.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import random
   np.random.seed(123456)
   from pandas import *
   import pandas as pd
   randn = np.random.randn
   randint = np.random.randint
   np.set_printoptions(precision=4, suppress=True)
   from pandas.compat import range, zip

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.

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.

In addition, whether a copy or a reference is returned for a selection operation, may depend on the context. See :ref:`Returning a View versus Copy <indexing.view_versus_copy>`

See the :ref:`cookbook<cookbook.selection>` for some advanced strategies

Choice

Starting in 0.11.0, 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 strictly label based, will raise KeyError when the items are not found, allowed inputs are:
- A single label, e.g. 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 list or array of labels ['a', 'b', 'c']
- A slice object with labels 'a':'f', (note that contrary to usual python slices, both the start and the stop are included!)
- A boolean array
See more at :ref:`Selection by Label <indexing.label>`
.iloc is strictly integer position based (from 0 to length-1 of the axis), will raise IndexError when the requested indicies are out of bounds. Allowed inputs are:
- An integer e.g. 5
- A list or array of integers [4, 3, 0]
- A slice object with ints 1:7
See more at :ref:`Selection by Position <indexing.integer>`
.ix supports mixed integer and label based access. It is primarily label based, but will fallback to integer positional access. .ix is the most general and will support any of the inputs to .loc and .iloc, as well as support for floating point label schemes. .ix is especially useful when dealing with mixed positional and label based hierarchial indexes.

As using integer slices with .ix have different behavior depending on whether the slice is interpreted as position based or label based, it's usually better to be explicit and use .iloc or .loc.

See more at :ref:`Advanced Indexing <indexing.advanced>`, :ref:`Advanced Hierarchical <indexing.advanced_hierarchical>` and :ref:`Fallback Indexing <indexing.fallback>`

Getting values from an object with multi-axes selection uses the following notation (using .loc as an example, but applies to .iloc and .ix 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 equiv 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]`

Deprecations

Beginning with version 0.11.0, it's recommended that you transition away from the following methods as they may be deprecated in future versions.

irow

icol

iget_value

See the section :ref:`Selection by Position <indexing.integer>` for substitutes.

Basics

As mentioned when introducing the data structures in the :ref:`last section <basics>`, 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,

Object Type	Selection	Return Value Type
Series	`series[label]`	scalar value
DataFrame	`frame[colname]`	`Series` corresponding to colname
Panel	`panel[itemname]`	`DataFrame` corresponing to the itemname

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

.. ipython:: python

   dates = date_range('1/1/2000', periods=8)
   df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
   df
   panel = Panel({'one' : df, 'two' : df - df.mean()})
   panel

Note

None of the indexing functionality is time series specific unless specifically stated.

Thus, as per above, we have the most basic indexing using []:

.. ipython:: python

   s = df['A']
   s[dates[5]]
   panel['two']

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:

.. ipython:: python

   df
   df[['B', 'A']] = df[['A', 'B']]
   df

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

Attribute Access

You may access a column on a DataFrame, and a item on a Panel directly as an attribute:

.. ipython:: python

   df.A
   panel.one

If you are using the IPython environment, you may also use tab-completion to see these accessable attributes.

Slicing ranges

The most robust and consistent way of slicing ranges along arbitrary axes is described in the :ref:`Selection by Position <indexing.integer>` section detailing the .iloc 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:

.. ipython:: python

   s[:5]
   s[::2]
   s[::-1]

Note that setting works as well:

.. ipython:: python

   s2 = s.copy()
   s2[:5] = 0
   s2

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

.. ipython:: python

   df[:3]
   df[::-1]

Selection By Label

Pandas provides a suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. ALL of the labels for which you ask, must be in the index or a KeyError will be raised! When slicing, the start bound is included, AND the stop bound is included. Integers are valid labels, but they refer to the label and not the position.

The .loc attribute is the primary access method. The following are valid inputs:

A single label, e.g. 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 list or array of labels ['a', 'b', 'c']
A slice object with labels 'a':'f' (note that contrary to usual python slices, both the start and the stop are included!)
A boolean array

.. ipython:: python

   s1 = Series(np.random.randn(6),index=list('abcdef'))
   s1
   s1.loc['c':]
   s1.loc['b']

Note that setting works as well:

.. ipython:: python

   s1.loc['c':] = 0
   s1

With a DataFrame

.. ipython:: python

   df1 = DataFrame(np.random.randn(6,4),
                   index=list('abcdef'),
                   columns=list('ABCD'))
   df1
   df1.loc[['a','b','d'],:]

Accessing via label slices

.. ipython:: python

   df1.loc['d':,'A':'C']

For getting a cross section using a label (equiv to df.xs('a'))

.. ipython:: python

   df1.loc['a']

For getting values with a boolean array

.. ipython:: python

   df1.loc['a']>0
   df1.loc[:,df1.loc['a']>0]

For getting a value explicity (equiv to deprecated df.get_value('a','A'))

.. ipython:: python

   # this is also equivalent to ``df1.at['a','A']``
   df1.loc['a','A']

Selection By Position

Pandas provides a suite of methods in order to get purely integer based indexing. The semantics follow closely python and numpy slicing. These are 0-based indexing. When slicing, the start bounds is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise a IndexError.

The .iloc attribute is the primary access method. The following are valid inputs:

An integer e.g. 5
A list or array of integers [4, 3, 0]
A slice object with ints 1:7

.. ipython:: python

   s1 = Series(np.random.randn(5),index=list(range(0,10,2)))
   s1
   s1.iloc[:3]
   s1.iloc[3]

Note that setting works as well:

.. ipython:: python

   s1.iloc[:3] = 0
   s1

With a DataFrame

.. ipython:: python

   df1 = DataFrame(np.random.randn(6,4),
                   index=list(range(0,12,2)),
                   columns=list(range(0,8,2)))
   df1

Select via integer slicing

.. ipython:: python

   df1.iloc[:3]
   df1.iloc[1:5,2:4]

Select via integer list

.. ipython:: python

   df1.iloc[[1,3,5],[1,3]]

For slicing rows explicitly (equiv to deprecated df.irow(slice(1,3))).

.. ipython:: python

   df1.iloc[1:3,:]

For slicing columns explicitly (equiv to deprecated df.icol(slice(1,3))).

.. ipython:: python

   df1.iloc[:,1:3]

For getting a scalar via integer position (equiv to deprecated df.get_value(1,1))

.. ipython:: python

   # this is also equivalent to ``df1.iat[1,1]``
   df1.iloc[1,1]

For getting a cross section using an integer position (equiv to df.xs(1))

.. ipython:: python

   df1.iloc[1]

There is one signficant departure from standard python/numpy slicing semantics. python/numpy allow slicing past the end of an array without an associated error.

.. ipython:: python

    # these are allowed in python/numpy.
    x = list('abcdef')
    x[4:10]
    x[8:10]

Pandas will detect this and raise IndexError, rather than return an empty structure.

>>> df.iloc[:,3:6]
IndexError: out-of-bounds on slice (end)

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 at and iat methods, which are implemented on all of the data structures.

Similary to loc, at provides label based scalar lookups, while, iat provides integer based lookups analagously to iloc

.. ipython:: python

   s.iat[5]
   df.at[dates[5], 'A']
   df.iat[3, 0]

You can also set using these same indexers. These have the additional capability of enlarging an object. This method always returns a reference to the object it modified, which in the case of enlargement, will be a new object:

.. ipython:: python

   df.at[dates[5], 'E'] = 7
   df.iat[3, 0] = 7

Boolean indexing

Another common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses.

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

.. ipython:: python

   s[s > 0]
   s[(s < 0) & (s > -0.5)]
   s[(s < -1) | (s > 1 )]
   s[~(s < 0)]

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):

.. ipython:: python

   df[df['A'] > 0]

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:

.. ipython:: python

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

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

.. ipython:: python

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

   df2[criterion]

   # equivalent but slower
   df2[[x.startswith('t') for x in df2['a']]]

   # Multiple criteria
   df2[criterion & (df2['b'] == 'x')]

Note, with the choice methods :ref:`Selection by Label <indexing.label>`, :ref:`Selection by Position <indexing.integer>`, and :ref:`Advanced Indexing <indexing.advanced>` you may select along more than one axis using boolean vectors combined with other indexing expressions.

.. ipython:: python

   df2.loc[criterion & (df2['b'] == 'x'),'b':'c']

DataFrame also has an isin method. When calling isin, pass a set of values as either an array or dict. If values is an array, isin returns a DataFrame of booleans that is the same shape as the original DataFrame, with True wherever the element is in the sequence of values.

.. ipython:: python

   df = DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
                'ids2': ['a', 'n', 'c', 'n']})

   values = ['a', 'b', 1, 3]

   df.isin(values)

Oftentimes you'll want to match certain values with certain columns. Just make values a dict where the key is the column, and the value is a list of items you want to check for.

.. ipython:: python

   values = {'ids': ['a', 'b'], 'vals': [1, 3]}

   df.isin(values)

You can also describe columns using integer location:

.. ipython:: python

   values = {0: ['a', 'b']}

   df.isin(values, iloc=True)

Where and Masking

Selecting values from a Series with a boolean vector generally returns a subset of the data. To guarantee that selection output has the same shape as the original data, you can use the where method in Series and DataFrame.

To return only the selected rows

.. ipython:: python

   s[s > 0]

To return a Series of the same shape as the original

.. ipython:: python

   s.where(s > 0)

Selecting values from a DataFrame with a boolean critierion now also preserves input data shape. where is used under the hood as the implementation. Equivalent is df.where(df < 0)

.. ipython:: python
   :suppress:

   dates = date_range('1/1/2000', periods=8)
   df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])

.. ipython:: python

   df[df < 0]

In addition, where takes an optional other argument for replacement of values where the condition is False, in the returned copy.

.. ipython:: python

   df.where(df < 0, -df)

You may wish to set values based on some boolean criteria. This can be done intuitively like so:

.. ipython:: python

   s2 = s.copy()
   s2[s2 < 0] = 0
   s2

   df2 = df.copy()
   df2[df2 < 0] = 0
   df2

Furthermore, where aligns the input boolean condition (ndarray or DataFrame), such that partial selection with setting is possible. This is analagous to partial setting via .ix (but on the contents rather than the axis labels)

.. ipython:: python

   df2 = df.copy()
   df2[ df2[1:4] > 0 ] = 3
   df2

By default, where returns a modified copy of the data. There is an optional parameter inplace so that the original data can be modified without creating a copy:

.. ipython:: python

   df_orig = df.copy()

   df_orig.where(df > 0, -df, inplace=True);

   df_orig

mask is the inverse boolean operation of where.

.. ipython:: python

   s.mask(s >= 0)

   df.mask(df >= 0)

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. take will also accept negative integers as relative positions to the end of the object.

.. ipython:: python

   index = Index(randint(0, 1000, 10))
   index

   positions = [0, 9, 3]

   index[positions]
   index.take(positions)

   ser = Series(randn(10))

   ser.ix[positions]
   ser.take(positions)

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

.. ipython:: python

   frm = DataFrame(randn(5, 3))

   frm.take([1, 4, 3])

   frm.take([0, 2], axis=1)

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.

.. ipython:: python

   arr = randn(10)
   arr.take([False, False, True, True])
   arr[[0, 1]]

   ser = Series(randn(10))
   ser.take([False, False, True, True])
   ser.ix[[0, 1]]

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.

.. ipython::

   arr = randn(10000, 5)
   indexer = np.arange(10000)
   random.shuffle(indexer)

   timeit arr[indexer]
   timeit arr.take(indexer, axis=0)

   ser = Series(arr[:, 0])
   timeit ser.ix[indexer]
   timeit ser.take(indexer)

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.

.. ipython:: python

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

Dictionary-like `get` method

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

.. ipython:: python

   s = Series([1,2,3], index=['a','b','c'])
   s.get('a')               # equivalent to s['a']
   s.get('x', default=-1)

Advanced Indexing with `.ix`

Note

The recent addition of .loc and .iloc have enabled users to be quite explicit about indexing choices. .ix allows a great flexibility to specify indexing locations by label and/or integer position. Pandas will attempt to use any passed integer as label locations first (like what .loc would do, then to fall back on positional indexing, like what .iloc would do). See :ref:`Fallback Indexing <indexing.fallback>` for an example.

The syntax of using .ix is identical to .loc, in :ref:`Selection by Label <indexing.label>`, and .iloc in :ref:`Selection by Position <indexing.integer>`.

The .ix attribute takes the following inputs:

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:

.. ipython:: python

   subindex = dates[[3,4,5]]
   df.reindex(index=subindex, columns=['C', 'B'])
   df.ix[subindex, ['C', 'B']]

Assignment / setting values is possible when using ix:

.. ipython:: python

   df2 = df.copy()
   df2.ix[subindex, ['C', 'B']] = 0
   df2

Indexing with an array of integers can also be done:

.. ipython:: python

   df.ix[[4,3,1]]
   df.ix[dates[[4,3,1]]]

Slicing has standard Python semantics for integer slices:

.. ipython:: python

   df.ix[1:7, :2]

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

.. ipython:: python

   date1, date2 = dates[[2, 4]]
   print date1, date2
   df.ix[date1:date2]
   df['A'].ix[date1:date2]

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

.. ipython:: python

   df2 = df[:5].copy()
   df2.ix[3]
   df2.ix[3] = np.arange(len(df2.columns))
   df2

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

.. ipython:: python

   df.ix[df['A'] > 0, 'B']
   df.ix[date1:date2, 'B']
   df.ix[date1, 'B']

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

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:

.. ipython:: python

   df.select(lambda x: x == 'A', axis=1)

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,

.. ipython:: python

  dflookup = DataFrame(np.random.rand(20,4), columns = ['A','B','C','D'])
  dflookup.lookup(list(range(0,10,2)), ['B','C','A','B','D'])

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:

.. ipython:: python

   df2 = df[:4]
   df2['foo'] = 'bar'
   print df2
   df2.ix[2] = np.nan
   print df2
   print df2.dtypes

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.

In chained expressions, the order may determine whether a copy is returned or not:

.. ipython:: python


   dfb = DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
                    'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
                    'c' : randn(7)})
   dfb[dfb.a.str.startswith('o')]['c'] = 42  # goes to copy (will be lost)
   dfb['c'][dfb.a.str.startswith('o')] = 42  # passed via reference (will stay)

When assigning values to subsets of your data, thus, make sure to either use the pandas access methods or explicitly handle the assignment creating a copy.

Fallback indexing

Float indexes should be used only with caution. If you have a float indexed DataFrame and try to select using an integer, the row that Pandas returns might not be what you expect. Pandas first attempts to use the integer as a label location, but fails to find a match (because the types are not equal). Pandas then falls back to back to positional indexing.

.. ipython:: python

    df = pd.DataFrame(np.random.randn(4,4),
        columns=list('ABCD'), index=[1.0, 2.0, 3.0, 4.0])
    df
    df.ix[1]

To select the row you do expect, instead use a float label or use iloc.

.. ipython:: python

    df.ix[1.0]
    df.iloc[0]

Instead of using a float index, it is often better to convert to an integer index:

.. ipython:: python

    df_new = df.reset_index()
    df_new[df_new['index'] == 1.0]
    # now you can also do "float selection"
    df_new[(df_new['index'] >= 1.0) & (df_new['index'] < 2)]

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:

.. ipython:: python

   index = Index(['e', 'd', 'a', 'b'])
   index
   'd' in index

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

.. ipython:: python

   index = Index(['e', 'd', 'a', 'b'], name='something')
   index.name

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

.. ipython:: python

   index = Index(list(range(5)), name='rows')
   columns = Index(['A', 'B', 'C'], name='cols')
   df = DataFrame(np.random.randn(5, 3), index=index, columns=columns)
   df
   df['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:

.. ipython:: python

   a = Index(['c', 'b', 'a'])
   b = Index(['c', 'e', 'd'])
   a.union(b)
   a | b
   a & b
   a - b

`isin` method of Index objects

One additional operation is the isin method that works analogously to the Series.isin method found :ref:`here <indexing.boolean>`.

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 :ref:`group by <groupby>` and :ref:`pivoting and reshaping data <reshaping>`, we'll show non-trivial applications to illustrate how it aids in structuring data for analysis.

See the :ref:`cookbook<cookbook.multi_index>` for some advanced strategies

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).

.. ipython:: python

   arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
             ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
   tuples = list(zip(*arrays))
   tuples
   index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
   s = Series(randn(8), index=index)
   s

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

.. ipython:: python

   arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'])
   ,
             np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])
             ]
   s = Series(randn(8), index=arrays)
   s
   df = DataFrame(randn(8, 4), index=arrays)
   df

All of the MultiIndex constructors accept a names argument which stores string names for the levels themselves. If no names are provided, None will be assigned:

.. ipython:: python

   df.index.names

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

.. ipython:: python

   df = DataFrame(randn(3, 8), index=['A', 'B', 'C'], columns=index)
   df
   DataFrame(randn(6, 6), index=index[:6], columns=index[:6])

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:

.. ipython:: python

   Series(randn(8), index=tuples)

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:

.. ipython:: python

   pd.set_option('display.multi_sparse', False)
   df
   pd.set_option('display.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:

.. ipython:: python

   index.get_level_values(0)
   index.get_level_values('second')

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:

.. ipython:: python

   df['bar']
   df['bar', 'one']
   df['bar']['one']
   s['qux']

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:

.. ipython:: python

   s + s[:-2]
   s + s[::2]

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

.. ipython:: python

   s.reindex(index[:3])
   s.reindex([('foo', 'two'), ('bar', 'one'), ('qux', 'one'), ('baz', 'one')])

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:

.. ipython:: python

   df = df.T
   df
   df.ix['bar']
   df.ix['bar', 'two']

"Partial" slicing also works quite nicely:

.. ipython:: python

   df.ix['baz':'foo']
   df.ix[('baz', 'two'):('qux', 'one')]
   df.ix[('baz', 'two'):'foo']

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

.. ipython:: python

   df.ix[[('bar', 'two'), ('qux', 'one')]]

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.

.. ipython:: python

    df.xs('one', level='second')

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:

.. ipython:: python

   midx = MultiIndex(levels=[['zero', 'one'], ['x','y']],
                     labels=[[1,1,0,0],[1,0,1,0]])
   df = DataFrame(randn(4,2), index=midx)
   print df
   df2 = df.mean(level=0)
   print df2
   print df2.reindex(df.index, level=0)
   df_aligned, df2_aligned = df.align(df2, level=0)
   print df_aligned
   print df2_aligned

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!

.. ipython:: python

   import random; random.shuffle(tuples)
   s = Series(randn(8), index=MultiIndex.from_tuples(tuples))
   s
   s.sortlevel(0)
   s.sortlevel(1)

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

.. ipython:: python

   s.index.names = ['L1', 'L2']
   s.sortlevel(level='L1')
   s.sortlevel(level='L2')

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:

.. ipython:: python

   s['qux']
   s.sortlevel(1)['qux']

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

.. ipython:: python

   df.T.sortlevel(1, axis=1)

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:

.. ipython:: python

   tuples = [('a', 'a'), ('a', 'b'), ('b', 'a'), ('b', 'b')]
   idx = MultiIndex.from_tuples(tuples)
   idx.lexsort_depth

   reordered = idx[[1, 0, 3, 2]]
   reordered.lexsort_depth

   s = Series(randn(4), index=reordered)
   s.ix['a':'a']

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:

.. ipython:: python

   df[:5]
   df[:5].swaplevel(0, 1, axis=0)

Reordering levels with `reorder_levels`

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

.. ipython:: python

   df[:5].reorder_levels([1,0], axis=0)

Some gory internal details

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

.. ipython:: python

   index
   index.levels
   index.labels
   index.names

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:

.. ipython:: python
   :suppress:

   data = DataFrame({'a' : ['bar', 'bar', 'foo', 'foo'],
                     'b' : ['one', 'two', 'one', 'two'],
                     'c' : ['z', 'y', 'x', 'w'],
                     'd' : [1., 2., 3, 4]})

.. ipython:: python

   data
   indexed1 = data.set_index('c')
   indexed1
   indexed2 = data.set_index(['a', 'b'])
   indexed2

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

.. ipython:: python

   frame = data.set_index('c', drop=False)
   frame = frame.set_index(['a', 'b'], append=True)
   frame

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

.. ipython:: python

   data.set_index('c', drop=False)
   data.set_index(['a', 'b'], inplace=True)
   data

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

.. ipython:: python

   data
   data.reset_index()

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:

.. ipython:: python

   frame
   frame.reset_index(level=1)

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:

data.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.

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

get_indexer_non_unique: Computes the indexing vector for reindexing / data alignment purposes when the index is non-unique. 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

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Indexing and Selecting Data

Choice

Deprecations

Basics

Attribute Access

Slicing ranges

Selection By Label

Selection By Position

Fast scalar value getting and setting

Boolean indexing

Where and Masking

Take Methods

Duplicate Data

Dictionary-like get method

Advanced Indexing with .ix

The select method

The lookup method

Setting values in mixed-type DataFrame

Returning a view versus a copy

Fallback indexing

Index objects

Set operations on Index objects

isin method of Index objects

Hierarchical indexing (MultiIndex)

Creating a MultiIndex (hierarchical index) object

Reconstructing the level labels

Basic indexing on axis with MultiIndex

Data alignment and using reindex

Advanced indexing with hierarchical index

Cross-section with hierarchical index

Advanced reindexing and alignment with hierarchical index

The need for sortedness

Swapping levels with swaplevel

Reordering levels with reorder_levels

Some gory internal details

Adding an index to an existing DataFrame

Add an index using DataFrame columns

Remove / reset the index, reset_index

Adding an ad hoc index

Indexing internal details

Dictionary-like `get` method

Advanced Indexing with `.ix`

The `select` method

The `lookup` method

`isin` method of Index objects

Data alignment and using `reindex`

Swapping levels with `swaplevel`

Reordering levels with `reorder_levels`

Remove / reset the index, `reset_index`