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} | 0 | 2013-12-30T21:53:13Z | 2013-12-30T22:54:10Z | 2013-12-30T22:54:10Z | CONTRIBUTOR | null | ```
In [1]: df = pd.DataFrame({ 'date1' : pd.Timestamp('20130101'), 'date2' : pd.date_range('20120101',periods=1000000,freq='s') })
In [2]: %timeit df['date1']-df['date2']
1 loops, best of 3: 2.17 s per loop
```
pretty easy fix; don't need to infer if its a series (as we know its dtype already)
should be more like this
```
In [3]: %timeit Series(df['date1'].astype('int64')-df['date2'].astype('int64'),dtype='m8[ns]')
10 loops, best of 3: 43 ms per loop
```
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} | 0 | 2013-12-30T22:20:36Z | 2014-06-15T19:45:24Z | 2013-12-30T22:54:10Z | CONTRIBUTOR | null | closes #5801
```
------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
timestamp_ops_diff2 | 21.7124 | 2472.3583 | 0.0088 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
```
really though this was in their before....oh well
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} | [] | closed | false | null | [] | null | 0 | 2013-12-31T00:37:30Z | 2014-06-15T18:45:46Z | 2013-12-31T00:37:43Z | NONE | null | 0.13.0 was the first time I tagged a release and I got it wrong.
Version strings are bumped only when tagging a new release.
```
% grh v0.13.0rc1^
% sudo python ./setup.py develop
% cat pandas/version.py
version = '0.12.0-1189-gd9b3340'
short_version = '0.12.0'
```
It makes sense since it leaves the next version undetermined until it happens.
Keeping the status-quo for now, until we agree on something better.
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} | 4 | 2013-12-31T01:34:26Z | 2014-01-03T02:58:01Z | 2014-01-03T02:58:01Z | NONE | null | Incresingly, python packages are being built on top of pandas.
Often, these solve problems pandas users need addressed
but which are a stretch for pandas' scope proper.
#2302, and [pandas-xlsxwriter-charts](http://pandas-xlsxwriter-charts.readthedocs.org/en/latest/chart_examples.html#scatter-chart), for example. vincent, yhat/ggplot, seaborn, etc'.
statsmodels too ofcourse, though it should be fairly well known.
Let's give them some exposure, encourage people to do their
own thing on top of pandas and help users locate existing solutions.
Any more mature(-ish) packages which deserve a mention?
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} | 11 | 2013-12-31T14:29:00Z | 2014-06-16T14:08:41Z | 2014-01-02T22:58:45Z | CONTRIBUTOR | null | This fixes issue #5797.
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} | [] | closed | false | null | [] | null | 1 | 2013-12-31T15:23:49Z | 2013-12-31T15:39:13Z | 2013-12-31T15:38:35Z | NONE | null | I believe the url for gathering data from yahoo has changed...
If i create a new DataReader... ticker_data = DataReader("SPY", "yahoo")
I get..
IOError: after 3 tries, Yahoo! did not return a 200 for url 'http://ichart.yahoo.com/table.csv?s=SPY&a=0&b=1&c=2010&d=11&e=31&f=2013&g=d&ignore=.csv'
I believe .finance needs to be added to the URL. (Ex) 'http://ichart.finance.yahoo.com/table.csv?s=SPY&a=0&b=1&c=2010&d=11&e=31&f=2013&g=d&ignore=.csv'
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} | 0 | 2013-12-31T15:31:03Z | 2014-01-04T22:30:39Z | 2014-01-04T22:30:39Z | CONTRIBUTOR | null | ```
In [4]: DataFrame(index=[1]) & DataFrame(index=[1])
TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
should just return an empty frame (also raises if one side is non-empty)
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} | 11 | 2013-12-31T15:46:31Z | 2014-06-18T06:21:16Z | 2014-01-04T22:30:39Z | CONTRIBUTOR | null | closes #5808
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https://api.github.com/repos/pandas-dev/pandas/issues/5811 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5811/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5811/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5811/events | https://github.com/pandas-dev/pandas/pull/5811 | 24,925,705 | MDExOlB1bGxSZXF1ZXN0MTExNjIxMjE= | 5,811 | Update yahoo url in data.py | {
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https://api.github.com/repos/pandas-dev/pandas/issues/5812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5812/events | https://github.com/pandas-dev/pandas/pull/5812 | 24,932,889 | MDExOlB1bGxSZXF1ZXN0MTExNjYwNzk= | 5,812 | BUG: Yahoo finance changed chart base url. Updated _get_hist_yahoo | {
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} | 25 | 2013-12-31T21:50:44Z | 2014-06-12T19:28:36Z | 2014-01-01T18:52:09Z | CONTRIBUTOR | null | The start of the old url was: `http://ichart.yahoo.com/` and yahoo now uses `http://ichart.finance.yahoo.com/`
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} | 8 | 2013-12-31T22:10:15Z | 2014-02-20T21:36:56Z | 2014-02-20T21:36:56Z | NONE | null | Pandas appears to use values for timestamp fields where 1 = January, 2 = February, etc., (vs. 0 = January) and 0 = Monday, 1 = Tuesday, 2 = Wednesday, 3 = Thursday, 4 = Friday, 5 = Saturday, 6 = Sunday. (vs. weekday numbering that starts with 1 instead of 0, or Sunday instead of Monday.)
```
dec31 = pd.Timestamp('2013-12-30 00:00:00')
dec31.weekday()
```
prints 0 in my system (Mac OSX + pandas 0.12.0)
This convention doesn't appear to be in the documentation + it would really help as it is one of several possibilities.
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https://api.github.com/repos/pandas-dev/pandas/issues/5814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5814/events | https://github.com/pandas-dev/pandas/pull/5814 | 24,942,952 | MDExOlB1bGxSZXF1ZXN0MTExNzA3Mzc= | 5,814 | DOC: add way to document DatetimeIndex field attributes | {
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} | 6 | 2014-01-01T17:11:07Z | 2014-06-29T12:30:55Z | 2014-01-02T20:17:17Z | MEMBER | null | Ping @rockg. The docstring is now listed in the autosummary table in api.rst.
Related to issue #5813 (but does not closes it, this only documents the DatetimeIndex field in the api.rst, not more general the Timestamp values).
I added the attribute docstring within the `_field_accessor` function. Is this a good approach?
You can also document attributes with a docstring line beneath the definition (see Sphinx docs: http://sphinx-doc.org/ext/autodoc.html#directive-autoattribute). The problem with this is that, for the moment, this works for the sphinx autodoc (so the generated pages), but not for the autosummary (there is an open PR for this: https://bitbucket.org/birkenfeld/sphinx/pull-request/142/make-autosummary-work-with-module-class/diff).
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} | 16 | 2014-01-01T18:50:03Z | 2014-07-16T08:44:54Z | 2014-01-01T19:13:26Z | CONTRIBUTOR | null | took out the apply on `count` and just compute directly
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_dropna_axis1_any | 147.5154 | 334.2137 | 0.4414 |
frame_dropna_axis1_all | 251.1443 | 437.9021 | 0.5735 |
frame_dropna_axis0_all | 80.6900 | 80.8613 | 0.9979 |
frame_dropna_axis0_any | 58.6040 | 54.6887 | 1.0716 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [c6e300d] : PERF: perf issue with dropna on frame
Base [5e176a9] : Merge pull request #5738 from y-p/PR_json_pr_ver
BLD: ci/print_versions.py learned to output json
```
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} | 15 | 2014-01-01T18:55:48Z | 2014-01-28T03:13:56Z | 2014-01-28T03:13:56Z | NONE | null | See discussion https://github.com/pydata/pandas/pull/5812
The skip behaviour was motivated by travis failing tests due to
wonky net cinnectivity which can mask actual problems such as broken urls.
Perhaps make the behavior conditional on environment (travis can be detected via
environment vars)
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} | 3 | 2014-01-02T00:46:13Z | 2014-07-16T08:44:56Z | 2014-01-03T00:06:25Z | CONTRIBUTOR | null | This could be useful to make pandas more resilient to basic url changes
like that which happened with yahoo finance. That said, clearly
wholesale API changes won't be helped by this.
What do you all think, worth it to make this relatively trivial change?
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} | 1 | 2014-01-02T06:38:36Z | 2014-01-24T22:20:51Z | 2014-01-24T22:20:51Z | NONE | null | It appears that if a dataframe has column headers of type DatetimeIndex, calling reset_index() throws `AttributeError: 'str' object has no attribute 'view'`. I see this both in v0.12 and the master branch.
If column headers are strings or integers, reset_index() works fine.
I'm guessing it's treating the new column array as type DatetimeIndex whereas it now has a string in the 0th position ('index'). Maybe the reset_index should first cast the columns as object and convert the DatetimeIndex values to strings?
``` python
In [48]: df = pd.DataFrame(data=np.random.rand(2, 2), columns=pd.date_range('1/1/2013', '1/2/2013'), index=['A', 'B'])
In [49]: df.reset_index()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-49-6983677cc901> in <module>()
----> 1 df.reset_index()
/home/user/environments/python3/src/pandas/pandas/core/frame.py in reset_index(self, level, drop, inplace, col_level, col_fill)
2447 else:
2448 values = _maybe_cast(self.index.values)
-> 2449 new_obj.insert(0, name, values)
2450
2451 new_obj.index = new_index
/home/user/environments/python3/src/pandas/pandas/core/frame.py in insert(self, loc, column, value, allow_duplicates)
1940 value = self._sanitize_column(column, value)
1941 self._data.insert(
-> 1942 loc, column, value, allow_duplicates=allow_duplicates)
1943
1944 def _sanitize_column(self, key, value):
/home/user/environments/python3/src/pandas/pandas/core/internals.py in insert(self, loc, item, value, allow_duplicates)
2899
2900 try:
-> 2901 new_items = self.items.insert(loc, item)
2902 self.set_items_norename(new_items)
2903
/home/user/environments/python3/src/pandas/pandas/tseries/index.py in insert(self, loc, item)
1539
1540 new_index = np.concatenate((self[:loc].asi8,
-> 1541 [item.view(np.int64)],
1542 self[loc:].asi8))
1543 return DatetimeIndex(new_index, freq='infer')
AttributeError: 'str' object has no attribute 'view'
In [50]: df = pd.DataFrame(data=np.random.rand(2, 2), columns=[1, 2], index=['A', 'B'])
In [51]: df.reset_index()
Out[51]:
index 1 2
0 A 0.947575 0.370406
1 B 0.664856 0.686524
[2 rows x 3 columns]
In [52]: df = pd.DataFrame(data=np.random.rand(2, 2), columns=['C', 'D'], index=['A', 'B'])
In [53]: df.reset_index()
Out[53]:
index C D
0 A 0.053455 0.599483
1 B 0.776364 0.680425
[2 rows x 3 columns]
```
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} | 8 | 2014-01-02T07:14:00Z | 2014-07-09T11:59:16Z | 2014-01-24T22:20:52Z | CONTRIBUTOR | null | Falls back to object Index instead. (previously wasn't checking for them), but _only_ strings are allowed.
Fixes #5818.
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} | 0 | 2014-01-02T11:54:16Z | 2014-06-19T08:09:48Z | 2014-01-02T12:09:55Z | CONTRIBUTOR | null | fixes dtype issue on 32-bit, related #5815
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} | 12 | 2014-01-02T12:40:54Z | 2017-01-12T21:28:55Z | 2014-04-09T13:00:23Z | CONTRIBUTOR | null | related #6066 (on Float64Index too)
The 'KeyError' on slicing was discussed multiple times but I'm still not sure if the issue below is a bug or just my misunderstanding.
I'm experiencing 'KeyError' from time to time when I try to slice my dataframes with datetime or Timestamp objects, however slicing with strings works perfectly. I was unable to construct synthetic example with pandas.date_range, so I needed to upload the piece of real data where the issue appears:
https://www.dropbox.com/s/ibzbwqs35tiydyc/tmp.h5
```
df = pd.read_hdf('tmp.h5', 'data')
print df.head()
```
When I try to slice it with the pandas.Timestamp objects it results in 'KeyError':
```
tt1 = pd.Timestamp('2006-11-16 18:30:00')
tt2 = pd.Timestamp('2006-11-16 18:40:00')
print df.ix[tt1:tt2].head()
```
Same for datetime objects:
```
tt1 = pd.Timestamp('2006-11-16 18:30:00').to_datetime()
tt2 = pd.Timestamp('2006-11-16 18:40:00').to_datetime()
print df.ix[tt1:tt2].head()
```
However this slicing works perfectly:
```
tt1 = pd.Timestamp('2006-11-16 18:30:00').strftime('%Y-%m-%d %H:%M:%S')
tt2 = pd.Timestamp('2006-11-16 18:40:00').strftime('%Y-%m-%d %H:%M:%S')
print df[tt1:tt2].head()
```
Numpy version 1.8.0
Pandas version 0.13.0
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} | 2 | 2014-01-02T16:16:14Z | 2014-02-14T13:09:40Z | 2014-02-14T13:09:40Z | NONE | null | I am confused by the dtype conversion function "astype" for pandas series. According to documentation, it should be the same as numpy.ndarray dtype conversion, but seems there is stil some difference. For example, if I try to convert columns of datetime type to object dtype, I got completely different results using astype function on pandas series versus on numpy array. My question is what sort of additional hanlding exists inside pandas Series dtype conversion? Example code as follows, note that pandas Series dtype conversion converts numpy.datetime64 to datetime.datetime, while numpy.array dtype conversion converts numpy.datetime64 to long. By the way, my numpy is version 1.8.0, and my pandas is version 0.12.0, and running on python 2.7.
> > > import datetime
> > > import numpy
> > > import pandas
> > > dd = pandas.DataFrame({"val": [datetime.datetime(2014, 1, 1, 9)]})
> > > dd["val"].astype("object")
> > > Out[1]:
> > > 0 2014-01-01 09:00:00
> > > Name: val, dtype: object
> > > numpy.array(dd["val"]).astype("object")
> > > Out[1]: array([1388566800000000000L], dtype=object)
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} | 0 | 2014-01-02T17:52:10Z | 2018-07-08T14:44:22Z | null | CONTRIBUTOR | null | http://stackoverflow.com/questions/20888226/pandas-hdfstore-tables-doesnt-accept-multiindex-columns/20888732#20888732
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https://api.github.com/repos/pandas-dev/pandas/issues/5824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5824/events | https://github.com/pandas-dev/pandas/issues/5824 | 24,979,132 | MDU6SXNzdWUyNDk3OTEzMg== | 5,824 | Numpy 1.8 `DeprecationWarning` in compat/scipy.py | {
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} | 20 | 2014-01-02T18:30:31Z | 2014-04-16T13:04:41Z | 2014-04-16T13:04:41Z | CONTRIBUTOR | null | Not sure how pressing this is, but with `DeprecationWarning` enabled, I notice that numpy 1.8 is raising a warning during the following call to `describe()`. [side note: enabled DeprecationWarning in my test suite after learning that it was changed in py2.7 to "ignore" by default.]
```
import pandas as pd
import warnings
warnings.simplefilter("once", DeprecationWarning)
df = pd.DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]})
print df.groupby('A')['B'].describe()
```
stdout:
```
$ python test_fail.py
.../pandas/compat/scipy.py:68: DeprecationWarning: using a non-integer
number instead of an integer will result in an error in the future
score = values[idx]
```
Here's the full traceback with DeprecationWarning escalated to an error (`warnings.simplefilter("error", DeprecationWarning)`):
```
Traceback (most recent call last):
File "test_fail.py", line 6, in <module>
print df.groupby('A')['B'].describe()
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 343, in wrapper
return self.apply(curried)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 424, in apply
return self._python_apply_general(f)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 427, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 883, in apply
res = f(group)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 422, in f
return func(g, *args, **kwargs)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/groupby.py", line 329, in curried
return f(x, *args, **kwargs)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/series.py", line 1386, in describe
lb), self.median(), self.quantile(ub),
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/core/series.py", line 1316, in quantile
result = _quantile(valid_values, q * 100)
File "/home/gmd/ENV/pandas-master-2/lib/python2.7/site-packages/pandas-0.13.0_29_g97860a1-py2.7-linux-i686.egg/pandas/compat/scipy.py", line 68, in scoreatpercentile
score = values[idx]
IndexError: cannot convert index to integer
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5825/events | https://github.com/pandas-dev/pandas/issues/5825 | 24,984,307 | MDU6SXNzdWUyNDk4NDMwNw== | 5,825 | ENH: Improved (JMP-like) Box Plot Hierarchy Labels | {
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} | [] | closed | false | null | [] | null | 4 | 2014-01-02T20:10:01Z | 2014-01-23T17:03:14Z | 2014-01-23T17:03:14Z | NONE | null | For a lot of work in statistics, box plots are still one of the most useful tools for at-a-glance comparison of experimental groups. JMP has a great default handler for plotting box plots when including multiple grouping levels for the data, including sane handling for missing data. Please see this example for what a simple case looks like:

In terms of 'sane defaults' and making a lot of use without a lot of work, I think this would be a great model for future box plot work for pandas given its rich GroupBy functionality. When creating a box plot based on a multi-level grouping in pandas/matplotlib, the only obvious method I can see is to use labels which are tuples of each of the values for the various group levels. This makes it hard to get much out of the plot visually for grouped data set.
JMP has a variety of other features for box plots that would be very useful in the future, but I believe this first visual enhancement would be more powerful for the most users.
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https://api.github.com/repos/pandas-dev/pandas/issues/5826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5826/events | https://github.com/pandas-dev/pandas/pull/5826 | 24,989,875 | MDExOlB1bGxSZXF1ZXN0MTExOTM2MTI= | 5,826 | DOC: Add example to extract docstring, and re-explain change to match. | {
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} | 7 | 2014-01-02T21:57:20Z | 2014-07-16T08:44:59Z | 2014-01-03T15:24:28Z | CONTRIBUTOR | null | @jreback, I added examples per your request in #5099. Also, I rewrote the explanation of how `str.match` is changing, which I left confusing and wordy before.
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https://api.github.com/repos/pandas-dev/pandas/issues/5827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5827/events | https://github.com/pandas-dev/pandas/issues/5827 | 24,990,966 | MDU6SXNzdWUyNDk5MDk2Ng== | 5,827 | setup.py breaks builds | {
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debian builder is broken after latest edits of the setup.py
https://github.com/pydata/pandas/commits/master/setup.py
error
```
raceback (most recent call last):
File "setup.py", line 213, in <module>
if pipe.returncode != 0:
NameError: name 'pipe' is not defined
dh_auto_clean: python setup.py clean -a returned exit code 1
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5828/events | https://github.com/pandas-dev/pandas/pull/5828 | 24,998,521 | MDExOlB1bGxSZXF1ZXN0MTExOTg3MDM= | 5,828 | DOC: add pandas-xlsxwriter-charts ipnb to cookbook | {
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} | 0 | 2014-01-03T02:07:56Z | 2014-07-16T08:45:01Z | 2014-01-03T02:58:27Z | NONE | null | cc @jmcnamara.
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https://api.github.com/repos/pandas-dev/pandas/issues/5829 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5829/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5829/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5829/events | https://github.com/pandas-dev/pandas/pull/5829 | 24,999,588 | MDExOlB1bGxSZXF1ZXN0MTExOTkyNTQ= | 5,829 | DOC: add 'pandas ecosystem' section to docs | {
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} | 3 | 2014-01-03T02:57:56Z | 2014-07-14T12:38:07Z | 2014-01-03T02:58:01Z | NONE | null | cc @jseabold, @glamp, @mwaskom, @kjordahl, @wrobstory
Feel free to suggest a better blurb if you'd like.
Replaces the `related libraries` section, which I feel is outdated.
closes #5804
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} | 4 | 2014-01-03T03:30:22Z | 2014-01-28T16:50:11Z | 2014-01-27T00:59:19Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_partially_invalid_plot_data (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python3/dist-packages/pandas/tests/test_graphics.py", line 939, in test_partially_invalid_plot_data
df.plot(kind=kind)
File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python3/dist-packages/pandas/util/testing.py", line 1331, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: TypeError not raised.
----------------------------------------------------------------------
Ran 4317 tests in 380.425s
FAILED (SKIP=325, failures=1)
['Idx']
```
seems to be just a sporadic failure (did test ok before in the same environment, ok on i386 in this round of builds), full log http://neuro.debian.net/_files/_buildlogs/pandas/0.13.0/pandas_0.13.0-1~nd13.04+1_amd64.build
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} | 4 | 2014-01-03T04:23:58Z | 2014-01-15T21:16:50Z | 2014-01-10T12:26:16Z | CONTRIBUTOR | null | on all aged Debian/Ubuntus (e.g. wheeze) where I build using pre-cythonized sources python3.2 seems to become blind and tries only to look for msgpack.c, not msgpack.cpp:
```
~/pandas-0.13.0# python3.2 setup.py build_ext
running build_ext
Traceback (most recent call last):
File "setup.py", line 583, in <module>
**setuptools_kwargs)
File "/usr/lib/python3.2/distutils/core.py", line 148, in setup
dist.run_commands()
File "/usr/lib/python3.2/distutils/dist.py", line 917, in run_commands
self.run_command(cmd)
File "/usr/lib/python3.2/distutils/dist.py", line 936, in run_command
cmd_obj.run()
File "/usr/lib/python3.2/distutils/command/build_ext.py", line 344, in run
self.build_extensions()
File "setup.py", line 346, in build_extensions
self.check_cython_extensions(self.extensions)
File "setup.py", line 343, in check_cython_extensions
""" % src)
Exception: Cython-generated file 'pandas/msgpack.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
~/pandas-0.13.0# ls -l pandas/msgpack.cpp
-rw-r--r-- 1 pbuilder pbuilder 570047 Jan 3 03:44 pandas/msgpack.cpp
```
I wonder if anyone ran into this misbehavior (before I start patching left and right ;-) )
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] | closed | false | null | [] | null | 10 | 2014-01-03T13:35:07Z | 2017-08-10T23:17:37Z | 2015-10-20T22:25:32Z | MEMBER | null | Inspired by this [SO question](http://stackoverflow.com/questions/20875140/apply-function-to-sets-of-columns-in-pandas-looping-over-entire-data-frame-co), I was wondering if the possibility of being able to give an axis keyword to `GroupBy.apply` would be interesting.
An example. Considering this dataframe:
```
In [1]: idx = pd.MultiIndex.from_arrays([list('AAABBB'), list('xyzxyz')])
In [2]: df = pd.DataFrame(np.random.randn(5,6), columns=idx)
In [3]: df
Out[3]:
A B
x y z x y z
0 1.085834 -1.247589 0.174311 1.415633 0.788986 0.443246
1 1.036490 0.223306 -0.920102 0.068396 1.577690 -0.757387
2 -0.587843 -1.223218 -0.153409 1.863440 2.169254 0.282485
3 1.485550 0.494427 -0.257822 1.360076 -0.049386 -0.264166
4 0.232604 -0.972644 0.877186 -0.875215 1.198826 -0.370832
```
and then groupby the first level of the columns (`A` and `B`):
```
In [4]: g = df.groupby(axis=1, level=0)
```
On the individual groups of `g`, you could do a following apply (to calculate the length of the vector (x,y,z)):
```
In [5]: df['A'].apply(lambda x: np.sqrt(x**2).sum(), axis=1)
Out[5]:
0 2.507734
1 2.179897
2 1.964471
3 2.237799
4 2.082434
dtype: float64
```
However, to do this on all groups, you need a second apply inside the apply to provide the axis keyword:
```
In [6]: g.apply(lambda g: g.apply(lambda x: np.sqrt(x**2).sum(), axis=1))
Out[6]:
A B
0 2.507734 2.647866
1 2.179897 2.403474
2 1.964471 4.315179
3 2.237799 1.673628
4 2.082434 2.444873
```
Would it be a interesting enhancement that you could directly provide this keyword like this?
```
g.apply(lambda x: np.sqrt(x**2).sum(), axis=1)
```
Or Is there another way I am missing to simply do this?
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} | 5 | 2014-01-03T14:38:04Z | 2014-02-18T20:24:11Z | 2014-02-18T20:24:11Z | CONTRIBUTOR | null | ```
pd.version.version
'0.12.0-1149-g141e93a'
```
Maybe already fixed, but I'm afraid to upgrade in the middle of working on a project given past experiences.
As I recall there were several ways proposed to join a series to a DataFrame with join being the preferred way.
```
df = pd.DataFrame(np.random.random((30,2)), columns=["A","B"])
series = pd.Series(np.random.random(30), name="C")
```
I know I can do
```
df["C"] = series
```
or
```
df = df.join(series)
```
But it'd be nice if the following also worked given that I'd like to write general code.
```
pd.concat((df, series), axis=1) # ok error message at least
df.merge(series) # just call join under the hood? bad error message
```
This is "unexpected" that it somehow tries to append the rows. Looks like a bug to me. Should it take an axis keyword?
```
df.append(series)
```
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} | 0 | 2014-01-03T16:47:19Z | 2014-01-04T18:53:09Z | 2014-01-04T18:53:09Z | CONTRIBUTOR | null | Hard to reprod.
Select from a duplicate indexed axis in a frame with ix where part of the
selection set is missing
```
In [22]: df = DataFrame(np.random.randn(5,5),columns=['A.1','B.1','B.2','B.3','A.2'],index=date_range('20130101',periods=5))
In [23]: df2 = df.rename(columns=lambda x: x.split('.')[0])
In [24]: df2
Out[24]:
A B B B A
2013-01-01 -1.029245 -0.782139 0.584956 1.097301 -0.150675
2013-01-02 -0.723246 -0.356150 -0.441952 0.027012 -1.851583
2013-01-03 -1.001412 0.129464 0.093433 0.952615 -1.338390
2013-01-04 0.165987 0.227918 0.557940 -0.102501 -1.194053
2013-01-05 0.249493 -1.102096 -0.977755 -0.529540 0.783277
[5 rows x 5 columns]
In [25]: df2.ix[:,['A','B','C']]
AssertionError: Number of manager items must equal union of block items
# manager items: 6, # tot_items: 14
```
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} | 0 | 2014-01-03T17:37:35Z | 2014-01-13T16:30:15Z | 2014-01-13T16:30:15Z | NONE | null | To 2008-2014.
a formality, but indicates the project is active.
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} | 11 | 2014-01-03T18:31:37Z | 2016-10-12T23:04:28Z | 2014-01-25T17:13:33Z | CONTRIBUTOR | null | Template page is here:
https://github.com/pydata/pandas/blob/master/doc/source/tutorials.rst
## Samples
www.bearrelroll.com/2013/05/python-pandas-tutorial
bunch on this page
http://datacommunitydc.org/blog/2013/07/python-for-data-analysis-the-landscape-of-tutorials/
http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/
http://conference.scipy.org/scipy2013/tutorial_detail.php?id=109
one mentioned on #3061
http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb
of course from the master! @wesm
http://blog.wesmckinney.com/
http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/
https://bitbucket.org/hrojas/learn-pandas
https://github.com/pydata/pandas/issues/5994 for this: http://pandas-xlsxwriter-charts.readthedocs.org/
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} | 19 | 2014-01-03T21:02:49Z | 2018-05-25T17:40:08Z | 2016-05-09T20:37:17Z | NONE | null | I have reached a corner case in the wonderful groupby().apply() method. A groupby with only **one** group causes the apply method to return the wrong output shape. Instead of a series with a multi-index the result is retunred as a DataFrame with last row index level as columns. :frowning:
``` python
In [1]: import numpy as np
...: import pandas as pd
...: from sklearn.cluster import DBSCAN as DBSCAN
...:
...: print pd.__version__
...:
...: # Generate Test DataFrame
...: NUM_ROWS = 1000
...: NUM_COLS = 10
...: col_names = ['A'+num for num in map(str,np.arange(NUM_COLS).tolist())]
...: index_cols = col_names[:5]
...:
...: # Set DataFrame to have 5 level Hierarchical Index.
...: # Sort the index!
...: df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names)
...: df = df.set_index(index_cols).sort_index()
...: df
...:
...: # Group by first 4 index columns.
...: grp = df.groupby(level=index_cols[:4])
...:
...: # Find index of largest group.
...: big_loc = grp.size().idxmax()
...:
...: # Create function to apply clustering on groups
...: def grp_func(df):
...: """Run clustering on subgroup and return series of results."""
...: db = DBSCAN(eps=1, min_samples=1, metric='euclidean').fit(df.values)
...: return pd.Series(db.labels_, name='cluster_id', index=df.index.get_level_values(4))
...:
0.12.0
In [2]: # Apply clustering on each subgroup of DataFrame
...: out_good = grp.apply(grp_func)
...: out_good
...: out_good.shape
Out[2]: (1000L,)
In [3]: # Select out biggest group wihile keeping index levels and try same apply
...: out_bad = df[[big_loc == a[:4] for a in df.index.values]].groupby(level=index_cols[:4]).apply(grp_func)
...: out_bad
...: out_bad.shape
Out[3]: (1, 7)
In [4]: out_good
Out[4]:
A0 A1 A2 A3 A4
0 0 0 0 0 1
3 0
1 3 0
2 1 1
3 2
3 0
3 3 1
3 0
1 1 1 0
2 0 0
2 1
4 2
3 4 0
4 1
4 0 0
...
4 4 3 0 2 0
1 1 1
3 2
4 0
3 1 0
2 1
4 4 0
4 0 4 0
1 1 3
1 1
2 2
2 0
3 1 1
3 0
4 4 0
Name: cluster_id, Length: 1000, dtype: float64
In [5]: out_bad
Out[5]:
A4 1 1 2 3 3 3 4
A0 A1 A2 A3
3 0 0 3 6 5 3 0 1 4 2
# If you stack the bad result it comes out looking OK, but now I need a workaround for this corner case to use apply.
In [17]: out_bad.stack()
Out[17]: A0 A1 A2 A3 A4
3 0 0 3 1 3
1 6
2 5
3 1
3 4
3 0
4 2
dtype: float64
```
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} | 34 | 2014-01-03T21:19:20Z | 2014-06-30T19:30:22Z | 2014-06-30T19:30:22Z | NONE | null | When using the `read_gbq()` function on a BigQuery table, incorrect results are returned.
I compare the output from `read_gbq()` to that of a CSV export from BigQuery directly. Interestingly, there are the same number of rows in each output - however, there are many duplicates in the `read_gbq()` output.
I'm using Pandas '0.13.0rc1-125-g4952858' on a Mac 10.9 using Python 2.7. Numpy '1.8.0'.
The code I execute to load the data in pandas:
`churn_data = gbq.read_gbq(train_query, project_id = projectid)`
I can't share the underlying data. What additional data/info would be useful for root causing?
The output data is ~400k lines.
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} | 3 | 2014-01-03T21:32:59Z | 2014-04-05T15:36:03Z | 2014-01-04T18:48:48Z | CONTRIBUTOR | null | Should PeriodIndex pass through infer_freq and return freqstr or should this be the user's problem? Either is a simple fix. The latter should just have a better error message. Right now
```
from pandas.tseries.api import infer_freq
from pandas import PeriodIndex
infer_freq(PeriodIndex(start="1/1/1990", periods=20, freq="M"))
```
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I haven't been able to test that this displays correctly as I can't figure out how to build the Sphinx docs.
If someone can tell me how to build the docs, I can test it.
```
bork@kiwi ~/c/p/pandas> pwd
/home/bork/clones/pandas/pandas
bork@kiwi ~/c/p/pandas> python ../doc/make.py html
Error: Cannot find source directory.
Building HTML failed
```
```
bork@kiwi ~/c/p/doc> pwd
/home/bork/clones/pandas/doc
bork@kiwi ~/c/p/doc> python make.py html
Running Sphinx v1.1.3
cannot import name hashtable
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "/opt/anaconda/lib/python2.7/site-packages/sphinx/cmdline.py", line 188, in main
warningiserror, tags)
File "/opt/anaconda/lib/python2.7/site-packages/sphinx/application.py", line 102, in __init__
confoverrides or {}, self.tags)
File "/opt/anaconda/lib/python2.7/site-packages/sphinx/config.py", line 216, in __init__
exec code in config
File "/home/bork/clones/pandas/doc/source/conf.py", line 74, in <module>
import pandas
File "/home/bork/clones/pandas/pandas/__init__.py", line 6, in <module>
from . import hashtable, tslib, lib
ImportError: cannot import name hashtable
> /home/bork/clones/pandas/pandas/__init__.py(6)<module>()
-> from . import hashtable, tslib, lib
```
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} | 4 | 2014-01-04T03:28:50Z | 2014-06-24T04:26:01Z | 2014-01-10T12:26:16Z | NONE | null | closes #5831
@yarikoptic, can you confirm this fixes the problem?
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} | 9 | 2014-01-04T05:27:17Z | 2014-01-26T22:58:21Z | 2014-01-26T22:58:21Z | CONTRIBUTOR | null | while building for Ubuntu 12.04, bulk of tests fail due e.g.
`ValueError: Unknown keyword argument 'truediv'` from numexpr and `AssertionError: Did not use numexpr as expected` in test_expressions
Full log http://neuro.debian.net/_files/_buildlogs/pandas/0.13.0/pandas_0.13.0-2~nd12.04+1_i386.build
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} | 6 | 2014-01-04T15:22:19Z | 2014-01-15T04:31:05Z | 2014-01-15T00:34:38Z | CONTRIBUTOR | null | currently, tail throws an exception on an empty frame but head does not.
```
import pandas as pd
df = pd.DataFrame()
df.tail()
```
traceback:
https://gist.github.com/MichaelWS/8256143
Should this be fixed by returning the frame if empty on calls to iloc?
We can check for empty in tail on length as well or create a new slice with start being None)
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} | 3 | 2014-01-04T15:34:02Z | 2014-07-16T08:45:16Z | 2014-01-04T18:48:48Z | CONTRIBUTOR | null | closes #5841. I'm still not sure we shouldn't just return `freq` if a PeriodIndex is given. Seems easy enough.
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} | 15 | 2014-01-04T16:18:26Z | 2014-06-29T04:32:50Z | 2014-01-15T00:34:38Z | CONTRIBUTOR | null | closes #5846
calling tail on an empty frame threw an exception. In addition, df.tail(0) returns self without indexing.
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} | 0 | 2014-01-04T17:20:44Z | 2014-07-09T13:06:17Z | 2014-01-04T18:53:09Z | CONTRIBUTOR | null | closes #5835
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https://api.github.com/repos/pandas-dev/pandas/issues/5850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5850/events | https://github.com/pandas-dev/pandas/pull/5850 | 25,057,418 | MDExOlB1bGxSZXF1ZXN0MTEyMjk4NzU= | 5,850 | ENH: enhancements to Panel.apply to enable arbitrary functions and multi-dim slicing (GH1148) | {
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} | 0 | 2014-01-04T18:03:12Z | 2014-06-25T15:23:43Z | 2014-01-15T02:53:46Z | CONTRIBUTOR | null | closes #1148
A reproduction of the new docs section
Applying with a Panel will pass a Series to the applied function. If the applied function returns a Series, the result of the application will be a Panel. If the applied function reduces to a scalar, the result of the application will be a DataFrame.
Note Prior to 0.13.1 apply on a Panel would only work on ufuncs (e.g. np.sum/np.max).
```
In [120]: import pandas.util.testing as tm
In [121]: panel = tm.makePanel(5)
In [122]: panel
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [123]: panel['ItemA']
A B C D
2000-01-03 0.166882 -0.597361 -1.200639 0.174260
2000-01-04 -1.759496 -1.514940 -1.872993 -0.581163
2000-01-05 0.901336 -1.640398 0.825210 0.087916
2000-01-06 -0.317478 -1.130643 -0.392715 0.416971
2000-01-07 -0.681335 -0.245890 -1.994150 0.666084
[5 rows x 4 columns]
```
A transformational apply.
```
In [124]: result = panel.apply(lambda x: x*2, axis='items')
In [125]: result
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [126]: result['ItemA']
A B C D
2000-01-03 0.333764 -1.194722 -2.401278 0.348520
2000-01-04 -3.518991 -3.029880 -3.745986 -1.162326
2000-01-05 1.802673 -3.280796 1.650421 0.175832
2000-01-06 -0.634955 -2.261286 -0.785430 0.833943
2000-01-07 -1.362670 -0.491779 -3.988300 1.332168
[5 rows x 4 columns]
```
A reduction operation.
```
In [127]: panel.apply(lambda x: x.dtype, axis='items')
A B C D
2000-01-03 float64 float64 float64 float64
2000-01-04 float64 float64 float64 float64
2000-01-05 float64 float64 float64 float64
2000-01-06 float64 float64 float64 float64
2000-01-07 float64 float64 float64 float64
[5 rows x 4 columns]
```
A similar reduction type operation
```
In [128]: panel.apply(lambda x: x.sum(), axis='major_axis')
ItemA ItemB ItemC
A -1.690090 1.840259 0.010754
B -5.129232 0.860182 0.178018
C -4.635286 0.545328 2.456520
D 0.764068 -3.623586 1.761541
[4 rows x 3 columns]
```
This last reduction is equivalent to
```
In [129]: panel.sum('major_axis')
ItemA ItemB ItemC
A -1.690090 1.840259 0.010754
B -5.129232 0.860182 0.178018
C -4.635286 0.545328 2.456520
D 0.764068 -3.623586 1.761541
[4 rows x 3 columns]
```
A transformation operation that returns a Panel, but is computing the z-score across the major_axis.
```
In [130]: result = panel.apply(lambda x: (x-x.mean())/x.std(), axis='major_axis')
In [131]: result
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: A to D
In [132]: result['ItemA']
A B C D
2000-01-03 0.509389 0.719204 -0.234072 0.045812
2000-01-04 -1.434116 -0.820934 -0.809328 -1.567858
2000-01-05 1.250373 -1.031513 1.499214 -0.138629
2000-01-06 0.020723 -0.175899 0.457175 0.564271
2000-01-07 -0.346370 1.309142 -0.912988 1.096405
[5 rows x 4 columns]
```
Apply can also accept multiple axes in the axis argument. This will pass a DataFrame of the cross-section to the applied function.
```
In [133]: f = lambda x: (x-x.mean(1)/x.std(1))
In [134]: result = panel.apply(f, axis = ['items','major_axis'])
In [135]: result
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [136]: result.loc[:,:,'ItemA']
A B C D
2000-01-03 0.748886 -0.323319 -1.172352 0.370451
2000-01-04 -1.594544 -1.659365 -1.444732 -0.162764
2000-01-05 0.908832 -1.220236 0.237668 0.754405
2000-01-06 -1.024669 -0.081850 -0.792957 0.641960
2000-01-07 -0.884333 -0.472889 -1.474646 -0.671871
[5 rows x 4 columns]
```
This is equivalent to the following
```
In [137]: result = Panel(dict([ (ax,f(panel.loc[:,:,ax])) for ax in panel.minor_axis ]))
In [138]: result
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
Items axis: A to D
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
Minor_axis axis: ItemA to ItemC
In [139]: result.loc[:,:,'ItemA']
A B C D
2000-01-03 0.748886 -0.323319 -1.172352 0.370451
2000-01-04 -1.594544 -1.659365 -1.444732 -0.162764
2000-01-05 0.908832 -1.220236 0.237668 0.754405
2000-01-06 -1.024669 -0.081850 -0.792957 0.641960
2000-01-07 -0.884333 -0.472889 -1.474646 -0.671871
[5 rows x 4 columns]
```
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} | 8 | 2014-01-04T19:37:13Z | 2014-01-22T02:14:39Z | 2014-01-22T02:14:39Z | CONTRIBUTOR | null | ```
======================================================================
FAIL: test_encoding (pandas.io.tests.test_pytables.TestHDFStore)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/io/tests/test_pytables.py", line 856, in test_encoding
tm.assert_frame_equal(store['df'], df)
File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 498, in assert_frame_equal
check_less_precise=check_less_precise)
File "/tmp/buildd/pandas-0.13.0/debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py", line 447, in assert_series_equal
assert_almost_equal(left.values, right.values, check_less_precise)
File "testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2561)
File "testing.pyx", line 93, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1803)
File "testing.pyx", line 69, in pandas._testing.assert_almost_equal (pandas/src/testing.c:1499)
AssertionError: 'f' != 'foo'
```
pytables 2.3.1-3
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} | 4 | 2014-01-04T23:31:59Z | 2014-04-09T03:03:32Z | 2014-04-09T03:03:32Z | CONTRIBUTOR | null | Likely take me a while to work my way through this again completely. Maybe I'm just misremembering this code, but should MS map to millisecond or month begin? (I was looking for a function to easily and generally say that M, MS, M-Jan, M-Dec, etc. are all monthly frequencies.)
```
pd.tseries.frequencies.get_standard_freq('MS')
pd.tseries.frequencies.get_freq_group('MS')
```
vs. things like
```
pd.tseries.frequencies.get_offset('MS')
```
and all the rest of the code that gives freq and freqstr MS for month start code like.
```
pd.tseries.offsets.MonthBegin().freqstr
```
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https://github.com/ipython/ipython/blob/master/tools/gh_api.py
https://github.com/ipython/ipython/blob/master/tools/github_stats.py
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re SetttingWithCopy.
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compatibility.
Fixes #5857, #5855, #5845.
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related #5853
DataFrame._ixs will properly record a cache change (similar to _get_item_cache)
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} | 7 | 2014-01-06T15:38:36Z | 2014-01-15T11:56:41Z | 2014-01-15T02:08:37Z | CONTRIBUTOR | null | I'm really salivating at the chance to use the `._metadata` and `__finalize__` mechanisms in pandas-0.13-dev to create my own subclass of DataFrame and to have metadata propagate after calling functions inherited from DataFrame like `dropna()`, `resample()` etc.
Using the latest 0.13-dev version of Pandas, I think I might have bumped into a small bug (although I'm not sure if this is a bug or not??):
`._metadata` propagates after calling `.resample(rule='D')` and `dropna()` but not after calling `.resample(rule='D', how='max')`.
More details, including the full code of my subclass, are given under the "experiments" heading of this issue: https://github.com/nilmtk/nilmtk/issues/83
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} | 7 | 2014-01-06T21:34:29Z | 2014-01-07T00:41:00Z | 2014-01-07T00:41:00Z | NONE | null | Hi,
I have a situation where I have "far away" end dates (3 examples included below). Normal Python strptime conversion handles this correctly, but the to_datetime functionality does a Martin McFly and ends up in the 1800s. See this example that reproduces it:
```
# input
time_ser = pd.Series([np.nan, '2013-04-08 00:00:00.000', '2013-06-04 00:00:00.000', '2013-09-06 00:00:00.000', '2013-10-02 00:00:00.000', '2013-10-03 00:00:00.000', '2013-10-30 00:00:00.000', '2013-10-31 00:00:00.000', '2013-11-30 00:00:00.000', '2013-12-02 00:00:00.000', '2013-12-17 00:00:00.000', '2013-12-31 00:00:00.000', '2014-01-15 00:00:00.000', '2014-01-31 00:00:00.000', '2014-02-15 00:00:00.000', '2014-02-28 00:00:00.000', '2014-03-15 00:00:00.000', '2014-03-31 00:00:00.000', '2014-06-15 00:00:00.000', '2014-06-30 00:00:00.000', '2099-12-31 00:00:00.000', '2999-12-31 00:00:00.000', '9990-12-31 00:00:00.000', '9999-12-31 00:00:00.000'])
# see last 3 results
pd.to_datetime(time_ser, '%Y-%m-%d %H:%M:%S.%f')
# normal python
import math
for x in sorted(time_ser.unique()):
if isinstance(x, basestring):
print '{} converts to {}'.format( x, datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f'))
# This is the erroneous result:
21 1830-11-22 00:50:52.580896768
22 1807-03-30 05:56:08.066277376
23 1816-03-29 05:56:08.066277376
```
I'm running Pandas 0.12 on Ubuntu 12.04LTS with Python 2.7.3. I could not find this issue registered yet.
Kind regards,
Carst
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} | 0 | 2014-01-06T23:47:36Z | 2014-06-27T05:14:01Z | 2014-01-07T00:41:00Z | CONTRIBUTOR | null | closes #5863
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] | closed | false | null | [] | null | 7 | 2014-01-07T07:10:17Z | 2014-01-15T02:32:45Z | 2014-01-15T02:32:45Z | NONE | null | When DataFrame().values[0,0] is a 0 integer, the excel output is corrupted.
When opening the excel file in Microsoft Excel 2010 I get:
```
Excel found unreadable content in 'a.xlsx'.
Do you want to recover the contents of this workbook?
If you trust the source of this workbook, click Yes.
```
When I click Yes, I get:
```
Excel was able to open the file by repairing or removing the unreadable content.
Removed Part: /xl/sharedStrings.xml part with XML error.
(Strings) Illegal xml character. Line 1, column 95.
Removed Records: Cell information from /xl/worksheets/sheet1.xml part
```
The cell that should have been 0 is now empty.
Here is an example:
``` python
import pandas
import numpy as np
a = np.arange(25).reshape(5,5)
pandas.DataFrame(a).to_excel('./a.xlsx') # this is 'unreadable'
pandas.DataFrame(a.astype(float)).to_excel('./afloat.xlsx') # this is OK
a[0,0] = 1
pandas.DataFrame(a).to_excel('./a1.xlsx') # this is OK
```
Packages: (from 'pip list' as I couldn't find [ci/print_versions.py](https://github.com/pydata/pandas/blob/master/ci/print_versions.py) mentioned in [CONTRIBUTING](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md))
pandas (0.12.0)
xlrd (0.9.2)
openpyxl (1.6.2)
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https://api.github.com/repos/pandas-dev/pandas/issues/5866 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5866/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5866/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5866/events | https://github.com/pandas-dev/pandas/pull/5866 | 25,156,500 | MDExOlB1bGxSZXF1ZXN0MTEyNzkwMjI= | 5,866 | DOC: Flesh out the R comparison section of docs (GH3980) | {
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#3980
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https://api.github.com/repos/pandas-dev/pandas/issues/5867 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5867/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5867/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5867/events | https://github.com/pandas-dev/pandas/issues/5867 | 25,180,063 | MDU6SXNzdWUyNTE4MDA2Mw== | 5,867 | DOC: shouldn't the call signature changes in 0.13 for Panel.reindex (and others) be listed explicitly in the API changes? | {
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} | [] | closed | false | null | [] | null | 15 | 2014-01-07T16:54:39Z | 2014-01-08T12:32:44Z | 2014-01-07T18:55:11Z | CONTRIBUTOR | null | ie. was:
`Panel.reindex(major=None, minor=None, method=None, major_axis=None, minor_axis=None, copy=True, **kwargs)`
now:
`Panel.reindex(items=None, major_axis=None, minor_axis=None, **kwargs)`
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} | 5 | 2014-01-07T18:57:13Z | 2014-07-16T08:45:37Z | 2014-01-07T19:25:21Z | NONE | null | recent changes (fe9c0ae0) broke setup.py for some users:
https://github.com/pydata/pandas/issues/5867#issuecomment-31759183
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} | 0 | 2014-01-07T20:10:08Z | 2014-01-27T11:32:09Z | 2014-01-07T20:44:31Z | CONTRIBUTOR | null | ```
In [30]: DataFrame(dict(A = Timestamp('20130101'), B = np.arange(5))).groupby('A')['A'].max()
Out[30]:
A
2013-01-01 1356998400000000000
Name: A, dtype: int64
```
should match
```
In [31]: DataFrame(dict(A = Timestamp('20130101'), B = np.arange(5))).groupby('A')['A'].apply(lambda x: x.max())
Out[31]:
A
2013-01-01 2013-01-01 00:00:00
dtype: datetime64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5871 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5871/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5871/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5871/events | https://github.com/pandas-dev/pandas/issues/5871 | 25,201,197 | MDU6SXNzdWUyNTIwMTE5Nw== | 5,871 | Possible bug in DataFrame.plot when duplicates in DatetimeIndex | {
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} | [] | closed | false | null | [] | null | 2 | 2014-01-07T22:10:30Z | 2014-01-08T07:40:27Z | 2014-01-08T07:40:27Z | NONE | null | There seems to be a bug when plotting dataframes that has duplicates in a DatetimeIndex. It seems to only be triggered when there is at least one more non-duplicate item in the index as in the first two cases below. These result in a:
``` python
ValueError: Shape of passed values is (1, 3), indices imply (1, 2)
```
While the last case seems to be fine.
``` python
# Case 1: fails
df = pandas.DataFrame(
index = pandas.to_datetime([
"2002-07-18 13:49:49",
"2002-07-18 13:49:49",
"2002-07-18 10:38:58"
]),
data = [1, 1, 1]
)
df.plot()
# Case 2: fails
df = pandas.DataFrame(
index = pandas.to_datetime([
"2002-07-19 10:51:29",
"2002-07-18 13:49:49",
"2002-07-18 13:49:49",
]),
data = [1, 1, 1]
)
df.plot()
# Case 3: works
df = pandas.DataFrame(
index = pandas.to_datetime([
"2002-07-18 13:49:49",
"2002-07-18 13:49:49",
]),
data = [1, 1]
)
df.plot()
```
This is with a recent master checkout: 52a82efe98a52df7a2dbb1208df19622ccdff39c
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} | [] | closed | false | null | [] | null | 1 | 2014-01-07T23:47:59Z | 2014-01-08T22:14:07Z | 2014-01-07T23:49:11Z | NONE | null | df['diff'] = df.apply(lambda x: (datetime.now() - x['Date']).days, axis=1)
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} | 5 | 2014-01-08T06:33:14Z | 2015-01-02T15:41:39Z | 2015-01-02T15:41:39Z | NONE | null | When (at least) one element in a MultiIndex contains a NaN, has_duplicates starts to behave strangely:
``` python
>>> idx = pd.MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
>>> idx
MultiIndex
[(101, 3.5), (102, nan)]
>>> idx.has_duplicates
True
>>> idx.get_duplicates()
[]
```
I would expect has_duplicates to return False here, because 102 is not the same as 101.
I would also expect it to return false for the MultiIndex
``` python
MultiIndex
[(101, 3.5), (101, nan)]
```
since 3.5 != NaN, but this case is more debatable.
This is important because you can't call .unstack() on a series with a MultiIndex for which has_duplicates is True, even if the MultiIndex is of high dimension and the dimensions containing the NaN(s) are not involved in the operation.
This is with pandas 0.12.0
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} | 4 | 2014-01-08T06:39:12Z | 2014-02-04T05:40:06Z | 2014-01-09T17:15:11Z | NONE | null | The API design issue discussed in #5655 again. pd.read_json guesses that a 150KB long string is a filename.
``` python
======================================================================
ERROR: test_round_trip_exception_ (pandas.io.tests.test_json.test_pandas.TestPandasContainer)
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\workspace\pandas_tests@2\BITS\64\PYTHONVER\33\pandas\util\testing.py", line 1057, in network_wrapper
return t(*args, **kwargs)
File "C:\workspace\pandas_tests@2\BITS\64\PYTHONVER\33\pandas\io\tests\test_json\test_pandas.py", line 593, in test_round_trip_exception_
result = pd.read_json(s)
File "C:\workspace\pandas_tests@2\BITS\64\PYTHONVER\33\pandas\io\json.py", line 176, in read_json
if os.path.exists(filepath_or_buffer):
File "c:\envs\33-64\lib\genericpath.py", line 18, in exists
os.stat(path)
nose.proxy.ValueError: ValueError: path too long for Windows
-------------------- >> begin captured stdout << ---------------------
Failed: ValueError('path too long for Windows',)
--------------------- >> end captured stdout << ----------------------
```
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} | 1 | 2014-01-08T09:21:06Z | 2014-06-26T12:22:49Z | 2014-01-15T16:15:01Z | CONTRIBUTOR | null | Doesn't close #3850 but at least the `%in%` operator is now in the comparison with R docs. I've lumped it with the `match` function since thats the page you see the `%in%` operator in the R docs http://finzi.psych.upenn.edu/R/library/base/html/match.html.
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} | 8 | 2014-01-08T12:56:58Z | 2018-07-06T22:02:59Z | 2018-07-06T21:39:26Z | CONTRIBUTOR | null | Not sure if that's a pandas or a mathplotlib bug.
``` python
import pandas
df = pandas.DataFrame({"year":[1,2,3,4],"name": [u"abc", u"bcd", u"cde", u"abc"]})
df["year"].hist() # Works
df["name"].hist()
TypeError Traceback (most recent call last)
<ipython-input-11-79a5ab7bbbd5> in <module>()
----> 1 df["name"].hist()
C:\portabel\Python27\lib\site-packages\pandas\tools\plotting.pyc in hist_series(self, by, ax, grid, xlabelsize, xrot, ylabelsize, yrot, figsize, **kwds)
2113 values = self.dropna().values
2114
-> 2115 ax.hist(values, **kwds)
2116 ax.grid(grid)
2117 axes = np.array([ax])
C:\portabel\Python27\lib\site-packages\matplotlib\axes.pyc in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
8325 # this will automatically overwrite bins,
8326 # so that each histogram uses the same bins
-> 8327 m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
8328 m = m.astype(float) # causes problems later if it's an int
8329 if normed and not stacked:
C:\portabel\Python27\lib\site-packages\numpy\lib\function_base.pyc in histogram(a, bins, range, normed, weights, density)
174 else:
175 range = (a.min(), a.max())
--> 176 mn, mx = [mi+0.0 for mi in range]
177 if mn == mx:
178 mn -= 0.5
TypeError: coercing to Unicode: need string or buffer, float found
```
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} | 6 | 2014-01-08T13:38:56Z | 2014-01-08T17:23:27Z | 2014-01-08T17:23:12Z | NONE | null | For some reason, with 0.13, an empty object Series is interpreted as a boolean one, when using to fetch elements in another Series:
```
pandas.Series(['A', 'B'])[pandas.Series([], dtype=object)]
```
The following works though, so the dtype confusion seems to be confined to empty Series:
```
pandas.Series(['A', 'B'])[pandas.Series(['C'], dtype=object)]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5878/events | https://github.com/pandas-dev/pandas/issues/5878 | 25,243,331 | MDU6SXNzdWUyNTI0MzMzMQ== | 5,878 | pivot function on timezone aware objects does not preserve timezone info in resulting dataframe index | {
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} | 1 | 2014-01-08T14:33:35Z | 2014-05-12T13:34:58Z | 2014-05-12T13:34:58Z | NONE | null | This bug is in 0.12.0
Using example DataFrame like below:
```
col data time
0 1 0 2013-03-22 11:00:00-04:00
1 2 1 2013-03-22 15:00:00-04:00
2 2 2 2013-03-22 11:00:00-04:00
3 1 3 2013-03-22 15:00:00-04:00
```
After pivoting, the old behavior in 0.10.1 properly preserved the timezone info in the index, resulting in a new DataFrame like such:
```
col 1 2
time
2013-03-22 11:00:00-04:00 0 2
2013-03-22 15:00:00-04:00 3 1
```
However in 0.12.0 this behavior is lost resulting in an index that does not have the timezone information
```
col 1 2
time
2013-03-22 15:00:00 0 2
2013-03-22 19:00:00 3 1
```
Below is the code to reproduce this issue:
``` python
import pandas
print pandas.__version__
import datetime
import pandas as pn
import pytz
est = pytz.timezone('US/Eastern')
dt1 = est.localize(datetime.datetime(2013,3,22,11,0,0))
dt2 = est.localize(datetime.datetime(2013,3,22,15,0,0))
df = pn.DataFrame({'time': [dt1, dt2, dt1, dt2], 'col': [1, 2, 2, 1], 'data': range(4)})
pivotDf = df.pivot('time', 'col', 'data')
print df
print pivotDf
print pivotDf.index
```
the output from 0.10.1 is:
```
0.10.1
col data time
0 1 0 2013-03-22 11:00:00-04:00
1 2 1 2013-03-22 15:00:00-04:00
2 2 2 2013-03-22 11:00:00-04:00
3 1 3 2013-03-22 15:00:00-04:00
col 1 2
time
2013-03-22 11:00:00-04:00 0 2
2013-03-22 15:00:00-04:00 3 1
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-03-22 11:00:00, 2013-03-22 15:00:00]
Length: 2, Freq: None, Timezone: US/Eastern
```
the output from 0.12.0 is:
```
0.12.0
col data time
0 1 0 2013-03-22 11:00:00-04:00
1 2 1 2013-03-22 15:00:00-04:00
2 2 2 2013-03-22 11:00:00-04:00
3 1 3 2013-03-22 15:00:00-04:00
col 1 2
time
2013-03-22 15:00:00 0 2
2013-03-22 19:00:00 3 1
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-03-22 15:00:00, 2013-03-22 19:00:00]
Length: 2, Freq: None, Timezone: None
```
Notice the None in the "Timezone: " infor of the DatetimeIndex.
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} | 24 | 2014-01-08T15:17:03Z | 2014-06-15T00:35:15Z | 2014-01-15T00:26:34Z | CONTRIBUTOR | null | Using regex=False can be faster when full regex searching is not needed.
See http://stackoverflow.com/q/20951840/190597
Example use case:
```
import string
import itertools as IT
import numpy as np
from pandas import Series
def make_series(letters, strlen, size):
return Series(
np.fromiter(IT.cycle(letters), count=size*strlen, dtype='|S1')
.view('|S{}'.format(strlen)))
many = make_series('matchthis'+string.uppercase, strlen=19, size=10000) # 31% matches
few = make_series('matchthis'+string.uppercase*42, strlen=19, size=10000) # 1% matches
```
```
In [115]: %timeit many.str.contains('matchthis')
100 loops, best of 3: 4.93 ms per loop
In [116]: %timeit many.str.contains('matchthis', regex=False)
100 loops, best of 3: 2.44 ms per loop
In [118]: %timeit few.str.contains('matchthis')
100 loops, best of 3: 4.81 ms per loop
In [119]: %timeit few.str.contains('matchthis', regex=False)
100 loops, best of 3: 2.37 ms per loop
```
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https://api.github.com/repos/pandas-dev/pandas/issues/5880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/5880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/5880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/5880/events | https://github.com/pandas-dev/pandas/pull/5880 | 25,247,701 | MDExOlB1bGxSZXF1ZXN0MTEzMjU1MDk= | 5,880 | BUG: Regresssion in handling of empty Series as indexers to Series (GH5877) | {
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} | 0 | 2014-01-08T15:19:02Z | 2014-06-16T09:26:31Z | 2014-01-08T17:23:12Z | CONTRIBUTOR | null | closes #5877
```
In [1]: s = Series(['A', 'B'])
In [2]: s[Series(['C'], dtype=object)]
Out[2]:
C NaN
dtype: object
In [3]: s[Series([], dtype=object)]
Out[3]: Series([], dtype: object)
```
This didn't work in 0.12
```
In [4]: s[Series([], dtype=bool)]
Out[4]: Series([], dtype: object)
```
This raises though (in both 0.12 and 0.13)
```
In [5]: s[Series([True], dtype=bool)]
IndexingError: Unalignable boolean Series key provided
``
```
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} | 11 | 2014-01-08T15:35:29Z | 2021-04-11T04:03:28Z | 2021-04-11T04:03:27Z | CONTRIBUTOR | null | related #4130
I haven't looked at the code at how easy this is to do, but it looks like things like expanding_apply (moving_apply too I suspect) expects a float returned from the function. It might make sense to have an optional no_return flag (or just be smart about it, though perhaps it makes sense to have a flag as a sanity check for the user.)
Use case, I'm using expanding_apply to run some code in a subprocess and write to disk. I'm able to get around the expectation by having my function return 0.0 or something, but it's a bit of a hack.
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} | [] | closed | false | null | [] | null | 2 | 2014-01-08T16:10:04Z | 2015-11-05T12:55:52Z | 2014-01-08T16:14:09Z | NONE | null | There are two groups below, so I expected the function `f` to be called only twice, but you can see in the output below, it was called 3 times.
I am using version 0.13.0rc1-92-gf6fd509
Code
```
import pandas as pd
from cStringIO import StringIO
# get sample dataframe
s = '''Col1 Col2 Col3
1 1 1
1 2 2
3 3 3'''
df = pd.read_csv(StringIO(s), sep=' ')
# to count how many times f is called
i = 0
# given the dataframe above, shouldn't this be called only twice?
def f(df):
global i
i += 1
print '[%d] Calling function' % i
print 'Performing complex aggregation'
return 'Using result %d' % i
print df.groupby('Col1').apply(f)
```
Output
```
[1] Calling function
Performing complex aggregation
[2] Calling function
Performing complex aggregation
[3] Calling function
Performing complex aggregation
Col1
1 Using result 2
3 Using result 3
dtype: object
```
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} | 11 | 2014-01-08T18:19:03Z | 2014-09-23T14:24:46Z | 2014-09-23T14:24:46Z | NONE | null | It looks newey-west adjustment is not working properly in OLS when 'cluster' is set to 'time' or 'entity'. Specifically, pandas.stats.plm.py lines 791-794 don't have any effect. Should that be replaced with: xox = math.newey_west(m, nw_lags, nobs, df, nw_overlap)?
Here is some code to reproduce the issue.
import numpy
from pylab import *
from pandas import *
T = 100
panel_size = 3
data_dimensions = [T, panel_size]
xs_per_y = WidePanel({
'predictor a' : numpy.random.normal(size=data_dimensions),
'predictor b' : numpy.random.normal(size=data_dimensions)
})
# y = B_a + B_b + noise
ys = xs_per_y['predictor a'] + xs_per_y['predictor b'] + numpy.random.normal(size=data_dimensions)
print ols(y=ys, x=xs_per_y, pool=True, cluster = 'time')
# we expect the following t-stats to be smaller, but they are the same as the previous OLS
print ols(y=ys, x=xs_per_y, pool=True, cluster = 'time', nw_lags=10)
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} | [] | closed | false | null | [] | null | 1 | 2014-01-08T19:46:08Z | 2014-01-08T21:28:34Z | 2014-01-08T20:03:17Z | CONTRIBUTOR | null | Surely this is a typo.
I noticed this because I was going add more robust parsing to datetools.parse [1], and I was surprised to find this function there. Two suggestions / questions. 1) Why not just roll this into the parse function like statsmodels instead of having it fall down on things like "1989Q1"? Extra function seems unneeded to me.
2) Why return the "resolution"? The parse function doesn't do this, and I'd expect them to have the same return signature. Optionally, the parse function should also try to infer a frequency.
[1] https://github.com/statsmodels/statsmodels/blob/master/statsmodels/tsa/base/datetools.py#L117
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} | 22 | 2014-01-08T20:20:38Z | 2016-08-29T12:18:28Z | 2016-08-28T11:04:22Z | CONTRIBUTOR | null | - [ ] document in docstring / cookbook / timeseries.rst usage of combing integer columns into YYYYMMDD and parsing to datetimes
- [ ] clean up imports of `_parse/parse` from dateutils
- [ ] `to_period` to create `PeriodIndex`
I started to make a PR #5885 to fix what I thought was a typo before realizing that this was intentional. It still doesn't make much sense to me though why I would want this return of datetime, _result, resolution. Maybe the whole approach could use a refactor. Otherwise what am I missing?
My typical use case for datetools.parse is something like
`dates = map(lambda x : parse(' '.join(x)), zip(df.day, df.month, df.year))`
A couple of questions.
1. Is there a better way to do this vectorized to datetime operation? AFAICT pd.to_datetime doesn't actually use the 'advanced' parsing for quarterly and monthly dates.
2. Should this all be unified? Assuming I haven't missed it, should there be, e.g., a function `pd.parse_dates` that is a general parser for both strings and works on array-like input, deprecating datetools.parse, datetools.parse_time_string, and datetools.to_datetime. This function could also have a flag to return Period or TimeStamp objects with frequency information instead of the current return of the parsed object and resolution. Given that I'm having to do things like
`dates = [x[0] for x in map(lambda x : parse(' '.join(x)), zip(df.day, df.month, df.year))]`
Thoughts?
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} | 12 | 2014-01-09T05:55:57Z | 2016-11-11T19:28:50Z | 2016-05-26T23:56:09Z | CONTRIBUTOR | null | I am getting the following warning:
```
/usr/local/lib/python2.7/dist-packages/pandas-0.13.0rc1_78_g142ca62-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1050:
DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.
data = self._reader.read(nrows)
```
but I can find no documentation for `low_memory`
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} | 2 | 2014-01-09T08:00:54Z | 2014-01-13T16:18:13Z | 2014-01-13T16:18:13Z | CONTRIBUTOR | null | This issue is closely related to bug #2680 and I believe it is also a bug.
The problem appears when adding a number of BDay n with n > 5, n % 5 == 0 to
a starting date which is not on offset.
For instance
``` python
dt = Timestamp('20140105') # Sunday
print (dt + BDay(10)).strftime('%A')
```
```
Sunday
```
We except Friday and not Sunday. Likewise, Friday is 'gone' from the week in the following loop:
``` python
for i in range(8,12):
print i, (Timestamp('20140104') + BDay(i)).strftime('%A')
```
```
8 Wednesday
9 Thursday
10 Saturday
11 Monday
```
It does yield the correct results for substraction however:
``` python
for i in range(9,13):
print i, (Timestamp('20140105') - BDay(i)).strftime('%A')
```
```
9 Tuesday
10 Monday
11 Friday
12 Thursday
```
I have patched tseries/offsets.py with a couple of lines and it corrects the problem without apparently breaking any of the existing tests (version 0.13.0-94-g0bab303). I am not super familiar with git but can try to write a PR about this if appropriate.
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} | 34 | 2014-01-09T10:03:52Z | 2016-03-25T19:44:54Z | 2014-02-18T02:27:11Z | NONE | null | I'm reading some excel spreadsheets (xlsx format) into pandas using `read_excel`, which generally works great. The problem I have is that when a column contains numbers, pandas converts these to float64 type, and I would like them to be treated as strings. After reading them in, I can convert the column to str:
```
my_frame.my_col = my_frame.my_col.astype('str')
```
This works as far as assigning the right type to the column, but when I view the values in this column, the strings are formatted in scientific-format e.g. 8.027770e+14, which is not what I want. I like to be able to tell Pandas to read the columns as strings. My current solution involves dropping down to xlrd to read the spreadsheet.
See stackoverflow question: http://stackoverflow.com/q/20970483/690890
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} | 0 | 2014-01-09T13:04:20Z | 2014-07-12T14:28:53Z | 2014-01-09T15:22:22Z | CONTRIBUTOR | null | related #5727
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} | 19 | 2014-01-09T14:53:03Z | 2014-06-15T07:02:50Z | 2014-01-09T15:53:24Z | NONE | null | Our CI situation could be better. Travis is great but doesn't do windows or 32 bit, the SPARC
buildbot provided by @yarikoptic lives in another location and the windows jenkins box isn't
publicly viewable. That means :red_circle: builds have been going unnoticed, sometimes for months
until it's time to do a release and we inevitably go spelunking through commits from 2 months
ago to find the problem.
Each platform features some uncorrelated failures so grabbing as much as we can will improve
our overall testing coverage.
To address these types of problems, I've started working on a side-project called ScatterCI.
You can check out the beta version here: [](http://scatterci.github.io/ScatterCI-Pandas/).
This PR should start getting travis jobs on the status page and the windows jenkins box already
submits results from it's nightlies.
If you hit problems with the status page, please open an issue up on the [ScatterCI-Pandas](http://scatterci.github.io/ScatterCI-Pandas/issues/new) repo, not here.
It's not a Pandas thing just a service useful to the project.
@yarikoptic, @neirbowj if you're interested, I can send you an API key and have you
and your favorite platform join the CI fun. It should just take a few minutes to setup and
a full-blown CI server is not even required, a nightly cron job would work just as well.
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} | 3 | 2014-01-09T16:38:56Z | 2014-06-26T01:49:11Z | 2014-01-09T17:15:11Z | CONTRIBUTOR | null | closes #5874
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} | 2 | 2014-01-09T16:40:22Z | 2014-01-27T09:52:34Z | 2014-01-15T19:00:50Z | NONE | null | Intermittent, seen on 2.7 64bits
``` python
======================================================================
ERROR: test_round_trip_frame_string (classname)
----------------------------------------------------------------------
Traceback (most recent call last):
File "c:\Python27-AMD64\Lib\unittest\case.py", line 331, in run
testMethod()
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\io\tests\test_clipboard.py", line 63, in test_round_trip_frame_string
self.check_round_trip_frame(dt,excel=False)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\io\tests\test_clipboard.py", line 50, in check_round_trip_frame
data.to_clipboard(excel=excel, sep=sep)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\core\generic.py", line 934, in to_clipboard
clipboard.to_clipboard(self, excel=excel, sep=sep, **kwargs)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\io\clipboard.py", line 76, in to_clipboard
clipboard_set(objstr)
File "C:\workspace\pandas_tests\BITS\64\PYTHONVER\27\pandas\util\clipboard.py", line 72, in winSetClipboard
ctypes.cdll.msvcrt.strcpy(ctypes.c_char_p(pchData), bytes(text))
WindowsError: exception: access violation writing 0x0000000000000000
```
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} | 3 | 2014-01-09T17:45:23Z | 2014-06-23T20:21:50Z | 2014-01-09T23:26:26Z | CONTRIBUTOR | null | ...ounds index for columns. This is a regression as it used to work in pandas 0.12.0. The code incorrectly checks the column index against the len() of the dataframe to see if it is in bounds not against the number of columns. Have added 2 tests with non-square dataframes to test that the fix works.
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} | 2 | 2014-01-10T01:13:38Z | 2014-07-19T14:13:11Z | 2014-07-01T15:28:58Z | NONE | null | Minimal snippet:
``` python
import pandas as pd
from numpy.random import randn
import matplotlib.pyplot as plt
d = pd.DataFrame({'one':randn(5), 'two':randn(5), 'three':randn(5), 'label':['label'] * 5},
columns = ['one','two','three', 'label'])
bp= d.boxplot(by='label', rot=45)
plt.savefig('xxx.png')
```
Output produced:

Issue:
The label on the left is rotated but the label on the right is not.
Versions:
pandas '0.12.0'
matplotlib '1.3.1'
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} | [] | closed | false | null | [] | null | 9 | 2014-01-10T02:08:01Z | 2014-07-10T14:59:30Z | 2014-01-18T03:34:46Z | CONTRIBUTOR | null | When reading data from a generator type collection only reads the firsts number (count) of values.
First step to solve #2305, known the length of the data to allocate memory.
- Add count parameter to Series, DataFrame, and DataFrame.from_records().
- In DataFrame.from_records() deprecate the existing parameter nrows. Count it's more general and only refers to quantity of data units. Also exists on numpy API (fromiter).
- Some refactor in DataFrame.from_records().
- Add tests too.
- Lack of release docs for now.
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] | closed | false | null | [] | null | 31 | 2014-01-10T10:31:06Z | 2014-01-25T22:11:01Z | 2014-01-20T08:21:30Z | MEMBER | null | I always build the docs with Sphinx 1.1.3, but recently (in december) a new Sphinx version was released: 1.2.
And it seems that the docs won't build with this new version (also mentioned in #5530 and https://github.com/pydata/pandas/pull/5701#discussion_r8715560). You get the following error:
```
File "C:\Users\vdbosscj\Scipy\pandas-joris\doc\source\_templates\autosummary/class.rst", line 1, in top-level template code
{% extends "!autosummary/class.rst" %}
File "C:\Anaconda\envs\devel2\lib\site-packages\jinja2\utils.py", line 339, in get
return self[key]
File "C:\Anaconda\envs\devel2\lib\site-packages\jinja2\utils.py", line 389, in __getitem__
if self._queue[-1] != key:
RuntimeError: maximum recursion depth exceeded in cmp
```
This has something to do with the class.rst template for autosummary (which we extend in [doc/source/_templates/autosummary/class.rst](https://github.com/pydata/pandas/blob/master/doc/source/_templates/autosummary/class.rst))
If you remove the template class.rst, the docs will build (you only will get a bunch of warnings because of links to non-existing documents, because not all docstring pages are built (that was the reason for the template)).
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} | 9 | 2014-01-10T17:07:04Z | 2016-01-31T17:58:48Z | 2016-01-31T17:58:48Z | NONE | null | This would allow chaining operations like:
``` python
pd.read_csv('imdb.txt')
.sort(columns='year')
.filter(lambda x: x['year']>1990) # <---this is missing in Pandas
.to_csv('filtered.csv')
```
For current alternatives see:
http://stackoverflow.com/questions/11869910/pandas-filter-rows-of-dataframe-with-operator-chaining
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