.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   np.random.seed(123456)
   np.set_printoptions(precision=4, suppress=True)
   import pandas as pd
   pd.options.display.max_rows=15

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 in 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 subclass 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:`MultiIndex / Advanced Indexing <advanced>` for MultiIndex and more advanced indexing documentation.

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

Different Choices for Indexing

.. 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 primarily label based, but may also be used with a boolean array. .loc will raise KeyError when the items are not found. Allowed inputs are:
- 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 primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with python/numpy slice semantics). Allowed inputs are:
- An integer e.g. 5
- A list or array of integers [4, 3, 0]
- A slice object with ints 1:7
- A boolean array
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 unless the corresponding axis is of integer type. .ix is the most general and will support any of the inputs in .loc and .iloc. .ix also supports floating point label schemes. .ix is exceptionally useful when dealing with mixed positional and label based hierachical indexes.

However, when an axis is integer based, ONLY label based access and not positional access is supported. Thus, in such cases, it's usually better to be explicit and use .iloc or .loc.

See more at :ref:`Advanced Indexing <advanced>` and :ref:`Advanced Hierarchical <advanced.advanced_hierarchical>`.

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]`

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 = pd.date_range('1/1/2000', periods=8)
   df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
   df
   panel = pd.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 = pd.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.
Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items, labels.
In any of these cases, standard indexing will still work, e.g. s['1'], s['min'], and s['index'] will access the corresponding element or column.
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.

You can also assign a dict to a row of a DataFrame:

.. ipython:: python

   x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})
   x.iloc[1] = dict(x=9, y=99)
   x

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>`

Warning

.loc is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a DatetimeIndex. These will raise a TypeError.

.. ipython:: python

   dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5))
   dfl

In [4]: dfl.loc[2:3]
TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>

String likes in slicing can be convertible to the type of the index and lead to natural slicing.

.. ipython:: python

   dfl.loc['20130102':'20130104']

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 = pd.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 = pd.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
A boolean array

.. ipython:: python

   s1 = pd.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 = pd.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]]

.. ipython:: python

   df1.iloc[1:3,:]

.. ipython:: python

   df1.iloc[:,1:3]

.. 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 = pd.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 = pd.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

Selecting Random Samples

A random selection of rows or columns from a Series, DataFrame, or Panel with the :meth:`~DataFrame.sample` method. The method will sample rows by default, and accepts a specific number of rows/columns to return, or a fraction of rows.

.. ipython :: python

    s = pd.Series([0,1,2,3,4,5])

    # When no arguments are passed, returns 1 row.
    s.sample()

    # One may specify either a number of rows:
    s.sample(n=3)

    # Or a fraction of the rows:
    s.sample(frac=0.5)

By default, sample will return each row at most once, but one can also sample with replacement using the replace option:

.. ipython :: python

   s = pd.Series([0,1,2,3,4,5])

    # Without replacement (default):
    s.sample(n=6, replace=False)

    # With replacement:
    s.sample(n=6, replace=True)

By default, each row has an equal probability of being selected, but if you want rows to have different probabilities, you can pass the sample function sampling weights as weights. These weights can be a list, a numpy array, or a Series, but they must be of the same length as the object you are sampling. Missing values will be treated as a weight of zero, and inf values are not allowed. If weights do not sum to 1, they will be re-normalized by dividing all weights by the sum of the weights. For example:

.. ipython :: python

    s = pd.Series([0,1,2,3,4,5])
    example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]
    s.sample(n=3, weights=example_weights)

    # Weights will be re-normalized automatically
    example_weights2 = [0.5, 0, 0, 0, 0, 0]
    s.sample(n=1, weights=example_weights2)

When applied to a DataFrame, you can use a column of the DataFrame as sampling weights (provided you are sampling rows and not columns) by simply passing the name of the column as a string.

.. ipython :: python

    df2 = pd.DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]})
    df2.sample(n = 3, weights = 'weight_column')

sample also allows users to sample columns instead of rows using the axis argument.

..      ipython :: python

    df3 = pd.DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
    df3.sample(n=1, axis=1)

Finally, one can also set a seed for sample's random number generator using the random_state argument, which will accept either an integer (as a seed) or a numpy RandomState object.

..      ipython :: python

    df4 = pd.DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})

    # With a given seed, the sample will always draw the same rows.
    df4.sample(n=2, random_state=2)
    df4.sample(n=2, random_state=2)

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

A DataFrame can be enlarged on either axis via .loc

.. ipython:: python

   dfi = pd.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 = pd.Series(range(-3, 4))
   s
   s[s > 0]
   s[(s < -1) | (s > 0.5)]
   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 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],
                       'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],
                       'c' : np.random.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 <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 = pd.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 = pd.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 = pd.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 = pd.date_range('1/1/2000', periods=8)
   df = pd.DataFrame(np.random.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

   n = 10
   df = pd.DataFrame(np.random.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 = pd.DataFrame(np.random.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 = pd.DataFrame(np.random.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 = pd.DataFrame({'a': np.random.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

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

   index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food'])
   df = pd.DataFrame(np.random.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 = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))
   df
   df2 = pd.DataFrame(np.random.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 = pd.DataFrame(np.random.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 = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),
                      'c': np.random.randint(5, size=12),
                      'd': np.random.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 = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))
   df['bools'] = np.random.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

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

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

.. ipython:: python
   :suppress:

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

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 keep parameter to specify targets to be kept.

keep='first' (default): mark / drop duplicates except for the first occurrence.
keep='last': mark / drop duplicates except for the last occurrence.
keep=False: mark / drop all duplicates.

.. ipython:: python

   df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'two', 'two', 'three', 'four'],
                       'b': ['x', 'y', 'x', 'y', 'x', 'x', 'x'],
                       'c': np.random.randn(7)})
   df2
   df2.duplicated('a')
   df2.duplicated('a', keep='last')
   df2.duplicated('a', keep=False)
   df2.drop_duplicates('a')
   df2.drop_duplicates('a', keep='last')
   df2.drop_duplicates('a', keep=False)

Also, you can pass a list of columns to identify duplications.

.. ipython:: python

   df2.duplicated(['a', 'b'])
   df2.drop_duplicates(['a', 'b'])

To drop duplicates by index value, use Index.duplicated then perform slicing. Same options are available in keep parameter.

.. ipython:: python

   df3 = pd.DataFrame({'a': np.arange(6),
                       'b': np.random.randn(6)},
                      index=['a', 'a', 'b', 'c', 'b', 'a'])
   df3
   df3.index.duplicated()
   df3[~df3.index.duplicated()]
   df3[~df3.index.duplicated(keep='last')]
   df3[~df3.index.duplicated(keep=False)]

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

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

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

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

.. ipython:: python

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

The name, if set, will be shown in the console display:

.. ipython:: python

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

Setting metadata

.. 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.

See :ref:`Advanced Indexing <advanced>` for usage of MultiIndexes.

.. ipython:: python

  ind = pd.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 = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])
  index
  index.levels[1]
  index.set_levels(["a", "b"], level=1)

Set operations on Index objects

Warning

In 0.15.0. the set operations + and - were deprecated in order to provide these for numeric type operations on certain index types. + can be replace by .union() or |, and - by .difference().

The two main operations are union (|), intersection (&) These can be directly called as instance methods or used via overloaded operators. Difference is provided via the .difference() method.

.. ipython:: python

   a = pd.Index(['c', 'b', 'a'])
   b = pd.Index(['c', 'e', 'd'])
   a | b
   a & b
   a.difference(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.difference(idx2).union(idx2.difference(idx1)), with duplicates dropped.

.. ipython:: python

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

Set / Reset Index

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.

Set an index

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 = pd.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

Reset the 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

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 = pd.DataFrame([list('abcd'),
                        list('efgh'),
                        list('ijkl'),
                        list('mnop')],
                       columns=pd.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 = pd.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 = pd.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.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

indexing.rst

indexing.rst

Indexing and Selecting Data

Different Choices for Indexing

Basics

Attribute Access

Slicing ranges

Selection By Label

Selection By Position

Selecting Random Samples

Setting With Enlargement

Fast scalar value getting and setting

Boolean indexing

Indexing with isin

The :meth:`~pandas.DataFrame.where` Method and Masking

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

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

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

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

The `in` and `not in` operators

Special use of the `==` operator with `list` objects

Boolean Operators

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

Duplicate Data

Dictionary-like :meth:`~pandas.DataFrame.get` method

The :meth:`~pandas.DataFrame.select` Method

The :meth:`~pandas.DataFrame.lookup` Method

Index objects

Setting metadata

Set operations on Index objects

Set / Reset Index

Set an index

Reset the index

Adding an ad hoc index

Returning a view versus a copy

Why does the assignment when using chained indexing fail!

Evaluation order matters

Files

indexing.rst

Latest commit

History

indexing.rst

File metadata and controls

Indexing and Selecting Data

Different Choices for Indexing

Basics

Attribute Access

Slicing ranges

Selection By Label

Selection By Position

Selecting Random Samples

Setting With Enlargement

Fast scalar value getting and setting

Boolean indexing

Indexing with isin

The :meth:`~pandas.DataFrame.where` Method and Masking

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

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

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

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

The in and not in operators

Special use of the == operator with list objects

Boolean Operators

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

Duplicate Data

Dictionary-like :meth:`~pandas.DataFrame.get` method

The :meth:`~pandas.DataFrame.select` Method

The :meth:`~pandas.DataFrame.lookup` Method

Index objects

Setting metadata

Set operations on Index objects

Set / Reset Index

Set an index

Reset the index

Adding an ad hoc index

Returning a view versus a copy

Why does the assignment when using chained indexing fail!

Evaluation order matters

The `in` and `not in` operators

Special use of the `==` operator with `list` objects