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https://api.github.com/repos/pandas-dev/pandas/issues/2901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2901/events | https://github.com/pandas-dev/pandas/issues/2901 | 11,180,899 | MDU6SXNzdWUxMTE4MDg5OQ== | 2,901 | Assign to Series slice of a Panel | {
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x = Series([2, 3, 4, 5], index=p.minor_axis)
p.ix[0, 0] = 4 # works
p.ix[0, 0, :] = x # works
p.ix[0, 0] = x # doesn't work
pandas.version.version
'0.10.1'
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} | 1 | "2013-02-20T03:22:35Z" | "2013-03-28T17:44:22Z" | "2013-03-28T17:44:22Z" | CONTRIBUTOR | null | Seem to be converting MultiIndex into tuples when I do a complex apply:
```
import pandas as pd
s = pd.DataFrame([[1,2], [3,4], [5,6]])
s.index = pd.MultiIndex.from_arrays([['a','a','b'], ['c','d','d']])
s.columns = ['col1','col2']
s
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>col1</th>
<th>col2</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2" valign="top"><strong>a</strong></td>
<td><strong>c</strong></td>
<td> 1</td>
<td> 2</td>
</tr>
<tr>
<td><strong>d</strong></td>
<td> 3</td>
<td> 4</td>
</tr>
<tr>
<td><strong>b</strong></td>
<td><strong>d</strong></td>
<td> 5</td>
<td> 6</td>
</tr>
</tbody>
</table>
```
s_1 = s.apply(lambda s: pd.Series({'min': s.min(), 'max': s.max()}), 1)
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>max</th>
<th>min</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>(a, c)</strong></td>
<td> 2</td>
<td> 1</td>
</tr>
<tr>
<td><strong>(a, d)</strong></td>
<td> 4</td>
<td> 3</td>
</tr>
<tr>
<td><strong>(b, d)</strong></td>
<td> 6</td>
<td> 5</td>
</tr>
</tbody>
</table>
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} | 3 | "2013-02-20T07:42:05Z" | "2013-04-17T00:13:10Z" | "2013-03-22T17:37:35Z" | CONTRIBUTOR | null | ``` python
In [40]: columns = pd.MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), ('b', 'hello'), ('b', 'world')], names=['lvl0', 'lvl1'])
In [41]: df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
In [42]: df
Out[42]:
lvl0 a b
lvl1 foo bar hello world
0 2.560515 1.579242 0.727753 2.207781
1 -0.599632 0.254395 0.784745 0.340575
2 1.262004 0.409085 -0.229616 1.096679
3 1.637929 -0.694670 -0.356355 0.410580
In [43]: df.xs('a', level='lvl0', axis=1)
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<...>
<...>/lib/python2.7/site-packages/pandas-0.11.0.dev_14a04dd-py2.7-linux-x86_64.egg/pandas/core/internals.pyc in set_axis(self, axis, value)
831 if len(value) != len(cur_axis):
832 raise Exception('Length mismatch (%d vs %d)'
--> 833 % (len(value), len(cur_axis)))
834 self.axes[axis] = value
835
Exception: Length mismatch (2 vs 4)
In [44]: df.xs('a', axis=1)
Out[44]:
lvl1 foo bar
0 2.560515 1.579242
1 -0.599632 0.254395
2 1.262004 0.409085
3 1.637929 -0.694670
In [45]: pd.__version__
Out[45]: '0.11.0.dev-14a04dd'
```
```
[1227][i] uname -a
Linux xl-mec-03 2.6.18-308.11.1.el5 #1 SMP Fri Jun 15 15:41:53 EDT 2012 x86_64 x86_64 x86_64 GNU/Linux
```
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} | 11 | "2013-02-20T10:29:37Z" | "2017-01-19T22:11:43Z" | "2017-01-19T22:11:43Z" | CONTRIBUTOR | null | ``` python
In [42]: from itertools import product
In [43]: import pandas as pd
In [44]: import numpy as np
In [45]: index = pd.MultiIndex.from_tuples([t for t in product([10, 20, 30], ['a', 'b'])])
In [46]: df = pd.DataFrame(np.random.randn(6, 6), index, index)
In [47]: df
Out[47]:
10 20 30
a b a b a b
10 a 0.077368 0.360018 0.649403 -0.221877 -1.527411 0.485647
b 0.890805 -2.142297 0.758411 -1.650710 0.041276 -0.040894
20 a -0.401678 0.481390 -1.080735 0.621861 1.410940 -1.106015
b -0.504422 -1.555415 -0.023859 0.211287 -0.321643 0.140895
30 a -0.118969 -0.432082 -0.888786 1.167191 -1.642356 -0.281661
b -0.580182 2.920769 -0.685617 1.327784 0.691514 -0.692361
```
Slicing ranges is consistent between both axis.
``` python
In [48]: df.ix[10:20, :]
Out[48]:
10 20 30
a b a b a b
10 a 0.077368 0.360018 0.649403 -0.221877 -1.527411 0.485647
b 0.890805 -2.142297 0.758411 -1.650710 0.041276 -0.040894
20 a -0.401678 0.481390 -1.080735 0.621861 1.410940 -1.106015
b -0.504422 -1.555415 -0.023859 0.211287 -0.321643 0.140895
In [49]: df.ix[:, 10:20]
Out[49]:
10 20
a b a b
10 a 0.077368 0.360018 0.649403 -0.221877
b 0.890805 -2.142297 0.758411 -1.650710
20 a -0.401678 0.481390 -1.080735 0.621861
b -0.504422 -1.555415 -0.023859 0.211287
30 a -0.118969 -0.432082 -0.888786 1.167191
b -0.580182 2.920769 -0.685617 1.327784
```
This is inconsistent to me:
``` python
In [50]: df.ix[10, :]
Out[50]:
10 20 30
a b a b a b
a 0.077368 0.360018 0.649403 -0.221877 -1.527411 0.485647
b 0.890805 -2.142297 0.758411 -1.650710 0.041276 -0.040894
In [51]: df.ix[:, 10]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
...
IndexError: index out of bounds
```
and this also
``` python
In [52]: df.ix[0, :]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
...
KeyError: 0
In [53]: df.ix[:, 0]
Out[53]:
10 a 0.077368
b 0.890805
20 a -0.401678
b -0.504422
30 a -0.118969
b -0.580182
Name: (10, a), Dtype: float64
```
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} | 7 | "2013-02-20T21:52:07Z" | "2014-05-29T06:11:32Z" | "2014-05-29T06:11:32Z" | NONE | null | I find myself writing this utility over and over in my usual work:
``` python
def count_uniques(df, groupby, unique_on):
"""Takes a dataframe, groups it by groupby, and counts the unique
values of unique_on."""
return df.groupby(groupby).agg({unique_on: lambda x: len(x.unique())})
```
This is easiest to explain by example. Suppose I had a dataframe with two columns, both with repeated values, and I want to count the number of unique values in one column rela
``` python
In [1]: from analysis import count_uniques
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(pd.np.array([['CBS', 'Old Spice'],
['ABC', 'Budweiser'],
['CBS', 'Budweiser'],
['CBS', 'Budweiser'],
['ESPN', 'Burger King'],
['Disney', 'Budweiser'],
['ESPN', None]]), columns=["Channel", "Advertiser"])
In [4]: count_uniques(df, "Channel", "Advertiser")
Out[4]:
Advertiser
Channel
ABC 1
CBS 2
Disney 1
ESPN 2
In [5]: count_uniques(df, "Advertiser", "Channel")
Out[5]:
Channel
Advertiser
Budweiser 3
Burger King 1
Old Spice 1
```
I feel like I should be able to call this right off of the dataframe, as `df.count_uniques("Advertiser", "Channel")`. My particular implementation of the function is not optimized whatsoever and I'm pretty confident there are speedier ways to do this calculation.
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https://api.github.com/repos/pandas-dev/pandas/issues/2906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2906/events | https://github.com/pandas-dev/pandas/issues/2906 | 11,217,478 | MDU6SXNzdWUxMTIxNzQ3OA== | 2,906 | date_range bug? | {
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} | 2 | "2013-02-20T22:00:55Z" | "2013-03-13T23:53:50Z" | "2013-03-13T23:53:50Z" | NONE | null | If 'start' is a timezone-aware datetime.datetime or Timestamp, then it will get shifted by several hours.
``` python
In [1]: from pandas import *
In [2]: start = Timestamp('20130220 10:00', tz='US/Eastern')
In [3]: start
Out[3]: <Timestamp: 2013-02-20 10:00:00-0500 EST, tz=US/Eastern>
In [4]: dr = date_range(start, periods=2)
In [5]: dr
Out[5]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-02-20 15:00:00, 2013-02-21 15:00:00]
Length: 2, Freq: D, Timezone: US/Eastern
In [6]: dr[0]
Out[6]: <Timestamp: 2013-02-20 15:00:00-0500 EST, tz=US/Eastern>
```
It seems that start' time component was treated as if it's UTC by date_range.
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} | [] | closed | false | null | [] | null | 1 | "2013-02-20T23:05:39Z" | "2013-02-21T19:32:29Z" | "2013-02-21T19:32:29Z" | CONTRIBUTOR | null | reindexing with all axes of None right now returns the same object
copy is not triggered, even though copy flag is True by default
in the same situation align will copy, and reindex_axis will as well
should I fix this? or leave as is?
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} | 10 | "2013-02-21T00:35:13Z" | "2014-10-07T00:15:24Z" | "2014-10-07T00:15:24Z" | NONE | null | It would be helpful to iterate through rows returned from an sql query (sqlite specifically) chunk by chunk just as is done in the read_csv and text files function as described here: http://pandas.pydata.org/pandas-docs/stable/io.html#iterating-through-files-chunk-by-chunk
The return value should be an iterable object. This will prevent queries from returning too large an amount of data, (possibly) exceeding the system memory.
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```
> df = pandas.DataFrame(data=[1,"a"])
> print(df)
0
0 1
1 a
> print(df.applymap(lambda x: x))
0
0 1
1 a
```
This does not (note the float "1." instead if "1"):
```
> df = pandas.DataFrame(data=[1.,"a"])
> print(df)
0
0 1
1 a
> print(df.applymap(lambda x: x))
0
0 1
1 NaN
```
This is a problem in statsmodels/statsmodels#636
pandas.**version** -> '0.11.0.dev-14a04dd'
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] | closed | false | null | [] | null | 2 | "2013-02-21T16:50:34Z" | "2014-06-22T07:36:52Z" | "2013-02-23T01:19:53Z" | CONTRIBUTOR | null | showing up in applymap (#2909)
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} | 4 | "2013-02-21T17:38:18Z" | "2013-09-21T12:21:43Z" | "2013-09-21T12:21:37Z" | CONTRIBUTOR | null | what is the best way to safely slice using multiple index labels? (i.e. not create new NaN rows?) is the best way to do this to intersect the index with the searched values before searching?
```
import pandas
df = pandas.DataFrame([[1,2], [3,4]], index=['X', 'Y'], columns=['A', 'B'])
df.ix[['X', 'Z'], :]
Out[1]:
A B
X 1 2
Z NaN NaN < -- why does this not error?
```
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} | 2 | "2013-02-21T18:45:31Z" | "2013-03-20T01:22:50Z" | "2013-03-20T01:22:50Z" | NONE | null | Using pandas 0.10.1:
```
df1 = pd.DataFrame(np.random.randn(10))
df1.groupby(np.arange(len(df1))).transform(lambda x: x)
```
produces:
```
FutureWarning: rename with inplace=True will return None from pandas 0.11 onward.
```
In the release notes for 0.10.1 I saw the changes regarding functions with the `inplace` option. However, it didn't seem like the above code should naturally lead to such a warning. No warning is produced if the same operation is done on a Series.
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} | 2 | "2013-02-22T01:43:13Z" | "2013-09-21T14:17:43Z" | "2013-09-21T14:17:43Z" | NONE | null | I'm using version 0.10.0
```
import pandas as pd
import numpy as np
df = pd.DataFrame({
"A":np.random.randint(0, 5, 20).astype(float),
"B":np.random.random(20)})
df2 = df.groupby("A", as_index=False).mean()
print df2.dtypes
```
the result is
```
A object
B float64
```
I think this is because float index use object array, but I think the dtype of the result should be float64. Compare this with apply:
```
df2 = df.groupby("A", as_index=False).apply(lambda x:x.mean())
print df2.dtypes
```
the result is
```
A float64
B float64
```
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] | closed | false | null | [] | null | 0 | "2013-02-22T01:53:33Z" | "2014-06-13T05:35:05Z" | "2013-02-23T16:43:51Z" | CONTRIBUTOR | null | This is conceptually (and implemented) like an `isin`
You can give a list of values that you want for a particular column, rather
than a boolean expression. This was already speced in the API, just
not implemented correctly!
Something like this
```
store.select('df',[pd.Term('users',[ 'a%03d' % i for i in xrange(60) ])])
```
originally from the mailing list
https://groups.google.com/forum/?fromgroups#!topic/pystatsmodels/oTjfOb0gazw
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} | 4 | "2013-02-22T19:27:53Z" | "2013-04-03T19:03:07Z" | "2013-04-03T19:03:07Z" | CONTRIBUTOR | null | - Refactor of frame.py/panel.py to move common code to generic.py
all axis creation and manipulation code is now common (except for Series)
- added _setup_axes to create generic NDFrame structures
- moved methods (some methods moved from series as well)
- from_axes,_wrap_array,axes,ix,shape,empty,swapaxes,transpose,pop
- **str**,**bytes**,**repr**
- **iter**,keys,**contains**,**len**,**neg**,**invert**
- convert_objects,as_blocks,as_matrix,values
- **getstate**,**setstate** (though compat remains in frame/panel)
- **getattr**,**setattr**
- _indexed_same,reindex_like,reindex,align,where,mask
- filter (added axis argument to selectively filter on a different axis)
- reindex : changed a fair amount
- truncate (moved to become part of PandasObject)
- API Changes in Panel which make Panel more consistent with DataFrame
- swapaxes on a Panel with the same axes specified now return a copy
- support attribute access for setting
- filter supports same api as original DataFrame filter
- API change in that reindex called with no arguments will now return a copy of the input object (e.g. `df.reindex()`)
_performance is comparable to the existing codebase_
Reasoning for this change:
1. this is in preparation for a change in series to sub-class NDFrame, rather than ndarray directly, with proper support for BlockManager. _this will also theoretically allow a way to do `integer NaNs`, via a mask in the IntBlock_
2. preparation for adding SparseBlocks to BlockManager
3. future changes/additions in indexing will be more straightforward, e.g. adding an integer like `ix` can be done, hopefully in one place
4. future API compatibiliy across objects (e.g. won't have to add methods to both Frame and Panel, and there API can be pretty consistent)
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} | 11 | "2013-02-23T20:20:45Z" | "2019-01-01T17:28:25Z" | "2019-01-01T17:28:25Z" | NONE | null | Just pushed `scripts/find_undoc_args.py`. putting this here
for the occasional drive-by contributor, and newcomers who would appreciate
an opportunity to make a "low-risk" first PR.
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} | 1 | "2013-02-23T21:51:01Z" | "2013-09-19T07:56:45Z" | "2013-02-23T22:07:36Z" | CONTRIBUTOR | null | Changes accepted `kind` parameter values from `{'line', 'bar', 'barh'}` to `{'line', 'bar', 'barh', 'kde', 'density'}`
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} | 15 | "2013-02-23T22:17:49Z" | "2014-06-13T07:22:05Z" | "2013-04-10T07:34:26Z" | CONTRIBUTOR | null | Treating DataFrame content as plain text, rather than HTML markup, by escaping
everything (#2617) seems like the right default for `to_html()`, however, if a DataFrame contains HTML ([example](http://stackoverflow.com/questions/14627380/pandas-html-output-with-conditional-formatting)) or text already HTML escaped, it results in either unwanted escaping or double-escaping.
Changes in this PR:
- make HTML escaping programmable through a new `to_html()` parameter named
`escape` (default True), allowing users to restore old `to_html()` behavior (<=0.10.0) by setting `escape=False`.
- add `&` to the list of HTML chars escaped, so strings that happen to contain
HTML escape sequences or reserved entities, such as `"<"`, are displayed
properly.
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- added frame_to_csv_mixed to vbench
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} | 122 | "2013-02-25T03:49:56Z" | "2014-07-28T19:25:24Z" | "2013-03-07T04:17:17Z" | CONTRIBUTOR | null | Updated to include new indexers:
`.iloc` for pure integer based indexing
`.loc` for pure label based indexing
`.iat` for fast scalar access by integer location
`.at` for fast scalar access by label location
Much updated docs, test suite, and example
In the new `test_indexing.py`, you can change the `_verbose` flag to True to get more test output
anybody interested can investigate a couple of cases marked `no comp` which are where the new
indexing behavior differs from `.ix` (or `.ix` doesn't work); this doesn't include cases where a `KeyError/IndexError` is raised (but `.ix` let's these thru)
Also, I wrote `.iloc` on top of `.ix` but most methods are overriden, it is possible that this let's something thru that should not, so pls take a look
_Please try this out and let me know if any of the docs or interface semantics are off_
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"description": "RC1 ANN went out on 2013-12-6: https://groups.google.com/forum/#!topic/pydata/OHutLByJvK0\r\nTwo weeks, clear all reported bugs then call it on 2013-12-20. \r\n(@y-p)\r\n\r\nsince we've had some bugs reported at a high enough rate, extending rc by one\r\nweek until 2013-12-27. \r\n(@y-p)\r\n\r\nBumping to clear debian sparc\r\n2013-12-30. \r\n(@y-p)",
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} | 2 | "2013-02-25T08:10:10Z" | "2013-09-21T15:10:08Z" | "2013-09-21T15:10:08Z" | NONE | null | Plotting a Series, whose index is not monotonic, gives wrong plot.
The following example gives markers at 4 different positions, whereas it should be 2 different positions.
```
In [168]: x1
Out[168]:
2012-07-03 17:16:07+00:00 1.784596
2012-07-03 15:42:00+00:00 1.786576
In [169]: x1.sort_index().plot(style='g^')
Out[169]: <matplotlib.axes.AxesSubplot at 0x12fb2cc0>
In [170]: x1.plot(style='bv')
Out[170]: <matplotlib.axes.AxesSubplot at 0x12fb2cc0>
In [171]: plt.show()
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2924 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2924/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2924/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2924/events | https://github.com/pandas-dev/pandas/issues/2924 | 11,354,483 | MDU6SXNzdWUxMTM1NDQ4Mw== | 2,924 | Multilevel indexing with .to_latex() method merges two index columns | {
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using the .to_latex method on a multilevel frame (specifically using a pivot_table) merges the two index columns so that it does not render correctly in latex. Looks like it is missing the & for blank cells in index. and also an & for the columns. Manually inserted those in output and it fixes problem
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] | closed | false | null | [] | null | 26 | "2013-02-25T14:49:58Z" | "2014-06-13T03:30:45Z" | "2013-03-09T19:42:52Z" | CONTRIBUTOR | null | simple usage of using numexpr on boolean type frame comparison
```
name
indexing_dataframe_boolean 13.3200 125.3521 0.1063
frame_mult 21.7157 36.6353 0.5928
frame_add 22.0432 36.5021 0.6039
frame_multi_and 385.4241 407.4359 0.9460
```
- optional use of numexpr (though out to suggest it highly)
- example usage, many more cases like this
_frame_multi_and is the case we are talking about below, a boolean express anded with another,
can only optimize each sub-expression, and not the 3 terms together_
original issue is #724
here some runs (on travis ci), for these 4 routines
- with no numexpr (no_ne)
- with numexpr single threaded (_st)
- with numexpr (no suffix)
So numexpr helps in boolean case with and w/o parallelization
add/multiply the parallelization helps (and just numexpr doesn't do much)
```
frame_mult : 27.0671 [ms]
frame_mult_st : 85.1929 [ms]
frame_mult_no_ne : 86.2602 [ms]
frame_add : 26.3325 [ms]
frame_add_st : 74.8805 [ms]
frame_add_no_ne : 73.9357 [ms]
indexing_dataframe_boolean : 15.0413 [ms]
indexing_dataframe_boolean_st : 25.8873 [ms]
indexing_dataframe_boolean_no_ne : 288.4722 [ms]
frame_multi_and : 637.8641 [ms]
frame_multi_and_st : 675.7760 [ms]
frame_multi_and_no_ne : 589.3791 [ms]
```
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} | 0 | "2013-02-25T17:23:56Z" | "2013-03-28T05:35:58Z" | "2013-03-28T05:35:58Z" | CONTRIBUTOR | null | I am using pandas version 0.11.0.dev-eb3134f
This code will raise [this assert](https://github.com/pydata/pandas/blob/master/pandas/tseries/index.py#L303-304)
My debugging shows that the code is testing the equality of a timezone object (inferred_tz) to a string (tz).
date_range si supposed to receive a string so I think that it is a legit use of date_range.
``` python
from pandas import *
start = Timestamp('20130220 10:00', tz='US/Eastern')
dr = date_range(start, tz='US/Eastern')
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2927 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2927/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2927/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2927/events | https://github.com/pandas-dev/pandas/pull/2927 | 11,369,466 | MDExOlB1bGxSZXF1ZXN0NDMwNzg5OA== | 2,927 | BUG: Fix tz handling in date_range when start arg has tz, #2926 | {
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} | 6 | "2013-02-25T17:31:11Z" | "2013-03-28T05:35:13Z" | "2013-03-28T05:35:13Z" | CONTRIBUTOR | null | This commit should fix [issue 2926](https://github.com/pydata/pandas/issues/2926).
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} | 13 | "2013-02-25T19:36:33Z" | "2013-03-10T17:44:04Z" | "2013-03-10T17:44:04Z" | MEMBER | null | not good for long series. do we really need the dtype anyway?
```
you 12
the 8
and 8
a 7
it 7
for 6
in 6
gcc-40 5
to 5
installed 5
some 4
pip 4
if 4
have 4
is 4
Dtype: int64
...
'-arch' 1
off 1
unless 1
tools 1
know 1
well 1
general 1
another 1
fink 1
laptop 1
don't 1
/usr/bin 1
-arch 1
been 1
re-install 1
Dtype: int64
Length: 167, Dtype: int64
```
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} | 1 | "2013-02-25T19:55:42Z" | "2013-03-14T00:38:12Z" | "2013-03-14T00:38:12Z" | NONE | null | When I unstack() one level of a DataFrame having only int64 column dtypes (and a MultiIndex created only from int64 values), the result will have float64 columns. This happens even when the unstacked DataFrame does not have any missing data.
Is this expected? Is there any way to prevent unstack() from changing the dtypes?
Behavior confirmed with pandas 0.8.1 and 0.10.1.
```
In [1]: import pandas as pd
In [2]: rows = [[1, 1, 3, 4],
...: [1, 2, 3, 4],
...: [2, 1, 3, 4],
...: [2, 2, 3, 4]]
In [3]: df = pd.DataFrame(rows, columns=list('ABCD'))
In [4]: print df.dtypes
A int64
B int64
C int64
D int64
In [5]: print df
A B C D
0 1 1 3 4
1 1 2 3 4
2 2 1 3 4
3 2 2 3 4
In [6]: df2 = df.set_index(['A', 'B'])
In [7]: print df2.dtypes
C int64
D int64
In [8]: print df2
C D
A B
1 1 3 4
2 3 4
2 1 3 4
2 3 4
In [9]: df3 = df2.unstack('B')
In [10]: print df3.dtypes
B
C 1 float64
2 float64
D 1 float64
2 float64
In [11]: print df3
C D
B 1 2 1 2
A
1 3 3 4 4
2 3 3 4 4
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2931/events | https://github.com/pandas-dev/pandas/issues/2931 | 11,385,938 | MDU6SXNzdWUxMTM4NTkzOA== | 2,931 | hdf5 compression breaks on sparse series which have all values censored | {
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Compression works fine if there's at least one non-sparse value in each series of a sparse dataframe. And dataframes with series that are completely sparse can be stored in hdf5 without compression.
But combining the two, as in case 4 below, breaks:
``` python
import pandas as pd
import numpy as np
# make sparse dataframe
df = pd.DataFrame(np.random.binomial(n=1, p=.01, size=(1e4, 1e2))).to_sparse(fill_value=0)
# case 1: store uncompressed (works)
store1 = pd.HDFStore('sparse_uncompressed.h5')
store1['sparse_df'] = df
store1.close()
# case 2: store compressed (works)
store2 = pd.HDFStore('sparse_compressed.h5', complib='zlib', complevel=9)
store2['sparse_df'] = df
store2.close()
# set one series to be completely sparse
df[0] = np.zeros(1e4)
# case 3: store df with completely sparse series uncompressed (works)
store3 = pd.HDFStore('sparser_uncompressed.h5')
store3['sparse_df'] = df
store3.close()
# case 4: try storing df with completely sparse series compressed (fails)
store4 = pd.HDFStore('sparser_compressed.h5', complib='zlib', complevel=9)
store4['sparse_df'] = df
store4.close()
```
The resulting error comes from `tables`:
``` python
ValueError: shape parameter cannot have zero-dimensions.
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2932 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2932/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2932/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2932/events | https://github.com/pandas-dev/pandas/issues/2932 | 11,388,553 | MDU6SXNzdWUxMTM4ODU1Mw== | 2,932 | BUG date_parser not accurate with Timestamp | {
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} | 0 | "2013-02-26T00:55:57Z" | "2013-03-28T05:28:02Z" | "2013-03-28T05:28:02Z" | CONTRIBUTOR | null | When reading in this DataFrame (with `parse_dates=[[0,1]]`) we see the datetimes have (29000) microseconds but when using `date_parser=pd.Timestamp` we lose this information:
```
from StringIO import StringIO
data = """05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25
05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25"""
```
```
In [3]: pd.read_csv(StringIO(data), sep=',', header=None, parse_dates=[[0,1]])
Out[3]:
0_1 2 3 4 5 6 7
0 2012-05-31 15:30:00.029000 1306.25 1 E 0 NaN 1306.25
1 2012-05-31 15:30:00.029000 1306.25 8 E 0 NaN 1306.25
In [4]: pd.read_csv(StringIO(data), sep=',', header=None, parse_dates=[[0,1]], date_parser=pd.Timestamp)
Out[4]:
0_1 2 3 4 5 6 7
0 2012-05-31 00:00:00 1306.25 1 E 0 NaN 1306.25
1 2012-05-31 00:00:00 1306.25 8 E 0 NaN 1306.25
```
_Note: This works accurately individually:_
```
In [5]: pd.Timestamp('05/31/2012,15:30:00.029')
Out[5]: <Timestamp: 2012-05-31 15:30:00.029000>
```
Migrated from [StackOverflow](http://stackoverflow.com/questions/14446744/how-to-efficiently-handle-pandas-read-csv-with-datetime-index).
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https://api.github.com/repos/pandas-dev/pandas/issues/2933 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2933/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2933/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2933/events | https://github.com/pandas-dev/pandas/pull/2933 | 11,389,362 | MDExOlB1bGxSZXF1ZXN0NDMxNzIwNw== | 2,933 | BUG: fixes issue in HDFStore w.r.t. compressed empty sparse series (GH #2931) | {
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} | [] | closed | false | null | [] | null | 0 | "2013-02-26T01:26:17Z" | "2014-06-27T14:00:23Z" | "2013-02-26T02:44:59Z" | CONTRIBUTOR | null | - SparseSeries that had 0 - len were not saving when the store was compressed
Closes GH #2931
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https://api.github.com/repos/pandas-dev/pandas/issues/2934 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2934/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2934/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2934/events | https://github.com/pandas-dev/pandas/pull/2934 | 11,390,843 | MDExOlB1bGxSZXF1ZXN0NDMxNzk1Nw== | 2,934 | CLN autopep8 applied to all py files | {
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} | [] | closed | false | null | [] | null | 2 | "2013-02-26T02:30:11Z" | "2014-07-04T02:44:01Z" | "2013-03-06T18:02:20Z" | CONTRIBUTOR | null | Round 2 of autopepping (after #2632).
:)
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} | 5 | "2013-02-26T04:36:16Z" | "2014-08-04T14:34:43Z" | "2013-03-13T23:53:49Z" | CONTRIBUTOR | null | fixes #2906, #2938
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} | 10 | "2013-02-26T06:08:53Z" | "2019-03-26T04:38:34Z" | "2019-03-26T04:38:34Z" | NONE | null | ```
>>> def applym(df):
... print df.irow(0)['a']
... return DataFrame({'a':[1],'b':[1]})
>>> df = DataFrame({'a':[1,1,1,2,3],'b':['a','a','a','b','c']})
>>> df.groupby('b').apply(applym)
1
1
2
3
a b
b
a 0 1 1
b 0 1 1
c 0 1 1
>>>
```
applym is called twice on the first group.
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https://api.github.com/repos/pandas-dev/pandas/issues/2937 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2937/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2937/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2937/events | https://github.com/pandas-dev/pandas/pull/2937 | 11,411,296 | MDExOlB1bGxSZXF1ZXN0NDMyNjc2NA== | 2,937 | BUG: fix handling of the 'kind' argument in Series.order (GH #2811) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2938 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2938/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2938/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2938/events | https://github.com/pandas-dev/pandas/issues/2938 | 11,429,134 | MDU6SXNzdWUxMTQyOTEzNA== | 2,938 | Bugs related to DatetimeIndex, date_range. Question: Is is possible to create a index of datetime, without being auto converted to a DatetimeIndex? | {
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I wrote a lot of code using Index containing timezone-aware datetime.datetime. This caused me a lot of headache when I tried to port my codes from 07.3 to 10.1.
I also encountered several weird bugs (in 10.1).
(I) When DatetimeIndex is assigned as a column of a DataFrame, a silent error occurs:
In [1]: from pandas import *
In [2]: a = date_range('20130101', periods=3, tz='US/Eastern')
In [3]: b = date_range('20130215', periods=3, tz='US/Eastern')
In [4]: Series(a, b) #works as expected
Out[4]:
2013-02-15 00:00:00-05:00 2013-01-01 05:00:00
2013-02-16 00:00:00-05:00 2013-01-02 05:00:00
2013-02-17 00:00:00-05:00 2013-01-03 05:00:00
Freq: D
In [5]: DataFrame({'a': a}, b) #???
Out[5]:
a
2013-02-15 00:00:00-05:00 1970-01-18 05:00:00
2013-02-16 00:00:00-05:00 1970-01-19 05:00:00
2013-02-17 00:00:00-05:00 1970-01-20 05:00:00
(II) concat two Series will result in an incorrect DatetimeIndex:
In [18]: dates_a = [Timestamp('20130219 13:00', tz='US/Eastern'), Timestamp('20130220 13:00', tz='US/Eastern')]
In [19]: dates_b = [Timestamp('20130222 13:00', tz='US/Eastern'), Timestamp('20130223 13:00', tz='US/Eastern')]
In [20]: a = Series(range(2), dates_a)
In [21]: b = Series(range(2), dates_b)
In [22]: concat([a, b])
Out[22]:
2013-02-19 18:00:00-05:00 0
2013-02-20 18:00:00-05:00 1
2013-02-22 18:00:00-05:00 0
2013-02-23 18:00:00-05:00 1
Note that the index of Out[22] is wrong (time shifted by 5 hours).
(III) issue #2906 (already fixed in #2935)
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} | 0 | "2013-02-27T03:26:13Z" | "2013-03-07T10:57:15Z" | "2013-03-07T10:57:15Z" | MEMBER | null | http://stackoverflow.com/questions/15102158/pandas-why-does-dataframe-droplabels-return-a-dataframe-with-removed-label-na
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} | 4 | "2013-02-27T05:54:34Z" | "2015-08-12T15:23:54Z" | "2015-08-12T15:23:24Z" | CONTRIBUTOR | null | Am I missing something here?
```
import numpy as np
import pandas
x = np.array([[0,2,4],[2,0,7],[4,7,0]])
df = pandas.DataFrame(x, index=["a","b", "c"], columns=["a","b","c"])
df.sort("b", inplace=True)
#raises error for axis=1
#df.sort("c", axis=1, inplace=True)
# same with sort_index
# desired result
df.sort("b").T.sort("c").T
```
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} | 11 | "2013-02-27T15:45:52Z" | "2015-06-13T03:37:23Z" | "2015-06-13T03:37:23Z" | CONTRIBUTOR | null | like Series.asof, should also take skipna={none, 'any', 'all'}
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} | 3 | "2013-02-27T15:47:43Z" | "2015-12-08T01:13:34Z" | "2015-12-08T01:13:34Z" | NONE | null | The to_latex method produces wrong latex code for multi-indexed funds. looks like it are missing a few blank {} and &. Net result is that the current method merges the two indeces. when used with output from pivot_table with major index giving categories and other one details this produces totally garbled results.
The formatters= parameter of to_latex method also creates exceptions for multi-indexed data
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https://api.github.com/repos/pandas-dev/pandas/issues/2943 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2943/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2943/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2943/events | https://github.com/pandas-dev/pandas/issues/2943 | 11,467,494 | MDU6SXNzdWUxMTQ2NzQ5NA== | 2,943 | Problem with import using rpy2 | {
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```
import pandas
names = ["a","b","c"]
df = pandas.DataFrame([[0,2,4],[2,0,7],[4,7,0]], index=names, columns=names)
df
# a b c
#a 0 2 4
#b 2 0 7
#c 4 7 0
df2 = df.sort("b").T.sort("c").T
df.where(df <= 4, 0)
# a b c
#a 0 2 4
#b 2 0 0
#c 4 0 0
df2.where(df <= 4, 0)
# c a b
#b 7 2 0
#a 4 0 0
#c 0 0 7
```
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} | 3 | "2013-02-27T22:00:01Z" | "2013-02-27T22:34:45Z" | "2013-02-27T22:34:45Z" | CONTRIBUTOR | null | In [34]: df
Out[34]:
0 1 time
0 -0.602181 -0.657444 2009-07-01 00:00:00
1 -2.102987 -0.486064 2009-07-02 00:00:00
2 0.819956 -0.800478 2009-07-03 00:00:00
3 1.118629 -0.008019 2009-07-04 00:00:00
4 0.404452 0.606187 2009-07-05 00:00:00
In [35]: df.time.dtype
Out[35]: dtype('datetime64[ns]')
In [36]: image_time
Out[36]: datetime.datetime(2009, 7, 28, 13, 39, 2)
## In [37]: df.time - image_time
ValueError Traceback (most recent call last)
<ipython-input-37-32815ed7fa8b> in <module>()
----> 1 df.time - image_time
/Users/changshe/code/pandas/pandas/core/series.pyc in wrapper(self, other)
94 dtype = 'timedelta64[ns]'
95 else:
---> 96 raise ValueError("cannot operate on a series with out a rhs of a series/ndarray of type datetime64[ns] or a timedelta")
97
98 lvalues = lvalues.view('i8')
ValueError: cannot operate on a series with out a rhs of a series/ndarray of type datetime64[ns] or a timedelta
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} | 1 | "2013-02-28T00:03:02Z" | "2013-03-28T05:43:47Z" | "2013-03-28T05:43:47Z" | NONE | null | Trouble parsing quotechars with escaped quotechars inside them
``` python
line = 'col1\n"\\"a,b,c\\""\n'
pandas.read_csv(StringIO(line))
Out[25]:
col1
a b c""
pandas.read_csv(StringIO(line)).ix[0]
Out[26]:
col1 c""
Name: (a, b)
```
i was expecting
``` python
pandas.read_csv(StringIO(line))
col1
0 "a,b,c"
```
and
``` python
pandas.read_csv(StringIO(line)).ix[0]['col1']
'"a,b,c"'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2947 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2947/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2947/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2947/events | https://github.com/pandas-dev/pandas/pull/2947 | 11,499,848 | MDExOlB1bGxSZXF1ZXN0NDM3MTUxMA== | 2,947 | DOC: updated dtypes docs | {
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} | 43 | "2013-02-28T17:40:56Z" | "2013-03-06T19:27:36Z" | "2013-03-05T17:31:20Z" | CONTRIBUTOR | null | I am trying to find the row of a DataFrame that is closest to a certain datetime. I have my data in a DataFrame which has a DatetimeIndex, and I can't use functions like `asof` because I need to get the closest row (either before _or_ after the time), so I am trying to implement the standard way of doing this which is getting the absolute difference between each index time and the time I want to find, then finding the row with the minimum difference.
However, there seems to be a strange bug with using np.abs with the pandas index, and it seems to be in the printing of the results stage (relating to `__get_repr`). Pandas prints things differently depending on how many rows the DataFrame has, and I think this is related here - as running the code on a 200 row DataFrame fails, but on a 100 row DataFrame it works! I've checked that its not some strange value in the DataFrame which is causing the problem, as a 100 row segment from anywhere in my original input file works, whereas a 200 row segment from anywhere fails.
Two input files (aeronet_large.txt with 200 rows and aeronet.txt with 100 rows) and the code I've been using are available at this gist: https://gist.github.com/robintw/631b53fa0cd9dbdabb36, and I asked a StackOverflow question about this (http://stackoverflow.com/questions/15115547/find-closest-row-of-dataframe-to-given-time-in-pandas) where I was recommended to raise an issue.
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} | 2 | "2013-02-28T19:10:09Z" | "2016-09-29T22:44:19Z" | "2016-09-29T22:23:20Z" | NONE | null | I've created a slightly modified version of PeriodIndex for some projects I'm working on that you may find useful for pandas. I rewrote the date-math code from scratch in Cython, it has a wide range of frequencies, it's very easy to extend to new frequencies, and the new code is quite compact.
Code is at https://github.com/abielr/pandasreg. There isn't much documentation yet, but all the key date-math code is in pandasreg/src/rfreq.pyx. Most of the rest of the code is a straight copy of the pandas code for Period and PeriodIndex and some other utility functions to work with the custom index.
Basic approach for handling frequencies is as follows:
- There is a base class RFrequency that defines an interface to convert datetime-like objects to an ordinal period at a given frequency (just like PeriodIndex), and to do the reverse.
- Periods at one frequency can be converted to periods at another frequency.
- Classes that inherit from RFrequency define the date-math logic that is particular to a given frequency. However, there is another flexibility for a single child class to define multiple frequencies. For example, the monthly class defines the monthly, bimonthly, quarterly, semiannual, and annual frequency without having to write custom code for each variant.
- You can force a frequency that is a multiple of a base frequency to pass through a particular period. For example, the semiannual frequency is created from the monthly frequency with a 6-month stride and the requirement that it pass through Jun 1970, which ensures that 1H=Jan-Jun and 2H=Jul-Dec always. (see [this](http://stackoverflow.com/questions/13384795/using-built-in-pandas-frequencies-to-simulate-semiannual-frequency) stackoverflow post for more background).
Looking at the pandas code, it appears that much of the current PeriodIndex functionality is based on the old scikits.timeseries project. I haven't done a detailed performance comparison, but relative to the scikits.timeseries codebase my code could be of benefit in pandas since there is no pure-C code to maintain, there is much less date-math code, and its easier to extend to new frequencies.
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https://api.github.com/repos/pandas-dev/pandas/issues/2950 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2950/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2950/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2950/events | https://github.com/pandas-dev/pandas/pull/2950 | 11,515,661 | MDExOlB1bGxSZXF1ZXN0NDM3OTYwMg== | 2,950 | BUG: in-place conversion of integer series to float (on putmasking), GH #2746. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2951 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2951/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2951/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2951/events | https://github.com/pandas-dev/pandas/pull/2951 | 11,516,050 | MDExOlB1bGxSZXF1ZXN0NDM3OTc3MA== | 2,951 | CLN: argument order and dtype formatting to be consistent with standard | {
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} | 3 | "2013-03-01T03:49:03Z" | "2013-12-04T00:57:33Z" | "2013-03-14T10:42:46Z" | CONTRIBUTOR | null | I am reporting a bug in which a TimeSeries containing datetime values are converted to float64 values upon application of the shift(self, offset) function. Version 0.10.1, python 2.7.
As an example below, I excerpted the first ten rows of end_time from the data frame listed. The dtype of this timeseries is datetime64[ns]. When I apply shift(2), the values are converted to float64. I tried applying the convert_type and astype functions to this timeseries, but neither would return the timeseries to its original datetime64[ns].
Expected behavior: data type of time series should not change upon shifting.
```
DatetimeIndex: 88449 entries, 2012-09-04 07:03:00 to 2013-02-25 20:00:00
Data columns:
time 88449 non-null values
end_time 88448 non-null values
mid 88449 non-null values
dtypes: datetime64[ns](2), float64(1)
```
```
In [70]: end_times
Out[70]:
time
2012-09-04 07:05:00 2012-09-04 07:06:00
2012-09-04 07:06:00 2012-09-04 07:07:00
2012-09-04 07:07:00 2012-09-04 07:08:00
2012-09-04 07:08:00 2012-09-04 07:09:00
2012-09-04 07:09:00 2012-09-04 07:10:00
2012-09-04 07:10:00 2012-09-04 07:11:00
2012-09-04 07:11:00 2012-09-04 07:12:00
2012-09-04 07:12:00 2012-09-04 07:13:00
2012-09-04 07:13:00 2012-09-04 07:14:00
In [71]: end_times.shift(2)
Out[71]:
time
2012-09-04 07:05:00 NaN
2012-09-04 07:06:00 NaN
2012-09-04 07:07:00 1.346742e+18
2012-09-04 07:08:00 1.346742e+18
2012-09-04 07:09:00 1.346742e+18
2012-09-04 07:10:00 1.346743e+18
2012-09-04 07:11:00 1.346743e+18
2012-09-04 07:12:00 1.346743e+18
2012-09-04 07:13:00 1.346743e+18
Name: end_time
```
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} | 20 | "2013-03-01T14:04:06Z" | "2020-12-28T00:07:08Z" | "2020-12-28T00:07:08Z" | NONE | null | Seems like there is a bug in the centered mooving window functions, although the examples [here](http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments) don't show it.
Also `np.convolve` used like in [this post](http://stackoverflow.com/a/11352216/1301710) on Stackoverflow shows different results for the first values:
``` python
In [109]: ser = pd.Series(randn(20), index=pd.date_range('1/1/2000', periods=20))
In [110]: df = pd.DataFrame(ser)
In [111]: df['rc'] = pd.rolling_count(ser, 10, center=True)
In [112]: df['rm'] = pd.rolling_mean(ser, 10, center=True)
In [113]: df['rm_min_1'] = pd.rolling_mean(ser, 10, center=True, min_periods=1)
In [114]: def movingaverage(interval, window_size):
.....: window = numpy.ones(int(window_size))/float(window_size)
.....: return numpy.convolve(interval, window, 'same')
.....:
In [116]: df['ma'] = movingaverage(ser, 10)
In [117]: df
Out[117]:
0 rc rm rm_min_1 ma
2000-01-01 1.177692 5 NaN 0.048306 0.024153
2000-01-02 -0.068567 6 NaN -0.094287 -0.056572
2000-01-03 0.194942 7 NaN -0.225103 -0.157572
2000-01-04 -0.179778 8 NaN -0.160040 -0.128032
2000-01-05 -0.882760 9 NaN -0.262176 -0.235959
2000-01-06 -0.807252 10 -0.250828 -0.250828 -0.250828
2000-01-07 -1.009999 10 -0.365703 -0.365703 -0.365703
2000-01-08 0.295402 10 -0.431110 -0.431110 -0.431110
2000-01-09 -1.079267 10 -0.408968 -0.408968 -0.408968
2000-01-10 -0.148689 10 -0.456946 -0.456946 -0.456946
2000-01-11 0.028935 10 -0.290453 -0.290453 -0.290453
2000-01-12 -0.722632 10 -0.204166 -0.204166 -0.204166
2000-01-13 0.416364 10 -0.175837 -0.175837 -0.175837
2000-01-14 -0.659560 10 -0.266773 -0.266773 -0.266773
2000-01-15 0.782165 10 -0.184003 -0.184003 -0.184003
2000-01-16 0.055617 10 -0.338359 -0.338359 -0.338359
2000-01-17 -0.726706 0 NaN NaN -0.341252
2000-01-18 -0.613954 0 NaN NaN -0.268989
2000-01-19 -0.251574 0 NaN NaN -0.310625
2000-01-20 -1.692243 0 NaN NaN -0.244669
```
```
In [5]: import numpy
In [6]: In [20]: ser = pd.Series(range(10))
...: ...: df = pd.DataFrame(ser)
...: ...: N=3
...: ...: df['rc'] = ser.rolling(N, center=True).count()
...: ...: df['rm'] = ser.rolling(N, center=True).mean()
...: ...: df['rm_min_1'] = ser.rolling(N, center=True, min_periods=1).mean()
...: ...: def movingaverage(interval, window_size):
...: ...: window = numpy.ones(int(window_size))/float(window_size)
...: ...: return numpy.convolve(interval, window, 'same')
...: ...:
...: ...: df['ma'] = movingaverage(ser, N)
In [7]: df
Out[7]:
0 rc rm rm_min_1 ma
0 0 2.0 NaN 0.5 0.333333
1 1 3.0 1.0 1.0 1.000000
2 2 3.0 2.0 2.0 2.000000
3 3 3.0 3.0 3.0 3.000000
4 4 3.0 4.0 4.0 4.000000
5 5 3.0 5.0 5.0 5.000000
6 6 3.0 6.0 6.0 6.000000
7 7 3.0 7.0 7.0 7.000000
8 8 3.0 8.0 8.0 8.000000
9 9 2.0 NaN 8.5 5.666667
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} | 6 | "2013-03-01T14:53:25Z" | "2013-09-21T01:29:24Z" | "2013-09-21T01:29:24Z" | CONTRIBUTOR | null | so min across the columns is clearly non-sensical,
what should we do about this when an op crosses dtype boundaries and is wrong ?
(this is harder than it looks, because you can't always just get the numeric data,
a frame with only say timedelta64[ns] is ok)
maybe have a raise_on_error parameter to min? (default of True)?
(if its mixed (and not all numeric)) types?
```
(Pdb) mixed
A B C D E
0 00:05:05 -1 days, 00:00:00 foo 1 1
1 1 days, 00:05:05 -1 days, 00:00:00 foo 1 1
2 2 days, 00:05:05 -1 days, 00:00:00 foo 1 1
(Pdb) mixed.min()
A 0:05:05
B -1 day, 0:00:00
C foo
D 1
E 1
Dtype: object
(Pdb) mixed.min(axis=1)
0 -8.640000e+13
1 -8.640000e+13
2 -8.640000e+13
Dtype: float64
(Pdb) mixed.dtypes
A timedelta64[ns]
B timedelta64[ns]
C object
D int64
E float64
Dtype: object
```
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BUG: fixed timedelta (in nanops.py) to work with min/max....abs still not working
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} | 1 | "2013-03-01T18:27:47Z" | "2013-12-04T00:57:33Z" | "2013-03-06T12:43:56Z" | CONTRIBUTOR | null | This is a very subtle bug in how np.abs operates on timedelta64[ns]
It converts them to us for some reason on numpy <= 1.6.2. (maybe numpy 1.7 is better)
workaround is to wrap in a Series and astype
tricky to fix this because we really can't detect is except in finalize, but then
have to check all return dtypes from the operations.....
exhibited also in #2948
```
In [23]: s1 = pd.Series(pd.date_range('20120101',periods=3))
In [24]: s2 = pd.Series(pd.date_range('20120102',periods=3))
In [27]: s1-s2
Out[27]:
0 -1 days, 00:00:00
1 -1 days, 00:00:00
2 -1 days, 00:00:00
Dtype: timedelta64[ns]
#### notice this is timedelta64[us] and not ns!
In [28]: np.abs(s1-s2)
Out[28]:
0 1 days, 00:00:00
1 1 days, 00:00:00
2 1 days, 00:00:00
Dtype: timedelta64[us]
# workaround
In [30]: pd.Series(np.abs(s1-s2)).astype('timedelta64[ns]')
Out[30]:
0 1 days, 00:00:00
1 1 days, 00:00:00
2 1 days, 00:00:00
Dtype: timedelta64[ns]
```
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} | 4 | "2013-03-02T09:49:03Z" | "2014-07-01T23:59:35Z" | "2014-07-01T23:59:35Z" | CONTRIBUTOR | null | In the following I took the extreme case where there is no overlap between the two indexes for illustration purposes. When there is some overlap there is still the same issue and the last plotted timeseries is dictating the xaxis range.
Here is the code, tested on pandas 0.13.1:
``` python
import numpy as np
import pandas as pd
import pandas.util.testing
import matplotlib.pyplot as plt
np.random.seed(0)
ts = pd.util.testing.makeTimeSeries()[:20]
tsIrregular = ts.drop(ts.index[[3,7,11,16]])
def myPlot(ts):
fig, axes = plt.subplots(1,2)
ax = axes[0]
ax.set_title('left series first')
ts[:5].plot(ax=ax, label='left series')
ts[6:].plot(style='or', ax=ax, label='right series')
ax.legend(loc='best')
ax = axes[1]
ax.set_title('right series first')
ts[6:].plot(style='or', ax=ax, label='right series')
ts[:5].plot(ax=ax, label='left series')
ax.legend(loc='best')
return axes
myPlot(ts)
myPlot(tsIrregular)
```
What you get for the regular timeseries case is what you expect, i.e. the order you plot timeseries doesn't matter and the xaxis range is the union of the two timeseries indexes:
but what you get for the irregular timeseries case is rather suprising:
There are easy work-arounds, like aligning the timeseries before plotting, but it feels like irregular timeseries shouldn't be second-class citizens compared to regular ones.
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https://api.github.com/repos/pandas-dev/pandas/issues/2961 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2961/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2961/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2961/events | https://github.com/pandas-dev/pandas/pull/2961 | 11,576,280 | MDExOlB1bGxSZXF1ZXN0NDQwOTQ3Mg== | 2,961 | Keep name after DataFrame drop (issue 2939) | {
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} | [] | closed | false | null | [] | null | 17 | "2013-03-02T13:47:29Z" | "2013-03-04T17:42:31Z" | "2013-03-03T11:50:30Z" | CONTRIBUTOR | null | This should fix #2939.
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https://api.github.com/repos/pandas-dev/pandas/issues/2962 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2962/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2962/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2962/events | https://github.com/pandas-dev/pandas/pull/2962 | 11,590,280 | MDExOlB1bGxSZXF1ZXN0NDQxMzkyNA== | 2,962 | BUG keep name after DataFrame drop, add check_name to assert_dataframe_equal | {
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} | 4 | "2013-03-04T01:41:20Z" | "2015-08-12T23:56:46Z" | "2013-07-18T00:51:18Z" | CONTRIBUTOR | null | Would you be interested in adding statistics bootstrap/sample function to pandas, implementing bootstrap described at:
http://statistics.ats.ucla.edu/stat/r/library/bootstrap.htm
Here is an actual implementation:
http://stackoverflow.com/a/15192860/304690
Thanks
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https://api.github.com/repos/pandas-dev/pandas/issues/2964 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2964/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2964/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2964/events | https://github.com/pandas-dev/pandas/pull/2964 | 11,615,374 | MDExOlB1bGxSZXF1ZXN0NDQyMjYyMQ== | 2,964 | ENH Assert_frame_equal check_names to True | {
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} | 7 | "2013-03-04T11:44:25Z" | "2014-06-18T17:28:59Z" | "2013-03-07T07:22:41Z" | CONTRIBUTOR | null | This is a follow up to #2962, where I added a `check_names=False` argument to `assert_frame_equal`, this defaults it to `True`. That is, when asserting two DataFrames are equal, it compares `.columns.names` and `.index.names`, unless explicitly passing `check_names=False`.
I've added in `check_names=False` where it wouldn't make sense to check names. I've added a TODO comment (and `check_names=False`) where I think it probably _should_ work but isn't... This was in the following cases:
```
to_csv
reindex
reset_index
groupy-a-get
pop
join (dataframe to a series)
from_xls (when setting index as first column)
```
i.e. these appear to drop column names (`df.columns.names`). Whether this behaviour is correct / desired in these cases, I'm not sure, but here they all are. :)
_Note: By changing the A1 cells to 'index', I had all but one test (labelled "read xls ignores index name ?") in `test_excel.py` working locally (with `check_names=True`) , however Travis was less convinced throwing:_
```
InvalidFileException: "There is no item named 'xl/theme/theme1.xml' in the archive"
```
_so I reverted to the previous xls and xlsx, and added back in `check_names=False`. I suspect LibreOffice wasn't saving it properly...?_
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https://api.github.com/repos/pandas-dev/pandas/issues/2965 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2965/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2965/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2965/events | https://github.com/pandas-dev/pandas/pull/2965 | 11,642,606 | MDExOlB1bGxSZXF1ZXN0NDQzNTczNw== | 2,965 | BUG check both left and right indexers for None (fix 2843) | {
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} | [] | closed | false | null | [] | null | 1 | "2013-03-04T22:33:05Z" | "2014-06-21T22:19:56Z" | "2013-03-07T01:34:46Z" | CONTRIBUTOR | null | Fixes #2843 and adds the two test cases suggested there.
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} | 3 | "2013-03-05T01:05:56Z" | "2014-06-12T21:00:19Z" | "2013-04-10T00:10:11Z" | CONTRIBUTOR | null | See Github Issue [#1670](https://github.com/pydata/pandas/issues/1670).
Apologies for not having squashed my commits together; got rather confused by the process.
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] | closed | false | null | [] | null | 3 | "2013-03-05T01:21:44Z" | "2013-03-06T12:41:02Z" | "2013-03-06T12:41:02Z" | CONTRIBUTOR | null | pretty simple fix though, just need to make sure that the result array
is typed float64 (to accomodate the nans) or int64 (if no nans)
(rather than the same as input type)
(which could give weird results in some cases), e.g. you wouldn't
want a float array back just because you fed it nans....
heres the inspiration question
http://stackoverflow.com/questions/15207279/return-sorted-indexes-skipping-nan-values-in-pandas
```
In [29]: s = pd.Series([pd.Timestamp('201301%02d'% (i+1)) for i in range(5)])
In [30]: s
Out[30]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
3 2013-01-04 00:00:00
4 2013-01-05 00:00:00
dtype: datetime64[ns]
In [31]: s.argsort()
Out[31]:
0 0
1 1
2 2
3 3
4 4
dtype: int64
In [32]: s.shift().argsort()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-32-61a586f08c06> in <module>()
----> 1 s.shift().argsort()
/mnt/home/jreback/pandas/pandas/core/series.pyc in argsort(self, axis, kind, order)
2119 result = values.copy()
2120 notmask = -mask
-> 2121 result[notmask] = np.argsort(values[notmask], kind=kind)
2122 return Series(result, index=self.index, name=self.name)
2123 else:
TypeError: array cannot be safely cast to required type
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2968 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2968/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2968/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2968/events | https://github.com/pandas-dev/pandas/issues/2968 | 11,649,844 | MDU6SXNzdWUxMTY0OTg0NA== | 2,968 | A weird thing | {
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```
def volchange(df,col):
del df[col]
ext = pd.DataFrame(np.random.randn(4,2),columns=['add1','add2'])
df = df.join(ext)
return df
aa = pd.DataFrame(np.random.randn(4,2),columns=['a','b'])
cc = aa
bb = volchange(cc,'a')
```
The result from running this code is:
bb has 3 columns, 'b','add1','add2', that is what we expected.
both aa and cc only have column 'b'. This is hard for me to understand:
I guess 'cc=aa' only assigns the reference of aa to cc. This is OK...
but if we assume we passes by reference to the function volchange(df,col), why only the command 'del df[col]' has effect on df, while the command 'df=df.join(ext)' has no effect on df, hence cc?
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} | 4 | "2013-03-05T03:25:39Z" | "2014-02-18T22:20:28Z" | "2014-02-18T22:20:07Z" | NONE | null | Hi not sure the best way to describe this but this stackoverflow question explains the problem nicely: http://stackoverflow.com/questions/14149156/merge-multi-indexed-with-single-indexed-data-frames-in-pandas
Would be nice to be able to do this without the awkward reindex. Thanks again for a great project.
Currently on version 0.10.1
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} | 5 | "2013-03-05T23:29:10Z" | "2014-02-14T05:12:23Z" | "2014-02-14T05:12:23Z" | NONE | null | If if_exists='replace' and the table already exists, then the following error occurs:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\pandas\io\sql.py", line 202, in write_frame
cur.execute(create)
sqlite3.OperationalError: no such table: aggregatedData
```
This is the code I ran with no issue:
`psql.write_frame(outDf, 'aggregatedData', conn)`
This is the code I ran to get the above error:
`psql.write_frame(outDf, 'aggregatedData', conn, if_exists='replace')`
If the same code is re-run after the error it will work as expected because the table is not there anymore. Running the code a third time will cause the error; the error happens every other run.
Here's the fix I implemented in pandas.io.sql.py (~line 191):
```
# create or drop-recreate if necessary
create = None
if exists and if_exists == 'replace':
create = "DROP TABLE %s" % name
cur = con.cursor()
cur.execute(create)
cur.close()
create = get_schema(frame, name, flavor)
```
Basically, I just dropped the existing table and set it to be recreated later in the code.
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} | 8 | "2013-03-05T23:45:32Z" | "2013-03-16T02:31:33Z" | "2013-03-16T02:31:33Z" | CONTRIBUTOR | null | Kind of an edge case I would say, but still it would be nice to get an error or have an automatic renaming of the columns. If a simple error is acceptable as a first step I could definitely put together a PR.
The following snippet shows that when you concatenate a column of zeros and a column of ones the resulting dataframe has two columns of ones:
``` python
import pandas as pd
import numpy as np
ts0 = pd.Series(np.zeros(5))
ts1 = pd.Series(np.ones(5))
ts0.name = ts1.name = 'same name'
df = pd.concat([ts0, ts1], axis=1)
print df
```
and the output (pandas 0.10.1):
```
same name same name
0 1 1
1 1 1
2 1 1
3 1 1
4 1 1
```
whereas I would expect:
```
same name same name
0 0 1
1 0 1
2 0 1
3 0 1
4 0 1
```
Looks like this is due to `pandas.tools.merge._Concatenator._get_concat_axis` which doesn't check for name uniqueness, here is the relevant snippet:
``` python
names = []
for x in self.objs:
if x.name is not None:
names.append(x.name)
else:
return Index(np.arange(len(self.objs))
return Index(names)
```
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``` python
store.select('df', 'x != none') #this is not NaN, but the string 'none'
```
one receives exactly the same object as when doing
``` python
store.select('df', 'x = none')
```
The suggestion is that the query parser warns about the unknown or unimplemented operator.
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} | 7 | "2013-03-06T00:15:25Z" | "2014-01-15T13:44:09Z" | "2013-07-22T20:04:12Z" | NONE | null | [Exhibitionist](https://github.com/Exhibitionist/Exhibitionist) is a library geared towards integrating HTML/JS UIs into interactive python work.
One of the examples included, a grid UI for dataframes, can be seen in the image below.
Please check it out, report issues and help spread the word.
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https://api.github.com/repos/pandas-dev/pandas/issues/2976 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2976/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2976/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2976/events | https://github.com/pandas-dev/pandas/pull/2976 | 11,696,509 | MDExOlB1bGxSZXF1ZXN0NDQ2MjM5NQ== | 2,976 | BUG: HDFStore didn't implement != correctly for string columns query, GH #2973 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/2977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2977/events | https://github.com/pandas-dev/pandas/pull/2977 | 11,696,955 | MDExOlB1bGxSZXF1ZXN0NDQ2MjYzMQ== | 2,977 | BUG: Series.argsort failing on datetime64[ns] when NaT present, GH #2967 | {
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} | 1 | "2013-03-06T10:21:36Z" | "2013-03-06T11:23:39Z" | "2013-03-06T11:23:39Z" | NONE | null | Version: pandas 0.10.0 , via `pip`
OS: Ubuntu 12.04 x64
Description:
I put a user-defined object (which defines only **getitem**) into a dataframe. Printing the dataframe causes the system to hang. I suspect that the construction of a text representation of the object is calling my object's **getitem**.
A simple code snippet to fully reproduce the problem:
```
import pandas
class A:
def __getitem__(self, key):
return 3 # obviously simplified
df = pandas.DataFrame([A()])
print df
```
Desired behavior: pandas should not be calling the **getitem** method of objects in DataFrame.
Running this script causes my computer to hang. Ctrl+C dumps a long traceback. Here is the first bit of it:
Traceback (most recent call last):
File "test.py", line 6, in <module>
print df
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 636, in **str**
return self.**bytes**()
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 646, in **bytes**
return self.**unicode**().encode(encoding , 'replace')
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 663, in **unicode**
self.to_string(buf=buf, line_width=line_width)
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/frame.py", line 1503, in to_string
formatter.to_string()
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/format.py", line 297, in to_string
strcols = self._to_str_columns()
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/format.py", line 251, in _to_str_columns
fmt_values = self._format_col(i)
File "/home/jack/.local/lib/python2.7/site-packages/pandas/core/format.py", line 379, in _format_col
space=self.col_space)
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} | 3 | "2013-03-06T11:42:01Z" | "2014-06-13T08:16:29Z" | "2013-03-10T16:12:08Z" | NONE | null | There's a circuit-breaker in higher layers of the display code,
but just in case.
``` python
n [2]: pd.options.display.max_seq_items=10
In [3]: pd.core.common.pprint_thing(range(10000))
Out[3]: u'[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...]'
```
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} | 2 | "2013-03-06T19:33:23Z" | "2021-04-11T01:55:15Z" | null | NONE | null | I have some data that I have used to generate a stacked bar graph.
```
by_conclusion = measles_data.groupby(["AGE", "CONCLUSION"])
counts_by_cause = by_conclusion.size().unstack().fillna(0)
ax = counts_by_cause.plot(kind='bar', stacked=True)
```
However, I get a pretty crowded x-axis:
So, I try to use `set_xticks` to try and thin them out, either as an argument to plot, or as a separate command:
```
ax.xticks(arange(0,80,10))
```
However, for some reason, the labels end up being downscaled by a factor of 10:
(might be hard to see, but it is printing "0.0, 1.0, 2.0, ..." instead of "0, 10, 20, ...")
Its not clear why this is happening, or how to get around it, so I'm either going about this wrong or its a bug. I get the same behavior when I try using `MaxNLocator`:
```
ax.xaxis.set_major_locator( MaxNLocator(nbins = 7) )
```
[The data are here](https://dl.dropbox.com/u/233041/measles.csv)
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} | 4 | "2013-03-06T19:53:49Z" | "2013-04-08T07:36:14Z" | "2013-04-08T07:36:14Z" | MEMBER | null | http://stackoverflow.com/questions/15242746/handling-variable-number-of-columns-with-pandas-python
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} | [] | closed | false | null | [] | null | 9 | "2013-03-06T20:54:27Z" | "2015-02-26T19:07:31Z" | "2013-03-09T21:00:31Z" | CONTRIBUTOR | null | ``` python
In [65]: s
Out[65]:
a 2013-01-02 00:00:00
b 2013-01-03 00:00:00
c 2013-01-04 00:00:00
d 2013-01-05 00:00:00
e NaT
dtype: datetime64[ns]
In [66]: s.argmin()
Out[66]: 4
In [67]: s.idxmin()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule 'safe'
In [68]: s.argmax()
Out[68]: 3
In [69]: s.idxmax()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule 'safe'
```
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} | 1 | "2013-03-06T21:19:44Z" | "2014-02-16T22:12:44Z" | "2014-02-16T22:12:44Z" | NONE | null | pandas 0.10.1, on OSX mountain lion, installed through pip.
So, this works fine for me,
``` python
events = ga.read_ga(['visits'],
dimensions=["date"],
start_date='2013-01-15',
secrets=secretsfile,
account_name="******************",
property_id="***************")
```
But when I do the following,
``` python
events = ga.read_ga(['totalEvents'],
dimensions=["eventLabel"],
start_date='2013-01-15',
secrets=sec,
account_name="Ripped Athlete",
property_id="UA-37875661-1")
```
I get this traceback (abbreviated)
```
/usr/local/lib/python2.7/site-packages/pandas/io/ga.pyc in _parse_data(self, rows, col_info, index_col, parse_dates, keep_date_col, date_parser, dayfirst, na_values, converters, sort)
309 keep_date_col=keep_date_col,
310 converters=converters,
--> 311 header=None, names=col_names))
312
313 if isinstance(sort, bool) and sort:
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
206
207 # Create the parser.
--> 208 parser = TextFileReader(filepath_or_buffer, **kwds)
209
210 if nrows is not None:
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
505 self.options['has_index_names'] = kwds['has_index_names']
506
--> 507 self._make_engine(self.engine)
508
509 def _get_options_with_defaults(self, engine):
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
613 elif engine == 'python-fwf':
614 klass = FixedWidthFieldParser
--> 615 self._engine = klass(self.f, **self.options)
616
617 def _failover_to_python(self):
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, **kwds)
1157 else:
1158 self.data = f
-> 1159 self.columns = self._infer_columns()
1160
1161 # get popped off for index
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _infer_columns(self)
1331 line = self.buf[0]
1332 else:
-> 1333 line = self._next_line()
1334
1335 ncols = len(line)
/usr/local/lib/python2.7/site-packages/pandas/io/parsers.pyc in _next_line(self)
1352 line = self.data[self.pos]
1353 except IndexError:
-> 1354 raise StopIteration
1355 else:
1356 while self.pos in self.skiprows:
StopIteration:
```
Before I dig in further, I noticed that [`PythonParser._infer_columns`](https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1301) doesn't handle the `StopIteration` that can be raised by [`PythonParser._next_line`](https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1351). Should we consider raising that exception to be normal operation and change `_infer_columns` to handle it, or should we figure out what strange mix of circumstances is causing `_next_iteration` to raise it?
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} | [] | closed | false | null | [] | null | 4 | "2013-03-07T00:12:25Z" | "2013-03-19T23:54:33Z" | "2013-03-19T23:54:33Z" | CONTRIBUTOR | null | To get an auto-diff on a timeseries I do:
``` python
In [47]: ts
Out[47]:
0 2011-04-16 00:00:00.025000
1 2011-04-16 00:00:00.152999
2 2011-04-16 00:00:00.280999
3 2011-04-16 00:00:00.409000
4 2011-04-16 00:00:00.537000
5 2011-04-16 00:00:00.665000
6 2011-04-16 00:00:00.792999
7 2011-04-16 00:00:00.921000
8 2011-04-16 00:00:01.049000
9 2011-04-16 00:00:01.177000
10 2011-04-16 00:00:01.297000
11 2011-04-16 00:00:01.424999
12 2011-04-16 00:00:01.552999
13 2011-04-16 00:00:01.680999
14 2011-04-16 00:00:01.809000
...
25646 2011-04-16 00:59:58.143001
25647 2011-04-16 00:59:58.270999
25648 2011-04-16 00:59:58.398998
25649 2011-04-16 00:59:58.527000
25650 2011-04-16 00:59:58.654998
25651 2011-04-16 00:59:58.783000
25652 2011-04-16 00:59:58.910999
25653 2011-04-16 00:59:59.039001
25654 2011-04-16 00:59:59.166999
25655 2011-04-16 00:59:59.294998
25656 2011-04-16 00:59:59.423000
25657 2011-04-16 00:59:59.550998
25658 2011-04-16 00:59:59.679000
25659 2011-04-16 00:59:59.806999
25660 2011-04-16 00:59:59.935001
Length: 25661, dtype: datetime64[ns]
In [48]: ts.diff()
Out[48]:
0 NaN
1 127999000
2 128000000
3 128001000
4 128000000
5 128000000
6 127999000
7 128001000
8 128000000
9 128000000
10 120000000
11 127999000
12 128000000
13 128000000
14 128001000
...
25646 128002000
25647 127998000
25648 127999000
25649 128002000
25650 127998000
25651 128002000
25652 127999000
25653 128002000
25654 127998000
25655 127999000
25656 128002000
25657 127998000
25658 128002000
25659 127999000
25660 128002000
Length: 25661, dtype: float64
```
1. One thing would be nice if a diff on a time-series automatically becomes a timedelta?
2. When converting the result of the `ts.diff()` with astype('timedelta64[ns']) it should convert the NaN into a NaT, if I understand things correctly?
Instead this happens and I have to cut off the NaN to make it work:
``` python
In [49]: ts.diff().astype('timedelta[ns]')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-49-d1462d75fb33> in <module>()
----> 1 ts.diff().astype('timedelta[ns]')
/usr/local/epd/lib/python2.7/site-packages/pandas/core/series.pyc in astype(self, dtype)
830 See numpy.ndarray.astype
831 """
--> 832 casted = com._astype_nansafe(self.values, dtype)
833 return self._constructor(casted, index=self.index, name=self.name,
834 dtype=casted.dtype)
/usr/local/epd/lib/python2.7/site-packages/pandas/core/common.pyc in _astype_nansafe(arr, dtype, copy)
1361 """ return a view if copy is False """
1362 if not isinstance(dtype, np.dtype):
-> 1363 dtype = np.dtype(dtype)
1364
1365 if issubclass(arr.dtype.type, np.datetime64):
TypeError: data type not understood
In [50]: ts.diff().astype('timedelta64[ns]')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-50-e9c54d8f8d73> in <module>()
----> 1 ts.diff().astype('timedelta64[ns]')
/usr/local/epd/lib/python2.7/site-packages/pandas/core/series.pyc in astype(self, dtype)
830 See numpy.ndarray.astype
831 """
--> 832 casted = com._astype_nansafe(self.values, dtype)
833 return self._constructor(casted, index=self.index, name=self.name,
834 dtype=casted.dtype)
/usr/local/epd/lib/python2.7/site-packages/pandas/core/common.pyc in _astype_nansafe(arr, dtype, copy)
1370
1371 if np.isnan(arr).any():
-> 1372 raise ValueError('Cannot convert NA to integer')
1373 elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
1374 # work around NumPy brokenness, #1987
ValueError: Cannot convert NA to integer
In [51]: ts.diff()[1:].astype('timedelta64[ns]')
Out[51]:
1 00:00:00.127999
2 00:00:00.128000
3 00:00:00.128001
4 00:00:00.128000
5 00:00:00.128000
6 00:00:00.127999
7 00:00:00.128001
8 00:00:00.128000
9 00:00:00.128000
10 00:00:00.120000
11 00:00:00.127999
12 00:00:00.128000
13 00:00:00.128000
14 00:00:00.128001
15 00:00:00.128000
...
25646 00:00:00.128002
25647 00:00:00.127998
25648 00:00:00.127999
25649 00:00:00.128002
25650 00:00:00.127998
25651 00:00:00.128002
25652 00:00:00.127999
25653 00:00:00.128002
25654 00:00:00.127998
25655 00:00:00.127999
25656 00:00:00.128002
25657 00:00:00.127998
25658 00:00:00.128002
25659 00:00:00.127999
25660 00:00:00.128002
Length: 25660, dtype: timedelta64[ns]
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2985 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2985/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2985/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2985/events | https://github.com/pandas-dev/pandas/pull/2985 | 11,741,683 | MDExOlB1bGxSZXF1ZXN0NDQ4NTA2NA== | 2,985 | BUG: Bug in idxmin/idxmax of ``datetime64[ns]`` Series with ``NaT`` (GH2982) | {
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} | [] | closed | false | null | [] | null | 0 | "2013-03-07T01:11:33Z" | "2014-07-06T15:25:25Z" | "2013-03-07T01:54:08Z" | CONTRIBUTOR | null | fixes GH #2982
- Bug in idxmin/idxmax of `datetime64[ns]` Series with `NaT`
- refactor in core/nanops.py to generically handle ops with
datetime64[ns]/timedelta64[ns]
- Series.argsort to always return int64 dtype (and ignore numpy dtype on ints),
was failing a test, indicated in a711db075d5835f45f04867752f3db64be359490
- minor doc update in io.rst (pytables)
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] | closed | false | null | [] | null | 3 | "2013-03-07T01:33:08Z" | "2013-03-12T13:39:09Z" | "2013-03-12T13:39:09Z" | NONE | null | I am build the latest stable version Pandas on my Windows 7 32-bit. On first try, I got a "Error: Unable to find vcvarsall.bar" error. Then I installed VC++2008 Express correctly, but the same error still didn't pass. I can locate the vcvarsall.bat on the VC++ installation directory manually, and I read msvc9compiler.py in Python's distutils module, I am sure the module can find vcvarsall.bat.
Also build Pandas with mingw32 4.72 version failed either.
Could anyone got some ideas on how I made mistakes possibly?
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https://api.github.com/repos/pandas-dev/pandas/issues/2987 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2987/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2987/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2987/events | https://github.com/pandas-dev/pandas/pull/2987 | 11,742,821 | MDExOlB1bGxSZXF1ZXN0NDQ4NTYwMw== | 2,987 | add comparisons operator to the Period object and fixes issue 2781 | {
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} | 7 | "2013-03-07T01:55:44Z" | "2014-06-19T14:50:06Z" | "2013-03-28T05:40:01Z" | CONTRIBUTOR | null | This commit should fix [issue 2781](https://github.com/pydata/pandas/issues/2781). The Period object couldn't be sorted because it didn't have any **le**, **gt**, **lt**, **ge** or **cmp** methods.
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https://api.github.com/repos/pandas-dev/pandas/issues/2988 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2988/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2988/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2988/events | https://github.com/pandas-dev/pandas/issues/2988 | 11,747,709 | MDU6SXNzdWUxMTc0NzcwOQ== | 2,988 | timedelta64[ns] : argmin() failing | {
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} | [] | closed | false | null | [] | null | 2 | "2013-03-07T06:28:35Z" | "2013-03-07T15:50:02Z" | "2013-03-07T15:50:02Z" | CONTRIBUTOR | null | argmax() works fine, but argmin() throws an error:
``` python
In [22]: diff = ts - ts.shift()
In [23]: diff
Out[23]:
0 NaT
1 00:00:00.128001
2 00:00:00.127999
3 00:00:00.128001
4 00:00:00.127999
5 00:00:00.128001
6 00:00:00.127999
7 00:00:00.128001
8 00:00:00.127999
9 00:00:00.128001
10 00:00:00.127999
11 00:00:00.128001
12 00:00:00.128000
13 00:00:00.127999
14 00:00:00.128001
...
25646 00:00:00.128001
25647 00:00:00.128000
25648 00:00:00.128000
25649 00:00:00.128000
25650 00:00:00.127999
25651 00:00:00.128000
25652 00:00:00.128000
25653 00:00:00.128000
25654 00:00:00.128001
25655 00:00:00.128000
25656 00:00:00.128000
25657 00:00:00.127999
25658 00:00:00.128000
25659 00:00:00.128000
25660 00:00:00.128000
Length: 25661, dtype: timedelta64[ns]
In [24]: diff.argmax()
Out[24]: 20540
In [25]: diff.argmin()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-25-e4a9e608a754> in <module>()
----> 1 diff.argmin()
/usr/local/epd/lib/python2.7/site-packages/pandas/core/series.pyc in wrapper(self, other)
134
135 else:
--> 136 raise ValueError('cannot operate on a series with out a rhs '
137 'of a series/ndarray of type datetime64[ns] '
138 'or a timedelta')
ValueError: cannot operate on a series with out a rhs of a series/ndarray of type datetime64[ns] or a timedelta
In [26]: pd.__version__
Out[26]: '0.11.0.dev-819e0ad'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2989 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2989/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2989/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2989/events | https://github.com/pandas-dev/pandas/issues/2989 | 11,747,853 | MDU6SXNzdWUxMTc0Nzg1Mw== | 2,989 | timedelta64[ns]: max() and min() return numpy.int64 | {
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} | [] | closed | false | null | [] | null | 6 | "2013-03-07T06:35:34Z" | "2013-03-07T20:10:37Z" | "2013-03-07T19:50:16Z" | CONTRIBUTOR | null | I guess that max() and min() are not implementing yet fully:
``` python
In [28]: diff.head()
Out[28]:
0 NaT
1 00:00:00.128001
2 00:00:00.127999
3 00:00:00.128001
4 00:00:00.127999
Dtype: timedelta64[ns]
In [29]: diff.max()
Out[29]: 1934745001000
In [30]: diff.max().dtype
Out[30]: dtype('int64')
In [31]: diff[0].dtype
Out[31]: dtype('timedelta64[ns]')
In [32]: diff.min().dtype
Out[32]: dtype('int64')
```
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```
In [168]: df = DataFrame(dict(A = s - Timestamp('20120101')-timedelta(minutes=5,seconds=5),
.....: B = s - Series(date_range('2012-1-2', periods=3, freq='D'))))
.....:
In [169]: df
Out[169]:
A B
0 -00:05:05 -1 days, 00:00:00
1 23:54:55 -1 days, 00:00:00
2 1 days, 23:54:55 -1 days, 00:00:00
In [170]: df.min()
Out[170]:
A -00:05:05
B -1 days, 00:00:00
dtype: timedelta64[ns]
In [171]: df.min(axis=1)
Out[171]:
0 -1 days, 00:00:00
1 -1 days, 00:00:00
2 -1 days, 00:00:00
dtype: timedelta64[ns]
In [172]: df.idxmin()
Out[172]:
A 0
B 0
dtype: int64
In [173]: df.idxmax()
Out[173]:
A 2
B 0
dtype: int64
min, max operations are support on series, these return a single element timedelta64[ns] Series
(this avoids having to deal with numpy timedelta64 issues). idxmin, idxmax are supported as well.
In [306]: df.min().max()
Out[306]:
0 -00:05:05
dtype: timedelta64[ns]
In [307]: df.min(axis=1).min()
Out[307]:
0 -1 days, 00:00:00
dtype: timedelta64[ns]
In [308]: df.min().idxmax()
Out[308]: 'A'
In [309]: df.min(axis=1).idxmin()
Out[309]: 0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/2992 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2992/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2992/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2992/events | https://github.com/pandas-dev/pandas/pull/2992 | 11,784,541 | MDExOlB1bGxSZXF1ZXN0NDUwNjMwNQ== | 2,992 | DOC: doc updates/formatting in basics,indexing,10min | {
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} | 2 | "2013-03-09T00:09:17Z" | "2013-03-21T07:58:54Z" | "2013-03-21T07:58:54Z" | NONE | null | In 0.10.1, Timestamp constructor seems to have an issue localizing datetime.date objects.
```
In [19]: import datetime
In [20]: datetime.date(2013, 3, 9)
Out[20]: datetime.date(2013, 3, 9)
In [21]: d=datetime.date(2013, 3, 9)
In [22]: Timestamp(d, tz='US/Pacific') == Timestamp(d).tz_localize('US/Pacific')
Out[22]: False
In [23]: Timestamp(d, tz='US/Pacific')
Out[23]: <Timestamp: 2013-03-08 16:00:00-0800 PST, tz=US/Pacific>
```
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} | 3 | "2013-03-09T00:11:01Z" | "2013-03-21T12:31:46Z" | "2013-03-21T12:31:46Z" | MEMBER | null | I would expect like `df.replace(0, df.mean())` to work. And it does not
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} | [] | closed | false | null | [] | null | 11 | "2013-03-09T01:21:44Z" | "2013-03-15T00:25:11Z" | "2013-03-15T00:25:11Z" | NONE | null | The Pandas documentation states that a hierarchically indexed DataFrame can be partially selected from by passing a partial list of index values. For example if we have a 5-level index, then df.ix['A','B','C','D'] should return a DataFrame with the subset of rows where levels[0,1,2,3] match values ['A','B','C','D'] indexed with by the remaining lowest index level 5. I have found that this only works when partially selecting on the first two levels, anything beyond the first 2 levels requires the user to append a null slice at the end for .ix to work. This becomes cumbersome especially if you do not know exactly how many levels are available in a DataFrame after several processing steps. The same behavior persists regardless if your MultiIndex is int64 or string or mixed types.
It would be best if any partial tuple of index values passed to the .ix method returns a partial selection matching that tuple without requiring users to explicitly append the empty slice for the remaining levels. Let me know if this is how the .ix method is intended to work. It might be a simple fix of by checking if the tuple length == # levels and if not appending the empty slice within the .ix method.
### Example Showing the Issue:
Take an example data set with 5 index levels, group by the first 4 levels and count the group sizes. Select the group with the largest size.
Results: (Clean Code Pasted Below IPython Output)
``` python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: print pd.__version__
0.11.0.dev-80945b6
In [4]: # Generate Test DataFrame
...: NUM_ROWS = 100000
...:
In [5]: NUM_COLS = 10
In [6]: col_names = ['A'+num for num in map(str,np.arange(NUM_COLS).tolist())]
In [7]: index_cols = col_names[:5]
In [8]: # Set DataFrame to have 5 level Hierarchical Index.
...: # The dtype does not matter try str or np.int64 same results.
...: df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names)
...:
In [9]: df = df.set_index(index_cols)
In [10]: df
Out[10]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 100000 entries, (2, 2, 4, 0, 0) to (3, 2, 3, 1, 1)
Data columns:
A5 100000 non-null values
A6 100000 non-null values
A7 100000 non-null values
A8 100000 non-null values
A9 100000 non-null values
dtypes: int64(5)
In [11]: # Group by first 4 index columns.
....: grp = df.groupby(level=index_cols[:-1])
....:
In [12]: # Compute group size and find index of largest group.
....: grp_size = grp.size()
....:
In [13]: grp_size.sort()
In [14]: grp_size[::-1]
Out[14]: A0 A1 A2 A3
2 1 2 1 200
1 4 1 3 200
3 1 4 1 199
4 1 1 1 196
2 4 2 0 190
4 4 0 1 190
3 1 0 0 189
0 0 4 3 189
4 1 2 1 188
0 1 4 2 187
2 2 3 0 187
3 2 3 2 187
3 4 0 186
1 3 0 186
3 3 4 185
...
0 1 1 4 136
2 4 4 3 135
4 4 1 2 135
2 4 3 2 135
3 3 4 1 134
4 2 3 1 133
3 2 2 4 133
4 0 1 3 133
0 4 3 4 131
1 2 1 0 130
4 4 1 4 130
2 1 4 128
0 2 3 2 127
3 3 0 1 121
1 0 2 3 106
Length: 625, dtype: int64
In [15]: loc = grp.size().idxmax()
In [16]: loc
Out[16]: (1, 4, 1, 3)
In [17]: # The following line does not work.
....: # Can't use partial selection to select largest group.
....: df.ix[loc]
....:
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
<ipython-input-17-e26047d119d7> in <module>()
1 # The following line does not work.
2 # Can't use partial selection to select largest group.
----> 3 df.ix[loc]
4
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
44 pass
45
---> 46 return self._getitem_tuple(key)
47 else:
48 return self._getitem_axis(key, axis=0)
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_tuple(self, tup)
235 for i, key in enumerate(tup):
236 if i >= self.obj.ndim:
--> 237 raise IndexingError('Too many indexers')
238
239 if _is_null_slice(key):
IndexingError: Too many indexers
In [18]: # Appending the null slice at the end works!
....: # I was hoping for partial selection to work above.
....: df.ix[loc+(slice(None),)]
....:
Out[18]: <class 'pandas.core.frame.DataFrame'>
Int64Index: 200 entries, 1 to 0
Data columns:
A5 200 non-null values
A6 200 non-null values
A7 200 non-null values
A8 200 non-null values
A9 200 non-null values
dtypes: int64(5)
In [19]: # Selecting on first level works.
....: df.ix[loc[0]]
....:
Out[19]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 19796 entries, (3, 1, 1, 1) to (4, 1, 3, 0)
Data columns:
A5 19796 non-null values
A6 19796 non-null values
A7 19796 non-null values
A8 19796 non-null values
A9 19796 non-null values
dtypes: int64(5)
In [20]: # Selecting on first two levels works.
....: df.ix[loc[0], loc[1]]
....:
Out[20]: A1 A2 A3 A4
3 1 1 1 3
0 1 1 0 2
4 0 1 3
4 3 1 4 1
1 4 4 4 4
0 0 3 2 4
1 1 2 3 1
4 3 4 3 4
2 1 2 0 3
1 2 0 2 1
0 0 3 0 3
2 4 2 2
4 4 0 4 1
2 2 4 4
1 3 0 3 4
...
0 2 4 0 0
1 4 2 1 4
0 2 1 4
3 4 2 3 2
1 1 3 2 3
3 3 0 3 1
2 1 2 4 3
4 4 2 2 0
0 2 3 2
4 4 1 3
3 1 3 4 2
2 2 4 1 4
3 1 1 1 1
1 0 1 3 1
4 1 3 0 3
Name: A9, Length: 19796, dtype: int64
In [21]: # Selecting on first 3 levels fails.
....: df.ix[loc[0], loc[1], loc[2]]
....:
---------------------------------------------------------------------------
IndexingError Traceback (most recent call last)
<ipython-input-21-38eb049081e0> in <module>()
1 # Selecting on first 3 levels fails.
----> 2 df.ix[loc[0], loc[1], loc[2]]
3
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
44 pass
45
---> 46 return self._getitem_tuple(key)
47 else:
48 return self._getitem_axis(key, axis=0)
C:\Python27\lib\site-packages\pandas\core\indexing.pyc in _getitem_tuple(self, tup)
235 for i, key in enumerate(tup):
236 if i >= self.obj.ndim:
--> 237 raise IndexingError('Too many indexers')
238
239 if _is_null_slice(key):
IndexingError: Too many indexers
In [22]: # Adding the null slice and it works.
....: df.ix[loc[0], loc[1], loc[2], slice(None)]
Out[22]: <class 'pandas.core.frame.DataFrame'>
MultiIndex: 848 entries, (2, 1) to (3, 0)
Data columns:
A5 848 non-null values
A6 848 non-null values
A7 848 non-null values
A8 848 non-null values
A9 848 non-null values
dtypes: int64(5)
```
### Code:
``` python
import numpy as np
import pandas as pd
print pd.__version__
# Generate Test DataFrame
NUM_ROWS = 100000
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.
# The dtype does not matter try str or np.int64 same results.
df = pd.DataFrame(np.random.randint(5, size=(NUM_ROWS,NUM_COLS)), dtype=np.int64, columns=col_names)
df = df.set_index(index_cols)
df
# Group by first 4 index columns.
grp = df.groupby(level=index_cols[:-1])
# Compute group size and find index of largest group.
grp_size = grp.size()
grp_size.sort()
grp_size[::-1]
loc = grp.size().idxmax()
loc
# The following line does not work.
# Can't use partial selection to select largest group.
df.ix[loc]
# Appending the null slice at the end works!
# I was hoping for partial selection to work above.
df.ix[loc+(slice(None),)]
# Selecting on first level works.
df.ix[loc[0]]
# Selecting on first two levels works.
df.ix[loc[0], loc[1]]
# Selecting on first 3 levels fails.
df.ix[loc[0], loc[1], loc[2]]
# Adding the null slice and it works.
df.ix[loc[0], loc[1], loc[2], slice(None)]
```
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} | [] | closed | false | null | [] | null | 7 | "2013-03-09T12:01:40Z" | "2015-10-14T11:13:25Z" | "2013-03-10T14:24:19Z" | CONTRIBUTOR | null | Given the following DataFrame
| area | point | test | value |
| --- | --- | --- | --- |
| A | 11 | 0 | 1234234 |
| A | 11 | 1 | 12341234 |
| A | 16 | 0 | 234234 |
| A | 16 | 1 | 2343 |
| A | 16 | 2 | 234234 |
| C | 4 | 0 | 234234 |
| C | 4 | 1 | 234234 |
it would be nice if there were a way of grouping say columns `area` and `point` and comparing the `value` per `test` > 1 with the value for `test` - 1.
This can be done by iterating over `df.groupby(['area', 'point', 'test'])` and using the sorting provided by `groupby()` on the specified columns to compare current and previous `value`s. However, it would be neat if this could also be done in a more Pandas-esque way using something akin to a SQL self-join.
NB request first made in pystatsmodels Google Group; was asked by Wes to create a Github issue for this.
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} | 8 | "2013-03-09T19:00:00Z" | "2013-09-26T02:42:49Z" | "2013-09-26T02:42:49Z" | CONTRIBUTOR | null | Using .ix twice and assigning a value as in `df.ix[...].ix[...] = b` works in the first case and not in the second:
``` python
>>> # from http://stackoverflow.com/questions/15200598/partial-update-to-dataframe-with-multi-index-index-with-integer-labels/15213525#15213525
>>> print df
bucket start stop date x1 x2 x3
0 B1 1 1 2000-10-03 00:00:00 2 2 3
1 B1 1 1 2000-01-04 00:00:00 4 3 3
2 B1 1 2 2000-01-03 00:00:00 4 2 3
3 B1 1 2 2000-01-04 00:00:00 6 2 2
>>> df2 = df.set_index(['bucket','start','stop'])
>>> print df2
date x1 x2 x3
bucket start stop
B1 1 1 2000-10-03 00:00:00 2 2 3
1 2000-01-04 00:00:00 4 3 3
2 2000-01-03 00:00:00 4 2 3
2 2000-01-04 00:00:00 6 2 2
>>> df2.ix[('B1',1,2)].ix[:,'x1'] = 5 #works
>>> print df2
date x1 x2 x3
bucket start stop
B1 1 1 2000-10-03 00:00:00 2 2 3
1 2000-01-04 00:00:00 4 3 3
2 2000-01-03 00:00:00 5 2 3
2 2000-01-04 00:00:00 5 2 2
>>> df3 = pd.DataFrame(data={"z":[1,2,3,4],"a":["a","b","c","d"],"b":["X","Y","X","Y"], "c":[1,2,3,4],"d":[5,6,7,8]})
>>> df4 = df3.set_index(["z","a"])
>>> print df4
b c d
z a
1 a X 1 5
2 b Y 2 6
3 c X 3 7
4 d Y 4 8
>>> df4.ix[1:2,"b":].ix[:,"d"] = 7 # does not work
>>> print df4
b c d
z a
1 a X 1 5
2 b Y 2 6
3 c X 3 7
4 d Y 4 8
```
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} | 1 | "2013-03-09T23:49:16Z" | "2013-03-14T10:45:52Z" | "2013-03-14T10:45:52Z" | NONE | null | Code to reproduce:
rng1 = pd.date_range('1/1/2011 01:00', periods=10, freq='H')
rng2 = pd.date_range('1/1/2011 02:00', periods=10, freq='H')
ts1 = pd.Series(np.random.randn(len(rng1)), index=rng1).tz_localize('CET')
ts2 = pd.Series(np.random.randn(len(rng2)), index=rng2).tz_localize('CET')
ts_result = ts1.append(ts2)
Result in IPython:
In [208]:
ts1
Out[208]:
2011-01-01 01:00:00+01:00 0.164609
Freq: H
In [209]:
ts2
Out[209]:
2011-01-01 02:00:00+01:00 2.138849
Freq: H
In [210]:
ts_result
Out[210]:
2011-01-01 00:00:00+01:00 0.164609
2011-01-01 01:00:00+01:00 2.138849
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https://api.github.com/repos/pandas-dev/pandas/issues/2999 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/2999/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/2999/comments | https://api.github.com/repos/pandas-dev/pandas/issues/2999/events | https://github.com/pandas-dev/pandas/pull/2999 | 11,846,740 | MDExOlB1bGxSZXF1ZXN0NDUzMzMyMg== | 2,999 | DOC: added recommended dependencies section in install.rst | {
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} | 4 | "2013-03-10T01:19:09Z" | "2013-03-10T02:47:28Z" | "2013-03-10T02:47:27Z" | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/3000 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/3000/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/3000/comments | https://api.github.com/repos/pandas-dev/pandas/issues/3000/events | https://github.com/pandas-dev/pandas/issues/3000 | 11,847,968 | MDU6SXNzdWUxMTg0Nzk2OA== | 3,000 | DataFrame.from_records should be less strict about dict like types | {
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} | 2 | "2013-03-10T03:47:40Z" | "2013-03-15T20:44:14Z" | "2013-03-15T20:44:14Z" | NONE | null | Currently if you try `DataFrame.from_records(emails, columns=['Subject', 'From', 'Date'])` it'll fail. Emails are of type `email.message.Message`. However if you convert the email to dictionaries - the above code works.
The problem seems to stem from `_to_arrays` function in `frame.py` where the check is `elif instance(data[0], dict)`. This fails even though `email.message.Message` has the full dictionary interface.
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