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} | 2 | 2011-10-06T18:57:04Z | 2011-10-06T21:32:22Z | 2011-10-06T21:24:33Z | NONE | null | You are probably already aware, but .append and .join are not yet fully functional with MultiIndex.
1. .append turns the index into numbers
> > > s1=s[:4]
> > > s2=s[4:]
> > > s1
> > > bar one 0.229167564165
> > > two -1.12529475186
> > > baz one 1.03310069555
> > > two -0.859600695252
> > > s2
> > > foo one 1.75545035115
> > > two -1.44073899127
> > > qux one -0.239883854241
> > > two -1.22027857654
> > > s1.append(s2)
> > > 0 0.229167564165
> > > 1 -1.12529475186
> > > ...
> > > 6 -0.239883854241
> > > 7 -1.22027857654
> > >
> > > df1=df[:4]
> > > df2=df[4:]
> > > df1
> > > A B C
> > > bar one -1.165 0.6214 -0.4865
> > > two -0.8845 -0.3048 -0.1469
> > > baz one 1.533 -0.7665 1.133
> > > two -0.2581 2.067 -0.4839
> > >
> > > df2
> > > A B C
> > > foo one -1.321 -0.08075 -1.135
> > > two -0.3081 0.8832 0.09399
> > > qux one -0.7685 0.8034 -1.032
> > > two 0.3256 1.158 0.163
> > >
> > > df1.append(df2)
> > > A B C
> > > 0 -1.165 0.6214 -0.4865
> > > 1 -0.8845 -0.3048 -0.1469
> > > ...
> > > 6 -0.7685 0.8034 -1.032
> > > 7 0.3256 1.158 0.163
1. .join errors out
> > > df1t=df1.T
> > > df2t=df2.T
> > > df1t
> > > bar baz
> > > one two one two
> > > A -1.165 -0.8845 1.533 -0.2581
> > > B 0.6214 -0.3048 -0.7665 2.067
> > > C -0.4865 -0.1469 1.133 -0.4839
> > >
> > > df2t
> > > foo qux
> > > one two one two
> > > A -1.321 -0.3081 -0.7685 0.3256
> > > B -0.08075 0.8832 0.8034 1.158
> > > C -1.135 0.09399 -1.032 0.163
> > >
> > > df1t.join(df2t)
> > > ...
> > > IndexError: list index out of range
1. both .join and .append are fine when you are lining the data up with MultiIndex, rather than creating an expanded MultiIndex.
>>> dfa
A B
bar one 0.5341 0.5299
two -0.3412 0.8686
baz one 0.6352 1.835
two -0.4022 -0.2686
> > > dfb
> > > C D
> > > bar one -1.066 -1.029
> > > two -0.8317 -0.9012
> > > baz one -0.4223 0.9805
> > > two -1.348 1.204
> > >
> > > dfa.join(dfb)
> > > A B C D
> > > bar one 0.5341 0.5299 -1.066 -1.029
> > > two -0.3412 0.8686 -0.8317 -0.9012
> > > baz one 0.6352 1.835 -0.4223 0.9805
> > > two -0.4022 -0.2686 -1.348 1.204
> > >
> > > (dfa.T.append(dfb.T)).T
> > > A B C D
> > > bar one 0.5341 0.5299 -1.066 -1.029
> > > two -0.3412 0.8686 -0.8317 -0.9012
> > > baz one 0.6352 1.835 -0.4223 0.9805
> > > two -0.4022 -0.2686 -1.348 1.204
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} | 2 | 2011-10-06T20:26:31Z | 2011-10-06T20:49:08Z | 2011-10-06T20:46:52Z | NONE | null | .sortlevel does not retain .names in MultiIndex. For example:
> > > options
> > > ForwardPrice Strike CallPut Premium
> > > 105169 57.2024993896 15.03 15.03 C 2.206
> > > -41.0596008301 15.03 15.03 P 2.231
> > > ...
> > >
> > > options.delevel()
> > > SecurityID Delta ForwardPrice Strike CallPut Premium
> > > 0 105169 57.2 15.03 15.03 C 2.206
> > > 1 105169 -41.06 15.03 15.03 P 2.231
> > > ...
> > >
> > > options = options.sortlevel(0)
> > > options.delevel()
> > > level_0 level_1 ForwardPrice Strike CallPut Premium
> > > 0 105169 -41.06 15.03 15.03 P 2.231
> > > 1 105169 57.2 15.03 15.03 C 2.206
> > > ...
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} | 0 | 2011-10-07T11:55:26Z | 2011-10-09T16:52:47Z | 2011-10-09T16:52:47Z | CONTRIBUTOR | null | Feature suggestion:
I was using hackish solutions to select rows where a particular column is empty/non-empty, because I didn't know about isnull and notnull. I think these should be exposed as methods, particularly of `Series` (i.e. columns), so they're readily discoverable via tab completion.
Example use:
```
selection = df[df['col'].notnull()]
```
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} | 0 | 2011-10-08T20:25:44Z | 2011-10-09T14:25:43Z | 2011-10-09T14:25:43Z | MEMBER | null | Ops in intindex form are much faster than in block form, for some reason
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I've also added a simple test for this case.
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} | 2 | 2011-10-08T23:47:24Z | 2016-10-12T23:02:36Z | 2012-05-21T18:12:22Z | MEMBER | null | On larger datasets methods like
- sort_index
- rename
(add methods to list)
etc. would benefit from an inplace option, e.g.:
``` python
df.sort_index(by=col, inplace=True)
```
and this would be more memory efficient
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I've also slipped in a couple of other small tweaks - fixing a bug with writing series CSV, and adding trove classifiers to indicate Python 3 compatibility.
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} | 1 | 2011-10-10T01:37:35Z | 2011-10-11T20:13:30Z | 2011-10-11T20:13:30Z | MEMBER | null | Could achieve 15-20% savings over the current code, unnecessary computations are being performed in the float-only case
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} | 0 | 2011-10-10T03:16:22Z | 2011-10-13T04:17:16Z | 2011-10-13T04:17:16Z | MEMBER | null | With advances in the Cython code since 0.3 this should be relatively straightforward (maybe)
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If a column name in a dataframe is a valid Python name and doesn't conflict with any DataFrame attributes, this lets you type `df.MyColumn` than `df['MyColumn']`. That's much handier for interactive use, especially because IPython will tab complete `df.MyColumn.<tab>`, while it won't by default tab complete `df['MyColumn'].<tab>`.
This doesn't interfere with accessing columns through the dictionary interface.
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} | 1 | 2011-10-11T17:54:16Z | 2011-10-11T19:39:18Z | 2011-10-11T19:39:18Z | MEMBER | null | API would look like
``` python
a.join(b, on=['foo', 'bar'])
```
where 'foo', and 'bar' are columns in a and b is indexed by a MultiIndex with 2 levels
I am already working on this...just here so I don't forget
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} | 2 | 2011-10-11T17:55:08Z | 2011-10-12T14:35:57Z | 2011-10-12T14:35:57Z | MEMBER | null | General xs function in internals.py is way too slow
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} | 6 | 2011-10-12T12:03:52Z | 2011-10-12T15:04:10Z | 2011-10-12T15:04:10Z | CONTRIBUTOR | null | Calling plot from matplotlib with series (e.g. columns of a DataFrame) fails, because it tries to add an axis to the series objects. A traceback can be found here: https://gist.github.com/1281034 .
One way to solve this could be, if we try to index a series to multiple dimensions, simply return a plain numpy array of the data. Or maybe it makes more sense to make changes on the matplotlib side?
`Series.plot()` and `DataFrame.plot()` are designed to plot columns against the index, but don't appear to help with plotting one column against another.
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} | 6 | 2011-10-12T14:19:49Z | 2011-10-12T20:21:48Z | 2011-10-12T18:54:16Z | CONTRIBUTOR | null | I loaded a dataset with an index column going from 1 to 304. Then I try to get the repr of the dataframe, but it raises `ValueError: year=100 is before 1900; the datetime strftime() methods require year >= 1900`. Looking at `df.index` shows that the numeric indices have been transformed into datetimes (the first 31 to days of this month, 32-60 as years 20xx, 61-99 as years 19xx, and higher numbers as a raw year).
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} | 2 | 2011-10-12T14:53:39Z | 2011-10-13T08:40:25Z | 2011-10-13T03:27:27Z | CONTRIBUTOR | null | If I use `read_csv` to load a datafile with the default index_col, but the first column does not have unique values, it succeeds at first, but when I try to select rows later, I get `Exception: Index cannot contain duplicate values!`. This raises a couple of points:
1. Should the default be to use an index column? My data often doesn't have one. [Apparently](http://pinard.progiciels-bpi.ca/libR/library/base/html/read.table.html) R takes the first column as an index if it doesn't have a header, and otherwise does an integer index.
2. Non-unique values should be detected when we're creating the index, and either leave that column as a regular column (falling back to an integer index), or raise an exception at that point.
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I also think the autosummary API documentation isn't particularly good - it doesn't seem to handle classes at all. I'll see if I can copy what IPython uses (example output is [here](http://ipython.org/ipython-doc/stable/api/generated/IPython.testing.tools.html#tempfilemixin)).
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} | 6 | 2011-10-13T16:11:54Z | 2011-10-15T08:24:08Z | 2011-10-14T21:49:30Z | NONE | null | DataFrame.to_string() is printing directly to console and not returning a string :
``` Python
>>> import pandas
>>> df = pandas.DataFrame(range(5))
>>> df
0
0 0
1 1
2 2
3 3
4 4
>>> view_repr = repr(df)
>>> view_to_string = df.to_string()
0
0 0
1 1
2 2
3 3
4 4
>>> view_repr == view_to_string
False
```
For large DataFrame, we have to use to_string() to get a tabular view.
But the return value is not set (None) and can't be retrieved :
``` Python
>>> view_repr
' 0\n0 0\n1 1\n2 2\n3 3\n4 4\n'
>>> view_to_string
>>>
```
It would be usefull to get a string (or StringIO) returned by DataFrame.to_string()
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I'm not entirely happy about how we check whether IPython is imported, but it's the best option I could see, and I don't think we should unconditionally import IPython and dependent modules just because it's installed.
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Pandas 0.4.3
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} | 25 | 2011-10-14T12:40:38Z | 2013-09-25T20:08:12Z | 2013-09-25T20:08:12Z | MEMBER | null | Idea from conversation with @CRP in #235
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} | 0 | 2011-10-14T21:01:49Z | 2011-10-14T21:15:38Z | 2011-10-14T21:15:38Z | MEMBER | null | the following groupby transform results in things getting casted to object...
``` python
df = DataFrame({'d' : [1.,1.,1.,2.,2.,2.],
'c' : np.tile(['a','b','c'], 2),
'v' : np.arange(1., 7.)})
# in IPython
In [34]: df
Out[34]:
c d v
0 a 1 1
1 b 1 2
2 c 1 3
3 a 2 4
4 b 2 5
5 c 2 6
```
Now write a small transform function:
``` python
def f(group):
v = group['v']
group['v2'] = (v - v.min()) / (v.max() - v.min())
return group
```
Note that this also handles NAs since the v variable is a pandas Series object.
Now group by the d column and apply f:
``` python
In [36]: df.groupby('d').apply(f)
Out[36]:
c d v v2
0 a 1 1 0
1 b 1 2 0.5
2 c 1 3 1
3 a 2 4 0
4 b 2 5 0.5
5 c 2 6 1
```
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} | 4 | 2011-10-14T22:28:24Z | 2011-10-15T19:23:26Z | 2011-10-15T18:53:42Z | NONE | null | In some problems it is convenient to create an empty dataframe, and then append to it in a loop. The following code now breaks:
import pandas as ps
x = ps.DataFrame()
x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
x.join(ps.DataFrame([3], index=[0], columns=['A']))
Pandas 0.4.1
> > > import pandas as ps
> > > x = ps.DataFrame()
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
> > > A
> > > 0 3
> > >
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']))
> > > Empty DataFrame
> > > Index([], dtype=object)
Pandas 0.4.3
> > > import pandas as ps
> > > x = ps.DataFrame()
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
Traceback (most recent call last):
File "<pyshell#1197>", line 1, in <module>
x.join(ps.DataFrame([3], index=[0], columns=['A']), how='outer')
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2168, in join
return self._join_index(other, how, lsuffix, rsuffix)
File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2193, in _join_index
merged_data = join_managers(thisdata, otherdata, axis=1, how=how)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 958, in join_managers
lblocks = _maybe_upcast_blocks(left.blocks, lneed_masking)
File "C:\Python27\lib\site-packages\pandas\core\internals.py", line 1029, in _maybe_upcast_blocks
return _consolidate(new_blocks, newb.ref_items)
UnboundLocalError: local variable 'newb' referenced before assignment
> > > x.join(ps.DataFrame([3], index=[0], columns=['A']))
> > > Empty DataFrame
> > > Index([], dtype=object)
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https://api.github.com/repos/pandas-dev/pandas/issues/239 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/239/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/239/comments | https://api.github.com/repos/pandas-dev/pandas/issues/239/events | https://github.com/pandas-dev/pandas/issues/239 | 1,918,892 | MDU6SXNzdWUxOTE4ODky | 239 | Enable creation of empty Panel objects | {
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} | 2 | 2011-10-15T19:36:07Z | 2011-10-16T01:11:43Z | 2011-10-15T19:52:37Z | MEMBER | null | @creeson reported that this is not possible
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https://api.github.com/repos/pandas-dev/pandas/issues/241 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/241/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/241/comments | https://api.github.com/repos/pandas-dev/pandas/issues/241/events | https://github.com/pandas-dev/pandas/pull/241 | 1,922,618 | MDExOlB1bGxSZXF1ZXN0NDA2NTUx | 241 | .describe() for series of objects | {
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} | [] | closed | false | null | [] | null | 5 | 2011-10-16T18:06:10Z | 2014-06-14T07:05:34Z | 2011-10-19T20:34:41Z | CONTRIBUTOR | null | See #210.
I don't know if `series.dtype == object` is the best way to check for non-numeric data, but it appears to work.
At present, I've used fields 'count', 'unique', 'top' (i.e. most common) and 'freq' (i.e. count of the most common). These seem like obvious things for a summary.
I also noticed a problem doing `df.describe()` if df has integer columns, so I copied some code from series to fix that.
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} | 1 | 2011-10-16T22:55:48Z | 2011-10-21T04:32:49Z | 2011-10-21T04:32:49Z | MEMBER | null | This would be a very nice (and easy to implement) feature
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The delimiter parameter is by default ",", hence there are no side effects with existing code.
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```
df.join(df2, on=['key'])
```
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} | 1 | 2011-10-18T19:12:20Z | 2011-10-18T20:59:19Z | 2011-10-18T20:59:19Z | CONTRIBUTOR | null | This crashes the plot() command in matplotlib. Simple fix would be to just check that all only columns containing non-null values are plotted.
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} | 0 | 2011-10-18T19:36:14Z | 2011-10-19T21:07:19Z | 2011-10-19T21:07:19Z | MEMBER | null | ```
In [1]: df = DataFrame(np.random.randint(0, 5, 10), dtype=float)
In [2]: df
Out[2]:
0
0 1
1 4
2 0
3 0
4 1
5 1
6 2
7 4
8 3
9 3
In [3]: df[0] = df[0].astype('i8')
In [4]: df
Out[4]:
0
0 1
1 4
2 0
3 0
4 1
5 1
6 2
7 4
8 3
9 3
In [5]: df.dtypes
Out[5]:
0 float64
Name: None, Length: 1
```
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} | 1 | 2011-10-18T19:39:08Z | 2011-10-18T22:08:01Z | 2011-10-18T22:08:01Z | MEMBER | null | This should be reasonably trivial to implement (without breaking hierarchical indexing) and be a fairly high impact since it's one of the first things that people try. Disambiguating from passing a boolean vector may not be the easiest thing in the world though.
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} | 0 | 2011-10-20T15:11:40Z | 2011-10-20T22:54:13Z | 2011-10-20T22:54:13Z | MEMBER | null | ```
MultiIndex names get lost in the returned dataframe after sort_index is used.
Here is an example:
In [1]: import pandas
In [2]: pandas.__version__
Out[2]: '0.4.3'
In [3]: index = pandas.MultiIndex.from_tuples([('x','y','z'), ('a','b','c'), ('p','q','r')], names=['one','two','three'])
In [4]: df = pandas.DataFrame(randn(3, 5), index=index)
In [5]: df
Out[5]:
0 1 2 3 4
x y z -1.098 -0.3314 -0.5526 0.3791 1.12
a b c 1.141 0.3309 -0.6509 -0.561 -1.086
p q r 0.3399 -0.3135 1.677 0.533 0.1158
In [6]: df.index.names
Out[6]: ['one', 'two', 'three']
In [7]: dfs = df.sort_index()
In [8]: dfs
Out[8]:
0 1 2 3 4
a b c 1.141 0.3309 -0.6509 -0.561 -1.086
p q r 0.3399 -0.3135 1.677 0.533 0.1158
x y z -1.098 -0.3314 -0.5526 0.3791 1.12
In [9]: dfs.index.names
Out[9]: [None, None, None]
```
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} | 8 | 2011-10-20T15:49:22Z | 2011-10-21T16:10:38Z | 2011-10-21T04:48:45Z | NONE | null | An empty MultiIndex cannot be constructed:
```
In [120]: pa.MultiIndex([],[])
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
C:\Users\dfortunov\<ipython-input-120-ca5695a01949> in <module>()
----> 1 pa.MultiIndex([],[])
C:\VirtualEnvs\mss.dev\lib\site-packages\pandas-0.5.0.dev20111019_17461b7-py2.6-win32.egg\pandas\core\index.pyc in __new
__(cls, levels, labels, sortorder, names)
833 assert(len(levels) == len(labels))
834 if len(levels) == 0:
--> 835 raise Exception('Must pass non-zero number of levels/labels')
836
837 if len(levels) == 1:
Exception: Must pass non-zero number of levels/labels
```
This does not maintain parity with regular indices, where an empty index may be constructed:
```
In [119]: pa.Index([])
Out[119]: Index([], dtype=object)
```
And causes problems when doing set operations with MultiIndexes.
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```
Series([(1,2)])
```
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```
I always find Series.clip() a bit confusing because the order of
arguments are reversed compared with Numpy's clip() method:
Series.clip(self, upper=None, lower=None)
numpy.clip(a, a_min, a_max, out=None)
Also, the following doesn't work well:
x = Series(data=np.random.uniform(size=10))
np.clip(x, 0.4, 0.6)
I think numpy.clip is checking for the existence of the clip() method
on the Series and attempting to pass the arguments to Series.clip().
Since Series.clip() doesn't support the axis argument this approach
fails. Ideally, I'd prefer to be able to use clip() without having to
check whether the data is a numpy.ndarray or pandas.Series object.
```
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} | 0 | 2011-10-21T20:00:21Z | 2012-01-06T05:54:18Z | 2012-01-06T05:54:18Z | MEMBER | null | Leave this for 0.6.0
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I keep getting a 'module' object has no attribute 'core' when using with mod_wsgi. Please see http://stackoverflow.com/questions/7766400/pandas-django-mod-wsgi-virtualenv for the full error description.
Evan
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} | 0 | 2011-10-24T19:00:45Z | 2011-10-25T01:35:04Z | 2011-10-25T01:35:04Z | MEMBER | null | ```
>> I was just using this feature and noticed a curious behaviour -- if you have dates in the ISO8601 YYYYMMDD format then importing from csv with a MultiIndex turns them into ints, whereas importing with a regular index parses them to dates.
>>
>> Looking at pandas/io/parsers.py (https://github.com/wesm/pandas/commit/1a5a252d97cb3f691a6bf93412b921b085f1be76#L1L113) it seems that the _maybe_convert_int_mindex() method you added prefers ints over dates -- is this intentional or were you just not expecting any overlap between ints and dates?
>>
>
> Most likely the latter.
>
>> You also no longer call _maybe_convert_int, which was previously being called but is now unused, and instead do the int parsing with a map().
>>
>> Why not just implement _maybe_convert_int_mindex() along the lines of what was originally there, something like this:
>> for i in range(len(index)):
>> if parse_dates:
>> index = _try_parse_dates(index[i], parser=date_parser)
>> index[i] = _maybe_convert_int(np.array(index[i], dtype=object))
>>
>> I would just do it and submit a patch but I just wanted to check with you as you're more familiar with this code since you wrote it.
```
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Pull request to let you know that i added argmin and argmax methods to Series and DataFrame.
I also included unit tests. It`s not that much work/code, but anyway an interesting enhancement i think.
some examples:
In [23]: s
Out[23]:
a 0.80015881463
b 0.983432892599
c 0.965122593504
d 0.81910825442
e 0.726239458506
Name: None, Length: 5
In [24]: s.argmin()
Out[24]: 'e'
In [25]: s[s.argmax()] == s.max()
Out[25]: True
In [26]: df
Out[26]:
A B C D E
one two three
a b 10 -0.6155 1.237 1.371 1.24 -0.7725
p q 20 1.14 -0.3743 0.5251 0.2426 1.81
x y 30 0.6626 2.004 -0.01327 1.587 0.3524
In [27]: df.argmax(axis=0)
Out[27]:
A ('p', 'q', 20)
B ('x', 'y', 30)
C ('a', 'b', 10)
D ('x', 'y', 30)
E ('p', 'q', 20)
Name: None, Length: 5
In [28]: df.argmin(axis=1)
Out[28]:
a b 10 E
p q 20 B
x y 30 C
Name: None, Length: 3
What do you think?
Wouter
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Some syntactic sugar ;-)
Thinking about adding DataFrameGroupBy.boxplot(), now this call is transfered to DataFrame.boxplot().
But i would prefer the behavior to be different. When groupby is used i`m interested to see in a single plot boxplots for one particular column for all groupby keys and not in one plot to have boxpots for all columns for one groupby key. An input argument can be used to so both ways of boxplotting can be used.
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DataFrame's min() and max() functions operate only on single columns. The way to do it currently is
``` python
minimum, maximum = (min(df.min()), max(df.max())
```
It's not a critical limitation, but it is convenient to have at times.
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} | 2 | 2011-10-26T00:56:05Z | 2011-10-26T10:24:27Z | 2011-10-26T01:14:10Z | CONTRIBUTOR | null | Hi, i ran into a problem with the current master on github. Here's
import cStringIO as StringIO
import pandas
f1 = StringIO.StringIO('a,1.0\nb,2.0')
df = pandas.DataFrame.from_csv(f1,header=None)
newdf = pandas.DataFrame({'t': df[df.columns[0]]})
newdf.to_csv('/tmp/test.csv')
The last line gives me an exception like this:
Traceback (most recent call last):
File "/tmp/test.py", line 6, in <module>
newdf.to_csv('/tmp/test.csv')
File "/usr/lib/python2.7/dist-packages/pandas/core/frame.py", line 531, in to_csv
csvout.writerow(list(index_label) + list(cols))
TypeError: 'NoneType' object is not iterable
Code like this was working until quite recently. What i'm really trying to do is to read a bunch of csv files, each of which contains two columns of data, the first being the index, the second some values. The index is the same across files, so
i'm trying to combine these files into one DataFrame.
thanks
dieter
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} | 1 | 2011-10-26T13:52:34Z | 2011-11-04T20:04:21Z | 2011-11-04T20:04:21Z | CONTRIBUTOR | null | In [93]: pandas.**version**
Out[0]: '0.5.0'
In [94]: df = pandas.DataFrame({'A': [(1, 2)]})
---
AssertionError Traceback (most recent call last)
< .. snip ... >
The latter fails, however the following works fine:
In [95]: s = pandas.Series([(1, 2)]) ---> this was fixed in issue #270
In [96]: df = pandas.DataFrame({'A': s})
In [97]: s
Out[0]:
0 (1, 2)
Name: None, Length: 1
In [98]: df
Out[0]:
A
0 (1, 2)
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Two examples below. First one has no problem, second on shows issue.
pandas 0.5.0
python 2.7
# example1 - ok
``` python
In [15]: s = pandas.Series([1, 2.2352345, (3, 4), 'hello world'], index=['a', 'b', 'c', 'd'])
In [16]: s
Out[16]:
a 1
b 2.2352345
c (3, 4)
d hello world
Name: None, Length: 4
In [17]: s.values.dtype
Out[17]: dtype('object')
In [18]: s['a'] = 'cheese'
In [19]: s
Out[19]:
a cheese
b 2.2352345
c (3, 4)
d hello world
Name: None, Length: 4
```
# example2 - ValueError
``` python
In [20]: s = pandas.Series(data=np.random.rand(4), index=['a', 'b', 'c', 'd'])
In [21]: s
Out[21]:
a 0.056213774349
b 0.329577766125
c 0.463165873436
d 0.364501153967
Name: None, Length: 4
In [22]: s.values.dtype
Out[22]: dtype('float64')
In [23]: s['a'] = 'cheese'
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<..snip..>/lib/python2.7/site-packages/pandas/core/series.pyc in __setitem__(self, key, value)
325 try:
326 loc = self.index.get_loc(key)
--> 327 values[loc] = value
328 return
329 except KeyError:
ValueError: setting an array element with a sequence.
```
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```
def dist(x):
return len(np.unique(x))
```
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