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.. currentmodule:: pandas


.. ipython:: python
   :suppress:

   import numpy as np
   import random
   np.random.seed(123456)
   from pandas import *
   options.display.max_rows=15
   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 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, 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.

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 :ref:`Returning a View versus Copy <indexing.view_versus_copy>`

Warning

In 0.15.0 Index has internally been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This should be a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`)

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

Different Choices for Indexing (loc, iloc, and ix)

.. versionadded:: 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 if an indexer is requested and it is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with python/numpy slice semantics). 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 fall back 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 hierarchical 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 an index on a Series, column on a DataFrame, and a item on a Panel directly as an attribute:

.. ipython:: python

   sa = Series([1,2,3],index=list('abc'))
   dfa = df.copy()


.. ipython:: python

   sa.b
   dfa.A
   panel.one

You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it fails silently, creating a new attribute rather than a new column.

.. ipython:: python

   sa.a = 5
   sa
   dfa.A = list(range(len(dfa.index)))       # ok if A already exists
   dfa
   dfa['A'] = list(range(len(dfa.index)))    # use this form to create a new column�
   dfa

Warning

  • You can use this access only if the index element is a valid python identifier, e.g. s.1 is not allowed. see here for an explanation of valid identifiers.
  • The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed.
  • The Series/Panel accesses are available starting in 0.13.0.

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

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 :ref:`Returning a View versus Copy <indexing.view_versus_copy>`

pandas provides a suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. at least 1 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 explicitly (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

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 :ref:`Returning a View versus Copy <indexing.view_versus_copy>`

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]

Out of range slice indexes are handled gracefully just as in Python/Numpy.

.. ipython:: python

    # these are allowed in python/numpy.
    # Only works in Pandas starting from v0.14.0.
    x = list('abcdef')
    x
    x[4:10]
    x[8:10]
    s = Series(x)
    s
    s.iloc[4:10]
    s.iloc[8:10]

Note

Prior to v0.14.0, iloc would not accept out of bounds indexers for slices, e.g. a value that exceeds the length of the object being indexed.

Note that this could result in an empty axis (e.g. an empty DataFrame being returned)

.. ipython:: python

   dfl = DataFrame(np.random.randn(5,2),columns=list('AB'))
   dfl
   dfl.iloc[:,2:3]
   dfl.iloc[:,1:3]
   dfl.iloc[4:6]

A single indexer that is out of bounds will raise an IndexError. A list of indexers where any element is out of bounds will raise an IndexError

dfl.iloc[[4,5,6]]
IndexError: positional indexers are out-of-bounds

dfl.iloc[:,4]
IndexError: single positional indexer is out-of-bounds

Setting With Enlargement

.. versionadded:: 0.13

The .loc/.ix/[] operations can perform enlargement when setting a non-existant key for that axis.

In the Series case this is effectively an appending operation

.. ipython:: python

   se = Series([1,2,3])
   se
   se[5] = 5.
   se

A DataFrame can be enlarged on either axis via .loc

.. ipython:: python

   dfi = DataFrame(np.arange(6).reshape(3,2),
                   columns=['A','B'])
   dfi
   dfi.loc[:,'C'] = dfi.loc[:,'A']
   dfi

This is like an append operation on the DataFrame.

.. ipython:: python

   dfi.loc[3] = 5
   dfi

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.

Similarly to loc, at provides label based scalar lookups, while, iat provides integer based lookups analogously to iloc

.. ipython:: python

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

You can also set using these same indexers.

.. ipython:: python

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

at may enlarge the object in-place as above if the indexer is missing.

.. ipython:: python

   df.at[dates[-1]+1, 0] = 7
   df

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]

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

.. ipython:: python

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

   # 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']

Indexing with isin

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

   s = Series(np.arange(5),index=np.arange(5)[::-1],dtype='int64')
   s
   s.isin([2, 4, 6])
   s[s.isin([2, 4, 6])]

The same method is available for Index objects and is useful for the cases when you don't know which of the sought labels are in fact present:

.. ipython:: python

   s[s.index.isin([2, 4, 6])]

   # compare it to the following
   s[[2, 4, 6]]

In addition to that, MultiIndex allows selecting a separate level to use in the membership check:

.. ipython:: python

   s_mi = Series(np.arange(6),
                 index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]))
   s_mi
   s_mi.iloc[s_mi.index.isin([(1, 'a'), (2, 'b'), (0, 'c')])]
   s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)]

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)

Combine DataFrame's isin with the any() and all() methods to quickly select subsets of your data that meet a given criteria. To select a row where each column meets its own criterion:

.. ipython:: python

  values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}

  row_mask = df.isin(values).all(1)

  df[row_mask]

The :meth:`~pandas.DataFrame.where` Method 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 criterion 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

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

alignment

Furthermore, where aligns the input boolean condition (ndarray or DataFrame), such that partial selection with setting is possible. This is analogous 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


.. versionadded:: 0.13

Where can also accept axis and level parameters to align the input when performing the where.

.. ipython:: python

   df2 = df.copy()
   df2.where(df2>0,df2['A'],axis='index')

This is equivalent (but faster than) the following.

.. ipython:: python

   df2 = df.copy()
   df.apply(lambda x, y: x.where(x>0,y), y=df['A'])

mask

mask is the inverse boolean operation of where.

.. ipython:: python

   s.mask(s >= 0)
   df.mask(df >= 0)

The :meth:`~pandas.DataFrame.query` Method (Experimental)

.. versionadded:: 0.13

:class:`~pandas.DataFrame` objects have a :meth:`~pandas.DataFrame.query` method that allows selection using an expression.

You can get the value of the frame where column b has values between the values of columns a and c. For example:

.. ipython:: python
   :suppress:

   from numpy.random import randint, rand
   np.random.seed(1234)


.. ipython:: python

   n = 10
   df = DataFrame(rand(n, 3), columns=list('abc'))
   df

   # pure python
   df[(df.a < df.b) & (df.b < df.c)]

   # query
   df.query('(a < b) & (b < c)')

Do the same thing but fall back on a named index if there is no column with the name a.

.. ipython:: python

   df = DataFrame(randint(n / 2, size=(n, 2)), columns=list('bc'))
   df.index.name = 'a'
   df
   df.query('a < b and b < c')

If instead you don't want to or cannot name your index, you can use the name index in your query expression:

.. ipython:: python
   :suppress:

   old_index = index
   del index


.. ipython:: python

   df = DataFrame(randint(n, size=(n, 2)), columns=list('bc'))
   df
   df.query('index < b < c')


.. ipython:: python
   :suppress:

   index = old_index
   del old_index

Note

If the name of your index overlaps with a column name, the column name is given precedence. For example,

.. ipython:: python

   df = DataFrame({'a': randint(5, size=5)})
   df.index.name = 'a'
   df.query('a > 2') # uses the column 'a', not the index

You can still use the index in a query expression by using the special identifier 'index':

.. ipython:: python

   df.query('index > 2')

If for some reason you have a column named index, then you can refer to the index as ilevel_0 as well, but at this point you should consider renaming your columns to something less ambiguous.

:class:`~pandas.MultiIndex` :meth:`~pandas.DataFrame.query` Syntax

You can also use the levels of a DataFrame with a :class:`~pandas.MultiIndex` as if they were columns in the frame:

.. ipython:: python

   import pandas.util.testing as tm

   n = 10
   colors = tm.choice(['red', 'green'], size=n)
   foods = tm.choice(['eggs', 'ham'], size=n)
   colors
   foods

   index = MultiIndex.from_arrays([colors, foods], names=['color', 'food'])
   df = DataFrame(randn(n, 2), index=index)
   df
   df.query('color == "red"')

If the levels of the MultiIndex are unnamed, you can refer to them using special names:

.. ipython:: python

   df.index.names = [None, None]
   df
   df.query('ilevel_0 == "red"')

The convention is ilevel_0, which means "index level 0" for the 0th level of the index.

:meth:`~pandas.DataFrame.query` Use Cases

A use case for :meth:`~pandas.DataFrame.query` is when you have a collection of :class:`~pandas.DataFrame` objects that have a subset of column names (or index levels/names) in common. You can pass the same query to both frames without having to specify which frame you're interested in querying

.. ipython:: python

   df = DataFrame(rand(n, 3), columns=list('abc'))
   df
   df2 = DataFrame(rand(n + 2, 3), columns=df.columns)
   df2
   expr = '0.0 <= a <= c <= 0.5'
   map(lambda frame: frame.query(expr), [df, df2])

:meth:`~pandas.DataFrame.query` Python versus pandas Syntax Comparison

Full numpy-like syntax

.. ipython:: python

   df = DataFrame(randint(n, size=(n, 3)), columns=list('abc'))
   df
   df.query('(a < b) & (b < c)')
   df[(df.a < df.b) & (df.b < df.c)]

Slightly nicer by removing the parentheses (by binding making comparison operators bind tighter than &/|)

.. ipython:: python

   df.query('a < b & b < c')

Use English instead of symbols

.. ipython:: python

   df.query('a < b and b < c')

Pretty close to how you might write it on paper

.. ipython:: python

   df.query('a < b < c')

The in and not in operators

:meth:`~pandas.DataFrame.query` also supports special use of Python's in and not in comparison operators, providing a succinct syntax for calling the isin method of a Series or DataFrame.

.. ipython:: python
   :suppress:

   try:
       old_d = d
       del d
   except NameError:
       pass


.. ipython:: python

   # get all rows where columns "a" and "b" have overlapping values
   df = DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),
                   'c': randint(5, size=12), 'd': randint(9, size=12)})
   df
   df.query('a in b')

   # How you'd do it in pure Python
   df[df.a.isin(df.b)]

   df.query('a not in b')

   # pure Python
   df[~df.a.isin(df.b)]

You can combine this with other expressions for very succinct queries:

.. ipython:: python

   # rows where cols a and b have overlapping values and col c's values are less than col d's
   df.query('a in b and c < d')

   # pure Python
   df[df.b.isin(df.a) & (df.c < df.d)]

Note

Note that in and not in are evaluated in Python, since numexpr has no equivalent of this operation. However, only the in/not in expression itself is evaluated in vanilla Python. For example, in the expression

df.query('a in b + c + d')

(b + c + d) is evaluated by numexpr and then the in operation is evaluated in plain Python. In general, any operations that can be evaluated using numexpr will be.

Special use of the == operator with list objects

Comparing a list of values to a column using ==/!= works similarly to in/not in

.. ipython:: python

   df.query('b == ["a", "b", "c"]')

   # pure Python
   df[df.b.isin(["a", "b", "c"])]

   df.query('c == [1, 2]')

   df.query('c != [1, 2]')

   # using in/not in
   df.query('[1, 2] in c')

   df.query('[1, 2] not in c')

   # pure Python
   df[df.c.isin([1, 2])]

Boolean Operators

You can negate boolean expressions with the word not or the ~ operator.

.. ipython:: python

   df = DataFrame(rand(n, 3), columns=list('abc'))
   df['bools'] = rand(len(df)) > 0.5
   df.query('~bools')
   df.query('not bools')
   df.query('not bools') == df[~df.bools]

Of course, expressions can be arbitrarily complex too

.. ipython:: python

   # short query syntax
   shorter = df.query('a < b < c and (not bools) or bools > 2')

   # equivalent in pure Python
   longer = df[(df.a < df.b) & (df.b < df.c) & (~df.bools) | (df.bools > 2)]

   shorter
   longer

   shorter == longer


.. ipython:: python
   :suppress:

   try:
       d = old_d
       del old_d
   except NameError:
       pass

Performance of :meth:`~pandas.DataFrame.query`

DataFrame.query() using numexpr is slightly faster than Python for large frames

_static/query-perf.png

Note

You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200,000 rows

_static/query-perf-small.png

This plot was created using a DataFrame with 3 columns each containing floating point values generated using numpy.random.randn().

.. ipython:: python
   :suppress:

   df = DataFrame(randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
   df2 = df.copy()

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 :meth:`~pandas.DataFrame.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 :meth:`~pandas.DataFrame.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 :meth:`~pandas.DataFrame.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'])

Float64Index

Note

As of 0.14.0, Float64Index is backed by a native float64 dtype array. Prior to 0.14.0, Float64Index was backed by an object dtype array. Using a float64 dtype in the backend speeds up arithmetic operations by about 30x and boolean indexing operations on the Float64Index itself are about 2x as fast.

.. versionadded:: 0.13.0

By default a Float64Index will be automatically created when passing floating, or mixed-integer-floating values in index creation. This enables a pure label-based slicing paradigm that makes [],ix,loc for scalar indexing and slicing work exactly the same.

.. ipython:: python

   indexf = Index([1.5, 2, 3, 4.5, 5])
   indexf
   sf = Series(range(5),index=indexf)
   sf

Scalar selection for [],.ix,.loc will always be label based. An integer will match an equal float index (e.g. 3 is equivalent to 3.0)

.. ipython:: python

   sf[3]
   sf[3.0]
   sf.ix[3]
   sf.ix[3.0]
   sf.loc[3]
   sf.loc[3.0]

The only positional indexing is via iloc

.. ipython:: python

   sf.iloc[3]

A scalar index that is not found will raise KeyError

Slicing is ALWAYS on the values of the index, for [],ix,loc and ALWAYS positional with iloc

.. ipython:: python

   sf[2:4]
   sf.ix[2:4]
   sf.loc[2:4]
   sf.iloc[2:4]

In float indexes, slicing using floats is allowed

.. ipython:: python

   sf[2.1:4.6]
   sf.loc[2.1:4.6]

In non-float indexes, slicing using floats will raise a TypeError

In [1]: Series(range(5))[3.5]
TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index)

In [1]: Series(range(5))[3.5:4.5]
TypeError: the slice start [3.5] is not a proper indexer for this index type (Int64Index)

Using a scalar float indexer will be deprecated in a future version, but is allowed for now.

In [3]: Series(range(5))[3.0]
Out[3]: 3

Here is a typical use-case for using this type of indexing. Imagine that you have a somewhat irregular timedelta-like indexing scheme, but the data is recorded as floats. This could for example be millisecond offsets.

.. ipython:: python

   dfir = concat([DataFrame(randn(5,2),
                     index=np.arange(5) * 250.0,
                     columns=list('AB')),
                  DataFrame(randn(6,2),
                     index=np.arange(4,10) * 250.1,
                     columns=list('AB'))])
   dfir

Selection operations then will always work on a value basis, for all selection operators.

.. ipython:: python

   dfir[0:1000.4]
   dfir.loc[0:1001,'A']
   dfir.loc[1000.4]

You could then easily pick out the first 1 second (1000 ms) of data then.

.. ipython:: python

   dfir[0:1000]

Of course if you need integer based selection, then use iloc

.. ipython:: python

   dfir.iloc[0:5]

Returning a view versus a copy

When setting values in a pandas object, care must be taken to avoid what is called chained indexing. Here is an example.

.. ipython:: python

   dfmi = DataFrame([list('abcd'),
                     list('efgh'),
                     list('ijkl'),
                     list('mnop')],
                    columns=MultiIndex.from_product([['one','two'],
                                                     ['first','second']]))
   dfmi

Compare these two access methods:

.. ipython:: python

   dfmi['one']['second']


.. ipython:: python

   dfmi.loc[:,('one','second')]

These both yield the same results, so which should you use? It is instructive to understand the order of operations on these and why method 2 (.loc) is much preferred over method 1 (chained [])

dfmi['one'] selects the first level of the columns and returns a data frame that is singly-indexed. Then another python operation dfmi_with_one['second'] selects the series indexed by 'second' happens. This is indicated by the variable dfmi_with_one because pandas sees these operations as separate events. e.g. separate calls to __getitem__, so it has to treat them as linear operations, they happen one after another.

Contrast this to df.loc[:,('one','second')] which passes a nested tuple of (slice(None),('one','second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes if so desired.

Why does the assignment when using chained indexing fail!

So, why does this show the SettingWithCopy warning / and possibly not work when you do chained indexing and assignment:

dfmi['one']['second'] = value

Since the chained indexing is 2 calls, it is possible that either call may return a copy of the data because of the way it is sliced. Thus when setting, you are actually setting a copy, and not the original frame data. It is impossible for pandas to figure this out because their are 2 separate python operations that are not connected.

The SettingWithCopy warning is a 'heuristic' to detect this (meaning it tends to catch most cases but is simply a lightweight check). Figuring this out for real is way complicated.

The .loc operation is a single python operation, and thus can select a slice (which still may be a copy), but allows pandas to assign that slice back into the frame after it is modified, thus setting the values as you would think.

The reason for having the SettingWithCopy warning is this. Sometimes when you slice an array you will simply get a view back, which means you can set it no problem. However, even a single dtyped array can generate a copy if it is sliced in a particular way. A multi-dtyped DataFrame (meaning it has say float and object data), will almost always yield a copy. Whether a view is created is dependent on the memory layout of the array.

Evaluation order matters

Furthermore, in chained expressions, the order may determine whether a copy is returned or not. If an expression will set values on a copy of a slice, then a SettingWithCopy exception will be raised (this raise/warn behavior is new starting in 0.13.0)

You can control the action of a chained assignment via the option mode.chained_assignment, which can take the values ['raise','warn',None], where showing a warning is the default.

.. ipython:: python
   :okwarning:

   dfb = DataFrame({'a' : ['one', 'one', 'two',
                           'three', 'two', 'one', 'six'],
                    'c' : np.arange(7)})

   # This will show the SettingWithCopyWarning
   # but the frame values will be set
   dfb['c'][dfb.a.str.startswith('o')] = 42

This however is operating on a copy and will not work.

>>> pd.set_option('mode.chained_assignment','warn')
>>> dfb[dfb.a.str.startswith('o')]['c'] = 42
Traceback (most recent call last)
     ...
SettingWithCopyWarning:
     A value is trying to be set on a copy of a slice from a DataFrame.
     Try using .loc[row_index,col_indexer] = value instead

A chained assignment can also crop up in setting in a mixed dtype frame.

Note

These setting rules apply to all of .loc/.iloc/.ix

This is the correct access method

.. ipython:: python

   dfc = DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})
   dfc.loc[0,'A'] = 11
   dfc

This can work at times, but is not guaranteed, and so should be avoided

.. ipython:: python

   dfc = dfc.copy()
   dfc['A'][0] = 111
   dfc

This will not work at all, and so should be avoided

>>> pd.set_option('mode.chained_assignment','raise')
>>> dfc.loc[0]['A'] = 1111
Traceback (most recent call last)
     ...
SettingWithCopyException:
     A value is trying to be set on a copy of a slice from a DataFrame.
     Try using .loc[row_index,col_indexer] = value instead

Warning

The chained assignment warnings / exceptions are aiming to inform the user of a possibly invalid assignment. There may be false positives; situations where a chained assignment is inadvertantly reported.

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 :class:`~pandas.Index` class and its subclasses can be viewed as implementing an ordered multiset. Duplicates are allowed. However, if you try to convert an :class:`~pandas.Index` object with duplicate entries into a set, an exception will be raised.

:class:`~pandas.Index` also provides the infrastructure necessary for lookups, data alignment, and reindexing. The easiest way to create an :class:`~pandas.Index` directly is to pass a list or other sequence to :class:`~pandas.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

Also available is the sym_diff (^) operation, which returns elements that appear in either idx1 or idx2 but not both. This is equivalent to the Index created by (idx1 - idx2) + (idx2 - idx1), with duplicates dropped.

.. ipython:: python

   idx1 = Index([1, 2, 3, 4])
   idx2 = Index([2, 3, 4, 5])
   idx1.sym_diff(idx2)
   idx1 ^ idx2

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

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), an array of tuples (using MultiIndex.from_tuples), or a crossed set of iterables (using MultiIndex.from_product). The Index constructor will attempt to return a MultiIndex when it is passed a list of tuples. The following examples demo different ways to initialize MultiIndexes.

.. 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'])
   index

   s = Series(randn(8), index=index)
   s

When you want every pairing of the elements in two iterables, it can be easier to use the MultiIndex.from_product function:

.. ipython:: python

   iterables = [['bar', 'baz', 'foo', 'qux'], ['one', 'two']]
   MultiIndex.from_product(iterables, names=['first', 'second'])

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']

See :ref:`Cross-section with hierarchical index <indexing.xs>` for how to select on a deeper level.

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 .loc/.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.loc['bar']
   df.loc['bar', 'two']

"Partial" slicing also works quite nicely.

.. ipython:: python

   df.loc['baz':'foo']

You can slice with a 'range' of values, by providing a slice of tuples.

.. ipython:: python

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

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

.. ipython:: python

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

Multiindexing using slicers

.. versionadded:: 0.14.0

In 0.14.0 we added a new way to slice multi-indexed objects. You can slice a multi-index by providing multiple indexers.

You can provide any of the selectors as if you are indexing by label, see :ref:`Selection by Label <indexing.label>`, including slices, lists of labels, labels, and boolean indexers.

You can use slice(None) to select all the contents of that level. You do not need to specify all the deeper levels, they will be implied as slice(None).

As usual, both sides of the slicers are included as this is label indexing.

Warning

You should specify all axes in the .loc specifier, meaning the indexer for the index and for the columns. Their are some ambiguous cases where the passed indexer could be mis-interpreted as indexing both axes, rather than into say the MuliIndex for the rows.

You should do this:

df.loc[(slice('A1','A3'),.....),:]

rather than this:

df.loc[(slice('A1','A3'),.....)]

Warning

You will need to make sure that the selection axes are fully lexsorted!

.. ipython:: python

   def mklbl(prefix,n):
       return ["%s%s" % (prefix,i)  for i in range(n)]

   miindex = MultiIndex.from_product([mklbl('A',4),
                                      mklbl('B',2),
                                      mklbl('C',4),
                                      mklbl('D',2)])
   micolumns = MultiIndex.from_tuples([('a','foo'),('a','bar'),
                                       ('b','foo'),('b','bah')],
                                        names=['lvl0', 'lvl1'])
   dfmi = DataFrame(np.arange(len(miindex)*len(micolumns)).reshape((len(miindex),len(micolumns))),
                    index=miindex,
                    columns=micolumns).sortlevel().sortlevel(axis=1)
   dfmi

Basic multi-index slicing using slices, lists, and labels.

.. ipython:: python

   dfmi.loc[(slice('A1','A3'),slice(None), ['C1','C3']),:]

You can use a pd.IndexSlice to shortcut the creation of these slices

.. ipython:: python

   idx = pd.IndexSlice
   dfmi.loc[idx[:,:,['C1','C3']],idx[:,'foo']]

It is possible to perform quite complicated selections using this method on multiple axes at the same time.

.. ipython:: python

   dfmi.loc['A1',(slice(None),'foo')]
   dfmi.loc[idx[:,:,['C1','C3']],idx[:,'foo']]

Using a boolean indexer you can provide selection related to the values.

.. ipython:: python

   mask = dfmi[('a','foo')]>200
   dfmi.loc[idx[mask,:,['C1','C3']],idx[:,'foo']]

You can also specify the axis argument to .loc to interpret the passed slicers on a single axis.

.. ipython:: python

   dfmi.loc(axis=0)[:,:,['C1','C3']]

Furthermore you can set the values using these methods

.. ipython:: python

   df2 = dfmi.copy()
   df2.loc(axis=0)[:,:,['C1','C3']] = -10
   df2

You can use a right-hand-side of an alignable object as well.

.. ipython:: python

   df2 = dfmi.copy()
   df2.loc[idx[:,:,['C1','C3']],:] = df2*1000
   df2

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')


.. ipython:: python

   # using the slicers (new in 0.14.0)
   df.loc[(slice(None),'one'),:]

You can also select on the columns with :meth:`~pandas.MultiIndex.xs`, by providing the axis argument

.. ipython:: python

   df = df.T
   df.xs('one', level='second', axis=1)


.. ipython:: python

   # using the slicers (new in 0.14.0)
   df.loc[:,(slice(None),'one')]

:meth:`~pandas.MultiIndex.xs` also allows selection with multiple keys

.. ipython:: python

   df.xs(('one', 'bar'), level=('second', 'first'), axis=1)


.. ipython:: python

   # using the slicers (new in 0.14.0)
   df.loc[:,('bar','one')]


.. versionadded:: 0.13.0

You can pass drop_level=False to :meth:`~pandas.MultiIndex.xs` to retain the level that was selected

.. ipython:: python

   df.xs('one', level='second', axis=1, drop_level=False)

versus the result with drop_level=True (the default value)

.. ipython:: python

   df.xs('one', level='second', axis=1, drop_level=True)


.. ipython:: python
   :suppress:

   df = df.T

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 with :class:`~pandas.MultiIndex`

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.set_names(['L1', 'L2'], inplace=True)
   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')]
Traceback (most recent call last)
     ...
KeyError: Key length (3) was greater than MultiIndex lexsort depth (2)

Swapping levels with :meth:`~pandas.MultiIndex.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 :meth:`~pandas.MultiIndex.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.

Setting index metadata (name(s), levels, labels)

.. versionadded:: 0.13.0

Indexes are "mostly immutable", but it is possible to set and change their metadata, like the index name (or, for MultiIndex, levels and labels).

You can use the rename, set_names, set_levels, and set_labels to set these attributes directly. They default to returning a copy; however, you can specify inplace=True to have the data change in place.

.. ipython:: python

  ind = Index([1, 2, 3])
  ind.rename("apple")
  ind
  ind.set_names(["apple"], inplace=True)
  ind.name = "bob"
  ind


.. versionadded:: 0.15.0

set_names, set_levels, and set_labels also take an optional level` argument

.. ipython:: python

  index
  index.levels[1]
  index.set_levels(["a", "b"], level=1)

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. 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
  • PeriodIndex: An Index object with Period elements
  • DatetimeIndex: An Index object with Timestamp elements
  • 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