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} | 27 | 2015-03-06T13:18:12Z | 2017-01-22T12:15:28Z | 2017-01-22T12:15:10Z | CONTRIBUTOR | null | so we should put up a deprecation notice on `rpy`imports that in the future the user should use `rpy2` directly rather than the less-supported `pandas.rpy` converters that we can take action say in 0.17.0
discussed extensivley in #7385
xref #7385, #9187 #7309, #8255
- [x] Update the docs with some example of how to port your pandas examples to the `rpy2` API -> done with #10075
- [ ] Go over the functionality in pandas and check if some things should be ported to `rpy2` (what still works better in pandas?)
- [ ] Handling of NaN values (comment below: https://github.com/pydata/pandas/issues/9602#issuecomment-94716332)
- [ ] index is not preserved in `ri2py` -> this should be handled in https://bitbucket.org/rpy2/rpy2/issues/285
- [ ] The closed PRs -> push them to `rpy2`?
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} | 2 | 2015-03-06T16:45:01Z | 2015-04-29T23:48:14Z | 2015-04-29T23:48:14Z | NONE | null | ``` python
df = pandas.DataFrame({'a': [1, 0, 0, 0]})
df.groupby(pandas.cut(df.a, [0, 1, 2, 3, 4])).apply(lambda x: len(x))
Out[3]:
a
(0, 1] 1
(1, 2] 0
(2, 3] 0
(3, 4] 3
dtype: int64
```
final group length should be 0 not 3
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} | 7 | 2015-03-06T18:58:45Z | 2015-10-08T22:34:27Z | null | MEMBER | null | ```
In [1]: import pandas
In [2]: institute = 'Nan-Kai Institute of Technology'
In [3]: words = institute.replace("-", ' ').lower().split()
In [4]: words_idces = pandas.Series(range(len(words)), index=words)
In [5]: len([words_idces.loc[w] for w in words])
Out[5]: 5
In [6]: words_idces.to_hdf('/tmp/test.hdf', 'precious_idces')
In [7]: words_idces = pandas.read_hdf('/tmp/test.hdf', 'precious_idces')
In [8]: len([words_idces.loc[w] for w in words])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-8-d54a661c6a50> in <module>()
----> 1 len([words_idces.loc[w] for w in words])
<ipython-input-8-d54a661c6a50> in <listcomp>(.0)
----> 1 len([words_idces.loc[w] for w in words])
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1197 return self._getitem_tuple(key)
1198 else:
-> 1199 return self._getitem_axis(key, axis=0)
1200
1201 def _getitem_axis(self, key, axis=0):
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1339
1340 # fall thru to straight lookup
-> 1341 self._has_valid_type(key, axis)
1342 return self._get_label(key, axis=axis)
1343
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in _has_valid_type(self, key, axis)
1301 raise
1302 except:
-> 1303 error()
1304
1305 return True
/home/pietro/nobackup/repo/pandas/pandas/core/indexing.py in error()
1288 "cannot use label indexing with a null key")
1289 raise KeyError("the label [%s] is not in the [%s]" %
-> 1290 (key, self.obj._get_axis_name(axis)))
1291
1292 try:
KeyError: 'the label [nan] is not in the [index]'
```
I didn't have time to investigate whether the problem is in pandas or pytables.
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} | 4 | 2015-03-06T20:01:46Z | 2015-03-07T15:09:41Z | 2015-03-06T23:09:16Z | CONTRIBUTOR | null | closes #9572
Added days_in_month property (compatibility alias daysinmonth) to Timestamp, DatetimeIndex, Period, PeriodIndex,
Series.dt.
Although not mentioned in the GH issue, I added the property to Period and PeriodIndex to be consistent with the others: dayofweek, dayofyear, etc.
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} | 2 | 2015-03-06T20:08:11Z | 2020-10-29T01:18:57Z | 2020-10-29T01:18:57Z | CONTRIBUTOR | null | we have a slightly odd naming convention for several index (and .dt) accessors
`dayofweek`
`dayofyear`
(and adding `daysinmonth` in #9605)
so these should prob be `day_of_week`
we should be able to maintain compat on these and just add the new ones.
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} | 1 | 2015-03-06T22:14:18Z | 2015-06-26T23:29:51Z | 2015-06-26T23:29:51Z | CONTRIBUTOR | null | so that instead of always copying the returned blocks we can have the option
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} | 5 | 2015-03-07T00:51:48Z | 2018-07-06T22:21:31Z | 2018-07-06T22:21:24Z | CONTRIBUTOR | null | looks like `pymsyql` just got updated from 0.6.3 to 0.6.6, see here: https://travis-ci.org/pydata/pandas/jobs/53409636
[changelog](https://github.com/PyMySQL/PyMySQL/blob/master/CHANGELOG)
and started breaking our builds (only 3.4 oddly)
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| null | 0 | 2015-03-07T03:48:07Z | 2020-05-07T03:37:21Z | null | CONTRIBUTOR | null | Starting this master issue for things I see while going through the plotting kwargs for the `.plot` accessor.
### hist_frame
- [ ] `xlabelsize`, `ylabelsize`: nothing specific to hist. Could be for all (non-pie) plot types
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} | 2 | 2015-03-07T14:00:11Z | 2015-04-05T13:52:22Z | 2015-04-05T13:52:22Z | CONTRIBUTOR | null | The labels overlap
``` python
In [1]: df = pd.DataFrame(np.random.randn(30, 4), columns=list('abcd'))
In [2]: ax = df.a.plot(legend=True)
In [3]: df.b.plot(ax=ax, legend=True)
Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0x10938f6a0>
```
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} | 24 | 2015-03-07T16:57:19Z | 2015-03-10T01:23:04Z | 2015-03-10T01:23:04Z | CONTRIBUTOR | null | closes #9347
closes #9190
closes #9148
so this is option 4).
Default is now `ordered=False` (independently of whether you specify categories or not)
The ordering is still what factorize does (seen order, if no categories are specified).
Add `set_ordered` to change the `ordered` flag, which by default returns a new object.
```
In [1]: cat = pd.Categorical([0,1,2])
In [2]: cat
Out[2]:
[0, 1, 2]
Categories (3, int64): [0, 1, 2]
In [3]: cat.ordered
Out[3]: False
In [4]: cat.ordered=True
pandas/core/categorical.py:452: FutureWarning: Setting 'ordered' directly is deprecated, use 'set_ordered'
In [5]: cat = cat.set_ordered(True)
In [6]: cat
Out[6]:
[0, 1, 2]
Categories (3, int64): [0 < 1 < 2]
In [7]: cat.ordered
Out[7]: True
In [8]: cat = pd.Categorical([0,1,2],ordered=True)
In [9]: cat
Out[9]:
[0, 1, 2]
Categories (3, int64): [0 < 1 < 2]
In [12]: cat.ordered
Out[12]: True
```
Further added the ability for astype to pass on keywords to the constructor
```
In [1]: Series(["a","b","c","a"]).astype('category',ordered=True)
Out[1]:
0 a
1 b
2 c
3 a
dtype: category
Categories (3, object): [a < b < c]
In [2]: Series(["a","b","c","a"]).astype('category',categories=list('abcdef'),ordered=False)
Out[2]:
0 a
1 b
2 c
3 a
dtype: category
Categories (6, object): [a, b, c, d, e, f]
```
Furthermore I put a warning as seemingly simple operations will fail because the user had an _implicity_ ordered Categorical.
```
In [1]: df = DataFrame({ 'A' : Series(list('aabc')).astype('category'), 'B' : np.arange(4) })
In [2]: df['A'].order()
TypeError: Categorical not ordered
you can use .set_ordered(True) to change the Categorical to an ordered one
In [3]: df.groupby('A').sum()
ValueError: cannot sort by an unordered Categorical in the grouper
you can set sort=False in the groupby expression or
make the categorical ordered by using .set_ordered(True)
```
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} | 5 | 2015-03-07T23:37:38Z | 2015-03-08T16:52:18Z | 2015-03-08T16:29:06Z | CONTRIBUTOR | null | closes #9602
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| null | 5 | 2015-03-08T10:24:16Z | 2021-08-08T19:08:08Z | 2021-08-08T19:08:08Z | NONE | null | Github show me how cool can be use YAML front matter blocks, and now I want to use them to put metadata in my datasets, this must be the new standard. It's easy, human readable and simple.
File example of the format
https://raw.githubusercontent.com/jrovegno/csvy/master/data.csvy
Regards
Javier
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} | 3 | 2015-03-08T10:54:34Z | 2019-11-13T01:57:19Z | 2019-11-13T01:57:19Z | CONTRIBUTOR | null | In a `DataFrame` without a categorical, the following comparisons work as expected:
``` python
df = pd.DataFrame({'id': [None] * 3, 'spam': [None] * 3})
df['spam'] == 'spam'
df.groupby('id').first()['spam'] == 'spam'
```
However, when a column is `Categorical`, a `groupby` on the all-null column behaves unexpected:
``` python
df['spam'] = df['spam'].astype('category')
df['spam'] == 'spam' # works as expected
df.groupby('id').first()['spam'] == 'spam' # raises TypeError: invalid type comparison
```
Looks like the groupby converts all types in the group to `float64`:
``` python
>>> df.groupby('id').first().info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 0 entries
Data columns (total 1 columns):
spam 0 non-null float64
dtypes: float64(1)
memory usage: 0.0 bytes
```
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} | 0 | 2015-03-08T16:18:08Z | 2015-03-08T16:27:28Z | 2015-03-08T16:27:28Z | CONTRIBUTOR | null | not supported module/interface.
xref #1986, #8788
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} | 1 | 2015-03-09T01:44:19Z | 2015-04-07T09:09:46Z | 2015-04-07T09:09:40Z | NONE | null | Hi
I am getting error when creating DataFrame and I have no idea on how to fix it
In [6]: import pandas.io.data as web
In [7]: import datetime
In [8]: start = datetime.datetime(2010, 1, 1)
In [9]: end = datetime.datetime(2013, 1, 27)
In [10]: f=web.DataReader("F", 'yahoo', start, end)
In [11]: f
Out[11]: <repr(<pandas.core.frame.DataFrame at 0x7f0b9c856290>) failed: IOError: [Errno 10] No child processes>
In [12]: f.ix['2010-01-04']
Out[12]: <repr(<pandas.core.series.Series at 0x7f0b9c856150>) failed: IOError: [
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} | 10 | 2015-03-09T08:12:31Z | 2015-03-11T02:33:15Z | 2015-03-10T22:49:06Z | MEMBER | null | `AttributeError` is really the appropriate error to raise for an invalid
attribute. In particular, it is necessary to ensure that tests like
`hasattr(s, 'cat')` work consistently on Python 2 and 3: on Python 2,
`hasattr(s, 'cat')` will return `False` even if a `TypeError` was raised, but
Python 3 more strictly requires `AttributeError`.
This is an unfortunate trap that we should avoid. See this discussion in
Seaborn for a full report:
https://github.com/mwaskom/seaborn/issues/361#issuecomment-77770773
Note that technically, this is an API change, since these accessors (all but
`.str`, I think) raised TypeError in the last release.
This also suggests another possibility for testing for Series with a
Categorical dtype (#8814): just use `hasattr(s, 'cat')` (at least for Python
2 or pandas >=0.16).
CC @mwaskom @jorisvandenbossche @JanSchulz
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} | 4 | 2015-03-09T10:55:31Z | 2015-07-18T13:30:43Z | 2015-07-18T13:30:43Z | NONE | null | This happen with some Pandas dataframes with indexes.
``` python
from pandas import read_msgpack
msg = df.to_msgpack()
read_msgpack(msg) #==> exception!
```
Full Traceback (most recent call last):
File "/srv/python/venv/local/lib/python2.7/site-packages/pandas/io/packers.py", line 163, in read_msgpack
return read(fh)
File "/srv/python/venv/local/lib/python2.7/site-packages/pandas/io/packers.py", line 141, in read
l = list(unpack(fh))
File "pandas/msgpack.pyx", line 662, in pandas.msgpack.Unpacker.**next** (pandas/msgpack.cpp:8303)
File "pandas/msgpack.pyx", line 591, in pandas.msgpack.Unpacker._unpack (pandas/msgpack.cpp:7328)
File "/srv/python/venv/local/lib/python2.7/site-packages/pandas/io/packers.py", line 490, in decode
blocks = [create_block(b) for b in obj['blocks']]
File "/srv/python/venv/local/lib/python2.7/site-packages/pandas/io/packers.py", line 488, in create_block
placement=axes[0].get_indexer(b['items']))
File "/srv/python/venv/local/lib/python2.7/site-packages/pandas/core/index.py", line 1488, in get_indexer
raise InvalidIndexError('Reindexing only valid with uniquely'
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
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https://api.github.com/repos/pandas-dev/pandas/issues/9619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9619/events | https://github.com/pandas-dev/pandas/pull/9619 | 60,358,877 | MDExOlB1bGxSZXF1ZXN0MzA3Njk1MTE= | 9,619 | DOC: Shrink image asset for assign | {
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} | 3 | 2015-03-09T15:20:03Z | 2015-08-18T12:45:06Z | 2015-03-09T18:42:16Z | CONTRIBUTOR | null | Let's try this again. The sphinx directive didn't seem to work. I just shrunk the static asset.
I'm not at my regular computer and I haven't been able to get the docs to build on this one, so I'm exactly sure how it will look in the page :)
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| null | 3 | 2015-03-09T17:25:05Z | 2015-03-09T18:27:33Z | 2015-03-09T18:27:33Z | NONE | null | See a SO question I asked that has the relevant information: http://stackoverflow.com/questions/28840036/pandas-secondary-y-axis-for-boxplots
Essentially, the `secondary_y` argument of `plot()` doesn't do anything for boxplots and several other plot types. It will work for line plots however.
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} | 0 | 2015-03-09T20:55:16Z | 2015-03-09T21:39:59Z | 2015-03-09T21:39:59Z | CONTRIBUTOR | null | xref #9608
fix version to bypass failures for now
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} | 12 | 2015-03-09T22:56:46Z | 2015-03-11T12:12:05Z | 2015-03-11T12:11:49Z | CONTRIBUTOR | null | alternate to #9611
closes #9347
closes #9190
closes #9148
This implementes the alternate, and IMHO a nice compromise.
Groupby's will succeed whether ordered or not
ordering ops (sort/argsort/min/max) will still raise.
one though is that we could detect a non-reduction where the ordering matters (maybe we do this in another PR), and show a warning, e.g. imagine `df.groupby('A').head(2)` is technically 'wrong', while `df.groupby('A').sum()` would have no warning.
```
In [1]: df = DataFrame({ 'A' : Series(list('aabc')).astype('category'), 'B' : np.arange(4) })
In [2]: df.groupby('A').sum()
Out[2]:
B
A
a 1
b 2
c 3
In [3]: df['A'].order()
TypeError: Categorical is not ordered for operation argsort
you can use .as_ordered() to change the Categorical to an ordered one
```
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} | 17 | 2015-03-10T01:24:57Z | 2015-03-11T14:17:14Z | 2015-03-11T12:10:07Z | CONTRIBUTOR | null | closes #5172
closes #8744
closes #8653
closes #9173
closes #9468
closes #8794
fixes all issues but one attached to #5172. There were several issues as mentioned by my comments in the master list. They are:
1. Offset classes were not working properly over DST boundaries. I added flags on the class to handle specially.
2. `normalize_date` does not work with DST change dates as the tz offset is not adjusted. `normalize` is added to Timestamp as discussed in #8794 (I don't like how this was done, but it seemed the easiest rather than duplicating lots of code in `tslib.date_normalize`.
3. `_adjust_dates_anchored` was not localizing properly once times were adjusted.
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} | 2 | 2015-03-10T15:48:56Z | 2015-03-11T01:47:12Z | 2015-03-11T01:47:12Z | MEMBER | null | ```
In [21]: dtidx = pd.date_range('2012-01-01', periods=2)
In [22]: dtidx
Out[22]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2012-01-01, 2012-01-02]
Length: 2, Freq: D, Timezone: None
In [23]: dtidx + pd.Series([pd.Timedelta('10 days'), pd.Timedelta('20 days')])
Out[23]:
2012-01-01 00:00:00 NaT
2012-01-02 00:00:00 NaT
0 NaT
1 NaT
dtype: datetime64[ns]
```
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| null | 1 | 2015-03-10T18:15:44Z | 2021-09-09T14:18:47Z | 2021-09-09T14:18:47Z | NONE | null | ``` python
a = pd.DataFrame({'x': [1,2,3], 'y': [2,3,4]})
a.index.names = ['a']
a.to_html()
```
it looks like
```
x y
a
0 1 2
1 2 3
2 3 4
```
Can we add an option in `to_html` so that we can achieve the following easily?
For the moment I have to drop the index, and bold the first column via formatter which is not simple.
```
a x y
0 1 2
1 2 3
2 3 4
```
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} | 4 | 2015-03-10T18:46:43Z | 2015-05-05T00:09:03Z | 2015-05-04T22:41:49Z | CONTRIBUTOR | null | Currently if one imports from the source directory without building the C extension the error message is not very helpful:
`ImportError: cannot import name 'hashtable'`
I see a few previous PRs (https://github.com/pydata/pandas/pull/3827, https://github.com/pydata/pandas/pull/3821) that tried to address this but somehow this is still the behavior on `master`.
With this PR the error message would be a lot more informative:
```
ImportError: C extension: 'hashtable' not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
```
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} | 1 | 2015-03-10T22:51:50Z | 2015-04-22T00:04:45Z | 2015-04-22T00:04:45Z | CONTRIBUTOR | null | xref #9617
btw, if someone wants to do a followup at some point to remove the .str/.cat/.dt from the **dir** on types they don't apply would be gr8. (not that hard, but requires a bit of changing), prob have to make _local_dir a bit more flexible (to add and delete)
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} | 1 | 2015-03-10T23:15:28Z | 2015-03-11T00:18:33Z | 2015-03-11T00:18:33Z | CONTRIBUTOR | null | All the other examples use the table name "tips"
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} | 2 | 2015-03-11T00:21:03Z | 2015-03-11T01:35:35Z | 2015-03-11T01:35:33Z | MEMBER | null | Follow-up on #9617
Fixes #8814 -- is it fair to say that `hasattr(s, 'cat')` is probably
the best solution we're going to come up with for checking for categorical
data?
CC @jreback @jorisvandenbossche @JanSchulz
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} | 8 | 2015-03-11T01:53:16Z | 2015-03-11T10:21:42Z | 2015-03-11T10:21:42Z | CONTRIBUTOR | null | closes #9094
closes #9095
deprecate `DatetimeIndex/PeriodIndex` `+/-` ops which are set ops. In a future version `-` will produce a `TimeDeltaIndex` and `+` a `TypeError`
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} | 5 | 2015-03-11T02:07:42Z | 2017-10-08T16:15:52Z | 2017-10-08T16:15:52Z | CONTRIBUTOR | null | xref #9630
IIRC most of these are `TypeErrors` (see `tseries/tests_base.py`, section on addition/subtraction ops)
but I guess *some* are`NotImplementedError``
```
In [3]: pd.date_range('20130101',periods=5)+Timestamp('20140101')
NotImplementedError:
```
```
In [4]: pd.date_range('20130101',periods=5)+Timestamp('20140101').to_datetime64()
TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('<M8[ns]')
```
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} | 20 | 2015-03-11T10:48:17Z | 2015-05-04T18:58:56Z | 2015-05-04T18:58:49Z | CONTRIBUTOR | null | Subclassing pandas.DataFrame had some issues, when a multi-level index was used. This PR should fix that.
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| null | 1 | 2015-03-11T19:43:16Z | 2015-03-11T20:40:11Z | 2015-03-11T20:40:11Z | NONE | null | In a DataFrame, it's not possible to use `fillna` to replace NaN values by `None`:
``` python
df = pandas.DataFrame([np.nan, 42])
df.fillna(value=None)
```
returns a `ValueError` exception (_ValueError: must specify a fill method or value_). Any other value is of course accepted.
I'm not sure if it's a good idea to call `fillna` for that, but it should at least be explained in the documentation that `None` cannot be used.
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} | 2 | 2015-03-11T20:46:10Z | 2015-03-20T14:48:59Z | 2015-03-11T20:46:30Z | CONTRIBUTOR | null | closes #8934
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} | 2 | 2015-03-12T10:40:11Z | 2015-04-04T18:40:47Z | 2015-04-04T18:40:47Z | CONTRIBUTOR | null | When stored in HDFStore, TimedeltaIndex are converted into Int64Index.
``` python
df = pd.DataFrame(np.random.normal(size=(10,5)))
df.index = pd.timedelta_range(start='0s',periods=10,freq='1s')
store = pd.HDFStore('test.h5')
store['df'] = df
df = store['df']
print df.index
```
```
Int64Index([0, 1000000000, 2000000000, 3000000000, 4000000000, 5000000000, 6000000000, 7000000000, 8000000000, 9000000000], dtype='int64')
```
Although the backward conversion is easy (pd.TimedeltaIndex(df.index)) , it's surprising.
``` python
pd.show_versions()
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.15.2-269-ge3d1b0e
nose: 1.3.1
Cython: 0.20.1post0
numpy: 1.8.2
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: 1.2.2
patsy: None
dateutil: 2.4.1
pytz: 2013b
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
I would have been happy to help and add this myself, but I really have no idea were is this hiding. I don't even know if I should have looked toward PyTables instead of here.
Thanks !
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} | 7 | 2015-03-12T15:50:32Z | 2018-12-23T20:17:51Z | 2015-03-17T10:19:10Z | NONE | null | When searching the web I didn't find any examples of a working pandas to R data transfer using HDF5 files, even though pandas's documentation mentions the used HDF5 format "can easily be imported into R using the rhdf5 library". The pandas export works as expected and I inspected the file format using the HDF group's viewer (HDFView).
After some experimentation I have a working sample for dataframe export from Python/pandas and import into R, which could be added to the documentation to help future users:
``` python
# Example of HDF5 export for R
import numpy as np
import pandas as pd
np.random.seed(1)
df = pd.DataFrame({"first": np.random.rand(100),
"second": np.random.rand(100),
"class": np.random.randint(0, 2, (100,))},
index=range(100))
print(df.head())
store = pd.HDFStore("transfer.hdf5", "w", complib=str("zlib"), complevel=5)
store.put("dataframe", df, data_columns=df.columns)
store.close()
```
Output:
```
class first second
0 0 0.417022 0.326645
1 0 0.720324 0.527058
2 1 0.000114 0.885942
3 1 0.302333 0.357270
4 1 0.146756 0.908535
```
``` R
# Load values and column names for all datasets from corresponding nodes and
# insert them into one data.frame object.
library(rhdf5)
loadhdf5data <- function(h5File) {
listing <- h5ls(h5File)
# Find all data nodes, values are stored in *_values and corresponding column
# titles in *_items
data_nodes <- grep("_values", listing$name)
name_nodes <- grep("_items", listing$name)
data_paths = paste(listing$group[data_nodes], listing$name[data_nodes], sep = "/")
name_paths = paste(listing$group[name_nodes], listing$name[name_nodes], sep = "/")
columns = list()
for (idx in seq(data_paths)) {
data <- data.frame(t(h5read(h5File, data_paths[idx])))
names <- t(h5read(h5File, name_paths[idx]))
entry <- data.frame(data)
colnames(entry) <- names
columns <- append(columns, entry)
}
data <- data.frame(columns)
return(data)
}
```
Now you can import the DataFrame:
``` R
> data = loadhdf5data("transfer.hdf5")
> head(data)
first second class
1 0.4170220047 0.3266449 0
2 0.7203244934 0.5270581 0
3 0.0001143748 0.8859421 1
4 0.3023325726 0.3572698 1
5 0.1467558908 0.9085352 1
6 0.0923385948 0.6233601 1
```
I hope this helps someone. :-)
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} | 3 | 2015-03-12T16:01:26Z | 2015-05-12T12:19:49Z | 2015-05-12T12:19:49Z | CONTRIBUTOR | null | closes #4679
This is a very simple fix to allow passing a list go the header argument of read_excel so that it creates a MultiIndex for columns. Given what is said in issue #4679, I am not really sure it is supposed to work, but with this fix, it works for my files. One thing discussed in issue #4679, which I did not do anything about (and thus probably still does not work) is if the header cells are merged, but my opinion is that this should be a separate issue, as it is already useful to be able to read files with the header duplicated (the files I receive are usually like this). If this PR is accepted (at least in spirit), the docstring for read_excel should probably be updated to reflect the "new" capability and the changelog updated, but I wanted to check first what you think.
As a side note, I am not entirely sure that _trim_excel_header is a good thing to do anyway, but that is another debate entirely.
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| null | 4 | 2015-03-13T01:03:45Z | 2015-03-13T16:54:25Z | 2015-03-13T07:48:21Z | CONTRIBUTOR | null | This rearranges the Contributing page into more logical sections and provides some more useful links to wiki pages. I think this should be the place where all relevant documentation on pandas development can be found. For example, both this page and the pandas homepage mention `vbench` but the information on running tests was lacking and it turns out that the wiki has this information but it was difficult to find. Hopefully this is a step in the right direction.
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} | 3 | 2015-03-13T02:22:36Z | 2015-04-29T15:32:32Z | 2015-03-14T01:31:43Z | CONTRIBUTOR | null | this should fix https://github.com/pydata/pandas/issues/8285
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| null | 4 | 2015-03-13T10:17:34Z | 2015-03-13T13:44:09Z | 2015-03-13T13:21:07Z | MEMBER | null | Is something like [this](http://stackoverflow.com/a/29014295/2858145) is worth including in pandas (although it will not work i.e. in Windows)?
If yes, I will put together a PR (also supporting Python2).
(I could also implement a platform independent alternative... but locking would not be atomic, so race conditions would be possible - although improbable)
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} | 3 | 2015-03-13T14:07:44Z | 2015-03-14T20:57:02Z | 2015-03-14T20:57:01Z | MEMBER | null | - `pd.read_csv('tmp2.sv', sep=None)` was given the warning "_ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'_"
Is this correct? (assuming so I just added what the warning message was saying)
- with the ordered -> unordered change in the Categoricals, the example on comparing them was failing (see http://pandas-docs.github.io/pandas-docs-travis/categorical.html#comparisons)
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} | 2 | 2015-03-13T14:50:42Z | 2015-03-14T12:05:10Z | 2015-03-14T12:05:10Z | MEMBER | null | No real edits, just some rearranging.
I added an automatic 'contents' in the beginning, so this gives all headers automatically, and in this way restricted the list in the beginning to just the highlights (then it can be copied exactly like this to the release notes in release.rst
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} | 6 | 2015-03-13T15:05:22Z | 2016-05-25T14:23:36Z | null | MEMBER | null | If you now do `pd.read_csv(myfile.csv, sep=None)`, you get the warning:
> ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'
Is this warning correct that it is not implemented for the c engine?
If so, it could maybe be added.
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Right now it's on the same level as Series, DataFrame, and Panel. Should be indented one more.
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} | 4 | 2015-03-13T18:27:16Z | 2019-04-22T07:41:24Z | 2015-03-16T00:07:22Z | CONTRIBUTOR | null | from [here](http://stackoverflow.com/questions/29016093/pandas-pytable-memory-overhead-when-writing-to-hdf/29035532#29035532)
reduce memeory usage necessary for using `to_hdf`
- was copying always in astyping
- was ravelling then reshaping
- was constantly allocating a new chunked buffer, now re-uses the same buffer
```
In [1]: df = pd.DataFrame(np.random.rand(1000000,500))
df.info()
In [2]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000000 entries, 0 to 999999
Columns: 500 entries, 0 to 499
dtypes: float64(500)
memory usage: 3.7 GB
```
Previously
```
In [3]: %memit -r 1 df.to_hdf('test.h5','df',format='table',mode='w')
peak memory: 11029.49 MiB, increment: 7130.57 MiB
```
With PR
```
In [2]: memit -r 1 df.to_hdf('test.h5','df',format='table',mode='w')
peak memory: 4669.21 MiB, increment: 794.57 MiB
```
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} | 0 | 2015-03-13T19:48:05Z | 2015-03-13T23:46:32Z | 2015-03-13T23:46:29Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9646
I did this through the GH online editor. Let's see if it works.
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} | 5 | 2015-03-13T20:31:02Z | 2015-04-11T18:29:26Z | 2015-04-11T18:29:26Z | NONE | null | This fixed my problem when calling DataFrame.stack(<integer level number>). Not really familiar with pandas internals, but couldn't find any reason why unicode strings won't be accepted here.
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} | 2 | 2015-03-13T21:11:41Z | 2015-09-04T12:01:54Z | 2015-03-13T21:31:32Z | CONTRIBUTOR | null | Empirically, it seems to be the case that if one sorts a dataframe then takes a groupby, that sort is preserved within each groupby group. For example, it seems to be the case that `my_func` below always gets a df sorted by age. But I can't find any documentation on whether this is guaranteed, so I'm currently re-sorting data in `my_func` to be safe.
```
df.sort(columns = ['users', 'age'], inplace = True)
df.groupby('users').apply(my_func)
```
Does anyone know if this is a guaranteed behavior, and if so, might it be worth adding to docs?
This is particularly relevant for the `groupby().nth()` command, since if sort isn't preserved, it's not clear what it grabs!
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} | 5 | 2015-03-13T23:34:55Z | 2015-03-18T01:55:38Z | 2015-03-14T15:20:37Z | NONE | null | It's not clear to me that this is a "bug" per se, but it's certainly not intuitive behavior. My thought was that these two methods would result in identical DataFrames.
``` python
import numpy as np
import pandas as pd
def multiindex_version_1(data):
n, m = data.shape
tuples = [('a', _m) for _m in xrange(m-1)] + [('b', )]
columns = pd.MultiIndex.from_tuples(tuples)
df = pd.DataFrame(data, columns=columns)
return df
def multiindex_version_2(data):
n, m = data.shape
tuples = [('a', _m) for _m in xrange(m-1)]
columns = pd.MultiIndex.from_tuples(tuples)
df = pd.DataFrame(data[:, :(m-1)], columns=columns)
df['b'] = data[:, m-1]
return df
def test_goofy_multiindexing():
shape = (10, 4)
data = np.random.rand(*shape)
df1 = multiindex_version_1(data)
print "============= version_1 ============="
print df1
df2 = multiindex_version_2(data)
print "============= version_2 ============="
print df2
assert (df1==df2).all()
```
Note the NaN immediately below.
```
============= version_1 =============
a b
0 1 2 NaN
0 0.742381 0.981698 0.976639 0.212344
1 0.278328 0.188071 0.320450 0.236994
2 0.543064 0.786865 0.065103 0.233676
3 0.084130 0.081981 0.295404 0.113241
4 0.025517 0.522754 0.238385 0.284278
5 0.263382 0.176789 0.289079 0.472668
6 0.933632 0.328850 0.006974 0.191654
7 0.557348 0.313619 0.582076 0.897741
8 0.271253 0.815123 0.113981 0.787208
9 0.595073 0.887483 0.350284 0.815767
============= version_2 =============
a b
0 1 2
0 0.742381 0.981698 0.976639 0.212344
1 0.278328 0.188071 0.320450 0.236994
2 0.543064 0.786865 0.065103 0.233676
3 0.084130 0.081981 0.295404 0.113241
4 0.025517 0.522754 0.238385 0.284278
5 0.263382 0.176789 0.289079 0.472668
6 0.933632 0.328850 0.006974 0.191654
7 0.557348 0.313619 0.582076 0.897741
8 0.271253 0.815123 0.113981 0.787208
9 0.595073 0.887483 0.350284 0.815767
E
======================================================================
ERROR: test_scatter.test_goofy_multiindexing
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/br/test/test_scatter.py", line 99, in test_goofy_multiindexing
assert (df1==df2).all()
File "/home/br/.virtualenvs/cloudlab/local/lib/python2.7/site-packages/pandas/core/ops.py", line 912, in f
return self._compare_frame(other, func, str_rep)
File "/home/br/.virtualenvs/cloudlab/local/lib/python2.7/site-packages/pandas/core/frame.py", line 3136, in _compare_frame
raise ValueError('Can only compare identically-labeled '
ValueError: Can only compare identically-labeled DataFrame objects
-------------------- >> begin captured logging << --------------------
rospy.topics: INFO: topicmanager initialized
--------------------- >> end captured logging << ---------------------
----------------------------------------------------------------------
Ran 1 test in 0.010s
FAILED (errors=1)
```
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} | 3 | 2015-03-14T01:47:54Z | 2015-03-16T11:56:04Z | 2015-03-16T08:49:53Z | MEMBER | null | Added summarized section to the release note based on the discussion in #9386
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| null | 1 | 2015-03-14T01:52:50Z | 2015-03-16T11:56:11Z | 2015-03-14T01:54:08Z | MEMBER | null | "Indexing Changes" section is duplicated in what's new.
- http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html
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| null | 9 | 2015-03-14T03:18:21Z | 2015-03-23T06:15:19Z | 2015-03-14T15:18:11Z | NONE | null | This was counter-intuitive for me.
``` python
N = 5
columns = pd.MultiIndex.from_tuples([('a', 0), ('a', 1), ('b', 0), ('b', 1)])
df1 = pd.DataFrame(np.random.rand(N, 4), columns=columns)
df2 = pd.DataFrame(np.random.rand(N), columns=['c'])
df1_copy = df1.copy()
df2_copy = df2.copy()
df1_copy[df2.columns] = df2
df2_copy[df1.columns] = df1
print "===== A ====="
print df1_copy
print
print "===== B ====="
print df2_copy
```
gives
```
===== A =====
a b c
0 1 0 1
0 0.540405 0.814476 0.923961 0.935421 0.909791
1 0.679014 0.220417 0.511039 0.117898 0.282063
2 0.428563 0.915760 0.518118 0.780704 0.766191
3 0.928167 0.616540 0.151223 0.664102 0.037299
4 0.717449 0.453904 0.384850 0.953102 0.757288
===== B =====
c (a, 0) (a, 1) (b, 0) (b, 1)
0 0.909791 0.540405 0.814476 0.923961 0.935421
1 0.282063 0.679014 0.220417 0.511039 0.117898
2 0.766191 0.428563 0.915760 0.518118 0.780704
3 0.037299 0.928167 0.616540 0.151223 0.664102
4 0.757288 0.717449 0.453904 0.384850 0.953102
```
I don't care about the ordering of the columns here; most of the time we are completely agnostic to that with pandas. What I _do_ care about is that the MultiIndex gets "flattened" in B.
Seems like a more desirable behavior would be to "promote" the target's column structure so that you always get A. That presents a tricky question about how to deal with complicated MultiIndex schemes on either side of the assignment, but at least for this basic case the current behavior feels wonky.
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} | 1 | 2015-03-14T15:13:00Z | 2015-03-14T15:33:52Z | 2015-03-14T15:33:52Z | CONTRIBUTOR | null | puzzled why the travis ci error started happening. This seems to fix it (and I don't even remember why this was necessary in the first place).
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} | 9 | 2015-03-14T19:30:29Z | 2015-06-02T19:25:19Z | 2015-05-25T14:17:18Z | NONE | null | Hello, just wanted to create this issue to hopefully inspire someone to include the use of axis='index' or axis='columns' as an alternative to using axis=0 or 1 in the documentation/docstrings, since the latter form is less intuittive.
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was tricky to distinguish this case from others, but I think this is a reasonably robust soln.
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} | 17 | 2015-03-16T10:47:12Z | 2017-09-07T06:24:32Z | 2015-03-17T13:09:55Z | NONE | null | closes #9636
I had some trouble getting the HDF5 import of DataFrame objects to work on the R side (pandas is fine). I've added an example to the documentation with a simple load function in R which assembles an R data.frame from the corresponding *_items and *_values nodes in the HDF5 file.
NOTE: Any improvements to the R code are welcome, I'm just starting to work with R. :-)
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} | 5 | 2015-03-16T11:49:28Z | 2015-03-17T12:41:39Z | 2015-03-17T12:41:39Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9635
Small change to fix issue 9635. I'm a beginner contributor, feel free to crush it out.
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} | 3 | 2015-03-16T16:34:38Z | 2015-08-20T14:26:03Z | 2015-08-20T14:26:03Z | CONTRIBUTOR | null | The dateutil package allows you to create time zone (`tzfile`) objects two ways, either by using `dateutil.tz.gettz` to read time zone data on the file system (/usr/share/zoneinfo), or by using `dateutil.zoneinfo.gettz` to read time zone data from a tar file distributed in the dateutil package.
The `tslib.maybe_get_tz` function doesn't handle the `dateutil.tz.gettz` variant.
```
from datetime import datetime
import pandas as pd
import pandas.tslib as tslib
import dateutil.tz
import dateutil.zoneinfo
tz1 = dateutil.tz.gettz('America/New_York')
tz2 = dateutil.zoneinfo.gettz('America/New_York')
d1 = datetime(2015, 1, 1, tzinfo=tz1)
d2 = datetime(2015, 1, 1, tzinfo=tz2)
```
`maybe_get_tz` returns None for tz1, but works correctly for tz2:
```
>>> tslib.maybe_get_tz('dateutil/' + tz1._filename)
>>> tslib.maybe_get_tz('dateutil/' + tz2._filename)
tzfile('America/New_York')
```
And so DatetimeIndexes are missing time zone information for those cases.
```
>>> pd.to_datetime([d1])
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-01 05:00:00]
Length: 1, Freq: None, Timezone: None
>>> pd.to_datetime([d2])
<class 'pandas.tseries.index.DatetimeIndex'>
[2015-01-01 00:00:00-05:00]
Length: 1, Freq: None, Timezone: tzfile('America/New_York')
```
I think if `maybe_get_tz` where to first try dateutil.zoneinfo.gettz, and then fall back on dateutil.tz.gettz, then the problem is solved.
This was a regression between 0.14 and 0.15.
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| null | 2 | 2015-03-16T18:59:17Z | 2015-03-16T19:15:19Z | 2015-03-16T19:15:19Z | NONE | null | When grouping a data frame by multiple columns, one of which contains datetime64 objects, and applying a transform to a single column, a KeyError is thrown. For example:
```
x = pd.DataFrame() #Start with an empty data frame
x['name'] = ['Bob','Alice','Bob']
x['date'] = ['2015-02-01','2015-02-02','2015-02-03']
x['value1'] = [2,3,4]
x['value2'] = [5,6,7]
x.groupby(['name','date']).value1.transform(lambda x: x) #This works fine when x.date is a string
x.date = pd.to_datetime(x.date) #Make the date column a datetime64 object
x.groupby(['name','date']).value1.transform(lambda x: x) #Now it fails with KeyError: ('Alice', Timestamp('2015-02-02 00:00:00', tz=None))
#But the following work fine
x.groupby('date').value1.transform(lambda x: x)
x.groupby(['name','date']).transform(lambda x: x)
```
Here's the version information
```
INSTALLED VERSIONS
------------------
Python: 2.7.6.final.0
OS: Linux
Release: 2.6.18-164.el5
Processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.0
Cython: Not installed
Numpy: 1.9.0.dev-Unknown
Scipy: 0.9.0
statsmodels: Not installed
patsy: Not installed
scikits.timeseries: Not installed
dateutil: 2.1
pytz: 2011h
bottleneck: Not installed
PyTables: Not Installed
numexpr: Not Installed
matplotlib: 1.3.0
openpyxl: Not installed
xlrd: Not installed
xlwt: Not installed
xlsxwriter: Not installed
sqlalchemy: Not installed
lxml: Not installed
bs4: Not installed
html5lib: Not installed
bigquery: Not installed
apiclient: Not installed
```
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} | 34 | 2015-03-16T21:05:39Z | 2015-05-01T17:39:18Z | 2015-05-01T12:50:43Z | CONTRIBUTOR | null | closes #2419
Creates a function `.sample()` in generic.py to sample from pandas objects. Returns either a fixed number of rows or a share. Also includes a number of tests in `test_sample()` -- I am open to added suggestions.
Input from users with panel experience appreciated!
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} | 31 | 2015-03-17T03:06:25Z | 2015-04-29T16:56:12Z | 2015-04-10T13:50:14Z | CONTRIBUTOR | null | fixes #9068
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| null | 1 | 2015-03-17T03:54:16Z | 2015-03-17T10:28:26Z | 2015-03-17T10:27:45Z | CONTRIBUTOR | null | Currently categorical data is converted to full form when made an index.
``` python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a': pd.Categorical(['a', 'b', 'a'])},
... index=pd.Categorical(['a', 'b', 'a']))
In [3]: df.dtypes
Out[3]:
a category
dtype: object
In [4]: df.index.dtype
Out[4]: dtype('O')
In [5]: df.index
Out[5]: Index([u'a', u'b', u'a'], dtype='object')
```
Is there a way around this? I'm trying to keep my DataFrames free of object dtypes to reduce serialization costs. I would like to index off of a text column using categoricals.
This is with `0.15.2`
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| null | 5 | 2015-03-17T04:46:50Z | 2015-03-17T23:55:36Z | 2015-03-17T23:19:49Z | NONE | null | I have a large CSV file that contains a single column of integers. When loading the CSV file with read_csv, nearly 2/3rds of the approximately 1.5 million values are loaded as ints while the remaining values are loaded as strings. I see no obvious problem with the file that would lead to this behavior.
The file I used is available here: https://drive.google.com/file/d/0ByZvgdTf0yfAT2dPdHRvc2hLVkU/view?usp=sharing
```
test_df = pd.read_csv('test.csv',header=0)
from collections import Counter
c = Counter()
els = []
for el in test_df['SERIALNO'].values:
c[type(el)] += 1
print c
Counter({<type 'int'>: 952026, <type 'str'>: 524288})
```
It appears that a continguous block of rows were read as strings - a block in the middle of the file. Very curious.
pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.5.0
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.1.0
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
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https://api.github.com/repos/pandas-dev/pandas/issues/9670 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9670/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9670/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9670/events | https://github.com/pandas-dev/pandas/issues/9670 | 62,345,524 | MDU6SXNzdWU2MjM0NTUyNA== | 9,670 | Numerically unstable mean calculation for Timedeltas. | {
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| null | 1 | 2015-03-17T09:35:21Z | 2015-03-17T10:25:44Z | 2015-03-17T10:25:24Z | CONTRIBUTOR | null | I am not sure whether I should report this here or on `numpy`. But this is what lead me to the problem:
```
In [11]: dAllTags.describe()
Out [11]:
finalPeriod
count 74501
mean -1 days +02:40:08.792662
std 500 days 06:32:37.640848
min 2 days 00:51:49.730000
25% 498 days 19:11:28.576000
50% 846 days 00:46:56.656000
75% 1245 days 17:11:58.493000
max 2224 days 07:03:26.593000
```
All the values are positive (the minimum is `2 days`) but the `mean` calculated is negative. This happens because the underlying type of `np.timedelta64` is `int64` which overflows while calculating the mean.
Now the issue of numerical stability in `numpy` has had a long history:
- https://github.com/numpy/numpy/issues/4694
- https://github.com/numpy/numpy/issues/1033
- https://github.com/numpy/numpy/issues/1063
- https://github.com/numpy/numpy/issues/2448
And though some steps have been taken to introduce precision accuracy (e.g. by providing `fsum` and using pairwise summation), there doesn't seem to be a consensus for using a numerically stable method for `mean`.
I was wondering if something could be done on the Pandas level to resolve this issue.
---
Currently, I am working around the issue by using the rather elaborate scheme:
```
df.finalPeriod.view(int).astype(float).mean()
```
since `timedelta64` cannot be directly converted to `float64`. Is there a better/more intuitive way to do this?
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https://api.github.com/repos/pandas-dev/pandas/issues/9671 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9671/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9671/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9671/events | https://github.com/pandas-dev/pandas/issues/9671 | 62,391,896 | MDU6SXNzdWU2MjM5MTg5Ng== | 9,671 | DataFrame.plot error when both 'color' and 'style' arguments are passed | {
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} | 6 | 2015-03-17T13:11:45Z | 2015-04-06T12:24:11Z | 2015-04-06T12:24:11Z | NONE | null | In pandas 0.15.2, when passing both arguments 'color' and 'style' to the plot method, there is an error which I think should be raised only when there is a color symbol in the style string. But in the example below, the error is raised even though there is no color symbol in the line string:
```
from pandas import DataFrame
import numpy as np
df = DataFrame({'a': np.random.rand(10), 'b': np.random.rand(10), 'c': np.random.rand(10)})
df.plot(color = ['red', 'black', 'blue'], style = ['-', '--', '-'])
```
The error is not clear (TypeError: expected string or buffer). This should be fixed, although it is easy to work around:
```
df.plot(style = ['r-', 'k--', 'b-'])
```
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} | 2 | 2015-03-17T13:41:37Z | 2015-04-04T18:40:35Z | 2015-04-04T18:40:35Z | CONTRIBUTOR | null | closes #9635
Hop, revival of PR9662, after some bad git moves.
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https://api.github.com/repos/pandas-dev/pandas/issues/9673 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9673/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9673/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9673/events | https://github.com/pandas-dev/pandas/pull/9673 | 62,433,170 | MDExOlB1bGxSZXF1ZXN0MzEzNDcwMjQ= | 9,673 | Fix for issue #9671 | {
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} | 1 | 2015-03-17T15:40:07Z | 2015-03-18T16:14:24Z | 2015-03-18T16:14:24Z | NONE | null | closes https://github.com/pydata/pandas/issues/9671
`self.style` can be a list of strings or a string. If it is a string, we make a list containing that string, so that we only have to deal with a list of strings. We check every element in that list and raise an error if it has a color symbol.
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https://api.github.com/repos/pandas-dev/pandas/issues/9674 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9674/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9674/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9674/events | https://github.com/pandas-dev/pandas/pull/9674 | 62,731,660 | MDExOlB1bGxSZXF1ZXN0MzE0NDQ1Mjk= | 9,674 | DataFrame.plot error when both 'color' and 'style' arguments are passed (GH9671) | {
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} | 5 | 2015-03-18T16:12:59Z | 2015-04-06T12:23:51Z | 2015-04-06T12:23:51Z | NONE | null | closes #9671
`self.style` can be a list of strings or a string. If it is a string, we make a list containing that string, so that we only have to deal with a list of strings. We check every element in that list and raise an error if it has a color symbol.
The test makes sure that calling `plot()` and passing both `color` and `style` arguments is allowed if there is no color symbol in the style string(s), and that passing both `color` and `style` arguments is not allowed if there is a color symbol in the style string(s). This test is done for a dataframe and for a series.
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https://api.github.com/repos/pandas-dev/pandas/issues/9675 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9675/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9675/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9675/events | https://github.com/pandas-dev/pandas/issues/9675 | 62,745,414 | MDU6SXNzdWU2Mjc0NTQxNA== | 9,675 | global name 'iNaT' is not defined | {
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} | 3 | 2015-03-18T17:16:56Z | 2015-03-25T23:03:52Z | 2015-03-25T23:03:52Z | NONE | null | Trying to perform this operation on a groupby object called clusters:
`clusters.agg({'diffs': lambda x: x.quantile(.5)})`
where diffs is a column of Timedelta objects.
Getting the error:
```
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in aggregate(self, func_or_funcs, *args, **kwargs)
2305 return self._python_agg_general(func_or_funcs, *args, **kwargs)
2306 except Exception:
-> 2307 result = self._aggregate_named(func_or_funcs, *args, **kwargs)
2308
2309 index = Index(sorted(result), name=self.grouper.names[0])
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _aggregate_named(self, func, *args, **kwargs)
2392 for name, group in self:
2393 group.name = name
-> 2394 output = func(group, *args, **kwargs)
2395 if isinstance(output, (Series, Index, np.ndarray)):
2396 raise Exception('Must produce aggregated value')
<ipython-input-174-5be8bb847fd6> in <lambda>(x)
----> 1 clusters.agg({'diffs': lambda x: x.quantile(.5)})
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in quantile(self, q)
1264 return _quantile(values, qs*100)
1265
-> 1266 return self._maybe_box(lambda values: multi(values, q), dropna=True)
1267
1268 def ptp(self, axis=None, out=None):
/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc in _maybe_box(self, func, dropna)
2105
2106 if len(values) == 0:
-> 2107 return boxer(iNaT)
2108
2109 values = values.view('i8')
NameError: global name 'iNaT' is not defined
```
Looks like other references to iNaT are all tslib.iNaT.
https://github.com/pydata/pandas/blob/master/pandas/core/series.py#L2098
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} | 21 | 2015-03-18T19:09:37Z | 2015-05-07T00:20:21Z | 2015-05-07T00:20:21Z | NONE | null | When I select from a HDF store using a "where" string (locating entries in which one field matches a particular string value), the function returns fewer rows than when I load the entire dataframe into memory and then match on that field. Below is some code that reproduces the problem; unfortunately, I can't easily provide the code that generates the source HDF store, but I'm happy to provide the kept_tids_20150310.h5 file if it would help. There are no nan values in the dataframe.
Running ptrepack on the dataframe solves the problem, but I don't believe this should happen in the first place.
I am using pandas 0.15.2 but have not tried 0.16.0.
``` python
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: 1.3.4
Cython: 0.19.2
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.5.0
IPython: 2.4.0
sphinx: 1.2.3
patsy: 0.2.1
dateutil: 2.4.1
pytz: 2014.10
bottleneck: 0.8.0
tables: 3.1.0
numexpr: 2.3
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: 0.7.2
xlsxwriter: None
lxml: 2.3.2
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.7.2
apiclient: None
rpy2: 2.4.2
sqlalchemy: None
pymysql: None
psycopg2: None
>>> kept_tids = pd.read_hdf('kept_tids_20150310.h5', 'kept_tids', mode='r')
>>> kept_tids.to_hdf('kept_tids_20150310_resave.h5', 'kept_tids', mode='w', format='t', data_columns=True)
>>> chroms = kept_tids['chrom'].drop_duplicates().order().tolist()
>>> print chroms
['chr1', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr2', 'chr20', 'chr21', 'chr22', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chrM', 'chrX', 'chrY']
>>> len(kept_tids)
202836
>>> sum(len(pd.read_hdf('kept_tids_20150310_resave.h5', 'kept_tids', mode='r', where="chrom == '%s'"%x)) for x in chroms)
193757
>>> (kept_tids['chrom']=='chr16').sum()
10157
>>> len(pd.read_hdf('kept_tids_20150310_resave.h5', 'kept_tids', mode='r', where="chrom == 'chr16'"))
6278
```
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| null | 15 | 2015-03-18T22:07:56Z | 2015-03-19T02:52:20Z | 2015-03-18T23:11:25Z | NONE | null | Saving and Retrieving are basic to data operations. The defacto save / retrieve in
pandas, at least from Wes's book, seems to be to_csv and .read_csv These are great,
but what is saved is not what is returned:
In [49] frame
Out[49]:
fun people state
0 pinball Joe OH
1 food Mary NJ
In[50] frame.to_csv('experiment.csv')
In[51] new_frame=pd.read_csv('experiment.csv')
In[52] new_frame
Out[52]:
```
Unnamed: 0 fun people state
```
0 0 pinball Joe OH
1 1 food Mary NJ
As you can see, a new index column is added. I think it would probably be very easy to
write a 'save_DataFrame' and a 'read_DataFrame' that would save the index and the columns
and restore them as they were saved.
I realize this is no big deal once you know the program and the suggstion is, admittedly, mostly philosophical and to make things easier for newbies. But I think this is the correct structure for
a data manipulation program.
This is just my opinion and I will only write this once. I don't want to argue about it.
Please do not write to me and tell me how stupid I am. Feel free just to trash this or
close it out without informing me.
Thanks. -- Greg
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} | 6 | 2015-03-18T22:23:40Z | 2015-03-19T14:06:48Z | 2015-03-19T13:31:16Z | NONE | null | I found that when using `to_sql` on a dataframe with a datetime64 column with most of the values being `NaT`, sqlalchemy fails. The column `closed_at` is of type `TIMESTAMP` on redshift (Timestamp without timezone).
```
df.info()
[output]
<class 'pandas.core.frame.DataFrame'>
Int64Index: 11 entries, 0 to 1684
Data columns (total 11 columns):
created_at 11 non-null datetime64[ns]
updated_at 11 non-null datetime64[ns]
closed_at 2 non-null datetime64[ns]
state 11 non-null object
title 11 non-null object
body 11 non-null object
issue_submitter 11 non-null object
assignee 6 non-null object
labels 11 non-null object
html_url 11 non-null object
id 11 non-null int64
dtypes: datetime64[ns](3), int64(1), object(7)None
```
The column `closed_at` has only 2 non empty values. if I delete the column prior to writing everything goes fine. However, when i call `to_sql` with the column on the dataframe i get the following error:
```
Traceback (most recent call last):
df.to_sql(table, self.engine,if_exists='append',index=False)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/pandas/core/generic.py", line 950, in to_sql
index_label=index_label)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/pandas/io/sql.py", line 475, in to_sql
index_label=index_label)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/pandas/io/sql.py", line 842, in to_sql
table.insert()
File "/home/manugarri/anaconda/lib/python2.7/site-packages/pandas/io/sql.py", line 611, in insert
self.pd_sql.execute(ins, data_list)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/pandas/io/sql.py", line 810, in execute
return self.engine.execute(*args, **kwargs)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 1682, in execute
return connection.execute(statement, *multiparams, **params)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 720, in execute
return meth(self, multiparams, params)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/sql/elements.py", line 317, in _execute_on_connection
return connection._execute_clauseelement(self, multiparams, params)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 817, in _execute_clauseelement
compiled_sql, distilled_params
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 947, in _execute_context
context)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 1108, in _handle_dbapi_exception
exc_info
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/util/compat.py", line 185, in raise_from_cause
reraise(type(exception), exception, tb=exc_tb)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/base.py", line 917, in _execute_context
context)
File "/home/manugarri/anaconda/lib/python2.7/site-packages/sqlalchemy/engine/default.py", line 432, in do_executemany
cursor.executemany(statement, parameters)
sqlalchemy.exc.DataError: (DataError) invalid input syntax for type timestamp: "0001-255-255T00:00:00"
```
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| null | 1 | 2015-03-18T22:24:40Z | 2015-03-18T23:07:21Z | 2015-03-18T23:05:56Z | NONE | null | Hi everyone,
How can I do a ifelse statement R-like in a pandas dataframe.
I want to compute a new column via a calculation from the others columns.
It's been a while, so I ask to the community. I used to do a simple ifelse(cond, true, false) with R
on a dataframe.I look for the same result.
here is my code:
import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
f=web.DataReader("F", 'yahoo', start, end)
here is a print of the first 5 lines:
Open High Low Close Volume Adj Close
Date
2010-01-04 10.17 10.28 10.05 10.28 60855800 9.43
2010-01-05 10.45 11.24 10.40 10.96 215620200 10.05
2010-01-06 11.21 11.46 11.13 11.37 200070600 10.43
2010-01-07 11.46 11.69 11.32 11.66 130201700 10.69
2010-01-08 11.67 11.74 11.46 11.69 130463000 10.72
I try to calculate a new columns based on the others:
f["new column"] = if open > close then (high - open)
if close >= open then (high - close)
else nan
I just started to test the applymap function by doing this:
f.applymap(lambda x: x["open"]*2)
it gave me that error:
IndexError: ('invalid index to scalar variable.', u'occurred at index Open')
Anyone has a solution ?
regards,
mayouf
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} | 4 | 2015-03-19T10:17:38Z | 2015-04-28T00:54:11Z | 2015-04-28T00:54:11Z | CONTRIBUTOR | null | ``` python
ts = pd.Series(np.random.normal(size=10),index=pd.timedelta_range(start=0,periods=10,freq='1s'))
ts.foo
```
raises
```
ValueError: cannot create timedelta string converter for [foo]
```
Shouldn't it raise
```
AttributeError: 'Series' object has no attribute 'foo'
```
?
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.16.0rc1-28-gd7d868f
nose: 1.3.1
Cython: 0.20.1post0
numpy: 1.8.2
scipy: 0.13.3
statsmodels: None
IPython: 1.2.1
sphinx: 1.2.2
patsy: None
dateutil: 2.4.1
pytz: 2013b
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
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closes #9676
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} | 3 | 2015-03-19T15:10:08Z | 2015-04-28T00:53:57Z | 2015-04-28T00:53:52Z | CONTRIBUTOR | null | closes #9680
And now it raises AttributeError.
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} | 6 | 2015-03-19T20:52:09Z | 2015-04-28T01:14:14Z | 2015-04-28T01:14:14Z | CONTRIBUTOR | null | This case may be unusual, but I was writing a test to make sure results are sorted within groupby calls, and found that if the `func` in a `df.groupby().apply(func)` call doesn't return a value, it causes an error.
Minimal code to replicate:
```
test_df = pd.DataFrame({'groups': [0,0,1,1], 'random_vars': [8,7,4,5]})
def test_func(x):
pass
test_df.groupby('groups').apply(test_func)
```
Error:
Traceback (most recent call last):
```
File "<ipython-input-18-d520af3d5d86>", line 11, in <module>
z.groupby('d').apply(sort_test)
File "/Users/Nick/GitHub/pandas/pandas/core/groupby.py", line 663, in apply
return self._python_apply_general(f)
File "/Users/Nick/GitHub/pandas/pandas/core/groupby.py", line 670, in _python_apply_general
not_indexed_same=mutated)
File "/Users/Nick/GitHub/pandas/pandas/core/groupby.py", line 2811, in _wrap_applied_output
v = next(v for v in values if v is not None)
StopIteration
```
The problem seems to be due to the function `test_func()` not returning a value, though the error persists even if I `return None` in `test_func()`.
My use, as example of actual situation it might come up:
```
test = pd.DataFrame({'groups': np.random.randint(0,10, size = 100), 'random_vars': np.random.rand(1,100)[0]})
test.sort('random_vars', inplace = True)
def sort_test(x):
assert_frame_equal(x, x.sort('random_vars'))
from pandas.util.testing import assert_frame_equal
test.groupby('groups').apply(sort_test)
```
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} | 6 | 2015-03-19T22:15:41Z | 2015-04-28T01:13:59Z | 2015-04-28T01:13:59Z | CONTRIBUTOR | null | Closes issue: #9684
Returns empty object even if all entries are missing.
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} | 3 | 2015-03-20T02:13:43Z | 2015-07-28T21:51:47Z | 2015-07-28T21:51:47Z | CONTRIBUTOR | null | Start for #9660
These are changes to get vbench running under python3. There are quite a few tests that need modification, but with these changes the code will parse and the suite can run (along with [vbench#36](https://github.com/pydata/vbench/pull/36)).
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} | 5 | 2015-03-20T10:26:23Z | 2020-01-03T23:23:43Z | 2020-01-03T23:23:43Z | NONE | null | In pandas 0.15.2, DataFrame.interpolate( ... ) doesn't work with the keyword combination axis=1, method="time", inplace=True. It just returns the original DataFrame.
```
import pandas as pd
import numpy as np
periods=5
idx = pd.date_range(start="2014-01-01", periods=periods)
data = np.random.rand(periods, periods)
data[data < 0.5] = np.nan
df0 = pd.DataFrame(index=idx, columns=idx, data=data)
df1 = df0.copy()
print "(1) Original df"
print df0
print "\n(2a) axis=0, inplace=False"
print df0.interpolate(axis=0, method="time")
print "\n(2b) axis=0, inplace=True"
df0.interpolate(axis=0, method="time", inplace=True)
print df0
print "\n(3a) axis=1, inplace=False"
print df1.interpolate(axis=1, method="time")
print "\n(3b) axis=1, inplace=True"
df1.interpolate(axis=1, method="time", inplace=True)
print df1
```
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} | 2 | 2015-03-20T12:53:05Z | 2015-04-10T10:54:14Z | 2015-03-26T09:49:50Z | CONTRIBUTOR | null | This format is used, e.g., in datasets from the ECB.
Code example:
In [25]: PD.period_range(start = '2012Q1', periods = 4, freq = 'Q')
Out[25]:
<class 'pandas.tseries.period.PeriodIndex'>
[2012Q1, ..., 2012Q4]
Length: 4, Freq: Q-DEC
## In [26]: PD.period_range(start = '2012-Q1', periods = 4, freq = 'Q')
ValueError Traceback (most recent call last)
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\tools.py in
parse_time_string(arg, freq, dayfirst, yearfirst)
463 parsed, reso = dateutil_parse(arg, default, dayfirst=dayfirst,
--> 464 yearfirst=yearfirst)
465 except Exception as e:
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\tools.py in
dateutil_parse(timestr, default, ignoretz, tzinfos, **kwargs)
489 if res is None:
--> 490 raise ValueError("unknown string format")
491
ValueError: unknown string format
During handling of the above exception, another exception occurred:
DateParseError Traceback (most recent call last)
<ipython-input-26-a960a544a087> in <module>()
----> 1 PD.period_range(start = '2012-Q1', periods = 4, freq = 'Q')
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\period.py i
n period_range(start, end, periods, freq, name)
1433 """
1434 return PeriodIndex(start=start, end=end, periods=periods,
-> 1435 freq=freq, name=name)
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\period.py i
n **new**(cls, data, ordinal, freq, start, end, periods, copy, name, tz, **kwarg
s)
637 else:
638 data, freq = cls._generate_range(start, end, periods,
--> 639 freq, kwargs)
640 else:
641 ordinal, freq = cls._from_arraylike(data, freq, tz)
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\period.py i
n _generate_range(cls, start, end, periods, freq, fields)
651 raise ValueError('Can either instantiate from fields '
652 'or endpoints, but not both')
--> 653 subarr, freq = _get_ordinal_range(start, end, periods, freq)
```
654 elif field_count > 0:
655 subarr, freq = _range_from_fields(freq=freq, **fields)
```
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\period.py i
n _get_ordinal_range(start, end, periods, freq)
1281
1282 if start is not None:
-> 1283 start = Period(start, freq)
1284 if end is not None:
1285 end = Period(end, freq)
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\period.py i
n **init**(self, value, freq, ordinal, year, month, quarter, day, hour, minute,
second)
124 value = value.upper()
125
--> 126 dt, _, reso = parse_time_string(value, freq)
127 if freq is None:
128 try:
C:\Users\stefan\Anaconda\envs\py343\lib\site-packages\pandas\tseries\tools.py in
parse_time_string(arg, freq, dayfirst, yearfirst)
465 except Exception as e:
466 # TODO: allow raise of errors within instead
--> 467 raise DateParseError(e)
468
469 if parsed is None:
DateParseError: unknown string format
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} | 1 | 2015-03-20T14:26:00Z | 2015-03-20T15:27:34Z | 2015-03-20T15:27:34Z | MEMBER | null | Some missing spaced in the string-methods section, and added a highlight about the deprecations.
@jreback you suggested to remove the highlights alltogether now we have the automatic table of contents, but I think it is not fully equivalent (eg the deprecations I added now are not directly visible in the table of contents)
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| null | 0 | 2015-03-20T15:07:44Z | 2019-01-13T00:48:23Z | null | CONTRIBUTOR | null | If `escape=False` is specified, this dataframe fill fail horribly…
``` python
df = DataFrame(data={
'test': ['<a href="http://reallylongurlthateasilyislongerthan50characters.htm">short linkname</a>'],
})
```
…as the `<td><a href="http://reallylongurlthateasilyislongertha...</td>` will be broken HTML, and the `a` tag is closed by the closing `td` tag.
i propose that `display.max_rows` is ignored for cells that contain unescaped HTML tags (i.e. at least one `<` character)
Related to #8273
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} | 3 | 2015-03-20T15:16:08Z | 2015-03-25T10:55:44Z | 2015-03-25T10:55:23Z | CONTRIBUTOR | null | When converting to datetime64, pandas does:
``` python
if col_type == 'datetime64':
try:
tz = col.tzinfo
return DateTime(timezone=True)
except:
return DateTime
if col_type == 'timedelta64':
```
2 problems
1. The exception catch is way to broad. I think this was probably supposed to catch an AttributeException (For col with no tzinfo attribute at all)
2. columns can have a tzinfo =None, in which case a Datetime(timezone=False) should be created instead.
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} | 11 | 2015-03-20T20:46:38Z | 2016-11-26T11:26:53Z | 2016-11-26T11:26:53Z | NONE | null | I have been successfully parsing a csv file in 0.14.1, just upgraded yesterday and now the following code breaks:
```
df = pd.io.parsers.read_csv(fname,
skiprows=range(1, 9))
```
here is the file:
https://www.dropbox.com/s/grhi6e9vihjf92t/testtown2.csv?dl=0
Versions:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.14-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: None
Cython: 0.20.1
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: 2.1.0
sphinx: None
patsy: None
dateutil: 2.4.1
pytz: 2014.10
bottleneck: None
tables: 3.1.1
numexpr: 2.4
matplotlib: None
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.6.8
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext lo64)
```
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} | 23 | 2015-03-20T20:52:05Z | 2015-12-05T15:33:44Z | 2015-12-05T15:33:44Z | CONTRIBUTOR | null | Resolves #9263
Tested:
- [x] windows
- [x] macosx
- [x] linux
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} | 3 | 2015-03-21T01:39:57Z | 2015-03-25T23:03:40Z | 2015-03-25T23:03:40Z | NONE | null | Changes to be committed:
modified: pandas/core/series.py
modified: pandas/tests/test_series.py
Fixes global reference to iNaT (should be tslib.iNaT) in series._maybe_box.
Adds test `test_datetime_timedelta_quantiles` to check for proper return value
in test_series.py.
Issue #9675
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| null | 1 | 2015-03-21T13:40:01Z | 2015-08-18T12:45:09Z | 2015-05-09T16:16:26Z | CONTRIBUTOR | null | Closes https://github.com/pydata/pandas/issues/9687
There's still some weird issues with dtypes. In my test case there's a DataFrame with dtypes [int, float, int]. After the interpolation they should be downcasts to [int, int, int] when the missing value is filled. But along the way a transpose changes things to `[float, float, float]` before filling. And then (I think) `self._update_inplace` doesn't change dtypes, so the actual result is `[float, float, float]`.
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| null | 1 | 2015-03-21T22:32:17Z | 2015-03-22T14:44:45Z | 2015-03-22T14:44:38Z | NONE | null | I finally traced a problem I was having with options downloads to changes made between version 0.15.1 and version 0.15.2. Probably easiest is just to link the question I posed on Stack Overflow, because it shows the behavior: http://stackoverflow.com/questions/29182526/trouble-with-http-request-from-google-compute-engine
Weirdly, in 0.15.2, I was consistently able to get the options data for large cap companies ('aapl', 'ge' were my typical test cases) but not for small cap companies such as 'spwr' or 'ddd'. Not sure what was changed, but it looks to me like it might have to do with the list of expiration dates or with the handling of empty tables given an expiration date. Right now, in any case, if you hit the link shown in my stack trace (http://finance.yahoo.com/q/op?s=SPWR&date=1430438400), there's an empty table for puts and only 1 call. That would be something that's more common for smaller companies, too. The other possibility is that the initial `Options` object isn't getting good links in the newer version.
That's about all I know about it, but reverting to 0.15.1 seems to have solved the problems I was having.
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} | 10 | 2015-03-22T00:31:06Z | 2015-05-18T15:20:38Z | 2015-03-23T10:55:36Z | CONTRIBUTOR | null | Replicating Example (pandas 15.2 and 0.16.0rc1-32-g5a417ec):
```
import numpy as np
df = pd.DataFrame({'col1':[1,1,2,2], 'col2':[1,2,3,np.nan]})
# Works fine
df.groupby('col1').transform(sum)['col2']
# Throws error
df.groupby('col1')['col2'].transform(sum)
```
Error:
Traceback (most recent call last):
```
File "<ipython-input-7-f969e26273d4>", line 8, in <module>
df.groupby('col1')['col2'].transform(sum)
File "/Users/Nick/GitHub/pandas/pandas/core/groupby.py", line 2418, in transform
return self._transform_fast(cyfunc)
File "/Users/Nick/GitHub/pandas/pandas/core/groupby.py", line 2459, in _transform_fast
return self._set_result_index_ordered(Series(values))
File "/Users/Nick/GitHub/pandas/pandas/core/groupby.py", line 497, in _set_result_index_ordered
result.index = self.obj.index
File "/Users/Nick/GitHub/pandas/pandas/core/generic.py", line 2061, in __setattr__
return object.__setattr__(self, name, value)
File "pandas/src/properties.pyx", line 65, in pandas.lib.AxisProperty.__set__ (pandas/lib.c:41404)
obj._set_axis(self.axis, value)
File "/Users/Nick/GitHub/pandas/pandas/core/series.py", line 268, in _set_axis
self._data.set_axis(axis, labels)
File "/Users/Nick/GitHub/pandas/pandas/core/internals.py", line 2211, in set_axis
'new values have %d elements' % (old_len, new_len))
ValueError: Length mismatch: Expected axis has 3 elements, new values have 4 elements
```
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| null | 1 | 2015-03-22T15:46:59Z | 2015-09-10T14:36:02Z | 2015-03-23T01:27:08Z | CONTRIBUTOR | null | There is a xport file reader on Github here:
https://github.com/jcushman/xport
Would it be reasonable (with the author's permission) to port this into Pandas?
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} | 2 | 2015-03-22T17:05:19Z | 2015-03-23T10:55:42Z | 2015-03-23T10:55:36Z | CONTRIBUTOR | null | Switch count() to size(), so that when we build the expanded values we're using the size of the groups and not simply the number of non-null values they have. Fixes #9697.
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} | 0 | 2015-03-22T17:17:25Z | 2015-04-29T10:31:53Z | 2015-04-29T10:31:53Z | CONTRIBUTOR | null | Under some circumstances, `transform` is getting confused about what the output index should be:
```
>>> df = pd.DataFrame({"c1": [1], "c2": [2]})
>>> df
c1 c2
0 1 2
>>> df.groupby('c1').transform(sum)
c2
0 NaN
1 2
```
I haven't chased down why, but since we have
```
>>> df.groupby('c1').transform(lambda x: sum(x))
c2
0 2
```
it must be another fast-path problem, like #9697.
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} | 5 | 2015-03-22T20:52:39Z | 2015-03-26T10:43:40Z | 2015-03-26T09:49:51Z | CONTRIBUTOR | null | This closes #9688 by making years and quarters separable by dashes.
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