The axis labeling information in pandas objects serves many purposes:
In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.
Note
The Python and NumPy indexing operators []
and attribute operator .
provide quick and easy access to pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized pandas data access methods exposed in this chapter.
Warning
Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called chained assignment
and should be avoided. See Returning a View versus Copy.
Warning
Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see here.
See the MultiIndex / Advanced Indexing for MultiIndex
and more advanced indexing documentation.
See the cookbook for some advanced strategies.
Object selection has had a number of user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing.
.loc
is primarily label based, but may also be used with a boolean array. .loc
will raise KeyError
when the items are not found. Allowed inputs are:
5
or 'a'
(Note that 5
is interpreted as a label of the index. This use is not an integer position along the index.). ['a', 'b', 'c']
. 'a':'f'
(Note that contrary to usual python slices, both the start and the stop are included, when present in the index! See Slicing with labels.). A callable
function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above).
New in version 0.18.1.
See more at Selection by Label.
.iloc
is primarily integer position based (from 0
to length-1
of the axis), but may also be used with a boolean array. .iloc
will raise IndexError
if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:
5
. [4, 3, 0]
. 1:7
. A callable
function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above).
New in version 0.18.1.
See more at Selection by Position, Advanced Indexing and Advanced Hierarchical.
.loc
, .iloc
, and also []
indexing can accept a callable
as indexer. See more at Selection By Callable. Getting values from an object with multi-axes selection uses the following notation (using .loc
as an example, but the following applies to .iloc
as well). Any of the axes accessors may be the null slice :
. Axes left out of the specification are assumed to be :
, e.g. p.loc['a']
is equivalent to p.loc['a', :, :]
.
Object Type | Indexers |
---|---|
Series | s.loc[indexer] |
DataFrame | df.loc[row_indexer,column_indexer] |
Panel | p.loc[item_indexer,major_indexer,minor_indexer] |
As mentioned when introducing the data structures in the last section, the primary function of indexing with []
(a.k.a. __getitem__
for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing pandas objects with []
:
Object Type | Selection | Return Value Type |
---|---|---|
Series | series[label] | scalar value |
DataFrame | frame[colname] |
Series corresponding to colname |
Panel | panel[itemname] |
DataFrame corresponding to the itemname |
Here we construct a simple time series data set to use for illustrating the indexing functionality:
In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D']) In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 2000-01-03 -0.861849 -2.104569 -0.494929 1.071804 2000-01-04 0.721555 -0.706771 -1.039575 0.271860 2000-01-05 -0.424972 0.567020 0.276232 -1.087401 2000-01-06 -0.673690 0.113648 -1.478427 0.524988 2000-01-07 0.404705 0.577046 -1.715002 -1.039268 2000-01-08 -0.370647 -1.157892 -1.344312 0.844885 In [4]: panel = pd.Panel({'one' : df, 'two' : df - df.mean()}) In [5]: panel Out[5]: <class 'pandas.core.panel.Panel'> Dimensions: 2 (items) x 8 (major_axis) x 4 (minor_axis) Items axis: one to two Major_axis axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00 Minor_axis axis: A to D
Note
None of the indexing functionality is time series specific unless specifically stated.
Thus, as per above, we have the most basic indexing using []
:
In [6]: s = df['A'] In [7]: s[dates[5]] Out[7]: -0.67368970808837059 In [8]: panel['two']
© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/indexing.html