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} | 10 | 2013-11-12T20:51:13Z | 2016-05-24T20:04:37Z | 2016-05-24T20:04:37Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/19938734/is-there-a-simple-way-to-reference-the-current-object-after-applying-a-chain-of
```
df.where("A>0",1)
df.loc["A>0"]
```
so could process the condition via an 'eval' type of operation
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} | 7 | 2013-11-13T02:28:06Z | 2020-09-20T23:00:52Z | 2020-09-20T23:00:51Z | CONTRIBUTOR | null | Currently `normalize` does not work on a `Series` or a `DataFrame`:
```
pd.Series(range(5), index=pd.date_range('2013-1-1', periods=5,freq='D')).normalize()
AttributeError: 'Series' object has no attribute 'normalize'
```
However, changing the timezone, which I consider to be a similar type of operation, does work:
```
In [50]: pd.Series(range(5), index=pd.date_range('2013-1-1', periods=5,freq='D')).tz_localize('America/New_York')
Out[50]:
2013-01-01 00:00:00-05:00 0
2013-01-02 00:00:00-05:00 1
2013-01-03 00:00:00-05:00 2
2013-01-04 00:00:00-05:00 3
2013-01-05 00:00:00-05:00 4
Freq: D, dtype: int64
```
My current workaround:
```
s = pd.Series(range(5), index=pd.date_range('2013-1-1', periods=5,freq='D'))
s.index = s.index.normalize()
```
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I was thinking something along the lines of:
```
interesting_rows = raw_data.groupby(["city"]).argmax(over='obt_dt')
```
which would pull out the row in each group that has the max value in the column referred to by the `over` keyword,
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} | 0 | 2013-11-13T16:42:26Z | 2014-02-20T08:34:34Z | 2014-02-20T08:34:34Z | MEMBER | null | E.g. for `rolling_sum`: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.stats.moments.rolling_sum.html#pandas.stats.moments.rolling_sum
Some issues I encountered:
- it is not mentioned the result is set to the right edge of the window.
- the `center` keyword is not documented.
- the explanation for `freq` (_Frequency to conform to before computing statistic_) is totally unclear to me. Conform how? It seems from the code it is doing a default resample, so this calculates a mean first?
- How are NaN's handled?
And something else, an interesting feature (and what I first thought `freq` would do) would be that you can specify the window size as a frequency (if dealing with a datetimeindexed series/frame) instead of a number (eg a frequency of '15min' instead of n=15 if your data are every minute).
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} | 38 | 2013-11-13T18:24:13Z | 2014-06-12T15:38:56Z | 2014-03-14T22:41:31Z | CONTRIBUTOR | null | Right now, there is
```
df.pivot(index=foo, columns=bar, values=baz)
```
and
```
df.pivot_table(rows=foo, cols=bar, values=baz)
```
Because of dirtyness in my data I occasionally have to go back and forth between the two forms (and use `df.pivot_table(..., aggfunc='count')` to see who had the bad data). It would be nice if I didn't have to change the keyword arguments each time and one had an alias of the other. It would also be easier to remember, especially in the trivial case of `cols` vs `columns`. Finally, the two are related operations and having an option for consistent keyword parameters makes sense.
I could make a PR to solve this easy enough, if this is an enhancement you are interested in. I'm not really sure what your stance is on redundant keyword arguments.
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} | 3 | 2013-11-13T18:24:58Z | 2013-11-13T19:54:35Z | 2013-11-13T19:53:52Z | NONE | null | Trying df.to_msgpack() on a DataFrame with a DateTimeIndex produces an AttributeError.
Setting the index to an Int64Index fixes the issue, so it seems like it has to do with DateTimeIndex.
pd.**version** = '0.12.0-1095-g3239b29'
In [130]: from pandas.io.data import DataReader
In [131]: from pandas.io.packers import pack, unpack
In [132]: goog = DataReader('goog', 'yahoo')
In [133]: buf = pack(goog)
AttributeError Traceback (most recent call last)
<ipython-input-133-361b2be95a50> in <module>()
----> 1 buf = pack(goog)
/home/user/environments/python3/lib/python3.3/site-packages/pandas/io/packers.py in pack(o, default, encoding, unicode_errors, use_single_float)
507 return Packer(default=default, encoding=encoding,
508 unicode_errors=unicode_errors,
--> 509 use_single_float=use_single_float).pack(o)
510
511
/home/user/environments/python3/lib/python3.3/site-packages/pandas/msgpack.cpython-33m.so in pandas.msgpack.Packer.pack (pandas/msgpack.cpp:3509)()
/home/user/environments/python3/lib/python3.3/site-packages/pandas/msgpack.cpython-33m.so in pandas.msgpack.Packer.pack (pandas/msgpack.cpp:3372)()
/home/user/environments/python3/lib/python3.3/site-packages/pandas/msgpack.cpython-33m.so in pandas.msgpack.Packer._pack (pandas/msgpack.cpp:3251)()
/home/user/environments/python3/lib/python3.3/site-packages/pandas/msgpack.cpython-33m.so in pandas.msgpack.Packer._pack (pandas/msgpack.cpp:2882)()
/home/user/environments/python3/lib/python3.3/site-packages/pandas/msgpack.cpython-33m.so in pandas.msgpack.Packer._pack (pandas/msgpack.cpp:3189)()
/home/user/environments/python3/lib/python3.3/site-packages/pandas/msgpack.cpython-33m.so in pandas.msgpack.Packer._pack (pandas/msgpack.cpp:3236)()
/home/user/environments/python3/lib/python3.3/site-packages/pandas/io/packers.py in encode(obj)
273 'dtype': obj.dtype.num,
274 'data': convert(obj.asi8),
--> 275 'freq': obj.freqstr,
276 'tz': obj.tz}
277 elif isinstance(obj, MultiIndex):
/home/user/environments/python3/lib/python3.3/site-packages/pandas/tseries/index.py in freqstr(self)
1394 @property
1395 def freqstr(self):
-> 1396 return self.offset.freqstr
1397
1398 year = _field_accessor('year', 'Y')
AttributeError: 'NoneType' object has no attribute 'freqstr'
In [134]: goog
Out[134]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 973 entries, 2010-01-04 00:00:00 to 2013-11-12 00:00:00
Data columns (total 6 columns):
Open 973 non-null values
High 973 non-null values
Low 973 non-null values
Close 973 non-null values
Volume 973 non-null values
Adj Close 973 non-null values
dtypes: float64(5), int64(1)
In [135]: goog.index = np.arange(973)
In [136]: buf = pack(goog)
In [137]: len(buf)
Out[137]: 54908
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} | 2 | 2013-11-13T18:48:54Z | 2014-07-16T08:40:20Z | 2013-11-13T19:53:52Z | CONTRIBUTOR | null | BUG: bug in to_msgpack for no freqstr datetime index (GH5506)
closes #5506
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} | 4 | 2013-11-13T21:10:28Z | 2017-12-21T05:59:38Z | 2013-11-14T01:06:30Z | NONE | null | Using the latest git version of pandas (705b677) I have exerienced the
following problems:
``` python
In [1]: a = pd.DataFrame(index=pd.Index(xrange(1,11)))
In [2]: a['foo'] = np.zeros(10, dtype=np.float)
In [3]: a['bar'] = np.zeros(10, dtype=np.complex)
In [4]: a.ix[2:5, 'bar']
Out[4]:
2 0j
3 0j
4 0j
5 0j
Name: bar, dtype: complex128
In [5]: a.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2])
# invalid input (RHS has wrong size) -> does not throw an exception!
# (The reason why no exception is thrown is because of the different
# dtype of a['foo'] - see ``In[9]-In[10]``
In [6]: a
Out[6]:
foo bar
1 0 0j
2 0 2.33j
3 0 2.33j
4 0 2.33j
5 0 2.33j
6 0 0j
7 0 0j
8 0 0j
9 0 0j
10 0 0j
In [7]: a.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2, 1.0]) # valid
In [8]: a
Out[8]:
foo bar
1 0 0j
2 0 2.33j
3 0 (1.23+0.1j)
4 0 (2.2+0j)
5 0 (1+0j)
6 0 0j
7 0 0j
8 0 0j
9 0 0j
10 0 0j
In [9]: a = pd.DataFrame(index=pd.Index(xrange(1,11)))
In [10]: a['bar'] = np.zeros(10, dtype=np.complex)
In [11]: a.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2])
# invalid RHS-> exception raised OK!
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-bde45910cde6> in <module>()
----> 1 a.ix[2:5, 'bar'] = np.array([2.33j, 1.23+0.1j, 2.2])
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in __setitem__(self, key, value)
92 indexer = self._convert_to_indexer(key, is_setter=True)
93
---> 94 self._setitem_with_indexer(indexer, value)
95
96 def _has_valid_type(self, k, axis):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
387 value = self._align_panel(indexer, value)
388
--> 389 self.obj._data = self.obj._data.setitem(indexer,value)
390 self.obj._maybe_update_cacher(clear=True)
391
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in setitem(self, *args, **kwargs)
2182
2183 def setitem(self, *args, **kwargs):
-> 2184 return self.apply('setitem', *args, **kwargs)
2185
2186 def putmask(self, *args, **kwargs):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in apply(self, f, *args, **kwargs)
2162
2163 else:
-> 2164 applied = getattr(blk, f)(*args, **kwargs)
2165
2166 if isinstance(applied, list):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in setitem(self, indexer, value)
580 try:
581 # set and return a block
--> 582 values[indexer] = value
583
584 # coerce and try to infer the dtypes of the result
ValueError: could not broadcast input array from shape (3) into shape (4)
```
Here is the 2nd problem:
``` python
In [1]: b = pd.DataFrame(index=pd.Index(xrange(1,11)))
In [2]: b['foo'] = np.zeros(10, dtype=np.float)
In [3]: b['bar'] = np.zeros(10, dtype=np.complex)
In [4]: b
Out[4]:
foo bar
1 0 0j
2 0 0j
3 0 0j
4 0 0j
5 0 0j
6 0 0j
7 0 0j
8 0 0j
9 0 0j
10 0 0j
In [5]: b[2:5]
Out[5]:
foo bar
3 0 0j
4 0 0j
5 0 0j
In [6]: b[2:5] = np.arange(1,4)*1j
# invalid input (wrong size on RHS)
In [7]: b
Out[7]:
foo bar
1 0j 0j
2 0j 0j
3 1j 2j
4 1j 2j
5 1j 2j
6 0j 0j
7 0j 0j
8 0j 0j
9 0j 0j
10 0j 0j
# why does the expression in ``In [6]`` change the dtype of b['foo'].
# Is this intended ?
In [8]: b[2:5] = np.arange(1,4)*1j # invalid input (wrong size on RHS)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-a729140e3f7f> in <module>()
----> 1 b[2:5] = np.arange(1,4)*1j # invalid input (wrong size on RHS)
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in __setitem__(self, key, value)
1831 indexer = _convert_to_index_sliceable(self, key)
1832 if indexer is not None:
-> 1833 return self._setitem_slice(indexer, value)
1834
1835 if isinstance(key, (Series, np.ndarray, list)):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/frame.pyc in _setitem_slice(self, key, value)
1842
1843 def _setitem_slice(self, key, value):
-> 1844 self.ix._setitem_with_indexer(key, value)
1845
1846 def _setitem_array(self, key, value):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/indexing.pyc in _setitem_with_indexer(self, indexer, value)
387 value = self._align_panel(indexer, value)
388
--> 389 self.obj._data = self.obj._data.setitem(indexer,value)
390 self.obj._maybe_update_cacher(clear=True)
391
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in setitem(self, *args, **kwargs)
2182
2183 def setitem(self, *args, **kwargs):
-> 2184 return self.apply('setitem', *args, **kwargs)
2185
2186 def putmask(self, *args, **kwargs):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in apply(self, f, *args, **kwargs)
2162
2163 else:
-> 2164 applied = getattr(blk, f)(*args, **kwargs)
2165
2166 if isinstance(applied, list):
/home/thomas/.local/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in setitem(self, indexer, value)
580 try:
581 # set and return a block
--> 582 values[indexer] = value
583
584 # coerce and try to infer the dtypes of the result
ValueError: could not broadcast input array from shape (3) into shape (3,2)
```
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] | closed | false | null | [] | null | 4 | 2013-11-13T23:08:08Z | 2013-11-27T18:33:18Z | 2013-11-27T18:33:04Z | CONTRIBUTOR | null | I'm trying to pin down where this is happening still, maybe you'll be able to tell by looking at the values:
``` python
In [10]: a['timestamp'].head()
Out[10]:
1 2 \
HRHHID HUHHNUM PULINENO
45004066531 1 1 1996-12-01 00:00:00 1997-01-01 00:00:00
2 1996-12-01 00:00:00 1997-01-01 00:00:00
3 1996-12-01 00:00:00 1997-01-01 00:00:00
4 1996-12-01 00:00:00 1997-01-01 00:00:00
5 1996-12-01 00:00:00 1997-01-01 00:00:00
In [13]: a.to_hdf(tst, '/m1996_12', append=False)
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.py:2339: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->axis0] [items->None]
warnings.warn(ws, PerformanceWarning)
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.py:2339: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_items] [items->None]
warnings.warn(ws, PerformanceWarning)
/Users/tom/Envs/statsmodels/lib/python2.7/site-packages/pandas-0.12.0_1098_g705b677-py2.7-macosx-10.8-x86_64.egg/pandas/io/pytables.py:2339: PerformanceWarning:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block2_values] [items->['flow', 'unemployed_history', 'nonemployed_history', 'either_history']]
warnings.warn(ws, PerformanceWarning)
In [14]: tst.select('/m1996_12')['timestamp'].head()
Out[14]:
1 2 \
HRHHID HUHHNUM PULINENO
45004066531 1 1 2086-12-01 00:00:00 2087-01-01 00:00:00
2 2086-12-01 00:00:00 2087-01-01 00:00:00
3 2086-12-01 00:00:00 2087-01-01 00:00:00
4 2086-12-01 00:00:00 2087-01-01 00:00:00
5 2086-12-01 00:00:00 2087-01-01 00:00:00
```
I was thinking it was the `NaT`s (and it may still be) by I wasn't able to reproduce with a simple Panel.
[link](https://www.dropbox.com/s/o0qmwhh2rsy1b18/tst_comp.h5) (90 MB) to the file.
To get the correct dates, I'm using the following:
https://gist.github.com/TomAugspurger/7458121
just call `fix_timestamp` on the Panel and it modifies it in place.
In [29]: pd.**version**
Out[29]: '0.12.0-1098-g705b677'
In [30]: np.**version**
Out[30]: '1.7.1'
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https://api.github.com/repos/pandas-dev/pandas/issues/5510 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5510/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5510/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5510/events | https://github.com/pandas-dev/pandas/pull/5510 | 22,633,278 | MDExOlB1bGxSZXF1ZXN0OTk0NDgwNg== | 5,510 | ENH add cumcount groupby method | {
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} | 10 | 2013-11-14T00:13:44Z | 2014-06-14T09:29:13Z | 2013-11-14T22:04:00Z | CONTRIBUTOR | null | closes #4646
Happy to hear ways to make this faster, but I think this is a good convenience function as is.
Update: made significantly faster...
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} | [] | closed | false | null | [] | null | 2 | 2013-11-14T00:24:48Z | 2013-11-14T13:25:25Z | 2013-11-14T13:25:25Z | NONE | null | After I convert the index from String to DatetimeIndex, index 0 seems to always point to the data near the Unix epoch. In the example below, b['Close'][0] correctly displays 4, but after the index conversion, b['Close'][0] now shows 54 (1/1/1970). Same goes with Open, High, Low, and Volume. Index other than 0 appears okay. If I use the slice notation, it would display the correct data. The to_csv also displays the right data.
```
>>> pd.__version__
'0.12.0'
>>> np.__version__
'1.7.1'
>>> b = pd.read_csv('/mnt/Investing/data-new/staging/b.csv', index_col=0)
>>> b['Close'][0]
4
>>> b
Open High Low Close Volume
Date
12/27/1969 1 2 3 4 5
12/28/1969 11 12 13 14 15
12/29/1969 21 22 23 24 25
12/30/1969 31 32 33 34 35
12/31/1969 41 42 43 44 45
1/1/1970 51 52 53 54 55
1/2/1970 61 62 63 64 65
1/3/1970 71 72 73 74 75
1/4/1970 81 82 83 84 85
1/5/1970 91 92 93 94 95
>>> b.index = pd.DatetimeIndex(b.index)
>>> b['Close'][0]
54
>>> b
Open High Low Close Volume
1969-12-27 1 2 3 4 5
1969-12-28 11 12 13 14 15
1969-12-29 21 22 23 24 25
1969-12-30 31 32 33 34 35
1969-12-31 41 42 43 44 45
1970-01-01 51 52 53 54 55
1970-01-02 61 62 63 64 65
1970-01-03 71 72 73 74 75
1970-01-04 81 82 83 84 85
1970-01-05 91 92 93 94 95
>>> b['Close'][0:1]
1969-12-27 4
Name: Close, dtype: int64
>>> b['Close'][0:2]
1969-12-27 4
1969-12-28 14
Name: Close, dtype: int64
```
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} | 0 | 2013-11-14T00:32:11Z | 2014-06-24T20:52:21Z | 2013-11-14T01:06:31Z | CONTRIBUTOR | null | closes #5508
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https://api.github.com/repos/pandas-dev/pandas/issues/5513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5513/events | https://github.com/pandas-dev/pandas/issues/5513 | 22,658,467 | MDU6SXNzdWUyMjY1ODQ2Nw== | 5,513 | Exception when trying to get values from DataFrame with duplicate column names and | {
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} | [] | closed | false | null | [] | null | 7 | 2013-11-14T12:02:35Z | 2014-01-15T18:00:42Z | 2013-11-14T13:24:49Z | CONTRIBUTOR | null | Lately I've been working with DataFrames with duplicate column names. I've found that if I have duplicate column names and there're different types of data in these columns, I cannot get values property from DataFrame (there're some other exceptions too, with iloc for example):
```
# no duplicates - ok
>>> df = pd.DataFrame([range(2) + ['x'], range(2) + ['c']], columns=['a','b','c'])
>>> df
a b c
0 0 1 x
1 0 1 c
>>> df.values
array([[0L, 1L, 'x'],
[0L, 1L, 'c']], dtype=object)
# duplicate columns, same type - ok
>>> df = pd.DataFrame([range(3), range(3)], columns=['a','b','b'])
>>> df
a b b
0 0 1 2
1 0 1 2
>>> df.values
array([[0, 1, 2],
[0, 1, 2]], dtype=int64)
# duplicate columns, diff types - error
>>> df = pd.DataFrame([range(2) + ['x'], range(2) + ['c']], columns=['a','b','b'])
>>> df
a b b
0 0 1 x
1 0 1 c
>>> df.values
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas-0.12.0-py2.7-win32.egg\pandas\core\frame.py", line 1748, in as_matrix
return self._data.as_matrix(columns).T
File "C:\Python27\lib\site-packages\pandas-0.12.0-py2.7-win32.egg\pandas\core\internals.py", line 1517, in as_matrix
mat = self._interleave(self.items)
File "C:\Python27\lib\site-packages\pandas-0.12.0-py2.7-win32.egg\pandas\core\internals.py", line 1548, in _interleave
raise AssertionError('All items must be in block items')
AssertionError: All items must be in block items
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5514 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5514/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5514/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5514/events | https://github.com/pandas-dev/pandas/pull/5514 | 22,659,293 | MDExOlB1bGxSZXF1ZXN0OTk1ODYwOA== | 5,514 | CLN: Add more methods to whitelist (for now) | {
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} | [] | closed | false | null | [] | null | 9 | 2013-11-14T12:20:58Z | 2014-06-27T15:02:57Z | 2013-12-05T23:35:54Z | CONTRIBUTOR | null | No test cases because goal is just to make sure this is not blocked in
0.13. Can do more tweaks later. If I have time I will try to pull
together something to cover what we were discussing in #5480.
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https://api.github.com/repos/pandas-dev/pandas/issues/5515 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5515/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5515/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5515/events | https://github.com/pandas-dev/pandas/issues/5515 | 22,668,048 | MDU6SXNzdWUyMjY2ODA0OA== | 5,515 | isnull silently changes the frequency of the datetime index of a dataframe | {
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} | [] | closed | false | null | [] | null | 3 | 2013-11-14T15:07:09Z | 2013-11-14T15:32:23Z | 2013-11-14T15:32:23Z | CONTRIBUTOR | null | There is no problem in doing pd.isnull on a series, but apparently in the case of a DataFrame the index frequency is converted silently.
If I have an index with freq=None isnull tries to infer the right frequency.
This is bad not only because one would expect that isnull does not modify the object in the call, but also because the inference of the frequency is imperfect and generates wrong results.
Here an example.
'2001-04-03' is tuesday, so the index is entirely in the same week
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.12.0'
In [3]: df = pd.DataFrame(randn(3,2), index=pd.to_datetime(['2001-04-03', '2001-04-04', '2001-04-05']))
In [4]: df.index # Notice Freq: None
Out[4]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2001-04-03 00:00:00, ..., 2001-04-05 00:00:00]
Length: 3, Freq: None, Timezone: None
In [5]: pd.isnull(df)
Out[5]:
0 1
2001-04-03 False False
2001-04-04 False False
2001-04-05 False False
In [6]: df.index # Notice Freq: D
Out[6]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2001-04-03 00:00:00, ..., 2001-04-05 00:00:00]
Length: 3, Freq: D, Timezone: None
```
In this case the index skips saturday and sunday and the conversion is to business day
``` python
In [7]: df2 = pd.DataFrame(randn(5,3), index=pd.to_datetime(['2001-04-'+x for x in ['03', '04', '05', '06', '09']]))
In [8]: df2.index
Out[8]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2001-04-03 00:00:00, ..., 2001-04-09 00:00:00]
Length: 5, Freq: None, Timezone: None
In [9]: pd.isnull(df2)
Out[9]:
0 1 2
2001-04-03 False False False
2001-04-04 False False False
2001-04-05 False False False
2001-04-06 False False False
2001-04-09 False False False
In [10]: df2.index # Notice Freq: B
Out[10]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2001-04-03 00:00:00, ..., 2001-04-09 00:00:00]
Length: 5, Freq: B, Timezone: None
```
This is the tricky case: the index is business days except the 15 which is
sunday. isnull converts this to business days and generates a fake line full
of nans corresponding to the 13 (which is friday)
``` python
In [11]: df3 = pd.DataFrame(randn(9,3), index=pd.to_datetime(['2001-04-'+x for x in ['03', '04', '05', '06', '09', '10', '11', '12', '15']]))
In [12]: df3.index
Out[12]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2001-04-03 00:00:00, ..., 2001-04-15 00:00:00]
Length: 9, Freq: None, Timezone: None
In [13]: pd.isnull(df3)
Out[13]:
0 1 2
2001-04-03 False False False
2001-04-04 False False False
2001-04-05 False False False
2001-04-06 False False False
2001-04-09 False False False
2001-04-10 False False False
2001-04-11 False False False
2001-04-12 False False False
2001-04-15 False False False
In [14]: df3.index # Notice Freq: B
Out[14]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2001-04-03 00:00:00, ..., 2001-04-15 00:00:00]
Length: 9, Freq: B, Timezone: None
In [15]: pd.isnull(df3) # The 15 is replaced by the 13 with nans
Out[15]:
0 1 2
2001-04-03 False False False
2001-04-04 False False False
2001-04-05 False False False
2001-04-06 False False False
2001-04-09 False False False
2001-04-10 False False False
2001-04-11 False False False
2001-04-12 False False False
2001-04-13 NaN NaN NaN
```
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} | [] | closed | false | null | [] | null | 8 | 2013-11-14T17:52:36Z | 2014-09-23T13:35:45Z | 2013-11-14T18:58:53Z | NONE | null | ```
import datetime
import pandas as pd
from pandas.tslib import Timestamp
Timestamp.max
```
> Timestamp('2262-04-11 23:47:16.854775807', tz=None)
```
date = datetime.date(2262, 4, 12)
pd.to_datetime(date)
```
> Timestamp('2262-04-11 00:25:26.290448384', tz=None)
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} | 7 | 2013-11-14T22:45:52Z | 2014-08-30T14:10:48Z | 2014-08-30T14:10:48Z | NONE | null | `DataFrame.boxplot` fails to properly set the limits of the y-axis is this example:
``` python
df = pd.DataFrame({'X': [1, 0, 1, 0],
'Y': [1, 0, 1, 0],
'Z': [1, 0, 1, 0],
#'M': [1, 0, 1, 0],
'B': list("XXYY")},
index=list("UUVV"))
print df
"""
B X Y Z
U X 1 1 1
U X 0 0 0
V Y 1 1 1
V Y 0 0 0
"""
df.boxplot(by='B')
```
I would expect the y-axis to go from 0 to 1, not -0.06 to 0.06.
If I uncomment the `M` it works.
I use pandas 0.12.0 and matplotlib 1.3.1
![Uploading boxplot.png . . .]()
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} | [] | closed | false | null | [] | null | 8 | 2013-11-15T00:30:38Z | 2014-08-18T13:12:49Z | 2013-11-17T04:41:20Z | CONTRIBUTOR | null | This is some low hanging fruit, significantly faster than master.
To give some numbers, before the change is below the new:
```
In [1]: df = pd.DataFrame(np.random.randint(0, 100, 1000), columns=['A'], index=[0] * 1000); g = df.groupby('A')
In [2]: %timeit g.head(2)
1000 loops, best of 3: 429 µs per loop
#100 loops, best of 3: 9.67 ms per loop
In [3]: %timeit g.tail(2)
1000 loops, best of 3: 398 µs per loop
#100 loops, best of 3: 9.68 ms per loop
In [4]: %timeit g.size()
10000 loops, best of 3: 119 µs per loop
#1000 loops, best of 3: 649 µs per loop
In [11]: df = pd.DataFrame(np.random.randint(0, 10, 1000), columns=['A'], index=[0] * 1000); g = df.groupby('A')
In [12]: %timeit g.head(2)
10000 loops, best of 3: 189 µs per loop
#100 loops, best of 3: 2.1 ms per loop
In [13]: %timeit g.tail(2)
10000 loops, best of 3: 160 µs per loop
#100 loops, best of 3: 2.11 ms per loop
In [14]: %timeit g.size()
10000 loops, best of 3: 41.9 µs per loop
#1000 loops, best of 3: 598 µs per loop
```
...It's a bit messy to keep track of the as_index stuff (which you get when you apply), see below - am I missing some way to grab out the final index ??
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} | [] | closed | false | null | [] | null | 6 | 2013-11-15T03:14:59Z | 2014-05-22T14:28:55Z | 2014-02-18T01:35:30Z | CONTRIBUTOR | null | There is no documentations for this method: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.weekday.html
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} | [] | closed | false | null | [] | null | 2 | 2013-11-15T09:52:20Z | 2014-07-16T08:40:30Z | 2013-11-16T10:36:14Z | CONTRIBUTOR | null | Currently, git describe fails with "fatal: No names found, cannot describe anything."
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} | 11 | 2013-11-15T10:42:46Z | 2014-07-01T16:33:07Z | 2014-07-01T16:33:07Z | NONE | null | Note for 0.14.1: need to add back `#N/A N/A` to na_values defaults
in pandas 0.12
i found a little bug:
this:
_NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN',
'#N/A N/A', 'NA', '#NA', 'NULL', 'NaN',
'nan', ''])
must be:
_NA_VALUES = set(['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN',
'#N/A','N/A', 'NA', '#NA', 'NULL', 'NaN',
'nan', ''])
thank you
Glauco
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} | 9 | 2013-11-15T15:03:18Z | 2019-10-26T03:42:43Z | 2019-10-26T03:25:45Z | NONE | null | ```
len(alphas.index.levels)
```
> 2
```
%time alphas.index.lexsort_depth
```
> CPU times: user 0.03 s, sys: 0.00 s, total: 0.03 s
> Wall time: 0.02 s
> 1
```
%time alphas.index.is_monotonic
```
> CPU times: user 10.22 s, sys: 0.00 s, total: 10.22 s
> Wall time: 10.22 s
> False
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} | 22 | 2013-11-15T16:31:06Z | 2014-06-25T08:45:15Z | 2014-01-06T23:44:31Z | CONTRIBUTOR | null | I added a test case test_isnull_nat() to test_common.py and a check for NaT in lib.isnullobj. pd.isnull(np.array([pd.NaT])) now yields the correct results ([True]).
closes #5443
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https://api.github.com/repos/pandas-dev/pandas/issues/5525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5525/events | https://github.com/pandas-dev/pandas/pull/5525 | 22,754,246 | MDExOlB1bGxSZXF1ZXN0MTAwMTE4MTI= | 5,525 | StataWriter: Replace non-isalnum characters in variable names by _ inste... | {
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} | 3 | 2013-11-15T19:43:26Z | 2014-07-09T17:56:15Z | 2013-12-04T13:02:57Z | CONTRIBUTOR | null | ...ad of integral represantation of replaced character. Eliminate duplicates created by replacement.
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https://api.github.com/repos/pandas-dev/pandas/issues/5526 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5526/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5526/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5526/events | https://github.com/pandas-dev/pandas/issues/5526 | 22,756,084 | MDU6SXNzdWUyMjc1NjA4NA== | 5,526 | Example in 'Essential Basic Functionality' section of Docs incorrect | {
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} | [] | closed | false | null | [] | null | 2 | 2013-11-15T20:14:53Z | 2013-12-06T00:07:20Z | 2013-12-06T00:07:20Z | NONE | null | http://pandas.pydata.org/pandas-docs/dev/basics.html#function-application
The example in line [109](under) of this section does not produce the expected results, as the text indicates the goal of the exercise is to "extract the date where the maximum value for each column occurred". To do this we simply need:
tsdf.idxmax()
Perhaps replacing it with another example, or at least correcting it, would be beneficial.
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} | 2 | 2013-11-15T23:38:29Z | 2013-12-03T03:00:46Z | 2013-12-03T03:00:46Z | NONE | null | ```
store_id_map
```
> <class 'pandas.io.pytables.HDFStore'>
> File path: C:\output\identifier_map.h5
> /identifier_map frame_table (typ->appendable_multi,nrows->26779823,ncols->9,indexers->[index],dc->[RefIdentifierID])
```
store_id_map.select('identifier_map')
```
> ---------------------------------------------------------------------------
> KeyError Traceback (most recent call last)
> <ipython-input-21-4b0da9c6ab3c> in <module>()
> ----> 1 store_id_map.select('identifier_map')
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close, *_kwargs)
> 456 return TableIterator(self, func, nrows=s.nrows, start=start, stop=stop, chunksize=chunksize, auto_close=auto_close)
> 457
> --> 458 return TableIterator(self, func, nrows=s.nrows, start=start, stop=stop, auto_close=auto_close).get_values()
> 459
> 460 def select_as_coordinates(self, key, where=None, start=None, stop=None, *_kwargs):
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in get_values(self)
> 982
> 983 def get_values(self):
> --> 984 results = self.func(self.start, self.stop)
> 985 self.close()
> 986 return results
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in func(_start, _stop)
> 449 # what we are actually going to do for a chunk
> 450 def func(_start, _stop):
> --> 451 return s.read(where=where, start=_start, stop=_stop, columns=columns, **kwargs)
> 452
> 453 if iterator or chunksize is not None:
>
> C:\Python27\lib\site-packages\pandas\io\pytables.pyc in read(self, columns, *_kwargs)
> 3259 columns.insert(0, n)
> 3260 df = super(AppendableMultiFrameTable, self).read(columns=columns, *_kwargs)
> -> 3261 df.set_index(self.levels, inplace=True)
> 3262 return df
> 3263
>
> C:\Python27\lib\site-packages\pandas\core\frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity)
> 2827 names.append(None)
> 2828 else:
> -> 2829 level = frame[col].values
> 2830 names.append(col)
> 2831 if drop:
>
> C:\Python27\lib\site-packages\pandas\core\frame.pyc in **getitem**(self, key)
> 2001 # get column
> 2002 if self.columns.is_unique:
> -> 2003 return self._get_item_cache(key)
> 2004
> 2005 # duplicate columns
>
> C:\Python27\lib\site-packages\pandas\core\generic.pyc in _get_item_cache(self, item)
> 665 return cache[item]
> 666 except Exception:
> --> 667 values = self._data.get(item)
> 668 res = self._box_item_values(item, values)
> 669 cache[item] = res
>
> C:\Python27\lib\site-packages\pandas\core\internals.pyc in get(self, item)
> 1653 def get(self, item):
> 1654 if self.items.is_unique:
> -> 1655 _, block = self._find_block(item)
> 1656 return block.get(item)
> 1657 else:
>
> C:\Python27\lib\site-packages\pandas\core\internals.pyc in _find_block(self, item)
> 1933
> 1934 def _find_block(self, item):
> -> 1935 self._check_have(item)
> 1936 for i, block in enumerate(self.blocks):
> 1937 if item in block:
>
> C:\Python27\lib\site-packages\pandas\core\internals.pyc in _check_have(self, item)
> 1940 def _check_have(self, item):
> 1941 if item not in self.items:
> -> 1942 raise KeyError('no item named %s' % com.pprint_thing(item))
> 1943
> 1944 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
>
> KeyError: u'no item named None'
I am at a loss. What does that mean? I successfully saved the DataFrame but I cannot read it back.
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} | 1 | 2013-11-16T11:04:18Z | 2013-11-16T19:13:59Z | 2013-11-16T19:13:47Z | NONE | null | ```
import pandas as pd
import numpy as np
tup = zip(*[['a','a','b','b'],['x','y','x','y']])
index = pd.MultiIndex.from_tuples(tup)
df = pd.DataFrame(np.random.randn(4, 4), index=index)
df.iloc[[0, 1]]
```
This will raise:
```
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
```
I am using 0.12 version.
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} | 0 | 2013-11-16T17:27:53Z | 2014-06-18T13:18:30Z | 2013-11-16T19:13:47Z | CONTRIBUTOR | null | closes #5528
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https://api.github.com/repos/pandas-dev/pandas/issues/5530 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5530/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5530/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5530/events | https://github.com/pandas-dev/pandas/pull/5530 | 22,784,526 | MDExOlB1bGxSZXF1ZXN0MTAwMjY5NzU= | 5,530 | sphinxext py3 compatibility | {
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} | 82 | 2013-11-16T20:43:28Z | 2014-06-12T19:56:32Z | 2014-01-30T10:49:25Z | CONTRIBUTOR | null | Temporary sphinxext py3 compatibility fix as discussed in #5479 before #5221 is resolved. So far test_docscrape.py passes on both Python 2.7 and 3.3 (which should be the typical developer interpreters).
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https://api.github.com/repos/pandas-dev/pandas/issues/5531 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5531/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5531/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5531/events | https://github.com/pandas-dev/pandas/issues/5531 | 22,787,096 | MDU6SXNzdWUyMjc4NzA5Ng== | 5,531 | Join on Column Name not Working | {
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} | [] | closed | false | null | [] | null | 2 | 2013-11-16T23:11:14Z | 2013-12-05T23:43:18Z | 2013-12-05T23:43:18Z | CONTRIBUTOR | null | I assume I am doing something wrong here:
```
s1 = pd.DataFrame({
'Month':[pd.Timestamp('2010-05-01')],
'Baz':[100]},
index=[pd.Timestamp('2010-05-01')])
s2 = pd.DataFrame({
'Month':[pd.Timestamp('2010-05-01')],
'Foo':[100]})
```
and then I join:
```
In [58]: s1.join(s2, on="Month", rsuffix="_r")
Out[58]:
Baz Month Foo Month_r
2010-05-01 100 2010-05-01 00:00:00 NaN NaT
```
Why is the `join` not matching correctly (I get `NaT` for `Month_r` and `NaN` for `foo`)?
Even if I do not add an index to `s1`, the join still fails:
```
s3 = pd.DataFrame({'Month':[pd.Timestamp('2010-05-01')], 'Baz':[100]})
In [68]: s3.join(s2, on="Month", rsuffix="_r")
Out[68]:
Baz Month Foo Month_r
0 100 2010-05-01 00:00:00 NaN NaT
```
In fact, even a self join fails:
```
In [77]: s3.join(s3, on="Month", rsuffix="_r")
Out[77]:
Baz Month Baz_r Month_r
0 100 2010-05-01 00:00:00 NaN NaT
```
`Merge`, however, works fine:
```
In [79]: s3.merge(s3, on="Month")
Out[79]:
Baz_x Month Baz_y
0 100 2010-05-01 00:00:00 100
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5532 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5532/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5532/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5532/events | https://github.com/pandas-dev/pandas/issues/5532 | 22,787,398 | MDU6SXNzdWUyMjc4NzM5OA== | 5,532 | DataFrame.insert not working properly | {
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} | [] | closed | false | null | [] | null | 13 | 2013-11-16T23:30:17Z | 2013-11-18T14:01:03Z | 2013-11-18T14:01:03Z | CONTRIBUTOR | null | For some reason I am just ending up with `NaT`s:
```
def get_month(x):
return x.replace(day=1)
p1 = pd.DataFrame({ 'Baz':[100]}, index=[pd.Timestamp('2010-05-13')])
p2 = p1.copy()
p2.insert(1, "Month", p1.reset_index()['index'].apply(get_month))
p2
Out[1]:
Baz Month
2010-05-13 100 NaT
```
the apply does work:
```
In [2]: p1.reset_index()['index'].apply(get_month)
Out[2]:
0 2010-05-01 00:00:00
Name: index, dtype: datetime64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5533/events | https://github.com/pandas-dev/pandas/pull/5533 | 22,791,286 | MDExOlB1bGxSZXF1ZXN0MTAwMjk1NDE= | 5,533 | PERF faster head, tail and size groupby methods | {
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} | [] | closed | false | null | [] | null | 6 | 2013-11-17T04:47:17Z | 2014-07-16T08:40:40Z | 2013-11-20T01:42:36Z | CONTRIBUTOR | null | Try again with #5518.
Massive gains in groupby head and tail, adds more tests for these, slight speed improvement in size (not as much as I'd hoped, basically iterating through grouper.indices is slow :( ).
_As mentioned before, I added a helper function to prepend as_index to index (if that makes **any** sense), I think it could be faster, and also using it in apply may fix some bugs there..._
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} | 46 | 2013-11-17T10:10:29Z | 2014-06-16T20:51:36Z | 2014-05-13T14:29:21Z | CONTRIBUTOR | null | closes #3960.
_Implemented using kth_largest, could cython using better algo later._
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https://api.github.com/repos/pandas-dev/pandas/issues/5535 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5535/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5535/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5535/events | https://github.com/pandas-dev/pandas/issues/5535 | 22,796,763 | MDU6SXNzdWUyMjc5Njc2Mw== | 5,535 | computation.tests.test_eval.TestScope.test_global_scope generates errors with numexpr engine and numpy 1.7.0 | {
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} | 5 | 2013-11-17T13:49:43Z | 2016-10-12T23:04:23Z | 2013-12-09T20:19:46Z | CONTRIBUTOR | null | ```
$ nosetests -v pandas.computation.tests.test_eval:TestScope.test_global_scope
pandas.computation.tests.test_eval.TestScope.test_global_scope('_var_s * 2', 'python', 'python') ... ok
pandas.computation.tests.test_eval.TestScope.test_global_scope('_var_s * 2', 'python', 'pandas') ... ok
pandas.computation.tests.test_eval.TestScope.test_global_scope('_var_s * 2', 'numexpr', 'python') ... ERROR
pandas.computation.tests.test_eval.TestScope.test_global_scope('_var_s * 2', 'numexpr', 'pandas') ... ERROR
======================================================================
ERROR: pandas.computation.tests.test_eval.TestScope.test_global_scope('_var_s * 2', 'numexpr', 'python')
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/nose-1.3.0-py2.7.egg/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/obrienjw/src/github/pandas/pandas/computation/tests/test_eval.py", line 1430, in check_global_scope
parser=parser))
File "/home/obrienjw/src/github/pandas/pandas/computation/eval.py", line 208, in eval
ret = eng_inst.evaluate()
File "/home/obrienjw/src/github/pandas/pandas/computation/engines.py", line 53, in evaluate
self.expr.terms.return_type)
File "/home/obrienjw/src/github/pandas/pandas/computation/align.py", line 250, in _reconstruct_object
ret = ret_value.item()
IndexError: index 140737488325120 is out of bounds for axis 0 with size 10
======================================================================
ERROR: pandas.computation.tests.test_eval.TestScope.test_global_scope('_var_s * 2', 'numexpr', 'pandas')
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/nose-1.3.0-py2.7.egg/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/obrienjw/src/github/pandas/pandas/computation/tests/test_eval.py", line 1430, in check_global_scope
parser=parser))
File "/home/obrienjw/src/github/pandas/pandas/computation/eval.py", line 208, in eval
ret = eng_inst.evaluate()
File "/home/obrienjw/src/github/pandas/pandas/computation/engines.py", line 53, in evaluate
self.expr.terms.return_type)
File "/home/obrienjw/src/github/pandas/pandas/computation/align.py", line 250, in _reconstruct_object
ret = ret_value.item()
IndexError: index 140737488325120 is out of bounds for axis 0 with size 10
----------------------------------------------------------------------
Ran 4 tests in 0.007s
FAILED (errors=2)
```
Here are the version details of my environment.
```
$ git describe
v0.12.0-1110-gd250d64
$ ci/print_versions.py
INSTALLED VERSIONS
------------------
Python: 2.7.5.final.0
OS: FreeBSD 9.2-PRERELEASE FreeBSD 9.2-PRERELEASE #0 r254955: Tue Aug 27 11:01:00 EDT 2013 [email protected]:/usr/obj/usr/src/sys/NARB amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: Not installed
Cython: 0.19.1
Numpy: 1.7.0
Scipy: 0.12.1
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2013.8
bottleneck: Not installed
PyTables: Not Installed
numexpr: 2.2.2
matplotlib: 1.2.0
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
Edited title to reflect latest analysis; affected code depends on the version of numpy, not the platform.
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https://api.github.com/repos/pandas-dev/pandas/issues/5536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5536/events | https://github.com/pandas-dev/pandas/issues/5536 | 22,797,093 | MDU6SXNzdWUyMjc5NzA5Mw== | 5,536 | tools.tests.test_tile:TestCut.test_inf_handling generates error on FreeBSD | {
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$ nosetests -v pandas.tools.tests.test_tile:TestCut.test_inf_handling
test_inf_handling (pandas.tools.tests.test_tile.TestCut) ... ERROR
======================================================================
ERROR: test_inf_handling (pandas.tools.tests.test_tile.TestCut)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/obrienjw/src/github/pandas/pandas/tools/tests/test_tile.py", line 121, in test_inf_handling
result = cut(data, [-np.inf, 2, 4, np.inf])
File "/home/obrienjw/src/github/pandas/pandas/tools/tile.py", line 103, in cut
include_lowest=include_lowest)
File "/home/obrienjw/src/github/pandas/pandas/tools/tile.py", line 169, in _bins_to_cuts
include_lowest=include_lowest)
File "/home/obrienjw/src/github/pandas/pandas/tools/tile.py", line 205, in _format_levels
fa, fb = fmt(a), fmt(b)
File "/home/obrienjw/src/github/pandas/pandas/tools/tile.py", line 201, in <lambda>
fmt = lambda v: _format_label(v, precision=prec)
File "/home/obrienjw/src/github/pandas/pandas/tools/tile.py", line 235, in _format_label
return '%d' % (-whole - 1)
TypeError: %d format: a number is required, not numpy.float64
----------------------------------------------------------------------
Ran 1 test in 0.002s
FAILED (errors=1)
```
My environment:
```
$ git describe
v0.12.0-1110-gd250d64
$ ci/print_versions.py
INSTALLED VERSIONS
------------------
Python: 2.7.5.final.0
OS: FreeBSD 9.2-PRERELEASE FreeBSD 9.2-PRERELEASE #0 r254955: Tue Aug 27 11:01:00 EDT 2013 [email protected]:/usr/obj/usr/src/sys/NARB amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: Not installed
Cython: 0.19.1
Numpy: 1.7.0
Scipy: 0.12.1
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2013.8
bottleneck: Not installed
PyTables: Not Installed
numexpr: 2.2.2
matplotlib: 1.2.0
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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$ nosetests pandas.tools.tests.test_util:TestLocaleUtils.test_set_locale
F
======================================================================
FAIL: test_set_locale (pandas.tools.tests.test_util.TestLocaleUtils)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/obrienjw/src/github/pandas/pandas/tools/tests/test_util.py", line 71, in test_set_locale
pass
File "/home/obrienjw/src/github/pandas/pandas/util/testing.py", line 1308, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: Error not raised.
----------------------------------------------------------------------
Ran 1 test in 0.044s
FAILED (failures=1)
```
My environment:
```
$ git describe
v0.12.0-1110-gd250d64
$ ci/print_versions.py
INSTALLED VERSIONS
------------------
Python: 2.7.5.final.0
OS: FreeBSD 9.2-PRERELEASE FreeBSD 9.2-PRERELEASE #0 r254955: Tue Aug 27 11:01:00 EDT 2013 [email protected]:/usr/obj/usr/src/sys/NARB amd64
byteorder: little
LC_ALL: None
LANG: None
pandas: Not installed
Cython: 0.19.1
Numpy: 1.7.0
Scipy: 0.12.1
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2013.8
bottleneck: Not installed
PyTables: Not Installed
numexpr: 2.2.2
matplotlib: 1.2.0
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
I will submit a pull request for this shortly.
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https://api.github.com/repos/pandas-dev/pandas/issues/5538 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5538/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5538/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5538/events | https://github.com/pandas-dev/pandas/pull/5538 | 22,797,196 | MDExOlB1bGxSZXF1ZXN0MTAwMzE4MzQ= | 5,538 | TST: correctly predict locale setability | {
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} | [] | closed | false | null | [] | null | 1 | 2013-11-17T14:24:40Z | 2014-06-19T22:50:55Z | 2013-11-17T15:00:14Z | CONTRIBUTOR | null | Don't join the lang and encoding when performing the trial set, because
```
locale.setlocale(locale.LC_ALL, ('it_CH', 'utf-8'))
```
succeeds, but
```
locale.setlocale(locale.LC_ALL, 'it_CH.utf-8')
```
throws locale.Error.
Closes #5537.
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} | 5 | 2013-11-17T16:35:39Z | 2017-11-05T04:36:52Z | 2017-11-05T04:36:52Z | CONTRIBUTOR | null | This used to work (October 2012), but doesn't anymore:
``` python
from pandas import DataFrame
import numpy as np
import StringIO
a = ["a","b","a","b","a","b","a","b","a","b","a","b"]
b = ["c","d","e","c","d","e","c","d","e","c","d","e"]
c = [1,2,3,4,5,6,7,8,9,10,11,12]
d = list(reversed(c))
df = DataFrame({"a":a, "b":b, "c":c, "d":d})
_agg_funs = [np.mean, np.std, np.min, np.max]
groupby_variables = ["a","b"]
df_grouped = df.groupby(groupby_variables, as_index=True).agg(_agg_funs)
output = StringIO.StringIO()
df_grouped.to_csv(output, header=[var + "_" + agg for (var, agg) in df_grouped.columns])
index = output.getvalue().split("\n")[0].split(",")
expected_index = groupby_variables + [var + "_" + agg for (var, agg) in df_grouped.columns]
print(index == expected_index) # This was true in October 2012!
print(index)
print(expected_index)
False
['', '', 'c', 'c', 'c', 'c', 'd', 'd', 'd', 'd']
['a', 'b', 'c_mean', 'c_std', 'c_amin', 'c_amax', 'd_mean', 'd_std', 'd_amin', 'd_amax']
```
Probably related to #3575
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```
======================================================================
FAIL: test_cumcount_empty (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/gmd/github_shadow/pandas/pandas/tests/test_groupby.py", line 2579, in test_cumcount_empty
assert_series_equal(e, ge.cumcount())
File "/home/gmd/github_shadow/pandas/pandas/util/testing.py", line 423, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "/home/gmd/github_shadow/pandas/pandas/util/testing.py", line 407, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "/home/gmd/github_shadow/pandas/pandas/util/testing.py", line 390, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
```
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} | 9 | 2013-11-18T19:17:07Z | 2021-04-11T03:55:33Z | null | CONTRIBUTOR | null | I'm having trouble figuring out how DataFrame.replace() is supposed to work. I'm not sure if this is a bug or a documentation issue.
```
In [1]: import pandas
In [2]: df = pandas.DataFrame({"col1":range(5), "col2":[0.5]*3+[1.0]*2})
In [3]: df
Out[3]:
col1 col2
0 0 0.5
1 1 0.5
2 2 0.5
3 3 1.0
4 4 1.0
In [4]: df.replace(1.0, "a")
Out[4]:
col1 col2
0 0 0.5
1 a 0.5
2 2 0.5
3 3 a
4 4 a
In [5]: df.replace(1.0, "a").replace(0.5, "b")
Out[5]:
col1 col2
0 0 b
1 a b
2 2 b
3 3 a
4 4 a
```
So far, so good, everything makes sense. But I would have expected this to accomplish the same as above:
```
In [6]: df.replace({1.0:"a", 0.5:"b"})
Out[6]:
col1 col2
0 b b
1 a a
2 2 b
3 3 a
4 4 b
```
As you can see, I'm getting alternating "b" and "a". From a quick browse of the source code, it seems that the dictionary-replacement option should result in the same outcome as the following (which gives what I would have expected):
```
In [15]: df.replace([1.0, 0.5], ["a", "b"])
Out[15]:
col1 col2
a 0 b
b a b
c 2 b
d 3 a
e 4 a
```
I'm not sure what the to_replace=dict option is supposed to be doing but (at least for pandas v 0.12.0) it isn't doing what I would have expected.
Whether this is a bug or not, the df.replace() method needs better documentation. It's not enough to include a disclaimer that "This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works."
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} | 2 | 2013-11-18T22:25:07Z | 2013-11-19T01:49:54Z | 2013-11-19T01:49:54Z | CONTRIBUTOR | null | I'm not sure whether `del someseries[something]` is supposed to work or not, although there is a `Series.__delitem__` method so maybe at some point it was. And `.drop()` seems to work, so it's not super-important. But ISTM users shouldn't see this:
```
>>> pd.__version__
'0.12.0-1114-g4d0632b'
>>> ser = pd.Series(range(5))
>>> del ser[0]
Traceback (most recent call last):
File "<ipython-input-62-e967e45427c2>", line 1, in <module>
del ser[0]
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1114_g4d0632b-py2.7-linux-i686.egg/pandas/core/generic.py", line 1057, in __delitem__
self._data.delete(key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1114_g4d0632b-py2.7-linux-i686.egg/pandas/core/internals.py", line 2818, in delete
self.set_items_norename(new_items)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1114_g4d0632b-py2.7-linux-i686.egg/pandas/core/internals.py", line 2923, in set_items_norename
self.set_axis(0, value, maybe_rename=False, check_axis=False)
TypeError: set_axis() got an unexpected keyword argument 'maybe_rename'
>>> del ser[0]
Traceback (most recent call last):
File "<ipython-input-63-e967e45427c2>", line 1, in <module>
del ser[0]
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1114_g4d0632b-py2.7-linux-i686.egg/pandas/core/generic.py", line 1057, in __delitem__
self._data.delete(key)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1114_g4d0632b-py2.7-linux-i686.egg/pandas/core/internals.py", line 2814, in delete
self._delete_from_all_blocks(loc, item)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1114_g4d0632b-py2.7-linux-i686.egg/pandas/core/internals.py", line 2945, in _delete_from_all_blocks
i, _ = self._find_block(item)
TypeError: 'NoneType' object is not iterable
```
That the error message isn't the same both times is a little disturbing in any case, because that suggests something stateful changed, which it's hard to believe can be right.
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} | 0 | 2013-11-18T23:49:12Z | 2014-02-17T17:46:58Z | 2014-02-17T17:46:58Z | CONTRIBUTOR | null | I've had to use this a few times, maybe some other people could use it too.
At least for unique indices, the symmetric difference should be equivalent to `(df1.index - df2.index).union(df2.index - df1.index)`. Example:
``` python
In [54]: df1 = pd.DataFrame([1,2,3,4], index=['a', 'b', 'c', 'd'])
In [55]: df1
Out[55]:
0
a 1
b 2
c 3
d 4
In [56]: df2 = pd.DataFrame([5, 6, 7, 8], index=['a', 'd', 'e', 'f'])
In [57]: df2
Out[57]:
0
a 5
d 6
e 7
f 8
In [58]: (df1.index - df2.index).union(df2.index - df1.index)
Out[58]: Index([u'b', u'c', u'e', u'f'], dtype='object')
```
I'll need to look into how would work for
- Indicies with duplicates
- MultiIndexes
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} | 0 | 2013-11-19T01:00:56Z | 2014-06-23T11:03:02Z | 2013-11-19T01:49:54Z | CONTRIBUTOR | null | closes #5542
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} | 1 | 2013-11-19T04:59:29Z | 2013-11-20T14:28:49Z | 2013-11-20T14:28:49Z | NONE | null | Tested pandas 0.11, and 0.12, both exhibit this problem
```
import pandas as pd
data = pd.DataFrame({'id_field' : [100, 100, 200, 300], 'category' : ['a','b','c','c'], 'value' : [1,2,3,4]})
def filt1(x):
if x.shape[0] == 1:
return x.copy()
else:
return x[x.category == 'c']
def filt2(x):
if x.shape[0] == 1:
return x
else:
return x[x.category == 'c']
#works fine
print data.groupby('id_field').apply(filt1)
#throws error
print data.groupby('id_field').apply(filt2)
```
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} | 0 | 2013-11-19T05:55:38Z | 2014-04-13T07:14:55Z | 2014-04-13T07:14:55Z | CONTRIBUTOR | null | `DatetimeIndex` correctly handles `NaT`:
```
In [2]: pd.DatetimeIndex([pd.NaT])
Out[2]:
<class 'pandas.tseries.index.DatetimeIndex'>
[NaT]
Length: 1, Freq: None, Timezone: None
```
But calling `tz_localize` leads to garbage:
```
In [1]: pd.DatetimeIndex([pd.NaT]).tz_localize('US/Pacific')
Out[1]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2262-04-10 00:12:43.145224192]
Length: 1, Freq: None, Timezone: US/Pacific
```
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} | [] | closed | false | null | [] | null | 1 | 2013-11-19T15:35:19Z | 2013-11-19T15:51:24Z | 2013-11-19T15:51:24Z | NONE | null | ``` python
s = pd.Series( range(10), index = pd.PeriodIndex(start = '2013-10-1', end = '2013-10-10', freq = 'D'))
pd.rolling_median(s,5, min_periods = 1)
2013-10-01 0.0
2013-10-02 0.5
2013-10-03 1.0
2013-10-04 1.5
2013-10-05 2.0
2013-10-06 3.0
2013-10-07 4.0
2013-10-08 5.0
2013-10-09 6.0
2013-10-10 7.0
Freq: D, dtype: float64
```
When center is specified we get Nans in the last window//2 positions
```
s = pd.Series( range(10), index = pd.PeriodIndex(start = '2013-10-1', end = '2013-10-10', freq = 'D'))
pd.rolling_median(s,5, min_periods = 1, center = True)
2013-10-01 1.0
2013-10-02 1.5
2013-10-03 2.0
2013-10-04 3.0
2013-10-05 4.0
2013-10-06 5.0
2013-10-07 6.0
2013-10-08 7.0
2013-10-09 NaN
2013-10-10 NaN
Freq: D, dtype: float64
```
I was expecting the last two values to be 7.5 and 8.
I got the same results, nans at the end with rolling_mean as well
This is with pandas 12.0 and windows 64.
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https://api.github.com/repos/pandas-dev/pandas/issues/5550 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5550/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5550/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5550/events | https://github.com/pandas-dev/pandas/pull/5550 | 22,941,825 | MDExOlB1bGxSZXF1ZXN0MTAxMDM1ODI= | 5,550 | HTML (and text) reprs for large dataframes. | {
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} | 59 | 2013-11-19T19:49:46Z | 2014-06-12T17:07:25Z | 2013-11-26T01:04:01Z | CONTRIBUTOR | null | As discussed in #4886, the HTML representation of DataFrames currently starts off as a table, but switches to the condensed info view if the table exceeds a certain size (by default, more than 60 rows or 20 columns). I've seen this confusing users, who think that they suddenly have a completely different kind of object, and don't understand why.
With these changes, the HTML repr always displays the table, but truncates it when it exceeds a certain size. It reuses the same options, `display.max_rows` and `display.max_columns`.
Before:
After:
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https://api.github.com/repos/pandas-dev/pandas/issues/5551 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5551/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5551/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5551/events | https://github.com/pandas-dev/pandas/issues/5551 | 22,949,847 | MDU6SXNzdWUyMjk0OTg0Nw== | 5,551 | 0.12: Strange Series.reindex() error | {
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} | [] | closed | false | null | [] | null | 1 | 2013-11-19T21:16:45Z | 2013-11-19T21:25:57Z | 2013-11-19T21:25:57Z | NONE | null | Using 0.12 production version.
I have a series res that is indexed by strings:
```
res.index.shape
```
> (2578L,)
```
res.head()
```
> stock_id
> 000312 -0.000198
> 00036110 0.000142
> 00101J10 -0.000229
> 00103110 -0.000170
> 00105510 -0.001236
> dtype: float64
I have a DataFrame test also indexed by stock_id. res has some stock_ids missing compared to test.
```
test.index.shape
```
> (2607L,)
```
test.index[:5]
```
> Index([u'000312', u'00036110', u'00101J10', u'00103110', u'00105510'], dtype=object)
I am trying to add res as a column to test. For that, I am trying to reindex res:
```
res.reindex(test.index)
```
> ---------------------------------------------------------------------------
> AssertionError Traceback (most recent call last)
> <ipython-input-148-265ae924cbbf> in <module>()
> ----> 1 res.reindex(test.index)
>
> C:\Python27\lib\site-packages\pandas\core\series.pyc in reindex(self, index, method, level, fill_value, limit, copy, takeable)
> 2644
> 2645 # GH4246 (dispatch to a common method with frame to handle possibly duplicate index)
> -> 2646 return self._reindex_with_indexers(new_index, indexer, copy=copy, fill_value=fill_value)
> 2647
> 2648 def _reindex_with_indexers(self, index, indexer, copy, fill_value):
>
> C:\Python27\lib\site-packages\pandas\core\series.pyc in _reindex_with_indexers(self, index, indexer, copy, fill_value)
> 2648 def _reindex_with_indexers(self, index, indexer, copy, fill_value):
> 2649 new_values = com.take_1d(self.values, indexer, fill_value=fill_value)
> -> 2650 return Series(new_values, index=index, name=self.name)
> 2651
> 2652 def reindex_axis(self, labels, axis=0, **kwargs):
>
> C:\Python27\lib\site-packages\pandas\core\series.pyc in **new**(cls, data, index, dtype, name, copy)
> 490 else:
> 491 subarr = subarr.view(Series)
> --> 492 subarr.index = index
> 493 subarr.name = name
> 494
>
> C:\Python27\lib\site-packages\pandas\lib.pyd in pandas.lib.SeriesIndex.**set** (pandas\lib.c:29092)()
>
> AssertionError: Index length did not match values
This is strange as I thought this was exactly the reason for reindex. For example, I can easily do this:
```
x = Series(range(10))
x.reindex(range(20))
```
Why doesn't it work in my case?
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} | 14 | 2013-11-19T22:02:44Z | 2014-03-07T20:24:22Z | 2014-03-07T20:24:22Z | CONTRIBUTOR | null | The nth groupby method on a Series takes the nth **non-NaN** value in the Series.
This means on a DataFrame it's not going to be well defined...
Should we make this a Series only method?
```
In [1]: g = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],columns=['A']).groupby('A')
In [2]: g.nth(0)
Out[2]:
Empty DataFrame
Columns: []
Index: []
In [3]: g.A.nth(0)
Out[3]:
A
a a
b b
Name: A, dtype: object
```
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} | 11 | 2013-11-20T03:50:01Z | 2013-11-20T16:07:05Z | 2013-11-20T16:07:05Z | CONTRIBUTOR | null | I found that segfault that messed up my HDFStore earlier.
You'll need to csv files:
https://www.dropbox.com/s/55gcpkp3b8byfka/df1.csv
https://www.dropbox.com/s/mqqhfgocdg4qggp/df2.csv
To reproduce:
``` python
In [1]: df1 = pd.read_csv('df1.csv', index_col=[0,1,2])
In [2]: df2 = pd.read_csv('df2.csv', index_col=[0,1,2])
In [3]: df2.loc[df1.index]
Segmentation fault: 11
(pandas-dev)dhcp80fff527:Desktop tom$
```
I've never debugged a segfault before, but I gather there are some [methods](https://wiki.python.org/moin/DebuggingWithGdb). Can anyone reproduce this?
Also, that's a valid use of `.loc` right?
In [1]: pd.**version**
Out[1]: '0.12.0-1084-ged64416'
In [2]: np.**version**
Out[2]: '1.7.1'
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] | closed | false | null | [] | null | 4 | 2013-11-20T16:17:56Z | 2014-01-04T18:04:18Z | 2013-11-20T16:23:45Z | CONTRIBUTOR | null | `Panel.apply(func, axis)` currently just pulls out `.values` and passes them to `numpy.apply_along_axis` -- which makes its utility questionable since all the indices and labels get lost in the process.
Wouldn't it make much more sense to pass DataFrames to the func and then try and recombine the result into either Series, DataFrame or a Panel?
By the way, this behaviour is not in the docs and is inconsistent with how apply works for series/frames, so an unwary user might be in for a surprise.
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} | 3 | 2013-11-20T19:16:25Z | 2013-11-20T21:10:31Z | 2013-11-20T20:15:58Z | CONTRIBUTOR | null | I'm getting occasional KeyErrors during `.value_counts()` on trunk. It's pretty flaky, and this is the smallest example I had at hand (had one with only ~60 but lost it.)
```
>>> import pandas as pd
>>> print pd.__version__
0.12.0-1128-ge6aaf5e
>>>
>>> ser = {256: 2321.0, 1: 78.0, 2: 2716.0, 3: 0.0, 4: 369.0, 5: 0.0, 6: 269.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 3536.0, 11: 0.0, 12: 24.0, 13: 0.0, 14: 931.0, 15: 0.0, 16: 101.0, 17: 78.0, 18: 9643.0, 19: 0.0, 20: 0.0, 21: 0.0, 22: 63761.0, 23: 0.0, 24: 446.0, 25: 0.0, 26: 34773.0, 27: 0.0, 28: 729.0, 29: 78.0, 30: 0.0, 31: 0.0, 32: 3374.0, 33: 0.0, 34: 1391.0, 35: 0.0, 36: 361.0, 37: 0.0, 38: 61808.0, 39: 0.0, 40: 0.0, 41: 0.0, 42: 6677.0, 43: 0.0, 44: 802.0, 45: 0.0, 46: 2691.0, 47: 0.0, 48: 3582.0, 49: 0.0, 50: 734.0, 51: 0.0, 52: 627.0, 53: 70.0, 54: 2584.0, 55: 0.0, 56: 324.0, 57: 0.0, 58: 605.0, 59: 0.0, 60: 0.0, 61: 0.0, 62: 3989.0, 63: 10.0, 64: 42.0, 65: 0.0, 66: 904.0, 67: 0.0, 68: 88.0, 69: 70.0, 70: 8172.0, 71: 0.0, 72: 0.0, 73: 0.0, 74: 64902.0, 75: 0.0, 76: 347.0, 77: 0.0, 78: 36605.0, 79: 0.0, 80: 379.0, 81: 70.0, 82: 0.0, 83: 0.0, 84: 3001.0, 85: 0.0, 86: 1630.0, 87: 7.0, 88: 364.0, 89: 0.0, 90: 67404.0, 91: 9.0, 92: 0.0, 93: 0.0, 94: 7685.0, 95: 0.0, 96: 1017.0, 97: 0.0, 98: 2831.0, 99: 0.0, 100: 2963.0, 101: 0.0, 102: 854.0, 103: 0.0, 104: 0.0, 105: 0.0, 106: 0.0, 107: 0.0, 108: 0.0, 109: 0.0, 110: 0.0, 111: 0.0, 112: 0.0, 113: 0.0, 114: 0.0, 115: 0.0, 116: 0.0, 117: 0.0, 118: 0.0, 119: 0.0, 120: 0.0, 121: 0.0, 122: 0.0, 123: 0.0, 124: 0.0, 125: 0.0, 126: 67744.0, 127: 22.0, 128: 264.0, 129: 0.0, 260: 197.0, 268: 0.0, 265: 0.0, 269: 0.0, 261: 0.0, 266: 1198.0, 267: 0.0, 262: 2629.0, 258: 775.0, 257: 0.0, 263: 0.0, 259: 0.0, 264: 163.0, 250: 10326.0, 251: 0.0, 252: 1228.0, 253: 0.0, 254: 2769.0, 255: 0.0}
>>> s = pd.Series(ser)
>>> s.value_counts()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/base.py", line 55, in __repr__
return str(self)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/base.py", line 35, in __str__
return self.__bytes__()
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/base.py", line 47, in __bytes__
return self.__unicode__().encode(encoding, 'replace')
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/series.py", line 854, in __unicode__
result = self._tidy_repr(min(30, max_rows - 4))
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/series.py", line 873, in _tidy_repr
head = self[:num]._get_repr(print_header=True, length=False,
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/series.py", line 698, in __getslice__
return self._slice(slobj)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/series.py", line 478, in _slice
slobj = self.index._convert_slice_indexer(slobj, typ=typ or 'getitem')
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/index.py", line 1811, in _convert_slice_indexer
return self.slice_indexer(key.start, key.stop, key.step)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/index.py", line 1493, in slice_indexer
start_slice, end_slice = self.slice_locs(start, end)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/index.py", line 1555, in slice_locs
end_slice = self.get_loc(end)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1128_ge6aaf5e-py2.7-linux-i686.egg/pandas/core/index.py", line 1017, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "index.pyx", line 129, in pandas.index.IndexEngine.get_loc (pandas/index.c:3548)
File "index.pyx", line 149, in pandas.index.IndexEngine.get_loc (pandas/index.c:3428)
File "hashtable.pyx", line 674, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10784)
File "hashtable.pyx", line 682, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10737)
KeyError: 15
>>> 15 in s
True
>>> s[15]
0.0
```
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} | 0 | 2013-11-20T19:52:12Z | 2014-06-12T14:46:25Z | 2013-11-20T20:15:58Z | CONTRIBUTOR | null | closes #5557
```
In [4]: s.value_counts()
Out[4]:
0 85
70 3
78 3
2584 1
3001 1
734 1
163 1
3374 1
9643 1
42 1
8172 1
931 1
802 1
67744 1
2716 1
...
775 1
36605 1
1630 1
605 1
347 1
729 1
88 1
854 1
34773 1
2769 1
3536 1
67404 1
264 1
1228 1
324 1
Length: 61, dtype: int64
```
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} | [] | closed | false | null | [] | null | 2 | 2013-11-20T21:12:41Z | 2014-06-14T21:14:01Z | 2013-11-20T21:15:16Z | CONTRIBUTOR | null | @jreback is this what you mean? #4646
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} | [] | closed | false | null | [] | null | 4 | 2013-11-20T21:25:18Z | 2014-06-23T21:39:28Z | 2013-11-23T10:10:45Z | MEMBER | null | Some stylistic clean-up of the docstrings in frame.py.
Things changed are mainly:
- missing spaces aroung the `:` in `param : type`
- explanation of param on second line (instead of on the same line -> there should be the type info)
- add bullets to enumerations of possible values of a keyword (so they are rendered as a list in the html docstring instead of one continuous block of text) Eg the `method` keyword explanation in http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.corr.html
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} | 13 | 2013-11-20T21:29:00Z | 2015-12-16T01:01:04Z | 2014-10-07T21:52:42Z | CONTRIBUTOR | null | Here is a collection of build scripts/tools for windows:
a nice conda recipe:
https://github.com/rmcgibbo/python-appveyor-conda-example/blob/master/appveyor.yml
https://github.com/pydata/pandas/tree/master/scripts/windows_builder
I have found many confusing explanations/suggestion on the web on how to build python extensions on windows 32/64 bit. The following is very simple to build from master.
Follow the instructions on the following link to build on windows with a binary compat to the existing python. Bascially.
http://pybytes.com/pywavelets/dev/preparing_windows_build_environment.html#installing-windows-sdk-c-c-compiler
Essentially:
- download the Microsoft SDK (you need the 64-bit one of you are on a 64-bit machine, even if you are going to want to build 32-bit builds); this is just the install kit. I used http://www.slysoft.com/en/virtual-clonedrive.html to mount the `.iso` and install it
- open the CMD Shell (installed onto the start menu)
- set the env variables (I just copy pasted, but you can make a bat file if you want). for either 32 or 64 bit.
you can then just
```
c:\python27\python.exe setup.py build_ext --inplace
c:\python27\scripts\nosetests pandas
```
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} | 3 | 2013-11-20T22:41:47Z | 2018-07-06T22:12:23Z | 2018-07-06T22:12:12Z | NONE | null | see https://github.com/pydata/pandas/pull/5550#issuecomment-28931094
related #6062
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} | 10 | 2013-11-20T22:46:56Z | 2019-10-27T22:57:41Z | null | NONE | null | Not sure if it's always been this way or if refactoring broke something.
In any case it looks strange when in ipnb:
related to #5550, making reprs consistent.
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} | 16 | 2013-11-21T12:46:25Z | 2014-06-30T21:46:37Z | 2013-12-07T14:15:52Z | CONTRIBUTOR | null | closes #4920
I thought I submitted this PR long ago.
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} | 0 | 2013-11-21T13:21:48Z | 2014-06-26T05:41:17Z | 2013-11-21T13:22:16Z | NONE | null | Related #5147, #5457
before
```
In [4]: %timeit -r 10 pd.get_option("display.max_rows")
10000 loops, best of 10: 53.3 us per loop
```
after
```
In [1]: %timeit -r 10 pd.get_option("display.max_rows")
100000 loops, best of 10: 5.5 us per loop
```
jeff's version with no error checking or rerouting deprecated keys
```
In [1]: %timeit -r 10 pd.core.config._get_option_fast("display.max_rows")
1000000 loops, best of 10: 792 ns per loop
```
Probably makes no practical difference.
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} | 0 | 2013-11-21T14:24:45Z | 2014-07-16T08:41:13Z | 2013-11-21T15:07:41Z | CONTRIBUTOR | null | e.g. 'l1=selection.index' and 'l1=l', where l = selection.index were failing
example reproduced here:
http://stackoverflow.com/questions/20111542/selecting-rows-from-an-hdfstore-given-list-of-indexes
```
In [5]: parms = DataFrame({ 'A' : [1,1,2,2,3] })
In [6]: parms
Out[6]:
A
0 1
1 1
2 2
3 2
4 3
In [7]: parms.to_hdf('parms.hdf','df',mode='w',format='table',data_columns=['A'])
In [8]: selection = pd.read_hdf('parms.hdf','df',where='A=[2,3]')
In [9]: selection
Out[9]:
A
2 2
3 2
4 3
In [10]: hist = DataFrame(np.random.randn(25,1),columns=['data'],
....: index=MultiIndex.from_tuples([ (i,j) for i in range(5) for j in range(5) ],
....: names=['l1','l2']))
In [11]: hist
Out[11]:
data
l1 l2
0 0 1.232358
1 -2.677047
2 -0.168854
3 0.538848
4 -0.678224
1 0 0.092575
1 1.297578
2 -1.489906
3 -1.380054
4 0.701762
2 0 1.397368
1 0.198522
2 1.034036
3 0.650406
4 1.823683
3 0 0.045635
1 -0.213975
2 -1.221950
3 -0.145615
4 -1.187883
4 0 -0.782221
1 -0.626280
2 -0.331885
3 -0.975978
4 2.006322
```
this works in 0.12
```
In [15]: pd.read_hdf('hist.hdf','df',where=pd.Term('l1','=',selection.index.tolist()))
Out[15]:
data
l1 l2
2 0 1.397368
1 0.198522
2 1.034036
3 0.650406
4 1.823683
3 0 0.045635
1 -0.213975
2 -1.221950
3 -0.145615
4 -1.187883
4 0 -0.782221
1 -0.626280
2 -0.331885
3 -0.975978
4 2.006322
```
This works in 0.13 as well
```
In [16]: pd.read_hdf('hist.hdf','df',where='l1=selection.index')
Out[16]:
data
l1 l2
2 0 1.397368
1 0.198522
2 1.034036
3 0.650406
4 1.823683
3 0 0.045635
1 -0.213975
2 -1.221950
3 -0.145615
4 -1.187883
4 0 -0.782221
1 -0.626280
2 -0.331885
3 -0.975978
4 2.006322
```
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} | 13 | 2013-11-21T16:11:23Z | 2014-02-16T14:05:32Z | 2014-02-16T00:41:29Z | CONTRIBUTOR | null | Series xs when presented with a multi-index should use the data frame logic (whereby it can use the levels to avoid having to scan the entire set). Need to move the logic of `xs` to `core/generic.py` so both Series/Frame can use it.
---
I stumbled on a weird issue.
The first time I access a series location with ix I have a huge overhead.
This is not the case if I transform the series in a dataframe and the access it.
I use a mutliindex below because it makes the effect more visible, but the same problem is present for regular indexes.
``` python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.12.0'
In [3]: mi = pd.MultiIndex.from_tuples([(x,y) for x in range(1000) for y in range(1000)])
In [4]: s = pd.Series(randn(1000000), index=mi)
In [5]: %time s.ix[999]
CPU times: user 652 ms, sys: 4 ms, total: 656 ms
Wall time: 656 ms
Out[5]:
0 -0.271328
...
999 -0.832013
Length: 1000, dtype: float64
In [6]: %time s.ix[999]
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 249 us
Out[6]:
0 -0.271328
...
999 -0.832013
Length: 1000, dtype: float64
```
The behavior seems related to the index because recreating the series does not reproduce it, but sorting the index does:
``` python
In [7]: s = pd.Series(randn(1000000), index=mi)
In [8]: %time s.ix[999]
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 247 us
Out[8]:
0 -1.207499
...
999 -0.370578
Length: 1000, dtype: float64
In [9]: %time s.ix[999]
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 202 us
Out[9]:
0 -1.207499
...
999 -0.370578
Length: 1000, dtype: float64
In [10]: s = s.sort_index()
In [11]: %time s.ix[999]
CPU times: user 856 ms, sys: 32 ms, total: 888 ms
Wall time: 890 ms
Out[11]:
0 -1.207499
...
999 -0.370578
Length: 1000, dtype: float64
In [12]: %time s.ix[999]
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 213 us
Out[12]:
0 -1.207499
...
999 -0.370578
Length: 1000, dtype: float64
```
And now the weird thing. I convert the series into a df:
``` python
In [16]: s = s.sort_index()
In [17]: %time pd.DataFrame(s).ix[999]
CPU times: user 4 ms, sys: 0 ns, total: 4 ms
Wall time: 3.78 ms
Out[17]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000 entries, 0 to 999
Data columns (total 1 columns):
0 1000 non-null values
dtypes: float64(1)
In [18]: %time pd.DataFrame(s).ix[999]
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 397 us
Out[18]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000 entries, 0 to 999
Data columns (total 1 columns):
0 1000 non-null values
dtypes: float64(1)
```
There is still a little overhead but nothing compared to the previous case.
It might seem an innocent problem but for large time-series the lag becomes of the order of the second and can eat up a lot of performance.
Lastly, I'm sorry but I don't have easy access to current dev version, so it might be that the problem is already solved. (although I'd be curious to know where it comes from)
Edit: can it be linked to #4198 ?
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} | 0 | 2013-11-21T21:42:56Z | 2014-06-23T14:23:49Z | 2013-11-21T21:44:06Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_from_series | 0.1043 | 17.8473 | 0.0058 |
```
an oversight in handling a case like:
`DataFrame(a_series)`
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https://api.github.com/repos/pandas-dev/pandas/issues/5569 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5569/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5569/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5569/events | https://github.com/pandas-dev/pandas/issues/5569 | 23,118,344 | MDU6SXNzdWUyMzExODM0NA== | 5,569 | Feature request: Option to include NaNs in value_counts() | {
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} | 10 | 2013-11-22T05:17:44Z | 2021-02-02T06:27:18Z | 2014-06-17T12:01:37Z | CONTRIBUTOR | null | I find it highly valuable to also receive the information how many NaN values are in my Series.
Could we have an option in value_counts(), maybe `include_nans=True` that would add a count for those in the output of it?
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https://api.github.com/repos/pandas-dev/pandas/issues/5570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5570/events | https://github.com/pandas-dev/pandas/issues/5570 | 23,140,123 | MDU6SXNzdWUyMzE0MDEyMw== | 5,570 | Pandas sometimes converts multi-indexes to indexes when indexing with all elements | {
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} | [] | closed | false | null | [] | null | 5 | 2013-11-22T14:11:21Z | 2013-11-22T15:14:39Z | 2013-11-22T15:07:40Z | NONE | null | these is done with Pandas 0.12
I create a DataFrame with a MultiIndex
``` Python
>>> h = pd.DataFrame([{'a':1, 'b':1, 'c':1}, {'a':1, 'b':2, 'c':1}])
>>> h.set_index(['a', 'b'], inplace=True)
```
If I take a list of elements (here only a list with one) of a column, I get back a Series with a MultiIndex:
```
>>> h['c'][[(1,1)]].index
MultiIndex
[(1, 1)]
```
However, if I happen to include all elements in the indexing list, then somehow pandas convertes my MultiIndex to an Index:
``` Python
>>> h['c'][[(1,1), (1,2)]].index
Index([(1, 1), (1, 2)], dtype=object)
```
First indexing and taking the column of the DataFrame preserves the MultiIndex:
``` Python
>>> h.ix[[(1,1), (1,2)]]['c'].index
MultiIndex
[(1, 1), (1, 2)]
```
I noticed this while trying to unstack a Series that I indexed with a MultiIndex list and for a certain case the list included all elements leading to this exception:
``` Python
>>> h['c'][[(1,1), (1,2)]].unstack()
AttributeError Traceback (most recent call last)
<ipython-input-916-d897801d99a0> in <module>()
----> 1 h['c'][[(1,1), (1,2)]].unstack()
C:\x64\Python27\lib\site-packages\pandas\core\series.pyc in unstack(self, level)
2437 """
2438 from pandas.core.reshape import unstack
-> 2439 return unstack(self, level)
2440
2441 #----------------------------------------------------------------------
C:\x64\Python27\lib\site-packages\pandas\core\reshape.pyc in unstack(obj, level)
417 return obj.T.stack(dropna=False)
418 else:
--> 419 unstacker = _Unstacker(obj.values, obj.index, level=level)
420 return unstacker.get_result()
421
C:\x64\Python27\lib\site-packages\pandas\core\reshape.pyc in __init__(self, values, index, level, value_columns)
72 self.level = self.index._get_level_number(level)
73
---> 74 levels = index.levels
75 labels = index.labels
76 def _make_index(lev,lab):
AttributeError: 'Index' object has no attribute 'levels'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5571/events | https://github.com/pandas-dev/pandas/issues/5571 | 23,157,350 | MDU6SXNzdWUyMzE1NzM1MA== | 5,571 | Should Series.__contains__ look at indexes or values? | {
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} | 4 | 2013-11-22T18:23:36Z | 2014-03-19T23:16:04Z | 2014-03-19T23:16:04Z | CONTRIBUTOR | null | When writing this SO answer, I came upon this behavior, which I did not expect.
```
In [1]: 1 in Series([1])
Out[1]: False
In [2]: 1 in Series([1, 2])
Out[2]: True
```
Apparently, `__contains__` looks at the index.
```
In [3]: 1 in Series([1], index=[1])
Out[3]: True
```
I can see how that makes sense. But I think it's fair to say that it is potentially confusing. Do we rely on this behavior of `__contains__` for data alignment? Might it be reasonable to change it? I'm not sure that's a good idea, but I want to raise the issue.
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"ValueError: Expecting 23 columns, got 26 in row 64"
The dump is below
In [3]:
infile =open(easygui.fileopenbox(),"r")
pledge = read_csv(infile,parse_dates='true')
---
ValueError Traceback (most recent call last)
<ipython-input-3-b35e7a16b389> in <module>()
1 infile =open(easygui.fileopenbox(),"r")
2
----> 3 pledge = read_csv(infile,parse_dates='true')
C:\Python27\lib\site-packages\pandas-0.8.1-py2.7-win32.egg\pandas\io\parsers.pyc in read_csv(filepath_or_buffer, sep, dialect, header, index_col, names, skiprows, na_values, thousands, comment, parse_dates, keep_date_col, dayfirst, date_parser, nrows, iterator, chunksize, skip_footer, converters, verbose, delimiter, encoding, squeeze)
234 kwds['delimiter'] = sep
235
--> 236 return _read(TextParser, filepath_or_buffer, kwds)
237
238 @Appender(_read_table_doc)
C:\Python27\lib\site-packages\pandas-0.8.1-py2.7-win32.egg\pandas\io\parsers.pyc in _read(cls, filepath_or_buffer, kwds)
189 return parser
190
--> 191 return parser.get_chunk()
192
193 @Appender(_read_csv_doc)
C:\Python27\lib\site-packages\pandas-0.8.1-py2.7-win32.egg\pandas\io\parsers.pyc in get_chunk(self, rows)
779 msg = ('Expecting %d columns, got %d in row %d' %
780 (col_len, zip_len, row_num))
--> 781 raise ValueError(msg)
782
783 data = dict((k, v) for k, v in izip(self.columns, zipped_content))
ValueError: Expecting 23 columns, got 26 in row 64
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} | [] | closed | false | null | [] | null | 11 | 2013-11-22T21:54:25Z | 2014-06-16T03:18:06Z | 2013-11-24T07:31:28Z | CONTRIBUTOR | null | Minor change to the docstring. It said you must refer to the position (not keys). Keys work:
``` python
In [18]: res.head()
Out[18]:
same_employer stamp
-3 1996-02-01 2
-2 1996-01-01 1
1996-02-01 1
1996-03-01 4
1 1996-01-01 13877
dtype: int64
In [19]: res.reorder_levels(['stamp', 'same_employer']).head()
Out[19]:
stamp same_employer
1996-02-01 -3 2
1996-01-01 -2 1
1996-02-01 -2 1
1996-03-01 -2 4
1996-01-01 1 13877
dtype: int64
```
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I think it's better to keep master as bleeding edge so it's simpler for new contributors. If no one objects, wherever master is 24 hours-ish from this issue I'll mark current master as 0.14-dev and checkout a separate `rc/0.13` branch.
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] | closed | false | null | [] | null | 7 | 2013-11-23T02:25:17Z | 2019-11-05T16:54:45Z | 2013-12-05T23:41:43Z | CONTRIBUTOR | null | Currently pandas has the regular `mean` that can be called on a `DataFrame` or a `Series`, but does not have `gmean` or `hmean`:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.gmean.html
http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.hmean.html
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} | 2 | 2013-11-23T03:27:08Z | 2013-11-24T01:08:16Z | 2013-11-24T01:08:16Z | CONTRIBUTOR | null | Running into the issue below with pandas master and numpy compiled from 1.8.x maintenance branch. If i do "import numpy.ma.mrecords" in ipython and try again, then creating the DataFrame works fine. Maybe the best thing to do is to add "import numpy.ma.mrecords" in core/frame.py?
```
import numpy.ma as ma
import pandas
x = ma.masked_all((3,4))
df = pandas.DataFrame(x)
AttributeError Traceback (most recent call last)
<ipython-input-4-8935d2e6ea81> in <module>()
----> 1 df = pandas.DataFrame(x)
C:\Python27\lib\site-packages\pandas-0.12.0_1147_g39ad4cb-py2.7-win-amd64.egg\pandas\core\frame.pyc in __init__(self, data, index, columns, dtype, copy)
203
204 # masked recarray
--> 205 if isinstance(data, ma.mrecords.MaskedRecords):
206 mgr = _masked_rec_array_to_mgr(data, index, columns, dtype,
207 copy)
AttributeError: 'module' object has no attribute 'mrecords'
```
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} | [] | closed | false | null | [] | null | 5 | 2013-11-23T16:45:20Z | 2013-11-24T13:24:14Z | 2013-11-24T13:24:14Z | CONTRIBUTOR | null | `NDFrame` has a `pct_change` method that works as: `x_t/x_{t-period} - 1`
`log_return` would work as: `log(x_t/x_{t-period})`
These two values are approximately equal for small values of `x_t/x_{t-period}`.
The log return version tends to be used in finance more often than the pct_change.
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} | 0 | 2013-11-23T19:06:48Z | 2014-06-26T19:33:48Z | 2013-11-24T01:08:16Z | CONTRIBUTOR | null | Fixes https://github.com/pydata/pandas/issues/5577
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} | 5 | 2013-11-24T07:57:52Z | 2014-01-24T16:30:15Z | 2014-01-24T14:58:54Z | CONTRIBUTOR | null | Contrary to Pandas DataFrame, Pandas Series doesn't display as HTML into IPython notebook. Moreover Pandas Series doesn't provide to_html() method.
(I'm using 0.12 version)
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Put the following in a file called `test.py`.
``` python
from pandas import DataFrame
def myfunction():
df = DataFrame({"A": [1, 2, 3, 4, 5], "B": [3.125, 4.12, 3.1, 6.2, 7.]})
row = df.loc[0]
row["A"] = 0
def anotherfunction():
myfunction()
def third_function():
anotherfunction()
third_function()
```
Running it will generate this warning:
```
test.py:5: SettingWithCopyWarning: A value is trying to be set on a copy
of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
row["A"] = 0
```
I don't think it's worth testing (especially because there's not necessarily a simple way to do it).
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https://api.github.com/repos/pandas-dev/pandas/issues/5582 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5582/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5582/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5582/events | https://github.com/pandas-dev/pandas/issues/5582 | 23,222,822 | MDU6SXNzdWUyMzIyMjgyMg== | 5,582 | possible easier dataframe/series selection | {
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} | [] | closed | false | null | [] | null | 2 | 2013-11-25T05:20:36Z | 2014-01-23T21:14:10Z | 2014-01-23T21:14:10Z | NONE | null | I believe lots of pandas users need to do lots of selection/slicing. The existing methods are flexible, such as iloc, loc, ix, ["columnname"].
In practice, I found it is not convenient to do complex selection. For example:
Frame.ix((Frame["ColumnName"]=="MatchValue") & (Frame["AnotherColumn"]=="AnotherMatch"), ["SelectColumn"])
It is error prone and confusing. Therefore, I came up with my own solution:
now you just use Frame.Sel(["SelectColumn"],{"ColumnName":"MatchValue","AnotherColumn":"AnotherMatch"}) And you can mix index and labels for selection. For example Frame.Sel([0,"ColumnName"]) will select column 0 and "ColumnName". Things now are natural and simplier, in my opinions; at least you don't have to switch between iloc[] and loc[]
This is just my suggestions, and definitely not a bug or issue. I don't know how to give the feedback to the development team or other users, so I posted here.
The following is some doc of my method. This is from my personal "enhancement" to pandas module, called kungfu at https://github.com/jerryzhujian9/kungfu
Feel free to change my codes. Greatly appreciate the hard work of all developers!
---
frame.Sel(*args)
Select certain columns and rows from a frame.
Args:
1) [] for columns FIRST, {} or [] for rows SECOND
2) if only pass [], treated as columns; if only {}, as rows
3) additionally, a single int(or str) can be parsed to a list of that int(or str)
4) int alone or in a list treated as (column/row) index; str alone or in a list as (column/row) name
5) does NOT support slice; however, one can use the function range(start,stop) to generate a list
6) better see some examples:
('Session') column "Session"
(1) column 1 (index based, starts from 0)
(['Session', 1])
columns "Session" and 1
the order of selected columns does not have to follow the order in the original frame! could be [2,4,0,1]({'Session': 1}) all columns where "Session" == 1
('Session', '0') <---raise error because of '0'
('Session', 0) column "Session" and row 0 (index based, starts from 0)
('Session', [0, 1]) column "Session" and rows 0 and 1
('Session', {'Session': 1}) column "Session" where "Session" == 1
(0, '0') <---raise error because of '0'
(0, 0) column 0 and row 0
(0, [0, 1]) column 0 and rows 0 and 1
(0, {'Session': 1}) column 0 where "Session" == 1
(['Session', 1], '0') <---raise error because of '0'
(['Session', 1], 0) columns "Session" and 1, and row 0
(['Session', 1], [0, 1]) columns "Session" and 1, and, rows 0 and 1
(['Session', 1], {"Subject":5101,"Procedure[Trial]":["PresentPair","PresentPair1"]})
columns "Session" and 1 where "Subject" == 5101 and ("Procedure[Trial]" == "PresentPair" or "Procedure[Trial]" == "PresentPair1")
([],0) all columns and row 0
Returns:
a Frame object even for a frame with only one column or row
to select a column or row as a series, use SelCol() or SelRow()
However, if only a cell is selected, returns the value of that cell with being its own data type.
Raises:
None
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https://api.github.com/repos/pandas-dev/pandas/issues/5583 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5583/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5583/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5583/events | https://github.com/pandas-dev/pandas/pull/5583 | 23,240,129 | MDExOlB1bGxSZXF1ZXN0MTAyNTgxODg= | 5,583 | ENH: Excel writer takes locale setting for date and datetime into account | {
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} | 20 | 2013-11-25T12:45:46Z | 2014-06-27T10:05:29Z | 2014-01-26T18:58:10Z | CONTRIBUTOR | null | closes #4133
Based on the locale setting the date and datetime values are formatted in
excel exports. Moreover the this behaviour can be overruled by keyword
arguments. Old behavior can be enforced by
ExcelWriter(file, date_format='YYYY-MM-DD', datetime_format='YYYY-MM-DD HH:MM:SS')
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https://api.github.com/repos/pandas-dev/pandas/issues/5584 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5584/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5584/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5584/events | https://github.com/pandas-dev/pandas/pull/5584 | 23,244,402 | MDExOlB1bGxSZXF1ZXN0MTAyNjA1OTA= | 5,584 | BUG/TST: reset setitem_copy on object enlargement | {
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} | 20 | 2013-11-25T14:12:59Z | 2014-06-19T16:10:09Z | 2013-11-29T19:13:20Z | CONTRIBUTOR | null | related #5581
closes #5597
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} | [] | closed | false | null | [] | null | 5 | 2013-11-25T16:19:15Z | 2013-11-26T18:28:45Z | 2013-11-26T18:28:41Z | NONE | null | Concatenation can fail when indexes use nanoseconds and tseries.index.union_many is called.
```
def thisFails():
# May be related to https://github.com/pydata/pandas/issues/1238
timeStep = np.timedelta64( 1, 'ns' )
t0 = np.datetime64( datetime.datetime.now() )
ndx1 = [t0 + k*timeStep for k in xrange( 5 ) ]
ndx2 = [t0 + (k-1)*timeStep for k in xrange( 5 )]
df1 = pd.DataFrame( data = list( xrange( 5 ) ), index = ndx1, columns = ['Apples'] )
df2 = pd.DataFrame( data = list( xrange( 5 ) ), index = ndx2, columns = ['Bananas'] )
_both = pd.concat( [df1, df2], axis = 1 )
print "Failed to replicate bug"
def thisWorks():
# May be related to https://github.com/pydata/pandas/issues/1238
timeStep = np.timedelta64( 1, 'ms' )
t0 = np.datetime64( datetime.datetime.now() )
ndx1 = [t0 + k*timeStep for k in xrange( 5 ) ]
ndx2 = [t0 + (k-1)*timeStep for k in xrange( 5 )]
df1 = pd.DataFrame( data = list( xrange( 5 ) ), index = ndx1, columns = ['Apples'] )
df2 = pd.DataFrame( data = list( xrange( 5 ) ), index = ndx2, columns = ['Bananas'] )
_both = pd.concat( [df1, df2], axis = 1 )
print "Failed to replicate bug"
try:
thisFails()
raise ValueError(" This was expected to fail. It might be that pandas has been updated from 0.11.0 ")
except:
pass
thisWorks()
```
The error message is
```
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\tools\merge.py", li\
ne 872, in concat
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\tools\merge.py", li\
ne 942, in __init__
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\tools\merge.py", li\
ne 1104, in _get_new_axes
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\tools\merge.py", li\
ne 1130, in _get_comb_axis
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\core\index.py", lin\
e 2705, in _get_combined_index
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\core\index.py", lin\
e 2728, in _union_indexes
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\tseries\index.py", \
line 874, in union_many
File "D:\ATHENA~1\RELEAS~1\build\ext2\vc90_xp/lib\python2.6\site-packages\pandas-0.11.0-py2.6-win32.egg\pandas\tseries\frequencies\
.py", line 431, in to_offset
ValueError: Could not evaluate N
Locals:
_: ''
delta: 'None'
freqstr: 'N'
stride: ''
stride_sign: 'None'
```
Apologies if this has already been fixed.
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} | 12 | 2013-11-25T17:04:46Z | 2019-12-26T00:25:34Z | 2019-12-26T00:25:34Z | CONTRIBUTOR | null | ```
import datetime
import numpy
import pandas
index = pandas.DatetimeIndex(start=datetime.date(2012, 1, 31), freq='M', periods=12)
ts = pandas.Series(range(12), index=index)
df = pandas.DataFrame(dict(A=ts, B=ts+2))
print df
# Correct
print df.resample('M', how=numpy.sum)
# Incorrect
print df.resample('M', how=numpy.product)
# Correct
print df.resample('Q', how=numpy.sum)
# Incorrect
print df.resample('Q', how=numpy.product)
```
for print df.resample('Q', how=numpy.product), was expecting:
```
2012-03-31 2*1*0 4*3*2
2012-06-30 5*4*3 7*6*5
2012-09-30 8*7*6 10*9*8
2012-12-31 11*10*9 13*12*11
```
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} | [] | closed | false | null | [] | null | 1 | 2013-11-25T19:12:21Z | 2014-07-12T13:20:39Z | 2013-11-25T19:18:40Z | NONE | null | While I know timezone conversion might be difficult, I think you mean different here :smile:
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} | 15 | 2013-11-26T08:22:44Z | 2013-12-05T22:58:18Z | 2013-12-05T22:58:18Z | CONTRIBUTOR | null | In 044ee06, I could just do, with no apparent delay:
``` python
df
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10718114 entries, 0 to 10718113
Columns: 20 entries, classification_id to spread
dtypes: datetime64[ns](2), float64(11), object(7)
```
In be80898, the notebook gets stuck and CPU is boiling and nothing is returned after 5 mins.
related #5550
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} | 1 | 2013-11-26T12:39:53Z | 2014-06-24T12:51:13Z | 2013-11-26T12:55:50Z | NONE | null | @takluyver, missed an O(N) there.
#5588
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} | 0 | 2013-11-26T14:09:14Z | 2014-07-16T08:41:34Z | 2013-11-26T14:28:18Z | CONTRIBUTOR | null | perf improvement in eval/query (and general take-like indexing)
```
In [1]: N = 1000000
In [2]: df = DataFrame({'a': np.random.randn(N)})
In [3]: min_val = df['a'].min()
In [4]: max_val = df['a'].max()
```
current master: 66980c68d188842b1c4d80d3508f539baab3cbe8
```
In [5]: %timeit df.query('(a >= min_val) & (a <= max_val)')
10 loops, best of 3: 31.6 ms per loop
```
This PR
```
In [5]: %timeit df.query('(a >= min_val) & (a <= max_val)')
10 loops, best of 3: 20.2 ms per loop
```
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} | 7 | 2013-11-26T16:29:40Z | 2014-02-18T19:24:24Z | 2014-02-18T19:24:24Z | NONE | null | `__contains__` should validate the key to be of a correctly castable type, e.g. 0.1 should raise a `TypeError` with an `Int64Index`
---
I may well be missing something, but the following behavior seems unexpected:
"""
In [58]: 5 in pd.Series([1.5, 2.5, 3.5])
Out[58]: False
In [59]: 5.0 in pd.Series([1.5, 2.5, 3.5])
Out[59]: False
In [60]: 0.1 in pd.Series([1.5, 2.5, 3.5])
Out[60]: True
In [61]: 0 in pd.Series([1.5, 2.5, 3.5])
Out[61]: True
In [62]: 3.5 in pd.Series([1.5, 2.5, 3.5])
Out[62]: False
In [63]: 3.4 in pd.Series([1.5, 2.5, 3.5])
Out[63]: False
In [64]: 0.2 in pd.Series([1.5, 2.5, 3.5])
Out[64]: True
In [65]: 1.6 in pd.Series([1.5, 2.5, 3.5])
Out[65]: True
In [66]: 2.6 in pd.Series([1.5, 2.5, 3.5])
Out[66]: True
In [67]: 3.0 in pd.Series([1.5, 2.5, 3.5])
Out[67]: False
In [68]: 2.5 in pd.Series([1.5, 2.5, 3.5])
Out[68]: True
In [69]: 0.1 in pd.Series([1.5, 2.5, 3.5])
Out[69]: True
"""
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} | 0 | 2013-11-26T19:22:51Z | 2014-06-21T12:52:16Z | 2013-11-26T19:49:55Z | CONTRIBUTOR | null | closes #5592
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} | 5 | 2013-11-26T23:16:56Z | 2014-06-27T12:06:24Z | 2013-11-27T18:31:42Z | CONTRIBUTOR | null | This should have been in PR #5550, but the directory is gitignored, and I missed it.
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https://api.github.com/repos/pandas-dev/pandas/issues/5595 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5595/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5595/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5595/events | https://github.com/pandas-dev/pandas/issues/5595 | 23,367,540 | MDU6SXNzdWUyMzM2NzU0MA== | 5,595 | Test failures on windows | {
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} | 2 | 2013-11-27T05:09:57Z | 2013-11-27T13:28:17Z | 2013-11-27T13:28:17Z | CONTRIBUTOR | null | The included error message is on 32-bit windows for python 2.6 but it's the same for 64-bit (waiting for other python versions to finish)
this is a blocker for 0.13 rc1
```
======================================================================
FAIL: test_groupby_return_type (pandas.tests.test_groupby.TestGroupBy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test-py26\pandas\tests\test_groupby.py", line 331, in test_groupby_return_type
assert_frame_equal(result,expected)
File "C:\workspace\pandas-windows-test-py26\pandas\util\testing.py", line 475, in assert_frame_equal
check_less_precise=check_less_precise)
File "C:\workspace\pandas-windows-test-py26\pandas\util\testing.py", line 423, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\workspace\pandas-windows-test-py26\pandas\util\testing.py", line 407, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\workspace\pandas-windows-test-py26\pandas\util\testing.py", line 390, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int64') != dtype('int32')
```
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] | closed | false | null | [] | null | 7 | 2013-11-27T09:44:38Z | 2013-11-27T16:13:57Z | 2013-11-27T16:13:49Z | NONE | null | I'm using pandas 0.12.0 and having issue with accessing hierarchically indexed data frame elements using the ix. As a note, the issue is coming only when I have datetime objects in level 0 of hierarchical index.
arrays = [date_range('2013-11-15', periods=4).repeat(2),
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = DataFrame(randn(8, 3), index=index, columns=['A', 'B', 'C'])
df.ix['2013-11-15', 'one']
---
KeyError Traceback (most recent call last)
<ipython-input-99-c55bdd03164b> in <module>()
----> 1 df.ix['2013-11-15', 'one']
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in **getitem**(self, key)
45 pass
46
---> 47 return self._getitem_tuple(key)
48 else:
49 return self._getitem_axis(key, axis=0)
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
251 def _getitem_tuple(self, tup):
252 try:
--> 253 return self._getitem_lowerdim(tup)
254 except IndexingError:
255 pass
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
384 new_key, = new_key
385
--> 386 return getattr(section,self.name)[new_key]
387
388 raise IndexingError('not applicable')
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in **getitem**(self, key)
45 pass
46
---> 47 return self._getitem_tuple(key)
48 else:
49 return self._getitem_axis(key, axis=0)
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
251 def _getitem_tuple(self, tup):
252 try:
--> 253 return self._getitem_lowerdim(tup)
254 except IndexingError:
255 pass
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
361 for i, key in enumerate(tup):
362 if _is_label_like(key) or isinstance(key, tuple):
--> 363 section = self._getitem_axis(key, axis=i)
364
365 # we have yielded a scalar ?
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
411 return self._get_loc(key, axis=axis)
412
--> 413 return self._get_label(key, axis=axis)
414
415 def _getitem_iterable(self, key, axis=0):
~/env/lib/python2.6/site-packages/pandas/core/indexing.pyc in _get_label(self, label, axis)
59 return self.obj._xs(label, axis=axis, copy=False)
60 except Exception:
---> 61 return self.obj._xs(label, axis=axis, copy=True)
62
63 def _get_loc(self, key, axis=0):
~/env/lib/python2.6/site-packages/pandas/core/frame.pyc in xs(self, key, axis, level, copy)
2358
2359 if axis == 1:
-> 2360 data = self[key]
2361 if copy:
2362 data = data.copy()
/u/pullurur/dig/quantinvest/env/lib/python2.6/site-packages/pandas/core/frame.pyc in **getitem**(self, key)
2001 # get column
2002 if self.columns.is_unique:
-> 2003 return self._get_item_cache(key)
2004
2005 # duplicate columns
~/env/lib/python2.6/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
665 return cache[item]
666 except Exception:
--> 667 values = self._data.get(item)
668 res = self._box_item_values(item, values)
669 cache[item] = res
~/env/lib/python2.6/site-packages/pandas/core/internals.pyc in get(self, item)
1653 def get(self, item):
1654 if self.items.is_unique:
-> 1655 _, block = self._find_block(item)
1656 return block.get(item)
1657 else:
~/env/lib/python2.6/site-packages/pandas/core/internals.pyc in _find_block(self, item)
1933
1934 def _find_block(self, item):
-> 1935 self._check_have(item)
1936 for i, block in enumerate(self.blocks):
1937 if item in block:
~/env/lib/python2.6/site-packages/pandas/core/internals.pyc in _check_have(self, item)
1940 def _check_have(self, item):
1941 if item not in self.items:
-> 1942 raise KeyError('no item named %s' % com.pprint_thing(item))
1943
1944 def reindex_axis(self, new_axis, method=None, axis=0, copy=True):
KeyError: u'no item named one'
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} | 19 | 2013-11-27T10:57:51Z | 2015-03-07T22:51:45Z | 2013-11-29T19:13:20Z | CONTRIBUTOR | null | I'm getting spurious warnings on some old code that I'm running with new pandas. You can replicate by doing something like this (you have to take a subset of the data first, that's the key)
```
import pandas as pd
from pandas.core.common import SettingWithCopyWarning
from string import letters
import warnings
warnings.simplefilter('error', SettingWithCopyWarning)
def random_text(nobs=100):
df = []
for i in range(nobs):
idx= np.random.randint(len(letters), size=2)
idx.sort()
df.append([letters[idx[0]:idx[1]]])
return pd.DataFrame(df, columns=['letters'])
df = random_text(100000)
df = df.ix[df.letters.apply(lambda x : len(x) > 10)]
df['letters'] = df['letters'].apply(str.lower)
```
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} | 1 | 2013-11-27T17:50:11Z | 2014-06-14T11:01:36Z | 2013-12-01T20:00:10Z | CONTRIBUTOR | null | closes #5319
Essentitally this is `ffill`
```
In [1]: Series([1,np.nan,2]).replace([np.nan])
Out[1]:
0 1
1 1
2 2
dtype: float64
In [2]: Series([1,np.nan,2]).replace(np.nan)
Out[2]:
0 1
1 1
2 2
dtype: float64
```
A bit confusing on a Frame, so raise an error
```
In [3]: DataFrame(randn(5,2)).replace(np.nan)
TypeError: cannot replace [nan] with method pad on a DataFrame
```
This was the exception before this PR on a Frame
```
TypeError: If "to_replace" and "value" are both None and "to_replace" is not a list, then regex must be a mapping
```
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