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| null | 3 | 2016-02-12T01:07:23Z | 2016-02-12T17:50:47Z | 2016-02-12T17:32:28Z | NONE | null | I've been having a very frustrating problem where I could save a DataFrame with unique labels to HDF5, and load it again, and find that the loaded version had non-unique labels. It turns out this is because `pd.read_hdf` is replacing all labels containing non-ASCII characters with the empty string. For example, a row with the label `'café'` will have the label `''` when it is loaded again.
I looked at the bytes that were written in the HDF5 file, and confirmed that the proper UTF-8 text was in there, so the bug is in `read_hdf`.
Version information:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-51-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: 1.5.4
setuptools: 2.2
Cython: 0.23.3
numpy: 1.10.4
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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| null | 2 | 2016-02-12T03:59:02Z | 2016-02-15T00:05:29Z | 2016-02-12T04:20:39Z | NONE | null | Under Windows 8 (x64), with Pandas v0.17.0 (from Anaconda) and up, Pandas.DataFrame.where() function is found to behave sporadically, sometimes returning the expected result and sometimes returning a dataframe filled with zeros.
The following code demonstrates this behavior, observed for dataframes that have larger than 361 rows and columns.
I observed that this problem does NOT exist in Pandas v0.16.2 on the same machine (Windows 8 x64, Anaconda), and in Pandas v0.17.1 under Mac OS (Anaconda).
``` python
import numpy as np
import pandas as pd
n = 400
n_trials = 1000
df_1 = pd.DataFrame(data=np.random.random((n,n)), index=np.arange(0,n), columns=np.arange(0,n))
df_2 = pd.DataFrame(data=np.random.random((n,n)), index=np.arange(0,n), columns=np.arange(0,n))
count = 0
for i in xrange(0,n_trials):
res = df_1.where(df_2 > 0.5)
if (res < 1e-6).all().all():
count += 1
print "number of unexpected cases: ", count
```
returns non-zero counts.
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| null | 24 | 2016-02-12T09:30:57Z | 2016-11-22T11:44:44Z | 2016-11-22T11:44:44Z | NONE | null | Addresses indexing issues related to subclassing (#11559).
Also made some changes to base, groupby, reshape, strings.
So for example, if df was a SubclassedDataFrame and s was a SubclassedSeries
`df.groupby('A').apply(lambda x: x)`
and
`s.value_counts()`
would return SubclassedDataFrame and SubclassedSeries instances, respectively.
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} | 13 | 2016-02-12T14:42:19Z | 2016-06-14T13:54:38Z | 2016-06-14T13:53:54Z | MEMBER | null | ```
In [8]: i = Index(range(3),name='foo')
In [9]: Index(i)
Out[9]: Int64Index([0, 1, 2], dtype='int64', name=u'foo')
In [10]: pd.Int64Index(i)
Out[10]: Int64Index([0, 1, 2], dtype='int64')
```
`Float64Index` as well
ideally move the tests to `Base` to test all index types.
See #12288.
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} | 1 | 2016-02-12T15:52:34Z | 2016-02-13T02:35:59Z | 2016-02-13T02:35:59Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/108709963
```
S.......S.S........................................................................./home/travis/build/pydata/pandas/pandas/core/config.py:219: FutureWarning:
mpl_style had been deprecated and will be removed in a future version.
Use `matplotlib.pyplot.style.use` instead.
return self.__func__(*args, **kwds)
```
cc @TomAugspurger
prob should bump the stacklevel as well
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| null | 11 | 2016-02-12T16:23:56Z | 2019-04-24T15:55:17Z | 2019-04-24T15:55:16Z | NONE | null | The styler does not seem to like unicode?
```
Reproduce by:
pd.DataFrame(['♚']).style
UnicodeDecodeError: 'ascii' codec can't decode byte 0xf0 in position 0: ordinal not in range(128)
```
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| null | 5 | 2016-02-12T18:02:41Z | 2016-02-13T13:50:05Z | 2016-02-12T18:10:05Z | MEMBER | null | ```
In [2]: df = pd.DataFrame(columns=pd.MultiIndex.from_product([range(2), range(2)]))
In [3]: df.loc[0] = range(4)
In [4]: df.loc[1] = pd.Series(range(4))
In [5]: df
Out[5]:
0 1
0 1 0 1
0 0.0 1.0 2.0 3.0
1 NaN NaN NaN NaN
```
That is: the first `loc` is doing what we expect, but the second is only creating a row of `NaN`s. By the way, giving an explicit columns list does not help, and
```
In [6]: df.loc[2] = pd.Series(range(40))
```
does not even raise an error.
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} | 0 | 2016-02-12T18:28:39Z | 2016-02-13T02:35:59Z | 2016-02-13T02:35:59Z | CONTRIBUTOR | null | closes #12311
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} | 0 | 2016-02-12T19:09:50Z | 2016-02-13T01:15:50Z | 2016-02-13T01:15:50Z | CONTRIBUTOR | null | closes #12217
xref #12306 which I think fixed
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} | 1 | 2016-02-13T02:02:00Z | 2016-02-13T02:36:00Z | 2016-02-13T02:36:00Z | CONTRIBUTOR | null | Forgot them in https://github.com/pydata/pandas/pull/12162
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} | 10 | 2016-02-13T03:32:15Z | 2016-02-14T19:16:45Z | 2016-02-14T17:08:43Z | MEMBER | null | Addresses issue #12301 by filtering `kwargs` argument in stat functions to prevent the passage of clearly invalid arguments while at the same time maintaining compatibility with analogous `numpy` functions.
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} | 3 | 2016-02-13T12:35:14Z | 2021-04-23T00:54:29Z | null | MEMBER | null | Suppose the following dataframe and reindex operation:
```
In [65]: df = pd.DataFrame(np.arange(12).reshape(4,3), columns=pd.MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('B', 'a')]))
In [66]: df
Out[66]:
A B
a b a
0 0 1 2
1 3 4 5
2 6 7 8
3 9 10 11
In [67]: df.reindex(columns=['a', 'b'], level=1)
Out[67]:
A B
a b a
0 0 1 2
1 3 4 5
2 6 7 8
3 9 10 11
```
Should this give the following?
```
In [67]: df.reindex(columns=['a', 'b'], level=1)
Out[67]:
A B
a b a b
0 0 1 2 NaN
1 3 4 5 NaN
2 6 7 8 NaN
3 9 10 11 NaN
```
I am not sure what the exact behaviour of the `level` keyword should be, but eg in the following example it does the selection of columns for each of label of the other level:
```
In [69]: df2 = pd.DataFrame(np.arange(18).reshape(3,6), columns=pd.MultiIndex.from_product([('A', 'B'), ('a', 'b', 'c')]))
In [70]:
In [70]: df2
Out[70]:
A B
a b c a b c
0 0 1 2 3 4 5
1 6 7 8 9 10 11
2 12 13 14 15 16 17
In [71]: df2.reindex(columns=['a', 'c'], level=1)
Out[71]:
A B
a c a c
0 0 2 3 5
1 6 8 9 11
2 12 14 15 17
```
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} | 0 | 2016-02-13T14:34:57Z | 2016-02-13T15:18:23Z | 2016-02-13T15:18:23Z | CONTRIBUTOR | null | catch warning on reindexing a boolean indexer
add some documentation on a na scalar comparison test warning
remove warning for plotting multiple on a sub-plot
closes #8397
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} | 2 | 2016-02-13T15:00:18Z | 2016-08-15T22:18:22Z | 2016-08-15T22:18:22Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/108942384
other warnings are already taken care of
```
...........................................S....................................S..........................S/home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'iris'
result = self._query(query)
./home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'iris'
result = self._query(query)
...S...S.../home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'test_frame_roundtrip'
result = self._query(query)
./home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'test_frame1'
result = self._query(query)
................................................................................S................................................................................S....................................S.........................S...................................................../home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'test'
result = self._query(query)
../home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'test'
result = self._query(query)
../home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'table_if_exists'
result = self._query(query)
..../home/travis/miniconda/envs/pandas/lib/python3.5/site-packages/pymysql/cursors.py:146: Warning: Unknown table 'test_table'
result = self._query(query)
```
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} | 1 | 2016-02-13T17:22:31Z | 2016-02-15T20:05:56Z | 2016-02-15T20:05:56Z | CONTRIBUTOR | null | from #12246
@jorisvandenbossche
Some after merge feedback:
I took a look and I also like the logic now as how you explained it in your last post (for label based indexing: if the label evaluates as equal, then it is interpreted as the existing label)
A few things:
- The whatsnew needs to be updated (as it still says it raises now for all cases, as you started the PR)
```
maybe mention that it only is about positional indexing? Otherwise people could think FloatIndex is removed.
```
- (minor) When using `iloc` with a float (so the case it still should raise an error), the error message is not fully correct:
```
In [76]: s2 = s.copy()
In [77]: s2.iloc[2.0] = 10
TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'>
with these indexers [2.0] of <type 'float'>
```
This is not 'label' indexing, but 'integer' or 'positional' indexing
- The behaviour of using a float indexer with `ix` on a non-numerical index has changed:
```
In [78]: s3 = pd.Series(range(3), index=list('abc'))
In [79]: s4 = s3.copy()
In [80]: s4.ix[1.0] = 10
In [81]: s4
Out[81]:
a 0
b 1
c 2
1.0 10
dtype: int64
In [82]: s4.index
Out[82]: Index([u'a', u'b', u'c', 1.0], dtype='object')
In [83]: pd.__version__
Out[83]: '0.17.1+350.g5f7c9e9'
```
and with 0.16.2
```
In [1]: pd.__version__
Out[1]: '0.16.2'
In [2]: s3 = pd.Series(range(3), index=list('abc'))
In [3]: s4 = s3.copy()
In [4]: s4.ix[1.0] = 10
C:\Anaconda\lib\site-packages\pandas\core\index.py:805: FutureWarning: scalar i
dexers for index type Index should be integers and not floating point
type(self).__name__),FutureWarning)
In [5]: s4
Out[5]:
a 0
b 10
c 2
dtype: int64
In [6]: s4.index
Out[6]: Index([u'a', u'b', u'c'], dtype='object')
```
The change is logical, as before the float was interpreted as a positional indexer (and for this the warning was raised). But now a float cannot be a positional indexer anymore, so it is interpreted as a new label.
To be clear, I think this change is OK, but just wanted to point out a case where this change will not raise an error but alter your results (worth mentioning in the whatsnew docs?)
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} | 3 | 2016-02-13T19:38:38Z | 2016-02-13T20:51:09Z | 2016-02-13T20:51:09Z | MEMBER | null | @jreback I proofread the highlights and made some edits (but language is always a bit subjective). Comments?
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kwargs
Closes #12171
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} | 1 | 2016-02-14T12:27:11Z | 2016-02-14T17:04:07Z | 2016-02-14T17:04:07Z | MEMBER | null | @TomAugspurger There were some remaining usages of `options.display.mpl_style` in the docs, so removed those + also removed the reference to it in the text.
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| null | 4 | 2016-02-15T06:39:20Z | 2016-04-14T04:03:56Z | 2016-04-14T01:27:52Z | CONTRIBUTOR | null | closes #12133
level names are lost on `MultiIndex.from_arrays(cartesian_product(table.index.levels))`
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| null | 2 | 2016-02-15T09:31:10Z | 2016-02-15T14:17:01Z | 2016-02-15T14:14:17Z | CONTRIBUTOR | null | When setting the warnings filter to show DeprecationWarnings, I get a warning when importing Pandas (which I discovered because mne-python changes the global warning settings, see https://github.com/mne-tools/mne-python/issues/2870):
```
import warnings
warnings.simplefilter("default")
import pandas as pd
```
This results in the following warning message:
```
/usr/local/lib/python3.5/site-packages/pandas/__init__.py:7: DeprecationWarning: bad escape \s
from pandas import hashtable, tslib, lib
```
I have no idea what's wrong with the code in line 7 though. Here's the output of `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 20.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
IPython: 4.1.1
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
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: 2.8
```
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} | 0 | 2016-02-15T14:50:17Z | 2016-02-15T21:49:01Z | 2016-02-15T20:28:58Z | CONTRIBUTOR | null | closes #9052
closes #12332
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| null | 8 | 2016-02-15T17:46:31Z | 2016-05-17T12:42:29Z | 2016-05-07T17:46:49Z | MEMBER | null | closes #12309
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} | 4 | 2016-02-15T19:01:58Z | 2016-02-15T20:29:10Z | 2016-02-15T20:28:58Z | CONTRIBUTOR | null | ``` python
In [1]: import pandas as pd
In [2]: index = pd.date_range('1-1-2000', '2-15-2000', freq='h')
In [3]: index = index.union(pd.date_range('4-15-2000', '5-15-2000', freq='h'))
In [4]: s = pd.Series(range(len(index)), index=index)
In [5]: a = s.loc[:'4-15-2000'].resample('30T').ohlc()
In [6]: b = s.loc[:'4-14-2000'].resample('30T').ohlc()
In [7]: a.head()
Out[7]:
open high low close
2000-01-01 00:00:00 0.0 0.0 0.0 0.0
2000-01-01 00:30:00 NaN NaN NaN NaN
2000-01-01 01:00:00 1.0 1.0 1.0 1.0
2000-01-01 01:30:00 NaN NaN NaN NaN
2000-01-01 02:00:00 2.0 2.0 2.0 2.0
In [8]: b.head()
Out[8]:
2000-01-01 00:00:00 0.0
2000-01-01 00:30:00 NaN
2000-01-01 01:00:00 1.0
2000-01-01 01:30:00 NaN
2000-01-01 02:00:00 2.0
Freq: 30T, dtype: float64
In [9]: pd.__version__
Out[9]: u'0.18.0rc1'
```
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} | 21 | 2016-02-15T21:02:04Z | 2016-03-08T23:38:19Z | 2016-03-08T23:38:19Z | CONTRIBUTOR | null | from #12246
@jorisvandenbossche this looks odd
```
In [11]: s = pd.Series([1,2,3])
In [12]: s.loc[0.0] = 10
In [13]: s.loc[0.0]
TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'>
with these indexers [0.0] of <type 'float'>
```
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} | 0 | 2016-02-15T21:52:43Z | 2016-02-17T01:12:08Z | 2016-02-17T01:12:08Z | CONTRIBUTOR | null | xref 12329
For consistency, the proposal is to make `[4]` have a multi-level index `('C','sum'),('C','std')` as its not a rename (like `[5]`). This previously raised in 0.17.1
```
In [2]: df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'two',
'two', 'two', 'one', 'two'],
'C': np.random.randn(8) + 1.0,
'D': np.arange(8)})
In [3]: g = df.groupby(['A', 'B'])
In [4]: g['D'].agg({'C': ['sum', 'std']})
Out[4]:
sum std
A B
bar one 1 NaN
two 8 1.414214
foo one 6 4.242641
two 13 2.516611
In [5]: g['D'].agg({'C': 'sum', 'D' : 'std'})
Out[5]:
C D
A B
bar one 1 NaN
two 8 1.414214
foo one 6 4.242641
two 13 2.516611
```
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} | 2 | 2016-02-15T22:12:16Z | 2016-02-17T01:12:08Z | 2016-02-17T01:12:08Z | CONTRIBUTOR | null | closes #12334
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} | 2 | 2016-02-15T23:08:46Z | 2016-02-18T13:15:08Z | 2016-02-18T13:15:08Z | MEMBER | null | ```
In [2]: pd.Series([0,0,0], dtype="category")
Out[2]:
0 0
1 0
2 0
dtype: category
Categories (1, int64): [0]
In [3]: pd.Series(0, index=range(3), dtype="category")
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-2c1d7a241071> in <module>()
----> 1 pd.Series(0, index=range(3), dtype="category")
/home/nobackup/repo/pandas/pandas/core/series.pyc in __init__(self, data, index, dtype, name, copy, fastpath)
224 else:
225 data = _sanitize_array(data, index, dtype, copy,
--> 226 raise_cast_failure=True)
227
228 data = SingleBlockManager(data, index, fastpath=True)
/home/nobackup/repo/pandas/pandas/core/series.pyc in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
2961 if len(subarr) != len(index) and len(subarr) == 1:
2962 subarr = create_from_value(subarr[0], index,
-> 2963 subarr.dtype)
2964
2965 elif subarr.ndim > 1:
/home/nobackup/repo/pandas/pandas/core/series.pyc in create_from_value(value, index, dtype)
2929 else:
2930 if not isinstance(dtype, (np.dtype, type(np.dtype))):
-> 2931 dtype = dtype.dtype
2932 subarr = np.empty(len(index), dtype=dtype)
2933 subarr.fill(value)
AttributeError: 'CategoricalDtype' object has no attribute 'dtype'
```
(Not a particularly brilliant use of categories, but they can then be concatenated with others.)
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https://api.github.com/repos/pandas-dev/pandas/issues/12337 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12337/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12337/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12337/events | https://github.com/pandas-dev/pandas/issues/12337 | 133,844,750 | MDU6SXNzdWUxMzM4NDQ3NTA= | 12,337 | BUG: UnicodeEncodeError in test_to_latex_filename (pandas.tests.test_format.TestDataFrameFormatting) | {
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} | 17 | 2016-02-15T23:48:58Z | 2020-04-04T20:36:50Z | 2020-04-04T20:36:50Z | NONE | null | Getting this error if (and I think only if) `LANG` is not defined or not set to any `utf8`-conforming value on 0.18.0rc1 (Mac OS X 10.10, python 3.4.4, numpy 0.11.0b3):
```
ERROR: test_to_latex_filename (pandas.tests.test_format.TestDataFrameFormatting)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tests/test_format.py", line 2614, in test_to_latex_filename
df.to_latex(path)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 1593, in to_latex
encoding=encoding)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/format.py", line 641, in to_latex
latex_renderer.write_result(f)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/format.py", line 877, in write_result
buf.write(' & '.join(crow))
UnicodeEncodeError: 'ascii' codec can't encode character '\xdf' in position 7: ordinal not in range(128)
```
Don't know about the inner workings of this test; if there are any open files involved, this might be relevant: https://github.com/scipy/scipy/issues/5694
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} | 7 | 2016-02-16T00:26:27Z | 2016-02-16T01:47:48Z | 2016-02-16T01:47:48Z | NONE | null | Getting these errors in the 0.18.0rc1 test suite on Mac OS X 10.10 (numpy 0.11.0b3):
with Python 2.7.11:
```
ERROR: test_constructor_compound_dtypes (pandas.tests.frame.test_block_internals.TestDataFrameBlockInternals)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tests/frame/test_block_internals.py", line 290, in test_constructor_compound_dtypes
f('M8[ns]')
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tests/frame/test_block_internals.py", line 276, in f
dtype=dtype)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 262, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5349, in _to_arrays
dtype=dtype)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5428, in _list_to_arrays
coerce_float=coerce_float)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5495, in _convert_object_array
arrays = [convert(arr) for arr in content]
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5492, in convert
arr = com._possibly_cast_to_datetime(arr, dtype)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/common.py", line 1675, in _possibly_cast_to_datetime
value = to_datetime(value, errors=errors)._values
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tseries/tools.py", line 280, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tseries/tools.py", line 407, in _to_datetime
return _convert_listlike(arg, box, format)
File "/scratch.noindex/fink.build/pandas-py27-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tseries/tools.py", line 381, in _convert_listlike
require_iso8601=require_iso8601
File "pandas/tslib.pyx", line 1956, in pandas.tslib.array_to_datetime (pandas/tslib.c:39318)
cpdef array_to_datetime(ndarray[object] values, errors='raise',
File "pandas/tslib.pyx", line 2145, in pandas.tslib.array_to_datetime (pandas/tslib.c:38913)
raise
File "pandas/tslib.pyx", line 2139, in pandas.tslib.array_to_datetime (pandas/tslib.c:38803)
oresult[i] = parse_datetime_string(val, dayfirst=dayfirst,
File "pandas/tslib.pyx", line 1728, in pandas.tslib.parse_datetime_string (pandas/tslib.c:31663)
dt = parse_date(date_string, default=_DEFAULT_DATETIME,
File "/sw/lib/python2.7/site-packages/dateutil/parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "/sw/lib/python2.7/site-packages/dateutil/parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
```
with Python 3.4.4 and 3.5.1:
```
ERROR: test_constructor_compound_dtypes (pandas.tests.frame.test_block_internals.TestDataFrameBlockInternals)
----------------------------------------------------------------------
Traceback (most recent call last):
File "pandas/tslib.pyx", line 2055, in pandas.tslib.array_to_datetime (pandas/tslib.c:37286)
_string_to_dts(val, &dts, &out_local, &out_tzoffset)
File "pandas/src/datetime.pxd", line 141, in datetime._string_to_dts (pandas/tslib.c:83021)
raise ValueError('Unable to parse %s' % str(val))
ValueError: Unable to parse b'aa'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas/tslib.pyx", line 2075, in pandas.tslib.array_to_datetime (pandas/tslib.c:37657)
py_dt = parse_datetime_string(val, dayfirst=dayfirst,
File "pandas/tslib.pyx", line 1728, in pandas.tslib.parse_datetime_string (pandas/tslib.c:31663)
dt = parse_date(date_string, default=_DEFAULT_DATETIME,
File "/sw/lib/python3.4/site-packages/dateutil/parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "/sw/lib/python3.4/site-packages/dateutil/parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas/tslib.pyx", line 2081, in pandas.tslib.array_to_datetime (pandas/tslib.c:37756)
raise TypeError("invalid string coercion to datetime")
TypeError: invalid string coercion to datetime
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tests/frame/test_block_internals.py", line 290, in test_constructor_compound_dtypes
f('M8[ns]')
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tests/frame/test_block_internals.py", line 276, in f
dtype=dtype)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 262, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5349, in _to_arrays
dtype=dtype)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5428, in _list_to_arrays
coerce_float=coerce_float)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5495, in _convert_object_array
arrays = [convert(arr) for arr in content]
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5495, in <listcomp>
arrays = [convert(arr) for arr in content]
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/frame.py", line 5492, in convert
arr = com._possibly_cast_to_datetime(arr, dtype)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/core/common.py", line 1675, in _possibly_cast_to_datetime
value = to_datetime(value, errors=errors)._values
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/util/decorators.py", line 91, in wrapper
return func(*args, **kwargs)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tseries/tools.py", line 280, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tseries/tools.py", line 407, in _to_datetime
return _convert_listlike(arg, box, format)
File "/scratch.noindex/fink.build/pandas-py34-0.18.0rc1-1/pandas-0.18.0rc1/pandas/tseries/tools.py", line 381, in _convert_listlike
require_iso8601=require_iso8601
File "pandas/tslib.pyx", line 1956, in pandas.tslib.array_to_datetime (pandas/tslib.c:39318)
cpdef array_to_datetime(ndarray[object] values, errors='raise',
File "pandas/tslib.pyx", line 2145, in pandas.tslib.array_to_datetime (pandas/tslib.c:38913)
raise
File "pandas/tslib.pyx", line 2139, in pandas.tslib.array_to_datetime (pandas/tslib.c:38803)
oresult[i] = parse_datetime_string(val, dayfirst=dayfirst,
File "pandas/tslib.pyx", line 1728, in pandas.tslib.parse_datetime_string (pandas/tslib.c:31663)
dt = parse_date(date_string, default=_DEFAULT_DATETIME,
File "/sw/lib/python3.4/site-packages/dateutil/parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "/sw/lib/python3.4/site-packages/dateutil/parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12340 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12340/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12340/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12340/events | https://github.com/pandas-dev/pandas/issues/12340 | 133,860,302 | MDU6SXNzdWUxMzM4NjAzMDI= | 12,340 | read_csv behavior with large floats | {
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| null | 3 | 2016-02-16T01:48:05Z | 2016-02-16T02:15:02Z | 2016-02-16T02:14:53Z | NONE | null | I have a data file in text which has 4 columns
```
$head sample.txt
SourceID Photons AvgX AvgY
6144039378640938.000000 71 1457.802817 2692.802817
6144053114071082.000000 8 1498.125000 2696.375000
6145000614093866.000000 0 nan nan
6145000614092842.000000 0 nan nan
6144137763642410.000000 0 nan nan
```
The 0th column really ought to have been int, but this is how the data file looks.
On trying to read it into a pandas, the [0, 0] element is read in as 6144039378640936.0.
The behavior is correct is numpy, and might be related to the floating point representation of this huge
float. However, checking the dtypes both numpy and pandas seem to be using the same type.
(The sample file and an ipython notebook is also in https://github.com/rbiswas4/pandasProblem/blob/master/checkproblem.ipynb)
```
python
>>> import pandas as pd
>>> df = pd.read_csv('sample.txt', sep='\s+')
>>> df.ix[0,0]
6144039378640936.0
>>>import numpy as np
>>> ddf = pd.read_csv('sample.txt', sep='\s+', dtype={'SourceID': np.int64})
>>> ddf.ix[0, 0]
6144039378640936.0
>>> arr = np.loadtxt('sample.txt', skiprows=1)
>>> arr[0,0]
6144039378640938.0
>>> df.dtypes
SourceID float64
Photons int64
AvgX float64
AvgY float64
dtype: object
>>> ddf.dtypes
SourceID int64
Photons int64
AvgX float64
AvgY float64
dtype: object
>>> arr.dtype
dtype('float64')
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.15.1
statsmodels: 0.5.0
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: 0.99999
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 14 | 2016-02-16T06:46:51Z | 2021-04-23T00:55:06Z | null | NONE | null | Just hoping to see if it might be possible to support categorical data in the fantastic parallel coordinates plot function.
I've uploaded an example script and dataset where the current code fails:
[Archive.zip](https://github.com/pydata/pandas/files/131996/Archive.zip)
Also, variable line widths that reflect frequency would also be highly appreciated. :)
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| null | 5 | 2016-02-16T07:17:22Z | 2016-02-16T14:40:53Z | 2016-02-16T14:40:53Z | CONTRIBUTOR | null | Closes #12261
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} | 2 | 2016-02-16T09:39:04Z | 2019-10-21T19:34:17Z | null | NONE | null | I came across some weird behavior when using assignment with a multi-index in Pandas 0.17.1
```
import pandas as pd
lvl0=range(2)
lvl1 = range(3)
index=pd.MultiIndex.from_product([lvl0,lvl1])
columns=['A','B']
df1=pd.DataFrame(data=1, columns=columns, index=index)
df2 = pd.DataFrame(data=2, index=lvl1, columns=['A'])
#this returns a dataframe without a multi index
print(df1.loc[0,df2.columns])
#but when you try an assignment it doesn't work
df1.loc[0,df2.columns]=df2
print(df1)
```
I debugged the pandas package a little bit and I think I know what is going. When you call `df1.loc[0,df2.columns]` it uses some getitem method. But, `df1.loc[0,df2.columns]=df2` uses some setitem method. When you set a break point in indexing.py in _NDFrameIndexer._get_setitem_indexer, then you see that the key that is being looked up in the index of the DataFrame is (0,['A']). Obviously, this key cannot be found, since A is a column. The result is that nothing is assigned.
I think this is a bug because there is inconsistent behaviour between setitem and getitem: when df1.loc[0,df2.columns] returns a DataFrame without a multindex (which isn't a copy) then assignment should be possible.
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} | 3 | 2016-02-16T10:36:18Z | 2016-03-06T15:28:42Z | 2016-03-06T15:28:42Z | NONE | null | ```
# Pandas version 0.17.1
import pandas as pd
df = pd.DataFrame({'Same': 1.0, ' Same': pd.np.nan, ' Same': pd.np.nan}, index=[0,1,2])
df.columns = [c.strip() for c in df.columns]
df.iloc[:, 2] # Returns all 1.0
df.iloc[:, 0] = df.iloc[:, 0].fillna(df.iloc[:, 1])
df.iloc[:, 2] # Column 2 is corrupted and returns all NaN
```
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} | 1 | 2016-02-16T10:59:35Z | 2016-02-17T11:21:10Z | 2016-02-16T14:08:19Z | NONE | null | I've made small documentation update for gbq features.
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| null | 1 | 2016-02-16T12:07:08Z | 2016-02-16T14:06:56Z | 2016-02-16T14:06:56Z | NONE | null | Comparing indexes returns two different data types. This is problematic since one of the types is a bool while the other is a Series which [explicitly can't be treated in a bool context](http://pandas.pydata.org/pandas-docs/stable/gotchas.html#using-if-truth-statements-with-pandas). Not to mention that the next section says `==` returns a Series object.
```
import pandas as pd
print pd.__version__
#0.14.0
index = pd.Int64Index([1, 2, 3])
print index == pd.Int64Index([])
# False
print index == pd.Int64Index([1])
# [True, False, False]
print index == pd.Int64Index([1, 2])
# False
print index == pd.Int64Index([4, 5, 6])
# [False, False, False]
series = pd.Series([1, 2, 3])
print series == pd.Series([1, 2])
# ValueError: Series lengths must match to compare
```
I'd suggest that comparing indexes with different lengths raise a `ValueError` like `Series` do. Possibly keeping the 1 element vs many as a special case
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| null | 1 | 2016-02-16T14:22:59Z | 2016-02-16T14:32:42Z | 2016-02-16T14:32:35Z | NONE | null | I have a data frame as below:
```
#user item rating time word_count review
3 2 4.0 131600 123 1 1 3 3 3 4 4 5 ..
......
```
The number in review denotes the word_id. When I apply the `to_csv` function:
```
df.to_csv("data.votes", sep = " ", cols = ['user','item','rating','time','word count','review'], header = False, index = False)
```
The resulting text file is like:
```
3 2 4.0 131600 123 "1 1 3 3 3 4 4 5... 101 "
```
How can I remove the annoying `"` ? It has a bad effect on running my model. Thank you in advance!
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} | 1 | 2016-02-16T15:07:01Z | 2016-02-16T15:24:58Z | 2016-02-16T15:15:22Z | NONE | null | Hi- I am observing odd behavior when trying to use resample on an uneven datetimeindex. See the below code to reproduce:
```
import pandas as pd
import numpy as np
bug_datetimeindex = pd.date_range('2013-12-30', '2014-01-07')
bug_datetimeindex = bug_datetimeindex.drop([ \
pd.Timestamp('2014-01-01'), \
pd.Timestamp('2013-12-31'), \
pd.Timestamp('2014-01-04'), \
pd.Timestamp('2014-01-05')
])
temp_df = pd.DataFrame(index=bug_datetimeindex, data=np.random.randn(len(bug_datetimeindex), 2))
temp_df.resample('B')
```
throws up a ValueError: Length mismatch: Expected axis has 5 elements, new values have 7 elements
Using asfreq() does not throw up this error on my machine.
I am running pd 0.17.1 on Python 3.4.4. off Anaconda for Windows.
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} | 0 | 2016-02-16T15:24:36Z | 2016-02-16T16:00:18Z | 2016-02-16T16:00:18Z | CONTRIBUTOR | null | xref #12348
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https://api.github.com/repos/pandas-dev/pandas/issues/12350 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12350/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12350/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12350/events | https://github.com/pandas-dev/pandas/pull/12350 | 134,010,158 | MDExOlB1bGxSZXF1ZXN0NTk1MDYxMjM= | 12,350 | docs: add hyperlinks to str.get_dummies | {
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} | 1 | 2016-02-16T15:25:49Z | 2016-02-17T01:09:19Z | 2016-02-17T01:09:18Z | CONTRIBUTOR | null | The description used in the summary table is the first sentence of
str_get_dummies()'s docstring, edited to match the style of other
summaries.
I tried to build this branch locally – the _Method Summary_ section doesn't render with this change and I can't see why, it looks syntactically correct to me…
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} | 8 | 2016-02-16T16:16:43Z | 2021-04-23T00:56:56Z | null | CONTRIBUTOR | null | I digged into a bit and found that `resample` by `BusinessHour` ran into here since the offset is neither string nor `Tick`:
https://github.com/pydata/pandas/blob/master/pandas/tseries/resample.py#L999
``` python
if not isinstance(offset, Tick): # and first.time() != last.time():
# hack!
first = first.normalize()
last = last.normalize()
```
`normalize` in `_get_range_edges` cut the time parts off from the offset so it causes the exception.
If I modify the line into following, the `resample` goes smoothly:
``` python
if not isinstance(offset, Tick) and not isinstance(offset, BusinessHour):
# hack!
first = first.normalize()
last = last.normalize()
```
If I made wrong approach, please point me out. I'll make a PR with test.
## Reproducible code
``` python
In [5]: df = pd.DataFrame(index=pd.date_range(
start='2016-02-11 00:00:00',
end='2016-02-12 23:00:00',
freq='H'))
In [6]: df.resample('BH').count()
```
## Traceback
```
In [6]: df.resample('BH').count()
> /Users/scari/codes/scari/pandas/pandas/tseries/resample.py(988)_get_range_edges()
-> if isinstance(offset, compat.string_types):
(Pdb) c
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-0ce6b0e15edc> in <module>()
----> 1 df.resample('BH').count()
/Users/scari/codes/scari/pandas/pandas/tseries/resample.py in f(self, _method)
457
458 def f(self, _method=method):
--> 459 return self._groupby_and_aggregate(None, _method)
460 f.__doc__ = getattr(GroupBy, method).__doc__
461 setattr(Resampler, method, f)
/Users/scari/codes/scari/pandas/pandas/tseries/resample.py in _groupby_and_aggregate(self, grouper, how, *args, **kwargs)
339
340 if grouper is None:
--> 341 self._set_binner()
342 grouper = self.grouper
343
/Users/scari/codes/scari/pandas/pandas/tseries/resample.py in _set_binner(self)
193
194 if self.binner is None:
--> 195 self.binner, self.grouper = self._get_binner()
196
197 def _get_binner(self):
/Users/scari/codes/scari/pandas/pandas/tseries/resample.py in _get_binner(self)
201 """
202
--> 203 binner, bins, binlabels = self._get_binner_for_time()
204 bin_grouper = BinGrouper(bins, binlabels)
205 return binner, bin_grouper
/Users/scari/codes/scari/pandas/pandas/tseries/resample.py in _get_binner_for_time(self)
476 if self.kind == 'period':
477 return self.groupby._get_time_period_bins(self.ax)
--> 478 return self.groupby._get_time_bins(self.ax)
479
480 def _downsample(self, how, **kwargs):
/Users/scari/codes/scari/pandas/pandas/tseries/resample.py in _get_time_bins(self, ax)
879 # general version, knowing nothing about relative frequencies
880 bins = lib.generate_bins_dt64(
--> 881 ax_values, bin_edges, self.closed, hasnans=ax.hasnans)
882
883 if self.closed == 'right':
/Users/scari/codes/scari/pandas/pandas/lib.pyx in pandas.lib.generate_bins_dt64 (pandas/lib.c:20883)()
1160
1161 if values[lenidx-1] > binner[lenbin-1]:
-> 1162 raise ValueError("Values falls after last bin")
1163
1164 bins = np.empty(lenbin - 1, dtype=np.int64)
ValueError: Values falls after last bin
```
## INSTALLED VERSIONS
commit: 8f1a31819f6b1fea1bfd25d973c28501ecaf5b3c
python: 3.4.4.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: ko_KR.UTF-8
LANG: ko_KR.UTF-8
pandas: 0.18.0rc1+8.g8f1a318.dirty
nose: 1.3.7
pip: 7.1.2
setuptools: 19.2
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
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} | 5 | 2016-02-16T16:34:52Z | 2016-02-17T13:22:18Z | 2016-02-17T13:22:18Z | NONE | null | This used to work in the past:
```
tmp = pd.DataFrame({
'ID': {pd.Timestamp('2015-06-05 00:00:00'): '0010100903', pd.Timestamp('2015-06-08 00:00:00'): '0010150847'},
'DATE': {pd.Timestamp('2015-06-05 00:00:00'): '2015-06-05', pd.Timestamp('2015-06-08 00:00:00'): '2015-06-08'}})
tmp.groupby(pd.TimeGrouper('D')).ID.nunique()
```
but now I get the obscure:
```
Traceback (most recent call last):
File "test.py", line 6, in <module>
tmp.groupby(pd.TimeGrouper('D')).ID.nunique()
File "/usr/lib/python3/dist-packages/pandas/core/groupby.py", line 2697, in nunique
name=self.name)
File "/usr/lib/python3/dist-packages/pandas/core/series.py", line 227, in __init__
data = SingleBlockManager(data, index, fastpath=True)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 3736, in __init__
ndim=1, fastpath=True)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 2454, in make_block
placement=placement)
File "/usr/lib/python3/dist-packages/pandas/core/internals.py", line 87, in __init__
len(self.values), len(self.mgr_locs)))
ValueError: Wrong number of items passed 2, placement implies 4
```
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} | 23 | 2016-02-16T17:01:56Z | 2020-04-10T03:33:18Z | null | CONTRIBUTOR | null | ``` python
In [1]: import xarray as xr
In [2]: import pandas as pd
In [3]: df = pd.DataFrame({'a': pd.np.random.rand(10), 'b': pd.np.random.rand(10)})
In [4]: df
Out[4]:
a b
0 0.711341 0.636954
1 0.199090 0.370938
2 0.486677 0.274427
3 0.407370 0.282419
4 0.760676 0.069163
5 0.098402 0.820085
6 0.710977 0.777998
7 0.687722 0.764163
8 0.297734 0.740927
9 0.554381 0.388324
In [5]: xr.Dataset(df)
Out[5]:
<xarray.Dataset>
Dimensions: (dim_0: 10)
Coordinates:
* dim_0 (dim_0) int64 0 1 2 3 4 5 6 7 8 9
Data variables:
a (dim_0) float64 0.7113 0.1991 0.4867 0.4074 0.7607 0.0984 0.711 ...
b (dim_0) float64 0.637 0.3709 0.2744 0.2824 0.06916 0.8201 0.778 ...
In [6]: pd.DataFrame(xr.Dataset(df))
---------------------------------------------------------------------------
PandasError Traceback (most recent call last)
<ipython-input-6-be1c7b096414> in <module>()
----> 1 pd.DataFrame(xr.Dataset(df))
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
303 copy=False)
304 else:
--> 305 raise PandasError('DataFrame constructor not properly called!')
306
307 NDFrame.__init__(self, mgr, fastpath=True)
PandasError: DataFrame constructor not properly called!
```
I _think_ because this looks for `dict` rather than 'dict-like' or `Mapping`: https://github.com/pydata/pandas/blob/master/pandas/core/frame.py#L222
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} | 13 | 2016-02-16T18:20:00Z | 2019-05-22T20:08:55Z | null | NONE | null | I'd like to be able to set the hatch styles (using a cycler) in plot(kind='area').
This would allow me to produce black&white plots correctly with more than two categories.
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| null | 4 | 2016-02-16T19:13:07Z | 2020-01-27T15:55:16Z | 2016-09-22T15:17:11Z | NONE | null | When using `pd.date_range` we work our way forwards from start to end. It would be nice to pass a flag or use the dates in reverse to go from end to start when creating the date_range.
``` python
#EX:
pd.date_range(end, start)
#or
pd.date_range(start, end, end_to_start=True)
```
``` python
Ex:
start = datetime.datetime(2016 ,2, 8)
end = datetime.datetime(2018 , 6, 1)
pd.date_range(start end, freq='6m', start_to_end=True)
>>> DatetimeIndex(['2016-02-08', '2016-06-01', '2016-12-01', '2017-06-01',
'2017-12-01', '2018-06-01'])
```
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} | 1 | 2016-02-16T20:06:40Z | 2016-03-23T11:57:39Z | 2016-03-23T11:57:39Z | CONTRIBUTOR | null | closes #12353
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| null | 3 | 2016-02-16T23:41:22Z | 2016-02-17T00:24:34Z | 2016-02-16T23:44:54Z | NONE | null | Having seen some inconsistent results from processing DataFrames - literally, running scripts over static data would generate different outcomes - we were able to boil down the following example:
``` python
def compare(df):
df1 = df.query('x < 2000')
df2 = df.query('x < 2000')
return df1.equals(df2)
```
If I understand correctly, calling `compare` should never return false and yet it does, randomly:
```
>>> df = pd.DataFrame({'x': range(100000), 'y': range(100000)})
>>> for i in xrange(1000):
... if not compare(df):
... print ('failed at iteration', i)
...
failed at iteration 10
failed at iteration 66
failed at iteration 80
failed at iteration 141
failed at iteration 149
...
failed at iteration 878
failed at iteration 999
```
This seems like a defect in query?
Relevant versions:
```
>python --version
Python 2.7.11 :: Anaconda 2.4.0 (64-bit)
>systeminfo | findstr /B /C:"OS Name" /C:"OS Version" /C:"System Type"
OS Name: Microsoft Windows 8.1 Pro
OS Version: 6.3.9600 N/A Build 9600
System Type: x64-based PC
```
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} | 9 | 2016-02-16T23:44:34Z | 2016-02-17T14:29:42Z | 2016-02-17T14:29:42Z | NONE | null | This is another issue I've found in code that used to work:
```
import pandas as pd
tm = pd.DatetimeIndex(pd.to_datetime(["2014-01-01 10:10:10"]), tz='UTC').tz_convert('Europe/Rome')
df = pd.DataFrame({'tm': tm})
df.set_index(df.tm, inplace=True)
print(df.tm[0].hour)
print(df.index[0].hour)
```
writes:
```
11
10
```
It's unclear to me why the time is shifted. If we take a pd.DatetimeIndex which is not directly contained in the df, it works as it should:
```
tm = pd.DatetimeIndex(pd.to_datetime(["2014-01-01 10:10:10"]), tz='UTC').tz_convert('Europe/Rome')
df = pd.DataFrame({'tm': tm})
df.set_index(tm, inplace=True)
print(df.tm[0].hour)
print(df.index[0].hour)
```
```
11
11
```
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} | 36 | 2016-02-16T23:55:01Z | 2016-03-18T16:06:36Z | 2016-03-18T00:01:28Z | CONTRIBUTOR | null | Addresses the case where 'compress' was not none. The old implementation
would decompress the data and then call np.frombuffer on a bytes
object. Because a bytes object is not a mutable buffer, the resulting
ndarray had writeable=False. The new implementation ensures that the
pandas is the only owner of this new buffer and then sets it to mutable
without copying it. This means that we don't need to do a copy of the
data coming in AND we can mutate it later. If we are not the only owner
of this data then we just copy it with np.fromstring.
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} | 0 | 2016-02-17T00:14:15Z | 2016-02-17T13:18:51Z | 2016-02-17T13:18:51Z | CONTRIBUTOR | null | closes #12303
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} | 18 | 2016-02-17T02:04:45Z | 2016-04-01T13:13:36Z | 2016-04-01T13:13:27Z | CONTRIBUTOR | null | The `BinGrouper.apply` (used by the `TimeGrouper`) and `BaseGrouper.apply` (used by the `Grouper`) have different output types. To make them consistent, remove `BinGrouper.apply` and let it use the same method as the superclass `BaseGrouper`. This requires changing `BinGrouper.groupings` to return an object which looks sufficiently like a `Grouping` object for `BaseGrouper.apply` to be happy. Use a namedtuple to create an object with the needed attributes.
I think this change will only affect the output of `apply` with a custom function, and only when grouping by a `TimeGrouper` (although a `Grouper` with a `freq` specified turns into a `TimeGrouper`, so that counts).
Closes #11742 .
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} | 1 | 2016-02-17T02:48:12Z | 2016-04-26T15:03:02Z | 2016-04-26T15:03:02Z | CONTRIBUTOR | null | I would think `[7]` and `[8]` would be identical. Note this is true for all ops I have looked at, so it is a general issue.
```
In [6]: df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'two',
'two', 'two', 'one', 'two'],
'C': np.random.randn(8) + 1.0,
'D': np.arange(8)})
In [7]: df.groupby(['A']).B.count()
Out[7]:
A
bar 3
foo 5
Name: B, dtype: int64
In [8]: df.B.groupby(df.A).count()
Out[8]:
A
bar 3
foo 5
dtype: int64
```
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| null | 4 | 2016-02-17T15:52:09Z | 2016-02-18T13:45:46Z | 2016-02-18T13:22:04Z | NONE | null | `datetime.time` objects are useful as index values, e.g. in a data frame
containing data of an experiment lasting a few minutes or hours. When
plotting such data frame, the x-axis ticks are formatted as `'%H:%M:%S'`
as standard. In most cases, either `'%H:%M'` or `'%M:%S'` contains enough
information, and the `'%H:%M:%S'` format is superfluous.
The xticks were formatted based on the fmt variable in the `__call__` method. In this commit, the tick format is saved in the `TimeFormatter.fmt`
variable. This is set in the `__init__` method to `'%H:%M:%S'`, but can
later be changed, e.g.:
```
ax = df.plot()
ax.xaxis.get_major_formatter().fmt = '%M:%S'
```
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} | 3 | 2016-02-17T16:36:01Z | 2016-02-18T13:15:13Z | 2016-02-18T13:15:08Z | CONTRIBUTOR | null | closes #12336
Changed the tests to match the other ones in `test_fromValue`.
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| null | 4 | 2016-02-17T16:58:21Z | 2016-02-22T08:20:35Z | 2016-02-17T17:03:01Z | MEMBER | null | Sorry in advance if this was already reported - I searched a bit but didn't know exactly what to search.
```
In [2]: df = pd.DataFrame(0, index=range(1), columns=pd.MultiIndex.from_product([['a', 'b'], ['c']]))
In [3]: cols = [('a',), ('b',)]
In [4]: df[cols[0]]
Out[4]:
c
0 0
In [5]: df[cols[1]]
Out[5]:
c
0 0
In [6]: df[cols]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-6-957cf8a677a9> in <module>()
----> 1 df[cols]
/home/pietro/nobackup/repo/pandas/pandas/core/frame.pyc in __getitem__(self, key)
1975 if isinstance(key, (Series, np.ndarray, Index, list)):
1976 # either boolean or fancy integer index
-> 1977 return self._getitem_array(key)
1978 elif isinstance(key, DataFrame):
1979 return self._getitem_frame(key)
/home/pietro/nobackup/repo/pandas/pandas/core/frame.pyc in _getitem_array(self, key)
2019 return self.take(indexer, axis=0, convert=False)
2020 else:
-> 2021 indexer = self.ix._convert_to_indexer(key, axis=1)
2022 return self.take(indexer, axis=1, convert=True)
2023
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
1203 mask = check == -1
1204 if mask.any():
-> 1205 raise KeyError('%s not in index' % objarr[mask])
1206
1207 return _values_from_object(indexer)
KeyError: "[('a',) ('b',)] not in index"
```
Notice that `df[[c[0] for c in cols]]` works just fine... but still we probably want to consider as valid a list of valid keys.
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} | 1 | 2016-02-17T22:27:30Z | 2019-06-28T16:15:02Z | 2019-06-28T16:15:02Z | CONTRIBUTOR | null | xref: https://github.com/pydata/pandas/pull/12370
This should be done inside `index.pyx`
if the lookup fails and if its a non-float index, then coerce to int, and if its the same
lookup again.
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```
(py3.5)[Tue Apr 05 08:16:18 ~/miniconda/envs/py3.5/pandas]$ ipython
Python 3.5.1 |Continuum Analytics, Inc.| (default, Dec 7 2015, 11:24:55)
In [1]: pd.Series.sum.__qualname__
Out[1]: 'Series.sum'
In [2]: pd.Series.cumsum.__qualname__
Out[2]: 'Series.cumsum'
In [3]: pd.Series.cumsum.__module__
Out[3]: 'pandas.core.series'
In [4]: pd.Series.cumsum.__name__
Out[4]: 'cumsum'
```
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} | 1 | 2016-02-17T23:00:05Z | 2016-03-06T00:45:44Z | 2016-03-06T00:45:44Z | CONTRIBUTOR | null | pd version: v0.18.0rc1
minimal example:
```
import pandas as pd
import numpy as np
pd.DataFrame(np.arange(5, dtype=np.float32)).rolling(window=3).max()
```
stack trace:
```
<ipython-input-25-795ba6485799> in <module>()
----> 1 pd.DataFrame(np.arange(5, dtype=np.float32)).rolling(window=3).max()
/venv/lib/python3.4/site-packages/pandas/core/window.py in max(self, **kwargs)
767 @Appender(_shared_docs['max'])
768 def max(self, **kwargs):
--> 769 return super(Rolling, self).max(**kwargs)
770
771 @Substitution(name='rolling')
/venv/lib/python3.4/site-packages/pandas/core/window.py in max(self, how, **kwargs)
541 if self.freq is not None and how is None:
542 how = 'max'
--> 543 return self._apply('roll_max', how=how, **kwargs)
544
545 _shared_docs['min'] = dedent("""
/venv/lib/python3.4/site-packages/pandas/core/window.py in _apply(self, func, window, center, check_minp, how, **kwargs)
474
475 if values.ndim > 1:
--> 476 result = np.apply_along_axis(calc, self.axis, values)
477 else:
478 result = calc(values)
/venv/lib/python3.4/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
89 outshape = asarray(arr.shape).take(indlist)
90 i.put(indlist, ind)
---> 91 res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
92 # if res is a number, then we have a smaller output array
93 if isscalar(res):
/venv/lib/python3.4/site-packages/pandas/core/window.py in calc(x)
471
472 def calc(x):
--> 473 return func(x, window, min_periods=self.min_periods)
474
475 if values.ndim > 1:
/venv/lib/python3.4/site-packages/pandas/core/window.py in func(arg, window, min_periods)
458 def func(arg, window, min_periods=None):
459 minp = check_minp(min_periods, window)
--> 460 return cfunc(arg, window, minp, **kwargs)
461
462 # calculation function
pandas/algos.pyx in pandas.algos.roll_max (pandas/algos.c:37363)()
ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'
```
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| null | 5 | 2016-02-17T23:49:54Z | 2021-04-23T00:58:31Z | null | CONTRIBUTOR | null | I find myself running into a situation where I don't want to see small numbers as scientific notation fairly frequently, things like:
```
In [3]: pd.set_option('display.precision', 2)
In [4]: pd.DataFrame(np.random.randn(5, 5)).corr()
Out[4]:
0 1 2 3 4
0 1.00 -0.57 2.15e-02 -3.48e-02 -0.64
1 -0.57 1.00 2.59e-01 -5.56e-01 0.51
2 0.02 0.26 1.00e+00 2.91e-03 -0.06
3 -0.03 -0.56 2.91e-03 1.00e+00 0.36
4 -0.64 0.51 -6.21e-02 3.63e-01 1.00
```
or
```
In [16]: pd.Series(np.random.poisson(size=1000)).value_counts(normalize=True)
Out[16]:
0 3.80e-01
1 3.63e-01
2 1.75e-01
3 5.70e-02
4 1.80e-02
5 5.00e-03
7 1.00e-03
6 1.00e-03
dtype: float64
```
Scientific notation isn't helpful when you are trying to make quick comparisons across elements, and have a well-defined notion of a -1 to 1 or 0 to 1 range.
I propose adding some sort of display flag to suppress scientific notation on small numbers, and just report zeros in these cases instead. Alternatively we could also suppress it on large numbers, but I am not sure how helpful that is. I usually only find myself going up against it on small numbers, in exactly the use cases (correlations, proportions) above.
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} | 6 | 2016-02-18T01:54:17Z | 2017-02-17T13:41:15Z | 2017-02-17T13:41:08Z | CONTRIBUTOR | null | ## Pandas Issue Submission Template
This template will help you and us respond to your issues in an efficient and timely manner.
Please fill out all sections and use [github markdown for code blocks](https://help.github.com/articles/basic-writing-and-formatting-syntax/)
#### Actual Behavior
#### Expected Behavior
#### output of `pd.show_versions()`
so this is what the new ISSUE_TEMPLATE looks like its in `.github/ISSUE_TEMPLATE.md`. Furthermore, now the CONTRIBUTING.md shows up when you create a new issue.
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} | 23 | 2016-02-18T04:52:09Z | 2016-03-06T01:11:31Z | 2016-03-06T00:45:44Z | CONTRIBUTOR | null | - [x] closes #12373
- [x] tests added
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry added
- [x] add test for various other dtypes
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} | 1 | 2016-02-18T06:25:04Z | 2021-04-23T00:59:22Z | null | CONTRIBUTOR | null | [52–53-week fiscal year](https://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar#52.E2.80.9353-week_fiscal_year) are designed to end on the same day of the week each quarter / year. Therefore it should be possible to upsample / downsample to/from weeks. I made an initial attempt at this in https://github.com/pydata/pandas/pull/5148 but that PR was never merged. This logic still makes sense.
/CC @fdion
Currently attempts to do so yield:
``` python
import pandas as pd
from pandas.tseries.offsets import FY5253Quarter, Week, WeekDay
from pandas.tseries.period import Period, PeriodIndex, period_range
from datetime import datetime
offset_fyq = FY5253Quarter(weekday=WeekDay.SAT, startingMonth=12,
variation="last", qtr_with_extra_week=4)
freq_week = Week(weekday=WeekDay.SAT)
prange = period_range(datetime(2013, 1, 5),
periods=2 * 13,
freq=freq_week)
df = pd.DataFrame({"A": [1] * 13 + [2] * 13}, index=prange)
resampled = df.resample(offset_fyq, fill_method="mean")
```
```
ValueError Traceback (most recent call last)
<ipython-input-8-38fa44432b0b> in <module>()
13
14 df = pd.DataFrame({"A": [1] * 13 + [2] * 13}, index=prange)
---> 15 resampled = df.resample(offset_fyq, fill_method="mean")
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
3641 fill_method=fill_method, convention=convention,
3642 limit=limit, base=base)
-> 3643 return sampler.resample(self).__finalize__(self)
3644
3645 def first(self, offset):
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/tseries/resample.pyc in resample(self, obj)
90
91 if self.kind is None or self.kind == 'period':
---> 92 rs = self._resample_periods()
93 else:
94 obj = self.obj.to_timestamp(how=self.convention)
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/tseries/resample.pyc in _resample_periods(self)
340 return obj.reindex(new_index)
341 else:
--> 342 start = axlabels[0].asfreq(self.freq, how=self.convention)
343 end = axlabels[-1].asfreq(self.freq, how='end')
344
pandas/src/period.pyx in pandas._period.Period.asfreq (pandas/src/period.c:14801)()
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in get_freq_code(freqstr)
253 # e.g., freqstr = ('T', 5)
254 try:
--> 255 code = _period_str_to_code(freqstr[0])
256 stride = freqstr[1]
257 except:
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in _period_str_to_code(freqstr)
809 FutureWarning, stacklevel=3)
810 except KeyError:
--> 811 raise ValueError("Unknown freqstr: %s" % freqstr)
812
813 return _period_code_map[alias]
ValueError: Unknown freqstr: REQ-L-DEC-SAT-4
```
Also should be able to downsample to Day and upsample to FY5254
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| null | 1 | 2016-02-18T06:40:36Z | 2016-02-18T13:07:44Z | 2016-02-18T13:07:44Z | CONTRIBUTOR | null | Reposted from: https://github.com/pydata/pandas/pull/5148/files#r10454189
Currently you get the following behavior because there is not a canonicalized offset name:
```
In [10]: get_offset("QS-JAN") == get_offset("QS")
Out[10]: True
In [29]: get_offset("QS-JAN").name == get_offset("QS").name
Out[29]: False
n [31]: get_offset("QS-JAN") is get_offset("QS")
Out[31]: False
```
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} | 3 | 2016-02-18T06:46:51Z | 2021-04-23T01:02:03Z | null | CONTRIBUTOR | null | ``` python
from pandas.tseries.offsets import FY5253Quarter
offset_fyq = FY5253Quarter(weekday=6, startingMonth=12,
variation="last", qtr_with_extra_week=4)
str(pd.Period("2011-1-2", freq=offset_fyq))
```
```
ValueError Traceback (most recent call last)
<ipython-input-1-b92e5a634df4> in <module>()
5 variation="last", qtr_with_extra_week=4)
6
----> 7 str(Period("2011-1-2", freq=offset_fyq))
pandas/src/period.pyx in pandas._period.Period.__init__ (pandas/src/period.c:12139)()
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in get_freq_code(freqstr)
253 # e.g., freqstr = ('T', 5)
254 try:
--> 255 code = _period_str_to_code(freqstr[0])
256 stride = freqstr[1]
257 except:
/Users/alex/.virtualenvs/work/lib/python2.7/site-packages/pandas/tseries/frequencies.pyc in _period_str_to_code(freqstr)
809 FutureWarning, stacklevel=3)
810 except KeyError:
--> 811 raise ValueError("Unknown freqstr: %s" % freqstr)
812
813 return _period_code_map[alias]
ValueError: Unknown freqstr: REQ-L-DEC-SAT-4
```
Expected: `'2010Q4'`.
See for more tests: https://github.com/pydata/pandas/pull/5148/files#diff-f9b276c4aa39a6161726d8b43ce62516R2420
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} | 3 | 2016-02-18T08:12:17Z | 2021-01-27T15:47:43Z | 2021-01-27T15:47:43Z | CONTRIBUTOR | null | ``` python
import pandas as pd
pd.set_option('max_rows', 10)
import sklearn.datasets as datasets
import pandas_ml as pdml # https://github.com/pandas-ml/pandas-ml
import matplotlib.pyplot as plt
df = pdml.ModelFrame(datasets.load_iris())
print(df)
.target sepal length (cm) sepal width (cm) petal length (cm) \
0 0 5.1 3.5 1.4
1 0 4.9 3.0 1.4
2 0 4.7 3.2 1.3
3 0 4.6 3.1 1.5
4 0 5.0 3.6 1.4
.. ... ... ... ...
145 2 6.7 3.0 5.2
146 2 6.3 2.5 5.0
147 2 6.5 3.0 5.2
148 2 6.2 3.4 5.4
149 2 5.9 3.0 5.1
petal width (cm)
0 0.2
1 0.2
2 0.2
3 0.2
4 0.2
.. ...
145 2.3
146 1.9
147 2.0
148 2.3
149 1.8
[150 rows x 5 columns]
print(df.dtypes)
.target int64
sepal length (cm) float64
sepal width (cm) float64
petal length (cm) float64
petal width (cm) float64
dtype: object
df.plot.scatter(x='sepal length (cm)', y='sepal width (cm)', c='.target')
plt.show()
```
#### Expected Output
A scatter plot with a colobar with a discrete colormap
#### Additional information
Converting `.target` column to a category doesn't fix the problem.
Maybe I should open an other issue about it.
``` python
df['.target'] = df['.target'].astype('category')
df.plot.scatter(x='sepal length (cm)', y='sepal width (cm)', c='.target')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgb(self, arg)
305 else:
--> 306 fl = float(argl)
307 if fl < 0 or fl > 1:
ValueError: could not convert string to float: '.target'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgba(self, arg, alpha)
369 else:
--> 370 r, g, b = self.to_rgb(arg)
371 if alpha is None:
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgb(self, arg)
327 raise ValueError(
--> 328 'to_rgb: Invalid rgb arg "%s"\n%s' % (str(arg), exc))
329 # Error messages could be improved by handling TypeError
ValueError: to_rgb: Invalid rgb arg ".target"
could not convert string to float: '.target'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgba_array(self, c, alpha)
398 # Single value? Put it in an array with a single row.
--> 399 return np.array([self.to_rgba(c, alpha)], dtype=np.float)
400 except ValueError:
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgba(self, arg, alpha)
375 raise ValueError(
--> 376 'to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
377
ValueError: to_rgba: Invalid rgba arg ".target"
to_rgb: Invalid rgb arg ".target"
could not convert string to float: '.target'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgb(self, arg)
305 else:
--> 306 fl = float(argl)
307 if fl < 0 or fl > 1:
ValueError: could not convert string to float: '.'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgba(self, arg, alpha)
369 else:
--> 370 r, g, b = self.to_rgb(arg)
371 if alpha is None:
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgb(self, arg)
327 raise ValueError(
--> 328 'to_rgb: Invalid rgb arg "%s"\n%s' % (str(arg), exc))
329 # Error messages could be improved by handling TypeError
ValueError: to_rgb: Invalid rgb arg "."
could not convert string to float: '.'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-8-19145e105567> in <module>()
----> 1 df.plot.scatter(x='sepal length (cm)', y='sepal width (cm)', c='.target')
//anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in scatter(self, x, y, s, c, **kwds)
3847 axes : matplotlib.AxesSubplot or np.array of them
3848 """
-> 3849 return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds)
3850
3851 def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
//anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
3669 fontsize=fontsize, colormap=colormap, table=table,
3670 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 3671 sort_columns=sort_columns, **kwds)
3672 __call__.__doc__ = plot_frame.__doc__
3673
//anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2554 yerr=yerr, xerr=xerr,
2555 secondary_y=secondary_y, sort_columns=sort_columns,
-> 2556 **kwds)
2557
2558
//anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2382 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2383
-> 2384 plot_obj.generate()
2385 plot_obj.draw()
2386 return plot_obj.result
//anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in generate(self)
985 self._compute_plot_data()
986 self._setup_subplots()
--> 987 self._make_plot()
988 self._add_table()
989 self._make_legend()
//anaconda/lib/python3.5/site-packages/pandas/tools/plotting.py in _make_plot(self)
1557 label = None
1558 scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
-> 1559 label=label, cmap=cmap, **self.kwds)
1560 if cb:
1561 img = ax.collections[0]
//anaconda/lib/python3.5/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1810 warnings.warn(msg % (label_namer, func.__name__),
1811 RuntimeWarning, stacklevel=2)
-> 1812 return func(ax, *args, **kwargs)
1813 pre_doc = inner.__doc__
1814 if pre_doc is None:
//anaconda/lib/python3.5/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)
3891 offsets=offsets,
3892 transOffset=kwargs.pop('transform', self.transData),
-> 3893 alpha=alpha
3894 )
3895 collection.set_transform(mtransforms.IdentityTransform())
//anaconda/lib/python3.5/site-packages/matplotlib/collections.py in __init__(self, paths, sizes, **kwargs)
829 """
830
--> 831 Collection.__init__(self, **kwargs)
832 self.set_paths(paths)
833 self.set_sizes(sizes)
//anaconda/lib/python3.5/site-packages/matplotlib/collections.py in __init__(self, edgecolors, facecolors, linewidths, linestyles, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)
115
116 self.set_edgecolor(edgecolors)
--> 117 self.set_facecolor(facecolors)
118 self.set_linewidth(linewidths)
119 self.set_linestyle(linestyles)
//anaconda/lib/python3.5/site-packages/matplotlib/collections.py in set_facecolor(self, c)
610 c = mpl.rcParams['patch.facecolor']
611 self._facecolors_original = c
--> 612 self._facecolors = mcolors.colorConverter.to_rgba_array(c, self._alpha)
613 self.stale = True
614
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgba_array(self, c, alpha)
420 result = np.zeros((nc, 4), dtype=np.float)
421 for i, cc in enumerate(c):
--> 422 result[i] = self.to_rgba(cc, alpha)
423 return result
424
//anaconda/lib/python3.5/site-packages/matplotlib/colors.py in to_rgba(self, arg, alpha)
374 except (TypeError, ValueError) as exc:
375 raise ValueError(
--> 376 'to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc))
377
378 def to_rgba_array(self, c, alpha=None):
ValueError: to_rgba: Invalid rgba arg "."
to_rgb: Invalid rgb arg "."
could not convert string to float: '.'
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: 2.8
```
Pinging @sinhrks
See also http://stanford.edu/~mwaskom/software/seaborn/examples/scatterplot_matrix.html
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| null | 11 | 2016-02-18T09:15:40Z | 2016-02-18T16:55:52Z | 2016-02-18T12:59:05Z | NONE | null | there are a couple of questions on this on Stackoverflow:
http://stackoverflow.com/questions/35465534/how-do-i-save-the-entire-workspace-in-pandas-like-rdata
http://stackoverflow.com/questions/12504951/save-session-in-ipython-like-in-matlab
Even the individual dataframe saving is a bit confusing - http://stackoverflow.com/questions/17098654/how-to-store-data-frame-using-pandas-python
From IRC, the following comments were made
- You can use hdf5, but in this case, always roundtrip it before using it, even when it was just computed -- unfortunately, the to_hdf/read_hdf won't necessarily give you the dataframe you started with
- pickle is undebuggable, non-version-compatible, Python-only, insecure-to-unserliazed, and potentially-incorrect-given-the-right-data
The recent news that Pandas is collaborating with Apache Arrow is encouraging - but what is going to be the long term official way for persisting the workspace ?
A lot of workflows come from R, where the entire workspace is snapshotted (and backed up to s3,etc) . This is perfectly production ready in R - but in Pandas a lot of creative invention is needed to make this work (for example - http://cyrille.rossant.net/moving-away-hdf5/).
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https://api.github.com/repos/pandas-dev/pandas/issues/12382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12382/events | https://github.com/pandas-dev/pandas/issues/12382 | 134,540,744 | MDU6SXNzdWUxMzQ1NDA3NDQ= | 12,382 | apply() silently removes time zone information from DateTime values | {
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| null | 2 | 2016-02-18T10:35:01Z | 2016-02-18T12:48:30Z | 2016-02-18T12:48:30Z | NONE | null | Hi,
apply() silently removes time zone information from DateTime values. I think this bug is related to https://github.com/pydata/pandas/issues/10668, but it is apparently not the same.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
date_range = pd.date_range('1/1/2011', periods=5, freq='H')
date_range = date_range.tz_localize('Europe/Budapest')
s = pd.Series(date_range, index=date_range)
s
```
```
2011-01-01 00:00:00+01:00 2011-01-01 00:00:00+01:00
2011-01-01 01:00:00+01:00 2011-01-01 01:00:00+01:00
2011-01-01 02:00:00+01:00 2011-01-01 02:00:00+01:00
2011-01-01 03:00:00+01:00 2011-01-01 03:00:00+01:00
2011-01-01 04:00:00+01:00 2011-01-01 04:00:00+01:00
Freq: H, dtype: datetime64[ns, Europe/Budapest]
```
Create a simple apply function which does nothing but returns each value. Notice that that values are converted to UTC by default and the timezone information is lost, and a timezone-naive value is returned instead.
``` python
def apply_function(dt):
return dt
s.apply(apply_function)
```
```
2011-01-01 00:00:00+01:00 2010-12-31 23:00:00
2011-01-01 01:00:00+01:00 2011-01-01 00:00:00
2011-01-01 02:00:00+01:00 2011-01-01 01:00:00
2011-01-01 03:00:00+01:00 2011-01-01 02:00:00
2011-01-01 04:00:00+01:00 2011-01-01 03:00:00
Freq: H, dtype: datetime64[ns]
```
#### Expected Output
Apply is expected to return the original series, values unchanged, including the time zone information.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-27-generic
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 20.1.1
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.1.1
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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| null | 2 | 2016-02-18T17:53:31Z | 2016-02-18T18:05:29Z | 2016-02-18T18:02:09Z | NONE | null | When ran on a pd.Series np.nanmax returns Series, while `np.nanmean` and `np.max` return a scalar:
#### Code Sample, a copy-pastable example if possible
```
li = [1,2,3,4,5]
ds = pd.Series(li, index= ["%s"%x for x in li])
# these returns a scalar
print(np.nanmean(ds))
print(np.max(ds))
#this returns a Series
print(np.nanmax(ds))
```
#### Expected Output
a scalar
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-73-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1+306.g8cdfd4d
nose: 1.3.7
pip: 8.0.2
setuptools: 20.0
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.7.3
```
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} | 5 | 2016-02-18T21:25:40Z | 2016-02-18T22:01:41Z | 2016-02-18T21:42:50Z | CONTRIBUTOR | null | A hidden code snippet uses `tm.choice()` when it should use `np.random.choice()`
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| null | 1 | 2016-02-18T21:31:40Z | 2016-02-18T21:55:16Z | 2016-02-18T21:55:07Z | NONE | null | Fixed typo (replaced tm with np.random as there is no tm module in this doc) so that the choice method at line 220 will work. @ellisonbg
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} | 4 | 2016-02-18T21:54:16Z | 2016-03-02T12:46:00Z | 2016-03-02T12:46:00Z | CONTRIBUTOR | null | xref #12384
so I think this was for numpy < 1.7 compat.
we should remove `pandas.util.testing.choice`
https://github.com/pydata/pandas/blob/master/pandas/util/testing.py#L173
there might be some latent doc references
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| null | 9 | 2016-02-19T00:44:06Z | 2021-06-22T13:24:33Z | 2016-02-19T01:13:41Z | CONTRIBUTOR | null | `DataFrame.from_dict` does not seem to behave [according to the documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_dict.html).
```
import pandas as pd
print("pandas.from_dict(<dict>, orient='index')")
print(pd.DataFrame.from_dict(dict([ ['key1', 1],['key2',2] ]), orient='index'))
print("pandas.from_dict(<list [<list>]>, orient='columns')")
print(pd.DataFrame.from_dict([ ['key1', 1],['key2',2] ], orient='columns'))
print("pandas.from_dict(<dict>, orient='columns')")
print(pd.DataFrame.from_dict(dict([ ['key1', 1],['key2',2] ]), orient='columns'))
```
#### Produces
```
pandas.from_dict(<dict>, orient='index')
0
key2 2
key1 1
pandas.from_dict(<list [<list>]>, orient='columns')
0 1
0 key1 1
1 key2 2
pandas.from_dict(<dict>, orient='columns')
Traceback (most recent call last):
File "../../bin/pandaserr.py", line 10, in <module>
print(pd.DataFrame.from_dict(dict([ ['key1', 1],['key2',2] ]), orient='columns'))
File "/home/cwarth/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 804, in from_dict
return cls(data, index=index, columns=columns, dtype=dtype)
File "/home/cwarth/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 226, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "/home/cwarth/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 363, in _init_dict
dtype=dtype)
File "/home/cwarth/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 5158, in _arrays_to_mgr
index = extract_index(arrays)
File "/home/cwarth/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 5197, in extract_index
raise ValueError('If using all scalar values, you must pass'
ValueError: If using all scalar values, you must pass an index
```
According to the reference documentation, "If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’" That doesn't appear to be what is happening.
#### Expected Output
`from_dict(<dict>, orient='index')` works as expected.
I would expect `from_dict(..., orient='columns')` to return a dataframe with the dictionary keys forming the column index, like this:
```
key1 key2
0 1 2
```
I would expect `from_dict()` to take a dict as a parameter in either case. Instead it appears to take a a dict for `orient='index'` and a list of lists (or tuples) for `orient='columns'`. Passing a dict with integer values when `orient='columns'` causes a crash.
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.6
pip: 8.0.2
setuptools: 18.8
Cython: 0.23.2
numpy: 1.10.4
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.5.0
openpyxl: 2.2.0-b1
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.3
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 13 | 2016-02-19T01:44:40Z | 2016-05-30T14:39:34Z | 2016-05-30T14:39:34Z | CONTRIBUTOR | null | The current behavior is inconsistent with normal python division of two `DataFrame`s (see code sample).
Pandas upcasts both terms to 64-bit floats when it detects a division, see:
https://github.com/pydata/pandas/blob/528108bba4104b939bcfe6923677ddacc916ff00/pandas/computation/ops.py#L453
I think numexpr can handle different types too, and upcast automatically, though I am not 100% sure. I can submit a PR, but how do you recommend fixing this? Something like the following?
```
if truediv or PY3:
for term in com.flatten(self):
try:
dt = term.values.dtype # can .values be expensive?
except AttributeError:
dt = type(term)
if dt == np.float32:
continue
else:
_cast_inplace([term], np.float_)
```
The downside is that if someone does `2 + df`, they'll probably still end up upcasting it. But this proposal is still better than what we have today
I might re-write the above using `filter` too, but at this time I just wanted to discuss the general approach
#### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(3, dtype=np.float32))
print('normal', (df/df).values.dtype)
print('pd_eval', pd.eval('df/df').values.dtype)
assert ((df/df).dtypes == pd.eval('df/df').dtypes).all()
```
#### Expected Output
```
normal float32
pd_eval float32
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: 1.0.9
pymysql: 0.6.7.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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} | 1 | 2016-02-19T02:25:02Z | 2016-02-19T13:06:42Z | 2016-02-19T13:06:42Z | CONTRIBUTOR | null | numpy >= 1.11failing on these
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} | 1 | 2016-02-19T02:47:06Z | 2016-02-21T15:15:56Z | 2016-02-21T15:15:56Z | CONTRIBUTOR | null | `1.12.0.dev0+f4cc58c (good)`
`1.12.0.dev0+a2f5392 (bad)`
https://travis-ci.org/pydata/pandas/jobs/110022540 (bad)
skipping with #12389
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} | 5 | 2016-02-19T03:36:30Z | 2016-02-21T00:41:34Z | 2016-02-20T18:27:34Z | MEMBER | null | Title is self-explanatory. Closes #11934.
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} | 44 | 2016-02-19T04:12:45Z | 2017-10-13T08:46:27Z | 2017-10-13T08:46:22Z | CONTRIBUTOR | null | `rename` accepts a `columns` argument or an `index` argument, while `drop` looks for a `labels` and `axis` pair. I don't know about anyone else, but I have to check the help file every time I come back to pandas to remember which takes which.
How would people feel about adding `columns` and `index` arguments to `drop`? They could just be added in addition to `labels`/`axis` if we want to provide backwards compatibility and just raise an exception if the user tries to mix them.
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} | 16 | 2016-02-19T11:53:45Z | 2016-02-26T02:14:52Z | 2016-02-26T02:13:43Z | NONE | null | This is as much a documentation issue as anything else. Basically it seems confusing that df.empty != df.dropna().empty I.e. that a a dataframe consiting entirely of na is not treated as empty. Obviously this is a bit of an edge case, but it caused a bunch of failures for me when used eith pd.read_sql methods, as database tables will often have columns that are not available for partiicular entities, and so can return an entire series of na.
It seems to me that in all cases df.empty should be the same as df.dropna().empty, but I understand that opinions might differ on this point, but at least the behaviour should be clearly documented.
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} | 4 | 2016-02-19T13:47:05Z | 2016-02-19T14:32:23Z | 2016-02-19T14:32:23Z | CONTRIBUTOR | null | I tried to produce a stand-alone example but couldn't so far. Maybe the answer is already apparent though. This started happening just recently (maybe after updated pandas to 0.17.1) but worked well before. I'm doing a multi-column `agg` in a groupby. One of the columns is a datetime of which I want the first element:
```
(Pdb) t.groupby(['block_dir']).first()
dt sid amount price symbol \
block_dir
1 2003-01-02 15:56:00+00:00 21719 -62 0.963811 AIRN
```
Works fine, however:
```
order_sign block_time
block_dir
1 False 0
(Pdb) t.groupby(['block_dir']).agg({'dt': 'first'})
dt
block_dir
1 1041522960000000000
```
#### Expected Output
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
Jinja2: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/12395 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12395/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12395/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12395/events | https://github.com/pandas-dev/pandas/pull/12395 | 134,943,936 | MDExOlB1bGxSZXF1ZXN0NTk5ODk4MDk= | 12,395 | Doc/df filter | {
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| null | 10 | 2016-02-19T18:44:32Z | 2016-02-20T18:19:05Z | 2016-02-19T23:59:24Z | CONTRIBUTOR | null | Updates doc comments for `DataFrame.filter` and adds usage examples.
<dl class="method">
<dt id="pandas.DataFrame.filter">
<code class="descclassname">DataFrame.</code><code class="descname">filter</code><span class="sig-paren">(</span><em>items=None</em>, <em>like=None</em>, <em>regex=None</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pandas.DataFrame.filter" title="Permalink to this definition">¶</a></dt>
<dd><p>Subset rows or columns of dataframe according to labels in the index.</p>
<p>Note that this routine does not filter a dataframe on its contents. The filter is
applied to the labels of the index.
This method is a thin veneer on top of <span class="xref std std-ref">DateFrame Select</span></p>
<table class="docutils field-list" frame="void" rules="none">
<colgroup><col class="field-name">
<col class="field-body">
</colgroup><tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>items</strong> : list-like</p>
<blockquote>
<div><p>List of info axis to restrict to (must not all be present)</p>
</div></blockquote>
<p><strong>like</strong> : string</p>
<blockquote>
<div><p>Keep info axis where “arg in col == True”</p>
</div></blockquote>
<p><strong>regex</strong> : string (regular expression)</p>
<blockquote>
<div><p>Keep info axis with re.search(regex, col) == True</p>
</div></blockquote>
<p><strong>axis</strong> : int or None</p>
<blockquote>
<div><p>The axis to filter on.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">same type as input object with filtered info axis</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>The <code class="docutils literal"><span class="pre">items</span></code>, <code class="docutils literal"><span class="pre">like</span></code>, and <code class="docutils literal"><span class="pre">regex</span></code> parameters should be mutually exclusive, but this is not checked.</p>
<p><code class="docutils literal"><span class="pre">axis</span></code> defaults to the info axis that is used when indexing with <code class="docutils literal"><span class="pre">[]</span></code>.</p>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">df</span>
<span class="go"> one two three</span>
<span class="go">mouse 1 2 3</span>
<span class="go">rabbit 4 5 6</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c1"># select columns by name</span>
<span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">items</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span>
<span class="go"> one three</span>
<span class="go">mouse 1 3</span>
<span class="go">rabbit 4 6</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c1"># select columns by regular expression</span>
<span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">regex</span><span class="o">=</span><span class="s1">'e$'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go"> one three</span>
<span class="go">mouse 1 3</span>
<span class="go">rabbit 4 6</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c1"># select rows containing 'bbi'</span>
<span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'bbi'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go"> one two three</span>
<span class="go">rabbit 4 5 6</span>
</pre></div>
</div>
</dd></dl>
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https://api.github.com/repos/pandas-dev/pandas/issues/12396 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12396/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12396/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12396/events | https://github.com/pandas-dev/pandas/issues/12396 | 134,951,968 | MDU6SXNzdWUxMzQ5NTE5Njg= | 12,396 | Concatenation of DFs with all NaT columns and TZ-aware ones breaks | {
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} | 9 | 2016-02-19T19:26:24Z | 2018-05-30T16:19:34Z | 2018-05-13T13:17:49Z | CONTRIBUTOR | null | Concatenating DFs that have columns with all NaTs and TZ-aware ones breaks as of 0.17.1:
```
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame([[pd.NaT], [pd.NaT]])
In [3]: df1
Out[2]:
0
0 NaT
1 NaT
In [4]: df2 = pd.DataFrame([[pd.Timestamp('2015/01/01', tz='UTC')], [pd.Timestamp('2016/01/01', tz='UTC')]])
In [5]: df2
Out[4]:
0
0 2015-01-01 00:00:00+00:00
1 2016-01-01 00:00:00+00:00
In [6]: pd.concat([df1, df2])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-f61a1ab4009e> in <module>()
----> 1 pd.concat([df1, df2])
/.../env/local/lib/python2.7/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
811 verify_integrity=verify_integrity,
812 copy=copy)
--> 813 return op.get_result()
814
815
/.../env/local/lib/python2.7/site-packages/pandas/tools/merge.py in get_result(self)
993
994 new_data = concatenate_block_managers(
--> 995 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
996 if not self.copy:
997 new_data._consolidate_inplace()
/.../env/local/lib/python2.7/site-packages/pandas/core/internals.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
4454 copy=copy),
4455 placement=placement)
-> 4456 for placement, join_units in concat_plan]
4457
4458 return BlockManager(blocks, axes)
/.../env/local/lib/python2.7/site-packages/pandas/core/internals.py in concatenate_join_units(join_units, concat_axis, copy)
4551 to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
4552 upcasted_na=upcasted_na)
-> 4553 for ju in join_units]
4554
4555 if len(to_concat) == 1:
/.../env/local/lib/python2.7/site-packages/pandas/core/internals.py in get_reindexed_values(self, empty_dtype, upcasted_na)
4799
4800 if self.is_null and not getattr(self.block,'is_categorical',None):
-> 4801 missing_arr = np.empty(self.shape, dtype=empty_dtype)
4802 if np.prod(self.shape):
4803 # NumPy 1.6 workaround: this statement gets strange if all
TypeError: data type not understood
```
Possibly related to #11693, #11705 and the #11456 family. However, this doesn't appear to be caused by the TZ-aware vs. non-TZ aware problems referenced there.
Versions:
```
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-77-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.6
pip: 6.0.8
setuptools: 12.0.5
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1.post1
IPython: 3.1.0
sphinx: 1.3.1
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.4
blosc: 1.2.8
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: None
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 8 | 2016-02-19T21:44:56Z | 2017-01-25T21:20:37Z | 2017-01-25T21:20:37Z | NONE | null | I find the `Index.to_series` method is a convenient way to allow indices to act as columns of a dataframe where desired. However, the behavior of `MultiIndex.to_series`, which gives a `Series` of tuples, is less useful.
Would it be convenient to provide a `to_dataframe` method for index classes? This would be a natural extension of the utility of `to_series`, and more useful for `MultiIndex` objects I would think.
I'm something equivalent to:
> ```
> def to_dataframe(self):
> DataFrame(self.tolist(), columns=self.names, index=self)
> ```
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} | 6 | 2016-02-19T23:14:12Z | 2016-04-30T14:35:56Z | 2016-04-30T14:35:52Z | NONE | null | Allow ability to distinguish inclusiveness of left and right boundaries. I've written the code and tests, but need a GH tracking number prior to submitting a PR.
```
def between(self, left, right, inclusive=True):
"""
Return boolean Series equivalent to left <= series <= right. NA values
will be treated as False
Parameters
----------
left : scalar
Left boundary
right : scalar
Right boundary
inclusive : boolean for both left and right or iterable pair.
Whether or not to include the left and right boundary.
If a tuple pair, the boundaries can be set separately
(e.g. (False, True) for left < series <= right).
```
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} | 15 | 2016-02-20T00:49:44Z | 2016-06-03T21:01:39Z | 2016-06-03T21:01:24Z | CONTRIBUTOR | null | Updates doc comments for `DataFrame.filter` and adds usage examples.
Fixed errors identified by `flake8` and correctly rebase my branch before issuing PR.
<dl class="method">
<dt id="pandas.DataFrame.filter">
<code class="descclassname">DataFrame.</code><code class="descname">filter</code><span class="sig-paren">(</span><em>items=None</em>, <em>like=None</em>, <em>regex=None</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pandas.DataFrame.filter" title="Permalink to this definition">¶</a></dt>
<dd><p>Subset rows or columns of dataframe according to labels in the index.</p>
<p>Note that this routine does not filter a dataframe on its contents. The filter is
applied to the labels of the index.
This method is a thin veneer on top of <span class="xref std std-ref">DateFrame Select</span></p>
<table class="docutils field-list" frame="void" rules="none">
<colgroup><col class="field-name">
<col class="field-body">
</colgroup><tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>items</strong> : list-like</p>
<blockquote>
<div><p>List of info axis to restrict to (must not all be present)</p>
</div></blockquote>
<p><strong>like</strong> : string</p>
<blockquote>
<div><p>Keep info axis where “arg in col == True”</p>
</div></blockquote>
<p><strong>regex</strong> : string (regular expression)</p>
<blockquote>
<div><p>Keep info axis with re.search(regex, col) == True</p>
</div></blockquote>
<p><strong>axis</strong> : int or None</p>
<blockquote>
<div><p>The axis to filter on.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">same type as input object with filtered info axis</p>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>The <code class="docutils literal"><span class="pre">items</span></code>, <code class="docutils literal"><span class="pre">like</span></code>, and <code class="docutils literal"><span class="pre">regex</span></code> parameters should be mutually exclusive, but this is not checked.</p>
<p><code class="docutils literal"><span class="pre">axis</span></code> defaults to the info axis that is used when indexing with <code class="docutils literal"><span class="pre">[]</span></code>.</p>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">df</span>
<span class="go"> one two three</span>
<span class="go">mouse 1 2 3</span>
<span class="go">rabbit 4 5 6</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c1"># select columns by name</span>
<span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">items</span><span class="o">=</span><span class="p">[</span><span class="s1">'one'</span><span class="p">,</span> <span class="s1">'three'</span><span class="p">])</span>
<span class="go"> one three</span>
<span class="go">mouse 1 3</span>
<span class="go">rabbit 4 6</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c1"># select columns by regular expression</span>
<span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">regex</span><span class="o">=</span><span class="s1">'e$'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go"> one three</span>
<span class="go">mouse 1 3</span>
<span class="go">rabbit 4 6</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="c1"># select rows containing 'bbi'</span>
<span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">like</span><span class="o">=</span><span class="s1">'bbi'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go"> one two three</span>
<span class="go">rabbit 4 5 6</span>
</pre></div>
</div>
</dd></dl>
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| null | 23 | 2016-02-20T06:59:54Z | 2017-08-17T10:28:43Z | 2017-08-17T10:28:43Z | CONTRIBUTOR | null | - [x] closes #12353
- [x] tests added
- [ ] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
I'm changed the dict / mapping issue, but I can't get this part working (so the tests fail at the moment):
> But pd.Series(dict(xr.Dataset({'a': 2, 'b': 3}))) doesn't work either, because the values in the dict are 0-th dimension arrays, and Series doesn't unpack them (list(dict(xr.Dataset({'a': 2, 'b': 3})).values())[0] for the full code)
> https://github.com/pydata/pandas/issues/12353#issuecomment-184968747
Is that a reasonable goal? Where would be a good place to do the conversion?
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} | 15 | 2016-02-20T18:23:15Z | 2018-09-07T22:31:36Z | 2017-10-04T11:07:59Z | CONTRIBUTOR | null | do we need label selectors? we should for sure just have a single method for this. maybe call it `query_labels`? to be consistent with `.query` as the workhorse for data selection.
- [x] ``.select`` (#17633)
- [ ] ``.filter``
xref #6599
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} | 3 | 2016-02-20T20:34:31Z | 2016-04-30T14:36:12Z | 2016-04-29T22:08:57Z | NONE | null | - [ X ] closes #12398
- [ X ] tests added / passed
- [ X ] passes `git diff upstream/master | flake8 --diff`
- [ X ] whatsnew entry
Amends pandas.Series.between method to set different inclusive/exclusive endpoints.
inclusive : Boolean to indicate whether or not to include both the left
and right endpoints (True: a <= series <= b, False: a < series < b),
or an iterable boolean pair to set them separately, e.g
inclusive=(False, True) for a < series <= b.
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| null | 29 | 2016-02-20T23:06:11Z | 2018-01-20T02:21:29Z | 2016-11-16T22:22:21Z | CONTRIBUTOR | null | - [X] closes #12396
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
- `concat` to handle tz-aware all NaT/mixed cases
- added unit tests
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