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} | 42 | 2013-10-31T20:53:13Z | 2020-11-26T00:31:47Z | 2020-11-26T00:31:47Z | CONTRIBUTOR | null | related here: https://github.com/pydata/pandas/issues/7203
If I haven't missed something, there's no simple way to make [cartesian product](http://pandas.pydata.org/pandas-docs/stable/merging.html#brief-primer-on-merge-methods-relational-algebra) method in pandas. For example, see http://stackoverflow.com/questions/19684765/pandas-extending-the-dataframe-by-adding-another-levels/19684948#19684948.
To do cross join, one have to add some keys to DataFrames, like this:
```
>>> dfA = pd.DataFrame(['a','b','c'],columns=['A'])
>>> dfB = pd.DataFrame(range(3),columns=['B'])
>>> dfA['key'] = 1
>>> dfB['key'] = 1
>>> pd.merge(dfB, dfA, on='key').ix[:, ('A','B')]
A B
0 a 0
1 b 0
2 c 0
3 a 1
4 b 1
5 c 1
6 a 2
7 b 2
8 c 2
```
How about adding possibility to pass how='cross' into merge() and join() methods?
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} | 8 | 2013-10-31T21:41:07Z | 2014-01-15T16:12:41Z | 2014-01-15T16:12:41Z | CONTRIBUTOR | null | Should this be doable?
``` python
In [39]: df = pd.DataFrame({'A': [1, 2], 'B': pd.to_datetime(['a', 'b'])},
index=pd.MultiIndex.from_tuples([(1, 'one'), (1, 'two')]))
In [40]: df
Out[40]:
A B
1 one 1 a
two 2 b
In [41]: wp = pd.Panel({'i1': df, 'i2': df})
In [42]: wp.to_frame()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-42-e49d9f2f9609> in <module>()
----> 1 wp.to_frame()
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/core/panel.pyc in to_frame(self, filter_observations)
846 index = MultiIndex(levels=[self.major_axis, self.minor_axis],
847 labels=[major_labels, minor_labels],
--> 848 names=[maj_name, min_name], verify_integrity=False)
849
850 return DataFrame(data, index=index, columns=self.items)
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.pyc in __new__(cls, levels, labels, sortorder, names, copy, verify_integrity)
1880 if names is not None:
1881 # handles name validation
-> 1882 subarr._set_names(names)
1883
1884 if sortorder is not None:
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.pyc in _set_names(self, values, validate)
2150 # set the name
2151 for name, level in zip(values, self.levels):
-> 2152 level.rename(name, inplace=True)
2153
2154 names = property(
/Users/tom/Envs/pandas-dev/lib/python2.7/site-packages/pandas-0.12.0_993_gda89834-py2.7-macosx-10.8-x86_64.egg/pandas/core/index.pyc in set_names(self, names, inplace)
333 """
334 if not com.is_list_like(names):
--> 335 raise TypeError("Must pass list-like as `names`.")
336 if inplace:
337 idx = self
TypeError: Must pass list-like as `names`.
```
I think the issue comes when the index of the lower dimensional DataFrame (`df` in this case) is already a MultiIndex. These two work:
``` python
In [45]: wp.transpose(1, 0, 2).to_frame()
Out[45]:
1
one two
major minor
i1 A 1 2
B a b
i2 A 1 2
B a b
In [46]: wp.transpose(1, 2, 0).to_frame()
Out[46]:
1
one two
major minor
A i1 1 2
i2 1 2
B i1 a b
i2 a b
```
I was expecting that `wp.to_frame()` would create a new MultiIndex with 3 levels:
``` python
In [63]: df = pd.DataFrame({'A': [1, 2, 1, 2], 'B': pd.to_datetime(['a', 'b', 'a', 'b'])},
index=pd.MultiIndex.from_tuples([('i1', 1, 'one'), ('i1', 1, 'two'), ('i2', 1, 'one'), ('i2', 1, 'two')]))
In [64]: df
Out[64]:
A B
i1 1 one 1 a
two 2 b
i2 1 one 1 a
two 2 b
```
The ordering of the new MultiIndex (with `wp.items` inserted) is ambiguous... But something like that. You could always swaplevels later.
(side note to myself: check on if `verify_integrity` is `validate` in MultiIndex land. It doesn't get passed to `_set_names`).
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} | [] | closed | false | null | [] | null | 0 | 2013-10-31T22:20:02Z | 2014-06-30T12:55:30Z | 2013-11-01T00:21:36Z | CONTRIBUTOR | null | Only `__new__` is really necessary. And subclasses just have to deal with
being passed an 'engine' keyword argument + a tad bit of style cleanup.
Thanks to @jseabold and @josef-pkt for making me realize that this was
unnecessary in https://github.com/statsmodels/statsmodels/issues/1167
:)
@jmcnamara - this has completely trivial impact on ExcelWriter
subclasses, just removes some magic.
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} | 11 | 2013-10-31T22:22:25Z | 2014-01-01T02:43:06Z | 2014-01-01T02:43:06Z | CONTRIBUTOR | null | I was looking into expanding the pandas.io.data functionality to read options data from Google Finance. After reading
http://pandas.pydata.org/pandas-docs/stable/gotchas.html#html-gotchas
I tried various combinations for parsing
http://www.google.com/finance/option_chain?q=GOOG
without success. The page formats itself using javascript, so it has to be "executed" in a browser.
[selenium](http://seleniumhq.org/)/[phantomjs](http://phantomjs.org/) seems to allow to process the page:
```
$ sudo aptitude install phantomjs
$ pip install selenium
$ ipython
In [1]: from selenium import webdriver
In [2]: browser=webdriver.PhantomJS()
In [3]: browser.get('http://www.google.com/finance/option_chain?q=IBM')
In [4]: exp=browser.find_element_by_id('expirations')
In [5]: exp.find_elements_by_tag_name('option')[2].text
```
Can they be considered for inclusion as parsing dependency?
Using phantomjs might also help with other HTML parsing issues experienced when using bs4/lxml/html5lib.
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This PR fixes those issues and add some more tests.
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} | 3 | 2013-11-01T06:46:39Z | 2014-06-19T04:37:08Z | 2014-06-19T04:37:08Z | CONTRIBUTOR | null | The computational tool `rolling_corr` in `pandas.stats.moments` has the very useful `center=True` parameter.
Can we have it for `rolling_corr_pairwise` as well?
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} | 11 | 2013-11-01T21:00:49Z | 2014-06-19T13:22:40Z | 2014-01-21T11:46:04Z | CONTRIBUTOR | null | ``` python
Python 2.7.5 (default, May 15 2013, 22:44:16) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Imported NumPy 1.7.1, SciPy 0.12.0, Matplotlib 1.3.0 + guidata 1.6.1, guiqwt 2.3.1
Type "scientific" for more details.
>>> from csv import QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONNUMERIC, QUOTE_NONE
>>> QUOTE_MINIMAL
0
>>> QUOTE_ALL
1
>>> QUOTE_NONNUMERIC
2
>>> QUOTE_NONE
3
>>>
```
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} | 1 | 2013-11-01T21:18:25Z | 2013-11-01T22:13:32Z | 2013-11-01T22:13:32Z | CONTRIBUTOR | null | This started after I merged #5375 (which passed cleanly before merging)
https://travis-ci.org/pydata/pandas/jobs/13376953
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} | 7 | 2013-11-01T22:12:36Z | 2013-11-04T21:19:02Z | 2013-11-04T21:19:02Z | CONTRIBUTOR | null | Currently
```
In [10]: pd.to_timedelta(Series(['00:03:37', '00:05:05'])) - pd.to_timedelta('00:03:00')
Out[10]:
0 00:00:37
1 NaT
dtype: timedelta64[ns]
```
because
```
In [13]: pd.to_timedelta('00:03:00')
Out[13]:
0 00:03:00
dtype: timedelta64[ns]
```
More expected and consistent behavior, in my opinion, would be
```
In [12]: pd.to_timedelta(Series(['00:03:37', '00:05:05'])) - pd.to_timedelta('00:03:00')
Out[12]:
0 00:00:37
1 00:02:05
dtype: timedelta64[ns]
```
If, instead, `pd.to_timedelta('00:03:00')` gave `numpy.timedelta64(180000000000,'ns')`, it would broadcast properly, as demonstrated by this work-around
```
In [12]: pd.to_timedelta(Series(['00:03:37', '00:05:05'])) - pd.to_timedelta('00:03:00')[0]
Out[12]:
0 00:00:37
1 00:02:05
dtype: timedelta64[ns]
```
I have not regularly used timedeltas until now. Am I misjudging the expected usage?
**Update**, OK, one can also get the expected result by setting `box=False`. But `box=False` should be default for a scalar, no?
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} | 0 | 2013-11-01T22:13:26Z | 2014-07-16T08:38:35Z | 2013-11-01T22:13:32Z | CONTRIBUTOR | null | close #5409
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} | 7 | 2013-11-01T22:26:35Z | 2013-11-02T23:54:38Z | 2013-11-02T23:54:38Z | CONTRIBUTOR | null | Numpy 1.8 was released recently - https://github.com/numpy/numpy/blob/master/doc/release/1.8.0-notes.rst
We should probably support it and add/change a Travis build for it. Additionally, might be interesting to benchmark numpy's nanmean, nanvar and nanstd against pandas versions...
numpy also notes that they are planning to make more datetime changes in 1.9 - whee!
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still pretty good coverage of all numpys
build take a tad longer w/o the wheels
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Maybe write tests for this? That much is over my head currently.
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} | 19 | 2013-11-02T19:37:15Z | 2014-06-14T22:38:45Z | 2014-01-15T16:12:41Z | CONTRIBUTOR | null | Closes #5402 WIP for now.
So this is a hack to get things "working".
I mainly just wanted to ask if there was a better way to get the levels and labels to the MultiIndex constructor.
Don't spend long thinking about it (this is my PR after all), but if you know of a quick way off the top of your head, I'd be interested.
I essentially need to
1. flatten the MultiIndex from major axis (levels and labels)
2. combine that flattened index with the minor axis (levels and labels)
Step 2 could be complicated by `minor_axis` being a MultiIndex also, I should be able to refactor this pretty easily to handle that. I think my was is making several copies of each index, and I'm not sure that they're all necessary.
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} | 1 | 2013-11-02T23:51:00Z | 2015-07-14T15:52:23Z | 2015-07-14T15:52:23Z | CONTRIBUTOR | null | I will fix as part of #5148
For example:
```
parse_time_string('2013Q1', freq="INVLD-L-DEC-SAT")
```
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} | 29 | 2013-11-03T00:50:08Z | 2014-06-26T00:27:11Z | 2013-11-03T23:42:17Z | CONTRIBUTOR | null | If you really need to, possible to opt-out with make_tempfile=False.
Something weird with HDF where, even though it's _supposed_ to be
removing the file every time, it causes issues to use a real temp
file... Not sure why (maybe I missed something there).
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} | 16 | 2013-11-03T01:42:11Z | 2014-06-19T00:29:52Z | 2014-06-19T00:29:52Z | CONTRIBUTOR | null | https://groups.google.com/forum/m/#!topic/pydata/W0e3l0UvNwI
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} | 3 | 2013-11-03T15:36:11Z | 2018-07-06T23:02:56Z | 2018-07-06T23:02:41Z | CONTRIBUTOR | null | related #5420
ML issue: https://groups.google.com/forum/#!topic/pydata/Ke8kntiPHqw
existing tests here (and tests/test_frame.py, test_multilevel.py)
https://github.com/pydata/pandas/blob/master/pandas/tests/test_indexing.py
- create new top-level advanced indexing tab (move hierarchical and sections below here)
- add an indexer matrix (generated from a function), which can document a cross of the indexers with the index types and accessors, e.g. something like this (the values should be filled in with maybe a code of somesort, e.g. `Y` for supported, `NI` for not-implemented, `nan` for not-applicable, `KeyError/IndexError` if raises
```
In [31]: from itertools import product
In [35]: index = MultiIndex.from_tuples([ (typ,indexer) for typ,indexer in product(['MultiIndex','DateTimeIndex','FloatIndex','Int64Index','mixed_labels'],['iloc','loc','ix','[]','xs']) ])
In [36]: columns = ['scalar_label_in_bounds','oob_scalar_label','scalar_position','oob_scalar_position','list_of_positions','list_of_positions_some_oob','list_of_labels','list_of_labels_some_oob','slice_of_positions','slice_of_labels','boolean_indexer']
In [37]: DataFrame(np.nan,index=index,columns=columns)
Out[37]:
scalar_label_in_bounds oob_scalar_label scalar_position oob_scalar_position list_of_positions list_of_positions_some_oob list_of_labels list_of_labels_some_oob slice_of_positions slice_of_labels boolean_indexer
MultiIndex iloc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
loc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ix NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
xs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
DateTimeIndex iloc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
loc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ix NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
xs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
FloatIndex iloc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
loc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ix NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
xs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Int64Index iloc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
loc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ix NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
xs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
mixed_labels iloc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
loc NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ix NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
[] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
xs NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
```
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} | 4 | 2013-11-03T17:26:01Z | 2014-07-16T08:38:46Z | 2013-11-03T17:44:08Z | CONTRIBUTOR | null | TST/BUG: incorrect way of testing for r+ modes
related #5419
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} | 31 | 2013-11-03T20:42:24Z | 2014-06-16T22:40:15Z | 2013-11-06T01:42:23Z | CONTRIBUTOR | null | Allow optional formatting of MultiIndex and Hierarchical Rows
as merged cells. closes #5254.
Some notes on this PR:
- The required xlsxwriter version was up-revved in `ci/requirements*.txt`to pick up a fix in that module that makes working with charts from pandas easier.
- Added a comment to `release.rst`.
- Modified `format.py` to format MultiIndex and Hierarchical Rows as merged cells in Excel. The old code path/option is still available. The new formatting must be explicitly invoked via the `merge_cells` option in `to_excel`:
``` python
df.to_excel('file.xlsx', merge_cells=True)
```
- The `merge_cells` option can be renamed if required. During development I also used `multi_index` and `merge_range`.
- Updated the API and docs in `frame.py` to reflect the new option.
- Fixed openpyxl merge handling in `excel.py`.
- Modified the `test_excel.py` test cases so that they could be used to test the existing `dot notation` or the new `merge_cells` options for MultiIndex and Hierarchical Rows handling.
- Did some minor PEP8 formatting to the `test_excel.py`.
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} | 3 | 2013-11-03T20:58:31Z | 2013-11-04T00:40:53Z | 2013-11-04T00:40:53Z | CONTRIBUTOR | null | These two codeblocks result in different dataframes:
``` python
import pandas as pd
import numpy as np
cont = ['one', 'two','three', 'four', 'five', 'six', 'seven']
dfb = pd.DataFrame({'a' : cont, "b":cont[3:]+cont[:3] ,'c' : np.arange(7)})
#print dfb.ix[0,"c"]
dfb.ix[7,'c'] = 1
print dfb
print dfb.ix[7,"c"]
print dfb.ix[0,"c"]
a b c
0 one four 0
1 two five 1
2 three six 2
3 four seven 3
4 five one 4
5 six two 5
6 seven three 6
7 NaN NaN 1
1.0
0.0
```
and
``` python
import pandas as pd
import numpy as np
cont = ['one', 'two','three', 'four', 'five', 'six', 'seven']
dfb = pd.DataFrame({'a' : cont, "b":cont[3:]+cont[:3] ,'c' : np.arange(7)})
print dfb.ix[0,"c"]
dfb.ix[7,'c'] = 1
print dfb
print dfb.ix[7,"c"]
print dfb.ix[0,"c"]
0
a b c
0 one four 0
1 two five 1
2 three six 2
3 four seven 3
4 five one 4
5 six two 5
6 seven three 6
7 NaN NaN NaN
nan
0.0
```
pd.**version** = '0.12.0-922-gac1609e'
In the first case I also don't understand why the last column is changed to a float when I set an int.
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} | 5 | 2013-11-03T23:42:01Z | 2014-07-16T08:38:56Z | 2013-11-04T01:10:47Z | CONTRIBUTOR | null | related #5419
Includes @jreback's commits from #5422 and hdf_temp:
- TST: make pytables tests go thru a temporary dir and file
- TST/BUG: incorrect way of testing for r+ modes
TST: fix temporary files by using mktemp (rather than mkstemp) which opens them
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} | 0 | 2013-11-04T00:13:08Z | 2014-06-23T22:17:11Z | 2013-11-04T00:40:53Z | CONTRIBUTOR | null | closes #5424
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} | 1 | 2013-11-04T01:11:18Z | 2013-11-06T22:16:21Z | 2013-11-06T22:16:21Z | CONTRIBUTOR | null | This is an issue that I introduced when fixing #5235.
The `cols` option in `to_excel` no longer works correctly after the fix for the above issue. For example:
``` python
import pandas as pd
df = pd.DataFrame({'A': ['a', 'a', 'a'],
'B': ['b', 'b', 'b']})
df.to_excel('frame.xlsx', sheet_name='Sheet1', cols=['B', 'A'])
```
Gives:
Note, the headers are changed but not the column data.
I have a proposed fix and test for this. Should I create a new branch/PR or merge it into the Excel MultiIndex PR: #5423
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} | 6 | 2013-11-04T01:11:37Z | 2018-07-06T20:21:02Z | 2017-07-10T21:09:21Z | CONTRIBUTOR | null | related #4889
Currently the to_json() method only works for Series and DataFrame, and raises NotImplementedError when called on Panel. Would be great to implement it for Panel as well.
For me it is sufficient to loop through all items and convert each item (DataFrame) to json separately and then stitch them together.
Happy to contribute code if needed.
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} | 18 | 2013-11-04T01:38:29Z | 2014-07-02T22:44:20Z | 2013-11-06T22:16:21Z | CONTRIBUTOR | null | closes #5427
Notes on this PR:
- This fix addresses an issue introduced in #5235 (Issue with Excel writers when column names are duplicated).
- Basically it needs to handle 2 different use cases:
- The user uses duplicate column names
- The user specifies a different order via the `cols` option
``` python
# Case 1
df = pd.DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
df.columns = ['A', 'B', 'B']
```
``` python
# Case 2
df = pd.DataFrame({'A': ['a', 'a', 'a'],
'B': ['b', 'b', 'b']})
df.to_excel('frame.xlsx', sheet_name='Sheet1', cols=['B', 'A'])
```
The proposed solution is to iterate through self.columns in the default or user supplied order. If a duplicate column name is encountered (i.e. if `df['B']` returns more than one series) then we select the first series and keep track of that index. If the duplicate column name is encountered again then we select the next available series from `df['B']` up to the last series available. If the duplicate name is encountered again then we return the last series again.
The patch is slightly kludgy. I didn't know how to check if `df[col]` contained more than one series so I used `len(self.df[col_name].columns)` in a `try:catch`. Hopefully there is something cleaner.
The change is repeated twice in the code.
There is a new test for this issue and an existing test for the previous issue.
I don't think this needs a release note since it is a fix for an issue that was never released.
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} | 23 | 2013-11-04T02:07:13Z | 2013-11-06T01:07:39Z | 2013-11-06T00:23:40Z | CONTRIBUTOR | null | I wrote a test case that consistently crashes the entire process. It looks like it requires a Series with data localized to a timezone that has a DST and the data crosses the DST boundary. Finally, you have to use `kind='period'`for the `resample()` operation. Oddly, it's not just the actual boundary, because I can create a smaller dataset, and it resamples fine (included in the test case with the `_works` suffix.
With that combination, the code crashes the entire process with a glibc error.
### Crashing Test Case
https://gist.github.com/kevinastone/7297033
```
>>> series.resample('D', how='sum', kind='period')
*** glibc detected *** ${VENV_DIR}/bin/python: double free or corruption (!prev): 0x00007fd6845cbb60 ***
======= Backtrace: =========
/lib/x86_64-linux-gnu/libc.so.6(+0x7eb96)[0x7fd693178b96]
${VENV_DIR}/local/lib/python2.7/site-packages/numpy/core/multiarray.so(+0x5f7a6)[0x7fd68f0f67a6]
${VENV_DIR}/local/lib/python2.7/site-packages/numpy/core/multiarray.so(+0xc4026)[0x7fd68f15b026]
${VENV_DIR}/local/lib/python2.7/site-packages/pandas/algos.so(+0x1184b2)[0x7fd689b004b2]
${VENV_DIR}/local/lib/python2.7/site-packages/pandas/algos.so(+0x11933c)[0x7fd689b0133c]
...
```
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https://pypi.python.org/pypi/xlrd/0.9.2
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} | 3 | 2013-11-04T20:11:40Z | 2014-07-16T08:39:13Z | 2013-11-06T00:23:40Z | CONTRIBUTOR | null | There's a failing test case and the patch in subsequent commits.
closes #5430
closes #3609
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} | 14 | 2013-11-04T20:37:14Z | 2014-01-31T20:43:11Z | 2014-01-17T13:37:15Z | NONE | null | This seems to be a very common issue with Python projects (Numpy has the same issue).
If you try to download this:
http://pandas.pydata.org/pandas-docs/stable/pandas.pdf
You'll get the documentation for version 0.11.0
Check the links for the pdf documentation at the main web page here:
http://pandas.pydata.org/
The same problem...
Here:
http://pandas.pydata.org/pandas-docs/stable/
Again...
This might be a detail (maybe docs don't change too much), but I expect the pandas project to be of high quality!
Besides, there are a lot of people that don't have an internet connection available all the time (lots of my students don't). Therefore, being able to download a PDF to read it offline is good. Also some people prefer to read PDFs instead of a long web page...
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} | 0 | 2013-11-04T22:01:12Z | 2014-07-11T22:41:28Z | 2013-11-04T22:49:49Z | CONTRIBUTOR | null | noticed in #5429
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} | 1 | 2013-11-04T22:46:06Z | 2014-01-28T02:11:13Z | 2014-01-28T01:05:02Z | CONTRIBUTOR | null | ExcelFile the class isn't actually in the API anywhere. We should rectify that. Also, I realized from [this SO question on ExcelFile](http://stackoverflow.com/questions/19760741/how-do-you-read-excel-sheets-using-index-instead-of-name-with-pandas-read-excel/19778206) that the release notes aren't explicitly clear that `ExcelFile` is just _moved_ not deprecated.
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} | 0 | 2013-11-04T23:52:29Z | 2014-11-26T02:30:02Z | 2014-11-26T02:30:02Z | CONTRIBUTOR | null | have to return timedelta64[ns] scalars
```
type: timedelta64[ns]
In [23]: s = pd.to_timedelta(np.arange(5),unit='d')
In [24]: s
Out[24]:
0 00:00:00
1 1 days, 00:00:00
2 2 days, 00:00:00
3 3 days, 00:00:00
4 4 days, 00:00:00
dtype: timedelta64[ns]
In [25]: s + timedelta(hours=1)
Out[25]:
0 01:00:00
1 1 days, 01:00:00
2 2 days, 01:00:00
3 3 days, 01:00:00
4 4 days, 01:00:00
dtype: timedelta64[ns]
In [26]: s + pd.offsets.Hour(1)
Out[26]:
0 01:00:00
1 1 days, 01:00:00
2 2 days, 01:00:00
3 3 days, 01:00:00
4 4 days, 01:00:00
dtype: timedelta64[ns]
In [27]: s.iloc[2] + pd.offsets.Hour(1)
TypeError: ufunc add cannot use operands with types dtype('<m8[ns]') and dtype('O')
In [29]: s.iloc[2] + timedelta(hours=1)
TypeError: ufunc add cannot use operands with types dtype('<m8[ns]') and dtype('O')
```
This works (wrapping in a np.timedeltat64
```
In [31]: s.iloc[2] += np.timedelta64(timedelta(hours=1))
In [32]: s
Out[32]:
0 00:00:00
1 1 days, 00:00:00
2 2 days, 01:00:00
3 3 days, 00:00:00
4 4 days, 00:00:00
dtype: timedelta64[ns]
```
Reevaluate whether summary ops should just return the np.timdelta64 scalar
(not as pretty though)
```
In [33]: s.mean()
Out[33]:
0 2 days, 00:12:00
dtype: timedelta64[ns]
In [34]: s.mean().iloc[0]
Out[34]: numpy.timedelta64(173520000000000,'ns')
```
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} | 2 | 2013-11-05T01:28:24Z | 2013-11-06T23:51:20Z | 2013-11-06T23:51:20Z | CONTRIBUTOR | null | ```
In [1]: Series(['2011-01-01', np.nan]).apply(pd.to_datetime)
Out[1]:
0 2011-01-01 00:00:00
1 NaT
dtype: datetime64[ns]
In [2]: Series(['00:00:01', np.nan]).apply(pd.to_timedelta)
AssertionError: Invalid type for timedelta scalar: <type 'float'>
```
May not be a trivial fix. So far I have seen that inserting np.nan and pd.tslib.iNaT into the result turns the dtype to object, which is no good.
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} | 20 | 2013-11-05T03:27:15Z | 2014-06-24T21:15:32Z | 2013-11-06T23:51:20Z | CONTRIBUTOR | null | closes #5437
**Updated**: Mostly following @jreback's suggestion, but we need `np.timedelta64('NaT')`, not `pd.tslib.iNaT`, because numpy cannot cast `np.datetime64('NaT')` as `np.timedelta64` type, and throws an `AssertionError`.
```
In [1]: pd.to_timedelta(Series(['00:00:01', '00:00:02']))
Out[1]:
0 00:00:01
1 00:00:02
dtype: timedelta64[ns]
In [2]: pd.to_timedelta(Series(['00:00:01', np.nan]))
Out[2]:
0 00:00:01
1 NaT
dtype: timedelta64[ns]
In [3]: pd.to_timedelta(Series(['00:00:01', pd.NaT]))
Out[3]:
0 00:00:01
1 NaT
dtype: timedelta64[ns]
```
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} | 6 | 2013-11-05T11:46:16Z | 2014-06-23T22:30:35Z | 2013-11-05T22:53:10Z | CONTRIBUTOR | null | Close #5356.
Note: this version actually forces `a / b` to always do truediv as well. It's
less complicated to maintain if we do it that way, but it's also possible to
have this `a / b` use div and `a.div(b)` use truediv, just might be strange.
This is definitely different from numpy behavior.
We also may need to add the division future import to more places.
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} | 3 | 2013-11-05T12:31:45Z | 2021-04-11T03:49:51Z | null | NONE | null | Hi,
Coming back to issue #2665, I'm still not convinced that the closed-argument of the resampling function does what it should.
When resampling daily to monthly values with closed = 'left', the closed argument should relate to the actual time the months change, but instead it seems to refer to the time 24 hours earlier.
```
In [25]: import pandas as pd
...: import numpy as np
...: dates = pd.date_range('3/29/2000', '4/3/2000', freq='1D')
In [27]: ts1 = pd.Series(np.ones(len(dates)), index=dates)
In [28]: ts2 = ts1.resample('1M', closed='left',label='left').sum()
In [29]: ts3 = ts1.resample('1M').sum()
In [30]: ts1
Out[30]:
2000-03-29 1.0
2000-03-30 1.0
2000-03-31 1.0
2000-04-01 1.0
2000-04-02 1.0
2000-04-03 1.0
Freq: D, dtype: float64
# resampling and asking that the first sample of the month (midnight April 1st) is part of that month does not result in what you think. Instead pandas does not consider 01/04/2000 00:00 as the time the months change, but it thinks the months change at 31/03/2000 00:00 (one day too early)
In [31]: ts2
Out[31]:
2000-02-29 2.0
2000-03-31 4.0
Freq: M, dtype: float64
# keeping it simple is a possible solution but it would be better to have the closed-argument working correctly ...
In [32]: ts3
Out[32]:
2000-03-31 3.0
2000-04-30 3.0
Freq: M, dtype: float64
```
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} | 6 | 2013-11-05T14:45:22Z | 2013-11-06T15:22:59Z | 2013-11-06T13:59:15Z | CONTRIBUTOR | null | I realized when profiling a slow select (200% more wall-time as direct pytables call and high memory usage) that most of the time is spend inside bytes.decode called by [_unconvert_strings_array](https://github.com/pydata/pandas/blob/master/pandas/io/pytables.py#L3966), even when selecting only int64 columns. It seems spend time and memory to decode string that are never returned.
I'm using python 3.3 and latest pandas (commit 2d2e8b5146024a90001f96862cdd0172adb4d1b8).
I gladly get back with more details if needed.
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} | 8 | 2013-11-05T16:16:29Z | 2016-05-04T13:04:00Z | 2016-05-04T13:04:00Z | NONE | null | In our project Zipline, https://github.com/quantopian/zipline/, we consume time series data one bar at a time. We provide support for calculations that are performed over a fixed length, e.g. over the last 15 days of data. The data which those calculations are performed over are updated with the most recent bar as it comes in.
Instead of creating a fresh Panel on each bar and reindexing all of the data into the underlying data storage, @wesm helped us with a RollingPanel object, https://github.com/quantopian/zipline/blob/2492feb938c21f7577e6c05fa8cd7bfd57863d25/zipline/utils/data.py
The RollingPanel is working well for our purposes, and we'd like to upstream it to make the behavior available to all users of **pandas**.
There are a couple idionsyncratic to our codebase parameters that would need to be generalized.
e.g.:
- the use `sids` would need to be a more general `minor_axis` parameter.
- the `dtype` of the index buffer would also need to be more general, where the version in Zipline is tuned/hard code for being a `pd.DatetimeIndex` in UTC.
@wesm mentioned that instead of a rolling version `pd.Panel` it may make more sense to start a rolling version of a `NDFrame`.
(And then I assume have a `RollingPanel`, `RollingSeries`, etc. that use `RollingNDFrame` instead of `NDFrame`, though that level of the API design I am not sure I am qualified to speak to as of yet.)
attn: @jreback, I believe this is under your purview?
If the idea passes muster, what would be a good next step for getting the rolling behavior into pandas?
(Or has there been work on a `RollingNDFrame` started already, that I could help pitch in on?)
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} | 0 | 2013-11-05T19:07:26Z | 2014-01-23T03:55:50Z | 2014-01-06T23:44:29Z | CONTRIBUTOR | null | ```
In [1]: pd.isnull(np.array([pd.NaT]))
Out[1]: array([False], dtype=bool)
```
this should be true
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https://api.github.com/repos/pandas-dev/pandas/issues/5444 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5444/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5444/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5444/events | https://github.com/pandas-dev/pandas/issues/5444 | 22,153,673 | MDU6SXNzdWUyMjE1MzY3Mw== | 5,444 | Panel4D can not be saved to hdf using the .to_hdf call | {
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import pandas as pd, numpy
print pd.__version__
with pd.get_store('test.h5') as store:
df = pd.Panel4D(data=numpy.random.randn(2,3,4,5))
store.append('test',df)
print store
df.to_hdf('test2.h5','test')
```
Output:
```
0.12.0
<class 'pandas.io.pytables.HDFStore'>
File path: test.h5
/test wide_table (typ->appendable,nrows->240,ncols->2,indexers->[items,major_axis,minor_axis])
Traceback (most recent call last):
File "pandas_hdf5_bug.py", line 10, in <module>
df.to_hdf('test2.h5','test')
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/core/generic.py", line 521, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/io/pytables.py", line 194, in to_hdf
f(store)
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/io/pytables.py", line 190, in <lambda>
f = lambda store: store.put(key, value, **kwargs)
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/io/pytables.py", line 585, in put
self._write_to_group(key, value, table=table, append=append, **kwargs)
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/io/pytables.py", line 909, in _write_to_group
encoding=encoding, **kwargs)
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/io/pytables.py", line 846, in _create_storer
error('_STORER_MAP')
File "/Users/kghose/Library/Python/2.7/lib/python/site-packages/pandas/io/pytables.py", line 815, in error
(t,group,type(value),table,append,kwargs))
TypeError: cannot properly create the storer for: [_STORER_MAP] [group->/test (Group) '',value-><class 'pandas.core.panelnd.Panel4D'>,table->None,append->False,kwargs->{'encoding': None}]
```
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} | 3 | 2013-11-05T22:57:26Z | 2017-06-01T20:13:30Z | 2017-06-01T20:13:30Z | CONTRIBUTOR | null | Now that all division on NDFrame is truediv, we should add the division future import to most/all files (alongside print) to avoid any inconsistencies. At least on the pure Python parts. Nor sure about Cython.
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} | [] | closed | false | null | [] | null | 1 | 2013-11-06T02:34:52Z | 2014-07-16T08:39:26Z | 2013-11-07T03:23:10Z | CONTRIBUTOR | null | Since xlsxwriter has a smaller install base (because it's newer),
reasonable to assume that if you have it installed, you want xlsxwriter.
Plus, it has better performance characteristics in general (esp. in
terms of memory usage)
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And in the same time, fixes a build error in the newly added docs on `truediv` in the release notes.
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} | 0 | 2013-11-06T12:39:07Z | 2014-06-16T19:05:47Z | 2013-11-06T13:59:15Z | CONTRIBUTOR | null | closes #5441
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pretty sure this is fixed with xlsxwriter...maybe need a warning with other writers?
http://stackoverflow.com/questions/19812934/pandas-dataframe-concat-and-to-excel-output
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} | 1 | 2013-11-06T14:05:13Z | 2014-07-09T18:47:42Z | 2013-11-06T15:31:36Z | MEMBER | null | Further fixes #5331 with @jtratner 's suggestion (https://github.com/pydata/pandas/issues/5142#issuecomment-27871973).
This removes the docstring of all attributes with the name `flags` during the doc building process.
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} | 17 | 2013-11-06T14:26:25Z | 2016-07-27T11:06:51Z | 2016-07-27T11:06:51Z | CONTRIBUTOR | null | See SO question: http://stackoverflow.com/questions/19802708/add-column-to-pandas-dataframe-not-in-place
I would suggest something like:
```
df_new = df.alter(new_col_name=existing_series)
```
where alter would wake one or more columns:
```
df_new = df.alter(
new_col_name1=existing_series1,
new_col_name2=existing_series2,
)
```
This can further be a superset of `drop` allowing columns to be removed by setting them to `None`
```
df_new = df.alter(old_col_name=None)
```
Thoughts?
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} | 59 | 2013-11-06T14:38:00Z | 2015-01-18T21:39:57Z | 2015-01-18T21:39:57Z | CONTRIBUTOR | null | I've used a try except block as opposed to and if else (below) to keep the non-default code path fast. What I didn't do:
```
if default is None :
inds = self.indices[name]
else :
inds = self.indices.get(name, default)
```
Here is an example use case:
```
def foo(df, keys) :
g = df.groupby('key')
for key in keys :
for x in g.get_group(key, default=[]).x :
yield key, x
```
vs
```
def foo(df, keys) :
g = df.groupby('key')
for key in keys :
if key in g.indices :
for x in g.get_group(key).x :
yield key, x
```
Many of the simple cases are actually handled by other higher level functions
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https://api.github.com/repos/pandas-dev/pandas/issues/5453 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5453/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5453/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5453/events | https://github.com/pandas-dev/pandas/issues/5453 | 22,200,481 | MDU6SXNzdWUyMjIwMDQ4MQ== | 5,453 | Matplotlib cursor position wrong after using asfreq method to change freq of DateTimeIndex from None to something | {
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} | 4 | 2013-11-06T15:15:53Z | 2014-06-12T21:07:21Z | 2014-05-01T17:50:35Z | CONTRIBUTOR | null | After using the `asfreq` method to change the frequency of a time-series DataFrame from `None` to something e.g. `15Min` the cursor position in matplotlib graphs of that DataFrame is no longer correct (usually shows a datetime just after the unix epoch). The following demonstrates this (NB dt in df1 is not a constant):
```
df1 = pandas.read_csv('tseries1.csv', names=['tstamp', 'Q'], parse_dates=True,
index_col='tstamp').clip_lower(0).fillna(0)
df1['T'] = pandas.read_csv('tseries2.csv', names=['tstamp', 'T'], parse_dates=True,
index_col='tstamp', squeeze=True).clip_lower(0).fillna(0)
df2 = df1.asfreq(freq='15Min', method='ffill')
# NB df1.index.freq is None
# NB df2.index.freq is <15 * Minutes>
df1.plot()
df2.plot()
plt.show()
```
I find the Matplotlib cursor position to be invaluable when looking for features in very long time-series.
Versions:
- pandas master (commit ID 764b444)
- numpy 1.8
- matplotlib 1.3.0
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https://api.github.com/repos/pandas-dev/pandas/issues/5454 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5454/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5454/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5454/events | https://github.com/pandas-dev/pandas/pull/5454 | 22,205,858 | MDExOlB1bGxSZXF1ZXN0OTcyNDA3NQ== | 5,454 | DOC: small error in cookbook (doc build warning) | {
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For example:
```
df = pd.DataFrame({'a':[1],'B':[2],'c':[3]})
df.drop(labels=lambda x:x.lower()==x, axis=1)
```
would drop columns 'a' and 'c'
This would be consistent with `DataFrame.rename` taking a function for `index` or `columns`.
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} | 5 | 2013-11-06T21:32:28Z | 2019-10-21T19:52:14Z | null | NONE | null | Add more explicit docs / work-around for dealing with groupby and NA groups
(see comments)
Changelog: 07.Nov.2013: Add line to example below to preprocess table content.
I expect the following behavior: A `DataFrame.groupby` splits the dataframe/table into subtables according to the grouping-condition. A column name as a grouping-condition will give me subtables for each individual value in that column. Similarly, grouping with multiple columns (a list of column names) gives me a group for each occurring combination of these columns (or let me put it differently, the unique "values" of multiple columns to group for are tuples).
So if I'm wrong with my expectations, I couldn't read a different meaning or to-expect-behavior from the documentation (e.g. `pandas.DataFrame.groupby.__doc__`), then there is a lake of clarification.
Otherwise I found a bug and I am in the need for a fix: Some existing combinations are not provided with a group or splited subtable -- I checked it with `drop_duplicates`. And, finally, `grouped.__iter__` ignores more/other combinations as `grouped.groups.keys()` -- Here, I also would expect, that both follows the same implementation...
I tracked it to the depth of pandas to `pandas.core.Grouper._get_group_keys` or better `_KeyMapper.get_key`, `self.levels`looks good, but the list-comprehension-getmethod-zip-action goes wrong or eventually `pandas.core.Grouper.group_info` provides a too small `ngroups` value oorr something else.
`pandas.__version__` : 0.12.0-1062-g3c57949 (from 6.11.2013)
`numpy.__version__` : 1.7.2
MacOSX 10.9
Test Example:
``` python
import pickle
import sys
import os
import pandas as pd
grp_cols = ['algorithm', 'customalpha']
df = "ccopy_reg\n_reconstructor\np0\n(cpandas.core.frame\nDataFrame\np1\nc__builtin__\nobject\np2\nNtp3\nRp4\ng0\n(cpandas.core.internals\nBlockManager\np5\ng2\nNtp6\nRp7\n((lp8\ncnumpy.core.multiarray\n_reconstruct\np9\n(cpandas.core.index\nIndex\np10\n(I0\ntp11\nS'b'\np12\ntp13\nRp14\n((I1\n(I2\ntp15\ncnumpy\ndtype\np16\n(S'O8'\np17\nI0\nI1\ntp18\nRp19\n(I3\nS'|'\np20\nNNNI-1\nI-1\nI63\ntp21\nbI00\n(lp22\nS'algorithm'\np23\naS'customalpha'\np24\natp25\n(Ntp26\ntp27\nbag9\n(cpandas.core.index\nInt64Index\np28\n(I0\ntp29\ng12\ntp30\nRp31\n((I1\n(I13\ntp32\ng16\n(S'i8'\np33\nI0\nI1\ntp34\nRp35\n(I3\nS'<'\np36\nNNNI-1\nI-1\nI0\ntp37\nbI00\nS'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x05\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x08\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x0c\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\r\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x11\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x15\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x17\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x18\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x1a\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x1e\\x00\\x00\\x00\\x00\\x00\\x00\\x00 \\x00\\x00\\x00\\x00\\x00\\x00\\x00'\np38\ntp39\n(Ntp40\ntp41\nba(lp42\ng9\n(cnumpy\nndarray\np43\n(I0\ntp44\ng12\ntp45\nRp46\n(I1\n(I2\nI13\ntp47\ng16\n(S'O8'\np48\nI0\nI1\ntp49\nRp50\n(I3\nS'|'\np51\nNNNI-1\nI-1\nI63\ntp52\nbI00\n(lp53\nS'ScenarioAlgoLocalHeuristicM'\np54\naS'ScenarioAlgoLocalHeuristicM'\np55\naS'ScenarioAlgoLocalHeuristicM'\np56\naS'ScenarioAlgoLocalHeuristicM'\np57\naS'ScenarioAlgoLocalHeuristicMC'\np58\naS'ScenarioAlgoCMTFLP'\np59\naS'ScenarioAlgoLocalHeuristicMC'\np60\naS'ScenarioAlgoLocalHeuristicMC'\np61\naS'ScenarioAlgoLocalHeuristicMC'\np62\naS'ScenarioAlgoLocalHeuristicMC'\np63\naS'ScenarioAlgoLocalHeuristicM'\np64\naS'ScenarioAlgoLocalHeuristicM'\np65\naS'ScenarioAlgoLocalHeuristicMC'\np66\naS'exp'\np67\naS'r100'\np68\naNaS'r333'\np69\naNaNaS'r333'\np70\naS'r100'\np71\naS'linear'\np72\naS'exp'\np73\naS'r10'\np74\naS'linear'\np75\naS'r10'\np76\natp77\nba(lp78\ng9\n(g10\n(I0\ntp79\ng12\ntp80\nRp81\n((I1\n(I2\ntp82\ng19\nI00\n(lp83\ng23\nag24\natp84\n(Ntp85\ntp86\nbatp87\nbb."
df = pickle.loads(df)
# Unexpected behavior was caused by None - values (which are treaded as NaN values), thanks jreback
df.fillna("default", inplace=True) # replaces None/NaN values
print "raw data: (", len(df), ")\n", df
print
print
df_grps1 = df[grp_cols].drop_duplicates()
df_grps2 = df.groupby(grp_cols)
df_grps3 = [grp for grp, _ in df.groupby(grp_cols)]
print "df_grps1 (#", len(df_grps1), "): \n", df_grps1
print
print "df_grps2 (#", len(df_grps2), "): "
for tpl in df_grps2.groups.keys():
print tpl
print
print "df_grps3 (#", len(df_grps3), "): "
for tpl in df_grps3:
print tpl
assert len(df_grps1) == len(df_grps2), "baad bug !!!"
assert len(df_grps2) == len(df_grps3), "baad bug !!!"
assert len(df_grps1) == len(df_grps3), "baad bug!!!"
print "passed without error"
```
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} | 1 | 2013-11-06T22:22:46Z | 2013-11-21T13:29:16Z | 2013-11-21T13:29:16Z | CONTRIBUTOR | null | @jreback is about to introduce this with the warn on setting with copy PR. We should use it in pandas internals - will be cleaner, especially when debugging. https://github.com/pydata/pandas/commit/7aa7880f#diff-1e746a348e7db9596f3d053134d00e74R515
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} | 0 | 2013-11-06T23:30:00Z | 2014-02-04T15:53:19Z | 2014-01-20T23:37:17Z | CONTRIBUTOR | null | _For later (post 0.13)..._
Good:
```
In [1]: Series(['2013-01-05']).apply(pd.to_datetime)
Out[1]:
0 2013-01-05 00:00:00
dtype: datetime64[ns]
```
Bad:
```
In [2]: Series(['00:00:01']).apply(pd.to_timedelta)
Out[2]:
0 1000000000 nanoseconds
dtype: object
```
This problem turned up in #5438 , resovling #5437 . Refer to the discussion there.
See `tseries/tests/test_timedeltas.py`, specifically `test_apply_to_timedelta`. Currently, Lines 304 and 311 of the latter contain commented out assert statements that, after this is resolved should be uncommented and pass.
Also see `tests/test_frame.py/test_operators_timedelta64.py`.
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} | 15 | 2013-11-07T01:45:06Z | 2014-06-14T22:38:30Z | 2013-11-27T20:00:21Z | CONTRIBUTOR | null | I added some simple documentation to the GroupBy and Grouper classes
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} | 6 | 2013-11-07T02:12:35Z | 2021-04-11T03:52:44Z | 2021-04-11T03:52:44Z | CONTRIBUTOR | null | I've been contributing a little bit to GeoPandas / working on some of my own custom code using pandas that I want to share with others and I realized that I wasn't sure what is and is not part of the public API for pandas.
Here's what I have in my head:
Definitely public:
- anything in pandas toplevel namespace (except for modules imported into that namespace)
- non-`_` classmethods on NDFrame
- DateOffsets (in that they have a set interface), but not necessarily direct use of get_offset and friends
Somewhat public:
- Subset of Index methods:
- Definitely `union`, `intersection`, `difference`, `levels`, `labels`, `names` (and the set methods for them)
- Somewhat: `get_indexer`, `get_indexer_non_unique`, `groupby`, `get_loc`, `slice_locs`, `equals`, `identical`, `values` property
- Not public: `is_`, `is_unique`, lexsort properties on MI
- `get_offset` and `DateOffset` subclasses
Nice to make public right now:
- `compat` module (useful to provide this for modules that depend on pandas...at least in terms of guaranteeing that whatever's in the namespace now will go through a deprecation period).
- test utils (otherwise have to reinvent the wheel)
- `cache_readonly` decorator
Internal methods/properties that subclasses may rely on/manipulate:
- `__finalize__`
- `_metadata` property
- `_constructor`, `_constructor_sliced`
- `_internal_names` (maybe - weird behavior with `__setattr__` if you don't do this)
- `_reset_cache`, `_update_inplace`, possibly `_maybe_update_cacher`
Things we could consider making public in the future:
- many of the `is_*` methods in core/common.
Am I missing anything here? Does this make sense?
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] | closed | false | null | [] | null | 10 | 2013-11-07T02:48:08Z | 2013-11-08T12:46:51Z | 2013-11-08T12:46:51Z | NONE | null | ```
import pandas as pd
import numpy as np
didx = pd.DatetimeIndex(np.array([1380585623454345752, 1380585612343234312]).astype("datetime64[ns]"))
didx.normalize()
```
the output is:
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-10-01 00:00:00.000000752, 2013-10-01 00:00:00.000000312]
Length: 2, Freq: None, Timezone: None
```
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} | 0 | 2013-11-07T13:06:16Z | 2014-07-16T08:39:41Z | 2013-11-07T14:44:58Z | CONTRIBUTOR | null | ```
In [1]: df = DataFrame(np.random.randn(1000,1000))
In [2]: %timeit df.apply(pd.isnull)
10 loops, best of 3: 121 ms per loop
In [3]: %timeit pd.isnull(df)
100 loops, best of 3: 3.15 ms per loop
```
from 0.12
```
In [2]: %timeit df.apply(pd.isnull)
10 loops, best of 3: 74.3 ms per loop
In [3]: %timeit pd.isnull(df)
10 loops, best of 3: 81.8 ms per loop
```
now evaluates block-by-block rather than column-by-column (via apply)
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} | [] | closed | false | null | [] | null | 10 | 2013-11-07T19:32:25Z | 2013-11-08T01:02:41Z | 2013-11-08T00:43:23Z | NONE | null | I got the following error when appending data to an HDF5 file:
> File "/opt/epd/7.3-2_pandas0.11/lib/python2.7/site-packages/pandas/io/pytables.py", line 610, in append
> self._write_to_group(key, value, table=True, append=True, *_kwargs)
> File "/opt/epd/7.3-2_pandas0.11/lib/python2.7/site-packages/pandas/io/pytables.py", line 871, in _write_to_group
> s.write(obj = value, append=append, complib=complib, *_kwargs)
> File "/opt/epd/7.3-2_pandas0.11/lib/python2.7/site-packages/pandas/io/pytables.py", line 2998, in write
> return super(AppendableMultiFrameTable, self).write(obj=obj.reset_index(), data_columns=data_columns, *_kwargs)
> File "/opt/epd/7.3-2_pandas0.11/lib/python2.7/site-packages/pandas/io/pytables.py", line 2740, in write
> min_itemsize=min_itemsize, *_kwargs)
> File "/opt/epd/7.3-2_pandas0.11/lib/python2.7/site-packages/pandas/io/pytables.py", line 2484, in create_axes
> self.validate(existing_table)
> File "/opt/epd/7.3-2_pandas0.11/lib/python2.7/site-packages/pandas/io/pytables.py", line 2098, in validate
> raise ValueError("invalid combinate of [%s] on appending data [%s] vs current table [%s]" % (c,sax,oax))
> ValueError: invalid combinate of [values_axes] on appending data [name->values_block_0,cname->values_block_0,axis->None,pos->1,kind->integer] vs current table [name->values_block_0,cname->values_block_0,axis->None,pos->1,kind->float]
The file only contains one DataFrame and I verified that, if I read data from the file, the file DataFrame and the DataFrame I am trying to append have the same column order and the same dtypes for those columns. In addition, index columns have the same types <class 'pandas.tseries.index.DatetimeIndex'>, <class 'pandas.core.index.Int64Index'>. Why am I still getting this error?
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} | 1 | 2013-11-08T02:07:55Z | 2013-11-08T18:19:17Z | 2013-11-08T18:19:17Z | CONTRIBUTOR | null | This is on the VM running pandas windows builds.
Windows XP
Python 2.7 32-bit
```
ERROR: test suite for <class 'pandas.io.tests.test_data.TestGoogle'>
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\python27\x32\lib\site-packages\nose\suite.py", line 208, in run
self.setUp()
File "c:\python27\x32\lib\site-packages\nose\suite.py", line 291, in setUp
self.setupContext(ancestor)
File "c:\python27\x32\lib\site-packages\nose\suite.py", line 314, in setupContext
try_run(context, names)
File "c:\python27\x32\lib\site-packages\nose\util.py", line 478, in try_run
return func()
File "C:\workspace\pandas-windows-test-py27\pandas\io\tests\test_data.py", line 41, in setUpClass
cls.locales = tm.get_locales(prefix='en_US')
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 181, in get_locales
raw_locales = locale_getter()
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 151, in _default_locale_getter
"system" % e)
TypeError: __init__() takes at least 3 arguments (2 given)
```
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} | 2 | 2013-11-08T02:09:25Z | 2013-11-08T18:18:25Z | 2013-11-08T18:18:25Z | CONTRIBUTOR | null | This is on the VM running pandas windows builds.
Windows XP
Python 2.7 32-bit
numpy 1.7.1
```
======================================================================
ERROR: test_timedelta_ops_with_missing_values (pandas.tseries.tests.test_timedeltas.TestTimedeltas)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test-py27\pandas\tseries\tests\test_timedeltas.py", line 247, in test_timedelta_ops_with_missing_values
actual = s1 + timedelta_NaT
File "C:\workspace\pandas-windows-test-py27\pandas\core\ops.py", line 461, in wrapper
time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name)
File "C:\workspace\pandas-windows-test-py27\pandas\core\ops.py", line 428, in maybe_convert_for_time_op
return cls(left, right, name)
File "C:\workspace\pandas-windows-test-py27\pandas\core\ops.py", line 246, in __init__
rvalues = self._convert_to_array(right, name=name, other=lvalues)
File "C:\workspace\pandas-windows-test-py27\pandas\core\ops.py", line 363, in _convert_to_array
" operation".format(pa.array(values).dtype))
TypeError: incompatible type [timedelta64[ns]] for a datetime/timedelta operation
```
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} | 1 | 2013-11-08T02:11:07Z | 2013-11-08T18:19:00Z | 2013-11-08T18:18:25Z | CONTRIBUTOR | null | This is on the VM running pandas windows builds.
Windows XP
Python 2.7 32-bit
numpy 1.7.1
```
======================================================================
ERROR: test_multiindex_assignment (pandas.tests.test_indexing.TestIndexing)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test-py27\pandas\tests\test_indexing.py", line 1003, in test_multiindex_assignment
df.ix[name, 'new_col'] = new_vals
File "C:\workspace\pandas-windows-test-py27\pandas\core\indexing.py", line 94, in __setitem__
self._setitem_with_indexer(indexer, value)
File "C:\workspace\pandas-windows-test-py27\pandas\core\indexing.py", line 363, in _setitem_with_indexer
setter(labels[0], value)
File "C:\workspace\pandas-windows-test-py27\pandas\core\indexing.py", line 318, in setter
self.obj[item] = s
File "C:\workspace\pandas-windows-test-py27\pandas\core\frame.py", line 1841, in __setitem__
self._set_item(key, value)
File "C:\workspace\pandas-windows-test-py27\pandas\core\frame.py", line 1907, in _set_item
self._check_setitem_copy()
File "C:\workspace\pandas-windows-test-py27\pandas\core\generic.py", line 1006, in _check_setitem_copy
raise SettingWithCopyError(t)
SettingWithCopyError: A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_index,col_indexer] = value instead
```
and also:
```
======================================================================
FAIL: test_inplace_mutation_resets_values (pandas.tests.test_index.TestMultiIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test-py27\pandas\tests\test_index.py", line 1352, in test_inplace_mutation_resets_values
assert_almost_equal(exp_values, new_values)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas\src\testing.c:2440)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1692)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1692)
File "testing.pyx", line 113, in pandas._testing.assert_almost_equal (pandas\src\testing.c:1973)
AssertionError: First object is numeric, second is not: 1 != 1L
```
and also:
```
======================================================================
FAIL: test_detect_chained_assignment (pandas.tests.test_indexing.TestIndexing)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test-py27\pandas\tests\test_indexing.py", line 1695, in test_detect_chained_assignment
assert_frame_equal(df, expected)
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 469, in assert_frame_equal
check_less_precise=check_less_precise)
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 417, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 401, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 384, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int32') != dtype('int64')
```
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} | 1 | 2013-11-08T02:14:43Z | 2013-11-08T18:18:25Z | 2013-11-08T18:18:25Z | CONTRIBUTOR | null | This is on the VM running pandas windows builds.
Windows XP
Python 2.7 32-bit
numpy 1.7.1
```
======================================================================
FAIL: pandas.computation.tests.test_eval.TestAlignment.test_basic_series_frame_alignment('numexpr', 'python')
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\python27\x32\lib\site-packages\nose\case.py", line 197, in runTest
self.test(*self.arg)
File "C:\workspace\pandas-windows-test-py27\pandas\computation\tests\test_eval.py", line 873, in check_basic_series_frame_alignment
testit(r_idx_type, c_idx_type, index_name)
File "C:\workspace\pandas-windows-test-py27\pandas\computation\tests\test_eval.py", line 868, in testit
assert_frame_equal(res, expected)
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 448, in assert_frame_equal
assert_index_equal(left.columns, right.columns)
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 394, in assert_index_equal
right.dtype))
AssertionError: [index] left [object Index([0, 2, 2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 1, 2000-01-10 00:00:00, 3, 2000-01-11 00:00:00, 4], dtype='object')], right [Index([2000-01-03 00:00:00, 2000-01-04 00:00:00, 2000-01-05 00:00:00, 2000-01-06 00:00:00, 2000-01-07 00:00:00, 2000-01-10 00:00:00, 2000-01-11 00:00:00, 0, 1, 2, 3, 4], dtype='object') object]
```
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} | 5 | 2013-11-08T02:16:30Z | 2013-11-08T05:01:49Z | 2013-11-08T04:48:37Z | CONTRIBUTOR | null | This is on the VM running pandas windows builds.
Windows XP
Python 2.7 32-bit
numpy 1.7.1
```
======================================================================
FAIL: test_mode (pandas.tests.test_series.TestSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas-windows-test-py27\pandas\tests\test_series.py", line 1727, in test_mode
assert_series_equal(Series([1, 2, 3]).mode(), Series([], dtype=int))
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 417, in assert_series_equal
assert_attr_equal('dtype', left, right)
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 401, in assert_attr_equal
assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr))
File "C:\workspace\pandas-windows-test-py27\pandas\util\testing.py", line 384, in assert_equal
assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b)
AssertionError: attr is not equal [dtype]: dtype('int64') != dtype('int32')
```
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} | [] | closed | false | null | [] | null | 0 | 2013-11-08T03:59:58Z | 2014-07-16T08:39:46Z | 2013-11-08T04:38:59Z | CONTRIBUTOR | null | #5468
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} | 3 | 2013-11-08T07:45:06Z | 2019-09-18T14:02:17Z | 2019-09-18T14:02:17Z | NONE | null | You can create a normal DataFrame with, e.g., `pandas.DataFrame(0, index=[1, 2, 3], columns=["A", "B", "C"])`. However, this fails with a SparseDataFrame:
```
>>> x = pandas.SparseDataFrame(0, index=[1, 2, 3], columns=["A", "B", "C"])
Traceback (most recent call last):
File "<pyshell#97>", line 1, in <module>
x = pandas.SparseDataFrame(0, index=[1, 2, 3], columns=["A", "B", "C"])
File "c:\users\brenbarn\documents\python\extensions\pandas\pandas\sparse\frame.py", line 123, in __init__
NDFrame.__init__(self, mgr)
UnboundLocalError: local variable 'mgr' referenced before assignment
```
It looks like the code in `SparseDataFrame.__init__` is not handling the case where the data is a single value. It has a bunch of if statements to handle different kinds of data, but falls through without creating `mgr` if none of them are met.
This makes it difficult to create an "empty" SparseDataFrame whose default fill value is something other than `nan`, e.g., `pandas.SparseDataFrame(0, index=[1, 2, 3], columns=["A", "B", "C"], default_fill_value=0)`. In fact, I would suggest that if a default_fill_value is provided but no data is provided, the SparseDataFrame should be filled with the fill value (not `nan`), but perhaps that warrants a separate issue.
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https://api.github.com/repos/pandas-dev/pandas/issues/5471 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5471/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5471/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5471/events | https://github.com/pandas-dev/pandas/pull/5471 | 22,324,429 | MDExOlB1bGxSZXF1ZXN0OTc4ODExNA== | 5,471 | BUG: bool(NaT) was True | {
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} | [] | closed | false | null | [] | null | 3 | 2013-11-08T08:52:18Z | 2013-11-08T16:51:27Z | 2013-11-08T16:51:27Z | CONTRIBUTOR | null | bool(NaT) evaluates to True. I am not sure if this is by design or if it is a bug, but I think it makes more sense to evaluate it to False.
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https://api.github.com/repos/pandas-dev/pandas/issues/5472 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5472/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5472/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5472/events | https://github.com/pandas-dev/pandas/pull/5472 | 22,333,055 | MDExOlB1bGxSZXF1ZXN0OTc5MjcxOQ== | 5,472 | BUG: DatetimeIndex.normalize now accounts for nanoseconds (#5461). | {
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} | [] | closed | false | null | [] | null | 1 | 2013-11-08T12:04:03Z | 2014-07-15T19:20:42Z | 2013-11-08T12:46:21Z | CONTRIBUTOR | null | closes #5461
datetime64(ns) uses the dts->ps which was not being set to zero in the
normalize function.
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https://api.github.com/repos/pandas-dev/pandas/issues/5473 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5473/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5473/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5473/events | https://github.com/pandas-dev/pandas/issues/5473 | 22,344,911 | MDU6SXNzdWUyMjM0NDkxMQ== | 5,473 | Test suite throws lots of DateParseErrors | {
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} | [] | closed | false | null | [] | null | 11 | 2013-11-08T15:52:45Z | 2013-12-19T04:12:09Z | 2013-11-08T16:32:46Z | NONE | null | Hej hej,
I built pandas from source on [python 2.7, numpy 1.8, dateutil 2.2] using
```
python setup.py build_ext --inplace
```
When running the test suite,
```
nosetests pandas
```
I encounter lots of DateParseErrors. The error message is basically always the same, so here is an example one:
<pre>
======================================================================
ERROR: test_cdaterange_holidays (pandas.tseries.tests.test_daterange.TestCustomDateRange)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/cmsaf/cmsaf-ops3/sfinkens/usr/src/pandas-0.12.0/pandas/tseries/tests/test_daterange.py", line 597, in test_c
daterange_holidays
xp = DatetimeIndex(['2013-05-02', '2013-05-03', '2013-05-06'])
File "/cmsaf/cmsaf-ops3/sfinkens/usr/src/pandas-0.12.0/pandas/tseries/index.py", line 197, in __new__
yearfirst=yearfirst)
File "/cmsaf/cmsaf-ops3/sfinkens/usr/src/pandas-0.12.0/pandas/tseries/index.py", line 1812, in _str_to_dt_array
data = _algos.arrmap_object(arr, parser)
File "generated.pyx", line 2170, in pandas.algos.arrmap_object (pandas/algos.c:62269)
File "/cmsaf/cmsaf-ops3/sfinkens/usr/src/pandas-0.12.0/pandas/tseries/index.py", line 1808, in parser
yearfirst=yearfirst)
File "/cmsaf/cmsaf-ops3/sfinkens/usr/src/pandas-0.12.0/pandas/tseries/tools.py", line 240, in parse_time_string
raise DateParseError(e)
DateParseError: 'tuple' object has no attribute 'year'
</pre>
It occurs in the following functions:
<pre>
test_cdaterange
test_cdaterange_holidays
test_cdaterange_weekmask
test_cdaterange_weekmask_and_holidays
test_annual_ambiguous
test_business_daily
test_day_corner
test_monthly_ambiguous
test_non_datetimeindex
test_not_monotonic
test_raise_if_too_few
test_equal
test_equal_Raises_Type
test_equal_Raises_Value
test_greater
test_greaterEqual
test_greaterEqual_Raises_Value
test_greater_Raises_Type
test_greater_Raises_Value
test_notEqual
test_smaller
test_smallerEqual
test_smallerEqual_Raises_Type
test_smallerEqual_Raises_Value
test_smaller_Raises_Type
test_smaller_Raises_Value
test_sort
test_align_frame
test_align_series
test_as_frame_columns
test_asfreq
test_asfreq_ts
test_constructor
test_constructor_use_start_freq
test_convert_array_of_periods
test_dti_to_period
test_fields
test_frame_setitem
test_frame_to_time_stamp
test_get_loc_msg
test_getitem_datetime
test_intersection
test_iteration
test_joins
test_make_time_series
test_nested_dict_frame_constructor
test_period_index_unicode
test_pickle_freq
test_pindex_slice_index
test_shift
test_slice_keep_name
test_take
test_to_datetime_1703
test_to_timestamp
test_to_timestamp_preserve_name
test_tolist
test_ts_repr
test_union
test_anchor_week_end_time
test_constructor_corner
test_constructor_infer_freq
test_period_cons_weekly
test_period_constructor
test_repr
test_strftime
test_timestamp_tz_arg
test_business_daily
test_daily
test_hourly
test_minutely
test_secondly
test_weekly
test_ax_plot
test_axis_limits
test_both_style_and_color
test_business_freq
test_business_freq_convert
test_dataframe
test_fake_inferred_business
test_finder_annual
test_finder_daily
test_finder_hourly
test_finder_minutely
test_finder_monthly
test_finder_monthly_long
test_finder_quarterly
test_format_date_axis
test_frame_inferred
test_from_weekly_resampling
test_gap_upsample
test_gaps
test_get_datevalue
test_get_finder
test_high_freq
test_irreg_dtypes
test_irreg_hf
test_irregular_datetime64_repr_bug
test_line_plot_datetime_frame
test_line_plot_datetime_series
test_line_plot_inferred_freq
test_line_plot_period_frame
test_line_plot_period_series
test_mixed_freq_alignment
test_mixed_freq_hf_first
test_mixed_freq_irreg_period
test_mixed_freq_irregular_first
test_mixed_freq_lf_first
test_mixed_freq_regular_first
test_mpl_nopandas
test_nonnumeric_exclude
test_nonzero_base
test_plot_multiple_inferred_freq
test_plot_offset_freq
test_secondary_bar
test_secondary_bar_frame
test_secondary_frame
test_secondary_kde
test_secondary_legend
test_secondary_upsample
test_secondary_y
test_secondary_y_ts
test_time
test_time_musec
test_to_weekly_resampling
test_ts_plot_with_tz
test_tsplot
test_uhf
test_ohlc_5min
test_all_values_single_bin
test_annual_upsample
test_annual_upsample_B_e_b
test_annual_upsample_B_e_f
test_annual_upsample_B_s_b
test_annual_upsample_B_s_f
test_annual_upsample_D_e_b
test_annual_upsample_D_e_f
test_annual_upsample_D_s_b
test_annual_upsample_D_s_f
test_annual_upsample_M_e_b
test_annual_upsample_M_e_f
test_annual_upsample_M_s_b
test_annual_upsample_M_s_f
test_basic_downsample
test_basic_upsample
test_monthly_upsample
test_not_subperiod
test_quarterly_upsample
test_resample_5minute
test_resample_empty
test_resample_irregular_sparse
test_resample_to_timestamps
test_resample_tz_localized
test_upsample_daily_business_daily
test_upsample_with_limit
test_weekly_upsample
test_datetimeindex_constructor
test_dti_snap
test_fancy_getitem
test_fancy_setitem
test_argmin_argmax
test_constructor_coverage
test_get_duplicates
test_insert
test_map_bug_1677
test_misc_coverage
test_order
test_stringified_slice_with_tz
test_to_period_nofreq
test_union_bug_1745
test_union_coverage
test_partial_slice
test_partial_slice_daily
test_partial_slice_hourly
test_partial_slice_minutely
test_shift
test_at_time
test_ctor_str_intraday
test_first_subset
test_frame_to_period
test_last_subset
test_reasonable_keyerror
test_to_period
test_indexing
test_indexing_unordered
test_range_slice
test_date_range_localize
Test
test_field_access_localize
test_pass_dates_localize_to_utc
test_timestamp_tz_convert
test_agg_period_index
test_index_ctor_infer_periodindex
test_asof_periodindex
test_getitem_setitem_datetimeindex
test_getitem_setitem_periodindex
test_timeseries_periodindex
</pre>
Kind regards,
Stephan
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} | 4 | 2013-11-08T16:04:19Z | 2014-07-16T08:39:49Z | 2013-11-08T18:18:25Z | CONTRIBUTOR | null | TST: test_indexing dtype comps, closes #5466
TST don't compare invalid indices in eval operations, closes #5467
TST: invalid assignment for np.timedelta64 closes #5465
TST: skip locale checks on windows cloes #5464
TST: dtype comp issue on windows in test_indexing (GH5466)
TST: skip test_pow on windows (GH5467)
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} | 3 | 2013-11-08T21:26:41Z | 2013-11-09T01:56:27Z | 2013-11-09T01:56:27Z | CONTRIBUTOR | null | Note the duplication of the assignment is not a copy/paste error-- the first one works, the second one fails, presumably because a different path is taken if the column already exists than if it doesn't.
```
>>> pd.__version__
'0.12.0-1081-ge684bdc'
>>> df = pd.DataFrame({"A": [1,2]})
>>> df._is_copy
False
>>> df.to_pickle("tmp.pk")
>>> df2 = pd.read_pickle("tmp.pk")
>>> hasattr(df2, "_is_copy")
False
>>> df2["B"] = df2["A"]
>>> df2["B"] = df2["A"]
Traceback (most recent call last):
File "<ipython-input-155-e1fb2db534a8>", line 1, in <module>
df2["B"] = df2["A"]
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1081_ge684bdc-py2.7-linux-i686.egg/pandas/core/frame.py", line 1841, in __setitem__
self._set_item(key, value)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1081_ge684bdc-py2.7-linux-i686.egg/pandas/core/frame.py", line 1907, in _set_item
self._check_setitem_copy()
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1081_ge684bdc-py2.7-linux-i686.egg/pandas/core/generic.py", line 1001, in _check_setitem_copy
if self._is_copy:
File "/usr/local/lib/python2.7/dist-packages/pandas-0.12.0_1081_ge684bdc-py2.7-linux-i686.egg/pandas/core/generic.py", line 1525, in __getattr__
(type(self).__name__, name))
AttributeError: 'DataFrame' object has no attribute '_is_copy'
```
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] | closed | false | null | [] | null | 6 | 2013-11-08T22:22:21Z | 2013-12-03T10:23:04Z | 2013-11-09T01:59:57Z | NONE | null | ```
import pandas as pd, numpy
r = numpy.array([
[1,2],
[2,1],
[1,2],
[2,1]],dtype=float)
r[0,1] = numpy.nan
df = pd.DataFrame(data=r,columns=['s1','s2'])
print 'Original:'
print df
print 'Selected:'
print df[df.s2==1]
# As expected
# s1 s2
#1 2 1
#3 2 1
df.to_hdf('float_bug.h5','data',table=True,data_columns=['s1','s2'])
with pd.get_store('float_bug.h5') as store:
print 'Selected store'
print store.select('data',['s2=1'])
# Unexpected
# Empty DataFrame
#Columns: [s1, s2]
#Index: []
In [72]: pd.__version__
Out[72]: '0.12.0'
```
You can verify this does not happen in the column without the NaN or when there are no NaNs at all.
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} | 0 | 2013-11-09T01:43:33Z | 2014-07-04T19:23:41Z | 2013-11-09T01:56:27Z | CONTRIBUTOR | null | closes #5475
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} | 34 | 2013-11-09T15:31:46Z | 2014-06-13T17:02:18Z | 2014-02-14T14:46:40Z | CONTRIBUTOR | null | This is just 10 minutes of copy-paste cargo-culting to gauge interest, I haven't tested anything yet.
``` python
In [1]: df = pd.DataFrame(np.random.randn(1000, 2))
In [2]: df.plot(kind='hexbin', x=0, y=1)
Out[2]: <matplotlib.axes.AxesSubplot at 0x10eb76e10>
```
It's not terribly difficult to do this on your own, so my feeling wouldn't be hurt at all if people are -1 on this :)
EDIT: oops I branched from my `to_frame` branch. I'll clean up the commits.
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```
Running Sphinx v1.2b3
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "/usr/local/lib/python3.3/site-packages/sphinx/cmdline.py", line 245, in main
warningiserror, tags, verbosity, parallel)
File "/usr/local/lib/python3.3/site-packages/sphinx/application.py", line 119, in __init__
self.setup_extension(extension)
File "/usr/local/lib/python3.3/site-packages/sphinx/application.py", line 283, in setup_extension
mod = __import__(extension, None, None, ['setup'])
File "/Users/user/Optional/pandas/doc/sphinxext/numpydoc.py", line 40
title_re = re.compile(ur'^\s*[#*=]{4,}\n[a-z0-9 -]+\n[#*=]{4,}\s*',
^
SyntaxError: invalid syntax
> /usr/local/lib/python3.3/site-packages/sphinx/application.py(283)setup_extension()
-> mod = __import__(extension, None, None, ['setup'])
(Pdb)
```
I looked into doc/sphinxtest/numpydoc.py and noticed the `u''` strings in the re.\* functions. This incompatibility is not documented. https://github.com/pydata/pandas/commit/ff52cbb334f8b9c58b215efdc44752246ecc33ad says something about "py3 compat", but I am not sure if the "revert" refers to py3 compat or just to "docs".
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} | 19 | 2013-11-10T04:00:11Z | 2013-12-28T22:18:05Z | 2013-12-07T14:16:56Z | CONTRIBUTOR | null | Copying comment from #4887 to a new issue for discussion purposes:
While testing pandas-master, hit a method not in the new groupby dispatch whitelist: `value_counts` (on a `SeriesGroupBy` object). Another possible addition: `shift`, as used in this [SO answer](http://stackoverflow.com/a/17406083/243434).
Tried to generate a list of blacklisted methods for DataFrame and Series, see below -- needs further filtering, but may reveal useful blocked methods. any thoughts on the remaining methods?
```
In [18]: sorted([x for x in set(dir(pd.DataFrame)) - pd.core.groupby._apply_whitelist - set(dir(pd.core.groupby.GroupBy)) if not x.startswith(('_', 'T', 'to', 'from', 'as'))])
Out[18]: ['abs', 'add', 'add_prefix', 'add_suffix', 'align', 'all', 'any', 'append', 'applymap', 'at', 'at_time', 'axes', 'between_time', 'bfill', 'blocks', 'bool', 'clip', 'clip_lower', 'clip_upper', 'columns', 'combine', 'combineAdd', 'combineMult', 'combine_first', 'compound', 'consolidate', 'convert_objects', 'copy', 'corr', 'corrwith', 'cov', 'delevel', 'diff', 'div', 'divide', 'dot', 'drop', 'drop_duplicates', 'dropna', 'dtypes', 'duplicated', 'empty', 'eq', 'eval', 'ffill', 'filter', 'first_valid_index', 'floordiv', 'ftypes', 'ge', 'get', 'get_dtype_counts', 'get_ftype_counts', 'get_value', 'get_values', 'groupby', 'gt', 'iat', 'icol', 'idxmax', 'idxmin', 'iget_value', 'iloc', 'index', 'info', 'insert', 'interpolate', 'irow', 'isin', 'isnull', 'iteritems', 'iterkv', 'iterrows', 'itertuples', 'ix', 'join', 'keys', 'kurt', 'kurtosis', 'last_valid_index', 'le', 'load', 'loc', 'lookup', 'lt', 'mad', 'mask', 'merge', 'mod', 'mode', 'mul', 'multiply', 'ndim', 'ne', 'notnull', 'pct_change', 'pivot', 'pivot_table', 'pop', 'pow', 'product', 'query', 'radd', 'rdiv', 'reindex', 'reindex_axis', 'reindex_like', 'rename', 'rename_axis', 'reorder_levels', 'replace', 'reset_index', 'rfloordiv', 'rmod', 'rmul', 'rpow', 'rsub', 'rtruediv', 'save', 'select', 'set_index', 'set_value', 'shape', 'shift', 'skew', 'sort', 'sort_index', 'sortlevel', 'squeeze', 'stack', 'sub', 'subtract', 'swapaxes', 'swaplevel', 'take', 'transpose', 'truediv', 'truncate', 'tshift', 'tz_convert', 'tz_localize', 'unstack', 'update', 'values', 'where', 'xs']
In [19]: sorted([x for x in set(dir(pd.Series)) - pd.core.groupby._apply_whitelist - set(dir(pd.core.groupby.GroupBy)) if not x.startswith(('_', 'T', 'to', 'from', 'as'))])
Out[19]: ['abs', 'add', 'add_prefix', 'add_suffix', 'align', 'all', 'any', 'append', 'argmax', 'argmin', 'argsort', 'at', 'at_time', 'autocorr', 'axes', 'base', 'between', 'between_time', 'bfill', 'blocks', 'bool', 'clip', 'clip_lower', 'clip_upper', 'combine', 'combine_first', 'compound', 'consolidate', 'convert_objects', 'copy', 'corr', 'cov', 'data', 'diff', 'div', 'divide', 'dot', 'drop', 'drop_duplicates', 'dropna', 'duplicated', 'empty', 'eq', 'ffill', 'filter', 'first_valid_index', 'flags', 'floordiv', 'ftype', 'ge', 'get', 'get_dtype_counts', 'get_ftype_counts', 'get_value', 'get_values', 'groupby', 'gt', 'iat', 'idxmax', 'idxmin', 'iget', 'iget_value', 'iloc', 'imag', 'index', 'interpolate', 'irow', 'is_time_series', 'isin', 'isnull', 'item', 'iteritems', 'iterkv', 'ix', 'keys', 'kurt', 'kurtosis', 'last_valid_index', 'le', 'load', 'loc', 'lt', 'mad', 'map', 'mask', 'mod', 'mode', 'mul', 'multiply', 'ndim', 'ne', 'nonzero', 'notnull', 'nunique', 'order', 'pct_change', 'pop', 'pow', 'product', 'ptp', 'put', 'radd', 'ravel', 'rdiv', 'real', 'reindex', 'reindex_axis', 'reindex_like', 'rename', 'rename_axis', 'reorder_levels', 'repeat', 'replace', 'reset_index', 'reshape', 'rfloordiv', 'rmod', 'rmul', 'round', 'rpow', 'rsub', 'rtruediv', 'save', 'select', 'set_value', 'shape', 'shift', 'skew', 'sort', 'sort_index', 'sortlevel', 'squeeze', 'str', 'strides', 'sub', 'subtract', 'swapaxes', 'swaplevel', 'take', 'transpose', 'truediv', 'truncate', 'tshift', 'tz_convert', 'tz_localize', 'unique', 'unstack', 'update', 'valid', 'value_counts', 'values', 'view', 'weekday', 'where', 'xs']
```
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} | 7 | 2013-11-10T16:18:17Z | 2013-11-10T18:47:39Z | 2013-11-10T18:47:39Z | CONTRIBUTOR | null | 1 million x 3 dataframe, using numexpr takes 142ms to do multiplication, with numexpr disabled, takes 21.6ms. I'm going to investigate, but would be helpful to know if you can reproduce this anywhere else.
```
In [10]: df = pd.DataFrame({"A": np.arange(1000000), "B": np.arange(1000000, 0, -1), "C": np.random.randn(1000000)})
In [11]: df * df
Out[11]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
A 1000000 non-null values
B 1000000 non-null values
C 1000000 non-null values
dtypes: float64(1), int64(2)
In [12]: %timeit df * df
10 loops, best of 3: 142 ms per loop
In [13]: pd.computation.expressions.set_use_numexpr(False)
In [14]: %timeit df * df
10 loops, best of 3: 21.6 ms per loop
In [15]: pd.computation.expressions.set_use_numexpr(True)
In [16]: %timeit df * df
10 loops, best of 3: 141 ms per loop
In [17]: len(df)
Out[17]: 1000000
```
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} | 7 | 2013-11-10T17:44:26Z | 2014-06-18T10:04:21Z | 2013-11-10T18:47:39Z | CONTRIBUTOR | null | closes #5481
BUG: non-unique ops not aligning correctly
these are bascially a trivial op in numpy, so numexpr is slightly slower (but the
the dtype inference issue is fixed). Essentially the recreation of an int64 ndarray had to check if its a datetime-like. In this case just passing in the dtype on the reconstructed series fixes it.
Also handles non-unique columns now (no tests before, and it would fail).
```
In [1]: df = pd.DataFrame({"A": np.arange(1000000), "B": np.arange(1000000, 0, -1), "C": np.random.randn(1000000)})
In [2]: pd.computation.expressions.set_use_numexpr(False)
In [3]: %timeit df*df
100 loops, best of 3: 11 ms per loop
In [4]: pd.computation.expressions.set_use_numexpr(True)
In [5]: %timeit df*df
100 loops, best of 3: 15.7 ms per loop
In [6]: df = df.astype(float)
In [7]: pd.computation.expressions.set_use_numexpr(False)
In [8]: %timeit df*df
100 loops, best of 3: 5.16 ms per loop
In [9]: pd.computation.expressions.set_use_numexpr(True)
In [10]: %timeit df*df
100 loops, best of 3: 5.37 ms per loop
```
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import pandas as pd
import numpy as np
arr = np.random.randn(20)
factor = pd.cut(arr, [-np.inf, 0, np.inf])
```
gives this error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/weitzenfeld/anaconda/lib/python2.7/site-packages/pandas/tools/tile.py", line 102, in cut
include_lowest=include_lowest)
File "/Users/weitzenfeld/anaconda/lib/python2.7/site-packages/pandas/tools/tile.py", line 168, in _bins_to_cuts
include_lowest=include_lowest)
File "/Users/weitzenfeld/anaconda/lib/python2.7/site-packages/pandas/tools/tile.py", line 204, in _format_levels
fa, fb = fmt(a), fmt(b)
File "/Users/weitzenfeld/anaconda/lib/python2.7/site-packages/pandas/tools/tile.py", line 200, in <lambda>
fmt = lambda v: _format_label(v, precision=prec)
File "/Users/weitzenfeld/anaconda/lib/python2.7/site-packages/pandas/tools/tile.py", line 247, in _format_label
return sgn + '%d' % whole
TypeError: %d format: a number is required, not numpy.float64
I'm using pandas version 0.12.0. Removing the -np.inf and np.inf eliminates the error.
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} | 2 | 2013-11-10T18:07:08Z | 2018-10-25T03:22:33Z | 2018-10-25T03:22:33Z | CONTRIBUTOR | null | related #5482 / #5481
in theory an op like:
`df * df2` can be done block-based rather than column-by-column, so for large numbers of columns would see nice speedups. Though, passing to `pd.eval` is almost always faster because its passed to numexpr (at the cost of an upcasted dtype on the way back)
the 2 frames are aligned so that they have the same columns; then need to cast
each of the other (df2) in this case to a joint common type (happens in any event when you operate.
Then can just do block-by-block ops
prob `BlockManager.eval` can handle this with a kw arg to distinguish
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} | 2 | 2013-11-10T18:10:28Z | 2021-04-11T03:53:33Z | null | CONTRIBUTOR | null | ```
In [11]: df = pd.DataFrame({"A": np.arange(1000000), "B": np.arange(1000000, 0, -1), "C": np.random.randn(1000000)})
In [13]: %timeit pd.eval('df*df')
1 loops, best of 3: 635 ms per loop
In [14]: df = df.astype(float)
In [15]: %timeit pd.eval('df*df')
100 loops, best of 3: 5.87 ms per loop
```
the time is all in `_interleave` as we need to pass values to numexpr
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} | 2 | 2013-11-10T21:48:16Z | 2014-07-16T08:40:00Z | 2013-11-10T21:48:36Z | NONE | null | #4760
#4901
1. Path resolution was broken, so running the script outside the pandas root directory didn't work.
2. The use of `subprocess` to invoke the script from the source tree relied on system python,
ignoring the interpreter version actually used to invoke the scripts. Travis was unaffected since the system python there
always matches the python version of the env.
3. Using subprocess meant Ctrl-C didn't interrupt the script (meh).
cc @cpcloud
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} | 17 | 2013-11-10T22:20:49Z | 2015-04-30T06:40:27Z | 2015-04-29T06:05:20Z | NONE | null | I'd like to propose `read_netcdf` ([netCDF (Network Common Data Format)](http://www.unidata.ucar.edu/software/netcdf/)) a new Pandas I/O api feature with a similar top level interface as the other reader functions. This format is widely used in the scientific community. Furthermore, netCDF4 is implemented on top of the HDF5 library, making this a natural extension to functionality already in the api.
Most likely this would sit on top of the exiting [Python/numpy interface to netCDF](https://code.google.com/p/netcdf4-python/), and because each variables metadata is stored in the file header, no complicated parsing would necessary. Multidimensional variables could be handled in a similar manner as hdf.
This may have been brought up in the past but my search here and on the google didn't bring anything up.
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} | 4 | 2013-11-10T22:45:37Z | 2014-02-14T08:38:26Z | 2014-02-14T08:38:26Z | CONTRIBUTOR | null | For example here, http://pandas.pydata.org/pandas-docs/stable/io.html#yahoo-finance:
```
end = datetime.datetime(2013, 01, 27)
```
the `01` int literal is not valid python3.
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} | 19 | 2013-11-10T23:42:26Z | 2014-01-25T19:57:57Z | 2014-01-25T19:55:51Z | CONTRIBUTOR | null | At PyData I was introduced to this great library that wraps matplotlib called [prettyplotlib](https://github.com/olgabot/prettyplotlib) by @olgabot and it was used to good effect in @twiecki's presentation on PyMC3.
I want to incorporate this into pandas - it would be a great improvement and a light dep for the library.
@olgabot said she was interested in working on a PR to incorporate it, so going to track from this issue.
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} | 14 | 2013-11-11T07:46:16Z | 2014-02-14T13:11:12Z | 2014-01-24T21:49:48Z | CONTRIBUTOR | null | I had a series containing strings like these:
"November 1, 2013"
Series length was about 500,000
A)
running pd.to_datetime(s) takes just over a minute.
B)
running pd.to_datetime(s, format="%B %d, %Y") takes about 7 seconds!
My suggestion is a way to make case A (where user doesn't specify the format type) take about as long as case B (user does specify).
Basically it looks like the code is always using date_util parser for case A.
My suggestion is based upon the idea that it's highly likely that the date strings are all in a consistent format (it's highly unlikely in this case that they would be in 500K separate formats!).
In a nutshell:
- figure out the date format of the first entry.
- try to use that against the entire series, using the speedy code in tslib.array_strptime
- if that works, we've saved heaps of time, if not fall back to the current slower behaviour of using dateutil parse each time.
Here's some pseudo-code::
```
datestr1 = s[0]
# I'm assuming dateutil has something like this, that can tell you what the format is for a given date string.
date_format = figure_out_datetime_format(datestr1)
try:
# use the super speed code that pandas uses when you tell it what the format is.
dt_series = tslib.array_strptime(s, format=datestr1, *, ...)
except:
# date strings aren't consistent after all. Let's do it the old slow way.
dt_series = tslib.array_to_datetime(s, format=None)
```
return dt_series
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https://api.github.com/repos/pandas-dev/pandas/issues/5491 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5491/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5491/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5491/events | https://github.com/pandas-dev/pandas/issues/5491 | 22,447,122 | MDU6SXNzdWUyMjQ0NzEyMg== | 5,491 | Inconsistency between min and max in apply with multiindex | {
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```
>>> df = pd.DataFrame({'A': [1, -1, 2, -2], 'B': [1, 1, 1, 1]})
>>> df.columns = [['A', 'B'], ['a', '']]
>>> df
A B
a
0 1 1
1 -1 1
2 2 1
3 -2 1
>>> print df.apply(lambda row: max(row['A', 'a'] * row['B'], 0), axis=1)
0 1
1 0
2 2
3 0
>>> print df.apply(lambda row: min(row['A', 'a'] * row['B'], 0), axis=1)
0 0
1 [-1]
2 0
3 [-2]
dtype: object
```
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} | 3 | 2013-11-11T15:22:23Z | 2014-07-16T08:40:03Z | 2013-11-27T18:31:21Z | CONTRIBUTOR | null | closes #3413
Let me know if you want this for .13 or .14 and I'll fill in the release notes appropriately.
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https://api.github.com/repos/pandas-dev/pandas/issues/5493 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5493/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5493/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5493/events | https://github.com/pandas-dev/pandas/issues/5493 | 22,466,812 | MDU6SXNzdWUyMjQ2NjgxMg== | 5,493 | groupby apply does not work if single value for groupby key | {
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] | closed | false | null | [] | null | 2 | 2013-11-11T18:29:30Z | 2013-12-05T23:37:23Z | 2013-12-05T23:37:15Z | NONE | null | The following groupby fails if key1 contains only a single value. I am using pandas 12 and python 3.x
This works:
import pandas as pd
import numpy as np
df = pd.DataFrame({'key1': ['a','a','n','a', 'a'],
'data1': np.random.randn(5),
'data2': np.random.randn(5)})
def x(df):
return df['data2']
df['test'] = df.groupby(df['key1']).apply(x)
print (df)
This fails. Note the key1 values which are all the same.
import pandas as pd
import numpy as np
df = pd.DataFrame({'key1': ['a','a','a','a', 'a'],
'data1': np.random.randn(5),
'data2': np.random.randn(5)})
def x(df):
return df['data2']
df['test'] = df.groupby(df['key1']).apply(x)
print (df)
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} | 5 | 2013-11-11T19:00:23Z | 2018-07-06T22:34:47Z | 2018-07-06T22:34:37Z | CONTRIBUTOR | null | `itertools.groupby` groups things contiguously-- great for run length encoding, not so great for partitioning. This necessitates the `groupby(sorted(items,key=keyfn), keyfn)` dance if you want to separate it. That's not always what you want either, so you wind up writing
```
def partition(seq, keyfn):
d = {}
for x in seq:
d.setdefault(keyfn(x), []).append(x)
return d
```
and so on.
`DataFrame.groupby` is great for data partitioning, but merges discontiguous groups. Wanting to cluster timeseries -- first x since the last y, etc. -- is a common task. With some `cumsum` hacks you can do it, but "get a boolean series, see when it's equal to its shifted value to find the transitions, take advantage of the fact that False == 0 and True == 1 to `cumsum` that to get something which grows for each cluster, and then `groupby` on that" is maybe a little more than I'd expect a beginner to have to do to get back what `itertools.groupby` does naturally. And if there's an easier way, then we at least should at least make it more obvious.
I'm not sure what the best way to proceed is, but I've answered variants of this several times on SO, and people wanting a cumsum/cumprod-with-reset is a pretty common numpy request.
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} | 1 | 2013-11-12T08:25:07Z | 2014-01-07T18:59:02Z | 2013-12-30T20:55:38Z | NONE | null | When you download the current master as zip archive from github.com, assigning a version number fails (empty string). As a consequence, packages depending on pandas (e.g. statsmodels) will fail to build as well.
I assume, the reason is the following: The version number is determined using git, but since the downloaded archive is not a git repository, this action fails and the version number remains an empty string.
I dont't think, this should be changed, but it would be helpful, if you added a node in the installation instruction, e.g. for people intending to install pandas on a machine without internet connection (like me ;)).
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https://api.github.com/repos/pandas-dev/pandas/issues/5496 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5496/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5496/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5496/events | https://github.com/pandas-dev/pandas/issues/5496 | 22,512,555 | MDU6SXNzdWUyMjUxMjU1NQ== | 5,496 | DateParseError after upgrading matplotlib and others dependancies | {
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} | [] | closed | false | null | [] | null | 4 | 2013-11-12T11:36:38Z | 2013-11-27T18:36:43Z | 2013-11-27T18:36:43Z | NONE | null | Hi all,
using pandas 0.12, after upgrading matplotlib from 1.2.0 to 1.3.1, which in turn have upgraded numpy python-dateutil tornado pyparsing nose six, I can't now slice a datetime based dataframe:
In [16]: pm
Out[16]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1584 entries, 2013-09-01 00:00:00 to 2013-11-05 23:00:00
Freq: H
Data columns (total 4 columns):
PM10_JUN 94 non-null values
PM10_MAG 155 non-null values
PS_CHALUS 97 non-null values
PS_ST_MATH 69 non-null values
dtypes: float64(4)
In [17]: pm.PM10_MAG["2013-09-17"]
Out[17]: 11.0
## In [18]: pm.PM10_MAG["2013-09-17":"2013-09-18"]
DateParseError Traceback (most recent call last)
<ipython-input-18-15808ec96c6d> in <module>()
----> 1 pm.PM10_MAG["2013-09-17":"2013-09-18"]
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in **getitem**(self, key)
639 key = _check_bool_indexer(self.index, key)
640
--> 641 return self._get_with(key)
642
643 def _get_with(self, key):
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in _get_with(self, key)
652 indexer = key
653 else:
--> 654 indexer = self.ix._convert_to_indexer(key, axis=0)
655 return self._get_values(indexer)
656 else:
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis)
558 else:
559 try:
--> 560 indexer = labels.slice_indexer(start, stop, obj.step)
561 except Exception:
562 if _is_index_slice(obj):
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in slice_indexer(self, start, end, step)
1225 raise TypeError('Cannot index datetime64 with float keys')
1226
-> 1227 return Index.slice_indexer(self, start, end, step)
1228
1229 def slice_locs(self, start=None, end=None):
/usr/local/lib/python2.7/dist-packages/pandas/core/index.pyc in slice_indexer(self, start, end, step)
1180 This function assumes that the data is sorted, so use at your own peril
1181 """
-> 1182 start_slice, end_slice = self.slice_locs(start, end)
1183
1184 # return a slice
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in slice_locs(self, start, end)
1236 try:
1237 if start:
-> 1238 start_loc = self._get_string_slice(start).start
1239 else:
1240 start_loc = 0
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in _get_string_slice(self, key, use_lhs, use_rhs)
1206 freq = getattr(self, 'freqstr',
1207 getattr(self, 'inferred_freq', None))
-> 1208 _, parsed, reso = parse_time_string(key, freq)
1209 loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs, use_rhs=use_rhs)
1210 return loc
/usr/local/lib/python2.7/dist-packages/pandas/tseries/tools.pyc in parse_time_string(arg, freq, dayfirst, yearfirst)
238 yearfirst=yearfirst)
239 except Exception, e:
--> 240 raise DateParseError(e)
241
242 if parsed is None:
DateParseError: 'tuple' object has no attribute 'year'
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} | 3 | 2013-11-12T13:56:37Z | 2014-01-21T13:30:59Z | 2014-01-21T13:30:59Z | CONTRIBUTOR | null | I make extensive use of `scatter_matrix` and I find misalignments in the coordinates of the scatter plots (off-diagonal) and the histograms (on-diagonal) which seem undesirable to me.
Consider the picture 
).
In this case, there is a clear misalignment (of about .02) in the x-coordinate between the subplot in position 1,2 and the subplot in position 2,2.
In other words, the peeks in subplots 1,2 and 2,2 should occur at the same x location
(at least, IMHO, this would be more meaningful behavior).
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} | 7 | 2013-11-12T15:39:50Z | 2021-10-26T16:20:50Z | 2013-11-13T14:13:45Z | CONTRIBUTOR | null | API: disable sparse structure encodings and unicode indexes
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
packers_read_pack | 3.5113 | 17.8316 | 0.1969 |
packers_read_pickle | 0.6769 | 0.6770 | 0.9999 |
packers_write_pack | 1.8814 | 3.5230 | 0.5340 |
packers_write_pickle | 1.5387 | 1.4664 | 1.0493 |
packers_write_hdf_store | 12.1900 | 12.2033 | 0.9989 |
packers_read_csv | 52.3347 | 52.2310 | 1.0020 |
packers_write_csv | 536.2056 | 526.5187 | 1.0184 |
packers_write_hdf_table | 33.3436 | 32.4137 | 1.0287 |
packers_read_hdf_store | 8.3120 | 8.0493 | 1.0326 |
packers_read_hdf_table | 13.9607 | 12.9707 | 1.0763 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [381e86b] : PERF: msgpack encoding changnes to use to/from string for speed boosts
API: disable sparse structure encodings and unicode indexes
Base [46008ec] : DOC: add cookbook entry
```
```
In [1]: from pandas.io.packers import pack
In [2]: import cPickle as pkl
In [3]: df = pd.DataFrame(np.random.rand(1000, 100))
In [6]: %timeit buf = pack(df)
1000 loops, best of 3: 492 ﾵs per loop
In [7]: %timeit buf = pkl.dumps(df,pkl.HIGHEST_PROTOCOL)
1000 loops, best of 3: 681 ﾵs per loop
In [8]: df = pd.DataFrame(np.random.rand(100000, 100))
In [11]: %timeit buf = pack(df)
1 loops, best of 3: 184 ms per loop
In [12]: %timeit buf = pkl.dumps(df,pkl.HIGHEST_PROTOCOL)
10 loops, best of 3: 111 ms per loop
```
now pretty competitive with pickle
note on bigger frames, writing an in-memory hdf file is quite fast
```
In [3]: def f(x):
...: store = pd.HDFStore('test.h5',mode='w',driver='H5FD_CORE',driver_core_backing_store=0)
...: store['df'] = x
...: store.close()
...:
In [11]: df = pd.DataFrame(np.random.rand(100000, 100))
In [13]: %timeit -n 5 buf = pack(df)
5 loops, best of 3: 202 ms per loop
In [14]: %timeit -n 5 buf = pkl.dumps(df,pkl.HIGHEST_PROTOCOL)
5 loops, best of 3: 115 ms per loop
In [15]: %timeit -n 5 f(df)
5 loops, best of 3: 53.9 ms per loop
```
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} | 0 | 2013-11-12T17:25:52Z | 2014-07-16T08:40:12Z | 2013-11-12T18:09:27Z | CONTRIBUTOR | null | {
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} | 20 | 2013-11-12T17:51:22Z | 2018-11-06T14:17:52Z | 2016-08-22T13:11:30Z | NONE | null | While importing large text files using read_csv we occasionally get an EOF (End of File ) character within a string, which causes an exception: "Error tokenizing data. C error: EOF inside string starting at line. 844863" . This occurs even with "error_bad_lines = False"..
Further, the line stated in the error message is not the line containing the EOF character. In this particular case the actual row was approx. 230 rows before the one stated, which hinders exception handling. (I now see this difference was caused by other "bad_lines" that were being skipped - the quoted error line is correct but the imported rows was less.)
I feel it would be appropriate if "error_bad_lines = False" handled this exception and allowed such rows to be skipped.
I note that when importing this text file into Excel, the "premature" EOF is simply ignored.
We are running on Windows 8 , with python version 2.7 and pandas version 0.12
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