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| null | 2 | 2015-04-27T23:12:09Z | 2015-05-09T20:18:50Z | 2015-05-09T20:18:50Z | NONE | null | closes #3335
Changed code to raise an exception when margin=True and All was being used in the table
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| null | 9 | 2015-04-28T15:34:45Z | 2015-04-28T21:42:25Z | 2015-04-28T21:41:31Z | CONTRIBUTOR | null | The dtypes don't match:
```
In [16]: pd.DataFrame(pd.Series([], dtype="int64"), columns=["D"]).dtypes
Out[16]:
D object
dtype: object
In [17]: pd.DataFrame(pd.Series([], dtype="int64")).dtypes
Out[17]:
0 int64
dtype: object
```
This came up in #9983 .
When this is fixed, the `check_dtype` flag should also be removed in `TestDataFrame.test_mode` in `pandas/tests/test_frame.py` .
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} | 1 | 2015-04-28T19:12:04Z | 2015-04-28T19:33:05Z | 2015-04-28T19:32:57Z | CONTRIBUTOR | null | Closes #9962
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} | 4 | 2015-04-28T20:16:45Z | 2016-05-13T13:25:25Z | 2016-05-13T13:24:58Z | CONTRIBUTOR | null | Currently the `Index.str` methods only support returning `Index` results. However, analogous to the expansion from `Series.str.*` -> `DataFrame`, we should be able to do `Index.str.*` -> `MultiIndex` in certain cases, as discussed in https://github.com/pydata/pandas/pull/9870#issuecomment-92416184
Some of the string methods that can support this are: `str.get_dummies`, `str.extract`, `str.split`
- [x] `split` (#10085)
- [x] `rsplit` (#10303)
- [x] `partition` / `rpartition` (#9773)
- [x] `extract` (#11386, only supports one group )
- [x] `extractall` (not implemented in Index), #13156
- [x] `get_dummies` (#12842)
Here are the related PRs: https://github.com/pydata/pandas/pull/9667, https://github.com/pydata/pandas/pull/9843, https://github.com/pydata/pandas/pull/9870, https://github.com/pydata/pandas/pull/9985
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} | 6 | 2015-04-28T21:22:07Z | 2015-04-29T23:33:35Z | 2015-04-29T22:20:10Z | CONTRIBUTOR | null | fixes #10002
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} | 1 | 2015-04-28T21:40:17Z | 2015-04-29T15:15:15Z | 2015-04-29T10:29:14Z | CONTRIBUTOR | null | Just a minor cleanup. `diff` is deprecated and it triggers a warning.
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| null | 20 | 2015-04-28T21:56:33Z | 2015-11-18T03:26:14Z | 2015-11-18T01:43:42Z | NONE | null | I've seen many developers post about incredible slowness when writing pandas dataframe to a SQL Server table. Some people labeled the issue "chunk size doesn't work" or "data incompatibility slowness" and what not. After a lengthy research I found the following explanation from SQLAlchemy:
http://docs.sqlalchemy.org/en/rel_0_8/faq.html#i-m-inserting-400-000-rows-with-the-orm-and-it-s-really-slow
Briefly, I believe Dataframe.to_sql() method is using SQLAlchemy ORM which uses the Unit of Work method and ends up performing row maintenance work upon insertion of every row of the dataframe. This issue can be resolved if to_sql uses SQLAlchemy CORE instead (link above). Is there a workaround for using Dataframe.to_sql() with SQL Server (not lite) with acceptable performance? I'm really trying all I can to avoid exporting the dataframe to a file and import it to SQL with an SSIS package.
Thanks,
Sam
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} | 2 | 2015-04-29T00:48:57Z | 2015-04-29T15:15:43Z | 2015-04-29T10:27:10Z | CONTRIBUTOR | null | It seems inconsistent that all the other `fillna()` methods (`DataFrame.fillna`, `Series.fillna`, etc) have `value` as the first kwarg but `Categorical` has `fill_value` instead
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| null | 4 | 2015-04-29T04:38:57Z | 2015-05-01T17:08:02Z | 2015-04-29T10:40:36Z | NONE | null | ```
import pandas as pd
df = pd.DataFrame(0:'b', index = ['c'])
pd.util.testing.assert_almost_equal(df, df) # SUCCESS
df = pd.DataFrame({'a':'b'}, index = ['c'])
pd.util.testing.assert_almost_equal(df, df) # FAIL KeyError
```
Top of the trace stack is:
- pandas\hashtable.pyd in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12310)()
- pandas\hashtable.pyd in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12261)()
- KeyError: 0
I am sure this used to work without any problem, because I have unit tests that depend on it.
I tried uninstalling and re-installing pandas and the bug is still present.
I couldn't find a similar bug searching for get_item, assert_almost_equal or hashtable.
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https://api.github.com/repos/pandas-dev/pandas/issues/10014 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10014/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10014/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10014/events | https://github.com/pandas-dev/pandas/pull/10014 | 71,854,019 | MDExOlB1bGxSZXF1ZXN0MzQzNTQ5MzI= | 10,014 | BUG: null group spills into final group when grouping on a categorical | {
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} | 3 | 2015-04-29T11:48:50Z | 2015-04-29T23:08:00Z | 2015-04-29T23:05:42Z | CONTRIBUTOR | null | Fixes GH #9603
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| null | 2 | 2015-04-29T13:07:31Z | 2015-07-29T07:38:11Z | 2015-07-28T21:50:33Z | NONE | null | This checks if ga keys are already prefixed with ga: and only adds the
prefix if required. The previous behaviour was particularly anoying for
complex filters where keys within the filter would need a ga: prefix but
the first one could not have the prefix.
eg. Both the following would fail:
`ga:pagepath=/foo;ga:city=London`
`pagepath=/foo;city=London`
Now the following works
`ga:pagepath=/foo;ga:city=London`
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| null | 2 | 2015-04-29T13:18:03Z | 2015-07-29T07:38:01Z | 2015-07-28T21:50:26Z | NONE | null | See: http://stackoverflow.com/questions/26858077/pandas-io-ga-not-working-for-me-solved
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| null | 9 | 2015-04-29T14:37:29Z | 2020-06-11T09:07:33Z | 2015-04-29T22:26:27Z | NONE | null | I'm trying to add a column to an existing dataframe. The dataframe can be empty (0 rows) but I want the column to be added anyway.
```
import pandas as pd
import numpy as np
df = pd.DataFrame(index=np.arange(4))
df.loc[:,'col'] = 42 # this works fine!
df = pd.DataFrame(index=np.arange(0)) # empty dataframe
df.loc[:,'col'] = 42 # ERROR: ValueError: cannot set a frame with no defined index and a scalar
df['col'] = 42 # this works fine but seems deprecated
pd.__version__ is '0.15.2'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/10018 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10018/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10018/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10018/events | https://github.com/pandas-dev/pandas/issues/10018 | 71,923,352 | MDU6SXNzdWU3MTkyMzM1Mg== | 10,018 | newline defaults for read_csv and write_csv are not consistent | {
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| null | 12 | 2015-04-29T16:32:10Z | 2021-08-18T03:57:08Z | null | NONE | null | It's possible to write out a CSV file using the default settings (i.e., `line_terminator='\n'` in `DataFrame.to_csv`) that can't be read back in using the default settings (i.e., `lineterminator=None`).
This problem arises when there's a text column that doesn't get quotes around it (e.g., if it has no commas) but has a carriage return. `read_csv` by default thinks that either '\n' or '\r' can be line terminators, so extra rows appear. See example below.
```
In [12]: pd.DataFrame({"text": ["foo\rbar"]}).to_csv("foo.csv", index=False)
In [13]: pd.read_csv("foo.csv")
Out[13]:
text
0 foo
1 bar
```
It might be best to have the default line terminator for `read_csv` be '\n'. That might reduce usability (e.g., it's nice to be able to load CSVs from UNIX or Windows using the defaults), so maybe it'd be good just to add something to the documentation. Or, maybe a warning could be printed out or exception raised if a CSV file appears to use multiple types of line endings, which shouldn't happen.
(IIRC this can also cause the default parsing engine to make python segfault in some cases.)
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| null | 2 | 2015-04-29T18:24:21Z | 2019-01-13T00:58:43Z | null | NONE | null | Here is a workaround how to achieve this manually:
```
import pandas as pd
import numpy as np
from numpy.random import rand
dfPP=pd.DataFrame(rand(3,5),columns=rand(5),index=rand(3))
nanarr=np.empty(dfPP.shape[0])
nanarr[:]=np.NAN
new_point=dfPP.columns.values.mean()
dfPP.loc[:,new_point]=nanarr
dfPP.sort(axis=1,inplace=True)
dfPP.head()
```
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>0.211451507884</th> <th>0.574409309122</th> <th>0.626609642735</th> <th>0.728622716072</th> <th>0.752814445643</th> <th>0.865750234955</th> </tr> </thead> <tbody> <tr> <th>0.340822</th> <td>0.451636</td> <td>0.292137</td> <td>NaN</td> <td>0.162465</td> <td>0.481506</td> <td>0.384237</td> </tr> <tr> <th>0.996621</th> <td>0.997825</td> <td>0.957920</td> <td>NaN</td> <td>0.728265</td> <td>0.916599</td> <td>0.286137</td> </tr> <tr> <th>0.711955</th> <td>0.091902</td> <td>0.951715</td> <td>NaN</td> <td>0.363268</td> <td>0.344484</td> <td>0.661254</td> </tr> </tbody></table>
```
dfPP=dfPP.interpolate(method='values',axis=1)
dfPP.head()
```
<table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>0.211451507884</th> <th>0.574409309122</th> <th>0.626609642735</th> <th>0.728622716072</th> <th>0.752814445643</th> <th>0.865750234955</th> </tr> </thead> <tbody> <tr> <th>0.340822</th> <td>0.451636</td> <td>0.292137</td> <td>0.248244</td> <td>0.162465</td> <td>0.481506</td> <td>0.384237</td> </tr> <tr> <th>0.996621</th> <td>0.997825</td> <td>0.957920</td> <td>0.880183</td> <td>0.728265</td> <td>0.916599</td> <td>0.286137</td> </tr> <tr> <th>0.711955</th> <td>0.091902</td> <td>0.951715</td> <td>0.752529</td> <td>0.363268</td> <td>0.344484</td> <td>0.661254</td> </tr> </tbody></table>
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} | 4 | 2015-04-29T18:49:37Z | 2015-05-08T16:13:11Z | 2015-05-08T13:24:44Z | CONTRIBUTOR | null | closed #10083
Currently the following would raise a `TypeError`:
```
In [1]: import pandas as pd
In [2]: idx = pd.Index(list('abc'))
In [3]: idx + '1'
Out[3]: Index(['a1', 'b1', 'c1'], dtype='object')
In [4]: '1' + idx
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-5e2fbeff7266> in <module>()
----> 1 '1' + idx
TypeError: Can't convert 'Index' object to str implicitly
```
While for `Series` this works fine:
```
In [5]: s = pd.Series(list('abc'))
In [6]: '1' + s
Out[6]:
0 1a
1 1b
2 1c
dtype: object
In [7]: s + '1'
Out[7]:
0 a1
1 b1
2 c1
dtype: object
```
This PR implements the `__radd__` method so that adding from the left would work for `Index`.
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} | 8 | 2015-04-29T19:34:14Z | 2015-05-08T04:25:52Z | 2015-05-08T04:25:52Z | CONTRIBUTOR | null | I have a well-formed CSV file with about 70k lines that parses out to a DataFrame with about 170k rows, where the extra rows are just full of NaNs. It only happens with the 'c' engine.
This is with git master.
I won't bother to upload the CSV file in question because I think I've tracked down the problem. It seems that `tokenize_delimited()` runs on chunks of data at a time. This problem occurs when a chunk happens to start with `'\n'`. I'll send a pull request.
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} | 13 | 2015-04-29T19:37:05Z | 2015-05-09T00:42:05Z | 2015-05-08T04:25:52Z | CONTRIBUTOR | null | Slight change to the logic in the `tokenize_delimited()` and `tokenize_delim_customterm()` functions of the C parser.
Fixes #10022.
I believe the new logic is correct, but perhaps someone with more familiarity can double-check.
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} | 5 | 2015-04-29T20:41:36Z | 2015-04-30T15:09:31Z | 2015-04-30T15:09:30Z | MEMBER | null | Before:
``` python
In [5]: x = np.array(['abcd', 'abcde', 'abcdef', 'abcdefg'] * int(1e7), object)
In [2]: f = pd.lib.max_len_string_array
In [6]: %timeit f(x)
1 loops, best of 3: 501 ms per loop
```
After:
``` python
In [1]: x = np.array(['abcd', 'abcde', 'abcdef', 'abcdefg'] * int(1e7), object)
In [2]: f = pd.lib.max_len_string_array
In [3]: %timeit f(x)
10 loops, best of 3: 68.4 ms per loop
```
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} | 0 | 2015-04-30T02:33:53Z | 2015-05-04T10:48:26Z | 2015-05-04T10:48:26Z | MEMBER | null | ```
In [46]: tdi = pd.timedelta_range(start=0, periods=4, freq='D')
In [47]: pd.Index(tdi, name='foo').name is None
Out[47]: True
In [48]: pd.Index(tdi.values, name='foo').name is None
Out[48]: False
```
Other index types seem to work fine:
```
In [51]: dti = pd.date_range(start='2000-01-01', periods=4, freq='D')
In [52]: pd.Index(dti, name='foo').name is None
Out[52]: False
```
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} | 5 | 2015-04-30T05:52:33Z | 2015-05-04T18:18:30Z | 2015-05-04T10:48:26Z | MEMBER | null | Fixes #10025
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} | 5 | 2015-04-30T06:39:53Z | 2015-05-04T18:18:22Z | 2015-05-04T10:58:50Z | MEMBER | null | As discussed in #5487.
This would also be a nice place to mention other packages that connect the
pandas DataFrames with other file formats. Right now, the only one I can think
of off-hand is `root_pandas` (#9378, CC @ibab), but I'm sure there are more.
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https://api.github.com/repos/pandas-dev/pandas/issues/10028 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10028/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10028/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10028/events | https://github.com/pandas-dev/pandas/issues/10028 | 72,102,599 | MDU6SXNzdWU3MjEwMjU5OQ== | 10,028 | Pivot_table with categorical data gives ValueError | {
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| null | 1 | 2015-04-30T08:49:02Z | 2015-04-30T10:03:27Z | 2015-04-30T10:03:07Z | MEMBER | null | This errors for me on master as well: http://stackoverflow.com/questions/29945342/pandas-error-with-pivot-table-and-categorical-data
```
category1 category2 numeric
0 grey complete 1
1 grey complete 2
2 NaN incomplete 3
3 blue complete 4
```
```
In [13]: df = pd.read_clipboard()
# with plain strings it works
In [16]: pd.pivot_table(df, index = 'category1', columns = 'category2', values = 'numeric', aggfunc = 'count')
Out[16]:
category2 complete
category1
blue 1
grey 2
In [17]: df['category1'] = df['category1'].astype('category')
In [18]: df['category2'] = df['category2'].astype('category')
In [19]: pd.pivot_table(df, index = 'category1', columns = 'category2', values =
'numeric', aggfunc = 'count')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
...
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\common.py in _astype_nansafe(ar
r, dtype, copy)
2726
2727 if np.isnan(arr).any():
-> 2728 raise ValueError('Cannot convert NA to integer')
2729 elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.intege
r):
2730 # work around NumPy brokenness, #1987
ValueError: Cannot convert NA to integer
```
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| null | 1 | 2015-04-30T09:44:32Z | 2015-04-30T10:08:12Z | 2015-04-30T10:07:58Z | NONE | null | when entering an invalid string in pandas.to_datetime, the output is the given string. it should either raise an exception or output a NaT value
```
>>> a = pd.to_datetime('1991-02-30')
>>> a
'1991-02-30'
>>> a = pd.to_datetime('1991-02302')
>>> a
'1991-02302'
>>> a = pd.to_datetime('199102302')
>>> a
'199102302'
>>> a = pd.to_datetime('19sd')
>>> a
'19sd'
```
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} | 52 | 2015-04-30T15:24:04Z | 2021-03-16T17:27:00Z | null | NONE | null | A "weights" labeled parameter in the df.mean method would be extremely useful. In numpy this functionality is provided via np.average instead of np.mean which I'm assuming is how similar functionality would be added to pandas.
ex requested feature:
```
> a = np.array([[2,4,6],[8,10,12]])
> w = [.5, 0, .5]
> np_avg = np.average(a, weights = w, axis=1)
#output -> array([ 4., 10.])
> pd_avg = pandas.DataFrame(a).mean(weights = w, axis=1)
#desired output -> series with entries 4 and 10
```
If this is a desired feature I'll complete this and submit a pull request if desired. If this is somewhere already I've overlooked, my apologies, I've tried to look thoroughly.
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} | 3 | 2015-04-30T16:12:18Z | 2015-05-07T02:39:22Z | 2015-05-06T10:40:18Z | MEMBER | null | Derived from #9111. Can this be considered in v0.16.1? Otherwise will change the milestone.
> [`unicodedata.normalize`](https://docs.python.org/2/library/unicodedata.html?highlight=unicodedata#unicodedata.normalize) is quite useful to standardize multi-bytes characters. I think it is nice if `StringMethods.normalize` can perform this.
```
import pandas as pd
s = pd.Series([u'ＡＢＣＤＥ', u'１２３４５'])
s
#0 ＡＢＣＤＥ
#1 １２３４５
# dtype: object
s.str.normalize()
#0 ABCDE
#1 12345
# dtype: object
```
Another point I'd like to discuss here is the condition `Index.str` can be used. Currently, `inferred_type` must be `string`. I think the preferable condition is:
- `Index` must be normal Index, not `MultiIndex`.
- Its `inferred_type` should be either `string`, `unicode` or `mixed`.
This PR adds `unicode` currently, not `mixed`.
```
pd.Index([u'a', u'B']).inferred_type
# unicode
pd.Index(['a', u'B']).inferred_type
# mixed
# when we allow "mixed" to show str, we should exclude MultiIndex case.
pd.MultiIndex.from_tuples([('a', 'a'), ('a', 'b')]).inferred_type
# mixed
```
CC: @mortada
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| null | 1 | 2015-04-30T17:48:12Z | 2019-06-28T15:56:24Z | 2019-06-28T15:56:24Z | MEMBER | null | This is currently not the case (e.g., for `TimedeltaIndex`)
as discussed in #10026.
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| null | 2 | 2015-04-30T18:46:18Z | 2021-04-18T06:40:20Z | null | NONE | null | It would be nice if there was an option to have box plot X axis labels match line plot's X axis labels. Specifically, when plotting a time series, box plot labels each tick whereas line plot's labels are more spread apart.
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} | 2 | 2015-04-30T19:10:57Z | 2015-04-30T22:10:15Z | 2015-04-30T22:00:29Z | CONTRIBUTOR | null | kind of a shameless plug here, but would love to have my pandas-based project on this page :)
please let me know what you think. Thanks.
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} | 2 | 2015-04-30T19:25:35Z | 2015-05-05T00:08:19Z | 2015-05-04T21:35:28Z | CONTRIBUTOR | null | currently the docstring for `DataFrame.fillna` is shown for `Series.fillna` and `Panel.fillna`. http://pandas.pydata.org/pandas-docs/version/0.16.0/generated/pandas.Panel.fillna.html?highlight=fillna#pandas.Panel.fillna
this is not accurate in particular for the `axis` values. This PR makes it such that they share the same template with different `axis` descriptions.
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} | 9 | 2015-04-30T21:29:12Z | 2015-05-07T20:49:27Z | 2015-05-05T22:03:31Z | NONE | null | ```
df.to_csv(path_or_buf=f, sep=' ', header=False,
index=True, mode='a', quoting=QUOTE_NONE, escapechar=' ',
date_format='%m/%d/%Y %H:%M:%S')
```
generates 2 spaces in csv file between date and time. It should only be one space since sep=' '.
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https://api.github.com/repos/pandas-dev/pandas/issues/10038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10038/events | https://github.com/pandas-dev/pandas/issues/10038 | 72,300,419 | MDU6SXNzdWU3MjMwMDQxOQ== | 10,038 | No deprecation warning raised for index - list operation | {
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} | 0 | 2015-04-30T22:34:47Z | 2015-05-04T12:45:51Z | 2015-05-04T12:45:51Z | MEMBER | null | Ilustration:
```
idx = pd.Index(['a', 'b', 'c', 'd'])
idx - ['a', 'd']
idx - pd.Index(['a', 'd'])
```
gives:
```
In [1]: idx = pd.Index(['a', 'b', 'c', 'd'])
In [2]: idx - ['a', 'd']
Out[2]: Index([u'b', u'c'], dtype='object')
In [3]: idx - pd.Index(['a', 'd'])
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.py:1192: FutureWarning: u
sing '-' to provide set differences with Indexes is deprecated, use .difference(
)
"use .difference()",FutureWarning)
Out[3]: Index([u'b', u'c'], dtype='object')
```
So a list is treated a an index-like (as it performs a set operation), but does not raise a warning as it should (the same for arrays, series).
We have an occurrence in the docs of this one.
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} | 1 | 2015-04-30T23:02:08Z | 2015-05-04T12:45:51Z | 2015-05-04T12:45:51Z | MEMBER | null | Related with the issue I just opened (#10038), I relooked at the set operation deprecation issues we had before (xref #8227, #9095, #9630).
To make a summary:
- in 0.15.0 we deprecated set operations, only for new TimedeltaIndex it is already numeric operation
- in 0.15.1, for numeric indexes this was by accident converted to numeric operation
- in 0.16.0, explicit deprecation added for DatetimeIndex
- in a future release we want to have it all numeric operations (or TypeErrors if the dtype does not support that operation)
But now, for the CategoricalIndex added, it is a set operation again (but with a warning):
```
In [1]: idx = pd.Index(pd.Categorical(['a', 'b']))
In [2]: idx
Out[2]:
CategoricalIndex([u'a', u'b'],
categories=[u'a', u'b'],
ordered=False,
name=None)
In [3]: idx - idx
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\index.py:1191: FutureWarning: u
sing '-' to provide set differences with Indexes is deprecated, use .difference(
)
"use .difference()", FutureWarning)
Out[3]: Index([], dtype='object')
```
As this is a new index, we should at once use the correct behaviour?
(Which is raising a TypeError I think?)
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} | 2 | 2015-05-01T14:39:15Z | 2015-05-07T20:53:20Z | 2015-05-07T20:53:20Z | NONE | null | ``` python
from pandas import DataFrame
from numpy import datetime64
data = ['2015-02-03', '2015-02-07']
data = DataFrame(data, dtype=datetime64)
data.diff().median()
```
This code returns
``` python
0 -53374 days +00:06:21.572612
dtype: timedelta64[ns]
```
while I would expect it to return the same as data.diff().mean()
``` python
0 4 days
dtype: timedelta64[ns]
```
Here is my setup
``` python
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Darwin
OS-release: 14.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.16.0
nose: 1.3.4
Cython: None
numpy: 1.9.2
scipy: 0.14.0
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: 2.1.3
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
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} | 1 | 2015-05-01T15:59:58Z | 2016-03-06T20:44:23Z | 2016-03-06T20:44:23Z | CONTRIBUTOR | null | Looks like a bug: When passing a YYYYMMDDTHHMMSS.f string to `pd.Timestamp`, the resolution seems limited to micro- rather than nano-seconds:
``` python
>>> pd.Timestamp('20130101T000000.000001+0000')
Timestamp('2013-01-01 00:00:00.000001+0000', tz='UTC')
>>> pd.Timestamp('20130101T000000.0000001+0000')
Timestamp('2013-01-01 00:00:00+0000', tz='UTC')
>>> pd.Timestamp('20130101T000000.00000001+0000')
Timestamp('2013-01-01 00:00:00+0000', tz='UTC')
```
The behavior seems correct when using a different string format:
``` python
>>> pd.Timestamp('2013-01-01 00:00:00.00000001+0000')
Timestamp('2013-01-01 00:00:00.000000010+0000', tz='UTC')
```
Using pandas 0.16.0, python 2.7.6
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} | 4 | 2015-05-01T16:03:24Z | 2015-05-04T21:00:39Z | 2015-05-04T12:45:51Z | CONTRIBUTOR | null | closes #10038
closes #10039
- `CategoricalIndex` now raise `TypeError` on ops like : `idx - idx` (we are moving all non-numeric Indexes to this, but just deprecated for now on existing index types)
- Index ops with lists will now show the same warning (e.g. `idx - ['a','b']`)
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} | 28 | 2015-05-01T17:05:44Z | 2015-05-08T05:00:36Z | 2015-05-08T00:06:40Z | NONE | null | This is picking up #9928 again. I don't know if the behavior is expected, but it is a bit odd to me. Maybe I'm doing something wrong, I'm not that familiar with the pandas internals.
We call `df.iloc[indices]` and that breaks with a read-only dataframe. I feel that it shouldn't though, as it is not writing.
Minimal reproducing example:
``` python
import pandas as pd
import numpy as np
array = np.eye(10)
array.setflags(write=False)
X = pd.DataFrame(array)
X.iloc[[1, 2, 3]]
```
> ValueError buffer source array is read-only
Is there a way to slice the rows of the dataframe in another way that doesn't need a writeable array?
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} | 6 | 2015-05-01T20:36:49Z | 2015-05-08T19:29:40Z | 2015-05-08T17:46:06Z | CONTRIBUTOR | null | as a part of https://github.com/pydata/pandas/issues/9111
@sinhrks @jreback
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| null | 3 | 2015-05-02T01:02:45Z | 2015-06-18T15:22:10Z | 2015-06-18T15:22:10Z | NONE | null | The code generated by Cython from pandas/algos.pyx contains an undefined behavior (invalid cast of floating-point number to integer):
```
easy_install -U Cython
git clone git://github.com/pydata/pandas.git
python setup.py install
```
The generated code for the line from `pandas/algos.pyx`
``` python
cdef np.float16_t MINfloat16 = np.NINF
```
in `pandas/algos.c` contains:
``` c
typedef npy_float16 __pyx_t_5numpy_float16_t;
//... more code
__pyx_t_5numpy_float16_t __pyx_t_10;
//... more code
__pyx_t_10 = __pyx_PyFloat_AsFloat(__pyx_t_1);
```
The file `numpy-1.9.2-py2.7-linux-x86_64.egg/numpy/core/include/numpy/npy_common.h`
defines `npy_float16` as 16-bit _integer_ type (I guess numpy implementation has a custom code that carefully represents these 16 bits as mantiss+exponent). Casting floating-point value `-inf` to a 16-bit integer type is an undefined behavior according to C standard.
I wasn't able to find any place in pandas code that uses `MINfloat16` (and `MAXfloat16`). Is it safe to just delete them?
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} | 2 | 2015-05-02T02:00:36Z | 2015-05-05T22:26:27Z | 2015-05-05T22:26:27Z | CONTRIBUTOR | null | _Unclear whether this was intentionally not included in the dt accessor._
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} | 2 | 2015-05-02T05:56:09Z | 2018-11-12T23:18:17Z | 2018-11-12T23:18:17Z | CONTRIBUTOR | null | Not sure if this is a bug, but seems like weird behaviour.... it came up on [SO](http://stackoverflow.com/q/29997115/1240268).
```
In [11]: d = {"a": pd.DataFrame([[1], [2]], index=[0, 2], columns=['Z']),
"b": pd.DataFrame([[3], [4]], index=[0, 2], columns=['Z'])}
In [12]: pd.concat(d).unstack(0) # expected?
Out[12]:
Z
a b
0 1 3
2 2 4
In [13]: t = pd.DataFrame()
In [14]: for name, df in d.items():
t[name] = df
In [15]: t
Out[15]:
a b
0 1 3
1 NaN NaN
```
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| null | 2 | 2015-05-02T09:55:59Z | 2015-05-02T16:05:32Z | 2015-05-02T14:26:08Z | CONTRIBUTOR | null | Hello,
I don't know if that's a misunderstanding from my side but
```
import pandas as pd
df_task = pd.DataFrame({
"Name": ["Saint Auban", "Go", "Coupe S", "3 Eveches", "DORMILLOUSE FORT", "Saint Auban"],
"Lat": [44.054901123046875, 44.015087127685547, 44.053600311279297, 44.288883209228516, 44.414447784423828, 44.054901123046875],
"Lon": [5.9840002059936523, 6.0107221603393555, 6.3285999298095703, 6.5361166000366211, 6.3811173439025879, 5.9840002059936523],
"Altitude": [1500, 1500, 1600, 2700, 2200, 1500]
})
In [129]: map(lambda callable: callable(), df_task.apply(lambda tp: tp.to_dict(), axis=1))
Out[129]:
[[44.054901123046875, 1500, 5.984000205993652, 'Saint Auban'],
[44.01508712768555, 1500, 6.0107221603393555, 'Go'],
[44.0536003112793, 1600, 6.32859992980957, 'Coupe S'],
[44.288883209228516, 2700, 6.536116600036621, '3 Eveches'],
[44.41444778442383, 2200, 6.381117343902588, 'DORMILLOUSE FORT'],
[44.054901123046875, 1500, 5.984000205993652, 'Saint Auban']]
```
I was expecting a list of dict like:
```
[{'Altitude': 1500, 'Lat': 44.054901123046875, 'Lon': 5.984000205993652, 'Name': 'Saint Auban'},
{'Altitude': 1500, 'Lat': 44.01508712768555, 'Lon': 6.0107221603393555, 'Name': 'Go'},
{'Altitude': 1600, 'Lat': 44.0536003112793, 'Lon': 6.32859992980957, 'Name': 'Coupe S'},
{'Altitude': 2700, 'Lat': 44.288883209228516, 'Lon': 6.536116600036621, 'Name': '3 Eveches'},
{'Altitude': 2200, 'Lat': 44.41444778442383, 'Lon': 6.381117343902588, 'Name': 'DORMILLOUSE FORT'},
{'Altitude': 1500, 'Lat': 44.054901123046875, 'Lon': 5.984000205993652, 'Name': 'Saint Auban'}]
```
Why ? because
```
tp = df_task.irow(0)
In [137]: tp.to_dict()
Out[137]:
{'Altitude': 1500,
'Lat': 44.054901123046875,
'Lon': 5.9840002059936523,
'Name': 'Saint Auban'}
```
Maybe I missed something ?
Any idea ?
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} | 2 | 2015-05-02T14:16:40Z | 2015-05-12T19:58:49Z | 2015-05-12T19:58:49Z | NONE | null | Using Pandas' datetime64 dtype to convert to a datetime.timedelta object results in this type error:
``` python
TypeError: unsupported type for timedelta microseconds component: numpy.int32
```
Reproduce:
``` python
# set up a dataframe with a datetime value in it
from datetime import datetime
import pandas as pd
df = pd.DataFrame({'foo' : [datetime.now()] })
print df.dtypes
# =>
foo datetime64[ns]
dtype: object
# let's convert to timedelta
df.foo.apply(lambda r : timedelta(hours = r.hour, minutes = r.minute, seconds = r.second, microseconds = r.microseconds))
=> TypeError: unsupported type for timedelta microseconds component: numpy.int32
```
Expected:
``` python
# no excepetions, whatever the correct value
df.foo.apply(lambda r : timedelta(hours = r.hour, minutes = r.minute, seconds = r.second, microseconds = r.microseconds))
# =>
0 16:08:25.347571
Name: foo, dtype: timedelta64[ns]
```
Work around:
``` python
# if you can't control the timedelta conversion (i.e. it is somewhere embedded, e.g. in a library)
df.foo = df.foo.astype(datetime)
# if you can
df.foo.apply(lambda r : timedelta(hours = r.hour, minutes = r.minute, seconds = r.second, microseconds = int(r.microseconds))
```
Version information
``` python
> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.0
nose: None
Cython: None
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.2
patsy: None
dateutil: 2.3
pytz: 2014.10
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.1.4
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.8
apiclient: 1.2
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 9 | 2015-05-02T15:04:50Z | 2017-04-05T02:06:24Z | 2015-05-09T20:42:30Z | CONTRIBUTOR | null | Part of https://github.com/pydata/pandas/issues/6384
I guess PeriodIndex don't show up in the API docs at all. Not sure if I'll get to any more.
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} | 25 | 2015-05-02T20:09:54Z | 2015-05-08T19:28:56Z | 2015-05-08T17:55:24Z | CONTRIBUTOR | null | as a part of https://github.com/pydata/pandas/issues/9111
note that this handles both py2 and py3 as the signature for `str.translate` has changed from py2 to py3
@sinhrks @jreback
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} | 8 | 2015-05-03T15:51:18Z | 2015-05-05T10:45:23Z | 2015-05-05T10:45:19Z | CONTRIBUTOR | null | Was there a reason infer_freq and inferred_freq weren't in the docs? Did I pick the right spot to add it? Does the feature work well?
I set out looking for a feature like this, searching "guess frequency", "auto detect frequency".
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} | 28 | 2015-05-03T17:12:39Z | 2015-09-04T00:51:57Z | 2015-09-04T00:51:47Z | CONTRIBUTOR | null | xref #7412
closes #8790
Adds a new column (`_merge`) to DataFrames being merged that takes on a value of `1` if observation was only in left df, `2` if only in right df, and `3` if in both. Designed to address #7412 and #8790.
**Update:** new column now categorical with more informative labels of `left_only`, `right_only`, and `both`.
Still at draft stage, but want to get comments before trying to refine too much.
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} | 10 | 2015-05-04T03:07:47Z | 2015-05-26T04:11:28Z | 2015-05-26T04:06:27Z | CONTRIBUTOR | null | Closes #7212. If this object is a list, make a copy and then pass to process_axes - otherwise, just pass the object directly (in this case it is most likely none).
A test for this behavior seems complex and somewhat contrived when compared with what exists in test_pytables.py, so I didn't add one --- let me know any thoughts on this.
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| null | 11 | 2015-05-04T16:30:31Z | 2019-07-12T14:38:04Z | 2019-07-12T14:38:04Z | NONE | null | When passing a list OrderedDict objects to `pd.DataFrame`, the ordering of the columns is not kept:
``` python
od = OrderedDict([('Z', 5), ('xx', 8), ('uno', 2), ('etc', 1), ('j', 76), ('e', 7)])
l = [od]*4
pd.DataFrame(l).columns
#Out[23]: Index(['Z', 'e', 'etc', 'j', 'uno', 'xx'], dtype='object')
```
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} | 8 | 2015-05-04T21:34:41Z | 2015-06-02T19:26:59Z | 2015-05-12T13:06:06Z | CONTRIBUTOR | null | It fixes the performance drop.
See %timeit result [here](http://nbviewer.ipython.org/gist/scari/1173a9519d8b89000c08).
closes #7754
@jreback Would you review this PR? Thanks!
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} | 4 | 2015-05-05T07:45:14Z | 2015-06-02T19:26:59Z | 2015-05-18T11:45:06Z | CONTRIBUTOR | null | currently `Panel.from_dict` is just ignoring the `dtype` argument:
```
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame(np.array(range(6)).reshape(3, 2))
In [4]: pa = pd.Panel.from_dict({'a': df, 'b': df}, dtype=float)
In [5]: pa.dtypes
Out[5]:
a int64
b int64
dtype: object
```
this PR sets the `dtype` and fixes the docstrings in some places
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| null | 6 | 2015-05-05T07:56:52Z | 2015-05-05T12:07:25Z | 2015-05-05T10:30:36Z | NONE | null | Based off this [question](http://stackoverflow.com/q/29986826/232794). In fact more or less copied and pasted :)
I have a file in HDF5 format. It was created using the HDF5's C++ API using these:
``` c++
struct SignalDefH5
{
char id [128];
char name [ 64];
char units[ 16];
float min;
float max;
hvl_t tags; /* This right there does not work in Pandas... */
};
struct TagDefH5
{
char tag [ 64];
char desc[256];
};
```
Note that tags is supposed to be a list of strings. If I load the file using h5py, I get this:
``` python
>>> import h5py
>>> hfile = h5py.File('test.h5', 'r')
>>> signals = hfile['/signals']
>>> signals[0]
('id1', 'a pressure', 'bar', 0.0, 300.0, ['Pressure'])
>>> type(signals[0][5])
numpy.ndarray
```
However, if I use Pandas to load the same file, I get this:
``` python
>>> store = pd.HDFStore('test.h5')
>>> store.root.signals
/signals (Table(179,)) ''
description := {
"id": StringCol(itemsize=128, shape=(), dflt='', pos=0),
"name": StringCol(itemsize=64, shape=(), dflt='', pos=1),
"units": StringCol(itemsize=16, shape=(), dflt='', pos=2),
"min": Float32Col(shape=(), dflt=0.0, pos=3),
"max": Float32Col(shape=(), dflt=0.0, pos=4),
"tags": StringCol(itemsize=64, shape=(), dflt='', pos=5)}
byteorder := 'little'
chunkshape := (234,)
>>> store.root.signals[0]
('id1', 'a pressure', 'bar', 0.0, 300.0, '\x02\x00\x00\x00\x00\x00\x00\x00\xf0f\x1e\x04\x00\x00\x00\x00\xba\nVT\xd1!\xa7\xdd\xb0\xe3\x9a\x02\x00\x00\x00\x00@\xecR\x1f\xa2\x7f\x00\x00}B\x178\x96\xa4u\xe6\xb0\xdd\x7f\x02\x00\x00\x00\x00 \x01')
>>> type(store.root.signals[0][5])
numpy.string_
```
So, [Pandas cannot cope with this type](http://stackoverflow.com/a/29988505/232794) yet it would be nice if it could... I am quit happy to test any patch and help in as much as I can.
Here's all the versions:
``` python
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.1.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.16.0
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.3
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
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| null | 4 | 2015-05-05T10:25:51Z | 2015-05-05T11:19:50Z | 2015-05-05T10:43:02Z | NONE | null | When calling the Series constructor on a dict with tuple keys containing a datetime.date object
the values are converted to NaNs as this example demonstrates:
```
In [52]:
pd.Series({(1, datetime.date(2012, 1, 1)): 1,
(2, datetime.date(2012, 1, 2)): 2})
Out[52]:
1 2012-01-01 NaN
2 2012-01-02 NaN
```
It only happens with MultiIndex but otherwise arises regardless of the number and order of the levels.
I know having datetime.date objects in an index is not optimal and that calling the constructor in this way actually forces the dates to Timestamp objects but in my use case I'm receiving data which is structured this way and converting it to Pandas objects.
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} | 1 | 2015-05-05T11:37:01Z | 2015-05-05T22:27:26Z | 2015-05-05T22:27:26Z | CONTRIBUTOR | null | closes #10004
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| null | 8 | 2015-05-05T12:49:24Z | 2015-05-06T12:27:37Z | 2015-05-05T13:51:46Z | NONE | null | Hi,
Using the last pandas version, the command : "import pandas.rpy.common as com"
produces the following warning :
"The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2, found here: http://rpy.sourceforge.net "like rpy2, found here: ttp://rpy.sourceforge.net", FutureWarning) "
I use the following functionality heavily :
com.convert_to_r_dataframe(pandas_DF)
com.convert_robj(results_from_R)
How with rpy2 I can do the same ?
Regards,
Boris
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} | 2 | 2015-05-05T13:20:02Z | 2015-05-13T20:02:08Z | 2015-05-05T16:48:28Z | NONE | null | This might be a potential bug: doing grouped timeseries operations fails silently on a multi-indexed DataFrame.
``` python
import pandas as pd
import pandas.io.data as web
# Get some market data
df = web.DataReader(['AAPL', 'GOOG'], 'yahoo', pd.Timestamp('2013'), pd.Timestamp('2014')).to_frame()
df.index.names = ('dt', 'symbol')
In [21]: df.head()
Out[21]:
Open High Low Close Volume \
dt symbol
2013-01-02 AAPL 553.82001 555.00000 541.62994 549.03003 140129500
2013-01-03 AAPL 547.88000 549.67004 541.00000 542.10004 88241300
2013-01-04 AAPL 536.96997 538.63000 525.82996 527.00000 148583400
2013-01-07 AAPL 522.00000 529.30005 515.20001 523.90002 121039100
2013-01-08 AAPL 529.21002 531.89001 521.25000 525.31000 114676800
Adj Close
dt symbol
2013-01-02 AAPL 74.63931
2013-01-03 AAPL 73.69719
2013-01-04 AAPL 71.64438
2013-01-07 AAPL 71.22294
2013-01-08 AAPL 71.41463
```
Let's say we want to resample this to monthly data. This fails and returns an empty DataFrame:
``` python
df_M = df.groupby(level='symbol').resample('M', how='mean')
In [23]: df_M
Out[23]:
Empty DataFrame
Columns: []
Index: []
```
This, however, works, but requires a seemingly-unnecessary re-indexing:
``` python
df_M = df.reset_index().set_index('dt').groupby('symbol').resample('M', how='mean')
In [26]: df_M.head()
Out[26]:
Adj Close Close High Low Open \
symbol dt
AAPL 2013-01-31 67.677750 497.822382 504.407623 492.969997 500.083329
2013-02-28 62.388477 456.808942 463.231056 452.106325 458.503692
2013-03-31 60.417287 441.841000 446.803495 437.337996 442.011512
2013-04-30 57.398619 419.765001 425.553183 414.722271 419.766820
2013-05-31 61.340151 446.452734 451.658190 441.495455 446.400919
Volume
symbol dt
AAPL 2013-01-31 1.562312e+08
2013-02-28 1.229478e+08
2013-03-31 1.147110e+08
2013-04-30 1.245851e+08
2013-05-31 1.073583e+08
```
The fact that you need to do the `reset_index().set_index('dt')` and then `groupby('symbol')` instead of `groupby(level='symbol')` seems to defeat the purpose of a multi-index! What gives?
I also realize that data like this is perhaps more well-suited to a Panel than a DataFrame, but when dealing with very large amounts of (often sparse) data, the 3D Panel structure presents performance and memory issues compared to the flat DataFrame.
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} | 0 | 2015-05-05T14:34:47Z | 2015-05-05T16:48:28Z | 2015-05-05T16:48:28Z | CONTRIBUTOR | null | closes #10063
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} | 3 | 2015-05-05T21:48:40Z | 2019-09-02T23:52:10Z | 2019-09-02T23:52:10Z | NONE | null | The strings "Infinity", "+Infinity" and "-Infinity" are commonly used to represent infinite floating point values. It would be great for pandas pd.read_csv to correctly interpret these strings as infinite floating point values or to take a parameter "inf_values" analogous to "na_values" that would allow the caller to specify what they require.
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} | 0 | 2015-05-05T23:43:38Z | 2015-05-07T11:28:59Z | 2015-05-05T23:45:07Z | MEMBER | null | follow-up for #10038 and #10042 -> remove the occurence in the docs
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} | 3 | 2015-05-06T03:09:14Z | 2015-06-02T19:26:59Z | 2015-05-30T13:41:38Z | MEMBER | null | ```
import pandas.util.testing as tm
ts = tm.makeTimeSeries()
ts.name = 'ts'
a, b = ts[2:], ts[:5]
# OK (names are preserved)
aa, ba = a.align(b)
aa.name
# ts
ba.name
# ts
# NG, specifying fill_value resets names
aa, ba = a.align(b, fill_value=-1)
aa.name
# None
ba.name
# None
```
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} | 6 | 2015-05-06T05:24:39Z | 2015-06-02T19:25:20Z | 2015-06-02T10:38:48Z | MEMBER | null | Based on `Index` behavior, `Series` arithmetic should reset name when names are different?
```
import pandas as pd
s1 = pd.Series([1, 2, 3], name='a')
s2 = pd.Series([3, 4, 5], name='b')
(s1 + s2).name
# None
s1.add(s2).name
# a
```
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} | 3 | 2015-05-06T14:39:17Z | 2015-05-07T00:20:26Z | 2015-05-07T00:20:22Z | CONTRIBUTOR | null | closes #9676
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} | 16 | 2015-05-06T16:57:21Z | 2015-05-11T09:16:04Z | 2015-05-08T00:06:21Z | CONTRIBUTOR | null | This is a tentative fix for #10043. I have not run any benchmark yet. I think we should do that before considering merging this PR. Any recommended way to do it?
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| null | 6 | 2015-05-07T10:59:27Z | 2015-08-05T10:32:51Z | 2015-08-05T10:32:51Z | NONE | null | Copying of a `BlockPlacement` is currently slower than it could/should be:
- `copy()` cannot be accessed from python, one currently needs to re-implement `copy()` if one wants to duplicate a `BlockPlacement` instance
- `copy()` tries to infer a slice for any array contained in the `BlockPlacement`.
- After `copy()` passes an array to the constructor, a sanity check in the form of `np.require(val, dtype=np.int64, requirements='W')` checks that the array is a valid input for a `BlockPlacement`. This is unnecessary since the array originated from a `BlockPlacement` for which this check was already done.
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| null | 3 | 2015-05-07T11:19:02Z | 2015-06-02T10:53:55Z | 2015-06-02T10:53:55Z | NONE | null | closes #9266
np.uin8 should be np.uint8
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} | 6 | 2015-05-07T12:05:27Z | 2015-05-11T09:12:42Z | 2015-05-11T09:12:42Z | MEMBER | null | Addresses the doc part of #9602
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} | 2 | 2015-05-07T17:58:59Z | 2015-05-08T02:34:35Z | 2015-05-07T23:23:31Z | MEMBER | null | - Organize `.str` related enhancements
- Fixed broken link
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} | 4 | 2015-05-07T18:22:18Z | 2020-01-03T23:23:43Z | 2020-01-03T23:23:43Z | NONE | null | I am working through logs of web requests, and when I want to find the most common, say, user agent string for a (disguised) user, I run something like the following:
```
from pandas import Series, DataFrame, Timestamp
tdf = DataFrame({'day': {0: Timestamp('2015-02-24 00:00:00'), 1: Timestamp('2015-02-24 00:00:00'),
2: Timestamp('2015-02-24 00:00:00'), 3: Timestamp('2015-02-24 00:00:00'),
4: Timestamp('2015-02-24 00:00:00')},
'userAgent': {0: 'some UA string', 1: 'some UA string', 2: 'some UA string',
3: 'another UA string', 4: 'some UA string'},
'userId': {0: '17661101', 1: '17661101', 2: '17661101', 3: '17661101', 4: '17661101'}})
def most_common_values(df):
return Series({c: s.value_counts().index[0] for c,s in df.iteritems()})
tdf.groupby('day').apply(most_common_values)
```
Note that in this (admittedly unusual) example, all of the lines are identical. I'm not sure if that is necessary to recreate the issue. And, I'm obscuring the exact purpose of this code, but it reproduces the bug: The 'userId' comes back as a Timestamp, not a string. This happens after the function most_common_values returns, since that userId string is not returned as a timestamp. if we change the value of the userId to an int:
```
tdf['userId'] = tdf.userId.astype(int)
```
or if the value of the associated integer is small enough:
```
tdf['userId'] = '15320104`
```
then the results are what we'd expect (the most common value as its original type is returned.)
I imagine that for some reason something like a dateutil parser is being called on strings by default but that probably shoulnd't be happening...
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} | 0 | 2015-05-07T19:08:15Z | 2016-12-19T11:20:43Z | 2016-12-19T11:20:43Z | CONTRIBUTOR | null | From [SO](http://stackoverflow.com/questions/30108778/sparse-dataframe-returns-attributeerror/30109645#30109645)
```
trains = np.arange(100)
tresholds = [10, 20, 30, 40, 50, 60]
tuples = []
for i in trains:
for j in tresholds:
tuples.append((i, j))
index = pd.MultiIndex.from_tuples(tuples, names=['trains', 'tresholds'])
matrix = np.empty((len(index), len(trains)))
matrix.fill(np.nan)
df = pd.DataFrame(matrix, index=index, columns=trains, dtype=float)
```
Fails
```
df.to_sparse()
```
Ok
```
df.iloc[0] = 0
df.to_sparse()
```
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} | 2 | 2015-05-07T21:24:03Z | 2015-05-08T13:19:38Z | 2015-05-08T13:19:38Z | CONTRIBUTOR | null | Fixes #9992.
I wanted to take a stab at developing pandas. I am a novice at both git/github and I hope I did the merge and PR correctly.
@jreback, please let me know if there is anything I can fix.
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} | 11 | 2015-05-08T00:23:58Z | 2015-06-05T16:54:04Z | 2015-06-05T16:54:04Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/30113499/python-pandas-split-columns-to-slow-anything-to-get-more-performance)
on big enough strings this might be quite useful for a number of string ops.
```
import pandas as pd
import random
import numpy as np
from StringIO import StringIO
def make_ip():
return '.'.join(str(random.randint(0, 255)) for n in range(4))
df = pd.DataFrame({'ip': [make_ip() for i in range(20000)]})
%timeit df[['ip0', 'ip1', 'ip2', 'ip3']] = df.ip.str.split('.', return_type='frame')
#1 loops, best of 3: 3.06 s per loop
%timeit df[['ip0', 'ip1', 'ip2', 'ip3']] = df['ip'].apply(lambda x: pd.Series(x.split('.')))
#1 loops, best of 3: 3.1 s per loop
%timeit df[['ip0', 'ip1', 'ip2', 'ip3']] = \
pd.read_table(StringIO(df['ip'].to_csv(None,index=None)),sep='.')
#10 loops, best of 3: 46.4 ms per loop
```
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} | 1 | 2015-05-08T02:05:11Z | 2015-05-08T16:12:06Z | 2015-05-08T13:45:18Z | CONTRIBUTOR | null | 1. currently `Series.shift()` and `DataFrame.shift()` are both missing `axis` information in the docstring.
2. comment in `timeseries.rst` about shifting only possible along `index` or `major_axis` is not true anymore
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} | 3 | 2015-05-08T04:46:34Z | 2015-05-08T19:16:35Z | 2015-05-08T13:25:08Z | MEMBER | null | #### Series (OK)
```
s = pd.Series(['A', 'B'])
'x' + s
#0 xA
#1 xB
# dtype: object
s + 'y'
#0 Ay
#1 By
# dtype: object
```
#### Index (NG)
```
idx = pd.Index(['A', 'B'])
# NG
'x' + idx
# Index([u'Ax', u'Bx'], dtype='object')
# OK
idx + 'y'
# Index([u'Ay', u'By'], dtype='object')
```
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} | 1 | 2015-05-08T15:40:56Z | 2017-10-08T16:31:13Z | 2017-10-08T16:31:08Z | CONTRIBUTOR | null | so it appears that [9] is wrong, should be consistent with [7],[8]
```
In [1]: index = date_range('20130101',freq='2D',periods=6)
In [2]: df = DataFrame(np.arange(20).reshape(5,4),columns=list('ABCD'),index=index.take([0,1,2,3,4]))
In [3]: df.resample('2D',how='max')
Out[3]:
A B C D
2013-01-01 0 1 2 3
2013-01-03 4 5 6 7
2013-01-05 8 9 10 11
2013-01-07 12 13 14 15
2013-01-09 16 17 18 19
In [4]: df.groupby(pd.Grouper(level=0,freq='2D')).max()
Out[4]:
A B C D
2013-01-01 0 1 2 3
2013-01-03 4 5 6 7
2013-01-05 8 9 10 11
2013-01-07 12 13 14 15
2013-01-09 16 17 18 19
In [5]: df.groupby([pd.Grouper(level=0,freq='2D')]).max()
Out[5]:
A B C D
2013-01-01 0 1 2 3
2013-01-03 4 5 6 7
2013-01-05 8 9 10 11
2013-01-07 12 13 14 15
2013-01-09 16 17 18 19
In [6]:
In [6]: df = DataFrame(np.arange(20).reshape(5,4),columns=list('ABCD'),index=index.take([0,1,2,4,5]))
In [7]: df.resample('2D')
Out[7]:
A B C D
2013-01-01 0 1 2 3
2013-01-03 4 5 6 7
2013-01-05 8 9 10 11
2013-01-07 NaN NaN NaN NaN
2013-01-09 12 13 14 15
2013-01-11 16 17 18 19
In [8]: df.groupby(pd.Grouper(level=0,freq='2D')).max()
Out[8]:
A B C D
2013-01-01 0 1 2 3
2013-01-03 4 5 6 7
2013-01-05 8 9 10 11
2013-01-07 NaN NaN NaN NaN
2013-01-09 12 13 14 15
2013-01-11 16 17 18 19
In [9]: df.groupby([pd.Grouper(level=0,freq='2D')]).max()
Out[9]:
A B C D
2013-01-01 0 1 2 3
2013-01-03 4 5 6 7
2013-01-05 8 9 10 11
2013-01-09 12 13 14 15
2013-01-11 16 17 18 19
```
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} | 4 | 2015-05-08T21:07:34Z | 2015-05-09T21:15:32Z | 2015-05-09T15:34:05Z | MEMBER | null | Closes #9847, Closes #9870. Default value is `expand=False` to be compat with`return_type='series'`. May better to change the default to True in future (and show warning about it)?
CC @jreback @jorisvandenbossche @sreejata
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} | 4 | 2015-05-08T22:10:46Z | 2015-08-03T12:40:55Z | 2015-08-03T12:40:55Z | CONTRIBUTOR | null | I think `Series.dt.strftime()` might be useful
This is essentially just calling `.format()` on a datetimelike lindex (which handles `NaT` and such)
```
In [1]: dates = pd.to_datetime(pd.Series(['20010101', '20010331']), format = '%Y%m%d')
In [2]: dates
Out[2]:
0 2001-01-01
1 2001-03-31
dtype: datetime64[ns]
In [4]: pd.DatetimeIndex(dates.values).format()
Out[4]: ['2001-01-01', '2001-03-31']
In [5]: pd.DatetimeIndex(dates.values).format(date_format='%Y%m%d')
Out[5]: ['20010101', '20010331']
```
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} | 2 | 2015-05-08T23:13:05Z | 2015-06-02T19:25:20Z | 2015-05-12T10:47:23Z | CONTRIBUTOR | null | Categorical Series have a special repr that looks at display.width, which can be None if following the Options and Settings docs. Unlike Python 2, in Python 3 an integer vs None comparison throws an exception.
(on current master, and has been true for several releases now)
``` python
Python 3.4.0 (default, Apr 11 2014, 13:05:11)
[GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.core.config.set_option('display.width', None)
>>> import numpy as np
>>> x = pd.Series(np.random.randn(100))
>>> pd.cut(x, 10)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/base.py", line 67, in __repr__
return str(self)
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/base.py", line 46, in __str__
return self.__unicode__()
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/series.py", line 897, in __unicode__
max_rows=max_rows)
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/series.py", line 962, in to_string
name=name, max_rows=max_rows)
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/series.py", line 992, in _get_repr
result = formatter.to_string()
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/format.py", line 222, in to_string
footer = self._get_footer()
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/format.py", line 196, in _get_footer
level_info = self.tr_series.values._repr_categories_info()
File "/home/andrew/git/pandas-rosnfeld-py3/pandas/core/categorical.py", line 1323, in _repr_categories_info
if max_width != 0 and cur_col_len + sep_len + len(val) > max_width:
TypeError: unorderable types: int() > NoneType()
```
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| null | 1 | 2015-05-09T03:22:04Z | 2015-05-09T15:25:54Z | 2015-05-09T15:25:46Z | NONE | null | - Added yahoo-actions data_source to DataReader;
- Added get_data_yahoo_actions that returns a DataFrame containing
DIVIDEND and SPLIT corporate actions fetched from Yahoo! Finance;
- Added unit tests;
- Added documentation example.
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} | 2 | 2015-05-09T04:42:15Z | 2015-11-13T16:05:41Z | 2015-11-13T16:05:41Z | MEMBER | null | Related to #6194. There seems no direct way to replace `NaN`s in `Index`?
I think a point is what types are acceptable via `value`.
- scalar: OK
- dict: NG (or accept {iloc : value} dict?)
- Series : NG
- DataFrame : NG
- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html
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} | 12 | 2015-05-09T05:21:22Z | 2015-06-05T16:54:19Z | 2015-06-05T16:54:04Z | CONTRIBUTOR | null | Closes #10081.
This simply removes the unnecessary `Series()` in the creation of the DataFrame for the split values in `str_split`. It vastly improves performance (20ms vs 3s for a series of 20,000 strings as a test) while not changing current behavior.
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} | 5 | 2015-05-09T19:36:35Z | 2015-05-10T01:53:25Z | 2015-05-10T01:29:59Z | CONTRIBUTOR | null | First reported here but it's been dormant for a long time: https://github.com/pydata/pandas/pull/9043
I think it'd be good to patch this bug. @jreback please let me know which release this should go to and I can add a release note. Thanks.
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} | 0 | 2015-05-09T19:43:34Z | 2015-06-02T19:25:20Z | 2015-05-16T14:53:02Z | MEMBER | null | http://stackoverflow.com/questions/30144170/what-does-pandas-sub-operator-do/30144239#30144238
The docstring of `DataFrame.sub` states `Binary operator sub`, so if you don't directly see the link with "subtraction", this is not very clear
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} | 0 | 2015-05-09T20:44:18Z | 2019-04-18T08:05:12Z | 2019-04-17T20:43:59Z | MEMBER | null | After #9901 has been merged, we should go through the docstrings and update where there are examples with Index reprs.
The ones that changed the most are the datetime-like. A search for `<class 'pandas.tseries.period.PeriodIndex'>` and alike can help there.
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} | 0 | 2015-05-09T21:53:39Z | 2019-06-28T15:52:42Z | null | CONTRIBUTOR | null | follow up to #9901
- also splitting the attributes part over multiple lines if this is very long
- for truncated output: now hard-code at 10 elements, but this could maybe also be 1 line? (and possibly with a minimum number of elements for longer strings) Because now, small integers don't fill the full line with 10 elements, and eg floats there is one element on the second line:
This is a function of the `display.width`. But the truncated output should restrict to a single-line if this happens.
```
In [6]: pd.set_option('display.width',100)
In [7]: Index(np.arange(400.))
Out[7]:
Float64Index([ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,
...
390.0, 391.0, 392.0, 393.0, 394.0, 395.0, 396.0, 397.0, 398.0, 399.0],
dtype='float64', length=400)
In [8]: pd.set_option('display.width',80)
In [9]: Index(np.arange(400.))
Out[9]:
Float64Index([ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
9.0,
...
390.0, 391.0, 392.0, 393.0, 394.0, 395.0, 396.0, 397.0, 398.0,
399.0],
dtype='float64', length=400)
```
This could eg easily be restricted to two times one line.
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} | 4 | 2015-05-09T22:26:31Z | 2015-06-02T19:26:11Z | 2015-05-12T19:58:49Z | MEMBER | null | Closes #10050.
Also added `daysinmonth` and `dayofweek` properties to `NaT` and perform tests.
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} | 23 | 2015-05-09T22:52:55Z | 2015-07-31T22:37:53Z | 2015-07-31T22:37:48Z | CONTRIBUTOR | null | As per discussion in Issue 9382, changes all HDF functions from having default of dropping all rows with NA in all non-index rows.
closes #9382
Followup for #9484
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} | 11 | 2015-05-11T05:08:57Z | 2015-06-07T22:28:40Z | 2015-06-07T22:28:38Z | CONTRIBUTOR | null | Have DataFrame.to_hdf() raise an error when using pytables with non-string
column types
closes #9057
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| null | 9 | 2015-05-11T09:32:01Z | 2020-09-20T22:07:44Z | 2020-09-20T22:07:44Z | NONE | null | I often have the situation where I would like to apply multiple aggregation functions to all the columns of a grouped dataframe, like:
``` python
grouped = df.groupby('somekey')
dfAggregated = grouped.agg([np.mean, np.std])
```
That works well, but sometimes (all the time, actually) I would also like to be able to use lambda functions this way, like:
``` python
grouped = df.groupby('somekey')
dfAggregated = grouped.agg([np.mean, np.std, lambda v: v.mean()/v.max()])
```
This works fine, but the resulting column name will now be 'lambda', which is ugly. This can be resolved by using the much more verbose syntax where you specify a dictionary for every column separately, but I would propose to allow the following syntax:
``` python
grouped = df.groupby('somekey')
dfAggregated = grouped.agg([np.mean,np.std,{'normalized_mean': lambda v: v.mean()/v.max()}])
```
The dictionary key should then be used as the resulting column name.
Interestingly, using this syntax in the version 0.16 does not produce an error, but produces a column named 'Nan', that is filled with tupple values: ('n','o','r','m','a','l','i','z','e','d','_','m','e','a','n'), which I don't think is of use to anyone:)
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| null | 2 | 2015-05-11T13:28:11Z | 2015-05-11T19:32:04Z | 2015-05-11T19:32:04Z | CONTRIBUTOR | null | Huge fan of Pandas. Thanks for all the hard work!
I've noticed an inconsistency. The following `DatetimeIndex` object reports a different timezone compared to its elements:
``` python
In [1]: import pandas as pd
In [2]: idx = pd.date_range("2015-01-01", "2015-01-03", freq="D", tz="US/Eastern")
In [3]: idx.tz
Out[3]: <DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>
In [4]: [timestamp.tz for timestamp in idx]
Out[4]:
[<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>,
<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>,
<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>]
In [6]: idx[0].tz == idx.tz
Out[6]: False
```
And here are my installed versions:
``` python
In [5]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.16.1
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.1.0
sphinx: 1.2.3
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: 0.6.0
tables: 3.1.1
numexpr: 2.4
matplotlib: 1.4.3
openpyxl: 1.8.6
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.6
bs4: None
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec mx pq3 ext)
```
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