3.4/pandas/parser.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/parser.c:243:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/parser.c:246:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
In file included from pandas/parser.c:251:0:
pandas/src/parser/io.h: At top level:
pandas/src/parser/io.h:33:0: warning: "HAVE_MMAP" redefined [enabled by default]
#define HAVE_MMAP
^
In file included from /usr/include/python3.4m/pyconfig.h:3:0,
from /usr/include/python3.4m/Python.h:8,
from pandas/parser.c:4:
/usr/include/x86_64-linux-gnu/python3.4m/pyconfig.h:587:0: note: this is the location of the previous definition
#define HAVE_MMAP 1
^
pandas/parser.c:21243:18: warning: ‘__pyx_f_6pandas_6parser__try_bool’ defined but not used [-Wunused-function]
static PyObject *__pyx_f_6pandas_6parser__try_bool(parser_t *__pyx_v_parser, int __pyx_v_col, int __pyx_v_line_start, int __pyx_v_line_end, int __pyx_v_na_filter, kh_str_t *__pyx_v_na_hashset) {
^
pandas/parser.c: In function ‘__pyx_f_6pandas_6parser_10TextReader__get_header’:
pandas/parser.c:6333:10: warning: ‘__pyx_v_data_line’ may be used uninitialized in this function [-Wmaybe-uninitialized]
size_t __pyx_v_data_line;
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/parser/tokenizer.c -o build/temp.linux-x86_64-3.4/pandas/src/parser/tokenizer.o
In file included from pandas/src/parser/tokenizer.h:32:0,
from pandas/src/parser/tokenizer.c:20:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/parser/tokenizer.c: In function ‘xstrtod’:
pandas/src/parser/tokenizer.c:2188:16: warning: suggest parentheses around comparison in operand of ‘&’ [-Wparentheses]
p += (tsep != '\0' & *p == tsep);
^
pandas/src/parser/tokenizer.c: In function ‘precise_xstrtod’:
pandas/src/parser/tokenizer.c:2355:20: warning: suggest parentheses around comparison in operand of ‘&’ [-Wparentheses]
p += (tsep != '\0' & *p == tsep);
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/parser/io.c -o build/temp.linux-x86_64-3.4/pandas/src/parser/io.o
In file included from pandas/src/parser/tokenizer.h:32:0,
from pandas/src/parser/io.h:2,
from pandas/src/parser/io.c:1:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
In file included from pandas/src/parser/io.c:1:0:
pandas/src/parser/io.h: At top level:
pandas/src/parser/io.h:33:0: warning: "HAVE_MMAP" redefined [enabled by default]
#define HAVE_MMAP
^
In file included from /usr/include/python3.4m/pyconfig.h:3:0,
from /usr/include/python3.4m/Python.h:8,
from pandas/src/parser/io.h:1,
from pandas/src/parser/io.c:1:
/usr/include/x86_64-linux-gnu/python3.4m/pyconfig.h:587:0: note: this is the location of the previous definition
#define HAVE_MMAP 1
^
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.4/pandas/parser.o build/temp.linux-x86_64-3.4/pandas/src/parser/tokenizer.o build/temp.linux-x86_64-3.4/pandas/src/parser/io.o -o build/lib.linux-x86_64-3.4/pandas/parser.cpython-34m.so
building 'pandas.lib' extension
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/lib.c -o build/temp.linux-x86_64-3.4/pandas/lib.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/lib.c:242:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/lib.c:247:0:
pandas/src/datetime_helper.h:7:0: warning: "PyInt_AS_LONG" redefined [enabled by default]
#define PyInt_AS_LONG PyLong_AsLong
^
pandas/lib.c:141:0: note: this is the location of the previous definition
#define PyInt_AS_LONG PyLong_AS_LONG
^
pandas/lib.c: In function ‘__pyx_f_6pandas_3lib_slice_get_indices_ex’:
pandas/lib.c:27984:3: warning: passing argument 1 of ‘PySlice_GetIndicesEx’ from incompatible pointer type [enabled by default]
__pyx_t_4 = PySlice_GetIndicesEx(((PySliceObject *)__pyx_v_slc), __pyx_v_objlen, (&__pyx_v_start), (&__pyx_v_stop), (&__pyx_v_step), (&__pyx_v_length)); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1639; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
^
In file included from /usr/include/python3.4m/Python.h:97:0,
from pandas/lib.c:4:
/usr/include/python3.4m/sliceobject.h:43:17: note: expected ‘struct PyObject *’ but argument is of type ‘struct PySliceObject *’
PyAPI_FUNC(int) PySlice_GetIndicesEx(PyObject *r, Py_ssize_t length,
^
pandas/lib.c: In function ‘__pyx_f_6pandas_3lib_slice_len’:
pandas/lib.c:28216:3: warning: passing argument 1 of ‘PySlice_GetIndicesEx’ from incompatible pointer type [enabled by default]
__pyx_t_4 = PySlice_GetIndicesEx(((PySliceObject *)__pyx_v_slc), __pyx_v_objlen, (&__pyx_v_start), (&__pyx_v_stop), (&__pyx_v_step), (&__pyx_v_length)); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1662; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
^
In file included from /usr/include/python3.4m/Python.h:97:0,
from pandas/lib.c:4:
/usr/include/python3.4m/sliceobject.h:43:17: note: expected ‘struct PyObject *’ but argument is of type ‘struct PySliceObject *’
PyAPI_FUNC(int) PySlice_GetIndicesEx(PyObject *r, Py_ssize_t length,
^
pandas/lib.c: In function ‘__pyx_gb_6pandas_3lib_112generator’:
pandas/lib.c:26830:38: warning: ‘__pyx_t_22’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_cur_scope->__pyx_t_8 = __pyx_t_22;
^
pandas/lib.c:26827:38: warning: ‘__pyx_t_10’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_cur_scope->__pyx_t_4 = __pyx_t_10;
^
pandas/lib.c:26824:38: warning: ‘__pyx_t_5’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_cur_scope->__pyx_t_3 = __pyx_t_5;
^
pandas/lib.c:26829:38: warning: ‘__pyx_t_14’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_cur_scope->__pyx_t_7 = __pyx_t_14;
^
pandas/lib.c:26828:38: warning: ‘__pyx_t_11’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_cur_scope->__pyx_t_6 = __pyx_t_11;
^
pandas/lib.c: In function ‘__pyx_pw_6pandas_3lib_59maybe_booleans_to_slice’:
pandas/lib.c:12781:15: warning: ‘__pyx_v_end’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_t_6 = PyInt_FromSsize_t(__pyx_v_end); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
^
pandas/lib.c:12440:14: note: ‘__pyx_v_end’ was declared here
Py_ssize_t __pyx_v_end;
^
pandas/lib.c:12779:15: warning: ‘__pyx_v_start’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_t_7 = PyInt_FromSsize_t(__pyx_v_start); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
^
pandas/lib.c:12439:14: note: ‘__pyx_v_start’ was declared here
Py_ssize_t __pyx_v_start;
^
pandas/lib.c: In function ‘__pyx_pf_6pandas_3lib_177maybe_convert_objects.isra.282’:
pandas/lib.c:1465:73: warning: ‘__pyx_pybuffernd_itimedeltas.diminfo[0].strides’ may be used uninitialized in this function [-Wmaybe-uninitialized]
#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
^
pandas/lib.c:53510:21: note: ‘__pyx_pybuffernd_itimedeltas.diminfo[0].strides’ was declared here
__Pyx_LocalBuf_ND __pyx_pybuffernd_itimedeltas;
^
pandas/lib.c:54472:19: warning: ‘__pyx_pybuffernd_itimedeltas.diminfo[0].shape’ may be used uninitialized in this function [-Wmaybe-uninitialized]
} else if (unlikely(__pyx_t_30 >= __pyx_pybuffernd_itimedeltas.diminfo[0].shape)) __pyx_t_7 = 0;
^
pandas/lib.c:1465:73: warning: ‘__pyx_pybuffernd_idatetimes.diminfo[0].strides’ may be used uninitialized in this function [-Wmaybe-uninitialized]
#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
^
pandas/lib.c:53506:21: note: ‘__pyx_pybuffernd_idatetimes.diminfo[0].strides’ was declared here
__Pyx_LocalBuf_ND __pyx_pybuffernd_idatetimes;
^
pandas/lib.c:54379:19: warning: ‘__pyx_pybuffernd_idatetimes.diminfo[0].shape’ may be used uninitialized in this function [-Wmaybe-uninitialized]
} else if (unlikely(__pyx_t_29 >= __pyx_pybuffernd_idatetimes.diminfo[0].shape)) __pyx_t_7 = 0;
^
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.4/pandas/lib.o -o build/lib.linux-x86_64-3.4/pandas/lib.cpython-34m.so
building 'pandas.index' extension
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/index.c -o build/temp.linux-x86_64-3.4/pandas/index.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/index.c:242:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/index.c:246:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
In file included from pandas/index.c:249:0:
pandas/src/datetime_helper.h: At top level:
pandas/src/datetime_helper.h:7:0: warning: "PyInt_AS_LONG" redefined [enabled by default]
#define PyInt_AS_LONG PyLong_AsLong
^
pandas/index.c:141:0: note: this is the location of the previous definition
#define PyInt_AS_LONG PyLong_AS_LONG
^
pandas/index.c: In function ‘__pyx_f_6pandas_5index_11IndexEngine_get_loc’:
pandas/index.c:9305:13: warning: ‘__pyx_v_mid’ may be used uninitialized in this function [-Wmaybe-uninitialized]
__pyx_t_5 = PyInt_FromSsize_t(__pyx_v_mid); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 479; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
^
pandas/index.c:9077:14: note: ‘__pyx_v_mid’ was declared here
Py_ssize_t __pyx_v_mid;
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/datetime/np_datetime.c -o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime.c:18:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/src/datetime/np_datetime.c:10:0:
/usr/include/python3.4m/datetime.h:191:25: warning: ‘PyDateTimeAPI’ defined but not used [-Wunused-variable]
static PyDateTime_CAPI *PyDateTimeAPI = NULL;
^
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/src/datetime/np_datetime_strings.c -o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime_strings.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/src/datetime/np_datetime_strings.c:17:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.4/pandas/index.o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime.o build/temp.linux-x86_64-3.4/pandas/src/datetime/np_datetime_strings.o -o build/lib.linux-x86_64-3.4/pandas/index.cpython-34m.so
building 'pandas.tslib' extension
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -g -fstack-protector --param=ssp-buffer-size=4 -Wformat -Werror=format-security -D_FORTIFY_SOURCE=2 -fPIC -Ipandas/src/klib -Ipandas/src -I/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include -I/usr/include/python3.4m -I/home/rowan/sites/dash.mysite.com/virtualenv/include/python3.4m -c pandas/tslib.c -o build/temp.linux-x86_64-3.4/pandas/tslib.o
In file included from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1781:0,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,
from /home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from pandas/tslib.c:242:
/home/rowan/sites/dash.mysite.com/virtualenv/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning "Using deprecated NumPy API, disable it by " "#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
#warning "Using deprecated NumPy API, disable it by " \
^
In file included from pandas/tslib.c:244:0:
pandas/src/datetime_helper.h:7:0: warning: "PyInt_AS_LONG" redefined [enabled by default]
#define PyInt_AS_LONG PyLong_AsLong
^
pandas/tslib.c:141:0: note: this is the location of the previous definition
#define PyInt_AS_LONG PyLong_AS_LONG
^
In file included from pandas/src/klib/khash_python.h:3:0,
from pandas/tslib.c:253:
pandas/src/klib/khash.h: In function ‘kh_del_str’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash.h:573:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(str, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int32’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:527:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
^
pandas/src/klib/khash.h:574:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT’
KHASH_MAP_INIT_INT(int32, size_t)
^
pandas/src/klib/khash.h: In function ‘kh_del_int64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:548:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
^
pandas/src/klib/khash.h:575:1: note: in expansion of macro ‘KHASH_MAP_INIT_INT64’
KHASH_MAP_INIT_INT64(int64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_float64’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:11:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_float64_hash_equal)
^
pandas/src/klib/khash_python.h:13:1: note: in expansion of macro ‘KHASH_MAP_INIT_FLOAT64’
KHASH_MAP_INIT_FLOAT64(float64, size_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pymap’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:35:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
^
pandas/src/klib/khash_python.h:38:1: note: in expansion of macro ‘KHASH_MAP_INIT_PYOBJECT’
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_pyset’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash_python.h:41:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_pyobject_t, char, 0, \
^
pandas/src/klib/khash_python.h:44:1: note: in expansion of macro ‘KHASH_SET_INIT_PYOBJECT’
KHASH_SET_INIT_PYOBJECT(pyset)
^
pandas/src/klib/khash_python.h: In function ‘kh_del_strbox’:
pandas/src/klib/khash.h:155:38: warning: statement with no effect [-Wunused-value]
#define __ac_set_isdel_true(flag, i) (0)
^
pandas/src/klib/khash.h:321:4: note: in expansion of macro ‘__ac_set_isdel_true’
__ac_set_isdel_true(h->flags, x); \
^
pandas/src/klib/khash.h:327:2: note: in expansion of macro ‘KHASH_INIT2’
KHASH_INIT2(name, static PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
^
pandas/src/klib/khash.h:565:2: note: in expansion of macro ‘KHASH_INIT’
KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
^
pandas/src/klib/khash_python.h:49:1: note: in expansion of macro ‘KHASH_MAP_INIT_STR’
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
^
pandas/tslib.c: In function ‘__pyx_f_6pandas_5tslib__check_dts_bounds’:
pandas/tslib.c:26395:3: warning: implicit declaration of function ‘cmp_pandas_datetimestruct’ [-Wimplicit-function-declaration]
__pyx_t_2 = ((cmp_pandas_datetimestruct(__pyx_v_dts, (&__pyx_v_6pandas_5tslib__NS_MIN_DTS)) == -1) != 0);
^
pandas/tslib.c: At top level:
pandas/tslib.c:15364:18: warning: ‘__pyx_f_6pandas_5tslib__tz_format’ defined but not used [-Wunused-function]
static PyObject *__pyx_f_6pandas_5tslib__tz_format(PyObject *__pyx_v_obj, PyObject *__pyx_v_zone) {
^
x86_64-linux-gnu-gcc: internal compiler error: Killed (program cc1)
Please submit a full bug report,
with preprocessed source if appropriate.
See <file:///usr/share/doc/gcc-4.8/README.Bugs> for instructions.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 4
----------------------------------------
Command "/home/rowan/sites/dash.mysite.com/virtualenv/bin/python3 -c "import setuptools, tokenize;__file__='/tmp/pip-build-9f88lmba/pandas/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-9nux__c1-record/install-record.txt --single-version-externally-managed --compile --install-headers /home/rowan/sites/dash.mysite.com/virtualenv/include/site/python3.4/pandas" failed with error code 1 in /tmp/pip-build-9f88lmba/pandas
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11571/events | https://github.com/pandas-dev/pandas/issues/11571 | 116,318,208 | MDU6SXNzdWUxMTYzMTgyMDg= | 11,571 | Segmentatuin fault while read_csv | {
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| null | 1 | 2015-11-11T11:48:34Z | 2015-11-11T12:36:31Z | 2015-11-11T12:36:31Z | NONE | null | Following code crashes with segmentation fault (SIGSEGV) - [strace.txt](https://github.com/pydata/pandas/files/31816/strace.txt). When engine is set to `python` all works fine. Problem is in that too large numeber.
```
import pandas as pd
answers = pd.read_csv("questions_answer.csv", engine='c')
```
questions_answer.csv
```
id,number
4926,2222222222222222222222222
```
I observe this error on two Fedora 20 machines
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.8-100.fc20.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: cs_CZ.UTF-8
pandas: 0.17.0
nose: None
pip: 1.5.6
setuptools: 3.6
Cython: None
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11572 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11572/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11572/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11572/events | https://github.com/pandas-dev/pandas/pull/11572 | 116,365,126 | MDExOlB1bGxSZXF1ZXN0NTA0MDA0ODY= | 11,572 | Fixed typo in plotting documentation | {
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| null | 1 | 2015-11-11T16:22:07Z | 2015-11-12T00:51:18Z | 2015-11-12T00:51:09Z | CONTRIBUTOR | null | Given a small csv
```
City,State,Zip
Omaha,NE,68134
Chicago,IL,60290
Atlanta,GA,30303
```
then
```
df = pd.read_csv("test.csv")
df["Zip"].str.len()
```
not surprisingly yields
_AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas_
this will not throw an error, but doesn't give me the expected result
```
df["Zip"] = df["Zip"].astype("object")
df["Zip"].str.len()
0 NaN
1 NaN
2 NaN
```
However, when I leverage the dtype arg in read_csv() it works
```
df = pd.read_csv("test.csv", dtype={"Zip" : "object"})
df["Zip"].str.len()
0 5
1 5
2 5
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 14.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: None
pip: 7.1.2
setuptools: 18.3.2
Cython: 0.22
numpy: 1.10.0
scipy: 0.16.0
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: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.6
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext)
```
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| null | 3 | 2015-11-11T17:10:35Z | 2020-05-22T03:27:32Z | null | NONE | null | Observed in pandas 0.17.0 and the current development version (d78266e).
It seems to me that pandas fail to align timeseries plots with different frequency unit (or no frequency) when they are drawn on _different axes_ with one _shared axis_. The issue does _not_ occur if the two timeseries are plotted on the _same axes_.
Example:
``` python
"""Shared axes between timeseries plots of different frequency."""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.show_versions()
# prepare a random time series
t = pd.date_range('1/1/2000', periods=1000)
x = np.random.randn(1000)
ts = pd.Series(x, index=t).cumsum()
# initialize figure
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)
# plot original and resampled timeseries
ts.plot(ax=ax1) # works as expected
ts.resample('30D').plot(ax=ax2) # works as expected
ts.resample('1M').plot(ax=ax3) # seem to mess up x axis
# show
plt.show()
```
After the third series is drawn, the first two appear shifted out of the frame to wrong x-coordinates.
Version info:
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0+159.gd78266e
nose: 1.3.7
pip: 7.1.2
setuptools: 14.3.1
Cython: 0.22
numpy: 1.10.1
scipy: 0.16.0
statsmodels: None
IPython: 0.13.1
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.7.4
apiclient: None
sqlalchemy: 0.7.8
pymysql: None
psycopg2: 2.4.5 (dt dec mx pq3 ext)
```
Thanks!
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} | 12 | 2015-11-11T22:04:25Z | 2015-11-14T15:08:24Z | 2015-11-14T15:08:07Z | CONTRIBUTOR | null | assigning to `Series.str`, `Series.dt`, or `Series.cat` was not failing
although you couldn't get the value back:
```
In[10]: a = pandas.Series(pandas.Categorical(list("abc")))
In[11]: a.cat.labels = [1,2]
In[12]: a.cat.labels
Traceback (most recent call last):
File "C:\portabel\miniconda\envs\pandas_dev\lib\site-packages\IPython\core\interactiveshell.py", line 2883, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-a68ee763e4e8>", line 1, in <module>
a.cat.labels
AttributeError: 'CategoricalAccessor' object has no attribute 'labels'
```
Now we fail early:
```
In[10]: a = pandas.Series(pandas.Categorical(list("abc")))
In[11]: a.cat.labels = [1,2]
Traceback (most recent call last):
File "C:\data\external\pandas\pandas\tests\test_categorical.py", line 3633, in test_cat_accessor_no_new_attributes
c.cat.labels = [1,2]
File "C:\data\external\pandas\pandas\core\base.py", line 121, in __setattr__
raise AttributeError( "You cannot add any new attribute '{key}'".format(key=key))
AttributeError: You cannot add any new attribute 'labels'
```
Closes: https://github.com/pydata/pandas/issues/10673
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| null | 6 | 2015-11-11T23:39:12Z | 2015-11-12T01:50:16Z | 2015-11-12T00:44:57Z | NONE | null | This works:
```
(df1.col1/(df1.col1+df1.col2)).plot(ylim=[0,.1])
```
But this fails (even with `engine`/`parser` set to `python`):
```
df1.query('col1/(col1+col2)').plot(ylim=[0,.1])
```
with traceback:
```
C:\Python\Python27\lib\site-packages\pandas\core\frame.pyc in query(self, expr, **kwargs)
2053
2054 try:
-> 2055 return self.loc[res]
2056 except ValueError:
2057 # when res is multi-dimensional loc raises, but this is sometimes a
C:\Python\Python27\lib\site-packages\pandas\core\indexing.pyc 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):
C:\Python\Python27\lib\site-packages\pandas\core\indexing.pyc 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
C:\Python\Python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_iterable(self, key, axis)
940 def _getitem_iterable(self, key, axis=0):
941 if self._should_validate_iterable(axis):
--> 942 self._has_valid_type(key, axis)
943
944 labels = self.obj._get_axis(axis)
C:\Python\Python27\lib\site-packages\pandas\core\indexing.pyc in _has_valid_type(self, key, axis)
1278
1279 raise KeyError("None of [%s] are in the [%s]" %
-> 1280 (key, self.obj._get_axis_name(axis)))
1281
1282 return True
KeyError: 'None of [2015-07-01 00:00:36 1.000000\n2015-07-01 00:01:36 1.000000\n2015-07-01 00:02:36 1.000000\n2015-07-01 00:03:36 1.000000\n2015-07-01 00:04:36 1.000000\n2015-07-01 00:05:36 1.000000\n2015-07-01 00:06:37 1.000000\n2015-07-01
...
14:33:57 0.031360\n2015-11-06 14:34:57 0.031348\n2015-11-06 14:35:57 0.031365\ndtype: float64] are in the [index]'
```
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| null | 20 | 2015-11-12T01:07:49Z | 2017-04-05T02:06:36Z | 2015-11-14T20:49:13Z | CONTRIBUTOR | null | just trying from a different branch.
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| null | 1 | 2015-11-12T04:15:23Z | 2015-11-12T12:26:06Z | 2015-11-12T12:25:52Z | CONTRIBUTOR | null | The examples that I see for [`resample`](http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.resample.html) seem to only involve `pd.Series`, although the method is technically defined for a `pd.DataFrame`.
Can it be used to do the type of more complex resampling described [here](http://stackoverflow.com/questions/33662514/aggregation-every-x-days-in-pandas)? Can I perhaps do this combining `resample` with `transform` having it work on one-column at a time as in the examples with the `series`?
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| null | 4 | 2015-11-12T07:26:24Z | 2021-04-21T04:57:53Z | null | NONE | null | I was happy to see in the release notes for 0.17.0 that value_counts no longer discards the series name, but the implementation wasn't what I expected.
0.17.0 gives
``` py
>>> series = pd.Series([1731, 364, 813, 1731], name='user_id')
>>> series.value_counts()
1731 2
813 1
364 1
Name: user_id, dtype: int64
```
which doesn't set the index name.
In my opinion the old series name belongs in the index, not in the series name:
``` py
>>> series.value_counts()
user_id
1731 2
813 1
364 1
dtype: int64
```
Why:
- It's logical: the user_id has moved to the index, and the values now represent occurrence counts
- This would be consistent with how `.groupby().size()` behaves
- Adding a missing index name is cumbersome and requires creating a temporary variable
- In many cases the series name is discarded, while index names tend to stick around: for example, `pd.DataFrame({'n': series.value_counts(), 'has_duplicates': series.value_counts() > 1})` should really have user_id as an index name
There are three options:
- result.name = None and result.index.name = series.name
- result.name = series.name and result.index.name = series.name
- result.name = 'size' or 'count' and result.index.name = series.name
The first option seems more elegant to me but @sinhrks, who reported #10150, apparently expected result.name to be filled, so perhaps there are use cases where the second option is useful.
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| null | 1 | 2015-11-12T10:00:05Z | 2015-11-12T12:24:13Z | 2015-11-12T12:23:57Z | NONE | null | Hi,
I run the following code which uses the binary operator function .mul to multiply a dataframe by 1.0 and then assert that the operation should result in the same dataframe, however, the test fails indeterminately?
```
import pandas as pd, numpy as np
from pandas.util.testing import assert_frame_equal
np.random.seed(42)
d_index = pd.date_range(start='2000-01-01', end='2015-07-01', freq='B')
t_index = np.sort(np.random.choice(d_index,2000,replace=False))
tmp = pd.DataFrame(np.ones((t_index.shape[0], 1000)), index=t_index)
def same_dataframes(df1,df2):
try:
assert_frame_equal(df1,df2)
except:
return False
return True
for i in range(100):
if not same_dataframes(tmp.mul(1.0, axis=1), tmp.mul(1.0, axis=1)):
print i, 'The Frames Are Not Equal'
```
It returns the following:
```
2 The Frames Are Not Equal
4 The Frames Are Not Equal
9 The Frames Are Not Equal
17 The Frames Are Not Equal
20 The Frames Are Not Equal
62 The Frames Are Not Equal
63 The Frames Are Not Equal
67 The Frames Are Not Equal
69 The Frames Are Not Equal
85 The Frames Are Not Equal
```
Additionally, when I try the following it passes every time:
```
for i in range(500):
if not same_dataframes(tmp.apply(lambda x: x * 1.0), tmp.apply(lambda x: x * 1.0)):
print i, 'The Frames Are Not Equal'
```
I tried running other binary operators such as .ge, .gt, .lt, .le and also received fails in random cases
Thank you
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} | 6 | 2015-11-12T11:11:18Z | 2015-11-15T16:46:47Z | 2015-11-15T16:46:44Z | CONTRIBUTOR | null | closes #3335.
Adds margin_column parameter to pivot_table so that user can set it to
something other than 'All'.
Raises ValueError exception if there is a conflict between the value of
margin_column and one of the other values appearing in the indices of
the pivot table.
take into account notes from closed pull request: #10296
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} | 25 | 2015-11-12T13:21:27Z | 2015-11-18T13:39:25Z | 2015-11-18T11:46:16Z | CONTRIBUTOR | null | If a series is a type category and the underlying Categorical has categories of type string or datetime, then make it possible to use the `.str`/`.dt` assessor on such a series.
The string/dt methods work on the categories (and therefore fast if we have
only a few categories), but return a Series with a dtype other than
category (integer, boolean, string,...), so that it is no different if we use
`.str` / `.dt` on a series of type string or of type category.
The main reason for that is that I think things like `s.str.slice(...) + s.str.slice(...)` should work.
Closes: https://github.com/pydata/pandas/issues/10661
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} | 1 | 2015-11-12T19:58:33Z | 2021-04-21T04:58:11Z | null | NONE | null | Would be nice to be able to perform a fuzzy join between two dataframes based on string columns. This [thread](http://stackoverflow.com/questions/13636848/is-it-possible-to-do-fuzzy-match-merge-with-python-pandas) shows how to do it using difflib, but it may be nice to have this as built-in functionality.
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} | 8 | 2015-11-12T21:32:54Z | 2016-02-12T15:04:32Z | 2016-02-12T15:04:32Z | NONE | null | Test 1 works successfully with a square dataframe
``` python
import numpy as np
import pandas as pd
np.random.seed(1)
a = pd.DataFrame(np.random.rand(5, 5))
np.random.seed(1)
b = pd.DataFrame(np.random.rand(5, 5))
b += 0.00001
pd.util.testing.assert_almost_equal(a,b)
```
Test 2 fails with a dataframe where number rows > number of columns
``` python
np.random.seed(1)
a = pd.DataFrame(np.random.rand(6, 5))
np.random.seed(1)
b = pd.DataFrame(np.random.rand(6, 5))
b += 0.00001
pd.util.testing.assert_almost_equal(a,b)
```
KeyError: 5L
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} | 1 | 2015-11-13T07:42:25Z | 2015-11-23T01:20:25Z | 2015-11-23T01:20:25Z | MEMBER | null | `.loc` against `CategoricalIndex` with values included in its `categories` but not appears as `codes` results in normal `Index`
```
import numpy as np
import pandas as pd
index = pd.CategoricalIndex(list('aabbca'), categories=list('cabe'))
df = pd.DataFrame({'A' : np.arange(6,dtype='int64')}, index=index)
# OK
df.loc[['a', 'b']].index
# CategoricalIndex([u'a', u'a', u'a', u'b', u'b'], categories=[u'c', u'a', u'b', u'e'], ordered=False, dtype='category')
# NG, must be CategoricalIndex
df.loc[['a', 'b', 'e']].index
# Index([u'a', u'a', u'a', u'b', u'b', u'e'], dtype='object')
```
Even though this is tested here, it doesn't check dtype.
- https://github.com/pydata/pandas/blob/master/pandas/tests/test_indexing.py#L4781
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} | 2 | 2015-11-13T07:47:23Z | 2018-12-14T14:53:43Z | 2018-12-14T14:53:43Z | MEMBER | null | Setting invalid freq against ndarray(dtype=int) should raise `ValueError`.
```
import numpy as np
import pandas as pd
pd.DatetimeIndex(np.array([pd.Timestamp('2011-01-01').value, pd.Timestamp('2011-01-03').value]), freq='D')
# ValueError: Inferred frequency None from passed dates does not conform to passed frequency D
```
There seems to be 2 issues:
### 1. invalid frequency may set if `data` is list
```
pd.DatetimeIndex([pd.Timestamp('2011-01-01').value, pd.Timestamp('2011-01-03').value], freq='D')
# DatetimeIndex(['2011-01-01', '2011-01-03'], dtype='datetime64[ns]', freq='D')
```
### 2. ndarray `data` may raise unclear error message
```
pd.DatetimeIndex(np.array([pd.tslib.iNaT, pd.Timestamp('2011-01-01').value]), freq='D')
# ValueError: NaTType does not support time
```
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} | 1 | 2015-11-13T13:45:17Z | 2015-11-17T13:19:12Z | 2015-11-17T12:17:29Z | MEMBER | null | Enable `check_index_type` and `check_columns_type` by default to detect dtype related problems.
Fixed following 2 minor bugs at the same time, but I'm willing to split them to separate PR if required.
### 1. `Series.quantile` with empty list results in `object` index rather than `float`.
```
pd.Series([1, 2, 3]).quantile([]).index
# Index([], dtype='object')
```
### 2. `pd.merge` includes empty data may result in `Int64Index` rather than `object`
Sorry, #10826 was incomplete.
When fixing #11497, #11586, we can enable corresponding `check_index_type`
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} | 2 | 2015-11-13T13:57:42Z | 2015-11-13T14:07:05Z | 2015-11-13T14:07:05Z | CONTRIBUTOR | null | Here is the bad address:
http://wesmckinney.com/files/tips.csv
Found here in the docs:
https://github.com/pydata/pandas/blob/master/doc/source/visualization.rst#trellis-plotting-interface
Alternative link found elsewhere in the code base:
https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv
PR for a fix follows
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} | 2 | 2015-11-13T14:05:36Z | 2015-11-13T14:07:31Z | 2015-11-13T14:07:05Z | CONTRIBUTOR | null | closes #11589
Fixes a bad url
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} | 16 | 2015-11-13T14:17:48Z | 2016-01-13T13:41:59Z | 2016-01-13T13:41:59Z | CONTRIBUTOR | null | closes #12014
This PR fixes a minor bug and introduces some performance enhancements, all related to value label reading in Stata files.
The bug is that when reading a Stata file incrementally, the value labels will be read even when specifying convert_categoricals=False (this does not happen when reading the entire file at once).
The performance enhancements are:
1. Read key/value information as an ndarray using np.frombuffer rather than as a Python loop.
2. When splitting the value labels at the offsets, we currently pass txt[off[i]:] to null_terminate, which creates many copies of large segments of a potentially large byte array. I changed it so that
only the relevant part of the string is copied.
Relating to 2, further performance improvements might be possible since there is no trailing null byte to remove except for the last element of `txt` (thus some of the work in `_null_terminate` is superfluous).
Background: This is an issue when processing large Stata files with millions of distinct value labels.
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| null | 3 | 2015-11-13T16:40:56Z | 2015-11-13T17:04:05Z | 2015-11-13T17:04:05Z | NONE | null | There's a different behavior between 0.16.0 and 0.17.0 on a DatetimeIndex creation with a frequency greater than the dates range:
``` python
import pandas as pd
pd.DatetimeIndex(start=pd.Timestamp('2015-04-15'), end=pd.Timestamp('2015-04-18'), freq='7D')
```
With previous versions I had a DatetimeIndex of length 2: `[2015-04-15, 2015-04-22]`.
With 0.17.0 I get an index with only one value: `['2015-04-15']`.
Is it a bug/regression? Is there a way to reproduce the previous behavior?
Here are the the results of `pd.show_versions()`:
- 0.16.0
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.0
nose: 1.3.7
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.7
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
```
- 0.17.0
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.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: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.1
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
```
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} | 5 | 2015-11-13T17:18:47Z | 2016-03-23T17:56:45Z | 2016-03-23T17:56:45Z | MEMBER | null | All the same issue
- [ ] #12045
- [ ] #12211
```
In [1]: import datetime
In [2]: s = pd.Series([datetime.datetime(2012, 1, 1)]*10 + [datetime.datetime(1012,1,2)] + [datetime.datetime(2012, 1, 3)]*10)
In [8]: s
Out[8]:
0 2012-01-01 00:00:00
1 2012-01-01 00:00:00
2 2012-01-01 00:00:00
3 2012-01-01 00:00:00
4 2012-01-01 00:00:00
5 2012-01-01 00:00:00
6 2012-01-01 00:00:00
7 2012-01-01 00:00:00
8 2012-01-01 00:00:00
9 2012-01-01 00:00:00
10 1012-01-02 00:00:00
11 2012-01-03 00:00:00
12 2012-01-03 00:00:00
13 2012-01-03 00:00:00
14 2012-01-03 00:00:00
15 2012-01-03 00:00:00
16 2012-01-03 00:00:00
17 2012-01-03 00:00:00
18 2012-01-03 00:00:00
19 2012-01-03 00:00:00
20 2012-01-03 00:00:00
dtype: object
In [9]: pd.options.display.max_rows = 8
In [10]: s
Out[10]:
0 2012-01-01
1 2012-01-01
2 2012-01-01
3 2012-01-01
...
17 2012-01-03
18 2012-01-03
19 2012-01-03
20 2012-01-03
dtype: datetime64[ns]
In [11]: s.dtype
Out[11]: dtype('O')
```
So in some cases, you think you have a `datetime64` series, but actually you don't and eg `dt` properties don't work which can lead to quite some confusion ... :-)
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} | 14 | 2015-11-13T17:26:56Z | 2016-03-30T17:24:15Z | 2015-11-13T21:34:59Z | CONTRIBUTOR | null | I would like to know how many bytes my dataframe takes up in memory. The standard way to do this is the `memory_usage` method
```
df.memory_usage(index=True)
```
For object dtype columns this measures 8 bytes per element, the size of the reference not the size of the full object. In some cases this significantly underestimates the size of the dataframe.
It might be nice to optionally map `sys.getsizeof` on object dtype columns to get a better estimate of the size. If this ends up being expensive then it might be good to have this as an optional keyword argument.
```
df.memory_usage(index=True, measure_object=True)
```
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} | 9 | 2015-11-13T18:23:01Z | 2015-11-13T21:34:59Z | 2015-11-13T21:34:59Z | CONTRIBUTOR | null | closes #11595
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} | 1 | 2015-11-13T20:56:11Z | 2016-01-03T16:54:06Z | 2016-01-03T16:54:06Z | CONTRIBUTOR | null | As discussed: https://github.com/pydata/pandas/pull/11596#issuecomment-156552785
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} | 7 | 2015-11-13T21:40:37Z | 2015-11-15T16:55:19Z | 2015-11-15T16:55:16Z | CONTRIBUTOR | null | closes pydata/pandas#11433
Code. taken from @jreback comment on pydata/pandas#11433
asv benchmark:
```
before after ratio
[6c4d2c7d] [6439bd94]
- 1.68s 364.81μs 0.00 series_methods.series_constructor_no_data_datetime_index.time_series_constructor_no_data_datetime_index
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
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} | 0 | 2015-11-13T22:45:30Z | 2015-11-18T21:34:56Z | 2015-11-18T21:34:56Z | MEMBER | null | From SO: http://stackoverflow.com/questions/33693601/removing-unused-categories-in-series-results-in-duplicated-categories
`removed_unused_categories` gives a wrong result when there is a NaN in the values:
```
>>> s = pd.Series(["A", "B", pd.np.nan]).astype("category")
>>> s.cat.remove_unused_categories()
0 A
1 B
2 NaN
dtype: category
Categories (3, object): [B, A, B]
```
I think the `-1` in the codes (`NaN`) duplicates the last item in the categories
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} | 8 | 2015-11-14T00:21:57Z | 2015-11-19T01:13:54Z | 2015-11-19T01:13:54Z | NONE | null | From SO: http://stackoverflow.com/questions/33702198/do-python-pandas-sparse-dataframes-lose-multi-index-column-names-or-am-i-doing-i
Bug is simple in concept, multi-index with column level names loses those names when going into sparse dataframes.
Minimal example - first create a multi-index dataframe:
```
In[2]: import pandas as pd
In[3]: miindex = pd.MultiIndex.from_product([["x","y"], ["10","20"]],names=['row-foo', 'row-bar'])
micol = pd.MultiIndex.from_product([['a','b','c'], ["1","2"]],names=['col-foo', 'col-bar'])
df = pd.DataFrame(index=miindex, columns=micol).sortlevel().sortlevel(axis=1)
df = df.fillna(value=3.14)
df
Out[3]:
col-foo a b c
col-bar 1 2 1 2 1 2
row-foo row-bar
x 10 3.14 3.14 3.14 3.14 3.14 3.14
20 3.14 3.14 3.14 3.14 3.14 3.14
y 10 3.14 3.14 3.14 3.14 3.14 3.14
20 3.14 3.14 3.14 3.14 3.14 3.14
```
This gives us a nice test multi-index with column and row level names. Now if I make a sparse matrix out of that and show it, the column level names are gone.
```
In[4]: ds = df.to_sparse()
ds
Out[4]:
a b c
1 2 1 2 1 2
row-foo row-bar
x 10 3.14 3.14 3.14 3.14 3.14 3.14
20 3.14 3.14 3.14 3.14 3.14 3.14
y 10 3.14 3.14 3.14 3.14 3.14 3.14
20 3.14 3.14 3.14 3.14 3.14 3.14
```
And if I convert the sparse version back to dense those level names are still gone.
```
In[6]: ds.to_dense()
Out[6]:
a b c
1 2 1 2 1 2
row-foo row-bar
x 10 3.14 3.14 3.14 3.14 3.14 3.14
20 3.14 3.14 3.14 3.14 3.14 3.14
y 10 3.14 3.14 3.14 3.14 3.14 3.14
20 3.14 3.14 3.14 3.14 3.14 3.14
```
I AM aware that displaying the sparse version calls to_dense() but the loss appears to be happening at the conversion to sparse. I'm exploring moving to sparse to reduce memory usage for a code base and my attempts to access the levels within the sparse dataframe generate "KeyError: 'Level not found'"
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| null | 6 | 2015-11-14T01:19:42Z | 2015-11-16T21:26:31Z | 2015-11-14T14:51:09Z | NONE | null | Hello - I was wondering if someone could throw together a way to export a .tde file which stands for a Tableau Data Extract file. I spend a lot of time reading and writing .csv files and then extracting the data manually in Tableau but it would be cool to skip the .csv step and read/write directly to the .tde format.
http://onlinehelp.tableau.com/current/api/sdk/en-us/help.htm#SDK/tableau_sdk_using_python.htm%3FTocPath%3D_____4
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} | 6 | 2015-11-14T15:17:11Z | 2016-01-06T16:37:24Z | 2016-01-06T16:37:24Z | CONTRIBUTOR | null | this was on 3.5; I couldn't repro on macosx 2.7.
```
In [1]: pd.__version__
Out[1]: '0.17.0+185.g9cbc179'
```
```
[pandas3.5] C:\Users\Jeff Reback\pandas3.5>nosetests pandas\tests\test_graphics.py -v -m area_lim -s
test_area_lim (pandas.tests.test_graphics.TestDataFramePlots) ... C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning s
calar instead, but in the future will perform elementwise comparison
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
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if self._edgecolors == str('face'):
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on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
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C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
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C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
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if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
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if self._edgecolors == str('face'):
C:\Miniconda\envs\pandas3.5\lib\site-packages\matplotlib\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparis
on
if self._edgecolors == str('face'):
ok
test_ts_area_lim (pandas.tests.test_graphics.TestSeriesPlots) ... ok
----------------------------------------------------------------------
Ran 2 tests in 2.028s
OK
[pandas3.5] C:\Users\Jeff Reback\pandas3.5>
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11603 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11603/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11603/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11603/events | https://github.com/pandas-dev/pandas/pull/11603 | 116,942,772 | MDExOlB1bGxSZXF1ZXN0NTA3Mjg2MzI= | 11,603 | API: provide Rolling/Expanding/EWM objects for deferred rolling type calculations #10702 | {
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} | 55 | 2015-11-14T18:12:18Z | 2016-01-17T18:46:08Z | 2015-12-19T13:52:07Z | CONTRIBUTOR | null | closes #10702
closes #9052
xref #4950, removal of depr
So this basically takes all of the `pd.rolling_*`,`pd.expanding_*`,`pd.ewma_*` routines and allows an object oriented interface, similar to groupby.
Some benefits:
- nice tab completions on the Rolling/Expanding/EWM objects
- much cleaner code internally
- complete back compat, e.g. everything just works like it did
- added a `.agg/aggregate` function, similar to groupby, where you can do multiple aggregations at once
- added `__getitem__` accessing, e.g. `df.rolling(....)['A','B'].sum()` for a nicer API
- allows for much of #8659 to be done very easily
- fix for coercing Timedeltas properly
- handling nuiscance (string) columns
Other:
- along with window doc rewrite, fixed doc-strings for groupby/window to provide back-refs
ToDO:
- [x] I think that all of the doc-strings are correct, but need check
- [x] implement `.agg`
- [x] update API.rst, what's new
- [x] deprecate the `pd.expanding_*`,`pd.rolling_*`,`pd.ewma_*` interface as this is polluting the top-level namespace quite a bit
- [x] change the docs to use the new API
```
In [4]: df = DataFrame({'A' : range(5), 'B' : pd.timedelta_range('1 day',periods=5), 'C' : 'foo'})
In [5]: df.rolling(window=2).sum()
Out[5]:
A B C
0 NaN NaT foo
1 1 3 days foo
2 3 5 days foo
3 5 7 days foo
4 7 9 days foo
In [6]: df.rolling(window=2)['A','C'].sum()
Out[6]:
A C
0 NaN foo
1 1 foo
2 3 foo
3 5 foo
4 7 foo
In [2]: r = df.rolling(window=3)
In [3]: r.
r.A r.C r.corr r.cov r.max r.median r.name r.skew r.sum
r.B r.apply r.count r.kurt r.mean r.min r.quantile r.std r.var
```
do rolling/expanding/ewma ops
```
In [1]: s = Series(range(5))
In [2]: r = s.rolling(2)
I# pd.rolling_sum
In [3]: r.sum()
Out[3]:
0 NaN
1 1
2 3
3 5
4 7
dtype: float64
# nicer repr
In [4]: r
Out[4]: Rolling [window->2,center->False,axis->0]
In [5]: e = s.expanding(min_periods=2)
# pd.expanding_sum
In [6]: e.sum()
Out[6]:
0 NaN
1 1
2 3
3 6
4 10
dtype: float64
In [7]: em = s.ewm(com=10)
# pd.ewma
In [8]: em.mean()
Out[8]:
0 0.000000
1 0.523810
2 1.063444
3 1.618832
4 2.189874
dtype: float64
```
and allow the various aggregation type of ops (similar to groupby)
```
In [1]: df = DataFrame({'A' : range(5), 'B' : pd.timedelta_range('1 day',periods=5), 'C' : 'foo'})
In [2]: r = df.rolling(2,min_periods=1)
In [3]: r.agg([np.sum,np.mean])
Out[3]:
A B C
sum mean sum mean sum mean
0 0 0.0 1 days 1 days 00:00:00 foo foo
1 1 0.5 3 days 1 days 12:00:00 foo foo
2 3 1.5 5 days 2 days 12:00:00 foo foo
3 5 2.5 7 days 3 days 12:00:00 foo foo
4 7 3.5 9 days 4 days 12:00:00 foo foo
In [4]: r.agg({'A' : 'sum', 'B' : 'mean'})
Out[4]:
A B
0 0 1 days 00:00:00
1 1 1 days 12:00:00
2 3 2 days 12:00:00
3 5 3 days 12:00:00
4 7 4 days 12:00:00
```
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