pandas 0.9.0 documentation

Indexing and selecting data

The axis labeling information in pandas objects serves many purposes:

  • Identifies data (i.e. provides metadata) using known indicators, important for for analysis, visualization, and interactive console display
  • Enables automatic and explicit data alignment
  • Allows intuitive getting and setting of subsets of the data set

In this section / chapter, 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. Expect more work to be invested higher-dimensional data structures (including Panel) in the future, especially in label-based advanced indexing.

Basics

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. Thus,

  • Series: series[label] returns a scalar value
  • DataFrame: frame[colname] returns a Series corresponding to the passed column name
  • Panel: panel[itemname] returns a DataFrame corresponding to the passed item name

Here we construct a simple time series data set to use for illustrating the indexing functionality:

In [550]: dates = np.asarray(date_range('1/1/2000', periods=8))

In [551]: df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])

In [552]: df
Out[552]: 
                   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 [553]: panel = Panel({'one' : df, 'two' : df - df.mean()})

In [554]: panel
Out[554]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 8 (major) x 4 (minor)
Items: one to two
Major axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00
Minor 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 [555]: s = df['A']

In [556]: s[dates[5]]
Out[556]: -0.67368970808837059

In [557]: panel['two']
Out[557]: 
                   A         B         C         D
2000-01-01  0.409571  0.113086 -0.610826 -0.936507
2000-01-02  1.152571  0.222735  1.017442 -0.845111
2000-01-03 -0.921390 -1.708620  0.403304  1.270929
2000-01-04  0.662014 -0.310822 -0.141342  0.470985
2000-01-05 -0.484513  0.962970  1.174465 -0.888276
2000-01-06 -0.733231  0.509598 -0.580194  0.724113
2000-01-07  0.345164  0.972995 -0.816769 -0.840143
2000-01-08 -0.430188 -0.761943 -0.446079  1.044010

Fast scalar value getting and setting

Since indexing with [] must handle a lot of cases (single-label access, slicing, boolean indexing, etc.), it has a bit of overhead in order to figure out what you’re asking for. If you only want to access a scalar value, the fastest way is to use the get_value method, which is implemented on all of the data structures:

In [558]: s.get_value(dates[5])
Out[558]: -0.67368970808837059

In [559]: df.get_value(dates[5], 'A')
Out[559]: -0.67368970808837059

There is an analogous set_value method which has the additional capability of enlarging an object. This method always returns a reference to the object it modified, which in the fast of enlargement, will be a new object:

In [560]: df.set_value(dates[5], 'E', 7)
Out[560]: 
                   A         B         C         D   E
2000-01-01  0.469112 -0.282863 -1.509059 -1.135632 NaN
2000-01-02  1.212112 -0.173215  0.119209 -1.044236 NaN
2000-01-03 -0.861849 -2.104569 -0.494929  1.071804 NaN
2000-01-04  0.721555 -0.706771 -1.039575  0.271860 NaN
2000-01-05 -0.424972  0.567020  0.276232 -1.087401 NaN
2000-01-06 -0.673690  0.113648 -1.478427  0.524988   7
2000-01-07  0.404705  0.577046 -1.715002 -1.039268 NaN
2000-01-08 -0.370647 -1.157892 -1.344312  0.844885 NaN

Additional Column Access

You may access a column on a dataframe directly as an attribute:

In [561]: df.A
Out[561]: 
2000-01-01    0.469112
2000-01-02    1.212112
2000-01-03   -0.861849
2000-01-04    0.721555
2000-01-05   -0.424972
2000-01-06   -0.673690
2000-01-07    0.404705
2000-01-08   -0.370647
Name: A

If you are using the IPython environment, you may also use tab-completion to see the accessible columns of a DataFrame.

You can pass a list of columns to [] to select columns in that order: If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:

In [562]: df
Out[562]: 
                   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 [563]: df[['B', 'A']] = df[['A', 'B']]

In [564]: df
Out[564]: 
                   A         B         C         D
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
2000-01-04 -0.706771  0.721555 -1.039575  0.271860
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-06  0.113648 -0.673690 -1.478427  0.524988
2000-01-07  0.577046  0.404705 -1.715002 -1.039268
2000-01-08 -1.157892 -0.370647 -1.344312  0.844885

You may find this useful for applying a transform (in-place) to a subset of the columns.

Data slices on other axes

It’s certainly possible to retrieve data slices along the other axes of a DataFrame or Panel. We tend to refer to these slices as cross-sections. DataFrame has the xs function for retrieving rows as Series and Panel has the analogous major_xs and minor_xs functions for retrieving slices as DataFrames for a given major_axis or minor_axis label, respectively.

In [565]: date = dates[5]

In [566]: df.xs(date)
Out[566]: 
A    0.113648
B   -0.673690
C   -1.478427
D    0.524988
Name: 2000-01-06 00:00:00

In [567]: panel.major_xs(date)
Out[567]: 
        one       two
A -0.673690 -0.733231
B  0.113648  0.509598
C -1.478427 -0.580194
D  0.524988  0.724113

In [568]: panel.minor_xs('A')
Out[568]: 
                 one       two
2000-01-01  0.469112  0.409571
2000-01-02  1.212112  1.152571
2000-01-03 -0.861849 -0.921390
2000-01-04  0.721555  0.662014
2000-01-05 -0.424972 -0.484513
2000-01-06 -0.673690 -0.733231
2000-01-07  0.404705  0.345164
2000-01-08 -0.370647 -0.430188

Slicing ranges

The most robust and consistent way of slicing ranges along arbitrary axes is described in the Advanced indexing section detailing the .ix method. For now, we explain the semantics of slicing using the [] operator.

With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:

In [569]: s[:5]
Out[569]: 
2000-01-01   -0.282863
2000-01-02   -0.173215
2000-01-03   -2.104569
2000-01-04   -0.706771
2000-01-05    0.567020
Name: A

In [570]: s[::2]
Out[570]: 
2000-01-01   -0.282863
2000-01-03   -2.104569
2000-01-05    0.567020
2000-01-07    0.577046
Name: A

In [571]: s[::-1]
Out[571]: 
2000-01-08   -1.157892
2000-01-07    0.577046
2000-01-06    0.113648
2000-01-05    0.567020
2000-01-04   -0.706771
2000-01-03   -2.104569
2000-01-02   -0.173215
2000-01-01   -0.282863
Name: A

Note that setting works as well:

In [572]: s2 = s.copy()

In [573]: s2[:5] = 0

In [574]: s2
Out[574]: 
2000-01-01    0.000000
2000-01-02    0.000000
2000-01-03    0.000000
2000-01-04    0.000000
2000-01-05    0.000000
2000-01-06    0.113648
2000-01-07    0.577046
2000-01-08   -1.157892
Name: A

With DataFrame, slicing inside of [] slices the rows. This is provided largely as a convenience since it is such a common operation.

In [575]: df[:3]
Out[575]: 
                   A         B         C         D
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804

In [576]: df[::-1]
Out[576]: 
                   A         B         C         D
2000-01-08 -1.157892 -0.370647 -1.344312  0.844885
2000-01-07  0.577046  0.404705 -1.715002 -1.039268
2000-01-06  0.113648 -0.673690 -1.478427  0.524988
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-04 -0.706771  0.721555 -1.039575  0.271860
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632

Boolean indexing

Another common operation is the use of boolean vectors to filter the data.

Using a boolean vector to index a Series works exactly as in a numpy ndarray:

In [577]: s[s > 0]
Out[577]: 
2000-01-05    0.567020
2000-01-06    0.113648
2000-01-07    0.577046
Name: A

In [578]: s[(s < 0) & (s > -0.5)]
Out[578]: 
2000-01-01   -0.282863
2000-01-02   -0.173215
Name: A

You may select rows from a DataFrame using a boolean vector the same length as the DataFrame’s index (for example, something derived from one of the columns of the DataFrame):

In [579]: df[df['A'] > 0]
Out[579]: 
                   A         B         C         D
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-06  0.113648 -0.673690 -1.478427  0.524988
2000-01-07  0.577046  0.404705 -1.715002 -1.039268

Consider the isin method of Series, which returns a boolean vector that is true wherever the Series elements exist in the passed list. This allows you to select rows where one or more columns have values you want:

In [580]: df2 = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
   .....:                  'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
   .....:                  'c' : randn(7)})
   .....:

In [581]: df2[df2['a'].isin(['one', 'two'])]
Out[581]: 
     a  b         c
0  one  x  1.075770
1  one  y -0.109050
2  two  y  1.643563
4  two  y  0.357021
5  one  x -0.674600

List comprehensions and map method of Series can also be used to produce more complex criteria:

# only want 'two' or 'three'
In [582]: criterion = df2['a'].map(lambda x: x.startswith('t'))

In [583]: df2[criterion]
Out[583]: 
       a  b         c
2    two  y  1.643563
3  three  x -1.469388
4    two  y  0.357021

# equivalent but slower
In [584]: df2[[x.startswith('t') for x in df2['a']]]
Out[584]: 
       a  b         c
2    two  y  1.643563
3  three  x -1.469388
4    two  y  0.357021

# Multiple criteria
In [585]: df2[criterion & (df2['b'] == 'x')]
Out[585]: 
       a  b         c
3  three  x -1.469388

Note, with the advanced indexing ix method, you may select along more than one axis using boolean vectors combined with other indexing expressions.

Indexing a DataFrame with a boolean DataFrame

You may wish to set values on a DataFrame based on some boolean criteria derived from itself or another DataFrame or set of DataFrames. This can be done intuitively like so:

In [586]: df2 = df.copy()

In [587]: df2 < 0
Out[587]: 
                A      B      C      D
2000-01-01   True  False   True   True
2000-01-02   True  False  False   True
2000-01-03   True   True   True  False
2000-01-04   True  False   True  False
2000-01-05  False   True  False   True
2000-01-06  False   True   True  False
2000-01-07  False  False   True   True
2000-01-08   True   True   True  False

In [588]: df2[df2 < 0] = 0

In [589]: df2
Out[589]: 
                   A         B         C         D
2000-01-01  0.000000  0.469112  0.000000  0.000000
2000-01-02  0.000000  1.212112  0.119209  0.000000
2000-01-03  0.000000  0.000000  0.000000  1.071804
2000-01-04  0.000000  0.721555  0.000000  0.271860
2000-01-05  0.567020  0.000000  0.276232  0.000000
2000-01-06  0.113648  0.000000  0.000000  0.524988
2000-01-07  0.577046  0.404705  0.000000  0.000000
2000-01-08  0.000000  0.000000  0.000000  0.844885

Note that such an operation requires that the boolean DataFrame is indexed exactly the same.

Take Methods

Similar to numpy ndarrays, pandas Index, Series, and DataFrame also provides the take method that retrieves elements along a given axis at the given indices. The given indices must be either a list or an ndarray of integer index positions.

In [590]: index = Index(randint(0, 1000, 10))

In [591]: index
Out[591]: Int64Index([969, 412, 496, 195, 288, 101, 881, 900, 732, 658])

In [592]: positions = [0, 9, 3]

In [593]: index[positions]
Out[593]: Int64Index([969, 658, 195])

In [594]: index.take(positions)
Out[594]: Int64Index([969, 658, 195])

In [595]: ser = Series(randn(10))

In [596]: ser.ix[positions]
Out[596]: 
0   -0.968914
9   -1.131345
3    1.247642

In [597]: ser.take(positions)
Out[597]: 
0   -0.968914
9   -1.131345
3    1.247642

For DataFrames, the given indices should be a 1d list or ndarray that specifies row or column positions.

In [598]: frm = DataFrame(randn(5, 3))

In [599]: frm.take([1, 4, 3])
Out[599]: 
          0         1         2
1 -0.932132  1.956030  0.017587
4 -0.077118 -0.408530 -0.862495
3 -1.143704  0.215897  1.193555

In [600]: frm.take([0, 2], axis=1)
Out[600]: 
          0         2
0 -0.089329 -0.945867
1 -0.932132  0.017587
2 -0.016692  0.254161
3 -1.143704  1.193555
4 -0.077118 -0.862495

It is important to note that the take method on pandas objects are not intended to work on boolean indices and may return unexpected results.

In [601]: arr = randn(10)

In [602]: arr.take([False, False, True, True])
Out[602]: array([ 1.3461,  1.3461,  1.5118,  1.5118])

In [603]: arr[[0, 1]]
Out[603]: array([ 1.3461,  1.5118])

In [604]: ser = Series(randn(10))

In [605]: ser.take([False, False, True, True])
Out[605]: 
0   -0.105381
0   -0.105381
1   -0.532532
1   -0.532532

In [606]: ser.ix[[0, 1]]
Out[606]: 
0   -0.105381
1   -0.532532

Finally, as a small note on performance, because the take method handles a narrower range of inputs, it can offer performance that is a good deal faster than fancy indexing.

Duplicate Data

If you want to identify and remove duplicate rows in a DataFrame, there are two methods that will help: duplicated and drop_duplicates. Each takes as an argument the columns to use to identify duplicated rows.

duplicated returns a boolean vector whose length is the number of rows, and which indicates whether a row is duplicated.

drop_duplicates removes duplicate rows.

By default, the first observed row of a duplicate set is considered unique, but each method has a take_last parameter that indicates the last observed row should be taken instead.

In [607]: df2 = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
   .....:                  'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
   .....:                  'c' : np.random.randn(7)})
   .....:

In [608]: df2.duplicated(['a','b'])
Out[608]: 
0    False
1    False
2    False
3    False
4     True
5     True
6    False

In [609]: df2.drop_duplicates(['a','b'])
Out[609]: 
       a  b         c
0    one  x -0.339355
1    one  y  0.593616
2    two  y  0.884345
3  three  x  1.591431
6    six  x  0.435589

In [610]: df2.drop_duplicates(['a','b'], take_last=True)
Out[610]: 
       a  b         c
1    one  y  0.593616
3  three  x  1.591431
4    two  y  0.141809
5    one  x  0.220390
6    six  x  0.435589

Dictionary-like get method

Each of Series, DataFrame, and Panel have a get method which can return a default value.

In [611]: s = Series([1,2,3], index=['a','b','c'])

In [612]: s.get('a')               # equivalent to s['a']
Out[612]: 1

In [613]: s.get('x', default=-1)
Out[613]: -1

Advanced indexing with labels

We have avoided excessively overloading the [] / __getitem__ operator to keep the basic functionality of the pandas objects straightforward and simple. However, there are often times when you may wish get a subset (or analogously set a subset) of the data in a way that is not straightforward using the combination of reindex and []. Complicated setting operations are actually quite difficult because reindex usually returns a copy.

By advanced indexing we are referring to a special .ix attribute on pandas objects which enable you to do getting/setting operations on a DataFrame, for example, with matrix/ndarray-like semantics. Thus you can combine the following kinds of indexing:

  • An integer or single label, e.g. 5 or 'a'
  • A list or array of labels ['a', 'b', 'c'] or integers [4, 3, 0]
  • A slice object with ints 1:7 or labels 'a':'f'
  • A boolean array

We’ll illustrate all of these methods. First, note that this provides a concise way of reindexing on multiple axes at once:

In [614]: subindex = dates[[3,4,5]]

In [615]: df.reindex(index=subindex, columns=['C', 'B'])
Out[615]: 
                   C         B
2000-01-04 -1.039575  0.721555
2000-01-05  0.276232 -0.424972
2000-01-06 -1.478427 -0.673690

In [616]: df.ix[subindex, ['C', 'B']]
Out[616]: 
                   C         B
2000-01-04 -1.039575  0.721555
2000-01-05  0.276232 -0.424972
2000-01-06 -1.478427 -0.673690

Assignment / setting values is possible when using ix:

In [617]: df2 = df.copy()

In [618]: df2.ix[subindex, ['C', 'B']] = 0

In [619]: df2
Out[619]: 
                   A         B         C         D
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
2000-01-04 -0.706771  0.000000  0.000000  0.271860
2000-01-05  0.567020  0.000000  0.000000 -1.087401
2000-01-06  0.113648  0.000000  0.000000  0.524988
2000-01-07  0.577046  0.404705 -1.715002 -1.039268
2000-01-08 -1.157892 -0.370647 -1.344312  0.844885

Indexing with an array of integers can also be done:

In [620]: df.ix[[4,3,1]]
Out[620]: 
                   A         B         C         D
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-04 -0.706771  0.721555 -1.039575  0.271860
2000-01-02 -0.173215  1.212112  0.119209 -1.044236

In [621]: df.ix[dates[[4,3,1]]]
Out[621]: 
                   A         B         C         D
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-04 -0.706771  0.721555 -1.039575  0.271860
2000-01-02 -0.173215  1.212112  0.119209 -1.044236

Slicing has standard Python semantics for integer slices:

In [622]: df.ix[1:7, :2]
Out[622]: 
                   A         B
2000-01-02 -0.173215  1.212112
2000-01-03 -2.104569 -0.861849
2000-01-04 -0.706771  0.721555
2000-01-05  0.567020 -0.424972
2000-01-06  0.113648 -0.673690
2000-01-07  0.577046  0.404705

Slicing with labels is semantically slightly different because the slice start and stop are inclusive in the label-based case:

In [623]: date1, date2 = dates[[2, 4]]

In [624]: print date1, date2
1970-01-11 232:00:00 1970-01-11 24:00:00

In [625]: df.ix[date1:date2]
Out[625]: 
Empty DataFrame
Columns: array([A, B, C, D], dtype=object)
Index: <class 'pandas.tseries.index.DatetimeIndex'>
Length: 0, Freq: None, Timezone: None

In [626]: df['A'].ix[date1:date2]
Out[626]: TimeSeries([], dtype=float64)

Getting and setting rows in a DataFrame, especially by their location, is much easier:

In [627]: df2 = df[:5].copy()

In [628]: df2.ix[3]
Out[628]: 
A   -0.706771
B    0.721555
C   -1.039575
D    0.271860
Name: 2000-01-04 00:00:00

In [629]: df2.ix[3] = np.arange(len(df2.columns))

In [630]: df2
Out[630]: 
                   A         B         C         D
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
2000-01-04  0.000000  1.000000  2.000000  3.000000
2000-01-05  0.567020 -0.424972  0.276232 -1.087401

Column or row selection can be combined as you would expect with arrays of labels or even boolean vectors:

In [631]: df.ix[df['A'] > 0, 'B']
Out[631]: 
2000-01-05   -0.424972
2000-01-06   -0.673690
2000-01-07    0.404705
Name: B

In [632]: df.ix[date1:date2, 'B']
Out[632]: TimeSeries([], dtype=float64)

In [633]: df.ix[date1, 'B']
Out[633]: -0.86184896334779992

Slicing with labels is closely related to the truncate method which does precisely .ix[start:stop] but returns a copy (for legacy reasons).

Returning a view versus a copy

The rules about when a view on the data is returned are entirely dependent on NumPy. Whenever an array of labels or a boolean vector are involved in the indexing operation, the result will be a copy. With single label / scalar indexing and slicing, e.g. df.ix[3:6] or df.ix[:, 'A'], a view will be returned.

The select method

Another way to extract slices from an object is with the select method of Series, DataFrame, and Panel. This method should be used only when there is no more direct way. select takes a function which operates on labels along axis and returns a boolean. For instance:

In [634]: df.select(lambda x: x == 'A', axis=1)
Out[634]: 
                   A
2000-01-01 -0.282863
2000-01-02 -0.173215
2000-01-03 -2.104569
2000-01-04 -0.706771
2000-01-05  0.567020
2000-01-06  0.113648
2000-01-07  0.577046
2000-01-08 -1.157892

The lookup method

Sometimes you want to extract a set of values given a sequence of row labels and column labels, and the lookup method allows for this and returns a numpy array. For instance,

In [635]: dflookup = DataFrame(np.random.rand(20,4), columns = ['A','B','C','D'])

In [636]: dflookup.lookup(xrange(0,10,2), ['B','C','A','B','D'])
Out[636]: array([ 0.0227,  0.4199,  0.529 ,  0.9674,  0.5357])

Advanced indexing with integer labels

Label-based indexing with integer axis labels is a thorny topic. It has been discussed heavily on mailing lists and among various members of the scientific Python community. In pandas, our general viewpoint is that labels matter more than integer locations. Therefore, with an integer axis index only label-based indexing is possible with the standard tools like .ix. The following code will generate exceptions:

s = Series(range(5))
s[-1]
df = DataFrame(np.random.randn(5, 4))
df
df.ix[-2:]

This deliberate decision was made to prevent ambiguities and subtle bugs (many users reported finding bugs when the API change was made to stop “falling back” on position-based indexing).

Setting values in mixed-type DataFrame

Setting values on a mixed-type DataFrame or Panel is supported when using scalar values, though setting arbitrary vectors is not yet supported:

In [637]: df2 = df[:4]

In [638]: df2['foo'] = 'bar'

In [639]: print df2
                   A         B         C         D  foo
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632  bar
2000-01-02 -0.173215  1.212112  0.119209 -1.044236  bar
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804  bar
2000-01-04 -0.706771  0.721555 -1.039575  0.271860  bar

In [640]: df2.ix[2] = np.nan

In [641]: print df2
                   A         B         C         D  foo
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632  bar
2000-01-02 -0.173215  1.212112  0.119209 -1.044236  bar
2000-01-03       NaN       NaN       NaN       NaN  NaN
2000-01-04 -0.706771  0.721555 -1.039575  0.271860  bar

In [642]: print df2.dtypes
A      float64
B      float64
C      float64
D      float64
foo     object

Index objects

The pandas Index class and its subclasses can be viewed as implementing an ordered set in addition to providing the support infrastructure necessary for lookups, data alignment, and reindexing. The easiest way to create one directly is to pass a list or other sequence to Index:

In [643]: index = Index(['e', 'd', 'a', 'b'])

In [644]: index
Out[644]: Index([e, d, a, b], dtype=object)

In [645]: 'd' in index
Out[645]: True

You can also pass a name to be stored in the index:

In [646]: index = Index(['e', 'd', 'a', 'b'], name='something')

In [647]: index.name
Out[647]: 'something'

Starting with pandas 0.5, the name, if set, will be shown in the console display:

In [648]: index = Index(range(5), name='rows')

In [649]: columns = Index(['A', 'B', 'C'], name='cols')

In [650]: df = DataFrame(np.random.randn(5, 3), index=index, columns=columns)

In [651]: df
Out[651]: 
cols         A         B         C
rows                              
0     0.192451  0.629675 -1.425966
1     1.857704 -1.193545  0.677510
2    -0.153931  0.520091 -1.475051
3     0.722570 -0.322646 -1.601631
4     0.778033 -0.289342  0.233141

In [652]: df['A']
Out[652]: 
rows
0       0.192451
1       1.857704
2      -0.153931
3       0.722570
4       0.778033
Name: A

Set operations on Index objects

The three main operations are union (|), intersection (&), and diff (-). These can be directly called as instance methods or used via overloaded operators:

In [653]: a = Index(['c', 'b', 'a'])

In [654]: b = Index(['c', 'e', 'd'])

In [655]: a.union(b)
Out[655]: Index([a, b, c, d, e], dtype=object)

In [656]: a | b
Out[656]: Index([a, b, c, d, e], dtype=object)

In [657]: a & b
Out[657]: Index([c], dtype=object)

In [658]: a - b
Out[658]: Index([a, b], dtype=object)

isin method of Index objects

One additional operation is the isin method that works analogously to the Series.isin method found here.

Hierarchical indexing (MultiIndex)

Hierarchical indexing (also referred to as “multi-level” indexing) is brand new in the pandas 0.4 release. It is very exciting as it opens the door to some quite sophisticated data analysis and manipulation, especially for working with higher dimensional data. In essence, it enables you to store and manipulate data with an arbitrary number of dimensions in lower dimensional data structures like Series (1d) and DataFrame (2d).

In this section, we will show what exactly we mean by “hierarchical” indexing and how it integrates with the all of the pandas indexing functionality described above and in prior sections. Later, when discussing group by and pivoting and reshaping data, we’ll show non-trivial applications to illustrate how it aids in structuring data for analysis.

Note

Given that hierarchical indexing is so new to the library, it is definitely “bleeding-edge” functionality but is certainly suitable for production. But, there may inevitably be some minor API changes as more use cases are explored and any weaknesses in the design / implementation are identified. pandas aims to be “eminently usable” so any feedback about new functionality like this is extremely helpful.

Creating a MultiIndex (hierarchical index) object

The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex an array of tuples where each tuple is unique. A MultiIndex can be created from a list of arrays (using MultiIndex.from_arrays) or an array of tuples (using MultiIndex.from_tuples).

In [659]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
   .....:           ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
   .....:

In [660]: tuples = zip(*arrays)

In [661]: tuples
Out[661]: 
[('bar', 'one'),
 ('bar', 'two'),
 ('baz', 'one'),
 ('baz', 'two'),
 ('foo', 'one'),
 ('foo', 'two'),
 ('qux', 'one'),
 ('qux', 'two')]

In [662]: index = MultiIndex.from_tuples(tuples, names=['first', 'second'])

In [663]: s = Series(randn(8), index=index)

In [664]: s
Out[664]: 
first  second
bar    one      -0.223540
       two       0.542054
baz    one      -0.688585
       two      -0.352676
foo    one      -0.711411
       two      -2.122599
qux    one       1.962935
       two       1.672027

As a convenience, you can pass a list of arrays directly into Series or DataFrame to construct a MultiIndex automatically:

In [665]: arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
   .....:           np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
   .....:

In [666]: s = Series(randn(8), index=arrays)

In [667]: s
Out[667]: 
bar  one   -0.880984
     two    0.997289
baz  one   -1.693316
     two   -0.179129
foo  one   -1.598062
     two    0.936914
qux  one    0.912560
     two   -1.003401

In [668]: df = DataFrame(randn(8, 4), index=arrays)

In [669]: df
Out[669]: 
                0         1         2         3
bar one  1.632781 -0.724626  0.178219  0.310610
    two -0.108002 -0.974226 -1.147708 -2.281374
baz one  0.760010 -0.742532  1.533318  2.495362
    two -0.432771 -0.068954  0.043520  0.112246
foo one  0.871721 -0.816064 -0.784880  1.030659
    two  0.187483 -1.933946  0.377312  0.734122
qux one  2.141616 -0.011225  0.048869 -1.360687
    two -0.479010 -0.859661 -0.231595 -0.527750

All of the MultiIndex constructors accept a names argument which stores string names for the levels themselves. If no names are provided, some arbitrary ones will be assigned:

In [670]: index.names
Out[670]: ['first', 'second']

This index can back any axis of a pandas object, and the number of levels of the index is up to you:

In [671]: df = DataFrame(randn(3, 8), index=['A', 'B', 'C'], columns=index)

In [672]: df
Out[672]: 
first        bar                 baz                 foo                 qux          
second       one       two       one       two       one       two       one       two
A      -1.296337  0.150680  0.123836  0.571764  1.555563 -0.823761  0.535420 -1.032853
B       1.469725  1.304124  1.449735  0.203109 -1.032011  0.969818 -0.962723  1.382083
C      -0.938794  0.669142 -0.433567 -0.273610  0.680433 -0.308450 -0.276099 -1.821168

In [673]: DataFrame(randn(6, 6), index=index[:6], columns=index[:6])
Out[673]: 
first              bar                 baz                 foo          
second             one       two       one       two       one       two
first second                                                            
bar   one    -1.993606 -1.927385 -2.027924  1.624972  0.551135  3.059267
      two     0.455264 -0.030740  0.935716  1.061192 -2.107852  0.199905
baz   one     0.323586 -0.641630 -0.587514  0.053897  0.194889 -0.381994
      two     0.318587  2.089075 -0.728293 -0.090255 -0.748199  1.318931
foo   one    -2.029766  0.792652  0.461007 -0.542749 -0.305384 -0.479195
      two     0.095031 -0.270099 -0.707140 -0.773882  0.229453  0.304418

We’ve “sparsified” the higher levels of the indexes to make the console output a bit easier on the eyes.

It’s worth keeping in mind that there’s nothing preventing you from using tuples as atomic labels on an axis:

In [674]: Series(randn(8), index=tuples)
Out[674]: 
('bar', 'one')    0.736135
('bar', 'two')   -0.859631
('baz', 'one')   -0.424100
('baz', 'two')   -0.776114
('foo', 'one')    1.279293
('foo', 'two')    0.943798
('qux', 'one')   -1.001859
('qux', 'two')    0.306546

The reason that the MultiIndex matters is that it can allow you to do grouping, selection, and reshaping operations as we will describe below and in subsequent areas of the documentation. As you will see in later sections, you can find yourself working with hierarchically-indexed data without creating a MultiIndex explicitly yourself. However, when loading data from a file, you may wish to generate your own MultiIndex when preparing the data set.

Note that how the index is displayed by be controlled using the multi_sparse option in pandas.set_printoptions:

In [675]: pd.set_printoptions(multi_sparse=False)

In [676]: df
Out[676]: 
first        bar       bar       baz       baz       foo       foo       qux       qux
second       one       two       one       two       one       two       one       two
A      -1.296337  0.150680  0.123836  0.571764  1.555563 -0.823761  0.535420 -1.032853
B       1.469725  1.304124  1.449735  0.203109 -1.032011  0.969818 -0.962723  1.382083
C      -0.938794  0.669142 -0.433567 -0.273610  0.680433 -0.308450 -0.276099 -1.821168

In [677]: pd.set_printoptions(multi_sparse=True)

Reconstructing the level labels

The method get_level_values will return a vector of the labels for each location at a particular level:

In [678]: index.get_level_values(0)
Out[678]: array([bar, bar, baz, baz, foo, foo, qux, qux], dtype=object)

In [679]: index.get_level_values('second')
Out[679]: array([one, two, one, two, one, two, one, two], dtype=object)

Basic indexing on axis with MultiIndex

One of the important features of hierarchical indexing is that you can select data by a “partial” label identifying a subgroup in the data. Partial selection “drops” levels of the hierarchical index in the result in a completely analogous way to selecting a column in a regular DataFrame:

In [680]: df['bar']
Out[680]: 
second       one       two
A      -1.296337  0.150680
B       1.469725  1.304124
C      -0.938794  0.669142

In [681]: df['bar', 'one']
Out[681]: 
A   -1.296337
B    1.469725
C   -0.938794
Name: ('bar', 'one')

In [682]: df['bar']['one']
Out[682]: 
A   -1.296337
B    1.469725
C   -0.938794
Name: one

In [683]: s['qux']
Out[683]: 
one    0.912560
two   -1.003401

Data alignment and using reindex

Operations between differently-indexed objects having MultiIndex on the axes will work as you expect; data alignment will work the same as an Index of tuples:

In [684]: s + s[:-2]
Out[684]: 
bar  one   -1.761968
     two    1.994577
baz  one   -3.386631
     two   -0.358257
foo  one   -3.196125
     two    1.873828
qux  one         NaN
     two         NaN

In [685]: s + s[::2]
Out[685]: 
bar  one   -1.761968
     two         NaN
baz  one   -3.386631
     two         NaN
foo  one   -3.196125
     two         NaN
qux  one    1.825119
     two         NaN

reindex can be called with another MultiIndex or even a list or array of tuples:

In [686]: s.reindex(index[:3])
Out[686]: 
first  second
bar    one      -0.880984
       two       0.997289
baz    one      -1.693316

In [687]: s.reindex([('foo', 'two'), ('bar', 'one'), ('qux', 'one'), ('baz', 'one')])
Out[687]: 
foo  two    0.936914
bar  one   -0.880984
qux  one    0.912560
baz  one   -1.693316

Advanced indexing with hierarchical index

Syntactically integrating MultiIndex in advanced indexing with .ix is a bit challenging, but we’ve made every effort to do so. for example the following works as you would expect:

In [688]: df = df.T

In [689]: df
Out[689]: 
                     A         B         C
first second                              
bar   one    -1.296337  1.469725 -0.938794
      two     0.150680  1.304124  0.669142
baz   one     0.123836  1.449735 -0.433567
      two     0.571764  0.203109 -0.273610
foo   one     1.555563 -1.032011  0.680433
      two    -0.823761  0.969818 -0.308450
qux   one     0.535420 -0.962723 -0.276099
      two    -1.032853  1.382083 -1.821168

In [690]: df.ix['bar']
Out[690]: 
               A         B         C
second                              
one    -1.296337  1.469725 -0.938794
two     0.150680  1.304124  0.669142

In [691]: df.ix['bar', 'two']
Out[691]: 
A    0.150680
B    1.304124
C    0.669142
Name: ('bar', 'two')

“Partial” slicing also works quite nicely:

In [692]: df.ix['baz':'foo']
Out[692]: 
                     A         B         C
first second                              
baz   one     0.123836  1.449735 -0.433567
      two     0.571764  0.203109 -0.273610
foo   one     1.555563 -1.032011  0.680433
      two    -0.823761  0.969818 -0.308450

In [693]: df.ix[('baz', 'two'):('qux', 'one')]
Out[693]: 
                     A         B         C
first second                              
baz   two     0.571764  0.203109 -0.273610
foo   one     1.555563 -1.032011  0.680433
      two    -0.823761  0.969818 -0.308450
qux   one     0.535420 -0.962723 -0.276099

In [694]: df.ix[('baz', 'two'):'foo']
Out[694]: 
                     A         B         C
first second                              
baz   two     0.571764  0.203109 -0.273610
foo   one     1.555563 -1.032011  0.680433
      two    -0.823761  0.969818 -0.308450

Passing a list of labels or tuples works similar to reindexing:

In [695]: df.ix[[('bar', 'two'), ('qux', 'one')]]
Out[695]: 
                    A         B         C
first second                             
bar   two     0.15068  1.304124  0.669142
qux   one     0.53542 -0.962723 -0.276099

The following does not work, and it’s not clear if it should or not:

>>> df.ix[['bar', 'qux']]

The code for implementing .ix makes every attempt to “do the right thing” but as you use it you may uncover corner cases or unintuitive behavior. If you do find something like this, do not hesitate to report the issue or ask on the mailing list.

Cross-section with hierarchical index

The xs method of DataFrame additionally takes a level argument to make selecting data at a particular level of a MultiIndex easier.

In [696]: df.xs('one', level='second')
Out[696]: 
              A         B         C
first                              
bar   -1.296337  1.469725 -0.938794
baz    0.123836  1.449735 -0.433567
foo    1.555563 -1.032011  0.680433
qux    0.535420 -0.962723 -0.276099

Advanced reindexing and alignment with hierarchical index

The parameter level has been added to the reindex and align methods of pandas objects. This is useful to broadcast values across a level. For instance:

In [697]: midx = MultiIndex(levels=[['zero', 'one'], ['x','y']],
   .....:                   labels=[[1,1,0,0],[1,0,1,0]])
   .....:

In [698]: df = DataFrame(randn(4,2), index=midx)

In [699]: print df
               0         1
one  y  0.307453 -0.906534
     x -1.505397  1.392009
zero y -0.027793 -0.631023
     x -0.662357  2.725042

In [700]: df2 = df.mean(level=0)

In [701]: print df2
             0         1
zero -0.345075  1.047010
one  -0.598972  0.242737

In [702]: print df2.reindex(df.index, level=0)
               0         1
one  y -0.598972  0.242737
     x -0.598972  0.242737
zero y -0.345075  1.047010
     x -0.345075  1.047010

In [703]: df_aligned, df2_aligned = df.align(df2, level=0)

In [704]: print df_aligned
               0         1
one  y  0.307453 -0.906534
     x -1.505397  1.392009
zero y -0.027793 -0.631023
     x -0.662357  2.725042

In [705]: print df2_aligned
               0         1
one  y -0.598972  0.242737
     x -0.598972  0.242737
zero y -0.345075  1.047010
     x -0.345075  1.047010

The need for sortedness

Caveat emptor: the present implementation of MultiIndex requires that the labels be sorted for some of the slicing / indexing routines to work correctly. You can think about breaking the axis into unique groups, where at the hierarchical level of interest, each distinct group shares a label, but no two have the same label. However, the MultiIndex does not enforce this: you are responsible for ensuring that things are properly sorted. There is an important new method sortlevel to sort an axis within a MultiIndex so that its labels are grouped and sorted by the original ordering of the associated factor at that level. Note that this does not necessarily mean the labels will be sorted lexicographically!

In [706]: import random; random.shuffle(tuples)

In [707]: s = Series(randn(8), index=MultiIndex.from_tuples(tuples))

In [708]: s
Out[708]: 
baz  two   -1.847240
bar  two   -0.529247
foo  one    0.614656
qux  two   -1.590742
     one   -0.156479
foo  two   -1.696377
baz  one    0.819712
bar  one   -2.107728

In [709]: s.sortlevel(0)
Out[709]: 
bar  one   -2.107728
     two   -0.529247
baz  one    0.819712
     two   -1.847240
foo  one    0.614656
     two   -1.696377
qux  one   -0.156479
     two   -1.590742

In [710]: s.sortlevel(1)
Out[710]: 
bar  one   -2.107728
baz  one    0.819712
foo  one    0.614656
qux  one   -0.156479
bar  two   -0.529247
baz  two   -1.847240
foo  two   -1.696377
qux  two   -1.590742

Note, you may also pass a level name to sortlevel if the MultiIndex levels are named.

In [711]: s.index.names = ['L1', 'L2']

In [712]: s.sortlevel(level='L1')
Out[712]: 
L1   L2 
bar  one   -2.107728
     two   -0.529247
baz  one    0.819712
     two   -1.847240
foo  one    0.614656
     two   -1.696377
qux  one   -0.156479
     two   -1.590742

In [713]: s.sortlevel(level='L2')
Out[713]: 
L1   L2 
bar  one   -2.107728
baz  one    0.819712
foo  one    0.614656
qux  one   -0.156479
bar  two   -0.529247
baz  two   -1.847240
foo  two   -1.696377
qux  two   -1.590742

Some indexing will work even if the data are not sorted, but will be rather inefficient and will also return a copy of the data rather than a view:

In [714]: s['qux']
Out[714]: 
L2
two   -1.590742
one   -0.156479

In [715]: s.sortlevel(1)['qux']
Out[715]: 
L2
one   -0.156479
two   -1.590742

On higher dimensional objects, you can sort any of the other axes by level if they have a MultiIndex:

In [716]: df.T.sortlevel(1, axis=1)
Out[716]: 
       zero       one      zero       one
          x         x         y         y
0 -0.662357 -1.505397 -0.027793  0.307453
1  2.725042  1.392009 -0.631023 -0.906534

The MultiIndex object has code to explicity check the sort depth. Thus, if you try to index at a depth at which the index is not sorted, it will raise an exception. Here is a concrete example to illustrate this:

In [717]: tuples = [('a', 'a'), ('a', 'b'), ('b', 'a'), ('b', 'b')]

In [718]: idx = MultiIndex.from_tuples(tuples)

In [719]: idx.lexsort_depth
Out[719]: 2

In [720]: reordered = idx[[1, 0, 3, 2]]

In [721]: reordered.lexsort_depth
Out[721]: 1

In [722]: s = Series(randn(4), index=reordered)

In [723]: s.ix['a':'a']
Out[723]: 
a  b   -0.488326
   a    0.851918

However:

>>> s.ix[('a', 'b'):('b', 'a')]
Exception: MultiIndex lexsort depth 1, key was length 2

Swapping levels with swaplevel

The swaplevel function can switch the order of two levels:

In [724]: df[:5]
Out[724]: 
               0         1
one  y  0.307453 -0.906534
     x -1.505397  1.392009
zero y -0.027793 -0.631023
     x -0.662357  2.725042

In [725]: df[:5].swaplevel(0, 1, axis=0)
Out[725]: 
               0         1
y one   0.307453 -0.906534
x one  -1.505397  1.392009
y zero -0.027793 -0.631023
x zero -0.662357  2.725042

Reordering levels with reorder_levels

The reorder_levels function generalizes the swaplevel function, allowing you to permute the hierarchical index levels in one step:

In [726]: df[:5].reorder_levels([1,0], axis=0)
Out[726]: 
               0         1
y one   0.307453 -0.906534
x one  -1.505397  1.392009
y zero -0.027793 -0.631023
x zero -0.662357  2.725042

Some gory internal details

Internally, the MultiIndex consists of a few things: the levels, the integer labels, and the level names:

In [727]: index
Out[727]: 
MultiIndex
[('bar', 'one') ('bar', 'two') ('baz', 'one') ('baz', 'two')
 ('foo', 'one') ('foo', 'two') ('qux', 'one') ('qux', 'two')]

In [728]: index.levels
Out[728]: [Index([bar, baz, foo, qux], dtype=object), Index([one, two], dtype=object)]

In [729]: index.labels
Out[729]: [array([0, 0, 1, 1, 2, 2, 3, 3]), array([0, 1, 0, 1, 0, 1, 0, 1])]

In [730]: index.names
Out[730]: ['first', 'second']

You can probably guess that the labels determine which unique element is identified with that location at each layer of the index. It’s important to note that sortedness is determined solely from the integer labels and does not check (or care) whether the levels themselves are sorted. Fortunately, the constructors from_tuples and from_arrays ensure that this is true, but if you compute the levels and labels yourself, please be careful.

Adding an index to an existing DataFrame

Occasionally you will load or create a data set into a DataFrame and want to add an index after you’ve already done so. There are a couple of different ways.

Add an index using DataFrame columns

DataFrame has a set_index method which takes a column name (for a regular Index) or a list of column names (for a MultiIndex), to create a new, indexed DataFrame:

In [731]: data
Out[731]: 
     a    b  c  d
0  bar  one  z  1
1  bar  two  y  2
2  foo  one  x  3
3  foo  two  w  4

In [732]: indexed1 = data.set_index('c')

In [733]: indexed1
Out[733]: 
     a    b  d
c             
z  bar  one  1
y  bar  two  2
x  foo  one  3
w  foo  two  4

In [734]: indexed2 = data.set_index(['a', 'b'])

In [735]: indexed2
Out[735]: 
         c  d
a   b        
bar one  z  1
    two  y  2
foo one  x  3
    two  w  4

The append keyword option allow you to keep the existing index and append the given columns to a MultiIndex:

In [736]: frame = data.set_index('c', drop=False)

In [737]: frame = frame.set_index(['a', 'b'], append=True)

In [738]: frame
Out[738]: 
           c  d
c a   b        
z bar one  z  1
y bar two  y  2
x foo one  x  3
w foo two  w  4

Other options in set_index allow you not drop the index columns or to add the index in-place (without creating a new object):

In [739]: data.set_index('c', drop=False)
Out[739]: 
     a    b  c  d
c                
z  bar  one  z  1
y  bar  two  y  2
x  foo  one  x  3
w  foo  two  w  4

In [740]: df = data.set_index(['a', 'b'], inplace=True)

In [741]: data
Out[741]: 
         c  d
a   b        
bar one  z  1
    two  y  2
foo one  x  3
    two  w  4

Remove / reset the index, reset_index

As a convenience, there is a new function on DataFrame called reset_index which transfers the index values into the DataFrame’s columns and sets a simple integer index. This is the inverse operation to set_index

In [742]: df
Out[742]: 
         c  d
a   b        
bar one  z  1
    two  y  2
foo one  x  3
    two  w  4

In [743]: df.reset_index()
Out[743]: 
     a    b  c  d
0  bar  one  z  1
1  bar  two  y  2
2  foo  one  x  3
3  foo  two  w  4

The output is more similar to a SQL table or a record array. The names for the columns derived from the index are the ones stored in the names attribute.

You can use the level keyword to remove only a portion of the index:

In [744]: frame
Out[744]: 
           c  d
c a   b        
z bar one  z  1
y bar two  y  2
x foo one  x  3
w foo two  w  4

In [745]: frame.reset_index(level=1)
Out[745]: 
         a  c  d
c b             
z one  bar  z  1
y two  bar  y  2
x one  foo  x  3
w two  foo  w  4

reset_index takes an optional parameter drop which if true simply discards the index, instead of putting index values in the DataFrame’s columns.

Note

The reset_index method used to be called delevel which is now deprecated.

Adding an ad hoc index

If you create an index yourself, you can just assign it to the index field:

df.index = index

Indexing internal details

Note

The following is largely relevant for those actually working on the pandas codebase. And the source code is still the best place to look at the specifics of how things are implemented.

In pandas there are a few objects implemented which can serve as valid containers for the axis labels:

  • Index: the generic “ordered set” object, an ndarray of object dtype assuming nothing about its contents. The labels must be hashable (and likely immutable) and unique. Populates a dict of label to location in Cython to do O(1) lookups.
  • Int64Index: a version of Index highly optimized for 64-bit integer data, such as time stamps
  • MultiIndex: the standard hierarchical index object
  • date_range: fixed frequency date range generated from a time rule or DateOffset. An ndarray of Python datetime objects

The motivation for having an Index class in the first place was to enable different implementations of indexing. This means that it’s possible for you, the user, to implement a custom Index subclass that may be better suited to a particular application than the ones provided in pandas. For example, we plan to add a more efficient datetime index which leverages the new numpy.datetime64 dtype in the relatively near future.

From an internal implementation point of view, the relevant methods that an Index must define are one or more of the following (depending on how incompatible the new object internals are with the Index functions):

  • get_loc: returns an “indexer” (an integer, or in some cases a slice object) for a label
  • slice_locs: returns the “range” to slice between two labels
  • get_indexer: Computes the indexing vector for reindexing / data alignment purposes. See the source / docstrings for more on this
  • reindex: Does any pre-conversion of the input index then calls get_indexer
  • union, intersection: computes the union or intersection of two Index objects
  • insert: Inserts a new label into an Index, yielding a new object
  • delete: Delete a label, yielding a new object
  • drop: Deletes a set of labels
  • take: Analogous to ndarray.take