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https://api.github.com/repos/pandas-dev/pandas/issues/10203 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10203/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10203/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10203/events | https://github.com/pandas-dev/pandas/issues/10203 | 79,859,046 | MDU6SXNzdWU3OTg1OTA0Ng== | 10,203 | Panel.apply (2d) doesn't return a result of Series in 0.16.1 | {
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| null | 2 | 2015-05-23T14:18:07Z | 2015-06-11T17:24:22Z | 2015-06-11T17:24:22Z | NONE | null | Panel.apply (2d) doesn't return a result of Series in 0.16.1
y = panel.apply(lambda df: df + 1, axis=[1, 2])
--> Returns Panel. OK.
y = panel.apply(lambda df: df['Column'] + 1, axis=[1, 2])
--> Returns DataFrame. OK.
y = panel.apply(lambda df: df['Column'].mean(), axis=[1, 2])
--> Returns Series is expected. but got error as follows.
> File "test.py", line 28, in <module>
> y = panel.apply(lambda df: df['Column'].mean(), axis=[1, 2])
> File "C:\Anaconda3\lib\site-packages\pandas\core\panel.py", line 969, in apply
> return self._apply_2d(f, axis=axis)
> File "C:\Anaconda3\lib\site-packages\pandas\core\panel.py", line 1068, in _apply_2d
> return self._construct_return_type(dict(results))
> File "C:\Anaconda3\lib\site-packages\pandas\core\panel.py", line 1124, in _construct_return_type
> '[result->%s]' % (self, result))
> pandas.core.common.PandasError: invalid _construct_return_type [self-><class 'pandas.core.panel.Panel'>
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} | 39 | 2015-05-24T23:06:36Z | 2016-01-07T05:46:05Z | 2016-01-07T00:20:49Z | CONTRIBUTOR | null | Closes #10174.
I have added the new argument to the doc too.
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| null | 1 | 2015-05-25T03:46:27Z | 2015-05-26T10:29:25Z | 2015-05-26T10:29:12Z | NONE | null | Pandas stores strings (str and unicode) with dtype=object. As such, some unexpected things happen, like empty fields being filled with nan, which is a float. Expected behavior should fill with empty string "" or at least None.
```
>>> import pandas as pd
>>> from StringIO import StringIO
>>> pd.read_csv(StringIO('col1,col2,col3\nfoo,,bar'),dtype=str)
col1 col2 col3
0 foo NaN bar
>>> type(pd.read_csv(StringIO('col1,col2,col3\nfoo,,bar'),dtype=str).iloc[0,1])
float
```
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} | 1 | 2015-05-25T18:00:24Z | 2015-06-02T19:26:12Z | 2015-05-26T10:32:56Z | CONTRIBUTOR | null | Fixes #10145
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} | 2 | 2015-05-26T10:49:13Z | 2019-06-24T15:11:42Z | 2019-06-24T15:11:41Z | CONTRIBUTOR | null | these can be moved to `core.generic.NDFrame.quantile` (leave disabled for ndim > 2 though)
xref #10174
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} | 9 | 2015-05-26T14:12:06Z | 2019-10-14T23:37:49Z | 2016-04-03T15:41:14Z | CONTRIBUTOR | null | Using `pd.read_csv(..., sep=", ", ...)` I'm now getting a warning about falling back to the C engine because regex parsing isn't supported in the C engine. That's fine, but this isn't actually using regex.
I don't have an idea for a good transition strategy, and maybe the ship has sailed, but perhaps there should be a separate `read_regex` or a `regex` keyword instead of emitting this warning for any string greater than length 1.
Pandas 0.16.0.
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} | 5 | 2015-05-26T14:35:19Z | 2015-06-05T21:45:56Z | 2015-06-05T21:45:56Z | NONE | null | Since 0.16.1 the to_csv output of a dataframe has started including hh:mm:ss with date_format strings like:
date_format='%d-%m-%Y'
Reverting back to 0.16.0 the date output was then formatted properly (without time).
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| null | 1 | 2015-05-26T16:32:54Z | 2015-05-26T17:28:26Z | 2015-05-26T17:28:24Z | CONTRIBUTOR | null | {
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| null | 5 | 2015-05-26T20:04:53Z | 2015-07-28T22:04:14Z | 2015-07-28T22:04:14Z | CONTRIBUTOR | null | This is a proposal for a simple API enhancement that would make merging on indices much more convenient. I've put in an initial commit to show the change needed in the library. Here's the old way of merging on indices:
``` python
In [1]: from pandas import Index, DataFrame, merge
In [2]: df1 = DataFrame({'data1':[0, 1, 2]})
In [3]: df2 = DataFrame({'data2':[3, 4, 5]})
In [4]: merge(df1, df2, left_index=True, right_index=True)
Out[4]:
data1 data2
0 0 3
1 1 4
2 2 5
```
Here is the new behavior enabled by this PR:
``` python
In [5]: merge(df1, df2, on=Index)
Out[5]:
data1 data2
0 0 3
1 1 4
2 2 5
```
This is a bit hackish, I admit, but I find myself wanting to do this often and the alternatives (using `left_index` and `right_index`, or using `concat([df1, df2], axis='cols')`) are inconvenient and less clear to the reader of the code. That said, I completely understand if folks think this is not a good fit for the pandas API.
Any thoughts on this idea?
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} | 1 | 2015-05-26T23:08:37Z | 2015-06-02T19:26:12Z | 2015-05-27T02:48:47Z | CONTRIBUTOR | null | Closes #9792 .
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https://api.github.com/repos/pandas-dev/pandas/issues/10213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10213/events | https://github.com/pandas-dev/pandas/issues/10213 | 81,213,512 | MDU6SXNzdWU4MTIxMzUxMg== | 10,213 | PERF: additonal GIL releasing | {
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} | 10 | 2015-05-27T01:20:42Z | 2021-04-18T06:54:24Z | 2021-04-18T06:54:24Z | CONTRIBUTOR | null | after #10199 (all of the groupbys)
these require slightly different templates as the object cannot have the `with nogil:`. These are all in `pandas/src/generate_code.py`
~~\- [ ] `groupby_count`~~
~~\- [ ] `is_monotonic`~~
- [ ] `take_1d`
- [ ] `take_2d`
- [ ] `take_2d_multi`
- [ ] `pad`
- [ ] `backfill`
- [x] `diff`
- [x] `join_indexer` (outer/inner/left)
in `algos.pyx`
- [x] `kthsmallest/kthlargest`
- [x] `group_median`
- [x] `roll_*`
- [ ] drop_duplicates machinery
- [x] `nancorr`
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} | 6 | 2015-05-27T12:12:06Z | 2015-06-09T11:54:20Z | 2015-06-09T11:54:19Z | CONTRIBUTOR | null | Hello,
this code works fine:
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
bins = np.arange(80, 100 + 2, 1)
df = pd.DataFrame({"Name": ["AAA", "BBB"], "ByCol": [1, 2], "Mark": [85, 89]})
df
ByCol Mark Name
0 1 85 AAA
1 2 89 BBB
df["Mark"].hist(by=df["ByCol"], bins=bins)
plt.show()
```
but same code with a one row DataFrame such as
```
df = pd.DataFrame({"Name": ["AAA"], "ByCol": [1], "Mark": [85]})
df
ByCol Mark Name
0 1 85 AAA
```
raises:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-35-93bcd47d24f2> in <module>()
----> 1 df["Mark"].hist(by=df["Mark"], bins=bins)
//anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc in hist_series(self, by, ax, grid, xlabelsize, xrot, ylabelsize, yrot, figsize, bins, **kwds)
2847 **kwds)
2848
-> 2849 if axes.ndim == 1 and len(axes) == 1:
2850 return axes[0]
2851 return axes
AttributeError: 'AxesSubplot' object has no attribute 'ndim'
```
Kind regards
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} | 5 | 2015-05-27T12:38:30Z | 2015-08-12T11:37:32Z | 2015-08-12T11:37:32Z | MEMBER | null | We already use the `.. versionadded::` directive in the tutorial docs to indicate that a certain feature is only present starting from a specific pandas version.
But I propose that we also start to do this in the docstrings, when adding a new function/method or adding a new keyword to an existing function/method:
- When developing a package depending on pandas, you typically are using latest pandas but want to support also certain older versions. When using a certain method/function/keyword functionality, the easiest place to see if this is available is the docstring.
- Or, you also typically look at the stable docs even when using a maybe older pandas version and see functions/keywords that don't yet exist for your version.
- This is also how it is done in numpy:
- example of new method: http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.nanmean.html
- example of new keyword: http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html
So if we agree on this, some todo's:
- [ ] add guidelines to the developer docs on how to add this (and start asking for this when reviewing PRs)
- [ ] - optional - add this retrospectively to eg the new features of 0.16.0/1 (#10339 for 0.16.0/0.16.1)
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} | 3 | 2015-05-27T13:04:55Z | 2015-07-06T17:41:14Z | 2015-07-06T17:41:14Z | CONTRIBUTOR | null | closes #10154 to_datetime, Inconsistent behavior with invalid dates.
results are now
```
In [2]: pd.to_datetime('2015-02-29', format="%Y-%m-%d", coerce=True)
Out[2]: NaT
In [3]: pd.to_datetime('2015-03-32', format="%Y-%m-%d", coerce=True)
Out[3]: NaT
In [4]: pd.to_datetime('2015-02-32', format="%Y-%m-%d", coerce=True)
Out[4]: NaT
In [5]: pd.to_datetime('2015-04-31', format="%Y-%m-%d", coerce=True)
Out[5]: NaT
```
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} | 4 | 2015-05-27T16:09:38Z | 2015-06-02T19:25:20Z | 2015-06-01T11:05:05Z | NONE | null | Why would the below return 11/2/2015 as opposed to 11/30/2015 if 11/30 would be considered the next Monday from the 4th Saturday?
```
Holiday("Cyber Monday", month=11, day=1, offset=pd.DateOffset(weekday=SA(4)), observance=next_monday)
```
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} | 1 | 2015-05-27T16:28:14Z | 2015-08-26T01:41:14Z | 2015-08-26T01:41:14Z | NONE | null | Simple usecase of `DataFrame#where` fails on a freshly installed latest Pandas release:
```
In [1]: df = DataFrame([1, 2]); df
Out[1]:
0
0 1
1 2
In [2]: df.where(df[0]==1)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-7b64bb424f7b> in <module>()
----> 1 df.where(df[0]==1)
/home/dunpeal/.env/lib/python3.4/site-packages/pandas/core/generic.py in where(self, cond, other, inplace, axis, level, try_cast, raise_on_error)
3445
3446 if isinstance(cond, NDFrame):
-> 3447 cond = cond.reindex(**self._construct_axes_dict())
3448 else:
3449 if not hasattr(cond, 'shape'):
/home/dunpeal/.env/lib/python3.4/site-packages/pandas/core/series.py in reindex(self, index, **kwargs)
2142 @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
2143 def reindex(self, index=None, **kwargs):
-> 2144 return super(Series, self).reindex(index=index, **kwargs)
2145
2146 @Appender(generic._shared_docs['fillna'] % _shared_doc_kwargs)
/home/dunpeal/.env/lib/python3.4/site-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
1747 if kwargs:
1748 raise TypeError('reindex() got an unexpected keyword '
-> 1749 'argument "{0}"'.format(list(kwargs.keys())[0]))
1750
1751 self._consolidate_inplace()
TypeError: reindex() got an unexpected keyword argument "columns"
```
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} | 5 | 2015-05-27T20:26:27Z | 2021-04-18T06:55:07Z | null | CONTRIBUTOR | null | xref #9877
regression from 0.16.0
```
df = pd.DataFrame(columns=['A','B'])
df.ix['test1', 'A'] = dict({'1':'test2'})
```
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} | 4 | 2015-05-28T16:39:33Z | 2015-06-02T19:29:23Z | 2015-05-29T17:10:25Z | CONTRIBUTOR | null | Should fix https://github.com/pydata/pandas/issues/10214#issuecomment-106091734
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} | 6 | 2015-05-28T17:42:12Z | 2017-03-29T01:07:27Z | 2017-03-29T01:07:27Z | NONE | null | Would it make sense to make the backref from ix objects to the dataframe be weakrefs (and presumably, loc/iloc) The reason I ask is that it seems that the refcount is incremented once you use ix, and as a result the dataframe won't get garbage collected until the cycle detection runs
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| null | 6 | 2015-05-28T19:42:34Z | 2017-02-09T18:28:39Z | 2016-01-27T15:15:19Z | NONE | null | From the documentation for DataFrame.apply:
`
In the current implementation apply calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.
`
I am sure there are well-thought out reasons why it is currently implemented this way, but this behavior can be a real "gotcha". I grant that having side effects inside an apply function is not good standard practice, but I would argue that there are times when it is a good solution to a problem. ( I can elaborate on this if needed). Thankfully this behavior is documented, but I think it is reasonable to expect that most users will not always be mindful of secondary notes that exist throughout the documentation, and the source of problems caused by this behavior is not at all obvious.
While I don't understand the engineering issues regarding the fast/slow path, I would suggest that some better solution to the problem be introduced.
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| null | 1 | 2015-05-28T20:33:56Z | 2015-05-28T20:39:10Z | 2015-05-28T20:38:51Z | NONE | null | Hi,
It seems that read_csv() ignores the dtype=str when encountering NA values. For instance if the contents of file.csv is:
```
id,symbol
1,NA
NA,g2
3,g3
4,g4
```
Then `df=pandas.read_csv('file.csv', dtype={'id':float, 'symbol':str})` will return the following data frame:
```
id symbol
0 1 NaN
1 NaN g2
2 3 g3
3 4 g4
```
which means it casts NA value in the symbol column as a float. Is this a bug or part of the read_csv design? It would make sense that when dtype=str is specified all the values will be treated as string. Note that in the above example using 'keep_default_na=False' in read_csv will cause an error.
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| null | 1 | 2015-05-28T20:36:25Z | 2015-05-28T20:39:39Z | 2015-05-28T20:39:39Z | NONE | null | Hi,
It seems that read_csv() ignores the dtype=str when encountering NA values. For instance if the contents of file.csv is:
id,symbol
1,NA
NA,g2
3,g3
4,g4
Then df=pandas.read_csv('file.csv', dtype={'id':float, 'symbol':str}) will return the following data frame:
id symbol
0 1 NaN
1 NaN g2
2 3 g3
3 4 g4
which means it casts NA value in the symbol column as a float. Is this a bug or part of the read_csv design? It would make sense that when dtype=str is specified all the values will be treated as string. Note that in the above example using 'keep_default_na=False' in read_csv will cause an error.
Thanks,
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} | 6 | 2015-05-29T01:16:42Z | 2015-06-02T19:26:12Z | 2015-06-01T11:04:48Z | CONTRIBUTOR | null | Resolves #10217
Doesn't fix the underlying issue yet, but at least raises on the unsupported behavior.
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| null | 6 | 2015-05-29T01:26:55Z | 2015-09-30T16:52:04Z | 2015-05-29T23:30:39Z | NONE | null | I've updated Pandas from 0.14.1 (all was good) to 0.16.1 and now I have that exception:
```
Error
Traceback (most recent call last):
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/tests/test_reports.py", line 618, in test_no_reports
response = self.request(request)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/tests/test_common.py", line 56, in request
HTTP_COOKIE={})
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/django/test/client.py", line 479, in post
secure=secure, **extra)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/django/test/client.py", line 294, in post
secure=secure, **extra)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/django/test/client.py", line 355, in generic
return self.request(**r)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/django/test/client.py", line 437, in request
six.reraise(*exc_info)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/django/core/handlers/base.py", line 111, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/django/views/generic/base.py", line 69, in view
return self.dispatch(request, *args, **kwargs)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/views.py", line 160, in dispatch
result = self.view(request)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/views.py", line 236, in view
return getattr(handler, action)(**args)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/utils.py", line 74, in inner
result = func(*args, **kwargs)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/utils.py", line 102, in inner
return func(*args, **kwargs)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/utils.py", line 120, in inner
return F(*args, **kwargs)
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/utils.py", line 902, in get_stat
self._get_pandas_dataframe()
File "/home/nedr/progs/MEA-FormBuilder/formbuilder_core/utils.py", line 881, in _get_pandas_dataframe
self.data = pandas.read_csv(cStringIO.StringIO(self.report.body))
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/pandas/io/parsers.py", line 474, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/pandas/io/parsers.py", line 250, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/pandas/io/parsers.py", line 566, in __init__
self._make_engine(self.engine)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/pandas/io/parsers.py", line 705, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/home/nedr/progs/env/mea-formbuilder/lib/python2.7/site-packages/pandas/io/parsers.py", line 1072, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 512, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4814)
ValueError: No columns to parse from file
```
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} | 4 | 2015-05-29T05:29:41Z | 2015-06-26T23:24:40Z | 2015-06-26T23:24:40Z | NONE | null | Runing the code as is below results in a segmentation fault when it gets to resample()
The first pass runs, and then either the 2nd or 3rd run produces:
Segmentation fault: 11
It happens on:
- Python 2.7.9 and Pandas 0.16.1
- Python 3.4.3 and Pandas 0.16.1
It does not happen on:
- Python 2.7.9 with Pandas 0.12.0
Those are the only ones I've tested
```
import pandas as pd
frame = pd.DataFrame({'delay_category': {pd.Timestamp('2014-05-18 08:50:00'): 'yes',
pd.Timestamp('2014-05-19 14:35:00'): 'yes',
pd.Timestamp('2014-05-22 08:45:00'): 'yes',
pd.Timestamp('2014-05-27 10:00:00'): 'no',
pd.Timestamp('2014-05-28 11:05:00'): 'no'}})
frame_filter = frame[frame['delay_category'] == 'da']['delay_category']
for i in range(1,10):
frame_filter.resample('d', how='count')
```
Setting how=sum also does not result in the issue.
It only happens after the frame has been selected with a parameter that doesn't work, resulting in an empty series.
Eg. this works fine:
```
frame_filter = frame[frame['delay_category'] == 'yes']['delay_category']
for i in range(1,10):
frame_filter.resample('d', how='count')
```
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| null | 1 | 2015-05-29T08:39:44Z | 2017-11-23T16:23:55Z | 2015-08-15T23:56:31Z | MEMBER | null | New version of https://github.com/pydata/pandas/pull/10139 , which now completely removes unused attribute and method, avoiding a couple of useless checks.
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} | 4 | 2015-05-29T09:14:01Z | 2015-06-02T19:26:12Z | 2015-05-29T12:21:07Z | CONTRIBUTOR | null | The unit tests freeze on my machine without this change because you can't drop a table until other connections that are using that table disconnect.
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} | 15 | 2015-05-29T16:50:51Z | 2020-01-28T18:05:16Z | 2015-06-11T15:39:35Z | CONTRIBUTOR | null | From my previous line comment at https://github.com/pydata/pandas/commit/be80898920eab8f740cf61019327621a75d73947#commitcomment-11338872. cc @takluyver @jorisvandenbossche
Because DataFrame's `_repr_html_` returns hardcoded `div` tag with style `max-height:1000px`, if the height of DataFrame's output exceeds 1000px, it will generate a scroll bar.
First, it is unnecessary because IPython Notebook has "toggle output scrolling" feature. Second, it causes UX problem if the height of browser resolution is below 1000px (user must scroll twice: the whole page and the generated div). Third, it can't make use of "toggle output scrolling" feature (if user turn it on, it will generate nested scroll bars (worse UX)). For more detailed explanations and examples, see the steps to reproduce below.
Pandas version: 0.16.1.
Steps to reproduce:
1. Open an IPython Notebook and paste this code. It will generate an inner scroll bar because the output's height exceeds 1000px.
``` python
import pandas as pd
pd.DataFrame(list(range(100)))
```
2. If you want to see all rows, first you should scroll the page.
3. After you reach the bottom of the generated ouput's `div`, you should scroll again the inner scroll bar.
4. If you want to toggle the IPython Notebook's "toggle output scrolling" feature, you get this.
Desired behaviors:
- When "output scrolling" feature is off
- When "output scrolling" feature is on
Solution:
Remove style property from `div` tag that DataFrame's `_repr_html_` returns.
Old: `<div style="max-height:1000px;max-width:1500px;overflow:auto;">\n`
New: `<div>\n`
Why should `max-width` be removed as well? Because it also generates _far_ worse UX. If you have so many columns that the horizontal scroll bar generated, you cannot scroll it. You must refresh the page to be able to scroll it.
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} | 33 | 2015-05-29T17:09:03Z | 2015-08-03T12:33:05Z | 2015-06-11T15:39:21Z | CONTRIBUTOR | null | Closes #10231
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} | 4 | 2015-05-29T17:09:10Z | 2015-06-07T06:12:25Z | 2015-06-07T06:12:25Z | CONTRIBUTOR | null | Should closes https://github.com/pydata/pandas/issues/10214
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| null | 3 | 2015-05-29T22:13:53Z | 2019-01-21T04:18:15Z | 2019-01-21T04:18:00Z | NONE | null | Pandas version: **0.15.2**
I have a dataframe object with one column of timestamps:
```
ts value
2012-01-01 00:10:00 100
2012-01-01 00:20:00 200
...
```
And when I dumped them into csv file using to_csv, they are stored properly.
While when I dumped them into xlsx file using to_excel. the data in file are miss detailed timestamps. like:
```
ts value
2012-01-01 00:00:00 100
2012-01-01 00:00:00 200
...
```
Another error message would is:
```
File "C:\Anaconda\lib\site-packages\pandas\util\decorators.py", line 60, in wrapper
return func(*args, **kwargs)
File "C:\Anaconda\lib\site-packages\pandas\core\frame.py", line 1230, in to_excel
startrow=startrow, startcol=startcol)
File "C:\Anaconda\lib\site-packages\pandas\io\excel.py", line 813, in write_cells
cell.val, style)
File "C:\Anaconda\lib\site-packages\xlsxwriter\worksheet.py", line 62, in cell_wrapper
return method(self, *args, **kwargs)
File "C:\Anaconda\lib\site-packages\xlsxwriter\worksheet.py", line 381, in write
return self.write_datetime(row, col, *args)
File "C:\Anaconda\lib\site-packages\xlsxwriter\worksheet.py", line 62, in cell_wrapper
return method(self, *args, **kwargs)
File "C:\Anaconda\lib\site-packages\xlsxwriter\worksheet.py", line 675, in write_datetime
number = self._convert_date_time(date)
File "C:\Anaconda\lib\site-packages\xlsxwriter\worksheet.py", line 3454, in _convert_date_time
return datetime_to_excel_datetime(dt_obj, self.date_1904)
File "C:\Anaconda\lib\site-packages\xlsxwriter\utility.py", line 617, in datetime_to_excel_datetime
delta = dt_obj - epoch
File "tslib.pyx", line 752, in pandas.tslib._Timestamp.__sub__ (pandas\tslib.c:14192)
TypeError: can't subtract offset-naive and offset-aware datetimes
```
A quick fix is to downgrade the version from **0.15.2** to **0.14.0**. It will dump the data correctly.
If it already fixed in the later version, just ignore that.
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} | 3 | 2015-05-30T00:40:14Z | 2016-07-25T12:03:19Z | 2016-07-25T12:03:19Z | CONTRIBUTOR | null | `DataFrame.duplicated` correctly uses `hashtable.duplicated_int64` a specialized routine, while `core.series.IndexOpsMixin.duplicated` uses `lib.duplicated` an object based one.
as its almost always better to factorize then use the fast routine, than to do object comparisons.
but YMMV, so needs a couple of perf tests.
```
In [3]: s = Series(np.random.randint(0,10000,size=100000))
In [4]: %timeit s.duplicated()
100 loops, best of 3: 5.75 ms per loop
In [6]: df = s.to_frame()
In [8]: %timeit df.duplicated()
1000 loops, best of 3: 1.86 ms per loop
```
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} | 14 | 2015-05-30T13:00:05Z | 2015-08-11T01:23:04Z | 2015-08-08T19:46:01Z | MEMBER | null | Closes #6511, Closes #8505.
Introduce `keep` kw to handle first / last / all duplicates based on the discussion above. If there is better API/kw, please lmk.
### `duplicated`
- `keep='first'` (default): mark duplicates as `True` except for the first occurrence.
- `keep='last'`: mark duplicates as `True` except for the last occurrence.
- `keep=False`: mark all duplicates as `True`.
### `drop_duplicates`
- `keep='first'` (default): drop duplicates except for the first occurrence.
- `keep='last'`: drop duplicates except for the last occurrence.
- `keep=False`: drop all duplicates.
``` python
import pandas as pd
s = pd.Series(['A', 'B', 'C', 'A', 'B', 'D'])
# mark duplicates except for the first occurrence
s.duplicated()
#0 False
#1 False
#2 False
#3 True
#4 True
#5 False
# dtype: bool
# mark duplicates except for the last occurrence
s.duplicated(keep='last')
#0 True
#1 True
#2 False
#3 False
#4 False
#5 False
# dtype: bool
# mark all duplicates
s.duplicated(keep=False)
#0 True
#1 True
#2 False
#3 True
#4 True
#5 False
# dtype: bool
# drop duplicates except for the first occurrence
s.drop_duplicates()
#0 A
#1 B
#2 C
#5 D
# dtype: object
# drop duplicates except for the last occurrence
s.drop_duplicates(keep='last')
#2 C
#3 A
#4 B
#5 D
# dtype: object
# drop all duplicates
s.drop_duplicates(keep=False)
#2 C
#5 D
# dtype: object
```
CC @shoyer, @wikiped, @socheon
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} | 2 | 2015-05-30T17:18:51Z | 2015-05-31T09:06:30Z | 2015-05-30T20:14:03Z | NONE | null | Does .round support arbitrary precision rounding in Python 3.4.3/Pandas 0.16.1?
(Pdb) data['h'].round()
0 0
1 54
2 20
3 793
4 72
5 43
6 51
Name: h, dtype: float64
(Pdb) data['h'].round(1)
0 0.00000000000000000000000
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2 19.69999999999999928945726
3 793.00000000000000000000000
4 72.40000000000000568434189
5 42.79999999999999715782906
6 51.10000000000000142108547
Name: h, dtype: float64
(Pdb) data['h'].round(2)
0 0.00000000000000000000000
1 54.07999999999999829469743
2 19.67000000000000170530257
3 793.03999999999996362021193
4 72.39000000000000056843419
5 42.84000000000000341060513
6 51.06000000000000227373675
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} | 1 | 2015-05-30T19:47:19Z | 2015-05-31T09:07:38Z | 2015-05-30T20:11:59Z | NONE | null | I'd like to run pandas tests but not have it do any tests that require networking. I can get the tests to pass if I disconnect from the internet on my machine, but otherwise I'm getting SSL errors. This might be because am building pandas with nix, but presumably there are other reasons why one might want to disable any tests that require networking -- regardless of whether networking is actually available.
Poking around through the tests code, it looks like there's a check [here](https://github.com/pydata/pandas/blob/d5fdbc6beb81ed2aa76bac9bb1c7dd2b7330617c/pandas/util/testing.py#L1451) which attempts to connect to google and skips the test if it's unable to do so. This, therefore, is _nearly_ there. All that would be needed is to add an additional condition which would read a configuration option. For example an environment variable `PANDAS_NO_NET_TESTS` or something:
``` python
if check_before_test and not raise_on_error:
if 'PANDAS_NO_NET_TESTS' in os.environ or not can_connect(url, error_classes):
raise SkipTest
```
The particulars are relatively unimportant but the point is, is such a thing undesirable for some reason? Or should I perhaps make a pull request?
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| null | 1 | 2015-05-30T20:58:58Z | 2019-12-01T17:57:23Z | null | MEMBER | null | ``` python
df = pd.DataFrame(np.random.randn(2, 3), columns=['A', 'B', 'C'])
numexpr.__version__
# '2.4'
# all resets column name
pd.eval('df.A')
#0 -0.312782
#1 1.204993
# dtype: float64
pd.eval('df["A"]')
#0 -0.312782
#1 1.204993
# dtype: float64
df.eval('A')
#0 -0.312782
#1 1.204993
# dtype: float64
# python engine looks OK
pd.eval('df["A"]', engine='python')
#0 -0.312782
#1 1.204993
# Name: A, dtype: float64
pd.eval('df.A', engine='python')
#0 -0.312782
#1 1.204993
# Name: A, dtype: float64
df.eval('A', engine='python')
#0 -0.312782
#1 1.204993
# Name: A, dtype: float64
```
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} | 1 | 2015-05-30T21:14:49Z | 2015-06-02T19:26:12Z | 2015-06-02T10:38:48Z | MEMBER | null | Closes #10068.
Should handle the case `_maybe_match_name` is `None`, because of continuous `__finalize__`.
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} | 4 | 2015-05-30T21:47:52Z | 2015-06-02T19:26:12Z | 2015-06-02T10:39:04Z | MEMBER | null | ```
# OK: name is preserved
s = pd.Series([1, 2, -3], name='a')
s.abs()
#0 1
#1 2
#2 3
# Name: a, dtype: int64
# NG: name is reset
s = pd.SparseSeries([1, 0, -3], name='a')
s.name
# 'a'
s.abs()
#0 1
#1 0
#2 3
# dtype: int64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: array([3], dtype=int32)
```
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} | 14 | 2015-05-30T22:37:57Z | 2015-09-04T12:15:33Z | 2015-09-04T12:15:33Z | NONE | null | The var method acts inconsistently when used on the Series vs the numpy array returned from values:
``` python
dat = pd.DataFrame({'x': [1,2,3,4,0,0,0]})
print(dat['x'].values.var())
print(dat['x'].var())
```
```
Prints:
2.24489795918
2.61904761905
```
This is due to numpy using ddof=0 as its default (calculates the biased variance), whereas pandas calculates the unbiased variance by default. These two should be consistent, so either pandas should adapt or numpy should (maybe it should be numpy since we should calculate unbiased by default?).
The other problem is that pandas does not calculate the variance of this DataFrame properly.
Inconsistent definition aside, the variance should clearly not be zero when calculated from pandas.
``` python
dat = pd.DataFrame({'x': [9.0692124699,9.0692124692,9.0692124702,9.0692124686,
9.0692124687,9.0692124707,9.0692124679,9.0692124685,
9.0692124698,9.0692124719,9.0692124698,9.0692124692,
9.0692124689,9.0692124673,9.0692124707,9.0692124714,
9.0692124714,9.0692124734,9.0692124719,9.0692124710,
9.0692124694,9.0692124705,9.0692124713,9.0692124717
]})
print(dat['x'].values.var())
print(dat['x'].var())
```
```
Prints:
2.06817742558e-18
0.0
```
Here is the system information:
INSTALLED VERSIONS:
commit: None
python: 3.4.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.16.1
numpy: 1.9.2
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| null | 25 | 2015-05-31T14:55:02Z | 2015-12-07T17:40:51Z | 2015-11-25T15:44:00Z | NONE | null | addresses #6245
@jreback this isn't quite working yet for older arrays - the reason is because I have to address values_block_kind_0 and things like that. Currently what I'm doing is creating a group under `self.group` of the Table class, named 'attr', which has all the attributes as CArrays. However the issue is that values_block_kind_0 is actually written as an attr of the table dataset, not the group. I can definitely setup the code to read that stuff for backwards compatability, but is it ok if I put all carray attrs as datasets under the attr group, or is there some namespace collisioning that I'm not aware of?
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https://api.github.com/repos/pandas-dev/pandas/issues/10244 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/10244/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/10244/comments | https://api.github.com/repos/pandas-dev/pandas/issues/10244/events | https://github.com/pandas-dev/pandas/issues/10244 | 83,278,915 | MDU6SXNzdWU4MzI3ODkxNQ== | 10,244 | new feature/enhancement request: new JSON string option for pandas.DataFrame.to_json() | {
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} | 4 | 2015-06-01T02:48:35Z | 2019-08-27T19:01:46Z | 2019-08-27T18:43:41Z | NONE | null | Currently `pandas.DataFrame.to_json` supports `orient=records` which yields a file of `[{column -> value}, ... , {column -> value}]` (as a single line). I'd like to request a slight variation of this format which is a single `{column -> value}` per line. This yields a similar format as PostgreSQL's `row_to_json()` function.
Example of the TPC-H `region` table using `orient=records`
```
[{"r_regionkey":0,"r_name":"AFRICA","r_comment":"lar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are according to "},{"r_regionkey":1,"r_name":"AMERICA","r_comment":"hs use ironic, even requests. s"},{"r_regionkey":2,"r_name":"ASIA","r_comment":"ges. thinly even pinto beans ca"},{"r_regionkey":3,"r_name":"EUROPE","r_comment":"ly final courts cajole furiously final excuse"},{"r_regionkey":4,"r_name":"MIDDLE EAST","r_comment":"uickly special accounts cajole carefully blithely close requests. carefully final asymptotes haggle furiousl"}]
```
desired new format:
```
{"r_regionkey":0,"r_name":"AFRICA","r_comment":"lar deposits. blithely final packages cajole. regular waters are final requests. regular accounts are according to "}
{"r_regionkey":1,"r_name":"AMERICA","r_comment":"hs use ironic, even requests. s"}
{"r_regionkey":2,"r_name":"ASIA","r_comment":"ges. thinly even pinto beans ca"}
{"r_regionkey":3,"r_name":"EUROPE","r_comment":"ly final courts cajole furiously final excuse"}
{"r_regionkey":4,"r_name":"MIDDLE EAST","r_comment":"uickly special accounts cajole carefully blithely close requests. carefully final asymptotes haggle furiousl"}
```
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} | 31 | 2015-06-01T08:05:39Z | 2015-06-08T13:28:39Z | 2015-06-08T13:28:39Z | CONTRIBUTOR | null | Consider a file of the following format:
```
week,sow,prn,rxstatus,az,elv,l1_cno,s4,s4_cor,secsigma1,secsigma3,secsigma10,secsigma30,secsigma60,code_carrier,c_cstdev,tec45,tecrate45,tec30,tecrate30,tec15,tecrate15,tec00,tecrate00,l1_loctime,chanstatus,l2_locktime,l2_cno
1765,68460.00,126,00E80000,0.00,0.00,39.38,0.118447,0.107595,0.252663,0.532384,0.600540,0.603073,0.603309,-13.255543,0.114,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1692.182,8C023D84,0.000,0.00
1765,68460.00,23,00E80000,0.00,0.00,53.48,0.034255,0.021177,0.035187,0.042985,0.061142,0.061738,0.061801,-22.760003,0.015,24.955111,0.112239,25.115330,-0.119774,25.146603,-0.065852,24.747576,-0.243804,10426.426,08109CC4,10409.660,44.52
1765,68460.00,13,00E80000,0.00,0.00,54.28,0.046218,0.019314,0.037818,0.056421,0.060602,0.060698,0.060735,-20.679035,0.090,25.670250,-0.070761,25.752224,-0.055089,26.045048,-0.180056,25.360369,-0.062119,7553.020,18109CA4,7202.660,47.27
```
I try to read that with the following code
``` python
data = pd.read_csv(FILE, date_parser=GPStime2datetime,
parse_dates={'datetime': ['week', 'sow']},
index_col=['datetime', 'prn'])
```
Here I'm parsing `week` and `sow` into a `datetime` column using a custom function (this works properly) and using `datetime` and the `prn` column as a `MultiIndex`. The file is read successfully when `index_col='datetime'`, but not when trying to create the `MultiIndex` using `index_col=['datetime', 'prn']` (or when using column numbers instead of names). I get the following traceback:
```
File "C:\Anaconda\lib\site-packages\pandas\io\parsers.py", line 474, in parser_f
return _read(filepath_or_buffer, kwds)
File "C:\Anaconda\lib\site-packages\pandas\io\parsers.py", line 260, in _read
return parser.read()
File "C:\Anaconda\lib\site-packages\pandas\io\parsers.py", line 721, in read
ret = self._engine.read(nrows)
File "C:\Anaconda\lib\site-packages\pandas\io\parsers.py", line 1223, in read
index, names = self._make_index(data, alldata, names)
File "C:\Anaconda\lib\site-packages\pandas\io\parsers.py", line 898, in _make_index
index = self._agg_index(index, try_parse_dates=False)
File "C:\Anaconda\lib\site-packages\pandas\io\parsers.py", line 984, in _agg_index
index = MultiIndex.from_arrays(arrays, names=self.index_names)
File "C:\Anaconda\lib\site-packages\pandas\core\index.py", line 4410, in from_arrays
cats = [Categorical.from_array(arr, ordered=True) for arr in arrays]
File "C:\Anaconda\lib\site-packages\pandas\core\categorical.py", line 355, in from_array
return Categorical(data, **kwargs)
File "C:\Anaconda\lib\site-packages\pandas\core\categorical.py", line 271, in __init__
codes, categories = factorize(values, sort=False)
File "C:\Anaconda\lib\site-packages\pandas\core\algorithms.py", line 131, in factorize
(hash_klass, vec_klass), vals = _get_data_algo(vals, _hashtables)
File "C:\Anaconda\lib\site-packages\pandas\core\algorithms.py", line 412, in _get_data_algo
mask = com.isnull(values)
File "C:\Anaconda\lib\site-packages\pandas\core\common.py", line 230, in isnull
return _isnull(obj)
File "C:\Anaconda\lib\site-packages\pandas\core\common.py", line 240, in _isnull_new
return _isnull_ndarraylike(obj)
File "C:\Anaconda\lib\site-packages\pandas\core\common.py", line 330, in _isnull_ndarraylike
result = np.isnan(values)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
I am using Python 2.7, Pandas 0.16.1 and numpy 1.9.2.
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} | 12 | 2015-06-01T19:36:24Z | 2016-01-30T19:29:48Z | 2016-01-30T19:27:56Z | CONTRIBUTOR | null | …kw for filling NaN when unstack results in a sparse DataFrame
closes #9746
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} | 4 | 2015-06-02T06:13:50Z | 2015-06-05T21:45:43Z | 2015-06-05T21:45:43Z | CONTRIBUTOR | null | closes #10209
When datetimes are not dates only i.e. have fractional day values, date_format passed to to_csv is ignored.
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} | 15 | 2015-06-02T12:00:16Z | 2015-06-08T13:50:57Z | 2015-06-08T13:28:39Z | CONTRIBUTOR | null | Fixes #10245
Without this fix, if the `date_parse` function passed to e.g. `read_csv` outputs a `np.datetime64` array, then it must be of `ns` resolution. With this fix, all time resolutions may be used.
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} | 89 | 2015-06-02T12:41:28Z | 2020-06-24T18:30:57Z | 2015-11-16T00:02:02Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/3190
closes #4315
Not close to being done, but I wanted to put this here before the meeting. Maybe someone will have a chance to check it out.
~~http://nbviewer.ipython.org/github/TomAugspurger/pandas/blob/638bd3e361633a4c446ee02534e07b8a9332258a/style.ipynb~~
~~https://github.com/TomAugspurger/pandas/blob/stylely/style.ipynb~~
[latest example notebook](http://nbviewer.ipython.org/gist/TomAugspurger/82a53e11993469cb29a1)
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} | 2 | 2015-06-02T17:32:47Z | 2015-06-05T21:29:03Z | 2015-06-05T21:28:59Z | CONTRIBUTOR | null | this is a follow-up to https://github.com/pydata/pandas/pull/10172
In addition to `mean()`, this PR also makes sure the returned dtype is consistent for `std()`, `var()`, `skew()`, and `kurt()`
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} | 14 | 2015-06-02T22:33:49Z | 2015-06-20T12:47:00Z | 2015-06-20T12:47:00Z | CONTRIBUTOR | null | From the original issue, closes #10140
```
In [1]: df = pd.DataFrame()
In [2]: df['x'] = 100 * np.random.random(100)
In [3]: df['y'] = df.x**2
In [4]: binedges = np.arange(0,110,10)
In [5]: binlabels = np.arange(5,105,10)
```
This now yields an `Int64Index`
```
In [6]: df.groupby(pd.cut(df.x,bins=binedges ,labels=binlabels )).y.mean()
Out[6]:
5 43.485680
15 227.648305
25 680.165208
35 1244.704812
45 1977.675209
55 3159.067969
65 4435.735803
75 5649.164598
85 7027.956570
95 8997.975423
Name: y, dtype: float64
In [7]: df.groupby(pd.cut(df.x,bins=binedges ,labels=binlabels )).y.mean().index
Out[7]: Int64Index([5, 15, 25, 35, 45, 55, 65, 75, 85, 95], dtype='int64')
```
If you pass an index in, then you will get that out, specifically a `Categorical` will preserve the categories (note the 2nd example has reversed labels - why anyone would do this, don't know.......)
```
In [8]: df.groupby(pd.cut(df.x,bins=binedges ,labels=pd.Categorical(binlabels) )).y.mean().index
Out[8]: CategoricalIndex([5, 15, 25, 35, 45, 55, 65, 75, 85, 95], categories=[5, 15, 25, 35, 45, 55, 65, 75, ...], ordered=True, name=u'x', dtype='category')
In [9]: df.groupby(pd.cut(df.x,bins=binedges ,labels=pd.Categorical(binlabels,categories=binlabels[::-1]) )).y.mean().index
Out[9]: CategoricalIndex([95, 85, 75, 65, 55, 45, 35, 25, 15, 5], categories=[95, 85, 75, 65, 55, 45, 35, 25, ...], ordered=True, name=u'x', dtype='category')
In [10]: df.groupby(pd.cut(df.x,bins=binedges ,labels=pd.Categorical(binlabels,categories=binlabels[::-1]) )).y.mean()
Out[10]:
x
95 43.485680
85 227.648305
75 680.165208
65 1244.704812
55 1977.675209
45 3159.067969
35 4435.735803
25 5649.164598
15 7027.956570
5 8997.975423
Name: y, dtype: float64
```
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} | 18 | 2015-06-03T02:26:03Z | 2015-08-19T17:19:06Z | 2015-06-06T03:14:27Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/10129
In the dev meeting, we settled on the following:
- `.pipe` will **not** include a check for `__pipe_func__` on the function passed in.
- To avoid messiness with lambdas when a function takes the DataFrame other than in the first position, users can pass in a `(callable, data_keyword)` argument to `.pipe` (thanks @mwaskom)
``` python
import statsmodels.formula.api as sm
bb = pd.read_csv('data/baseball.csv', index_col='id')
(bb.query('h > 0')
.assign(ln_h = lambda df: np.log(df.h))
# sm.possion expects `formula, data`
.pipe((sm.poisson, 'data'), 'hr ~ ln_h + year + g + C(lg)')
.fit()
.summary()
)
## -- End pasted text --
Optimization terminated successfully.
Current function value: 2.116284
Iterations 24
Out[1]:
<class 'statsmodels.iolib.summary.Summary'>
"""
Poisson Regression Results
==============================================================================
Dep. Variable: hr No. Observations: 68
Model: Poisson Df Residuals: 63
Method: MLE Df Model: 4
Date: Tue, 02 Jun 2015 Pseudo R-squ.: 0.6878
Time: 20:57:27 Log-Likelihood: -143.91
converged: True LL-Null: -460.91
LLR p-value: 6.774e-136
===============================================================================
coef std err z P>|z| [95.0% Conf. Int.]
-------------------------------------------------------------------------------
Intercept -1267.3636 457.867 -2.768 0.006 -2164.767 -369.960
C(lg)[T.NL] -0.2057 0.101 -2.044 0.041 -0.403 -0.008
ln_h 0.9280 0.191 4.866 0.000 0.554 1.302
year 0.6301 0.228 2.762 0.006 0.183 1.077
g 0.0099 0.004 2.754 0.006 0.003 0.017
===============================================================================
"""
```
Thanks everyone for the input in the issue.
---
This is mostly ready. What's a good name for the argument to `pipe`? I have `func` right now, @shoyer you had `target` in the issue thread. I've been using `target` as the keyword expecting the data, i.e. where the DataFrame should be pipe to.
The tests are extremely minimal... but so is the implementation. Am I missing any obvious edge-cases?
We'll see how this goes. I don't think I push `.pipe` as a protocol at all in the documentation, though we can change that in the future. We should be forwards-compatible if we do ever go down the `__pipe_func__` route.
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} | 26 | 2015-06-03T08:06:49Z | 2021-04-18T06:56:40Z | null | MEMBER | null | To record the issue we discussed yesterday, to be solved for 0.16.2
Plotting of categorical:
- y-axis: the 'values' itself are used
- x-axis: they are not regarded as values, but as all unique items that just gets represented by a `range(len(cat))` x data
Overview: http://nbviewer.ipython.org/gist/jorisvandenbossche/992d9d34dbfcfd8bc326
Disclaimer: didn't yet look into the code to see why this is like this.
Way forward:
- for now: handle CategoricalIndex the same as column with Categorical values (so use the values itself)
- this will also mean that a CategoricalIndex with string categories will raise now (in 0.16.1 it did not but plotted all values just in order, also discarding the fact the values with the same category should be regarded as equal)
- later, we can try to implement more fancy / intelligent categorical plotting (there are already #9069 and #8712 about this)
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} | 1 | 2015-06-03T08:14:50Z | 2015-06-05T09:11:25Z | 2015-06-05T09:11:25Z | MEMBER | null | Reported at StackOverflow: http://stackoverflow.com/questions/30586292/valueerror-mysql-identifier-cannot-be-entirely-numeric-with-pandas-to-sql
MySQL supports numeric identifiers (if quoted) (http://dev.mysql.com/doc/refman/5.0/en/identifiers.html)
@artemyk Was there a reason we explicitely disallowed this? (https://github.com/pydata/pandas/blob/v0.16.1/pandas/io/sql.py#L1287)
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} | 3 | 2015-06-03T11:00:38Z | 2015-06-03T16:39:31Z | 2015-06-03T11:33:18Z | NONE | null | (Hopefully I did not find this issue in the list, because there was none...) I have experienced the following issue for a while (wonder if it ever worked actually):
I create a Series or a DataFrame with well-defined, floating-point indices. Then I try to slice some rows using a list. For some valid index values I get NaN instead of the value itself. An example:
``` python
>>> import numpy as np
>>> import pandas as pd
>>> i = np.linspace(0, 10, 101) # create a floating-point (i)ndex-range
>>> d = np.random.random(101) # add some random numbers as (d)ata
>>> s = pd.Series(d, index=i) # create a Series from the generated data
>>> type(s)
<class 'pandas.core.series.Series'>
>>> s.head()
0.0 0.969683
0.1 0.463655
0.2 0.879788
0.3 0.122202
0.4 0.466166
dtype: float64
>>> xs = [0.8, 1.1, 1.9, 2.2, 3.3] # we make a list with a few indexes of interest
>>> s[xs] # try to list Series-values for those indices, but for some we see only NaN!
0.8 0.996011
1.1 0.699921
1.9 NaN
2.2 0.919059
3.3 NaN
dtype: float64
R:\WinPython\python-3.4.3.amd64\lib\site-packages\pandas\core\format.py:2012: RuntimeWarning: invalid value encountered in greater
has_large_values = (abs_vals > 1e8).any()
R:\WinPython\python-3.4.3.amd64\lib\site-packages\pandas\core\format.py:2013: RuntimeWarning: invalid value encountered in less
has_small_values = ((abs_vals < 10 ** (-self.digits+1)) &
R:\WinPython\python-3.4.3.amd64\lib\site-packages\pandas\core\format.py:2014: RuntimeWarning: invalid value encountered in greater
(abs_vals > 0)).any()
>>> s[1:2] # we can see, that the values are really there, also at index 1.9
1.0 0.905714
1.1 0.699921
1.2 0.105968
1.3 0.809368
1.4 0.227002
1.5 0.825698
1.6 0.911150
1.7 0.027182
1.8 0.046001
1.9 0.456927
2.0 0.592820
dtype: float64
>>>
```
The warnings are actually came from the console, the IPython Notebook that I normally use does not even issue them. Same result using e.g. _s.loc[xs]_. The problem also exists for DataFrames, which can be reproduced in the same manner.
I'm using the latest 64-bit WinPython with Python 3.4.3 and Pandas 0.16.1:
``` python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: hu_HU
pandas: 0.16.1
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 3.1.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.7.2
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.4
pymysql: None
psycopg2: None
```
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} | 2 | 2015-06-03T12:07:26Z | 2015-06-04T13:16:14Z | 2015-06-03T21:21:22Z | MEMBER | null | When constructing `Series` from `Series`, name attribute is preserved otherwise specified.
```
import pandas as pd
s = pd.Series([1, 2, 3], name='a')
pd.Series(s)
#0 1
#1 2
#2 3
# Name: a, dtype: int64
pd.Series(s, name='x')
#0 1
#1 2
#2 3
# Name: x, dtype: int64
```
But `SparseSeries` doesn't preserve its name.
```
s = pd.SparseSeries([1, 2, 3], name='a')
pd.SparseSeries(s)
#0 1
#1 2
#2 3
# dtype: int64
# BlockIndex
# Block locations: array([0], dtype=int32)
# Block lengths: array([3], dtype=int32)
```
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} | 7 | 2015-06-03T13:39:51Z | 2018-03-30T21:56:37Z | 2018-03-30T21:56:37Z | MEMBER | null | Python 3.5 is being released on Sep.13. Maybe we can wait for v0.17?
- http://legacy.python.org/dev/peps/pep-0465/
- numpy fix: https://github.com/numpy/numpy/pull/5878/
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| null | 6 | 2015-06-03T14:54:47Z | 2015-06-04T12:39:01Z | 2015-06-03T17:00:34Z | NONE | null | As per http://stackoverflow.com/questions/30621887/return-a-column-vector-in-pandas-apply-with-different-length/30622209#30622209
It would be nice if an apply function automatically converted lists to pd.Series when they are returned to .apply(), in other words,
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame({'a':[0,10], 'b':[0,30]})
df.apply(lambda x: pd.Series(np.percentile(x, q=[10,20,30,40,50,75,100])), axis=0)
```
could become
``` python
df.apply(np.percentile, axis=0, q=[10,20,30,40,50,75,100])
```
(np.percentile currently returns a list, unless `axis=` is supplied, and then it returns a list of arrays o_O )
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| null | 4 | 2015-06-03T17:21:38Z | 2021-04-18T06:57:04Z | null | NONE | null | When read_fwf is used with iterator = True and skiprows = [list] arguments it doesn't properly skip all the rows in the skiprows list. Things work properly when either of those arguments is used in isolation.
Here is a simple bit of code to reproduce:
``` python
import pandas as pd
#Create a fixed width file to test with.
df = pd.DataFrame({'a': range(10)})
with open('testfwf.txt', 'w') as f:
f.write(df.to_string(index = False, header = False))
rows_to_skip = [0,1,2,6,9]
df_iter = pd.read_fwf('testfwf.txt', colspecs = [(0,2)], names = ['a'], iterator = True,
chunksize = 2, skiprows = rows_to_skip)
print('The fixed width file in chunks with rows [0,1,2,6,9] skipped: ')
for df in df_iter:
print(df)
print('Notice how row 6 of the fixed width file has not been skipped even though it should')
print('have been.')
```
It seems that all rows are skipped until there are rows that aren't skipped. For example, the leading rows 0,1,2 are skipped. But since there are then rows that aren't skipped the skipping stops for all rows until then end, when row 9 IS skipped.
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} | 5 | 2015-06-03T19:38:14Z | 2015-07-10T23:17:58Z | 2015-07-10T23:17:52Z | CONTRIBUTOR | null | Closes #10255 .
Rewrote some of the code in `test_sql.py` so that it now properly drops tables with names that need to be quoted.
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} | 4 | 2015-06-03T20:57:06Z | 2015-06-05T09:11:32Z | 2015-06-05T09:11:25Z | CONTRIBUTOR | null | Closes #10255 , alternative to #10262 .
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} | 15 | 2015-06-03T21:05:16Z | 2015-06-05T21:41:05Z | 2015-06-05T21:41:05Z | NONE | null | The following code snippet produces unexpected output:
``` python
import pandas as pd
import numpy as np
x = pd.DataFrame(data=np.zeros((5,5),dtype=np.int),columns=['a','b','c','d','e'])
x['f'] = 0
x.f.values[3]=1
y = x.iloc[np.arange(2,len(x))]
x.f.values[3]=2
print x
print x.f.values
```
```
Out[130]:
a b c d e f
0 0 0 0 0 0 0
1 0 0 0 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 1
4 0 0 0 0 0 0
[0 0 0 2 0]
```
The expected output is:
```
Out[131]:
a b c d e f
0 0 0 0 0 0 0
1 0 0 0 0 0 0
2 0 0 0 0 0 0
3 0 0 0 0 0 2
4 0 0 0 0 0 0
[0 0 0 2 0]
```
The behavior only manifests if the dataframe has had a column added to it (f in this case), if the columns are ints, and if the view is constructed with certain kinds of indexing (y=x.iloc[2:] works fine for instance).
Here is my version info:
## INSTALLED VERSIONS
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.4.2.el7.jump.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2.dev
nose: 1.3.4
Cython: 0.20.2
numpy: 1.9.0
scipy: 0.14.1rc1.dev-Unknown
statsmodels: None
IPython: 3.1.0
sphinx: 1.3b1
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.10
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.1.4
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.4.3
sqlalchemy: None
pymysql: 0.6.2.None
psycopg2: 2.5.4 (dt dec pq3 ext)
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} | 40 | 2015-06-03T21:53:02Z | 2015-07-15T21:14:39Z | 2015-07-14T12:47:25Z | CONTRIBUTOR | null | Changes behavior of convert objects so that passing 'coerce' will
ensure that data of the correct type is returned, even if all
values are null-types (NaN or NaT).
closes #9589
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} | 11 | 2015-06-04T02:37:01Z | 2015-06-12T23:39:57Z | 2015-06-12T22:23:59Z | CONTRIBUTOR | null | Fixes #2941
This can almost certainly be made quicker, still digging into the
internals to understand the various underlying indexers.
There are 4 distinct logics you can apply to how to deal with the
missings (as opposed to the Series asof, wheres theres just two,
either skipna or dont skipna). The default is to be equivalent to
`df.apply(lambda s: s.asof(where))`.
Likely not ready to actually be merged, but at a stage where I could
use some feedback on both the logic and implementation.
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| null | 0 | 2015-06-04T02:40:26Z | 2015-06-04T02:52:25Z | 2015-06-04T02:52:25Z | CONTRIBUTOR | null | This is to fix #10160, where `DataFrame` construction from nested `dict` with `datetime64` index returns a `DataFrame` of `NaN`s or `None`s.
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Added copy argument in as_blocks. Defaults to True as per the original code.
Wrote test cases to check the copy=True and copy=False cases work.
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closes #9456
where `DataFrame/Series` construction from nested `dict` with `datetime64` index returns a `DataFrame\Series` of `NaN`s.
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```
In [2]: df = DataFrame({'A' : pd.date_range('20130101',periods=100000,freq='s')})
In [3]: %timeit df.to_csv('test.csv',mode='w')
1 loops, best of 3: 282 ms per loop
```
0.16.1
```
In [2]: %timeit df.to_csv('test.csv',mode='w')
1 loops, best of 3: 352 ms per loop
```
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} | 0 | 2015-06-04T12:10:26Z | 2015-06-05T21:41:05Z | 2015-06-05T21:41:05Z | CONTRIBUTOR | null | closes #10264
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} | 3 | 2015-06-04T14:14:22Z | 2015-08-29T00:08:29Z | 2015-08-29T00:08:29Z | NONE | null | Sample code below throws ValueError. Ideally it should return a DataFrame with zero rows.
``` python
import pandas as pd
#This query returns zero rows
query = "SELECT * FROM [publicdata:samples.wikipedia] where timestamp=-9999999"
df = pd.io.gbq.read_gbq(query, project_id="my_bigquery_project_id")
```
```
Job not yet complete...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/io/gbq.py", line 341, in read_gbq
final_df = concat(dataframe_list, ignore_index = True)
File "pandas/tools/merge.py", line 754, in concat
copy=copy)
File "pandas/tools/merge.py", line 799, in __init__
raise ValueError('All objects passed were None')
ValueError: All objects passed were None
```
```
INSTALLED VERSIONS
------------------
commit: b144cc1285e8f353112075b3d4eff75a68ad1699
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.1-64-gb144cc1
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2015.4
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.1
apiclient: 1.4.0
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 14 | 2015-06-04T15:19:00Z | 2015-12-10T12:46:09Z | 2015-12-10T12:22:48Z | MEMBER | null | Add `_isnan` to `DatetimeIndexOpsMixin` to cache `NaT` mask.
This leads to some perf improvement which is noticeable in larger data.
### after fix
```
import pandas as pd
import numpy as np
np.random.seed(1)
idx = pd.DatetimeIndex(np.append(np.array([pd.tslib.iNaT]),
np.random.randint(500, 1000, size=100000000)))
%timeit idx.max()
1 loops, best of 3: 599 ms per loop
%timeit idx.min()
1 loops, best of 3: 608 ms per loop
```
### before fix:
```
%timeit idx.max()
1 loops, best of 3: 940 ms per loop
%timeit idx.min()
1 loops, best of 3: 883 ms per loop
```
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| null | 3 | 2015-06-04T17:24:41Z | 2020-03-31T04:52:59Z | null | CONTRIBUTOR | null | The definition of the USFederalHolidayCalendar is not correct.
```
from pandas.tseries.holiday import USFederalHolidayCalendar
print(USFederalHolidayCalendar.rules)
```
> [Holiday: New Years Day (month=1, day=1, observance=<function nearest_workday at 0x10d17dcf8>), Holiday: Dr. Martin Luther King Jr. (month=1, day=1, offset=<DateOffset: kwds={'weekday': MO(+3)}>), Holiday: Presidents Day (month=2, day=1, offset=<DateOffset: kwds={'weekday': MO(+3)}>), Holiday: MemorialDay (month=5, day=24, offset=<DateOffset: kwds={'weekday': MO(+1)}>), Holiday: July 4th (month=7, day=4, observance=<function nearest_workday at 0x10d17dcf8>), Holiday: Labor Day (month=9, day=1, offset=<DateOffset: kwds={'weekday': MO(+1)}>), Holiday: Columbus Day (month=10, day=1, offset=<DateOffset: kwds={'weekday': MO(+2)}>), Holiday: Veterans Day (month=11, day=11, observance=<function nearest_workday at 0x10d17dcf8>), Holiday: Thanksgiving (month=11, day=1, offset=<DateOffset: kwds={'weekday': TH(+4)}>), Holiday: Christmas (month=12, day=25, observance=<function nearest_workday at 0x10d17dcf8>)]
```
print(USFederalHolidayCalendar.start_date)
```
> 1970-01-01 00:00:00
But Martin Luther King Day was first observed in 1986, so the holiday is being applied inappropriately before then. I am going to work on a snippet for a correct US holiday calendar.
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} | 9 | 2015-06-04T19:03:41Z | 2018-09-05T11:58:27Z | 2018-09-05T11:58:19Z | NONE | null | read_sql_table handles columns with dots in the name incorrectly (removes name before dot)
```
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite://')
conn = engine.connect()
conn.execute("create table test (a int, `b.c` int)")
conn.execute("insert into test values (1,2)")
df = pd.read_sql_table("test", engine)
print(df)
```
->
```
a c
0 1 2
```
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} | 0 | 2015-06-04T19:25:04Z | 2015-06-09T14:43:27Z | 2015-06-09T14:43:27Z | CONTRIBUTOR | null | xref #10264
converts int -> floats for the entire block.
```
In [1]: x = pd.DataFrame(data=np.zeros((5,5),dtype=np.int),columns=['a','b','c','d','e'], index=['one','two','three','four','five'])
In [3]: x.loc['three','e'] = 3.1
In [4]: x
Out[4]:
a b c d e
one 0 0 0 0 0.0
two 0 0 0 0 0.0
three 0 0 0 0 3.1
four 0 0 0 0 0.0
five 0 0 0 0 0.0
In [5]: x.dtypes
Out[5]:
a float64
b float64
c float64
d float64
e float64
dtype: object
```
ok for other kinds of assignments
```
In [6]: x = pd.DataFrame(data=np.zeros((5,5),dtype=np.int),columns=['a','b','c','d','e'], index=['one','two','three','four','five'])
[8]: x.loc['three','e'] = 'foo'
In [9]: x
Out[9]:
a b c d e
one 0 0 0 0 0
two 0 0 0 0 0
three 0 0 0 0 foo
four 0 0 0 0 0
five 0 0 0 0 0
In [10]: x.dtypes
Out[10]:
a int64
b int64
c int64
d int64
e object
dtype: object
```
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} | 18 | 2015-06-04T20:34:51Z | 2015-08-18T16:58:13Z | 2015-08-18T11:09:17Z | CONTRIBUTOR | null | closes #9760
Fixing MLK day not observed before 1986, per https://github.com/pydata/pandas/issues/10278#issuecomment-109002244
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} | 1 | 2015-06-05T06:14:49Z | 2015-06-05T08:44:39Z | 2015-06-05T07:34:35Z | NONE | null | Currently i am having an question in python pandas. I want to filter a dataframe using url query string dynamically.
Detailed question:
http://stackoverflow.com/questions/30658017/filter-pandas-dataframes-using-dynamic-url-query-string
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} | 3 | 2015-06-05T07:45:39Z | 2021-09-03T10:15:55Z | 2020-12-23T23:28:23Z | NONE | null | When reading from SQL queries - especially when reading chunk-wise - issues with type conversion can occur.
It would be ideal if one could specify the types of the columns, so that type conversions go right.
A very common case is reading pure float results chunk-wise from a large table. The nuisance comes in when and chunk contains NULL values only in a column. In such cases these NULL values are stored as `None` rather than `float("nan")`. I see that this is an issue with inconsistent NULL types in pandas and missing type information in read_sql_query.
Another case could be when text columns happen to contain numbers only in a chunk.
Specifying types for a query manually could resolve these issues.
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} | 4 | 2015-06-05T09:04:00Z | 2015-06-05T09:16:44Z | 2015-06-05T09:16:00Z | CONTRIBUTOR | null | Added description of Pull Request 9544
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} | 0 | 2015-06-05T13:12:06Z | 2015-06-24T20:59:59Z | 2015-06-24T20:59:59Z | CONTRIBUTOR | null | from [SO](http://stackoverflow.com/questions/30657115/optimize-pandas-query-on-multiple-columns-multiindex/30667394#30667394)
```
idx = pd.IndexSlice
n=10000000
np.random.seed(1234)
mdt = pandas.DataFrame()
mdt['A'] = np.random.choice(range(10000,45000,1000), n)
mdt['B'] = np.random.choice(range(10,400), n)
mdt['C'] = np.random.choice(range(1,150), n)
mdt['D'] = np.random.choice(range(10000,45000), n)
mdt['x'] = np.random.choice(range(400), n)
mdt['y'] = np.random.choice(range(25), n)
test_A = 25000
test_B = 25
test_C = 40
test_D = 35000
eps_A = 5000
eps_B = 5
eps_C = 5
eps_D = 5000
mdt2 = mdt.set_index(['A','B','C','D']).sortlevel()
```
selection
```
In [106]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B:test_B+eps_B,test_C-eps_C:test_C+eps_C,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 4.34 s per loop
```
Repeated selection
```
In [105]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A],:].loc[idx[:,test_B-eps_B:test_B+eps_B],:].loc[idx[:,:,test_C-eps_C:test_C+eps_C],:].loc[idx[:,:,:,test_D-eps_D:test_D+eps_D],:]
10 loops, best of 3: 140 ms per loop
```
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} | 5 | 2015-06-05T13:36:36Z | 2015-06-05T14:12:57Z | 2015-06-05T14:00:38Z | MEMBER | null | I get a SettingWithCopyWarning after the use of `dropna`, which does not really seem logical:
```
In [102]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':['A', 'B', 'C']})
In [103]: df
Out[103]:
a b
0 1 A
1 2 B
2 NaN C
In [104]: df = df.dropna(subset=['a'])
In [105]: df
Out[105]:
a b
0 1 A
1 2 B
In [106]: df['new_col'] = 2
C:\Anaconda\envs\devel\Scripts\ipython-script.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/s
table/indexing.html#indexing-view-versus-copy
if __name__ == '__main__':
In [107]: df.loc[:,'new_col'] = 2
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\indexing.py:404: SettingWithCop
yWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/s
table/indexing.html#indexing-view-versus-copy
self.obj[item] = s
```
Further, also the indication were the warning occurred is not correct I think.
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} | 3 | 2015-06-05T17:09:53Z | 2015-07-02T19:04:02Z | 2015-06-07T23:05:24Z | NONE | null | ``` python
from pandas import DataFrame
df = DataFrame({
'one' : pandas.Series(['foo','bar','bee'],index=['a','b','c']),
'two' : pandas.Series([1,2,3],index=['a','b','c']) } )
df.to_json(orient='index')
```
Observe that the first index value 'a' is used for all the record returned, rather than for just the first one. I've reproduced this issue on a linux machine with the config below, but it was first observed on OSX and can reproduce it on Windows, all with pandas 0.16.1.
This issue was not present in pandas version 0.14.x
> > > pandas.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-274.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.16.1
nose: None
Cython: None
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
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} | 2 | 2015-06-05T19:16:08Z | 2015-06-24T20:59:59Z | 2015-06-24T20:59:59Z | CONTRIBUTOR | null | closes #10287
master
```
In [22]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A],:].loc[idx[:,test_B-eps_B:test_B+eps_B],:].loc[idx[:,:,test_C-eps_C:test_C+eps_C],:].loc[idx[:,:,:,test_D-eps_D:test_D+eps_D],:]
10 loops, best of 3: 141 ms per loop
In [23]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B:test_B+eps_B,test_C-eps_C:test_C+eps_C,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 4.23 s per loop
```
PR
```
# this actually is a bit faster in master. but this repeated chain indexing is frowned upon anyhow
In [22]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A],:].loc[idx[:,test_B-eps_B:test_B+eps_B],:].loc[idx[:,:,test_C-eps_C:test_C+eps_C],:].loc[idx[:,:,:,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 210 ms per loop
# this is the prefered method (as you can set with this and such), and such a huge diff.
In [23]: %timeit mdt2.loc[idx[test_A-eps_A:test_A+eps_A,test_B-eps_B:test_B+eps_B,test_C-eps_C:test_C+eps_C,test_D-eps_D:test_D+eps_D],:]
1 loops, best of 3: 425 ms per loop
```
master
```
In [10]: %timeit s3.isin([1,2])
100 loops, best of 3: 8.83 ms per loop
```
PR
```
In [2]: s3 = Series(np.random.randint(1, 10, 100000)).astype('int64')
In [5]: %timeit s3.isin([1,2])
100 loops, best of 3: 2.47 ms per loop
```
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} | 12 | 2015-06-05T23:20:24Z | 2016-02-12T03:02:26Z | 2016-02-12T03:02:26Z | NONE | null | get ~~ KeyError: '**dummy**' ~~ when I run the following:
```
np.random.seed(seed = 99)
s = np.random.randint(1,10,200)
s = pd.Series(np.where(s > 9, np.nan, s))
s1 = s[:100]
s2 = s[100:]
pd.crosstab(s1, s2)
KeyError: '__dummy__
```
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} | 1 | 2015-06-05T23:31:04Z | 2015-06-08T13:20:31Z | 2015-06-07T23:02:54Z | MEMBER | null | `DatetimeIndex` preserve freq after slicing.
```
di = pd.date_range('2001', '2005', freq='D')
di[1:5:2]
# DatetimeIndex(['2001-01-02', '2001-01-04'], dtype='datetime64[ns]', freq='2D', tz=None)
```
But `TimedeltaIndex` doesn't.
```
tdi = pd.timedelta_range('1day', '5day', freq='D')
tdi[1:5:2]
# TimedeltaIndex(['2 days', '4 days'], dtype='timedelta64[ns]', freq=None)
```
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| null | 3 | 2015-06-06T00:36:29Z | 2015-06-06T13:51:49Z | 2015-06-06T13:51:35Z | NONE | null | Hello All,
I am trying to replace certain pattern with empty value in my dataframe, this my snippet,
``` python
mydfv2['Values used'].replace(to_replace= r"\[.*?\].", value="",inplace = True, regex=True)
```
This column --> "Values used" will have a list of strings or list of list of strings in each cell. Every time I run this, my python app crashes. I tried with Ipython, sublimetext2 and eclipse. Here is the error log..
Process: Python [25441]
Path: /System/Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 2.7.6 (2.7.6)
Code Type: X86-64 (Native)
Parent Process: Sublime Text 2 [25001]
Responsible: Sublime Text 2 [25001]
User ID: 502
PlugIn Path: /Library/Python/2.7/site-packages/pandas/lib.so
PlugIn Identifier: lib.so
PlugIn Version: ??? (???)
Date/Time: 2015-06-06 12:23:26.847 +1200
OS Version: Mac OS X 10.10.3 (14D136)
Report Version: 11
Anonymous UUID: 2647067B-A02E-E9D9-B3EB-45A40988D449
Sleep/Wake UUID: A3B7949F-5AF1-4FCE-8B29-A26DCE9BE8F7
Time Awake Since Boot: 650000 seconds
Time Since Wake: 13000 seconds
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_BAD_ACCESS (SIGSEGV)
Exception Codes: KERN_INVALID_ADDRESS at 0x0000000000000000
VM Regions Near 0:
-->
__TEXT 0000000100e37000-0000000100e38000 [ 4K] r-x/rwx SM=COW /System/Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 lib.so 0x0000000101ef2148 __pyx_pw_6pandas_3lib_25isnullobj + 1320
1 org.python.python 0x0000000100ec6d2c PyEval_EvalFrameEx + 15766
2 org.python.python 0x0000000100ec960e 0x100e3f000 + 566798
3 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
4 org.python.python 0x0000000100ec960e 0x100e3f000 + 566798
5 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
6 org.python.python 0x0000000100ec960e 0x100e3f000 + 566798
7 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
8 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
9 org.python.python 0x0000000100ec957d 0x100e3f000 + 566653
10 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
11 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
12 org.python.python 0x0000000100ec957d 0x100e3f000 + 566653
13 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
14 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
15 org.python.python 0x0000000100ec957d 0x100e3f000 + 566653
16 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
17 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
18 org.python.python 0x0000000100ec957d 0x100e3f000 + 566653
19 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
20 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
21 org.python.python 0x0000000100e6730a 0x100e3f000 + 164618
22 org.python.python 0x0000000100e492ac PyObject_Call + 99
23 org.python.python 0x0000000100ec59ba PyEval_EvalFrameEx + 10788
24 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
25 org.python.python 0x0000000100ec957d 0x100e3f000 + 566653
26 org.python.python 0x0000000100ec63e3 PyEval_EvalFrameEx + 13389
27 org.python.python 0x0000000100ec2d62 PyEval_EvalCodeEx + 1413
28 org.python.python 0x0000000100ec27d7 PyEval_EvalCode + 54
29 org.python.python 0x0000000100ee27bd 0x100e3f000 + 669629
30 org.python.python 0x0000000100ee2860 PyRun_FileExFlags + 133
31 org.python.python 0x0000000100ee23fd PyRun_SimpleFileExFlags + 769
32 org.python.python 0x0000000100ef3b23 Py_Main + 3051
33 libdyld.dylib 0x00007fff8d9c35c9 start + 1
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x0000000000000000 rcx: 0x0000000100fd5cf8 rdx: 0x0000000000000000
rdi: 0x0000000103402368 rsi: 0x0000000100f3f1b8 rbp: 0x00007fff5edc73f0 rsp: 0x00007fff5edc7220
r8: 0x00000000000001ff r9: 0x00007f8af29e1200 r10: 0x0000000000000002 r11: 0x00007f8af1c00000
r12: 0x0000000000000416 r13: 0x00000000000020b0 r14: 0x0000000000000416 r15: 0x0000000000000001
rip: 0x0000000101ef2148 rfl: 0x0000000000010297 cr2: 0x0000000000000000
Logical CPU: 1
Error Code: 0x00000006
Trap Number: 14
Binary Images:
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External Modification Summary:
Calls made by other processes targeting this process:
task_for_pid: 0
thread_create: 0
thread_set_state: 0
Calls made by this process:
task_for_pid: 0
thread_create: 0
thread_set_state: 0
Calls made by all processes on this machine:
task_for_pid: 289347
thread_create: 0
thread_set_state: 0
VM Region Summary:
ReadOnly portion of Libraries: Total=109.6M resident=41.3M(38%) swapped_out_or_unallocated=68.3M(62%)
Writable regions: Total=79.5M written=35.7M(45%) resident=43.7M(55%) swapped_out=0K(0%) unallocated=35.8M(45%)
REGION TYPE VIRTUAL
=========== =======
Kernel Alloc Once 4K
MALLOC 70.1M
MALLOC (admin) 16K
STACK GUARD 56.0M
Stack 8192K
VM_ALLOCATE 16K
__DATA 3676K
__LINKEDIT 73.4M
__TEXT 36.2M
__UNICODE 552K
shared memory 4K
=========== =======
TOTAL 247.8M
Model: MacBookPro11,1, BootROM MBP111.0138.B14, 2 processors, Intel Core i5, 2.6 GHz, 8 GB, SMC 2.16f68
Graphics: Intel Iris, Intel Iris, Built-In
Memory Module: BANK 0/DIMM0, 4 GB, DDR3, 1600 MHz, 0x80AD, 0x484D54343531533641465238412D50422020
Memory Module: BANK 1/DIMM0, 4 GB, DDR3, 1600 MHz, 0x80AD, 0x484D54343531533641465238412D50422020
AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0x112), Broadcom BCM43xx 1.0 (7.15.166.24.3)
Bluetooth: Version 4.3.4f4 15601, 3 services, 27 devices, 1 incoming serial ports
Network Service: Wi-Fi, AirPort, en0
Serial ATA Device: APPLE SSD SM0256F, 251 GB
USB Device: Internal Memory Card Reader
USB Device: Apple Internal Keyboard / Trackpad
USB Device: BRCM20702 Hub
USB Device: Bluetooth USB Host Controller
Thunderbolt Bus: MacBook Pro, Apple Inc., 17.2
Thanks.
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} | 11 | 2015-06-06T01:34:57Z | 2015-11-14T07:30:10Z | 2015-11-02T12:09:03Z | CONTRIBUTOR | null | New urls_as_links boolean paramater that will output urls as href html
links. ref #2679
Thanks for @tdas14 for initial code.
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} | 3 | 2015-06-06T01:55:16Z | 2015-08-08T14:38:47Z | 2015-08-08T14:38:47Z | MEMBER | null | ### DatetimeIndex / TimedeltaIndex
```
import pandas as pd
idx = pd.date_range('2011-01-01', '2011-05-01', freq='M')
# freq must be preserved, because ``DatetimeIndex`` is ordered when it has freq
idx.order()
# DatetimeIndex(['2011-01-31', '2011-02-28', '2011-03-31', '2011-04-30'], dtype='datetime64[ns]', freq=None, tz=None)
# freq must be preserved like idx[::-1]
idx.order(ascending=False)
# DatetimeIndex(['2011-04-30', '2011-03-31', '2011-02-28', '2011-01-31'], dtype='datetime64[ns]', freq=None, tz=None)
idx[::-1]
# DatetimeIndex(['2011-04-30', '2011-03-31', '2011-02-28', '2011-01-31'], dtype='datetime64[ns]', freq='-1M', tz=None)
```
Internally, `order` may use `take` when `return_indexer` is `True` and should be fixed also.
### PeriodIndex
Results in `TypeError` because it doesn't pass `freq` ( `PeriodIndex` without freq is meaningless ). Required to fix #7832 first to use the same flow as `DatetimeIndex`, I think.
```
idx = pd.period_range('2011-01-01', '2011-05-01', freq='M')
idx.order()
# TypeError: expected string or buffer
idx.order().freq is None
# True
```
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| null | 6 | 2015-06-06T04:41:00Z | 2015-11-12T07:52:30Z | 2015-11-10T01:26:51Z | CONTRIBUTOR | null | ref #3335.
Adds margin_column parameter to pivot_table so that user can set it to
something other than 'All'.
Raises ValueError exception if there is a conflict between the value of
margin_column and one of the other values appearing in the indices of
the pivot table.
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| null | 3 | 2015-06-06T05:04:42Z | 2015-06-06T13:56:07Z | 2015-06-06T13:55:58Z | NONE | null | IMHO, two date functions from `SAS` can be very useful and straight forward. I wonder if they can be incorporated in Pandas.
### [`intck()`](http://support.sas.com/documentation/cdl/en/lefunctionsref/63354/HTML/default/viewer.htm#p1md4mx2crzfaqn14va8kt7qvfhr.htm)
This function computes the difference between 2 dates/datetime with specified units. For example
Assume: `d1=1992-03-20` and `d2=1992-06-12`. And these are all of `date` type in SAS.
`intck('month', d1, d2)` yields 3 (regardless of what day it is). `intck('qtr', d1, d2)` yields 1. `intck('year', d1, d2)` yields 0.
### [ `intnx()`](http://support.sas.com/documentation/cdl/en/lefunctionsref/63354/HTML/default/viewer.htm#p10v3sa3i4kfxfn1sovhi5xzxh8n.htm)
This function increments a given date by an interval. Assuming `d1=1992-03-20`, `intnx('month', d1, 3, 'end')` yields 1992-06-30 (increase month by 3 and return the last day of that month. `intnx('month', d1, 3, 'same')` yields 1992-06-20 (increase month by 3 and return the same day of that month.
---
As you can see, these two functions are very flexible and simple. The whole `offset` system seems to be a little too cumbersome comparing to these 2.
---
See this [question](http://stackoverflow.com/questions/23840797/convert-a-column-of-timestamps-into-periods-in-pandas) too. Especially the solution offered by @hayd
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} | 8 | 2015-06-06T05:29:13Z | 2015-06-09T11:54:08Z | 2015-06-09T11:54:08Z | CONTRIBUTOR | null | closes #10214
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} | 1 | 2015-06-06T05:47:58Z | 2015-10-15T14:20:58Z | 2015-10-15T14:20:58Z | CONTRIBUTOR | null | I was going to put this on SO but I think it might belong here instead:
I have a pandas DataFrame where I want to plot each column as a line with the same color. This might sound useless but I am showing groups of lines from certain selections of the DataFrame. The only problem is it appears that the `DataFrame.plot` method does not accept any matplotlib hex strings or named colors in the color kwarg.
A trivial example:
```
pd.DataFrame([range(10),range(10)]).plot(color='#008000')
```
gives a blank subplot with no errors, however,
```
pd.DataFrame([range(10),range(10)]).plot(color='g')
```
gives the expected result. To me this seems like a bug considering that `color='g'` works but not its hex string equivalent. The same issue occurs when trying to use a named matplotlib color like `color='lightgray'`. This does however work when using `Series.plot` or `DataFrame.plot` with one column or if a list of hex strings or named colors are given with length equal to the number of columns as in,
```
pd.DataFrame([range(10),range(10)]).plot(color=['#008000']*2)
```
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} | 3 | 2015-06-06T14:45:10Z | 2015-06-08T12:04:03Z | 2015-06-07T22:24:16Z | CONTRIBUTOR | null | closes #7828
Is this all we need?
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} | 1 | 2015-06-06T16:57:35Z | 2015-09-19T00:38:30Z | 2015-06-07T23:06:15Z | CONTRIBUTOR | null | Fixes GH #10184.
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