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https://api.github.com/repos/pandas-dev/pandas/issues/5901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5901/events | https://github.com/pandas-dev/pandas/issues/5901 | 25,409,549 | MDU6SXNzdWUyNTQwOTU0OQ== | 5,901 | Nested groupby using sum of columns changes return type unexpectedly in 0.13.0-111-ge29c8e8 | {
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} | [] | closed | false | null | [] | null | 7 | 2014-01-10T17:29:48Z | 2014-01-10T18:07:10Z | 2014-01-10T18:07:10Z | NONE | null | I believe this to be a regression error, and could not find information on this change in the release notes (so I don't think it is intentional).
With some DataFrame in python 0.12.0 I could group by timestamp and another column using the following:
``` python
mydf.set_index('timestamp').sort_index().groupby(pandas.TimeGrouper('1D')).apply(__grouped_by_time)
```
See this message by Jeff on StackOverflow: http://stackoverflow.com/questions/20788932/out-of-memory-when-dataframe-resample-is-called-in-pandas-on-small-dataset
The results of this call on a given dataframe differ on pandas 0.12 and pandas 0.13. In particular, the results in 0.13 are all quoted strings, but in 0.12 are all numeric. If the aggregation function is something like sum(), which returns numeric, the results probably shouldn't change into strings (or at least an explanation would be good). Here is a full test, first using pandas 0.12.0 and then using 0.13
``` python
>>> import pandas
>>> pandas.__version__
'0.12.0'
>>> def __grouped_by_time(df):
... return df.groupby(["hashed_username"]).sum()
...
>>> mydf=pandas.read_pickle("sample.pik")
>>> mydf
cnt hashed_username timestamp
0 1 cfc2ff00f9ff635a368f230329a3a464 2012-10-08 00:00:00
1 1 1babe8cf174b20951147ea80ecf2a251 2012-11-16 00:00:00
2 1 fdafdb2e21c47ff967b2705c31457d61 2012-11-20 00:00:00
3 1 f0afdad49d7b693f680dc26b9e1f0fc7 2012-10-02 00:00:00
4 1 febaa2c77250cf7a78ea1aef3c9b3527 2012-10-25 00:00:00
5 1 0cbaabd42eccb0dce44d8070f1d4e7b1 2012-10-07 00:00:00
6 1 ca0efe0e195035d193b78cb0ab1975ee 2012-10-25 00:00:00
7 1 6852cc5b3a9c08a3f675aea075d226a8 2012-10-08 00:00:00
8 1 f731fd87e810c399598b702928392089 2012-11-01 00:00:00
9 1 2792e61e451d0fd63da951759763f52f 2012-10-16 00:00:00
10 0 special 2013-01-01 00:00:00
>>> df_result = mydf.set_index('timestamp').sort_index().groupby(pandas.TimeGrouper('1D')).apply(__grouped_by_time)
>>> df_result["cnt"].unique()
array([1, 0])
```
And now, 0.13.0-111-ge29c8e8:
``` pandas
>>> import pandas
>>> pandas.__version__
'0.13.0-111-ge29c8e8'
>>> def __grouped_by_time(df):
... return df.groupby(["hashed_username"]).sum()
...
>>> mydf=pandas.read_pickle("sample.pik")
>>> mydf
cnt hashed_username timestamp
0 1 cfc2ff00f9ff635a368f230329a3a464 2012-10-08 00:00:00
1 1 1babe8cf174b20951147ea80ecf2a251 2012-11-16 00:00:00
2 1 fdafdb2e21c47ff967b2705c31457d61 2012-11-20 00:00:00
3 1 f0afdad49d7b693f680dc26b9e1f0fc7 2012-10-02 00:00:00
4 1 febaa2c77250cf7a78ea1aef3c9b3527 2012-10-25 00:00:00
5 1 0cbaabd42eccb0dce44d8070f1d4e7b1 2012-10-07 00:00:00
6 1 ca0efe0e195035d193b78cb0ab1975ee 2012-10-25 00:00:00
7 1 6852cc5b3a9c08a3f675aea075d226a8 2012-10-08 00:00:00
8 1 f731fd87e810c399598b702928392089 2012-11-01 00:00:00
9 1 2792e61e451d0fd63da951759763f52f 2012-10-16 00:00:00
10 0 special 2013-01-01 00:00:00
[11 rows x 3 columns]
>>> df_result = mydf.set_index('timestamp').sort_index().groupby(pandas.TimeGrouper('1D')).apply(__grouped_by_time)
>>> df_result["cnt"].unique()
array(['1', '0'], dtype=object)
```
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} | 21 | 2014-01-10T18:03:27Z | 2016-08-09T21:34:37Z | 2016-06-07T12:37:09Z | CONTRIBUTOR | null | I've been trying to find out the process of creating DataFrames in order to try to solve #2305 with minimal memory use. I've made some tests that put in a IPython notebook: http://nbviewer.ipython.org/gist/tinproject/7d0e0de9475b16910fcf
Currently when reads from a generator pandas internally allocate the whole generator in memory within a list, using at least twice the memory needed. I suspect sometimes could be even more if there is a later type conversion needed.
Also, as it can be viewed in the notebook, the input data collection it's completely readed many times before data it's loaded in the final data structure.
To read from an iterator, without put the whole it in memory, it's needed to process the values that yield one by one, or at least in a one pass form.
One it could read data from a iterator in one pass, it could read from every iterable collection only needing the count of elements to read. It includes collections with implicit lenght: sequences(list, tuple, str), sets, mappings, etc. and collections with unknown length: generators, iterators, etc. which size must be explicitly given.
I think this could end in a great improve on performance and enhance the input data types accepted (In my tests I saw that the tuple collection it's not a valid type for DataFrame!)
Relates to #2305, #2193, #5898
I want to help to solve this, but it's a bis task, and I'm lost when comes to the Blocks part. Is there anywhere documented what structure have and how Blocks works?
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} | [] | closed | false | null | [] | null | 2 | 2014-01-10T21:48:16Z | 2014-07-02T21:28:19Z | 2014-01-12T00:33:24Z | MEMBER | null | Some docs things:
- remove remaining use of `code-block`, closes #3430 (@y-p, I think that were the last ones, so now it can be really closed)
- fix some headers to avoid useless warnings during doc build (if the line under the header is not as long as the header itself, sphinx complains about it although it renders fine)
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} | 5 | 2014-01-11T05:13:41Z | 2017-08-07T11:05:59Z | 2017-08-07T11:05:59Z | CONTRIBUTOR | null | Would it be costly to issue a warning when one adds a column to a DataFrame that has the same name as a built-in DataFrame method?
As you know, in that case I can not do Dataframe.name to access the column but instead I get a method pointer.
But if I'm unaware (or it's late and I forgot.. ;) ) that a method with that name exists, I could be confused for quite a while before I find out what's going on.
Example I just had:
s = Series(somedata)
Investigating the difference with
s.diff()
Looks interesting, so for easier management I want to put things in a dataframe now.
df = DataFrame(s)
df['diff'] = s.diff()
And then I tried to filter the df for some values in diff and got this:
df[df.diff > 10]
KeyError: u'no item named True'
That somehow didn't make me at all think about a name clash. In this case I eventually guessed what was wrong, but if I wouldn't know that a fancy method with a fancy name exists and I picked it as well, I guess I never would find out, what's going on.
So, if it isn't too much costs in terms of performance a name check against core methods would be really nice, I think.
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] | closed | false | null | [] | null | 0 | 2014-01-11T14:49:50Z | 2014-01-11T15:15:28Z | 2014-01-11T15:15:28Z | CONTRIBUTOR | null | Fully reindexing a panel copies sub-frames when missing top-level items
IOW, this should have a `nan` frame for 'Item2'
```
In [1]: df = DataFrame(np.random.randn(4,3))
[4]: p = Panel({ 'Item1' : df })
In [12]: p['Item2'] = np.nan
In [13]: p
Out[13]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 2
In [5]: items = ['Item1','Item2']
In [8]: major_axis = np.arange(4)
In [9]: minor_axis = np.arange(3)
In [10]: result = p.reindex(items=items,major_axis=major_axis,minor_axis=minor_axis)
In [11]: result.ix['Item2']
Out[11]:
0 1 2
0 1.420715 0.972559 -1.334794
1 -0.165063 -1.811950 -1.107652
2 -1.412676 0.376777 -0.521125
3 2.119229 1.221648 -1.078299
[4 rows x 3 columns]
In [14]: p['Item2']
Out[14]:
0 1 2
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
[4 rows x 3 columns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5907 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5907/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5907/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5907/events | https://github.com/pandas-dev/pandas/pull/5907 | 25,447,385 | MDExOlB1bGxSZXF1ZXN0MTE0Mjg3NjE= | 5,907 | DOC: Add "How to reduce a sequence (e.g. of Series) using a binary opera... | {
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} | [] | closed | false | null | [] | null | 1 | 2014-01-11T17:39:55Z | 2014-06-14T21:09:21Z | 2014-01-11T18:29:52Z | CONTRIBUTOR | null | ...tor"
MAINT: Fixed some typos
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} | 2 | 2014-01-11T17:54:23Z | 2018-07-06T22:50:12Z | 2018-07-06T22:50:12Z | CONTRIBUTOR | null | This came up in https://github.com/pydata/pandas/issues/5264, especially [here](https://github.com/pydata/pandas/issues/5264#issuecomment-30571813), [here](https://github.com/pydata/pandas/issues/5264#issuecomment-32101503), and this [SO post](http://stackoverflow.com/questions/21057397/possible-bug-in-pandas-groupby-agg).
Essentially, there are cases now where the `agg` or `transform` or `apply` function is applied to the grouper. This can cause failures in some very odd ways. As a simple example:
``` python
In [25]: df = pd.DataFrame({"A": ['a', 'b', 'a', 'b'], 'B': [1, 2, 3, 4]})
In [26]: df
Out[26]:
A B
0 a 1
1 b 2
2 a 3
3 b 4
[4 rows x 2 columns]
In [28]: df.groupby('A').apply(lambda x: x+1)
TypeError: Could not operate 1 with block values cannot concatenate 'str' and 'int' objects
```
The example in the SO post linked above is especially subtle because the path the code follows depends on whether or not the aggfunc is successfully applied to the `group`ing column.
I'm going to poke around the code and see what breaks. I'd propose that we apply the function to the grouper in fewer (possibly zero) cases.
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} | 11 | 2014-01-11T20:25:12Z | 2021-04-11T04:06:19Z | null | NONE | null | Unless I'm missing something, there's no DataFrame method analogous to set_index() for setting column values. One can directly manipulate the .columns attribute of the DF, but it's often convenient to be able to alter columns in-line after some other operation--e.g.,
`data = pd.concat([df_a, df_b], axis=1).set_columns(['a', 'b', 'c'])`
Even if there's already some equivalent longer way to do this, it might be nice to add set_columns for convenience; I've now caught myself trying to call set_columns() on a DF several times.
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} | 8 | 2014-01-12T03:01:12Z | 2014-06-25T21:56:45Z | 2014-01-13T16:18:13Z | CONTRIBUTOR | null | closes #5890
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} | 3 | 2014-01-12T21:00:26Z | 2014-01-15T02:57:34Z | 2014-01-15T02:57:34Z | CONTRIBUTOR | null | See:
```
In [27]: str(pd.Timestamp(200))
Out[27]: '1970-01-01 00:00:00'
In [28]: repr(pd.Timestamp(200))
Out[28]: "Timestamp('1970-01-01 00:00:00.000000200', tz=None)"
```
whereas microseconds work fine:
```
In [30]: str(pd.Timestamp(2000))
Out[30]: '1970-01-01 00:00:00.000002'
```
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} | 21 | 2014-01-12T22:21:56Z | 2015-08-20T02:20:53Z | 2015-08-20T02:20:53Z | NONE | null | Linked Issue: https://github.com/PyTables/PyTables/issues/319
Hi,
I have a dataframe saved in HDF5 with 6.7 million records (about 425MB). As you can see below, it gives an incorrect result when it has to do multiple iterations (which is a serious issue for me actually). ->
```
# prepare
fact_hdf = pd.HDFStore('iteration_test3.h5', mode='r')
store_name = 'store_0'
column_list = ['o', 'm101']
where = [('o', '=', [-15534, -16280, -17113, -14786, -14790, -18074])]
# describe
fact_hdf[store_name]
'''
<class 'pandas.core.frame.DataFrame'>
Int64Index: 6708525 entries, 0 to 6708524
Columns: 27 entries, a1007 to m105_b
dtypes: float64(11), int64(16)
'''
# non iterated
fact_hdf.select(store_name, columns=column_list, where=where)['o'].unique()
'''
array([-14790, -16280, -15534, -14786, -18074, -17113])
'''
# iterated by 1mln
chunksize = 1000000 # 1 mln
for sub_df in fact_hdf.select(store_name, columns=column_list, where=where, iterator=True, chunksize=chunksize):
sub_df['o'].unique()
'''
array([-14790, -16280, -15534, -14786, -18074, -17113])
array([-14786, -14790, -15534, -18074, -16280, -17113])
array([-14790, -18074, -15534, -14786, -16280, -17113])
array([-16280, -14790, -15534, -18074, -14786, -17113])
array([-14790, -14786, -15534, -18074, -16280, -17113])
array([-18511, -17074, -17099, -16876, -18060, -15965, -14733, -16300,
-15534, -14790, -16280, -14786, -18074, -17113])
array([-16280, -14790, -18074, -15534, -14786, -17113])
'''
# iterated by 10mln
chunksize = 10000000 # 1 mln
for sub_df in fact_hdf.select(store_name, columns=column_list, where=where, iterator=True, chunksize=chunksize):
sub_df['o'].unique()
'''
array([-14790, -16280, -15534, -14786, -18074, -17113])
'''
```
Reproducing the bug can be hard (not all files have the same issue), but this specific case happens again (it's not a corrupted HDF5 file, if you write away the dataframe it happens again).
I can share the file with pandas/pytables developers if needed (it has to be personal though / not uploaded to a public location; message me for a sftp link)
Some technical stuff
- Pandas 0.12
- Pytables 3.0
- Ubuntu 12.04LTS 64bit
- File is saved with BLOSC, complevel=9
Kind regards,
Carst
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} | 4 | 2014-01-13T00:36:27Z | 2014-01-13T13:16:33Z | 2014-01-13T13:16:06Z | CONTRIBUTOR | null | When I cast a FloatIndex to a numpy array, I get an array of objects. The desired behavior should be float. This is backwards incompatible with pre-FloatIndex behavior.
``` python
In [119]: s = pd.Series([1,2,3],[1.,2.,3.])
array(s.index).dtype
Out[119]: dtype('O')
```
The Int64Index works as expected.
``` python
In [114]: s = pd.Series([1,2,3],[1,2,3])
array(s.index).dtype
Out[114]: dtype('int64')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5915 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5915/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5915/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5915/events | https://github.com/pandas-dev/pandas/issues/5915 | 25,474,844 | MDU6SXNzdWUyNTQ3NDg0NA== | 5,915 | Problem exporting dataframe to csv using to_csv() with data frames containing strings with accents (\xc9) | {
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] | closed | false | null | [] | null | 2 | 2014-01-13T02:00:48Z | 2014-01-13T19:52:15Z | 2014-01-13T19:50:11Z | NONE | null | Pandas' to_csv() method produces an error when strings in my data frame contain \xc9 accents. Any idea how I can quickly solve this?
Thanks
UnicodeEncodeError Traceback (most recent call last) /Users/slegroux1/Projects/FeaturEmo/en_features.py in () 360 print "----- No plot. Corresponding features are missing." 361 if name == 'main': --> 362 main()
/Users/slegroux1/Projects/FeaturEmo/en_features.py in main() 40 if output: 41 embed() ---> 42 dict2frame(features).to_csv(output) 43 if plot and not(directory or my_csv_list): 44 plot_features(features)
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/frame.pyc in to_csv(self, path_or_buf, sep, na_rep, float_format, cols, header, index, index_label, mode, nanRep, encoding, quoting, line_terminator, chunksize, tupleize_cols, **kwds) 1408 chunksize=chunksize,engine=kwds.get("engine"), 1409 tupleize_cols=tupleize_cols) -> 1410 formatter.save() 1411 1412 def to_excel(self, excel_writer, sheet_name='sheet1', na_rep='',
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/format.pyc in save(self) 970 971 else: --> 972 self._save() 973 974
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/format.pyc in _save(self) 1076 break 1077 -> 1078 self._save_chunk(start_i, end_i) 1079 1080 def _save_chunk(self, start_i, end_i):
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/core/format.pyc in _save_chunk(self, start_i, end_i) 1094 ix = data_index.to_native_types(slicer=slicer, na_rep=self.na_rep, float_format=self.float_format) 1095 -> 1096 lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer) 1097 1098 # from collections import namedtuple
/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/pandas/lib.so in pandas.lib.write_csv_rows (pandas/lib.c:13871)()
UnicodeEncodeError: 'ascii' codec can't encode character u'\xc9' in position 0: ordinal not in range(128)
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} | 0 | 2014-01-13T07:11:21Z | 2014-07-16T08:46:06Z | 2014-01-13T07:11:37Z | NONE | null | On master we use tags strictly to indicate releases.
In other clones, this may not be the case. Specifically, jenkins
sometimes likes to tags each build which can wreak [havoc](http://pandas.pydata.org/pandas-build/dev/) when
using git-describe naively to generate version strings.
git allows you to filter the tags to consider when constructing the version string. well then.
I tested this locally of course, but as always with changes to setup.py - duck and cover.
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} | 0 | 2014-01-13T12:57:39Z | 2014-06-26T01:25:52Z | 2014-01-13T13:16:06Z | CONTRIBUTOR | null | closes #5914
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] | closed | false | null | [] | null | 4 | 2014-01-13T13:14:51Z | 2017-03-15T06:44:20Z | 2014-01-13T21:16:49Z | NONE | null | Please add hive (Hadoop Hive Server 2) support into Pandas.
There is an example package: https://github.com/BradRuderman/pyhs2
I fixed a bug in it here: https://github.com/rzykov/pyhs2
See below a working example for pandas:
``` python
import pyhs2
import pandas as pd;
query = """select * from crosssell where siteid='529dee04ec92e60208567ca6' limit 1000"""
def read_hive(query):
conn = pyhs2.connect(host='example.com',
port=10000,
authMechanism="PLAIN",
user='XXXXX',
password='XXXXX',
database='default')
cur = conn.cursor()
cur.execute(query)
#Return column info from query
if cur.getSchema() is None:
cur.close()
conn.close()
return None
columnNames = [a['columnName'] for a in cur.getSchema()]
print columnNames
columnNamesStrings = [a['columnName'] for a in cur.getSchema() if a['type']=='STRING_TYPE']
output = pd.DataFrame(cur.fetch(),columns=columnNames)
cur.close()
conn.close()
return output
read_hive(query)
```
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/home/user1/src/pandas/pandas/io/pytables.py:658: FutureWarning: unique(key,column) is deprecated
use select_column(key,column).unique() instead
FutureWarning)
```
@jreback, any issues with making the change?
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closes #5899, following @jorisvandenbossche's advice
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`df.repr_html` currently raises `ValueError`. if IPython agree to the
proposed behaviour, we'll change it to `NotImplementedError` to restore things to normal.
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} | 7 | 2014-01-13T18:10:23Z | 2014-01-19T13:52:15Z | 2014-01-13T20:14:14Z | CONTRIBUTOR | null | related #5661
maybe `io.pickle.compat = 'global/local``
where we only allow local changes to the UnPickler.
This is not destructive, just 'bad practive', but weighing against more usability
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} | 12 | 2014-01-13T18:15:06Z | 2014-06-16T03:52:02Z | 2014-01-18T16:30:29Z | NONE | null | https://github.com/ipython/ipython/pull/4570
We'd prefer to get rid of the duplication, but we have some tweaks in place
which ipython does not.
I rebased everythingon top of ipython to generae a clean diff, if they pick it up
we can make the transition, otherwise we synched for now.
cc @takluyver, if you're interested, check the diff between 2e3c608 and 82b8c3a
for an unobstructed view.
@jorisvandenbossche, can you check this out and let me know if I missed anything?
I included the cython magic fixes you pointed out.
related https://github.com/pydata/pandas/issues/5221
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https://api.github.com/repos/pandas-dev/pandas/issues/5926 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5926/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5926/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5926/events | https://github.com/pandas-dev/pandas/pull/5926 | 25,517,254 | MDExOlB1bGxSZXF1ZXN0MTE0NjI0ODQ= | 5,926 | DOC: make io.rst utf8 only | {
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} | 8 | 2014-01-13T18:30:02Z | 2014-07-06T12:35:17Z | 2014-01-13T20:40:57Z | NONE | null | https://github.com/pydata/pandas/issues/5142
@JanSchulz , can you test whether this solves the problem for you?
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https://api.github.com/repos/pandas-dev/pandas/issues/5927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5927/events | https://github.com/pandas-dev/pandas/issues/5927 | 25,520,147 | MDU6SXNzdWUyNTUyMDE0Nw== | 5,927 | PERF: resample with max/min taking slow path | {
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} | 17 | 2014-01-13T19:09:04Z | 2014-01-16T14:28:12Z | 2014-01-16T14:28:12Z | CONTRIBUTOR | null | ```
In [21]: df = DataFrame(np.random.randn(100000,2),index=date_range('20130101',periods=100000,freq='50L'))
In [19]: %timeit df.resample('1s',how=np.max)[0]
1 loops, best of 3: 902 ms per loop
```
mean is ok
```
In [22]: %timeit df.resample('1s',how=np.mean)[0]
100 loops, best of 3: 2.42 ms per loop
```
The way to do this actually is ohlc
```
In [20]: %timeit df.groupby(pd.TimeGrouper('1s'))[0].ohlc()
1000 loops, best of 3: 1.16 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5928 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5928/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5928/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5928/events | https://github.com/pandas-dev/pandas/issues/5928 | 25,533,548 | MDU6SXNzdWUyNTUzMzU0OA== | 5,928 | Assignment using .ix[] raises ValueError | {
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} | 2 | 2014-01-13T22:21:55Z | 2014-01-14T03:05:23Z | 2014-01-14T00:24:21Z | CONTRIBUTOR | null | This works with `0.12` but not with master:
``` python
In [8]: import pandas as pd
In [10]: x = pd.DataFrame({'a': [1, 2, 3]})
In [13]: x['a'].ix[[0, 1, 2]] = -x['a'].ix[[0, 1, 2]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-30fcfd21bb22> in <module>()
----> 1 x['a'].ix[[0, 1, 2]] = -x['a'].ix[[0, 1, 2]]
/home/wiecki/envs/hddm/local/lib/python2.7/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
94 indexer = self._convert_to_indexer(key, is_setter=True)
95
---> 96 self._setitem_with_indexer(indexer, value)
97
98 def _has_valid_type(self, k, axis):
/home/wiecki/envs/hddm/local/lib/python2.7/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
409
410 if isinstance(value, ABCSeries):
--> 411 value = self._align_series(indexer, value)
412
413 elif isinstance(value, ABCDataFrame):
/home/wiecki/envs/hddm/local/lib/python2.7/site-packages/pandas/core/indexing.py in _align_series(self, indexer, ser)
516 return ser.reindex(ax).values
517
--> 518 raise ValueError('Incompatible indexer with Series')
519
520 def _align_frame(self, indexer, df):
ValueError: Incompatible indexer with Series
> /home/wiecki/envs/hddm/local/lib/python2.7/site-packages/pandas/core/indexing.py(518)_align_series()
517
--> 518 raise ValueError('Incompatible indexer with Series')
519
```
This is where it showed up: https://github.com/hddm-devs/hddm/issues/35
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https://api.github.com/repos/pandas-dev/pandas/issues/5929 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5929/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5929/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5929/events | https://github.com/pandas-dev/pandas/pull/5929 | 25,533,817 | MDExOlB1bGxSZXF1ZXN0MTE0NzE4MDY= | 5,929 | Tutorials | {
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} | 13 | 2014-01-13T22:25:52Z | 2014-07-16T08:46:22Z | 2014-01-17T22:17:13Z | NONE | null | added tutorials, let me know if you guys would like the output in a different format.
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} | 0 | 2014-01-13T23:21:18Z | 2014-06-22T07:37:28Z | 2014-01-14T00:24:21Z | CONTRIBUTOR | null | closes #5928
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} | 15 | 2014-01-14T05:38:28Z | 2015-03-03T01:54:06Z | 2015-03-03T01:54:06Z | CONTRIBUTOR | null | I just stumbled over time series indexing with a non-monotonic time series that came about because I concatenated data that should not have existed even. (JPL just got a bug report. ;)
So, if I have a dataframe like this, with the index clearly out of order:
``` python
0 1 2
2011-07-19 10:00:01.824000 -0.071762 -0.708509 -0.764987
2011-07-19 10:00:01.952000 1.487533 -0.111620 0.858629
2011-07-19 10:00:00.038000 0.600555 0.183954 -0.635211
```
Here as a json string, but: When you recreate it, the index is in order, so you got to do some magic to roll it around (how would i do that?)
``` python
'{"0":{"1311069601824":-0.0717615221,"1311069601952":1.4875326599,"1311069600038":0.6005551597},"1":{"1311069601824":-0.7085093684,"1311069601952":-0.111620072,"1311069600038":0.1839544789},"2":{"1311069601824":-0.7649867899,"1311069601952":0.8586288484,"1311069600038":-0.6352107949}}'
```
and I try an indexing with an automatically determined hour start, I get KeyError:
``` python
import datetime as dt
start = dt.datetime(2011, 7,19,10)
try:
df2[start:]
except KeyError as e:
print KeyError, e
```
I get
``` python
<type 'exceptions.KeyError'> Timestamp('2011-07-19 10:00:00', tz=None)
```
Leaving out the last, non-monotonic, index, it works:
``` python
df.iloc[:-1][start:]
```
Out:
``` python
0 1 2
2011-07-19 10:00:00.038000 0.600555 0.183954 -0.635211
2011-07-19 10:00:01.824000 -0.071762 -0.708509 -0.764987
```
I understand why it is happening (I think), but I believe it should be handled better, or at least a better error message so that the user knows what's happening.
Edit: This is on v0.13
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} | 2 | 2014-01-14T09:05:22Z | 2014-01-14T13:45:46Z | 2014-01-14T13:45:01Z | CONTRIBUTOR | null | The following line illustrates the problem:
```
df = pd.DataFrame(index=[0]).copy()
df["a"] = 0
```
This results in an `AttributeError` since the function `BlockManager.make_empty` initialises `df._data.blocks` with a numpy array object instead of an empty list. Thus subsequent calls to `blocks.pop` and `blocks.append` fail.
I have a patch coming up.
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} | 9 | 2014-01-14T09:49:12Z | 2021-04-11T04:08:00Z | null | MEMBER | null | When building the docs on Windows, I get some errors related to the removal of temporary files. For example, for building io.rst:
```
reading sources... [100%] io
---------------------------------------------------------------------------
WindowsError Traceback (most recent call last)
<ipython-input-124-e1699a291992> in <module>()
----> 1 os.remove('tmp.sv')
WindowsError: [Error 32] The process cannot access the file because it is being
used by another process: 'tmp.sv'
---------------------------------------------------------------------------
WindowsError Traceback (most recent call last)
<ipython-input-371-8e84b2979f2e> in <module>()
----> 1 os.remove('store.h5')
WindowsError: [Error 32] The process cannot access the file because it is being
used by another process: 'store.h5'
```
- For `temp.sv` this is due to:
```
reader = pd.read_table('tmp.sv', sep='|', iterator=True)
reader.get_chunk(5)
```
where `reader` is still 'open'. A following `os.remove('tmp.sv')` will generate this error. **Is there a way to close a `TextFileReader` object?**
- For `store.h5` this is due to the last `os.remove('store.h5')` (before 'SQL queries' http://pandas.pydata.org/pandas-docs/dev/io.html#sql-queries). For this, I don't directly see why this fails, as all the other calls to `os.remove('store.h5')` do work (and there is a `store.close()` command)
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} | 6 | 2014-01-14T10:20:40Z | 2018-07-06T22:43:01Z | 2018-07-06T22:43:00Z | MEMBER | null | Contributing to the docs (and so building the docs) should be as easy as possible to promote this, which is not always the case (eg see comment here https://github.com/pydata/pandas/pull/5530#issuecomment-32138220). Thanks to @y-p to pushing some things forward yesterday, and I thought of making an overview of all issues related to this:
- [x] Building of the docs fails with current stable sphinx 1.2 (#5899).
- It does work with 1.1.3 and with development version of sphinx (bug already fixed). In #5921 there was a discussion on this (removing template class.rst would temporarily solve this, but some of the generated docstring pages would be lost). For the moment this is left as is. At least, there should be a clear warning for this.
Does somebody see another solution?
- [x] On Windows, doc building fails on some unicode error in io.rst (#5142).
- See also #5245 and #5926.
- Fixed by #5925.
- [ ] On Windows, the removal of temporary files cause errors (#5933).
- [x] Building of the docs is not python 3 compatible at the moment (#5479, #5530)
- This should be solved after updating to ipython's version (#5221, #5925)
- [x] There should be a better guide to building the documentation.
- To be fixed by #5996.
Any others?
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} | 0 | 2014-01-14T13:10:28Z | 2014-06-15T15:32:47Z | 2014-01-14T13:45:01Z | CONTRIBUTOR | null | closes #5932
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} | 13 | 2014-01-14T15:25:46Z | 2014-02-20T21:28:07Z | 2014-02-20T21:28:07Z | NONE | null | @mangecoeur noted in https://github.com/pydata/pandas/issues/4163#issuecomment-32272752
```
http://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries
Also needs updating since it doesn't reflect the updated API
(that was already introduced in 0.13 I think) which uses read_sql
and to_sql for consistency with e.g. the CSV api.
```
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} | 2 | 2014-01-14T16:18:38Z | 2021-04-11T04:08:50Z | null | CONTRIBUTOR | null | Resampling weekly doesn't behave the same way as resampling daily when using label='right'.
``` python
import numpy as np
import pandas as pd
dates = pd.date_range('2001-01-01 10:00', '2001-01-15 16:00', freq='12h')
d = pd.DataFrame(dict(A=np.arange(len(dates))), index=dates)
```
so d looks like this:
```
A
2001-01-01 10:00:00 0
2001-01-01 22:00:00 1
2001-01-02 10:00:00 2
2001-01-02 22:00:00 3
2001-01-03 10:00:00 4
2001-01-03 22:00:00 5
2001-01-04 10:00:00 6
2001-01-04 22:00:00 7
2001-01-05 10:00:00 8
2001-01-05 22:00:00 9
2001-01-06 10:00:00 10
2001-01-06 22:00:00 11
<truncated>
```
Then we resample daily:
``` python
d.resample('D', label='right').last()
```
Produces the following output.
```
A
2001-01-02 1
2001-01-03 3
2001-01-04 5
2001-01-05 7
2001-01-06 9
<truncated>
```
Note that the value for 2001-01-05 is 7, the last value on 2001-01-04 in the original dataframe. If we resample weekly:
``` python
d.resample('W-FRI', label='right').last()
```
Output is
```
A
2001-01-05 9
2001-01-12 23
2001-01-19 29
```
This time, the value labelled 2001-01-05 is the last value on 2001-01-05 _not_ the last value on 2001-01-04. This is inconsistent with the behaviour for daily resample, where the label is always strictly after the data.
The result I expect is
```
A
2001-01-06 9
2001-01-13 23
2001-01-20 29
```
as the end of a W-FRI bucket should be midnight on the following Saturday. This is consistent with the daily resampling behaviour where the end of the 2001-01-05 bucket is midnight on 2001-01-06.
Output of installed versions below.
```
INSTALLED VERSIONS
------------------
Python: 2.7.3.final.0
OS: Linux
Release: 2.6.18-308.el5
Processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.13.0
Cython: Not installed
Numpy: 1.7.1
Scipy: 0.9.0
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 1.5
pytz: 2011k
bottleneck: Not installed
PyTables: 2.3.1-1
numexpr: 2.0.1
matplotlib: 1.1.1
openpyxl: Not installed
xlrd: 0.8.0
xlwt: 0.7.4
xlsxwriter: Not installed
sqlalchemy: 0.8.2
lxml: 2.3.6
bs4: Not installed
html5lib: 0.90
bigquery: Not installed
apiclient: Not installed
```
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} | [] | closed | false | null | [] | null | 5 | 2014-01-14T20:25:54Z | 2014-01-14T21:31:09Z | 2014-01-14T20:29:08Z | NONE | null | ```
In [82]: d = {'a': range(4), 'b': list('ab..'), 'c': ['a', 'b', nan, 'd']}
In [83]: df = pd.DataFrame(d)
In [84]: df
Out[84]:
a b c
0 0 a a
1 1 b b
2 2 . NaN
3 3 . d
In [85]: df[['c']][pd.isnull(df.c)] = df[['b']][pd.isnull(df.c)]
In [86]: df
Out[86]:
a b c
0 0 a a
1 1 b b
2 2 . NaN
3 3 . d
In [87]: df['c'][pd.isnull(df.c)] = df[['b']][pd.isnull(df.c)]
In [88]: df
Out[88]:
a b c
0 0 a a
1 1 b b
2 2 . .
3 3 . d
```
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} | 0 | 2014-01-14T21:31:09Z | 2014-07-16T08:46:31Z | 2014-01-15T00:37:31Z | CONTRIBUTOR | null | when detecting chained assignment, related, #5938
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} | 20 | 2014-01-14T23:41:02Z | 2014-03-14T02:40:26Z | 2014-03-14T02:33:24Z | NONE | null | Given that the entire Google BigQuery API returns UTF-8, it would make sense to handle UTF-8 output from BigQuery in the `gbq.read_gbq` IO module.
I'd love to do a pull request but I'm not sure the preferred way of handling this. I'd assume that [this line](https://github.com/pydata/pandas/blob/master/pandas/io/gbq.py#L123) should be changed to `field_value = field_value.decode('utf-8')`. I made that change and tests passed but figuring out how to properly test UTF-8 encoding is giving me some trouble (keeps making my test addition fail).
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} | 6 | 2014-01-15T13:19:13Z | 2014-06-14T23:57:13Z | 2014-01-15T22:17:33Z | CONTRIBUTOR | null | Building the result from a list of strings or a list of lists seems to improve performance over building the result via `arr.apply(f)`.
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} | 8 | 2014-01-15T14:57:06Z | 2014-04-24T01:24:31Z | 2014-04-24T01:24:31Z | NONE | null | related to #4332
I have an excel spread sheet (.xls) that contains a time column. The time is displayed in Excel as minutes:seconds.tenths of seconds. Such as "50:59.2" "50:59.4". The raw data contains hours:minutes:seconds.decimalseconds.
It seems that Pandas uses xldate_as_tuple() which apparently rounds all seconds, so the decimal part is dropped. So the above two data points both import as "50:59".
I suggest using a different conversion method, at least as an option. That way the data will not be dropped, as it is now.
I did also post this same issue to xlrd.
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} | 1 | 2014-01-15T15:52:46Z | 2018-07-06T22:37:51Z | 2018-07-06T22:37:51Z | CONTRIBUTOR | null | Since ohlc produces a multi-index and other aggregations an index,
should promote the index to multi-index and conjoin
http://stackoverflow.com/questions/21140630/resampling-trade-data-into-ohlcv-with-pandas
e.g.
`df.resample('30s',how={'price' : 'ohlc', 'volume' : 'sum'})`
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} | 1 | 2014-01-15T16:27:40Z | 2014-01-15T19:25:07Z | 2014-01-15T19:25:07Z | NONE | null | A rare/corner case, but thought I'd document it.
pandas.read_msgpack() throws a `ValueError: Dates do not conform to passed frequency` if the index is a DatetimeIndex and there are only two rows of data.
``` python
In [66]: df = pd.DataFrame([1, 2], index=pd.date_range('1/1/2013', '1/2/2013'))
In [67]: df
Out[67]:
0
2013-01-01 1
2013-01-02 2
[2 rows x 1 columns]
In [68]: pd.read_msgpack(df.to_msgpack())
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-68-fae93928effe> in <module>()
----> 1 pd.read_msgpack(df.to_msgpack())
/home/user/environments/python3/src/pandas/pandas/io/packers.py in read_msgpack(path_or_buf, iterator, **kwargs)
158 try:
159 fh = compat.BytesIO(path_or_buf)
--> 160 return read(fh)
161 finally:
162 fh.close()
/home/user/environments/python3/src/pandas/pandas/io/packers.py in read(fh)
136
137 def read(fh):
--> 138 l = list(unpack(fh))
139 if len(l) == 1:
140 return l[0]
/home/user/environments/python3/src/pandas/pandas/msgpack.cpython-33m.so in pandas.msgpack.Unpacker.__next__ (pandas/msgpack.cpp:7846)()
/home/user/environments/python3/src/pandas/pandas/msgpack.cpython-33m.so in pandas.msgpack.Unpacker._unpack (pandas/msgpack.cpp:6981)()
/home/user/environments/python3/src/pandas/pandas/io/packers.py in decode(obj)
448 elif typ == 'datetime_index':
449 data = unconvert(obj['data'], np.int64, obj.get('compress'))
--> 450 result = globals()[obj['klass']](data, freq=obj['freq'], name=obj['name'])
451 tz = obj['tz']
452
/home/user/environments/python3/src/pandas/pandas/tseries/index.py in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, **kwds)
283 inferred = subarr.inferred_freq
284 if inferred != offset.freqstr:
--> 285 raise ValueError('Dates do not conform to passed '
286 'frequency')
287
ValueError: Dates do not conform to passed frequency
```
Adding a third row makes the problem go away:
``` python
In [13]: df = pd.DataFrame([1, 2, 3], index=pd.date_range('1/1/2013', '1/3/2013'))
In [14]: df
Out[14]:
0
2013-01-01 1
2013-01-02 2
2013-01-03 3
[3 rows x 1 columns]
In [15]: pd.read_msgpack(df.to_msgpack())
Out[15]:
0
2013-01-01 1
2013-01-02 2
2013-01-03 3
[3 rows x 1 columns]
```
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} | 0 | 2014-01-15T16:41:07Z | 2014-07-16T08:46:41Z | 2014-01-15T19:25:07Z | CONTRIBUTOR | null | closes #5947
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} | 3 | 2014-01-15T17:25:01Z | 2018-03-07T09:56:47Z | 2018-03-07T09:56:42Z | CONTRIBUTOR | null | `pd.match` is a lurking function.....would be nice to add a short blurb to the main docs, maybe in `factorize` section
https://github.com/pydata/pandas/blob/master/doc/source/reshaping.rst#factorizing-values
used here in docs: https://github.com/pydata/pandas/pull/5875/files
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} | 0 | 2014-01-15T19:03:35Z | 2014-07-16T08:22:47Z | 2014-01-15T20:49:18Z | NONE | null | #5922. No word from ipython yet but the change makes sense regardless.
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} | 3 | 2014-01-15T19:36:40Z | 2014-01-23T20:17:02Z | 2014-01-23T20:17:02Z | NONE | null | The value "-nan" (yes, this is a valid type of NaN!) in a CSV input file causes that column to be treated as 'object' instead of float64.
```
pd.read_csv(StringIO.StringIO('a,b\n1,2.0\n2,nan\n3,-nan')).b
Out[15]:
0 2.0
1 NaN
2 -nan
Name: b, dtype: object
pd.read_csv(StringIO.StringIO('a,b\n1,2.0\n2,nan\n')).b
Out[16]:
0 2
1 NaN
Name: b, dtype: float64
```
When the file is sufficiently large, the following error is generated:
```
In [3]: pd.read_csv('big.bad.csv')
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py:1033: DtypeWarning: Columns (58,64) have mixed types. Specify dtype option on import or set low_memory=False.
data = self._reader.read(nrows)
```
If the string "-nan" is replaced with "nan" all is well. I don't really need to distinguish negative NaN from NaN but would like to be able to read my data files w/o having to pre-process them to scrub all the '-nan's.
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} | 0 | 2014-01-15T19:47:05Z | 2014-06-22T18:22:35Z | 2014-01-15T19:47:11Z | NONE | null | The more innocous bits and pieces of #5925, no sense in waiting.
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} | 17 | 2014-01-15T20:26:44Z | 2014-06-15T09:42:36Z | 2014-01-16T11:26:40Z | NONE | null | revisiting #5655 due to recent #5874, it's just not right.
most pandas read_\* methods take a path/url of file like. the newish read_json/read_msgpack
also accept a (byte)string of data, and tries to guess whether it's data or a filepath.
That creates weird corner cases and is sufficently wrong IMO that it's worth
making a breaking change. Users will now have to wrap their data in BytesIO/StringIO.
not much of a problem, except perhaps the lost convenience of `pd.read_json(j)`. But since json
usually comes from a file or url which are supported directly I'm hoping it won't be
that disruptive.
0.14.0 ofcourse.
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} | 2 | 2014-01-15T20:30:31Z | 2014-03-29T01:53:36Z | 2014-03-29T01:53:36Z | NONE | null | related to #3020
``` python
$ python
Python 2.7.4 (default, Apr 23 2013, 12:22:04)
[GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import datetime
>>> import pandas as pd
>>> pd.__version__
'0.12.0'
>>> indices = [datetime.datetime(1975, 1, i, 12, 0) for i in range(1, 6)]
>>> series = pd.Series(range(1, 6), index=indices)
>>> series = series.map(lambda x: float(x)) # range() returns ints, so force to float
>>> series = series.sort_index() # already sorted, but just to be clear
>>> series # here's what our input series looks like
1975-01-01 12:00:00 1
1975-01-02 12:00:00 2
1975-01-03 12:00:00 3
1975-01-04 12:00:00 4
1975-01-05 12:00:00 5
dtype: float64
>>> pd.rolling_mean(series, window=2, freq='D') # these results will be wrong
1975-01-01 NaN
1975-01-02 NaN
1975-01-03 NaN
1975-01-04 NaN
1975-01-05 NaN
Freq: D, dtype: float64
>>> better_series = series.append(pd.Series([3.0], index=[datetime.datetime(1975, 1, 3, 6, 0)]))
>>> better_series = better_series.sort_index()
>>> better_series # here's a revised input with more than one datapoint on one of the days
1975-01-01 12:00:00 1
1975-01-02 12:00:00 2
1975-01-03 06:00:00 3
1975-01-03 12:00:00 3
1975-01-04 12:00:00 4
1975-01-05 12:00:00 5
dtype: float64
>>> pd.rolling_mean(better_series, window=2, freq='D') # These results will be correct and are what I expected above
1975-01-01 NaN
1975-01-02 1.5
1975-01-03 2.5
1975-01-04 3.5
1975-01-05 4.5
Freq: D, dtype: float64
```
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related #5874
closes #5924
todo:
```
[ ] update io.rst docs / whatsnew
[ ] more tests (esp pickle/csv) for passing string to ``read_csv`` or file-handle to ``reads_csv``
```
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just be ignored.
#3944
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} | 2 | 2014-01-16T02:24:51Z | 2014-06-21T03:08:27Z | 2014-01-16T14:28:12Z | CONTRIBUTOR | null | closes #5927
Fixes numpy max/min slow path on resample and adds tests to vbench.
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} | 0 | 2014-01-16T03:46:06Z | 2014-01-16T11:54:39Z | 2014-01-16T11:54:39Z | CONTRIBUTOR | null | This works:
```
In [400]:
pd.to_datetime([pd.Timestamp("2013-1-1", tz=pytz.timezone('US/Eastern'))])
Out[400]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01 00:00:00-05:00]
Length: 1, Freq: None, Timezone: US/Eastern
```
but this (adding an NaT) does not. At the very least, the error message is misleading:
```
In [401]:
pd.to_datetime([pd.Timestamp("2013-1-1", tz=pytz.timezone('US/Eastern')), pd.NaT])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-401-3b5b8a67d2e1> in <module>()
----> 1 pd.to_datetime([pd.Timestamp("2013-1-1", tz=pytz.timezone('US/Eastern')), pd.NaT])
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0rc1_78_g142ca62-py2.7-linux-x86_64.egg/pandas/tseries/tools.pyc in to_datetime(arg, errors, dayfirst, utc, box, format, coerce, unit)
137 return Series(values, index=arg.index, name=arg.name)
138 elif com.is_list_like(arg):
--> 139 return _convert_listlike(arg, box=box)
140
141 return _convert_listlike(np.array([ arg ]), box=box)[0]
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0rc1_78_g142ca62-py2.7-linux-x86_64.egg/pandas/tseries/tools.pyc in _convert_listlike(arg, box)
127 return DatetimeIndex._simple_new(values, None, tz=tz)
128 except (ValueError, TypeError):
--> 129 raise e
130
131 if arg is None:
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
I tracked down this issue to a bug in `datetime_to_datetime64`
When iterating over the elements in `datetime_to_datetime64` the check for nullness is `util._checknull(val)` which is `False` for `NaT`.
The correct null check is to use `checknull` from `lib`
PR on the way.
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} | 4 | 2014-01-16T04:33:47Z | 2014-11-26T02:30:02Z | 2014-11-26T02:30:02Z | CONTRIBUTOR | null | ```
In [426]:
s = pd.Series([timedelta(days=1), timedelta(days=2)])
s > timedelta(days=1)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-426-9094b7c93677> in <module>()
1 s = pd.Series([timedelta(days=1), timedelta(days=2)])
----> 2 s > timedelta(days=1)
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0rc1_78_g142ca62-py2.7-linux-x86_64.egg/pandas/core/ops.pyc in wrapper(self, other)
561
562 # scalars
--> 563 res = na_op(values, other)
564 if np.isscalar(res):
565 raise TypeError('Could not compare %s type with Series'
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0rc1_78_g142ca62-py2.7-linux-x86_64.egg/pandas/core/ops.pyc in na_op(x, y)
528
529 try:
--> 530 result = getattr(x, name)(y)
531 if result is NotImplemented:
532 raise TypeError("invalid type comparison")
TypeError: can't compare datetime.timedelta to long
```
The comparison does work for `timedelta64`:
```
In [45]: s > np.timedelta64(timedelta(days=1))
Out[45]:
0 False
1 True
dtype: bool
```
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} | 1 | 2014-01-16T09:37:09Z | 2014-01-16T11:49:06Z | 2014-01-16T11:05:06Z | NONE | null | 0.16 install from pip on ubuntu 12.04 LTS 64 bit edition currently throws an error:
Exception: Cython-generated file 'pandas/msgpack.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
I did not specify a dev branch install from pip, simply ran
`sudo pip install pandas` and it produced the above error, python-dev and build-essential are installed, available and the latest updates on ubuntu 12.04 64
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} | 4 | 2014-01-16T13:21:52Z | 2016-11-11T10:21:27Z | 2016-11-11T10:21:23Z | CONTRIBUTOR | null | related #4147
Observe w/o tz:
```
In [46]: pd.Series([pd.NaT, pd.Timestamp("2013-1-1")]).min()
Out[46]: Timestamp('2013-01-01 00:00:00', tz=None)
```
however w/ tz:
```
In [48]: pd.Series([pd.NaT, pd.Timestamp("2013-1-1", tz="US/Eastern")]).min()
Out[48]: NaT
```
strangely, `max` works:
```
In [60]: pd.Series([pd.NaT, pd.Timestamp("2013-1-1", tz="US/Eastern")]).max()
Out[60]: Timestamp('2013-01-01 00:00:00-0500', tz='US/Eastern')
```
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} | 21 | 2014-01-16T13:42:26Z | 2014-01-16T22:14:44Z | 2014-01-16T17:58:40Z | NONE | null | For the FEC dataset, it takes about 1.5 sec to get a repr. and prun
puts it all in `infer_dtype`.
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} | 43 | 2014-01-16T16:08:44Z | 2016-10-12T23:04:30Z | 2014-01-26T23:16:10Z | CONTRIBUTOR | null | From the example at http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.query.html:
``` python
from numpy.random import randn
from pandas import DataFrame
df = DataFrame(randn(10, 2), columns=list('ab'))
df.query('a > b')
```
gives me:
```
---------------------------------------------------------------------------
NameResolutionError Traceback (most recent call last)
<ipython-input-19-47040e53b0e7> in <module>()
2 from pandas import DataFrame
3 df = DataFrame(randn(10, 2), columns=list('ab'))
----> 4 df.query('a > b')
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in query(self, expr, **kwargs)
1778 "query expression")
1779
-> 1780 res = self.eval(expr, **kwargs)
1781
1782 try:
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in eval(self, expr, **kwargs)
1829 kwargs['local_dict'] = _ensure_scope(resolvers=resolvers, **kwargs)
1830 kwargs['target'] = self
-> 1831 return _eval(expr, **kwargs)
1832
1833 def _slice(self, slobj, axis=0, raise_on_error=False, typ=None):
/usr/local/lib/python2.7/dist-packages/pandas/computation/eval.pyc in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target)
206 eng = _engines[engine]
207 eng_inst = eng(parsed_expr)
--> 208 ret = eng_inst.evaluate()
209
210 # assign if needed
/usr/local/lib/python2.7/dist-packages/pandas/computation/engines.pyc in evaluate(self)
48
49 # make sure no names in resolvers and locals/globals clash
---> 50 self.pre_evaluate()
51 res = self._evaluate()
52 return _reconstruct_object(self.result_type, res, self.aligned_axes,
/usr/local/lib/python2.7/dist-packages/pandas/computation/engines.pyc in pre_evaluate(self)
31
32 def pre_evaluate(self):
---> 33 self.expr.check_name_clashes()
34
35 def evaluate(self):
/usr/local/lib/python2.7/dist-packages/pandas/computation/expr.pyc in check_name_clashes(self)
797 lcl_keys = frozenset(env.locals.keys()) & names
798 gbl_keys = frozenset(env.globals.keys()) & names
--> 799 _check_disjoint_resolver_names(res_keys, lcl_keys, gbl_keys)
800
801 def add_resolvers_to_locals(self):
/usr/local/lib/python2.7/dist-packages/pandas/computation/expr.pyc in _check_disjoint_resolver_names(resolver_keys, local_keys, global_keys)
39 if res_locals:
40 msg = "resolvers and locals overlap on names {0}".format(res_locals)
---> 41 raise NameResolutionError(msg)
42
43 res_globals = list(com.intersection(resolver_keys, global_keys))
NameResolutionError: resolvers and locals overlap on names ['a']
```
This is with `0.13`.
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https://api.github.com/repos/pandas-dev/pandas/issues/5970 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5970/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5970/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5970/events | https://github.com/pandas-dev/pandas/pull/5970 | 25,737,727 | MDExOlB1bGxSZXF1ZXN0MTE1ODUyOTM= | 5,970 | PERF: perf improvments in dtypes/ftypes methods (GH5968) | {
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} | 3 | 2014-01-16T16:35:58Z | 2014-06-15T18:39:31Z | 2014-01-16T17:04:02Z | CONTRIBUTOR | null | closes #5968
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_dtypes | 0.2654 | 51.2140 | 0.0052 |
frame_wide_repr | 17.1947 | 525.7313 | 0.0327 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [6a7bfc1] : PERF: perf improvments in dtypes/ftypes methods (GH5968)
Base [2081fcc] : Merge pull request #5951 from y-p/PR_latest_ipython_rep_fixes
CLN: repr_html raises NotImplementedError rather then ValueError in qtconsole
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5971 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5971/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5971/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5971/events | https://github.com/pandas-dev/pandas/issues/5971 | 25,740,818 | MDU6SXNzdWUyNTc0MDgxOA== | 5,971 | Suggestion: percentile ranks | {
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} | 4 | 2014-01-16T17:14:47Z | 2014-03-27T12:07:42Z | 2014-02-16T21:23:12Z | CONTRIBUTOR | null | I realize I am computing percentile ranks constantly in my code.
df["pct_rank"] = df["field"].groupby("date").transform(lambda x: x.rank(ascending=False) / float(x.count()))
Would anyone have any use for a function that is computed in cython for this?
if so, would people prefer to it to be a separate function or an option in rank?
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} | 2 | 2014-01-16T17:38:28Z | 2014-01-17T09:48:52Z | 2014-01-17T09:48:52Z | CONTRIBUTOR | null | This is identical to Tom Augspurger's issue [here](https://groups.google.com/d/msg/pydata/pxFe_ISulyA/Upyjnx96P-UJ).
Under some circumstances, you can't run the performance tests:
```
~/sys/pandas$ ./test_perf.sh
Traceback (most recent call last):
File "/home/mcneil/sys/pandas/vb_suite/test_perf.py", line 55, in <module>
import pandas as pd
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_75_g7d9e9fa-py2.7-linux-i686.egg/pandas/__init__.py", line 37, in <module>
import pandas.core.config_init
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_75_g7d9e9fa-py2.7-linux-i686.egg/pandas/core/config_init.py", line 17, in <module>
from pandas.core.format import detect_console_encoding
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_75_g7d9e9fa-py2.7-linux-i686.egg/pandas/core/format.py", line 9, in <module>
from pandas.core.index import Index, MultiIndex, _ensure_index
File "/usr/local/lib/python2.7/dist-packages/pandas-0.13.0_75_g7d9e9fa-py2.7-linux-i686.egg/pandas/core/index.py", line 11, in <module>
import pandas.index as _index
File "index.pyx", line 34, in init pandas.index (pandas/index.c:16227)
File "/usr/local/lib/python2.7/dist-packages/pytz/__init__.py", line 29, in <module>
from pkg_resources import resource_stream
File "/usr/local/lib/python2.7/dist-packages/pkg_resources.py", line 76, in <module>
import parser
File "/home/mcneil/sys/pandas/vb_suite/parser.py", line 1, in <module>
from vbench.api import Benchmark
File "/usr/local/lib/python2.7/dist-packages/vbench/__init__.py", line 2, in <module>
import vbench.log
File "/usr/local/lib/python2.7/dist-packages/vbench/log.py", line 34, in <module>
from vbench.config import is_interactive
File "/usr/local/lib/python2.7/dist-packages/vbench/config.py", line 4, in <module>
TIME_ZONE = pytz.timezone('US/Eastern')
AttributeError: 'module' object has no attribute 'timezone'
```
This happens because pytz does
```
from pkg_resources import resource_stream
```
which itself calls
```
import parser
```
which is picking up `pandas/vb_suite/parser.py` instead of the builtin parser module. Net result: the `pytz` import never manages to get to the point where `timezone` is defined.
As expected, renaming `parser.py` solves the problem (and restoring the original name creates it again).
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https://api.github.com/repos/pandas-dev/pandas/issues/5973 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5973/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5973/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5973/events | https://github.com/pandas-dev/pandas/pull/5973 | 25,743,312 | MDExOlB1bGxSZXF1ZXN0MTE1ODg1NDY= | 5,973 | PERF: perf improvments in indexing with object dtypes (GH5968) | {
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```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_iloc_big | 0.2836 | 2.8454 | 0.0997 |
```
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} | 14 | 2014-01-16T17:52:57Z | 2014-06-12T19:14:09Z | 2014-01-16T18:06:13Z | NONE | null | #5550 deprecated options.display.max_info_rows, but df.info is still there
for the user to invoke and the null count can be very slow.
Un-deprecte the option and revamp `df.info` to do the right thing.
Now that @cpcloud add per column dtypes it will always show them,
and just supress the counts if needed, where previously if max_info_rows was
exceeded, it didn't even print the column names.
```
In [4]: df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 1000000 entries, C00410118 to C00431445
Data columns (total 18 columns):
cmte_id 1000000 non-null object
cand_id 1000000 non-null object
cand_nm 1000000 non-null object
contbr_nm 999975 non-null object
contbr_city 1000000 non-null object
contbr_st 999850 non-null object
contbr_zip 992087 non-null object
contbr_employer 994533 non-null object
contbr_occupation 1000000 non-null float64
contb_receipt_amt 1000000 non-null object
contb_receipt_dt 18038 non-null object
receipt_desc 383960 non-null object
memo_cd 391290 non-null object
memo_text 1000000 non-null object
form_tp 1000000 non-null int64
file_num 1000000 non-null object
tran_id 999998 non-null object
election_tp 0 non-null float64
dtypes: float64(2), int64(1), object(15)
In [5]: pd.options.display.max_info_rows
Out[5]: 1690785
In [6]: pd.options.display.max_info_rows=999999
In [7]: df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 1000000 entries, C00410118 to C00431445
Data columns (total 18 columns):
cmte_id object
cand_id object
cand_nm object
contbr_nm object
contbr_city object
contbr_st object
contbr_zip object
contbr_employer object
contbr_occupation float64
contb_receipt_amt object
contb_receipt_dt object
receipt_desc object
memo_cd object
memo_text object
form_tp int64
file_num object
tran_id object
election_tp float64
```
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} | 2 | 2014-01-16T18:14:57Z | 2014-01-19T09:43:24Z | 2014-01-19T09:43:24Z | CONTRIBUTOR | null | From [this SO question](http://stackoverflow.com/questions/21169182/interpolation-for-some-methods-is-failing-with-a-type-error).
Is this something we should worry about here, or report upstream to scipy? The workaround I posted on SO is just to explicitly recast the index as an int64, interpolate on that, and then reset the original index. Any reason we shouldn't do this internally?
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Is there a better way to check for a DatetimeIndex than with an `isinstance()`?
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} | 14 | 2014-01-16T19:05:10Z | 2014-06-12T15:46:42Z | 2014-02-16T21:23:12Z | CONTRIBUTOR | null | closes #5971
This pull allows people to compute percentage ranks in cython for a series. I found myself computing this all the time and it will make this calculation much faster.
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https://api.github.com/repos/pandas-dev/pandas/issues/5979 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5979/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5979/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5979/events | https://github.com/pandas-dev/pandas/pull/5979 | 25,755,447 | MDExOlB1bGxSZXF1ZXN0MTE1OTMwMzI= | 5,979 | VBENCH: rename parser.py to parser_vb.py | {
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} | 4 | 2014-01-16T19:38:24Z | 2014-06-21T02:57:58Z | 2014-01-17T09:48:52Z | NONE | null | closes #5972
@dsm054, does this resolve the issue for you?
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https://api.github.com/repos/pandas-dev/pandas/issues/5980 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5980/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5980/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5980/events | https://github.com/pandas-dev/pandas/pull/5980 | 25,768,473 | MDExOlB1bGxSZXF1ZXN0MTE1OTg2NDA= | 5,980 | DOC: add a couple plt.close('all') to avoid warning | {
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} | [] | closed | false | null | [] | null | 2 | 2014-01-16T21:52:15Z | 2014-07-02T01:21:00Z | 2014-01-17T10:47:13Z | MEMBER | null | Some small doc fixes:
- added some `plt.close('all')` calls to avoid the matplotlib warning that there are more than 20 figures open.
- some style errors in release.rst. Apparantly sphinx complains about something like ```NaN``s`.because there may not be a character directly after the closing ````
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} | 4 | 2014-01-16T23:18:14Z | 2014-01-17T12:55:11Z | 2014-01-17T12:55:11Z | CONTRIBUTOR | null | I am testing pandas 0.13 (built against numexpr 2.2.2, numpy 1.8.0, scipy 0.13.2, all linked to MKL 10.3 using Scientific Linux 6.4's stock gcc 4.7.4), and test_pow fails with an array not equal error:
#
## FAIL: test_pow (pandas.computation.tests.test_eval.TestEvalNumexprPandas)
Traceback (most recent call last):
File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/pandas/computation/tests/test_eval.py", line 192, in test_pow
self.check_pow(lhs, '*_', rhs)
File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/pandas/computation/tests/test_eval.py", line 94, in wrapper
f(self, lhs, arith1, rhs, *args, *_kwargs)
File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/pandas/computation/tests/test_eval.py", line 408, in check_pow
assert_array_equal(result, expected)
File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/numpy/testing/utils.py", line 718, in assert_array_equal
verbose=verbose, header='Arrays are not equal')
File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not equal
(mismatch 25.8064516129%)
x: array([[ 6.98492548e-01, 5.39900967e-01, 1.87991705e+00,
7.67724670e-01, nan],
[ nan, 1.18493281e+00, 4.15163610e+01,...
y: array([[ 6.98492548e-01, 5.39900967e-01, 1.87991705e+00,
7.67724670e-01, nan],
[ nan, 1.18493281e+00, 4.15163610e+01,...
---
I have modified the check_pow function in that test to identify the error and print out the difference in result and expected:
@skip_incompatible_operand
def check_pow(self, lhs, arith1, rhs):
ex = 'lhs {0} rhs'.format(arith1)
expected = self.get_expected_pow_result(lhs, rhs)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
```
if (np.isscalar(lhs) and np.isscalar(rhs) and
_is_py3_complex_incompat(result, expected)):
self.assertRaises(AssertionError, assert_array_equal, result,
expected)
else:
print result, expected, result-expected
assert_array_equal(result, expected,verbose=True)
ex = '(lhs {0} rhs) {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
expected = self.get_expected_pow_result(
self.get_expected_pow_result(lhs, rhs), rhs)
print result, expected, result-expected
assert_array_equal(result, expected)
```
Which yields the following (only differences reported for brevity):
[10 rows x 5 columns] 0 1 2 3 4
0 -2.775558e-17 NaN 0.000000e+00 -2.220446e-16 0.000000e+00
1 0.000000e+00 1.110223e-16 NaN NaN NaN
2 0.000000e+00 NaN NaN NaN NaN
3 0.000000e+00 NaN NaN 0.000000e+00 0.000000e+00
4 NaN 0.000000e+00 1.110223e-16 0.000000e+00 2.220446e-16
5 NaN -1.110223e-16 -5.551115e-17 0.000000e+00 0.000000e+00
6 NaN NaN 0.000000e+00 NaN -4.440892e-16
7 -1.110223e-16 NaN NaN 3.552714e-15 NaN
8 NaN 0.000000e+00 0.000000e+00 NaN 2.220446e-16
9 0.000000e+00 0.000000e+00 NaN 0.000000e+00 0.000000e+00
and
[10 rows x 5 columns] 0 1 2 3 4
0 0.000000e+00 NaN 0.000000e+00 0.000000e+00 0.000000e+00
1 0.000000e+00 1.110223e-16 NaN NaN NaN
2 2.220446e-16 NaN NaN NaN NaN
3 0.000000e+00 NaN NaN 5.551115e-17 0.000000e+00
4 NaN 0.000000e+00 1.110223e-16 0.000000e+00 2.220446e-16
5 NaN -1.110223e-16 0.000000e+00 0.000000e+00 0.000000e+00
6 NaN NaN 0.000000e+00 NaN 1.110223e-16
7 2.220446e-16 NaN NaN -4.336809e-19 NaN
8 NaN 0.000000e+00 0.000000e+00 NaN -1.110223e-16
9 0.000000e+00 1.110223e-16 NaN 0.000000e+00 0.000000e+00
The differences are only at machine precision, and may be due to the use of MKL rather than standard blas (I haven't dug any further). I have verified that changing the test from assert_array_equal to assert_allclose allows the test to succeed.
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} | 1 | 2014-01-16T23:36:12Z | 2014-07-16T08:47:12Z | 2014-01-17T12:54:59Z | CONTRIBUTOR | null | ...rt_allclose rather than assert_all_equal to avoid machine precision failures in TestEvalNumexprPandas/Numpy. Fixes issue #5981.
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} | 0 | 2014-01-17T00:42:42Z | 2014-07-16T08:47:14Z | 2014-01-17T13:42:13Z | CONTRIBUTOR | null | DOC: updated api docs for panel/panel4d
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https://api.github.com/repos/pandas-dev/pandas/issues/5984 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5984/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5984/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5984/events | https://github.com/pandas-dev/pandas/pull/5984 | 25,797,806 | MDExOlB1bGxSZXF1ZXN0MTE2MTQ4MjI= | 5,984 | DOC: correction of docstring dtypes/ftypes | {
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See #5970, you copied the docstring of `get_dtype_counts`. Only thing I was wondering: `columns` is maybe not generic enough?
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} | [] | closed | false | null | [] | null | 0 | 2014-01-17T13:37:10Z | 2014-06-22T08:33:03Z | 2014-01-17T13:37:15Z | NONE | null | This fix allows us to bring back the pdf documentation, albeit with some warnings.
I've uploaded the pdf to GH releases, and eventually hope to get it on the website.
Also updated the [release checklist](https://github.com/pydata/pandas/wiki/Release-Checklist) to make pdf publishing a standard part of our release process.
closes #5433
related #3518
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} | 1 | 2014-01-17T16:31:02Z | 2016-02-27T16:03:24Z | 2016-02-27T15:22:59Z | CONTRIBUTOR | null | check that these work / fix (prob just need tests and conversion to i8), datetime64
- [x] rank #12465
- [x] unique
- [x] factorize #12465
- [x] quantile
- [x] mode #12465
- [x] value_counts
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} | 10 | 2014-01-17T18:52:50Z | 2014-02-20T14:24:06Z | 2014-02-20T14:24:06Z | MEMBER | null | Currently, with `query` and `eval` you can use local variables a la the `@` symbol. It's a bit confusing since you're not allowed to have a local variable and a column name with the same name, but it will try to pull the local if possible.
Current API:
Fails with a `NameError`:
``` python
a = 1
df = DataFrame({'a': randn(10), 'b': randn(10)})
df.query('a > b')
```
But this works:
``` python
df.query('@a > b')
```
And so does this, which is confusing:
``` python
a = 1
df = DataFrame({'b': randn(10), 'c': randn(10)})
df.query('a < b < c')
```
As suggested by @y-p and @jreback, the following API is less confusing IMO.
From now on, all local variables will need an explicit reference and if there is a column name and a local with the same name then the column will be used. Thus you can always be sure that you're referring to a column, or it doesn't exist, in which case you'll get an error. And if you use `@` then you can be sure that you're referring to local, and likewise get an error if it doesn't exist. As a bonus ( :wolf: in :sheep: 's clothing), this allows you to use both a local _and_ a column name with the same name.
Examples:
``` python
a = 1
df = DataFrame({'a': randn(10), 'b': randn(10)})
# uses the column 'a'
df.query('a > b')
# uses the local
df.query('@a > b')
# fails because I didn't reference the local and there's no 'c' column
c = 1
df.query('a > c')
# local and a column name
df.query('b < @a < a')
```
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} | [] | closed | false | null | [] | null | 1 | 2014-01-17T19:27:47Z | 2014-01-17T22:46:43Z | 2014-01-17T22:46:43Z | NONE | null | I've started toying around with this today, its quite interesting and powerful for research. I thought it an interesting and at first thought "relatively easy" (famous last words) to add a column for the forward price of the security. When constructing the VIX index the CBOE has a realtively simple process to extract market participants forward price.
https://www.cboe.com/micro/vix/vixwhite.pdf page 6.
The only input that theoretically would be needed is the risk free rate used for discounting. But they essentially take the strike price for which the (call price - put price is smallest) at each expiration, and discount it back at the risk free rate.
I'm not sure if this is functionality one would want included in the main libraries or if its something that should be left to others, but appending a forward price column(it would be the same for all options for each expiration), one could then make an implied volatility column. Also, having a panel of important expiration dates, one could "realtively" easily interpolate the volatility surface with matplotlib, using the methodology of Gatheral (2006).
Just a discussion to start, having historical data on the volatility surface readily available could yield some interesting research results.
Thanks
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} | 1 | 2014-01-17T20:31:23Z | 2014-07-16T08:47:18Z | 2014-01-18T02:53:26Z | CONTRIBUTOR | null | Fixes a quick typo I noticed.
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} | [] | closed | false | null | [] | null | 2 | 2014-01-18T04:11:56Z | 2014-01-23T16:53:30Z | 2014-01-23T16:53:05Z | CONTRIBUTOR | null | Often on SO people looking for help say they can't put their DataFrame somewhere for others to look at because the data's private. In most cases, the data itself isn't relevant to answering the question, and only the structure matters.
It would be straightforward to add a method which replaces string data and index/column names by A,B,C, randomizes numbers and times (with or without preserving the overall ordering), etc. There's a reasonably popular [R question](http://stackoverflow.com/questions/10454973/how-to-create-example-data-set-from-private-data-replacing-variable-names-and-l) asking about the same.
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} | [] | closed | false | null | [] | null | 1 | 2014-01-18T16:38:07Z | 2014-01-19T11:27:59Z | 2014-01-19T11:25:08Z | CONTRIBUTOR | null | As requested in [#5530 (comment)] and after the merge of #5925, I am trying to test building the docs on python 3. I get the error:
```
Traceback (most recent call last):
File "/Users/user/.virtualenvs/pandas-dev/lib/python3.3/site-packages/sphinx/cmdline.py", line 245, in main
warningiserror, tags, verbosity, parallel)
File "/Users/user/.virtualenvs/pandas-dev/lib/python3.3/site-packages/sphinx/application.py", line 119, in __init__
self.setup_extension(extension)
File "/Users/user/.virtualenvs/pandas-dev/lib/python3.3/site-packages/sphinx/application.py", line 283, in setup_extension
mod = __import__(extension, None, None, ['setup'])
File "/Users/user/Optional/pandas-phaebz/doc/sphinxext/numpydoc.py", line 40
title_re = re.compile(ur'^\s*[#*=]{4,}\n[a-z0-9 -]+\n[#*=]{4,}\s*',
SyntaxError: invalid syntax
```
I thought about it and talked to @kermit666: where should this be taken care of?
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} | 0 | 2014-01-18T20:04:00Z | 2014-06-12T13:16:50Z | 2014-01-18T20:07:12Z | NONE | null | While investigating an optimization suggested in #5490 , I came
across a fast path in pd.read_csv that I was unaware of for parsing iso8601 datetimes.
It's undocumented as far as I can tell.
RIght the wrong.
```
N=10000
r=pd.date_range("2000-01-01","2013-01-01",freq="H")[:N]
df=pd.DataFrame(range(N),index=r)
df.to_csv("/tmp/1.csv")
def f1(s):
return dt.datetime.strptime(s,"%Y-%m-%d %H:%M:%S")
def f2(s):
return dateutil.parser.parse(s)
%timeit x1=pd.read_csv("/tmp/1.csv",parse_dates=True,index_col=0) # fast-path for iso8601, fallback to dateutil.parser
%timeit x2=pd.read_csv("/tmp/1.csv",parse_dates=True,index_col=0,date_parser=f1)
%timeit x2=pd.read_csv("/tmp/1.csv",parse_dates=True,index_col=0,date_parser=f2)
100 loops, best of 3: 14.1 ms per loop
1 loops, best of 3: 259 ms per loop
1 loops, best of 3: 956 ms per loop
```
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} | 2 | 2014-01-18T20:37:06Z | 2014-01-23T15:55:36Z | 2014-01-23T15:41:34Z | CONTRIBUTOR | null | add to
http://pandas.pydata.org/pandas-docs/dev/tutorials.html
the tutorial:
http://pandas-xlsxwriter-charts.readthedocs.org/
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} | 19 | 2014-01-18T21:00:18Z | 2014-06-16T22:59:26Z | 2014-01-20T23:37:17Z | CONTRIBUTOR | null | closes #5458
closes #5695
closes #5689
cleaned up / clarified timedelta inferences, bottom line is the following works, previously these would
return not-useful object dtypes
```
In [4]: Series([np.timedelta64(1,'s'),timedelta(days=1),pd.NaT])
Out[4]:
0 0 days, 00:00:01
1 1 days, 00:00:00
2 NaT
dtype: timedelta64[ns]
```
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} | 13 | 2014-01-18T22:54:08Z | 2014-07-02T10:56:37Z | 2014-01-25T01:09:54Z | MEMBER | null | First draft of some guidelines on contributing to and building the docs (following #5934). Comments are very welcome.
Rendered version can be seen here: https://github.com/jorisvandenbossche/pandas/blob/doc-guide/doc/README.rst
TODO:
- [x] update section on Sphinx 1.2 now 1.2.1 is released
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} | 41 | 2014-01-18T22:59:41Z | 2014-02-04T15:32:27Z | 2014-01-23T20:17:05Z | NONE | null | several test errors of this type when run against the maintenance/1.8.x branch of numpy.
```
ERROR: test_qcut_specify_quantiles (pandas.tools.tests.test_tile.TestCut)
File "hashtable.pyx", line 776, in pandas.hashtable.PyObjectHashTable.unique (pandas/hashtable.c:12071)
TypeError: unhashable type: 'numpy.ndarray'
FAIL: test_series_describe_multikey (pandas.tests.test_groupby.TestGroupBy)
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('O') != dtype('float64')
```
it seems you have coded a regression of 1.8 into the tests, it is intended to fix this in numpy 1.8.1 but now that pandas relies on it makes the situation a little tricky.
see https://github.com/numpy/numpy/pull/4109
How bad would it be for pandas if we release 1.8.1 with the regression fixed?
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} | 4 | 2014-01-19T00:49:53Z | 2014-06-23T03:21:11Z | 2014-01-19T09:43:24Z | CONTRIBUTOR | null | Closes #5975 (again), with the issue raised at https://github.com/pydata/pandas/pull/5977#issuecomment-32691271.
I'm using `astype` instead of `view` now, as Jeff suggested. I also didn't realize that `asi8` was a method on all `Index`es, and not just `DatetimeIndex`es. So instead of checking for that attr, I just check if it's a DatetimeIndex.
@y-p this should fix the doc example. It built for me anyway, but I've had trouble building the docs off my working branch.
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} | 0 | 2014-01-19T04:56:52Z | 2014-01-19T19:41:13Z | 2014-01-19T19:02:13Z | CONTRIBUTOR | null | full log https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=s390x&ver=0.13.0-2&stamp=1390095775&file=log
```
======================================================================
ERROR: test_flush (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 523, in test_flush
store.flush(fsync=True)
File "/«PKGBUILDDIR»/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/pytables.py", line 571, in flush
os.fsync(self._handle.fileno())
ValueError: file descriptor cannot be a negative integer (-1)
```
may be due to a recent update to pytables 3.0.0-2
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} | 7 | 2014-01-19T10:40:13Z | 2021-01-24T01:16:35Z | 2021-01-24T01:16:34Z | MEMBER | null | I think the documentation can use some reorganisation.
The current structure looks like this (updated with status of jan 2017):
```
What’s New
Installation
Contributing to pandas
Frequently Asked Questions (FAQ)
Package overview
10 Minutes to pandas
Tutorials
Cookbook
Intro to Data Structures
Essential Basic Functionality
Working with Text Data
Options and Settings
Indexing and Selecting Data
MultiIndex / Advanced Indexing
Computational tools
Working with missing data
Group By: split-apply-combine
Merge, join, and concatenate
Reshaping and Pivot Tables
Time Series / Date functionality
Time Deltas
Categorical Data
Visualization
Style
IO Tools (Text, CSV, HDF5, ...)
Remote Data Access
Enhancing Performance
Sparse data structures
Caveats and Gotchas
rpy2 / R interface
pandas Ecosystem
Comparison with R / R libraries
Comparison with SQL
Comparison with SAS
API Reference
Internals
Release Notes
```
which is a huge list that is somewhat unclear and difficult to navigate.
It is maybe also an issue that will have to go hand in hand with a better navigation provided in the html theme of the docs.
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