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| null | 4 | 2016-06-23T18:08:16Z | 2016-11-16T22:23:17Z | 2016-11-16T22:23:17Z | NONE | null | - [x] closes #13490
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes a bug where the symmetric difference of two equal MultiIndexes would raise a TypeError. MultiIndex used to use the `Index.symmetric_difference`. With this PR it it's own implementation that is more like `MultiIndex.difference`.
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| null | 2 | 2016-06-23T20:46:24Z | 2016-06-27T15:09:46Z | 2016-06-27T15:09:46Z | NONE | null | I am trying to read_csv from a csv buffer object while selecting an index column, but I am recieving an EmptyDataError. I can read the plain file and select an index_col no problem, but for some reason the buffer object is not achieving the same results. I pasted my code below.
```
import pandas as pd
from six import StringIO
# reading from a file and setting the index works
df = pd.read_csv('somedata.csv', index_col=0)
print(df.to_csv())
,Data
2008-01-01,2106195622.22
2008-02-01,2049275024.52
2008-03-01,2653068424.54
2008-04-01,2523254963.17
2008-05-01,2504513912.88
# reading from a buffer and trying to setting an index does not work,
buffy = StringIO()
df.to_csv(buffy)
buffy.seek(0)
print(buffy.read())
,Data
2008-01-01,2106195622.22
2008-02-01,2049275024.52
2008-03-01,2653068424.54
2008-04-01,2523254963.17
2008-05-01,2504513912.88
buffy.seek(0)
df_buff = pd.read_csv(buffy, index_col=0)
<ipython-input-243-95002384e2b3> in <module>()
----> 1 pd.read_csv(buffy, index_col=0)
560 skip_blank_lines=skip_blank_lines)
561
--> 562 return _read(filepath_or_buffer, kwds)
563
564 parser_f.__name__ = name
/home/eholmes/.virtualenv/py27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
313
314 # Create the parser.
--> 315 parser = TextFileReader(filepath_or_buffer, **kwds)
316
317 if (nrows is not None) and (chunksize is not None):
/home/eholmes/.virtualenv/py27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
643 self.options['has_index_names'] = kwds['has_index_names']
644
--> 645 self._make_engine(self.engine)
646
647 def close(self):
/home/eholmes/.virtualenv/py27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
797 def _make_engine(self, engine='c'):
798 if engine == 'c':
--> 799 self._engine = CParserWrapper(self.f, **self.options)
800 else:
801 if engine == 'python':
/home/eholmes/.virtualenv/py27/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, src, **kwds)
1211 kwds['allow_leading_cols'] = self.index_col is not False
1212
-> 1213 self._reader = _parser.TextReader(src, **kwds)
1214
1215 # XXX
pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:5214)()
EmptyDataError: No columns to parse from file
```
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.5
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0b1
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: 0.2.1
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| null | 0 | 2016-06-23T22:32:53Z | 2021-05-01T22:31:03Z | null | CONTRIBUTOR | null | xref #8640 #12699 #13361 #13410
There's been discussion of a few overlapping uses of `Categorical`:
1. as 'true' categorical data with a known set of values
2. as 'lazy' categorical data which adds new categories as needed
3. as an interned string data type, with no particular categorical interpretation
Option 1 is currently well-supported. Option 2 can be achieved explicitly by `union_categorical`, see #13361 #13410, but will not happen automatically e.g. when setting values or concatenating, see #12699. Option 3 is similar to 2, and has been discussed as a new `String` type, see #8640, but may have different semantics to 2.
While I completely agree that option 1 should be the default, I'd like to see more support for option 2 if possible; there are cases where I really do want to work with categorical data, but I don't yet know what the categories are, e.g. when concatenating a table together from several different source files.
One option would be to mimic Matlab's 'protected' flag for categorical data. By default, a `Categorical` would be created protected, and throw errors when performing actions which implicitly change the category set. However, the user could choose to declare a `Categorical` as unprotected, in which case these operations would be performed as intended.
While this behaviour could be achieved with the proposed `String` type, it's unclear whether that type would share the `Categorical` API, or be efficiently convertible to `Categorical`. Having the behaviour as part of `Categorical` would allow the user to build up a `Categorical` iteratively, from multiple sources, and then quickly mark it protected once the category set is known.
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} | 1 | 2016-06-23T23:17:34Z | 2016-06-23T23:59:45Z | 2016-06-23T23:59:45Z | CONTRIBUTOR | null | Fixes a build error from https://github.com/pydata/pandas/pull/12946 caused by mmap error being returned in Italian when `LOCALE_OVERRIDE="it_IT.UTF-8"`. The test fails with:
`AssertionError: "Invalid argument" does not match "[Errno 22] Argomento non valido"`
``` python
msg = "Invalid argument"
tm.assertRaisesRegexp(mmap.error, msg, common.MMapWrapper, non_file)
```
i.e. message is not being matched. Change to match the errno instead as that's the same across languages.
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} | 0 | 2016-06-24T17:30:57Z | 2017-03-03T09:17:29Z | 2017-03-03T09:16:46Z | NONE | null | It would be nice to have `\multicolumn` and `\multirow` in the `to_latex` output when using multilayer indexes or columns.
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https://api.github.com/repos/pandas-dev/pandas/issues/13509 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13509/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13509/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13509/events | https://github.com/pandas-dev/pandas/issues/13509 | 162,239,059 | MDU6SXNzdWUxNjIyMzkwNTk= | 13,509 | BUG: Float64Index getitem raises error with tuple-like values | {
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} | 3 | 2016-06-24T22:02:53Z | 2016-08-26T20:21:02Z | 2016-08-26T20:21:02Z | MEMBER | null | So when you have a Series with the combination of a Float64Index and tuple values, getitem/`[]` gives an error:
```
In [46]: s = pd.Series([1,2,3], index=[0.0,0.1,0.2])
In [47]: s[0.0]
Out[47]: 1
In [48]: s = pd.Series([(1,1),(2,2),(3,3)], index=[0.0,0.1,0.2])
In [49]: s[0.0]
...
TypeError: len() of unsized object
```
When the values are not tuples, or when you have a regular index, or when using `ix/loc/iloc` this issue does not appear.
This issue is coming from geopandas (https://github.com/geopandas/geopandas/issues/351), where a Series of Point geometries give the same error:
```
In [66]: import shapely.geometry
In [67]: s = pd.Series([(1,1),(2,2),(3,3)], index=[0.0,0.1,0.2])
In [68]: s = s.map(shapely.geometry.Point)
In [69]: s
Out[69]:
0.0 POINT (1 1)
0.1 POINT (2 2)
0.2 POINT (3 3)
dtype: object
In [70]: s[0.0]
...
TypeError: len() of unsized object
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13510 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13510/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13510/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13510/events | https://github.com/pandas-dev/pandas/pull/13510 | 162,239,741 | MDExOlB1bGxSZXF1ZXN0NzUxODA0NDQ= | 13,510 | BUG: date_range closed keyword with timezone aware start/end (GH12684) | {
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} | 2 | 2016-06-24T22:08:11Z | 2016-06-29T12:16:23Z | 2016-06-29T12:16:22Z | MEMBER | null | closes #12684
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} | 4 | 2016-06-25T09:54:49Z | 2016-07-25T15:07:00Z | 2016-07-25T15:07:00Z | CONTRIBUTOR | null | Given a DataFrame which has a MultiIndex. When a label of the MultiIndex has the value NaN and the DataFrame is written to an excel file, the label will have a value which is not NaN in the excel file.
#### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame({'c1': [1,1,2,2],
'c2': [None] + "b a b".split()})
df
```
returns a DataFrame where the first element of column 'c2' is NaN:
```
c1 c2
0 1
1 1 b
2 2 a
3 2 b
```
Set both columns as the index:
```
df_midx = df.set_index(['c1', 'c2'])
df_midx
```
returns
```
c1 c2
1
1 b
2 a
2 b
```
Write DataFrame to excel file and read it back in:
```
df_midx.to_excel('df_midx.xlsx')
df_midx_from_xlsx = pd.read_excel('df_midx.xlsx')
df_midx_from_xlsx
```
returns
```
c1 c2
0 1.0 b
1 b
2 2.0 a
3 b
```
The first element of column 'c2' is now set to 'b' instead of NaN.
#### Expected Output
```
c1 c2
0 1.0
1 b
2 2.0 a
3 b
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: ac174349b0e1525475c2354e1c0b8ee1ed1cabad
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-88-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.4
setuptools: 2.2
Cython: None
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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| null | 1 | 2016-06-25T16:27:22Z | 2016-06-27T06:42:30Z | 2016-06-27T06:42:30Z | CONTRIBUTOR | null | This is a test PR copy of #13425 to test travis - do not merge
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} | 71 | 2016-06-26T02:15:05Z | 2020-09-18T11:02:50Z | 2016-07-20T17:23:59Z | CONTRIBUTOR | null | xref #13327
closes #936
This [notebook shows the usecase](http://nbviewer.jupyter.org/gist/jreback/186d09a99902a17a095d99ac6a5e4cd3)
- implement lint checking for cython (currently only for windows.pyx), xref #12995
This implements time-ware windows, IOW, to a `.rolling()` you can now pass a ragged / sparse timeseries and have it work with an offset (e.g. like `2s`). Previously you _could_ achieve these results by resampling first, then using an integer period (though you would have to jump thru hoops when crossing day boundaries and such).
This now provides a nice easy / performant implementation (as indicated on the linked notebook, min/max impl is giving scaling issues).
```
In [1]: df = DataFrame({'B': range(5)})
In [2]: df.index = [Timestamp('20130101 09:00:00'),
...: Timestamp('20130101 09:00:02'),
...: Timestamp('20130101 09:00:03'),
...: Timestamp('20130101 09:00:05'),
...: Timestamp('20130101 09:00:06')]
In [3]: df
Out[3]:
B
2013-01-01 09:00:00 0
2013-01-01 09:00:02 1
2013-01-01 09:00:03 2
2013-01-01 09:00:05 3
2013-01-01 09:00:06 4
In [4]: df.rolling(2, min_periods=1).sum()
Out[4]:
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 5.0
2013-01-01 09:00:06 7.0
In [5]: df.rolling('2s', min_periods=1).sum()
Out[5]:
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 3.0
2013-01-01 09:00:06 7.0
```
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} | 29 | 2016-06-26T04:33:36Z | 2016-07-19T14:50:49Z | 2016-07-19T01:52:00Z | CONTRIBUTOR | null | - [x] fixes some issues from #13432
- [x] closes #12044
- [x] closes #12814
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
---
1. Added an internal `safe_sort` to safely sort mixed-integer
arrays in Python3.
2. Changed Index.difference and Index.symmetric_difference
in order to:
- sort mixed-int Indexes (#13432)
- improve performance (#12044)
3. Fixed DataFrame.join which raised in Python3 with mixed-int
non-unique indexes (issue with sorting mixed-ints, #12814)
4. Fixed Index.union returning an empty Index when one of
arguments was a named empty Index (#13432)
Benchmarks (for `index_object` only):
```
]$ asv compare bf47 a02f -f 1.5 -s
Benchmarks that have improved:
before after ratio
[bf47 ] [a02f ]
- 155.66ms 10.32ms 0.07 index_object.index_datetime_set_difference.time_index_datetime_difference
- 154.66ms 2.98ms 0.02 index_object.index_datetime_set_difference.time_index_datetime_difference_disjoint
- 391.45ms 10.65ms 0.03 index_object.index_datetime_set_difference.time_index_datetime_symmetric_difference
- 195.55ms 88.95ms 0.45 index_object.index_int64_set_difference.time_index_int64_difference
- 440.93ms 103.65ms 0.24 index_object.index_int64_set_difference.time_index_int64_symmetric_difference
- 8.77ms 4.88ms 0.56 index_object.index_str_set_difference.time_str_symmetric_difference
```
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} | 11 | 2016-06-26T12:14:51Z | 2016-06-27T13:04:04Z | 2016-06-27T13:04:03Z | CONTRIBUTOR | null | Force the rebuilding of cython files in PR as can't rely on the git history
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- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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| null | 2 | 2016-06-26T16:45:45Z | 2019-06-28T16:27:52Z | 2019-06-28T16:27:51Z | CONTRIBUTOR | null | Series.map works when passed a dict, but Index.map doesn't:
``` .python
>>> s = pd.Series(['a', 'b', 'c', 'd'], index=['a', 'b', 'c', 'd'])
>>> d = {'a': 0, 'b': 1, 'c': 2, 'd': 3}
>>> s.map(d)
a 0
b 1
c 2
d 3
dtype: int64
>>> s.index.map(d)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/evanpw/Workspace/pandas/pandas/indexes/base.py", line 2305, in map
return self._arrmap(self.values, mapper)
File "pandas/src/generated.pyx", line 2356, in pandas.algos.arrmap_object (pandas/algos.c:70221)
TypeError: 'dict' object is not callable
```
PR to follow shortly
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| null | 4 | 2016-06-26T17:22:49Z | 2016-11-16T22:23:39Z | 2016-11-16T22:23:39Z | CONTRIBUTOR | null | - [X] closes #13517
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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} | 7 | 2016-06-26T21:23:52Z | 2021-05-01T22:32:16Z | null | NONE | null | Using groupby + shift seems to have changed behaviour in 0.17 and 0.18 compared to 0.16.
With as_index=False, I would expect the columns that the groupby is made over to remain in the output dataframe, but they are no longer present.
#### Code Sample, a copy-pastable example if possible
``` python
>>>import pandas as pd
>>>import numpy as np
>>>df = pd.DataFrame({'A': [1.0, 1, 1, 2, 2, 3], 'B': [1.0, 2, 3, 4, 5, 6]})
>>>df
A B
0 1.0 1.0
1 1.0 2.0
2 1.0 3.0
3 2.0 4.0
4 2.0 5.0
5 3.0 6.0
>>>df_sorted.groupby('A', as_index=False).shift(1)
B
0 NaN
1 1.0
2 2.0
3 NaN
4 4.0
5 NaN
```
#### Expected Output
``` python
>>>pd.DataFrame({'A':[np.nan, 1, 1, np.nan, 2, np.nan], 'B':[np.nan, 1, 2, np.nan, 4, np.nan]})
A B
0 NaN NaN
1 1.0 1.0
2 1.0 2.0
3 NaN NaN
4 2.0 4.0
5 NaN NaN
```
#### output of `pd.show_versions()`
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
I have also confirmed the issue on an similar install Linux installation using pandas 0.18.1
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} | 5 | 2016-06-27T14:19:57Z | 2016-07-19T13:15:22Z | 2016-07-19T13:15:22Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
In [49]: df = pd.DataFrame({'a': 1.}, index=pd.date_range('2014-01-01', '2015-12-31'))
In [50]: df.resample('M').pct_change()
FutureWarning: .resample() is now a deferred operation
use .resample(...).mean() instead of .resample(...)
if __name__ == '__main__':
Out[50]:
a
2014-01-31 NaN
<snip>
```
#### Expected Output
no warning
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.22.1
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.0.2
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.0
```
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} | 7 | 2016-06-27T20:35:29Z | 2020-03-03T13:02:32Z | 2016-06-28T23:02:33Z | NONE | null | Currently DataFrame.query() takes a string to query a database in a way that's quite similar to how an SQL query reads data from a database. If a user writes code that uses pythons standard string replacement or that uses regex, the code is vunerable in a similar way to how SQL queries that are build with string replacement and regex are vulnerable.
Pythons [sqlite3 ](https://docs.python.org/2/library/sqlite3.html) writes in it's documentation:
```
# Never do this -- insecure!
symbol = 'RHAT'
c.execute("SELECT * FROM stocks WHERE symbol = '%s'" % symbol)
# Do this instead
t = ('RHAT',)
c.execute('SELECT * FROM stocks WHERE symbol=?', t)
```
I think it would be valuable to provide a similar API for DataFrame.query(). In most cases DataFrame.query() likely will be used in scientific computing context where injection attacks aren't a concern but sooner or later someone will write code with DataFrame.query() where injection attacks are a concern.
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} | 1 | 2016-06-27T23:14:37Z | 2016-08-17T22:03:59Z | 2016-08-17T22:03:59Z | NONE | null | If you assign the dataframe's index to as a column as well, without resetting the index, any subsequent assignments on the column seem to affect both the column as well as the index.
The behaviour is there for Index, Int64Index, and Float64Index but not DatetimeIndex.
#### Code Sample, a copy-pastable example if possible.
``` python
>>> import pandas as pd
>>> df=pd.DataFrame(index=['A', 'B', 'C'])
>>> df['X']=df.index
>>> df
X
A A
B B
C C
>>> df['X']=['x', 'y', 'z']
>>> df
X
x x
y y
z z
>>> df.ix[0, 'X']='q'
>>> df
X
q q
y y
z z
```
#### Expected Output
``` python
X
A x
B y
C z
```
#### output of `pd.show_versions()`
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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} | 10 | 2016-06-28T09:59:04Z | 2017-03-23T22:36:58Z | 2017-03-23T13:39:59Z | CONTRIBUTOR | null | https://groups.google.com/forum/m/?pli=1#!topic/pystatsmodels/VT24vE45oog
is out in pre release - would be nice to test with pandas (master and release) and report any issues
should also add to the ci once released
https://github.com/statsmodels/statsmodels/
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} | 13 | 2016-06-28T14:22:02Z | 2016-09-07T13:18:27Z | 2016-09-07T13:18:27Z | NONE | null | I recently posted this on StackOverflow. It seems to be a bug so I am posting here as well.
I want to generate a dataframe that is created by appended several separate dataframes generated in a for loop. Each individual dataframe consists of a name column, a range of integers and a column identifying a category to which the integer belongs (e.g. quintile 1 to 5). If I generate each dataframe individually and then append one to the other to create a 'master' dataframe then there are no problems. However, when I use a loop to create each individual dataframe then trying to append a dataframe to the master dataframe results in:
`ValueError: incompatible categories in categorical concat`
A work-around (suggested by jezrael) involved appending each dataframe to a list of dataframes and concatenating them using pd.concat.
I've written a simplified loop to illustrate:
#### Code Sample, a copy-pastable example if possible
``` python
import numpy as np
import pandas as pd
# Define column names
colNames = ('a','b','c')
# Define a dataframe with the required column names
masterDF = pd.DataFrame(columns = colNames)
# A list of the group names
names = ['Group1','Group2','Group3']
# Create a dataframe for each group
for i in names:
tempDF = pd.DataFrame(columns = colNames)
tempDF['a'] = np.arange(1,11,1)
tempDF['b'] = i
tempDF['c'] = pd.cut(np.arange(1,11,1),
bins = np.linspace(0,10,6),
labels = [1,2,3,4,5])
print(tempDF)
print('\n')
# Try to append temporary DF to master DF
masterDF = masterDF.append(tempDF,ignore_index=True)
print(masterDF)
```
#### Expected Output
```
a b c
0 1 Group1 1
1 2 Group1 1
2 3 Group1 2
3 4 Group1 2
4 5 Group1 3
5 6 Group1 3
6 7 Group1 4
7 8 Group1 4
8 9 Group1 5
9 10 Group1 5
10 11 Group2 1
11 12 Group2 1
12 13 Group2 2
13 14 Group2 2
...
28 29 Group3 5
29 30 Group3 5
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.4.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.6
setuptools: 20.1.1
Cython: None
numpy: 1.11.0
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.1.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: 0.7.4.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/13525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13525/events | https://github.com/pandas-dev/pandas/pull/13525 | 162,724,953 | MDExOlB1bGxSZXF1ZXN0NzU1MDU0MDM= | 13,525 | Fixes a missing ".reshape" call | {
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} | 4 | 2016-06-28T16:08:09Z | 2016-07-21T16:47:34Z | 2016-07-06T21:31:17Z | NONE | null | closes https://github.com/pydata/pandas/issues/13287
Currently, some 2D arrays can go into [_possibly_infer_to_datetimelike](https://github.com/andyljones/pandas/blob/59b9e835d49036cabad19cf2c53140ac9f2eb465/pandas/core/common.py#L1133-L1222) and come out 1D. The issue is that the input is ravelled [at one point](https://github.com/andyljones/pandas/blob/59b9e835d49036cabad19cf2c53140ac9f2eb465/pandas/core/common.py#L1165), but only unravelled back to it's original form [if a certain path it taken through the function](https://github.com/andyljones/pandas/blob/59b9e835d49036cabad19cf2c53140ac9f2eb465/pandas/core/common.py#L1187). This pull request adds the same `.reshape(shape)` call to the _other_ path through the function.
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} | 9 | 2016-06-28T19:21:03Z | 2016-07-03T23:25:04Z | 2016-07-03T23:24:49Z | CONTRIBUTOR | null | As talked about in #13425 with @gfyoung and @jreback this PR does cython caching by comparing the pyx files and not relying on the git history.
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} | 2 | 2016-06-28T22:42:54Z | 2016-06-29T19:17:57Z | 2016-06-28T22:58:58Z | NONE | null | ### Code Sample
I am trying to get a DataFrame from dict whose values are of variable length. However, I get a `ValueError`.
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: x_dict = {"A{}".format(i): np.random.randint(1, 25, data) for i, data in enumerate([5, 10, 15, 20])}
In [4]: x_dict
Out[4]:
{'A0': array([ 5, 7, 17, 10, 24]),
'A1': array([ 4, 8, 11, 7, 9, 1, 23, 22, 20, 15]),
'A2': array([ 9, 5, 20, 2, 21, 12, 21, 8, 1, 10, 5, 21, 7, 9, 5]),
'A3': array([ 2, 1, 24, 21, 24, 16, 6, 9, 20, 7, 24, 11, 23, 15, 3, 22, 5,
21, 1, 20])}
In [5]: x_df = pd.DataFrame.from_dict(x_dict)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-e23195a7d765> in <module>()
----> 1 x_df = pd.DataFrame.from_dict(x_dict)
/python2.7/site-packages/pandas/core/frame.pyc in from_dict(cls, data, orient, dtype)
807 raise ValueError('only recognize index or columns for orient')
808
--> 809 return cls(data, index=index, columns=columns, dtype=dtype)
810
811 @deprecate_kwarg(old_arg_name='outtype', new_arg_name='orient')
/python2.7/site-packages/pandas/core/frame.pyc in __init__(self, data, index, columns, dtype, copy)
222 dtype=dtype, copy=copy)
223 elif isinstance(data, dict):
--> 224 mgr = self._init_dict(data, index, columns, dtype=dtype)
225 elif isinstance(data, ma.MaskedArray):
226 import numpy.ma.mrecords as mrecords
/python2.7/site-packages/pandas/core/frame.pyc in _init_dict(self, data, index, columns, dtype)
358 arrays = [data[k] for k in keys]
359
--> 360 return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
361
362 def _init_ndarray(self, values, index, columns, dtype=None, copy=False):
/python2.7/site-packages/pandas/core/frame.pyc in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
5229 # figure out the index, if necessary
5230 if index is None:
-> 5231 index = extract_index(arrays)
5232 else:
5233 index = _ensure_index(index)
/python2.7/site-packages/pandas/core/frame.pyc in extract_index(data)
5277 lengths = list(set(raw_lengths))
5278 if len(lengths) > 1:
-> 5279 raise ValueError('arrays must all be same length')
5280
5281 if have_dicts:
ValueError: arrays must all be same length
```
### Expected output
DataFrame with keys as columns and values as rows.
```
In [11]: x_df
Out[11]:
A1 A0 A3 A2
0 4.0 5.0 2.0 9.0
1 8.0 7.0 1.0 5.0
2 11.0 17.0 24.0 20.0
3 7.0 10.0 21.0 2.0
4 9.0 24.0 24.0 21.0
5 1.0 NaN 16.0 12.0
6 23.0 NaN 6.0 21.0
7 22.0 NaN 9.0 8.0
8 20.0 NaN 20.0 1.0
9 15.0 NaN 7.0 10.0
10 NaN NaN 24.0 5.0
11 NaN NaN 11.0 21.0
12 NaN NaN 23.0 7.0
13 NaN NaN 15.0 9.0
14 NaN NaN 3.0 5.0
15 NaN NaN 22.0 NaN
16 NaN NaN 5.0 NaN
17 NaN NaN 21.0 NaN
18 NaN NaN 1.0 NaN
19 NaN NaN 20.0 NaN
```
### output of `pd.show_versions()`
INSTALLED VERSIONS
- commit: None
- python: 2.7.11.final.0
- python-bits: 64
- OS: Darwin
- OS-release: 15.5.0
- machine: x86_64
- processor: i386
- byteorder: little
- LC_ALL: None
- LANG: en_US.UTF
- pandas: 0.18.1
- nose: 1.3.7
- pip: 8.1.2
- setuptools: 23.1.0
- Cython: 0.24
- numpy: 1.11.1
- scipy: 0.17.1
- statsmodels: 0.6.1
- xarray: None
- IPython: 4.2.0
- sphinx: 1.4.4
- patsy: None
- dateutil: 2.5.3
- pytz: 2016.4
- blosc: None
- bottleneck: None
- tables: None
- numexpr: None
- matplotlib: 1.5.1
- openpyxl: None
- xlrd: 1.0.0
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- bs4: 4.4.1
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- apiclient: None
- sqlalchemy: None
- pymysql: None
- psycopg2: None
- jinja2: 2.8
- boto: None
- pandas_datareader: None
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} | 2 | 2016-06-29T01:54:32Z | 2016-06-29T10:16:18Z | 2016-06-29T10:15:56Z | CONTRIBUTOR | null | - [ X] passes `git diff upstream/master | flake8 --diff`
Minor Documentaion change to note that Vincent project has intentionally been abandoned and is not longer maintained. Add a pointer to IPython Vega successor.
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| null | 3 | 2016-06-29T09:36:16Z | 2016-06-29T12:28:54Z | 2016-06-29T11:17:32Z | NONE | null | I'm not sure if this is a bug, but I'm pretty sure I shouldn't be seeing these warnings. Any clarification would be a real help, as I need to ensure the accuracy of the calculations in all circumstances (including situations where the deviation is effectively zero)
Paste-ready code:
```
import traceback
import warnings
import sys
import pandas as pd
import numpy as np
np.seterr(all='raise')
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
traceback.print_stack()
log = file if hasattr(file,'write') else sys.stderr
log.write(warnings.formatwarning(message, category, filename, lineno, line))
warnings.showwarning = warn_with_traceback
pd.Series(np.random.randn(50)).pct_change().ewm(span=35, min_periods=35).std()
```
**Expected output:**
pd.Series of exponentially weighted standard deviation.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.0
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
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} | 5 | 2016-06-29T13:57:43Z | 2016-07-05T08:40:12Z | 2016-07-05T08:39:35Z | NONE | null | On 0.18.1 the following works without error
```
df = pd.DataFrame({'a':list('abssbab')})
df.groupby('a').count()
```
this results in an empty df:
```
Out[255]:
Empty DataFrame
Columns: []
Index: [a, b, s]
```
On Master it results in an error see related: https://github.com/pydata/pandas/issues/13393
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| null | 6 | 2016-06-29T14:48:23Z | 2016-09-11T16:48:44Z | 2016-09-11T16:48:39Z | NONE | null | Right now `read_gbq` runs a query and retrieves the results into a data_frame. If one wants to also save it into a dataset table, one then has to call `to_gbq` which would stream the results back.
The `bq` command line tool has an option to pass a destination dataset table immediately on query to store the results.
```
cat queries/sample.sql | bq query --format csv
--destination_table=collection.table_name > output.csv
```
Ideally `read_gbq` would have an additional option like `destination_table` that could store the results immediately.
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} | 8 | 2016-06-29T19:16:49Z | 2016-07-04T10:37:53Z | 2016-07-04T10:37:52Z | MEMBER | null | Simplified the version requirement for sphinx (>= 1.3.2). In principle >= 1.2.2 but < 1.3.0 also works, but left it out for simplicity.
@cswarth Do you have other things that you find unclear about the requirements for the doc build environment?
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} | 5 | 2016-06-30T01:26:49Z | 2016-06-30T22:59:17Z | 2016-06-30T22:59:11Z | CONTRIBUTOR | null | - [x] closes #13473
- [ ] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2016-06-30T12:06:29Z | 2016-07-19T01:59:54Z | 2016-07-19T01:59:54Z | NONE | null | I work with Pandas and Stata and found the DataFrame.to_stata() method very valuable. I would like to be able to assign variable labels in my .dta files but the data_label parameter of the DataFrame.to_stata() method is not documented so I do not know in which format to supply my variable labels.
I tried a dictionary of the form {'df column name' : 'wanted label'} but that returns
TypeError: unhashable type: 'slice'
Here is the page in the documentation I am referring to: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_stata.html#pandas.DataFrame.to_stata
If it could be updated to provide a working example of data_label I would be eternally grateful. This is my first issue on github, hope I specified it correctly, feel free to point out if I messed up the format somehow.
Thanks!
#### Code Sample, a copy-pastable example if possible
import pandas as pd
d = {'one' : [1., 2., 3., 4.]}
df = pd.DataFrame(d)
labdict = {'one': 'foo'}
df.to_stata('test.dta', write_index=False, data_label=labdict)
#### Expected Output
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.1
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
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} | 0 | 2016-06-30T13:14:56Z | 2016-07-19T01:59:54Z | 2016-07-19T01:59:54Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/issues/13535#issuecomment-229652201
cc @frehoy
> It seems that is is almost implemented here.
>
> See
> https://github.com/pydata/pandas/blob/master/pandas/io/stata.py#L2057
>
> and
>
> https://github.com/pydata/pandas/blob/master/pandas/io/stata.py#L2134
>
> As you can see, this always passes the default None and so no labels are written.
>
> Mostly needs an external interface and a tiny amount of wiring up. And testing, esp that the labels can be read into Stata.
A possible API
``` python
import pandas as pd
d = {'one' : [1., 2., 3., 4.]}
df = pd.DataFrame(d)
labdict = {'one': 'foo'}
df.to_stata('test.dta', write_index=False, variab_labels=labdict)
```
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} | 2 | 2016-06-30T14:01:26Z | 2016-07-01T07:31:13Z | 2016-07-01T02:25:00Z | NONE | null | It would be nice to have the possibility to plot expressions. Suppose I have a dataframe with three columns: col1, col2, col3. Now I can do:
```
df.plot.scatter(x='col1', y='col2')
```
it would be nice to be able to plot expressions directly:
```
df.plot.scatter(x='col1 ** 2', y = 'col2 / sqrt(col3)')
```
and also for other plotting methods (e.g. histograms)
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} | 3 | 2016-06-30T14:10:25Z | 2016-08-25T10:20:52Z | 2016-08-25T10:20:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
s1=pd.Series([True,False,True,True])
s2=pd.Series([True,True,False])
s1.index=pd.MultiIndex.from_tuples( [(0, 2), (1, 1), (1, 2), (2, 1)],names=['st', 'at'])
s2.index=pd.Index([0,1,2], name='st')
ds1=pd.DataFrame(s1)
ds2=pd.DataFrame(s2)
s3=pd.Series([True,False,True])
s4=pd.Series([True,True,False])
```
#### Expected Output
```
st at
0 2 True
1 1 False
1 2 True
2 1 False
dtype: bool
```
For series, `*` and `&` give different result:
```
In: s1 & s2
Out:
st at
0 2 False
1 1 False
1 2 False
2 1 False
dtype: bool
In: s1 * s2
Out:
st at
0 2 True
1 1 False
1 2 True
2 1 False
dtype: bool
```
BUT for dataframes they give the same:
```
In: ds1 * ds2
Out:
0
st at
0 2 True
1 1 False
1 2 True
2 1 False
In: ds1 & ds2
Out:
0
st at
0 2 True
1 1 False
1 2 True
2 1 False
```
and last for series with single index, also both `s3 & s4` or `s3 * s4` give:
```
0 True
1 False
2 False
dtype: bool
```
#### output of `pd.show_versions()`
pandas: 0.18.1
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} | 6 | 2016-06-30T18:33:52Z | 2016-07-01T12:42:26Z | 2016-07-01T11:45:42Z | NONE | null | Hi,
I have a simple dataframe with a multiindex.
```
stats.index.names
Out[67]: FrozenList([u'day', u'category'])
```
day is a time variable, category is a string.
```
stats.index.inferred_type
Out[69]: 'mixed'
stats.index.dtype_str
Out[68]: 'object'
```
if I type
```
idx=pd.IndexSlice
stats.loc[idx['2015-01-01',:],:]
```
I get the correct slice at '2015-01-01', that is all the observations for all the categories on that day.
If I type
```
idx=pd.IndexSlice
stats.loc[idx['2015',:],:]
```
I get back (almost) all my observations, even for years other than 2015.
What can be the problem here? I cannot paste the data unfortunately but I am happy to help in any way.
Thanks
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} | 3 | 2016-06-30T23:19:12Z | 2016-07-01T01:24:57Z | 2016-06-30T23:42:41Z | NONE | null | It would be great if Pandas Series got an info method, like Pandas DataFrame. My main interest is to find out memory usage.
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} | 2 | 2016-07-01T11:47:48Z | 2016-07-03T23:23:08Z | 2016-07-03T23:23:08Z | CONTRIBUTOR | null | xref #13167
cc @gfyoung
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} | 4 | 2016-07-01T12:17:55Z | 2016-08-15T17:52:44Z | 2016-08-15T17:52:44Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
np.random.seed(12345)
panel = pd.Panel(np.random.randn(125,200,10))
panel.iloc[:,:,0] = np.round(panel.iloc[:,:,0])
panel.iloc[:,:,1] = np.round(panel.iloc[:,:,1])
x = panel
cols = [0,1]
_x = x.swapaxes(0, 2).to_frame()
numeric_cols = []
for df_col in _x:
if df_col not in cols and pd.core.common.is_numeric_dtype(_x[df_col].dtype):
numeric_cols.append(df_col)
def _noop(df):
return df
groups = _x.groupby(cols)
groups.transform(_noop)
```
#### Expected Output
The original input. `_noop` is just a trivial example.
#### Error
``` python
C:\anaconda\lib\site-packages\pandas\core\internals.py in construction_error(tot_items, block_shape, axes, e)
3968 raise ValueError("Empty data passed with indices specified.")
3969 raise ValueError("Shape of passed values is {0}, indices imply {1}".format(
-> 3970 passed, implied))
3971
3972
ValueError: Shape of passed values is (8, 25000), indices imply (8, 25799)
```
#### Comments
The indices have reversed order, which is problem 1. Also, one index has lost a value.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.8.0.dev0+f060948
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
```
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} | 11 | 2016-07-01T13:01:41Z | 2017-03-25T18:40:39Z | 2017-03-25T18:40:39Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
np.random.seed(12345)
panel = pd.Panel(np.random.randn(125,200,10))
panel.iloc[:,:,0] = np.round(panel.iloc[:,:,0])
panel.iloc[:,:,1] = np.round(panel.iloc[:,:,1])
x = panel
cols = [0,1]
_x = x.swapaxes(0, 2).to_frame()
numeric_cols = []
for df_col in _x:
if df_col not in cols and pd.core.common.is_numeric_dtype(_x[df_col].dtype):
numeric_cols.append(df_col)
calls = 0
def _print_type(df):
print(type(df))
return df
index = _x.index
_x.index = pd.RangeIndex(0, _x.shape[0])
groups = _x.groupby(cols)
out = groups.transform(_print_type)
```
#### Comment
The `slow_path` operated series by series rather than on a group DataFrame. Once the slow path is accepted, it operated on the group DataFrames. I have 59 groups in my example with 8 columns, and so it runs 8 times with Series from the first group DataFrame and then, once happy, runs 58 more times on the DataFrames.
The description says that it onlly operated on the group DataFrames (which is the correct behavior IMO)
#### Expected Output
Many lines of
```
<class 'pandas.core.frame.DataFrame'>
```
#### Actual Output
```
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.frame.DataFrame'>
...
<class 'pandas.core.frame.DataFrame'>
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.8.0.dev0+f060948
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
```
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} | 6 | 2016-07-01T17:42:19Z | 2017-03-04T11:11:14Z | 2017-03-04T07:28:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
In area plots, the handles in the legend are made with filled rectangles, as for example in:
``` python
df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot(kind='area', linewidth=0.1)
```
However, if method `legend()` is used to alter the position of the legend, the handles are rendered as lines, which poses a problem if the line width is small, as in the following example:
``` python
df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot(kind='area', linewidth=0.1).legend(loc="center left", bbox_to_anchor=(1.02, 0.5))
```
#### output of `pd.show_versions()`
``` txt
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: None
numpy: 1.10.4
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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| null | 3 | 2016-07-01T19:01:47Z | 2016-07-06T21:00:05Z | 2016-07-06T21:00:05Z | NONE | null | Hi,
When using `pd.read_excel(..., sheetname=None, converters={...})` to load all data in an Excel workbook, the columns specified in `converters` gets applied to any matching column names across _all_ spreadsheets. I have a use case where I would like to use different converter functions for columns of the same name across multiple sheets.
One potential implementation could yield:
``` python
pd.read_excel("my_data.xlsx", sheetname=None, converters={
"Sheet1": { "my_column": do_something },
"Sheet2": { "my_column": do_something_else }})
```
Another implementation could be:
``` python
pd.read_excel("my_data.xlsx", sheetname=None, converters={
("Sheet1", "my_column"): do_something,
("Sheet2", "my_column"): do_something_else })
```
Thanks! Please let me know if I can help out with an implementation -- I'd love to contribute to Pandas!
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} | 1 | 2016-07-01T19:52:07Z | 2016-07-02T14:19:33Z | 2016-07-01T19:54:56Z | NONE | null | I have been trying to use some memory profiling tools, to profile my code. With two different memory profilers, both of which use `sys.getsizeof(obj)`, `pandas` has been the cause of a `TypeError` returned. I am posting below an example using the [`memory-tools`](https://github.com/maxzheng/memory-tools) package.
#### Code Sample
I have tested the following code independently in a Jupyter notebook environment, separate from my other code. It should (hopefully) be a minimal example that can be reproduced. I am happy to provide more information on my environment if needed.
```
from memorytools import summarize_objects
import pandas as pd
summarize_objects()
```
#### Error Returned
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-c46dbac041c7> in <module>()
----> 1 summarize_objects()
/Users/ericmjl/Documents/github/memory-tools/src/memorytools/__init__.py in summarize_objects(objs, echo, limit)
90 objs = gc.get_objects()
91
---> 92 objs_dict = _summarize_objects(objs)
93 size_summary = ['{0:>10s} {1:>5s} {2}'.format('Size', 'Count', 'Type')]
94 count_summary = ['{0:>5s} {1:>10s} {2}'.format('Count', 'Size', 'Type')]
/Users/ericmjl/Documents/github/memory-tools/src/memorytools/__init__.py in _summarize_objects(objs)
133 for obj in objs:
134 _incr(objs_dict, type(obj), 'count')
--> 135 _incr(objs_dict, type(obj), 'size', sys.getsizeof(obj))
136
137 return objs_dict
/Users/ericmjl/anaconda/lib/python3.5/site-packages/pandas/core/base.py in __sizeof__(self)
128 # no memory_usage attribute, so fall back to
129 # object's 'sizeof'
--> 130 return super(self, PandasObject).__sizeof__()
131
132
TypeError: must be type, not FrozenList
```
Please pardon my ignorance on this matter, but would someone be kind enough to educate me on why this error is thrown, and how I might be able to fix this? If there's a maintainer who could provide some guidance, I am happy to dig into the codebase and submit a PR if that will fix the error.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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} | 1 | 2016-07-01T19:55:16Z | 2019-10-21T19:34:18Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/38152606/pandas-hdfstore-append-to-multiple-with-pandas-multiindex
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} | 14 | 2016-07-02T00:08:39Z | 2016-08-01T09:59:34Z | 2016-07-03T23:35:44Z | NONE | null | I deal with DateTime columns extensively. Using strings to slice and select items is extremely useful, but it requires a DatetimeIndex.
I think it would be great to include the same functionality for any column.
Consider this DF:
```
>>> a = pd.DataFrame([pd.to_datetime('2016-4-3 15:32:04'), pd.to_datetime('2016-4-9 15:32:04')], columns=['a_col'])
>>> print(a)
a_col
0 2016-04-03 15:32:04
1 2016-04-09 15:32:04
```
This is what I usually do, but it's extremely cluttered, and fails when the index is already something useful.
```
>>>print(a.set_index('a_col')['2016-4-3'].reset_index())
a_col
0 2016-04-03 15:32:04
```
I imagine this is the preferred way of doing the same thing (please correct me if I'm wrong). Requires a lot of typing, and not elegant at all (especially for chosing a single day, as in this example).
```
>>> print(a[(a.a_col >= pd.to_datetime('2016-4-3')) & (a.a_col < pd.to_datetime('2016-4-4'))])
a_col
0 2016-04-03 15:32:04
```
I think something like the `ds` would be great:
```
>>> print(a.ds['a_col', '2016-4-3'])
```
The second element could be any argument that is accepted to DatetimeIndex elements.
What do you think? Am I missing something?
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} | 4 | 2016-07-02T10:28:39Z | 2016-08-22T20:47:18Z | 2016-08-22T20:47:18Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas
path = "test.csv"
pandas.DataFrame({"A": [0,1], "B": [2,3]}).to_csv(
path, encoding="utf-16")
for encoding in ["utf-16","utf_16","UTF_16","UTF-16"]:
try:
pandas.io.parsers.read_csv(
path,
engine='c',
encoding=encoding)
print(encoding, "succeeded in c-pandas")
except UnicodeDecodeError:
print(encoding, "failed in c-pandas")
try:
pandas.io.parsers.read_csv(
path,
engine='python',
encoding=encoding)
print(encoding, "succeeded in pandas")
except UnicodeDecodeError:
print(encoding, "failed in pandas")
try:
with open(path, encoding=encoding) as file:
file.read()
print(encoding, "succeeded in open")
except UnicodeDecodeError:
print(encoding, "failed in open")
```
#### Expected Output
```
utf-16 succeeded in c-pandas
utf-16 succeeded in pandas
utf-16 succeeded in open
utf_16 succeeded in c-pandas
utf_16 succeeded in pandas
utf_16 succeeded in open
UTF_16 succeeded in c-pandas
UTF_16 succeeded in pandas
UTF_16 succeeded in open
UTF-16 succeeded in c-pandas
UTF-16 succeeded in pandas
UTF-16 succeeded in open
```
#### Actual Output
```
utf-16 succeeded in c-pandas
utf-16 succeeded in pandas
utf-16 succeeded in open
utf_16 failed in c-pandas
utf_16 succeeded in pandas
utf_16 succeeded in open
UTF_16 failed in c-pandas
UTF_16 succeeded in pandas
UTF_16 succeeded in open
UTF-16 failed in c-pandas
UTF-16 succeeded in pandas
UTF-16 succeeded in open
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.2-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 9 | 2016-07-02T22:24:08Z | 2016-07-03T23:23:09Z | 2016-07-03T23:23:09Z | MEMBER | null | Picks up from #13167 by properly removing the parameters and ensuring that `numpy` compatibility has been maintained. The current test suite does a good job of checking that already, so no tests were added.
Closes #13541.
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} | 11 | 2016-07-03T01:46:10Z | 2016-07-25T15:07:12Z | 2016-07-25T15:07:00Z | CONTRIBUTOR | null | - [X] closes #13511
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
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} | 3 | 2016-07-03T13:39:28Z | 2016-07-04T16:01:16Z | 2016-07-04T16:01:16Z | CONTRIBUTOR | null | This test case assert `pivot_table` method.
So it should be defined on `TestPivotTable`.
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} | 11 | 2016-07-03T15:16:08Z | 2017-04-18T22:37:28Z | 2017-04-18T22:37:28Z | CONTRIBUTOR | null | - [x] closes #4386
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Before this commit, if
- `values` is not list like
- `columns` is `None`
- `aggfunc` is not instance of `list`
`pivot_table` returns a `Series`.
This commit adds checking for `columns.nlevels` is
greater than 1 to prevent from casting `table` to
a `Series`.
This will fix #4386.
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| null | 2 | 2016-07-03T17:02:27Z | 2016-07-03T23:31:29Z | 2016-07-03T23:31:29Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
import pandas as pd
import numpy as np
dates = pd.date_range('20160101', periods=12)
df = pd.DataFrame(np.random.randn(12, 4), index=dates, columns=list('ABCD'))
df.sort_index(axis=1, ascending=False) % df.sort_index(axis=1, ascending=True)
Output : -
A B C D
2016-01-01 0 -0 0 -0
2016-01-02 -0 0 0 0
2016-01-03 0 0 0 -0
2016-01-04 0 0 0 0
2016-01-05 0 -0 -0 0
2016-01-06 0 -0 0 0
2016-01-07 -0 -0 0 0
2016-01-08 0 0 -0 -0
2016-01-09 -0 -0 -0 -0
2016-01-10 -0 -0 -0 0
2016-01-11 -0 0 -0 0
2016-01-12 0 -0 -0 -0
#### Expected Output
```
A B C D
```
2016-01-01 0 0 0 0
2016-01-02 0 0 0 0
2016-01-03 0 0 0 0
2016-01-04 0 0 0 0
2016-01-05 0 0 0 0
2016-01-06 0 0 0 0
2016-01-07 0 0 0 0
2016-01-08 0 0 0 0
2016-01-09 0 0 0 0
2016-01-10 0 0 0 0
2016-01-11 0 0 0 0
2016-01-12 0 0 0 0
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.5-300.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.0
setuptools: 20.10.1
Cython: None
numpy: 1.9.2
scipy: 0.14.1
statsmodels: None
IPython: 3.2.1
sphinx: 1.2.3
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 0.6.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
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} | 1 | 2016-07-04T02:43:34Z | 2016-07-04T07:55:26Z | 2016-07-04T03:00:19Z | NONE | null | ### Summary of issue
By the name of it, I expected using the 'dropna=False' option with crosstab would have given me results that include rows for which my column value is missing. However, I instead found that missing values continue to be ignored.
#### Code Sample
```
df = pd.DataFrame({'geography': ['tropic', 'tropic', 'desert', None, 'tundra', 'tundra'],
'dragons': ['many', None, 'few', 'few', 'many', None]})
print(df)
pd.crosstab(df.geography, df.dragons)
pd.crosstab(df.geography, df.dragons, dropna=False)
pd.crosstab(df.dragons, df.geography, dropna=False)
```
#### Expected Output
```
geography NA desert tropic tundra
dragons
NA 0 0 1 1
few 1 1 0 0
many 0 0 1 1
```
#### Actual output
```
In [210]: print(df)
dragons geography
0 many tropic
1 None tropic
2 few desert
3 few None
4 many tundra
5 None tundra
In [211]: pd.crosstab(df.geography, df.dragons)
Out[211]:
dragons few many
geography
desert 1 0
tropic 0 1
tundra 0 1
In [212]: pd.crosstab(df.geography, df.dragons, dropna=False)
Out[212]:
dragons few many
geography
desert 1 0
tropic 0 1
tundra 0 1
In [213]: pd.crosstab(df.dragons, df.geography, dropna=False)
Out[213]:
geography desert tropic tundra
dragons
few 1 0 0
many 0 1 1
```
### This work-around gives me what I expected.
```
pd.crosstab(df.dragons.fillna('NA'), df.geography.fillna('NA'), dropna=False)
```
I'd hate for others to also not realize that the NAs are being dropped from the output, even when dropna=False. Perhaps altering the documentation or the behavior of this function would help.
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} | 5 | 2016-07-04T03:01:38Z | 2016-07-05T08:41:26Z | 2016-07-05T00:09:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: p = pd.Panel(np.arange(512).reshape((8,8,8)))
In [4]: p.to_frame()
Out[4]: <repr(<pandas.core.frame.DataFrame at 0x7fcd99f25590>) failed: TypeError: data type "major" not understood>
In [5]: p.axes
Out[5]:
[RangeIndex(start=0, stop=8, step=1),
RangeIndex(start=0, stop=8, step=1),
RangeIndex(start=0, stop=8, step=1)]
Issue is similar to [ Related TypeErrors in multi-indexed DataFrame #12893 ](https://github.com/pydata/pandas/issues/12893)
#### Expected Output
Similar to this...
In [6]: p = pd.Panel(np.arange(8).reshape((2,2,2)))
In [7]: p.to_frame()
Out[7]:
```
0 1
major minor
0 0 0 4
1 1 5
1 0 2 6
1 3 7
```
In [8]: p.axes
Out[8]:
[RangeIndex(start=0, stop=2, step=1),
RangeIndex(start=0, stop=2, step=1),
RangeIndex(start=0, stop=2, step=1)]
#### output of `pd.show_versions()`
In [9]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.12.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0+git114-g6c692ae
nose: 1.3.7
pip: None
setuptools: 20.10.1
Cython: None
numpy: 1.11.1rc1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 2.4.1
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.2rc2
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 7 | 2016-07-04T06:45:31Z | 2016-07-05T11:07:47Z | 2016-07-05T11:07:47Z | NONE | null | #### Copy-pastable Code Sample
``` python
import pandas as pd
import pandas.util.testing as tm
class myClass1:
def __call__(self):
return "Result 1"
def __str__(self):
return "Class 1"
class myClass2:
def __call__(self):
return "Result 2"
def __str__(self):
return "Class 2"
obj1 = myClass1()
obj2 = myClass2()
df = pd.DataFrame([], index=[0], columns=[obj1, obj2])
# Throws error because new pandas version wants to select by callable
df[obj1] = obj1()
df[obj2] = obj2()
```
#### Expected Output
``` python
expected = pd.DataFrame([["Result 1", "Result 2"]], columns=[obj1, obj2])
tm.assert_frame_equal(df, expected)
```
#### How I fixed the test for the moment
``` python
class myClass1:
def __call__(self, *args):
if args:
return self
return "Result 1"
def __str__(self):
return "Class 1"
class myClass2:
def __call__(self, *args):
if args:
return self
return "Result 2"
def __str__(self):
return "Class 2"
```
If pandas invokes a callable object it passes the frame itself as a parameter. Because i dont need arguments for my call function i can prevent this call by checking if a argument is passed. Is there a better way of doing this or can you add an option so i can disable the new behavior.
#### output of `pd.show_versions()`
``` python
commit: None
python: 3.4.3.final.0
python-bits: 32
OS: Windows
OS-release: 8
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 19.6.2
Cython: None
numpy: 1.11.0
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 1 | 2016-07-04T10:04:22Z | 2016-07-05T08:37:18Z | 2016-07-05T08:37:18Z | MEMBER | null | Closes #12685
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} | 3 | 2016-07-04T23:19:43Z | 2016-07-05T08:39:35Z | 2016-07-05T08:39:35Z | MEMBER | null | - [x] closes #13530
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
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} | 12 | 2016-07-05T00:13:43Z | 2016-07-11T22:22:16Z | 2016-07-09T16:38:59Z | MEMBER | null | It is two months since 0.18.1 is released, and there are already quite a lot of changes in master, so it is time to think about a next release.
Currently, the release is targeted to be 0.18.2.
Personally, I think current master has already more new features and slight API changes than would be expected from a bug-fix release, so my proposal would be to just call it 0.19.0 instead (as also discussed on the dev meeting).
If there is agreement on this, I can do the change this week (not much work, just changing all references of 0.18.2 to 0.19.0 and retargeting the issues).
Besides that, there are two other questions in case of a rename of master to 0.19.0:
- Do we want to release current master as 0.19.0 as is (in the short time, meaning we could tag a release candidate shortly)? Or do we want to give it some more time?
- Do we still want to release a 0.18.2 bugfix release? We could select some bug fixes that are now in master to put in a 0.18.2 release.
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| null | 10 | 2016-07-05T00:18:14Z | 2018-12-02T22:56:04Z | 2017-04-07T19:10:25Z | MEMBER | null | This is a topic that has come up recently (https://github.com/pydata/pandas/issues/10000, https://github.com/pydata/pandas/issues/8906, pandas-dev mailing list discussion), let's make this an issue to track the discussion about it.
- https://github.com/pydata/pandas/issues/8906#issuecomment-152613473 experience of the 'experimental' status of Panels by @MaximilianR and following discussion on pointing users to xarray
- Issue for docs on using xarray instead of Panels: https://github.com/pydata/pandas/issues/12283
- "WIP for transitioning from Panel docs" PR https://github.com/pydata/xarray/pull/832
Deprecating Panels would be a rather large change, so:
- Do we need to further discuss if we actually want to do this?
- Are there people who make intensive use of Panels to ask feedback?
- How do we go about such a deprecation? First making a note in the whatsnew / pinging mailing list or other fora before actually deprecating?
cc @pydata/pandas @MaximilianR
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} | 2 | 2016-07-05T00:23:38Z | 2016-07-25T23:34:18Z | 2016-07-25T23:34:18Z | MEMBER | null | Related to https://github.com/pydata/pandas/issues/13563 (deprecation of Panels), we could/should also deprecate Panel4D / PanelND.
(xref https://github.com/pydata/pandas/issues/5049)
Deprecating Panel4D/ND could probably be done sooner than deprecating Panels (e.g. actually deprecating in 0.19.0 ?)
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} | 11 | 2016-07-05T11:08:23Z | 2016-08-29T12:29:04Z | 2016-08-29T12:29:04Z | NONE | null | Call unique() on a timezone aware datetime series returns non timezone aware result.
#### Code Sample
import pandas as pd
import pytz
import datetime
In [242]: ts = pd.Series([datetime.datetime(2011,2,11,20,0,0,0,pytz.utc), datetime.datetime(2011,2,11,20,0,0,0,pytz.utc), datetime.datetime(2011,2,11,21,0,0,0,pytz.utc)])
In [243]: ts
Out[243]:
0 2011-02-11 20:00:00+00:00
1 2011-02-11 20:00:00+00:00
2 2011-02-11 21:00:00+00:00
dtype: datetime64[ns, UTC]
In [244]: ts.unique()
Out[244]: array(['2011-02-11T20:00:00.000000000', '2011-02-11T21:00:00.000000000'], dtype='datetime64[ns]')
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_AT.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.21.1
numpy: 1.11.0
scipy: 0.14.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.2.3
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: 2.3.5
xlrd: 0.9.2
xlwt: 0.7.4
xlsxwriter: None
lxml: 3.6.0
bs4: None
html5lib: 1.0b3
httplib2: 0.9
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
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} | 4 | 2016-07-05T11:29:17Z | 2016-07-21T16:47:01Z | 2016-07-09T18:16:40Z | CONTRIBUTOR | null | Testing 32-bit Manylinux1 wheels for Pandas 0.18.1 gives the following errors:
```
======================================================================
ERROR: test_read_dta12 (pandas.io.tests.test_stata.TestStata)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/io/tests/test_stata.py", line 244, in test_read_dta12
parsed_117 = self.read_dta(self.dta21_117)
File "/venv/local/lib/python2.7/site-packages/pandas/io/tests/test_stata.py", line 86, in read_dta
return read_stata(file, convert_dates=True)
File "/venv/local/lib/python2.7/site-packages/pandas/io/stata.py", line 170, in read_stata
return reader.read()
File "/venv/local/lib/python2.7/site-packages/pandas/io/stata.py", line 1499, in read
data = self._insert_strls(data)
File "/venv/local/lib/python2.7/site-packages/pandas/io/stata.py", line 1595, in _insert_strls
data.iloc[:, i] = [self.GSO[k] for k in data.iloc[:, i]]
KeyError: 4294967299
======================================================================
FAIL: test_isin (pandas.tseries.tests.test_timedeltas.TestTimedeltaIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/tseries/tests/test_timedeltas.py", line 1681, in test_isin
self.assertTrue(result.all())
AssertionError: False is not true
======================================================================
FAIL: test_isin (pandas.tseries.tests.test_timeseries.TestDatetimeIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/tseries/tests/test_timeseries.py", line 3452, in test_isin
self.assertTrue(result.all())
AssertionError: False is not true
======================================================================
FAIL: test_isin_with_i8 (pandas.tests.series.test_analytics.TestSeriesAnalytics)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/tests/series/test_analytics.py", line 1128, in test_isin_with_i8
assert_series_equal(result, expected)
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1049, in assert_series_equal
check_less_precise, obj='{0}'.format(obj))
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3887)
File "pandas/src/testing.pyx", line 147, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2769)
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 915, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Series are different
Series values are different (40.0 %)
[left]: [False, False, False, False, False]
[right]: [True, True, False, False, False]
======================================================================
FAIL: test_basic (pandas.tests.test_algos.TestIsin)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/tests/test_algos.py", line 371, in test_basic
tm.assert_numpy_array_equal(result, expected)
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 957, in assert_numpy_array_equal
raise_assert_detail(obj, msg, left, right)
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 915, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array values are different (33.33333 %)
[left]: [False, False, False]
[right]: [True, False, False]
======================================================================
FAIL: test_drop_level_nonunique_datetime (pandas.tests.test_multilevel.TestMultiLevel)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/venv/local/lib/python2.7/site-packages/pandas/tests/test_multilevel.py", line 1978, in test_drop_level_nonunique_datetime
assert_frame_equal(result, expected)
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 1119, in assert_frame_equal
'{0}, {1}'.format(right.shape[0], right.index))
File "/venv/local/lib/python2.7/site-packages/pandas/util/testing.py", line 915, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame are different
DataFrame shape (number of rows) are different
[left]: 5, MultiIndex(levels=[[2, 3, 4, 5], [2016-03-23 14:00:00, 2016-03-23 15:00:00, 2016-03-23 16:00:00, 2016-03-23 17:00:00]],
labels=[[0, 1, 2, 2, 3], [0, 1, 2, 2, 3]],
names=[u'id', u'tstamp'])
[right]: 3, MultiIndex(levels=[[2, 3, 4, 5], [2016-03-23 14:00:00, 2016-03-23 15:00:00, 2016-03-23 16:00:00, 2016-03-23 17:00:00]],
labels=[[0, 1, 3], [0, 1, 3]],
names=[u'id', u'tstamp'])
```
https://travis-ci.org/MacPython/pandas-wheels/jobs/142424407
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} | 3 | 2016-07-05T14:32:16Z | 2016-07-08T15:17:39Z | 2016-07-08T15:16:46Z | CONTRIBUTOR | null | - [x] closes #10690
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
the Datetime64Formatter class did not accept a `formatter` argument, so custom formatters passed in through `df.to_string` or `df.to_html` were silently ignored.
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} | 4 | 2016-07-05T15:06:42Z | 2016-07-11T15:03:02Z | 2016-07-11T15:02:25Z | NONE | null | #### Code Sample
``` 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)})
>>>
>>> gb = df.groupby('B')
>>>
>>> gb[['C', 'D']].apply(lambda group: group.C.mean() - group.D.mean())
```
Works fine on 0.18.0.
On 0.18.1 throws:
> TypeError: Series.name must be a hashable type
#### Expected Output
> B
> one 0.609650
> three -1.027308
> two 0.515237
> dtype: float64
(some other random numbers)
### Comment
With 0.18.1 update, some piece of my code stopped working, in a quite surprising way.
The code is easy to fix - just by removing [['C', 'D']] and applying on the whole groupby object.
Nevertheless, its a regression in a point release and I think someone else may run into the same problem.
I am guessing that pandas is trying to generate a name for the result series based on the column list - unfortunately I cannot investigate further right now.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 24.0.2
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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} | 2 | 2016-07-05T17:24:18Z | 2021-05-17T15:17:29Z | 2021-05-17T15:17:29Z | CONTRIBUTOR | null | When using `.loc` to expand a column, the dataframe and the constituent Series can get out of sync. This led to a strange issue in some legacy code I inherited. Because of the copy/view issues I wouldn't have written this particular code myself, but it still cost me some time tracking down what was happening:
```
df = pd.DataFrame({"a": [10,20,30]})
df["a"].loc[4] = 40
```
gives me
```
In [224]: df
Out[224]:
a
0 10
1 20
2 30
In [225]: df["a"]
Out[225]:
0 10
1 20
2 30
4 40
Name: a, dtype: int64
In [226]: df.shape, df["a"].shape
Out[226]: ((3, 1), (4,))
```
which is a little unnerving. I expected that `df` and `df["a"]` would both be unchanged or both be changed. Not sure if it's too much trouble to fix and we should just say "don't move your arm like that", though.
@jorisvandenbossche reported the same thing in master; here I'm on 0.18.0.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13570/events | https://github.com/pandas-dev/pandas/issues/13570 | 163,930,974 | MDU6SXNzdWUxNjM5MzA5NzQ= | 13,570 | Test failure with matplotlib 1.5.2rc2 on Debian | {
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} | 9 | 2016-07-05T19:48:28Z | 2016-07-14T08:44:19Z | 2016-07-14T08:44:19Z | NONE | null | When running the test suite after the package was built, we get the following failure:
```
ERROR: test_plot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/$BUILD/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_graphics.py", line 1322, in test_plot
df.plot.line(blarg=True)
File "/$BUILD/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 3758, in line
return self(kind='line', x=x, y=y, **kwds)
[...]
File "/$BUILD/debian/tmp/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py", line 1340, in _plot
return ax.plot(*args, **kwds)
File "/usr/lib/python2.7/dist-packages/matplotlib/__init__.py", line 1821, in inner
return func(ax, *args, **kwargs)
File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_axes.py", line 1432, in plot
for line in self._get_lines(*args, **kwargs):
File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 381, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 369, in _plot_args
seg = func(x[:, j % ncx], y[:, j % ncy], kw, kwargs)
File "/usr/lib/python2.7/dist-packages/matplotlib/axes/_base.py", line 276, in _makeline
seg = mlines.Line2D(x, y, **kw)
File "/usr/lib/python2.7/dist-packages/matplotlib/lines.py", line 380, in __init__
self.update(kwargs)
File "/usr/lib/python2.7/dist-packages/matplotlib/artist.py", line 859, in update
raise AttributeError('Unknown property %s' % k)
AttributeError: Unknown property blarg
```
Full log [here](https://tests.reproducible-builds.org/debian/rbuild/testing/amd64/pandas_0.18.0+git114-g6c692ae-1.rbuild.log) (from the [Debian bug report](https://bugs.debian.org/827938)).
`pandas.show_versions()` gives:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.6.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
pandas: 0.18.0+git114-g6c692ae
nose: 1.3.7
pip: None
setuptools: 20.10.1
Cython: None
numpy: 1.11.1rc1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: 1.4.4
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.2rc2
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.6.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 19 | 2016-07-05T21:32:40Z | 2016-08-21T20:47:08Z | 2016-08-19T23:07:50Z | CONTRIBUTOR | null | Rebased as PR https://github.com/pydata/pandas/pull/14060
---
- [x] closes #13549
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
-read_csv with engine=c throws error when encoding=UTF_16 (anything other than utf-16)
-improved nosetests and moved to in pandas/io/tests/common.py
-passes `pep8radius upstream/master --diff` and `git diff upstream/master | flake8 --diff`
-put what's new entry in 0.19.0 in accordance with milestone posted on issue
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} | 7 | 2016-07-06T12:23:22Z | 2019-06-28T16:32:00Z | 2019-06-28T16:32:00Z | NONE | null | There is an inconsistency when you look for dates in a DateTimeIndex if you query using strings and the index is non-monotonic. The first element in the index cannot be found in this way, but if you manually convert it to a timestamp object, or if you look for elements other than the first, then the lookup is successful.
#### Code Sample, a copy-pastable example if possible
``` python
>>> import pandas as pd
>>> idx = pd.to_datetime(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-04'])
>>> idx
Out[38]: DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-04'], dtype='datetime64[ns]', freq=None)
>>> idx.is_monotonic
Out[29]: False
>>> idx = pd.to_datetime(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-04'])
>>> '2015-01-01' in idx # This should work
Out[31]: False
>>> pd.Timestamp('2015-01-01') in idx
Out[32]: True
>>> '2015-01-02' in idx
Out[33]: True
>>> '2015-01-04' in idx
Out[34]: True
```
#### Expected Output
``` python
>>> '2015-01-01' in idx
Out[31]: True
```
#### output of `pd.show_versions()`
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-573.7.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7.commod
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
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---
- [x] closes #13570
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
The "blarg" argument is propagated to matplotlib and there it raises an
AttributeError (and not a TypeError).
However, since the test doesn't seem to have a rationale, it is removed here.
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} | 3 | 2016-07-06T21:21:25Z | 2016-07-16T23:04:06Z | 2016-07-16T23:04:06Z | NONE | null | - [ ] closes #13572
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pandas.tests.indexes.test_datetimelike.TestDatetimeIndex:test_contains
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
…ime index and the object in question is first in the index.
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} | 15 | 2016-07-07T06:47:15Z | 2016-07-21T14:21:03Z | 2016-07-21T14:16:50Z | CONTRIBUTOR | null | - [x] closes #13461
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} | 7 | 2016-07-07T07:15:51Z | 2016-07-08T09:56:54Z | 2016-07-08T09:56:41Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```
In [109]:
data = np.array([[ 'c1', 'c2'],
[ '', 0.285],
[ 10.1, 0.285]], dtype=object)
In [110]:
pd.DataFrame(data).to_csv('test.csv',header=False,index=False)
In [111]:
!cat test.csv
Out[111]:
c1,c2
,0.285
10.1,0.285
In [113]:
pd.read_csv('test.csv',converters={'c1':str},engine='c').values
Out[113]:
array([['', 0.285],
['10.1', 0.285]], dtype=object)
In [114]:
pd.read_csv('test.csv',converters={'c1':str},engine='python').values
Out[114]:
array([[nan, 0.285],
['10.1', 0.285]], dtype=object)
```
#### Expected Output
Notice that the output for the python engine and the c engine are different. I am not sure which one is preferable/expected.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 13.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 21.0.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
```
### Details
The culpruit seems to be this function: https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1299
Before that call, `results` and `values` both contain the `''` string in the python engine version of the parser. That call changes the `''`' value in `values` to `nan` and thus changes the value of `results` as well. Specifically, the change for the python engine happens here: https://github.com/pydata/pandas/blob/0c6226cbbc319ec22cf4c957bdcc055eaa7aea99/pandas/src/inference.pyx#L1009
```
if (convert_empty and val == '') or (val in na_values)
```
It seems that `na_values` contains `''` by default for certain parsing calls. This means that when `val == ''` triggers the `nan` conversion whether or not `convert_empty` is true.
I haven't tracked down the change in the `c` engine.
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https://api.github.com/repos/pandas-dev/pandas/issues/13577 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13577/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13577/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13577/events | https://github.com/pandas-dev/pandas/issues/13577 | 164,262,241 | MDU6SXNzdWUxNjQyNjIyNDE= | 13,577 | GbqConnector should be able to fetch default credentials on Google Compute Engine | {
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} | 2 | 2016-07-07T09:04:06Z | 2016-08-18T10:49:30Z | 2016-08-18T10:49:30Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py
```
class GbqConnector(object):
...
def get_user_account_credentials(self):
...
...
```
Google Compute Engine and Google Dataproc etc. already have default application credentials on them. Therefore, there is no need of running `OAuth2WebServerFlow` while the method `get_user_account_credentials` is being called - instead `return GoogleCredentials.get_application_default()` is all you need. This change will allow you to run `GbqConnector` on commandline-only systems on Google Compute Engine without a need of a service account json key.
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} | 1 | 2016-07-07T09:20:00Z | 2016-07-11T08:36:47Z | 2016-07-10T20:18:41Z | CONTRIBUTOR | null | - [x] closes #13577
- [x] passed all existing tests for GbqConnector
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 13 | 2016-07-07T14:51:30Z | 2019-10-25T16:37:24Z | 2017-04-14T23:54:36Z | MEMBER | null | Rebased in #16005
---
- [x] closes #12548
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
cleanup `tools/plotting` as `plotting` subpackage. Because the differences are being huge, would like to split 3 part if base direction is OK:
~~1. Move plotting related tests under `pandas/tests/plotting` splitting to `Series`, `DataFrame`, etc. (0.18.2)~~ (#13621)
2. Create `pd.plotting` and move related files (0.20.0)
3. Split / refactor `pd.plotting` to use small utility functions / property control classes. Add tests. (0.20.0)
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} | 4 | 2016-07-07T19:47:21Z | 2016-07-11T12:02:16Z | 2016-07-09T05:30:47Z | NONE | null | Enhancement Request:
- InStr Comparison between two values on the same row in the same DataFrame
Would be great if pandas would enable some 'instr' functionality between two columns.
For instance, being able to tell when the contents of one value exist in another:
- Excel does this in the following manner:
`=instr(0, a1&"."&b1, c1, vbTextCompare)`
- In Teradata SQL (not ansi compliant):
`select databsename, tablename, requeststring,`
`index(createstring, databasename||'.'||tablename)`
`from dbc.tables;`
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} | 2 | 2016-07-07T20:55:22Z | 2016-07-10T21:03:00Z | 2016-07-10T21:02:56Z | MEMBER | null | - [x] closes #13078
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} | 1 | 2016-07-07T22:02:16Z | 2016-07-15T00:30:34Z | 2016-07-15T00:30:34Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> None in pd.period_range('2015-01-01', periods=1, freq='D').insert(0, pd.NaT)
False
```
Incorrect behavior for `pd.NaT, None, float('nan'), np.nan`
#### Expected Output
``` python
>>> None in pd.period_range('2015-01-01', periods=1, freq='D').insert(0, pd.NaT)
True
```
#### output of `pd.show_versions()`
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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} | 2 | 2016-07-07T23:02:00Z | 2016-07-10T22:05:12Z | 2016-07-10T22:05:03Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Unable to concatenate `Series` with `tzlocal`
```
s = pd.Series([pd.Timestamp('2011-01-01', tz=dateutil.tz.tzlocal())])
s
#0 2011-01-01 00:00:00+09:00
# dtype: datetime64[ns, tzlocal()]
pd.concat([s, s])
# UnknownTimeZoneError: 'tzlocal()'
```
It is caused by 2 issues:
#### 1. `tslib.maybe_get_tz` can't handle `tzlocal`.
```
pd.tslib.maybe_get_tz('tzlocal()')
# UnknownTimeZoneError: 'tzlocal()'
```
#### 2. `DatetimeIndex.tz_localize` may return incorrect result when `tzlocal` is used.
it's internally used in `conat`.
```
pd.DatetimeIndex(['2011-01-01', '2011-01-02'], tz=dateutil.tz.tzlocal()).tz_localize(None)
# DatetimeIndex(['2011-01-01', '1970-01-01'], dtype='datetime64[ns]', freq=None)
```
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} | 8 | 2016-07-08T01:18:22Z | 2016-07-09T18:16:40Z | 2016-07-09T18:16:40Z | CONTRIBUTOR | null | closes #13566
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} | 6 | 2016-07-08T09:35:57Z | 2016-07-11T15:02:25Z | 2016-07-11T15:02:25Z | MEMBER | null | - [x] closes #13568
- [x] tests added / passed
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| null | 1 | 2016-07-08T15:18:17Z | 2016-07-12T10:21:21Z | 2016-07-12T10:21:21Z | NONE | null | In series all logical operators are by position (iloc) while all mathematical operators are by location (loc).
This is probably similar to https://github.com/pydata/pandas/issues/4581.
e.g.
`import pandas as pd`
`a = pd.Series([1, 4], index=[0, 1])`
`b = pd.Series([3, 7], index=[1, 0])`
`a>b`
Gives
`0 False`
`1 False`
`dtype: bool`
But
`a - b`
gives
`0 -6`
`1 1`
`dtype: int64`
#### output of `pd.show_versions()`
`
> > > pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.6
setuptools: 3.6
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
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: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
`
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have their own packages in official repositories, it is better
to point to them than the one from OpenSuse.
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} | 3 | 2016-07-08T19:27:36Z | 2016-12-04T20:32:42Z | 2016-12-04T20:32:42Z | CONTRIBUTOR | null | The sort_index method on DataFrame and NDFrame takes kwargs `kind` and `na_position`, but `Series` does not currently support it. (In addition their shared docstring says Series would support it.)
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} | 3 | 2016-07-09T05:56:57Z | 2016-07-19T01:30:42Z | 2016-07-19T01:30:42Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Follow-up of #10951. Remove legacy offsets deprecated in 0.17.0.
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} | 1 | 2016-07-09T08:30:49Z | 2019-10-12T23:51:05Z | 2019-10-12T23:51:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Step 1. Run the code with Python 2 and Python 3
``` python
from sys import version
from pandas import DataFrame
print(version)
vers = version[0]
file_name = 'foobar'
data = {'a': [1], 'b': [2]}
dtf = DataFrame(data=data)
dtf.to_msgpack(file_name + '_py{}.msg'.format(vers))
```
Step 2. Run the code with Python 2 and Python 3
``` python
from sys import version
from pandas import read_msgpack
print(version)
vers = version[0]
file_name = 'foobar'
dtf = read_msgpack(file_name + '_py3.msg'.format(vers))
print(dtf.columns)
print(dtf.a)
dtf = read_msgpack(file_name + '_py2.msg'.format(vers))
# dtf.columns = [i.decode("utf-8") for i in dtf.columns]
print(dtf.columns)
print(dtf.a)
```
#### Expected Output
Same result with Python 2 and Python 3
Python 2 output:
```
2.7.10 (default, Oct 23 2015, 19:19:21)
[GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.5)]
Index([u'a', u'b'], dtype='object')
0 1
Name: a, dtype: int64
Index([u'a', u'b'], dtype='object')
0 1
Name: a, dtype: int64
```
Python 3 output, the exception it is not expected:
```
3.5.1 (v3.5.1:37a07cee5969, Dec 5 2015, 21:12:44)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]
Index(['a', 'b'], dtype='object')
0 1
Name: a, dtype: int64
Index([b'a', b'b'], dtype='object')
Traceback (most recent call last):
File "/Users/VV/GitHub/doptimization/bug_msgpack_reader.py", line 16, in <module>
print(dtf.a)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/generic.py", line 2669, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'a'
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: None
pip: 8.1.2
setuptools: 20.6.7
Cython: None
numpy: 1.11.0
scipy: 0.16.1
statsmodels: None
xarray: None
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: None
pip: 8.1.2
setuptools: 20.6.7
Cython: None
numpy: 1.11.0
scipy: 0.16.1
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
None
3.5.1 (v3.5.1:37a07cee5969, Dec 5 2015, 21:12:44)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]
Index(['a', 'b'], dtype='object')
0 1
Name: a, dtype: int64
Index([b'a', b'b'], dtype='object')
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
None
```
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} | 5 | 2016-07-09T12:26:50Z | 2016-07-11T01:37:13Z | 2016-07-11T01:37:09Z | MEMBER | null | - [x] closes #9005
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
`Series` and `Index` compares `NaT == NaT` as `True` if it has `object` dtype.
```
pd.Series([pd.NaT])
#0 NaT
# dtype: datetime64[ns]
# OK
pd.Series([pd.NaT]) == pd.NaT
#0 False
# dtype: bool
# NG in object dtype
pd.Series([pd.NaT], dtype=object) == pd.NaT
#0 True
# dtype: bool
# OK
pd.Index([pd.NaT]) == pd.NaT
array([False], dtype=bool)
# NG in object dtype
pd.Index([pd.NaT], dtype=object) == pd.NaT
# array([ True], dtype=bool)
```
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} | 3 | 2016-07-09T15:07:20Z | 2016-07-15T00:21:58Z | 2016-07-15T00:21:47Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
Rebased version of https://github.com/pydata/pandas/pull/10229 which was [actually not](https://github.com/pydata/pandas/pull/10229#issuecomment-131470116) fixed by https://github.com/pydata/pandas/pull/10199.
Nothing particular relevant, just wanted to delete this branch locally and noticed it still applies: you'll judge what to do of it.
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} | 1 | 2016-07-09T15:10:42Z | 2017-08-15T22:44:55Z | 2017-08-15T22:44:55Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [3]: s = pd.Series([1, 2, np.inf])
In [4]: s.argmax()
Out[4]: 1
In [5]: s.argmax(skipna=False)
Out[5]: nan
```
#### Expected Output
2 in both cases.
#### output of `pd.show_versions()`
In [6]: pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit: a63bd12529ff309d957d714825b1753d0e02b7fa
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+174.ga63bd12
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 1 | 2016-07-09T15:15:53Z | 2016-07-09T16:49:44Z | 2016-07-09T16:48:34Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: mi = pd.MultiIndex.from_product([[1,2], ['a', 'b']])
In [3]: mi.values
Out[3]: array([(1, 'a'), (1, 'b'), (2, 'a'), (2, 'b')], dtype=object)
In [4]: mi.values[0] = (3, 'c')
In [5]: mi.values
Out[5]: array([(3, 'c'), (1, 'b'), (2, 'a'), (2, 'b')], dtype=object)
In [6]: mi.copy().values
Out[6]: array([(1, 'a'), (1, 'b'), (2, 'a'), (2, 'b')], dtype=object)
```
#### Expected Output
The same as `mi.values`, whatever it is.
#### output of `pd.show_versions()`
```
In [7]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: a63bd12529ff309d957d714825b1753d0e02b7fa
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+174.ga63bd12
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 4 | 2016-07-09T15:19:16Z | 2016-07-11T13:20:06Z | 2016-07-09T16:45:06Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: df = pd.DataFrame(index=pd.MultiIndex.from_tuples([['a', 'b', 'c']]), columns=[1, 2])
In [3]: df.loc['a', 'b', 'c']
Out[3]:
1 NaN
2 NaN
Name: (a, b, c), dtype: object
In [4]: df.loc['a', 'b', :]
Out[4]:
1 2
c NaN NaN
In [5]: df.loc[:, 'b', 'c']
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1397 if key not in ax:
-> 1398 error()
1399 except TypeError as e:
/home/nobackup/repo/pandas/pandas/core/indexing.py in error()
1392 raise KeyError("the label [%s] is not in the [%s]" %
-> 1393 (key, self.obj._get_axis_name(axis)))
1394
KeyError: 'the label [b] is not in the [columns]'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-5-116a40d4994c> in <module>()
----> 1 df.loc[:, 'b', 'c']
/home/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1295
1296 if type(key) is tuple:
-> 1297 return self._getitem_tuple(key)
1298 else:
1299 return self._getitem_axis(key, axis=0)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_tuple(self, tup)
790
791 # no multi-index, so validate all of the indexers
--> 792 self._has_valid_tuple(tup)
793
794 # ugly hack for GH #836
/home/nobackup/repo/pandas/pandas/core/indexing.py in _has_valid_tuple(self, key)
140 if i >= self.obj.ndim:
141 raise IndexingError('Too many indexers')
--> 142 if not self._has_valid_type(k, i):
143 raise ValueError("Location based indexing can only have [%s] "
144 "types" % self._valid_types)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1404 raise
1405 except:
-> 1406 error()
1407
1408 return True
/home/nobackup/repo/pandas/pandas/core/indexing.py in error()
1391 "key")
1392 raise KeyError("the label [%s] is not in the [%s]" %
-> 1393 (key, self.obj._get_axis_name(axis)))
1394
1395 try:
KeyError: 'the label [b] is not in the [columns]'
```
#### Expected Output
Either something analogous to the previous command, or all 3 should raise an error I guess.
#### output of `pd.show_versions()`
```
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: a63bd12529ff309d957d714825b1753d0e02b7fa
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+174.ga63bd12
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 6 | 2016-07-09T15:22:08Z | 2016-07-19T01:11:27Z | 2016-07-19T01:11:18Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [5]: pd.Series(['a', 'b']).str.zfill('hi')
Out[5]:
0 NaN
1 NaN
dtype: float64
```
#### Expected Output
A `ValueError`.
#### output of `pd.show_versions()`
```
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: a63bd12529ff309d957d714825b1753d0e02b7fa
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+174.ga63bd12
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 5 | 2016-07-09T15:24:31Z | 2016-07-20T22:25:22Z | 2016-07-20T22:25:22Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: pd.MultiIndex.from_arrays([[1,2,3], ['a', 'b']])
Out[2]:
MultiIndex(levels=[[1, 2, 3], ['a', 'b']],
labels=[[0, 1, 2], [0, 1]])
In [3]: pd.MultiIndex.from_arrays([[1,2,3], ['a', 'b']]).values
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-65e6fa14e07f> in <module>()
----> 1 pd.MultiIndex.from_arrays([[1,2,3], ['a', 'b']]).values
/home/nobackup/repo/pandas/pandas/indexes/multi.py in values(self)
552 values.append(taken)
553
--> 554 self._tuples = lib.fast_zip(values)
555 return self._tuples
556
/home/nobackup/repo/pandas/pandas/lib.pyx in pandas.lib.fast_zip (pandas/lib.c:11131)()
587 it = <flatiter> PyArray_IterNew(arr)
588 if len(arr) != n:
--> 589 raise ValueError('all arrays must be same length')
590
591 for i in range(n):
ValueError: all arrays must be same length
```
#### Expected Output
The first previous command should already raise an error.
#### output of `pd.show_versions()`
```
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: a63bd12529ff309d957d714825b1753d0e02b7fa
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+174.ga63bd12
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 0 | 2016-07-09T15:29:16Z | 2016-07-09T23:47:27Z | 2016-07-09T17:01:59Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2016-07-09T15:29:56Z | 2020-01-09T21:17:06Z | 2020-01-09T21:17:06Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [3]: s = pd.Series(0, range(4))
In [4]: s.index = np.array([[2,3]]*4)
In [5]: s
Out[5]: ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/nobackup/repo/ipython/IPython/core/formatters.py in __call__(self, obj)
683 type_pprinters=self.type_printers,
684 deferred_pprinters=self.deferred_printers)
--> 685 printer.pretty(obj)
686 printer.flush()
687 return stream.getvalue()
/home/nobackup/repo/ipython/IPython/lib/pretty.py in pretty(self, obj)
381 if callable(meth):
382 return meth(obj, self, cycle)
--> 383 return _default_pprint(obj, self, cycle)
384 finally:
385 self.end_group()
/home/nobackup/repo/ipython/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
501 if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs:
502 # A user-provided repr. Find newlines and replace them with p.break_()
--> 503 _repr_pprint(obj, p, cycle)
504 return
505 p.begin_group(1, '<')
/home/nobackup/repo/ipython/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
692 """A pprint that just redirects to the normal repr function."""
693 # Find newlines and replace them with p.break_()
--> 694 output = repr(obj)
695 for idx,output_line in enumerate(output.splitlines()):
696 if idx:
/home/nobackup/repo/pandas/pandas/core/base.py in __repr__(self)
65 Yields Bytestring in Py2, Unicode String in py3.
66 """
---> 67 return str(self)
68
69
/home/nobackup/repo/pandas/pandas/core/base.py in __str__(self)
44
45 if compat.PY3:
---> 46 return self.__unicode__()
47 return self.__bytes__()
48
/home/nobackup/repo/pandas/pandas/core/series.py in __unicode__(self)
979
980 self.to_string(buf=buf, name=self.name, dtype=self.dtype,
--> 981 max_rows=max_rows)
982 result = buf.getvalue()
983
/home/nobackup/repo/pandas/pandas/core/series.py in to_string(self, buf, na_rep, float_format, header, index, length, dtype, name, max_rows)
1020 the_repr = self._get_repr(float_format=float_format, na_rep=na_rep,
1021 header=header, index=index, length=length,
-> 1022 dtype=dtype, name=name, max_rows=max_rows)
1023
1024 # catch contract violations
/home/nobackup/repo/pandas/pandas/core/series.py in _get_repr(self, name, header, index, length, dtype, na_rep, float_format, max_rows)
1048 float_format=float_format,
1049 max_rows=max_rows)
-> 1050 result = formatter.to_string()
1051
1052 # TODO: following check prob. not neces.
/home/nobackup/repo/pandas/pandas/formats/format.py in to_string(self)
227 return 'Series([], ' + footer + ')'
228
--> 229 fmt_index, have_header = self._get_formatted_index()
230 fmt_values = self._get_formatted_values()
231
/home/nobackup/repo/pandas/pandas/formats/format.py in _get_formatted_index(self)
213 else:
214 have_header = index.name is not None
--> 215 fmt_index = index.format(name=True)
216 return fmt_index, have_header
217
/home/nobackup/repo/pandas/pandas/indexes/base.py in format(self, name, formatter, **kwargs)
1525 return header + list(self.map(formatter))
1526
-> 1527 return self._format_with_header(header, **kwargs)
1528
1529 def _format_with_header(self, header, na_rep='NaN', **kwargs):
/home/nobackup/repo/pandas/pandas/indexes/base.py in _format_with_header(self, header, na_rep, **kwargs)
1549
1550 else:
-> 1551 result = _trim_front(format_array(values, None, justify='left'))
1552 return header + result
1553
/home/nobackup/repo/pandas/pandas/formats/format.py in format_array(values, formatter, float_format, na_rep, digits, space, justify, decimal)
2021 space=space, justify=justify, decimal=decimal)
2022
-> 2023 return fmt_obj.get_result()
2024
2025
/home/nobackup/repo/pandas/pandas/formats/format.py in get_result(self)
2040
2041 def get_result(self):
-> 2042 fmt_values = self._format_strings()
2043 return _make_fixed_width(fmt_values, self.justify)
2044
/home/nobackup/repo/pandas/pandas/formats/format.py in _format_strings(self)
2226 def _format_strings(self):
2227 formatter = self.formatter or (lambda x: '% d' % x)
-> 2228 fmt_values = [formatter(x) for x in self.values]
2229 return fmt_values
2230
/home/nobackup/repo/pandas/pandas/formats/format.py in <listcomp>(.0)
2226 def _format_strings(self):
2227 formatter = self.formatter or (lambda x: '% d' % x)
-> 2228 fmt_values = [formatter(x) for x in self.values]
2229 return fmt_values
2230
/home/nobackup/repo/pandas/pandas/formats/format.py in <lambda>(x)
2225 class IntArrayFormatter(GenericArrayFormatter):
2226 def _format_strings(self):
-> 2227 formatter = self.formatter or (lambda x: '% d' % x)
2228 fmt_values = [formatter(x) for x in self.values]
2229 return fmt_values
TypeError: %d format: a number is required, not numpy.ndarray
```
#### Expected Output
It would be cool if this became a `MultiIndex` automatically, but otherwise it should just raise an error (or maybe it's just the string representation which is broken?)
#### output of `pd.show_versions()`
```
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: a63bd12529ff309d957d714825b1753d0e02b7fa
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.18.1+174.ga63bd12
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+111ddc0
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.1.2
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13602 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13602/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13602/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13602/events | https://github.com/pandas-dev/pandas/pull/13602 | 164,687,337 | MDExOlB1bGxSZXF1ZXN0NzY4NTgwMDA= | 13,602 | CLN: Removed coerce param in pd.to_timedelta and pd.to_datetime | {
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} | 27 | 2016-07-09T21:51:53Z | 2016-10-15T20:08:03Z | 2016-10-15T19:58:27Z | MEMBER | null | Deprecated back in `v0.17.0` <a href="https://github.com/pydata/pandas/commit/987b7e7e586b8df1d127406c69e0a9094a1a5322">here</a>. Seems appropriate to carry though now.
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} | 0 | 2016-07-09T21:57:45Z | 2016-07-19T01:14:14Z | 2016-07-19T01:14:14Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
>>> None in pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1)
True
```
Returns `True` for any one of `[pd.NaT, None, float('nan'), np.nan]`
#### Expected Output
``` python
>>> None in pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1)
False
```
#### output of `pd.show_versions()`
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 3c202b1cbcc73c4006c967c8abe1b8d9089c5be4
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.18.1+171.g3c202b1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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