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.

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.

See the MultiIndex / Advanced Indexing for MultiIndex and more advanced indexing documentation.

See the cookbook for some advanced strategies.

Different choices for indexing#

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, when present in the index! See Slicing with labels and Endpoints are inclusive.)

    • A boolean array (any NA values will be treated as False).

    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).

    • A tuple of row (and column) indices whose elements are one of the above inputs.

    See more at Selection by Label.

  • .iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:

    • An integer e.g. 5.

    • A list or array of integers [4, 3, 0].

    • A slice object with ints 1:7.

    • A boolean array (any NA values will be treated as False).

    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).

    • A tuple of row (and column) indices whose elements are one of the above inputs.

    See more at Selection by Position, Advanced Indexing and Advanced Hierarchical.

  • .loc, .iloc, and also [] indexing can accept a callable as indexer. See more at Selection By Callable.

    Note

    Destructuring tuple keys into row (and column) indexes occurs before callables are applied, so you cannot return a tuple from a callable to index both rows and columns.

Getting values from an object with multi-axes selection uses the following notation (using .loc as an example, but the following applies to .iloc as well). Any of the axes accessors may be the null slice :. Axes left out of the specification are assumed to be :, e.g. p.loc['a'] is equivalent to p.loc['a', :].

In [1]: ser = pd.Series(range(5), index=list("abcde"))

In [2]: ser.loc[["a", "c", "e"]]
Out[2]: 
a    0
c    2
e    4
dtype: int64

In [3]: df = pd.DataFrame(np.arange(25).reshape(5, 5), index=list("abcde"), columns=list("abcde"))

In [4]: df.loc[["a", "c", "e"], ["b", "d"]]
Out[4]: 
    b   d
a   1   3
c  11  13
e  21  23

Basics#

As mentioned when introducing the data structures in the last section, the primary function of indexing with [] (a.k.a. __getitem__ for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing pandas objects with []:

Object Type

Selection

Return Value Type

Series

series[label]

scalar value

DataFrame

frame[colname]

Series corresponding to colname

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

In [5]: dates = pd.date_range('1/1/2000', periods=8)

In [6]: df = pd.DataFrame(np.random.randn(8, 4),
   ...:                   index=dates, columns=['A', 'B', 'C', 'D'])
   ...: 

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

Note

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

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

In [8]: s = df['A']

In [9]: s[dates[5]]
Out[9]: -0.6736897080883706

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

In [10]: df
Out[10]: 
                   A         B         C         D
2000-01-01  0.469112 -0.282863 -1.509059 -1.135632
2000-01-02  1.212112 -0.173215  0.119209 -1.044236
2000-01-03 -0.861849 -2.104569 -0.494929  1.071804
2000-01-04  0.721555 -0.706771 -1.039575  0.271860
2000-01-05 -0.424972  0.567020  0.276232 -1.087401
2000-01-06 -0.673690  0.113648 -1.478427  0.524988
2000-01-07  0.404705  0.577046 -1.715002 -1.039268
2000-01-08 -0.370647 -1.157892 -1.344312  0.844885

In [11]: df[['B', 'A']] = df[['A', 'B']]

In [12]: df
Out[12]: 
                   A         B         C         D
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
2000-01-04 -0.706771  0.721555 -1.039575  0.271860
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-06  0.113648 -0.673690 -1.478427  0.524988
2000-01-07  0.577046  0.404705 -1.715002 -1.039268
2000-01-08 -1.157892 -0.370647 -1.344312  0.844885

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

Warning

pandas aligns all AXES when setting Series and DataFrame from .loc.

This will not modify df because the column alignment is before value assignment.

In [13]: df[['A', 'B']]
Out[13]: 
                   A         B
2000-01-01 -0.282863  0.469112
2000-01-02 -0.173215  1.212112
2000-01-03 -2.104569 -0.861849
2000-01-04 -0.706771  0.721555
2000-01-05  0.567020 -0.424972
2000-01-06  0.113648 -0.673690
2000-01-07  0.577046  0.404705
2000-01-08 -1.157892 -0.370647

In [14]: df.loc[:, ['B', 'A']] = df[['A', 'B']]

In [15]: df[['A', 'B']]
Out[15]: 
                   A         B
2000-01-01 -0.282863  0.469112
2000-01-02 -0.173215  1.212112
2000-01-03 -2.104569 -0.861849
2000-01-04 -0.706771  0.721555
2000-01-05  0.567020 -0.424972
2000-01-06  0.113648 -0.673690
2000-01-07  0.577046  0.404705
2000-01-08 -1.157892 -0.370647

The correct way to swap column values is by using raw values:

In [16]: df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy()

In [17]: df[['A', 'B']]
Out[17]: 
                   A         B
2000-01-01  0.469112 -0.282863
2000-01-02  1.212112 -0.173215
2000-01-03 -0.861849 -2.104569
2000-01-04  0.721555 -0.706771
2000-01-05 -0.424972  0.567020
2000-01-06 -0.673690  0.113648
2000-01-07  0.404705  0.577046
2000-01-08 -0.370647 -1.157892

However, pandas does not align AXES when setting Series and DataFrame from .iloc because .iloc operates by position.

This will modify df because the column alignment is not done before value assignment.

In [18]: df[['A', 'B']]
Out[18]: 
                   A         B
2000-01-01  0.469112 -0.282863
2000-01-02  1.212112 -0.173215
2000-01-03 -0.861849 -2.104569
2000-01-04  0.721555 -0.706771
2000-01-05 -0.424972  0.567020
2000-01-06 -0.673690  0.113648
2000-01-07  0.404705  0.577046
2000-01-08 -0.370647 -1.157892

In [19]: df.iloc[:, [1, 0]] = df[['A', 'B']]

In [20]: df[['A','B']]
Out[20]: 
                   A         B
2000-01-01 -0.282863  0.469112
2000-01-02 -0.173215  1.212112
2000-01-03 -2.104569 -0.861849
2000-01-04 -0.706771  0.721555
2000-01-05  0.567020 -0.424972
2000-01-06  0.113648 -0.673690
2000-01-07  0.577046  0.404705
2000-01-08 -1.157892 -0.370647

Attribute access#

You may access an index on a Series or column on a DataFrame directly as an attribute:

In [21]: sa = pd.Series([1, 2, 3], index=list('abc'))

In [22]: dfa = df.copy()
In [23]: sa.b
Out[23]: 2

In [24]: dfa.A
Out[24]: 
2000-01-01   -0.282863
2000-01-02   -0.173215
2000-01-03   -2.104569
2000-01-04   -0.706771
2000-01-05    0.567020
2000-01-06    0.113648
2000-01-07    0.577046
2000-01-08   -1.157892
Freq: D, Name: A, dtype: float64
In [25]: sa.a = 5

In [26]: sa
Out[26]: 
a    5
b    2
c    3
dtype: int64

In [27]: dfa.A = list(range(len(dfa.index)))  # ok if A already exists

In [28]: dfa
Out[28]: 
            A         B         C         D
2000-01-01  0  0.469112 -1.509059 -1.135632
2000-01-02  1  1.212112  0.119209 -1.044236
2000-01-03  2 -0.861849 -0.494929  1.071804
2000-01-04  3  0.721555 -1.039575  0.271860
2000-01-05  4 -0.424972  0.276232 -1.087401
2000-01-06  5 -0.673690 -1.478427  0.524988
2000-01-07  6  0.404705 -1.715002 -1.039268
2000-01-08  7 -0.370647 -1.344312  0.844885

In [29]: dfa['A'] = list(range(len(dfa.index)))  # use this form to create a new column

In [30]: dfa
Out[30]: 
            A         B         C         D
2000-01-01  0  0.469112 -1.509059 -1.135632
2000-01-02  1  1.212112  0.119209 -1.044236
2000-01-03  2 -0.861849 -0.494929  1.071804
2000-01-04  3  0.721555 -1.039575  0.271860
2000-01-05  4 -0.424972  0.276232 -1.087401
2000-01-06  5 -0.673690 -1.478427  0.524988
2000-01-07  6  0.404705 -1.715002 -1.039268
2000-01-08  7 -0.370647 -1.344312  0.844885

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, but s['min'] is possible.

  • Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.

  • 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.

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:

In [31]: x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})

In [32]: x.iloc[1] = {'x': 9, 'y': 99}

In [33]: x
Out[33]: 
   x   y
0  1   3
1  9  99
2  3   5

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 creates a new attribute rather than a new column and will this raise a UserWarning:

In [34]: df_new = pd.DataFrame({'one': [1., 2., 3.]})

In [35]: df_new.two = [4, 5, 6]

In [36]: df_new
Out[36]: 
   one
0  1.0
1  2.0
2  3.0

Slicing ranges#

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

Note

When the Series has float indices, slicing will select by position.

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

In [37]: s[:5]
Out[37]: 
2000-01-01    0.469112
2000-01-02    1.212112
2000-01-03   -0.861849
2000-01-04    0.721555
2000-01-05   -0.424972
Freq: D, Name: A, dtype: float64

In [38]: s[::2]
Out[38]: 
2000-01-01    0.469112
2000-01-03   -0.861849
2000-01-05   -0.424972
2000-01-07    0.404705
Freq: 2D, Name: A, dtype: float64

In [39]: s[::-1]
Out[39]: 
2000-01-08   -0.370647
2000-01-07    0.404705
2000-01-06   -0.673690
2000-01-05   -0.424972
2000-01-04    0.721555
2000-01-03   -0.861849
2000-01-02    1.212112
2000-01-01    0.469112
Freq: -1D, Name: A, dtype: float64

Note that setting works as well:

In [40]: s2 = s.copy()

In [41]: s2[:5] = 0

In [42]: s2
Out[42]: 
2000-01-01    0.000000
2000-01-02    0.000000
2000-01-03    0.000000
2000-01-04    0.000000
2000-01-05    0.000000
2000-01-06   -0.673690
2000-01-07    0.404705
2000-01-08   -0.370647
Freq: D, Name: A, dtype: float64

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

In [43]: df[:3]
Out[43]: 
                   A         B         C         D
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804

In [44]: df[::-1]
Out[44]: 
                   A         B         C         D
2000-01-08 -1.157892 -0.370647 -1.344312  0.844885
2000-01-07  0.577046  0.404705 -1.715002 -1.039268
2000-01-06  0.113648 -0.673690 -1.478427  0.524988
2000-01-05  0.567020 -0.424972  0.276232 -1.087401
2000-01-04 -0.706771  0.721555 -1.039575  0.271860
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804
2000-01-02 -0.173215  1.212112  0.119209 -1.044236
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632

Selection by label#

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.

In [45]: dfl = pd.DataFrame(np.random.randn(5, 4),
   ....:                    columns=list('ABCD'),
   ....:                    index=pd.date_range('20130101', periods=5))
   ....: 

In [46]: dfl
Out[46]: 
                   A         B         C         D
2013-01-01  1.075770 -0.109050  1.643563 -1.469388
2013-01-02  0.357021 -0.674600 -1.776904 -0.968914
2013-01-03 -1.294524  0.413738  0.276662 -0.472035
2013-01-04 -0.013960 -0.362543 -0.006154 -0.923061
2013-01-05  0.895717  0.805244 -1.206412  2.565646

In [47]: dfl.loc[2:3]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[47], line 1
----> 1 dfl.loc[2:3]

File ~/work/pandas/pandas/pandas/core/indexing.py:1197, in _LocationIndexer.__getitem__(self, key)
   1195 maybe_callable = com.apply_if_callable(key, self.obj)
   1196 maybe_callable = self._raise_callable_usage(key, maybe_callable)
-> 1197 return self._getitem_axis(maybe_callable, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1419, in _LocIndexer._getitem_axis(self, key, axis)
   1417 if isinstance(key, slice):
   1418     self._validate_key(key, axis)
-> 1419     return self._get_slice_axis(key, axis=axis)
   1420 elif com.is_bool_indexer(key):
   1421     return self._getbool_axis(key, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1451, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
   1448     return obj.copy(deep=False)
   1450 labels = obj._get_axis(axis)
-> 1451 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
   1453 if isinstance(indexer, slice):
   1454     return self.obj._slice(indexer, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:673, in DatetimeIndex.slice_indexer(self, start, end, step)
    665 # GH#33146 if start and end are combinations of str and None and Index is not
    666 # monotonic, we can not use Index.slice_indexer because it does not honor the
    667 # actual elements, is only searching for start and end
    668 if (
    669     check_str_or_none(start)
    670     or check_str_or_none(end)
    671     or self.is_monotonic_increasing
    672 ):
--> 673     return Index.slice_indexer(self, start, end, step)
    675 mask = np.array(True)
    676 in_index = True

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6537, in Index.slice_indexer(self, start, end, step)
   6486 def slice_indexer(
   6487     self,
   6488     start: Hashable | None = None,
   6489     end: Hashable | None = None,
   6490     step: int | None = None,
   6491 ) -> slice:
   6492     """
   6493     Compute the slice indexer for input labels and step.
   6494 
   (...)
   6535     slice(1, 3, None)
   6536     """
-> 6537     start_slice, end_slice = self.slice_locs(start, end, step=step)
   6539     # return a slice
   6540     if not is_scalar(start_slice):

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6777, in Index.slice_locs(self, start, end, step)
   6775 start_slice = None
   6776 if start is not None:
-> 6777     start_slice = self.get_slice_bound(start, "left")
   6778 if start_slice is None:
   6779     start_slice = 0

File ~/work/pandas/pandas/pandas/core/indexes/base.py:6681, in Index.get_slice_bound(self, label, side)
   6677 original_label = label
   6679 # For datetime indices label may be a string that has to be converted
   6680 # to datetime boundary according to its resolution.
-> 6681 label = self._maybe_cast_slice_bound(label, side)
   6683 # we need to look up the label
   6684 try:

File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:633, in DatetimeIndex._maybe_cast_slice_bound(self, label, side)
    628 if isinstance(label, dt.date) and not isinstance(label, dt.datetime):
    629     # Pandas supports slicing with dates, treated as datetimes at midnight.
    630     # https://github.com/pandas-dev/pandas/issues/31501
    631     label = Timestamp(label).to_pydatetime()
--> 633 label = super()._maybe_cast_slice_bound(label, side)
    634 self._data._assert_tzawareness_compat(label)
    635 return Timestamp(label)

File ~/work/pandas/pandas/pandas/core/indexes/datetimelike.py:369, in DatetimeIndexOpsMixin._maybe_cast_slice_bound(self, label, side)
    367     return lower if side == "left" else upper
    368 elif not isinstance(label, self._data._recognized_scalars):
--> 369     self._raise_invalid_indexer("slice", label)
    371 return label

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4069, in Index._raise_invalid_indexer(self, form, key, reraise)
   4067 if reraise is not lib.no_default:
   4068     raise TypeError(msg) from reraise
-> 4069 raise TypeError(msg)

TypeError: cannot do slice indexing on 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.

In [48]: dfl.loc['20130102':'20130104']
Out[48]: 
                   A         B         C         D
2013-01-02  0.357021 -0.674600 -1.776904 -0.968914
2013-01-03 -1.294524  0.413738  0.276662 -0.472035
2013-01-04 -0.013960 -0.362543 -0.006154 -0.923061

pandas provides a suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the start bound AND the stop bound are included, if present in the index. 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, when present in the index! See Slicing with labels.

  • A boolean array.

  • A callable, see Selection By Callable.

In [49]: s1 = pd.Series(np.random.randn(6), index=list('abcdef'))

In [50]: s1
Out[50]: 
a    1.431256
b    1.340309
c   -1.170299
d   -0.226169
e    0.410835
f    0.813850
dtype: float64

In [51]: s1.loc['c':]
Out[51]: 
c   -1.170299
d   -0.226169
e    0.410835
f    0.813850
dtype: float64

In [52]: s1.loc['b']
Out[52]: 1.3403088497993827

Note that setting works as well:

In [53]: s1.loc['c':] = 0

In [54]: s1
Out[54]: 
a    1.431256
b    1.340309
c    0.000000
d    0.000000
e    0.000000
f    0.000000
dtype: float64

With a DataFrame:

In [55]: df1 = pd.DataFrame(np.random.randn(6, 4),
   ....:                    index=list('abcdef'),
   ....:                    columns=list('ABCD'))
   ....: 

In [56]: df1
Out[56]: 
          A         B         C         D
a  0.132003 -0.827317 -0.076467 -1.187678
b  1.130127 -1.436737 -1.413681  1.607920
c  1.024180  0.569605  0.875906 -2.211372
d  0.974466 -2.006747 -0.410001 -0.078638
e  0.545952 -1.219217 -1.226825  0.769804
f -1.281247 -0.727707 -0.121306 -0.097883

In [57]: df1.loc[['a', 'b', 'd'], :]
Out[57]: 
          A         B         C         D
a  0.132003 -0.827317 -0.076467 -1.187678
b  1.130127 -1.436737 -1.413681  1.607920
d  0.974466 -2.006747 -0.410001 -0.078638

Accessing via label slices:

In [58]: df1.loc['d':, 'A':'C']
Out[58]: 
          A         B         C
d  0.974466 -2.006747 -0.410001
e  0.545952 -1.219217 -1.226825
f -1.281247 -0.727707 -0.121306

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

In [59]: df1.loc['a']
Out[59]: 
A    0.132003
B   -0.827317
C   -0.076467
D   -1.187678
Name: a, dtype: float64

For getting values with a boolean array:

In [60]: df1.loc['a'] > 0
Out[60]: 
A     True
B    False
C    False
D    False
Name: a, dtype: bool

In [61]: df1.loc[:, df1.loc['a'] > 0]
Out[61]: 
          A
a  0.132003
b  1.130127
c  1.024180
d  0.974466
e  0.545952
f -1.281247

NA values in a boolean array propagate as False:

In [62]: mask = pd.array([True, False, True, False, pd.NA, False], dtype="boolean")

In [63]: mask
Out[63]: 
<BooleanArray>
[True, False, True, False, <NA>, False]
Length: 6, dtype: boolean

In [64]: df1[mask]
Out[64]: 
          A         B         C         D
a  0.132003 -0.827317 -0.076467 -1.187678
c  1.024180  0.569605  0.875906 -2.211372

For getting a value explicitly:

# this is also equivalent to ``df1.at['a','A']``
In [65]: df1.loc['a', 'A']
Out[65]: 0.13200317033032932

Slicing with labels#

When using .loc with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:

In [66]: s = pd.Series(list('abcde'), index=[0, 3, 2, 5, 4])

In [67]: s.loc[3:5]
Out[67]: 
3    b
2    c
5    d
dtype: object

If the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:

In [68]: s.sort_index()
Out[68]: 
0    a
2    c
3    b
4    e
5    d
dtype: object

In [69]: s.sort_index().loc[1:6]
Out[69]: 
2    c
3    b
4    e
5    d
dtype: object

However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed type indexes). For instance, in the above example, s.loc[1:6] would raise KeyError.

For the rationale behind this behavior, see Endpoints are inclusive.

In [70]: s = pd.Series(list('abcdef'), index=[0, 3, 2, 5, 4, 2])

In [71]: s.loc[3:5]
Out[71]: 
3    b
2    c
5    d
dtype: object

Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, s.loc[2:5] would raise a KeyError.

For more information about duplicate labels, see Duplicate Labels.

Selection by position#

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

  • A callable, see Selection By Callable.

  • A tuple of row (and column) indexes, whose elements are one of the above types.

In [72]: s1 = pd.Series(np.random.randn(5), index=list(range(0, 10, 2)))

In [73]: s1
Out[73]: 
0    0.695775
2    0.341734
4    0.959726
6   -1.110336
8   -0.619976
dtype: float64

In [74]: s1.iloc[:3]
Out[74]: 
0    0.695775
2    0.341734
4    0.959726
dtype: float64

In [75]: s1.iloc[3]
Out[75]: -1.110336102891167

Note that setting works as well:

In [76]: s1.iloc[:3] = 0

In [77]: s1
Out[77]: 
0    0.000000
2    0.000000
4    0.000000
6   -1.110336
8   -0.619976
dtype: float64

With a DataFrame:

In [78]: df1 = pd.DataFrame(np.random.randn(6, 4),
   ....:                    index=list(range(0, 12, 2)),
   ....:                    columns=list(range(0, 8, 2)))
   ....: 

In [79]: df1
Out[79]: 
           0         2         4         6
0   0.149748 -0.732339  0.687738  0.176444
2   0.403310 -0.154951  0.301624 -2.179861
4  -1.369849 -0.954208  1.462696 -1.743161
6  -0.826591 -0.345352  1.314232  0.690579
8   0.995761  2.396780  0.014871  3.357427
10 -0.317441 -1.236269  0.896171 -0.487602

Select via integer slicing:

In [80]: df1.iloc[:3]
Out[80]: 
          0         2         4         6
0  0.149748 -0.732339  0.687738  0.176444
2  0.403310 -0.154951  0.301624 -2.179861
4 -1.369849 -0.954208  1.462696 -1.743161

In [81]: df1.iloc[1:5, 2:4]
Out[81]: 
          4         6
2  0.301624 -2.179861
4  1.462696 -1.743161
6  1.314232  0.690579
8  0.014871  3.357427

Select via integer list:

In [82]: df1.iloc[[1, 3, 5], [1, 3]]
Out[82]: 
           2         6
2  -0.154951 -2.179861
6  -0.345352  0.690579
10 -1.236269 -0.487602
In [83]: df1.iloc[1:3, :]
Out[83]: 
          0         2         4         6
2  0.403310 -0.154951  0.301624 -2.179861
4 -1.369849 -0.954208  1.462696 -1.743161
In [84]: df1.iloc[:, 1:3]
Out[84]: 
           2         4
0  -0.732339  0.687738
2  -0.154951  0.301624
4  -0.954208  1.462696
6  -0.345352  1.314232
8   2.396780  0.014871
10 -1.236269  0.896171
# this is also equivalent to ``df1.iat[1,1]``
In [85]: df1.iloc[1, 1]
Out[85]: -0.1549507744249032

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

In [86]: df1.iloc[1]
Out[86]: 
0    0.403310
2   -0.154951
4    0.301624
6   -2.179861
Name: 2, dtype: float64

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

# these are allowed in Python/NumPy.
In [87]: x = list('abcdef')

In [88]: x
Out[88]: ['a', 'b', 'c', 'd', 'e', 'f']

In [89]: x[4:10]
Out[89]: ['e', 'f']

In [90]: x[8:10]
Out[90]: []

In [91]: s = pd.Series(x)

In [92]: s
Out[92]: 
0    a
1    b
2    c
3    d
4    e
5    f
dtype: object

In [93]: s.iloc[4:10]
Out[93]: 
4    e
5    f
dtype: object

In [94]: s.iloc[8:10]
Out[94]: Series([], dtype: object)

Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned).

In [95]: dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))

In [96]: dfl
Out[96]: 
          A         B
0 -0.082240 -2.182937
1  0.380396  0.084844
2  0.432390  1.519970
3 -0.493662  0.600178
4  0.274230  0.132885

In [97]: dfl.iloc[:, 2:3]
Out[97]: 
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4]

In [98]: dfl.iloc[:, 1:3]
Out[98]: 
          B
0 -2.182937
1  0.084844
2  1.519970
3  0.600178
4  0.132885

In [99]: dfl.iloc[4:6]
Out[99]: 
         A         B
4  0.27423  0.132885

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.

In [100]: dfl.iloc[[4, 5, 6]]
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
File ~/work/pandas/pandas/pandas/core/indexing.py:1722, in _iLocIndexer._get_list_axis(self, key, axis)
   1721 try:
-> 1722     return self.obj.take(key, axis=axis)
   1723 except IndexError as err:
   1724     # re-raise with different error message, e.g. test_getitem_ndarray_3d

File ~/work/pandas/pandas/pandas/core/generic.py:4014, in NDFrame.take(self, indices, axis, **kwargs)
   4012     return self.copy(deep=False)
-> 4014 new_data = self._mgr.take(
   4015     indices,
   4016     axis=self._get_block_manager_axis(axis),
   4017     verify=True,
   4018 )
   4019 return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(
   4020     self, method="take"
   4021 )

File ~/work/pandas/pandas/pandas/core/internals/managers.py:1029, in BaseBlockManager.take(self, indexer, axis, verify)
   1028 n = self.shape[axis]
-> 1029 indexer = maybe_convert_indices(indexer, n, verify=verify)
   1031 new_labels = self.axes[axis].take(indexer)

File ~/work/pandas/pandas/pandas/core/indexers/utils.py:283, in maybe_convert_indices(indices, n, verify)
    282     if mask.any():
--> 283         raise IndexError("indices are out-of-bounds")
    284 return indices

IndexError: indices are out-of-bounds

The above exception was the direct cause of the following exception:

IndexError                                Traceback (most recent call last)
Cell In[100], line 1
----> 1 dfl.iloc[[4, 5, 6]]

File ~/work/pandas/pandas/pandas/core/indexing.py:1197, in _LocationIndexer.__getitem__(self, key)
   1195 maybe_callable = com.apply_if_callable(key, self.obj)
   1196 maybe_callable = self._raise_callable_usage(key, maybe_callable)
-> 1197 return self._getitem_axis(maybe_callable, axis=axis)

File ~/work/pandas/pandas/pandas/core/indexing.py:1751, in _iLocIndexer._getitem_axis(self, key, axis)
   1749 # a list of integers
   1750 elif is_list_like_indexer(key):
-> 1751     return self._get_list_axis(key, axis=axis)
   1753 # a single integer
   1754 else:
   1755     key = item_from_zerodim(key)

File ~/work/pandas/pandas/pandas/core/indexing.py:1725, in _iLocIndexer._get_list_axis(self, key, axis)
   1722     return self.obj.take(key, axis=axis)
   1723 except IndexError as err:
   1724     # re-raise with different error message, e.g. test_getitem_ndarray_3d
-> 1725     raise IndexError("positional indexers are out-of-bounds") from err

IndexError: positional indexers are out-of-bounds
In [101]: dfl.iloc[:, 4]
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[101], line 1
----> 1 dfl.iloc[:, 4]

File ~/work/pandas/pandas/pandas/core/indexing.py:1190, in _LocationIndexer.__getitem__(self, key)
   1188     if self._is_scalar_access(key):
   1189         return self.obj._get_value(*key, takeable=self._takeable)
-> 1190     return self._getitem_tuple(key)
   1191 else:
   1192     # we by definition only have the 0th axis
   1193     axis = self.axis or 0

File ~/work/pandas/pandas/pandas/core/indexing.py:1698, in _iLocIndexer._getitem_tuple(self, tup)
   1697 def _getitem_tuple(self, tup: tuple):
-> 1698     tup = self._validate_tuple_indexer(tup)
   1699     with suppress(IndexingError):
   1700         return self._getitem_lowerdim(tup)

File ~/work/pandas/pandas/pandas/core/indexing.py:975, in _LocationIndexer._validate_tuple_indexer(self, key)
    973 for i, k in enumerate(key):
    974     try:
--> 975         self._validate_key(k, i)
    976     except ValueError as err:
    977         raise ValueError(
    978             "Location based indexing can only have "
    979             f"[{self._valid_types}] types"
    980         ) from err

File ~/work/pandas/pandas/pandas/core/indexing.py:1600, in _iLocIndexer._validate_key(self, key, axis)
   1598     return
   1599 elif is_integer(key):
-> 1600     self._validate_integer(key, axis)
   1601 elif isinstance(key, tuple):
   1602     # a tuple should already have been caught by this point
   1603     # so don't treat a tuple as a valid indexer
   1604     raise IndexingError("Too many indexers")

File ~/work/pandas/pandas/pandas/core/indexing.py:1693, in _iLocIndexer._validate_integer(self, key, axis)
   1691 len_axis = len(self.obj._get_axis(axis))
   1692 if key >= len_axis or key < -len_axis:
-> 1693     raise IndexError("single positional indexer is out-of-bounds")

IndexError: single positional indexer is out-of-bounds

Selection by callable#

.loc, .iloc, and also [] indexing can accept a callable as indexer. The callable must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.

Note

For .iloc indexing, returning a tuple from the callable is not supported, since tuple destructuring for row and column indexes occurs before applying callables.

In [102]: df1 = pd.DataFrame(np.random.randn(6, 4),
   .....:                    index=list('abcdef'),
   .....:                    columns=list('ABCD'))
   .....: 

In [103]: df1
Out[103]: 
          A         B         C         D
a -0.023688  2.410179  1.450520  0.206053
b -0.251905 -2.213588  1.063327  1.266143
c  0.299368 -0.863838  0.408204 -1.048089
d -0.025747 -0.988387  0.094055  1.262731
e  1.289997  0.082423 -0.055758  0.536580
f -0.489682  0.369374 -0.034571 -2.484478

In [104]: df1.loc[lambda df: df['A'] > 0, :]
Out[104]: 
          A         B         C         D
c  0.299368 -0.863838  0.408204 -1.048089
e  1.289997  0.082423 -0.055758  0.536580

In [105]: df1.loc[:, lambda df: ['A', 'B']]
Out[105]: 
          A         B
a -0.023688  2.410179
b -0.251905 -2.213588
c  0.299368 -0.863838
d -0.025747 -0.988387
e  1.289997  0.082423
f -0.489682  0.369374

In [106]: df1.iloc[:, lambda df: [0, 1]]
Out[106]: 
          A         B
a -0.023688  2.410179
b -0.251905 -2.213588
c  0.299368 -0.863838
d -0.025747 -0.988387
e  1.289997  0.082423
f -0.489682  0.369374

In [107]: df1[lambda df: df.columns[0]]
Out[107]: 
a   -0.023688
b   -0.251905
c    0.299368
d   -0.025747
e    1.289997
f   -0.489682
Name: A, dtype: float64

You can use callable indexing in Series.

In [108]: df1['A'].loc[lambda s: s > 0]
Out[108]: 
c    0.299368
e    1.289997
Name: A, dtype: float64

Using these methods / indexers, you can chain data selection operations without using a temporary variable.

In [109]: bb = pd.read_csv('data/baseball.csv', index_col='id')

In [110]: (bb.groupby(['year', 'team']).sum(numeric_only=True)
   .....:    .loc[lambda df: df['r'] > 100])
   .....: 
Out[110]: 
           stint    g    ab    r    h  X2b  ...     so   ibb   hbp    sh    sf  gidp
year team                                   ...                                     
2007 CIN       6  379   745  101  203   35  ...  127.0  14.0   1.0   1.0  15.0  18.0
     DET       5  301  1062  162  283   54  ...  176.0   3.0  10.0   4.0   8.0  28.0
     HOU       4  311   926  109  218   47  ...  212.0   3.0   9.0  16.0   6.0  17.0
     LAN      11  413  1021  153  293   61  ...  141.0   8.0   9.0   3.0   8.0  29.0
     NYN      13  622  1854  240  509  101  ...  310.0  24.0  23.0  18.0  15.0  48.0
     SFN       5  482  1305  198  337   67  ...  188.0  51.0   8.0  16.0   6.0  41.0
     TEX       2  198   729  115  200   40  ...  140.0   4.0   5.0   2.0   8.0  16.0
     TOR       4  459  1408  187  378   96  ...  265.0  16.0  12.0   4.0  16.0  38.0

[8 rows x 18 columns]

Combining positional and label-based indexing#

If you wish to get the 0th and the 2nd elements from the index in the ‘A’ column, you can do:

In [111]: dfd = pd.DataFrame({'A': [1, 2, 3],
   .....:                     'B': [4, 5, 6]},
   .....:                    index=list('abc'))
   .....: 

In [112]: dfd
Out[112]: 
   A  B
a  1  4
b  2  5
c  3  6

In [113]: dfd.loc[dfd.index[[0, 2]], 'A']
Out[113]: 
a    1
c    3
Name: A, dtype: int64

This can also be expressed using .iloc, by explicitly getting locations on the indexers, and using positional indexing to select things.

In [114]: dfd.iloc[[0, 2], dfd.columns.get_loc('A')]
Out[114]: 
a    1
c    3
Name: A, dtype: int64

For getting multiple indexers, using .get_indexer:

In [115]: dfd.iloc[[0, 2], dfd.columns.get_indexer(['A', 'B'])]
Out[115]: 
   A  B
a  1  4
c  3  6

Reindexing#

The idiomatic way to achieve selecting potentially not-found elements is via .reindex(). See also the section on reindexing.

In [116]: s = pd.Series([1, 2, 3])

In [117]: s.reindex([1, 2, 3])
Out[117]: 
1    2.0
2    3.0
3    NaN
dtype: float64

Alternatively, if you want to select only valid keys, the following is idiomatic and efficient; it is guaranteed to preserve the dtype of the selection.

In [118]: labels = [1, 2, 3]

In [119]: s.loc[s.index.intersection(labels)]
Out[119]: 
1    2
2    3
dtype: int64

Having a duplicated index will raise for a .reindex():

In [120]: s = pd.Series(np.arange(4), index=['a', 'a', 'b', 'c'])

In [121]: labels = ['c', 'd']

In [122]: s.reindex(labels)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[122], line 1
----> 1 s.reindex(labels)

File ~/work/pandas/pandas/pandas/core/series.py:4841, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4824 @doc(
   4825     NDFrame.reindex,  # type: ignore[has-type]
   4826     klass=_shared_doc_kwargs["klass"],
   (...)
   4839     tolerance=None,
   4840 ) -> Series:
-> 4841     return super().reindex(
   4842         index=index,
   4843         method=method,
   4844         level=level,
   4845         fill_value=fill_value,
   4846         limit=limit,
   4847         tolerance=tolerance,
   4848         copy=copy,
   4849     )

File ~/work/pandas/pandas/pandas/core/generic.py:5375, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5372     return self._reindex_multi(axes, fill_value)
   5374 # perform the reindex on the axes
-> 5375 return self._reindex_axes(
   5376     axes, level, limit, tolerance, method, fill_value
   5377 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5397, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
   5394     continue
   5396 ax = self._get_axis(a)
-> 5397 new_index, indexer = ax.reindex(
   5398     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5399 )
   5401 axis = self._get_axis_number(a)
   5402 obj = obj._reindex_with_indexers(
   5403     {axis: [new_index, indexer]},
   5404     fill_value=fill_value,
   5405     allow_dups=False,
   5406 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4199, in Index.reindex(self, target, method, level, limit, tolerance)
   4196     raise ValueError("cannot handle a non-unique multi-index!")
   4197 elif not self.is_unique:
   4198     # GH#42568
-> 4199     raise ValueError("cannot reindex on an axis with duplicate labels")
   4200 else:
   4201     indexer, _ = self.get_indexer_non_unique(target)

ValueError: cannot reindex on an axis with duplicate labels

Generally, you can intersect the desired labels with the current axis, and then reindex.

In [123]: s.loc[s.index.intersection(labels)].reindex(labels)
Out[123]: 
c    3.0
d    NaN
dtype: float64

However, this would still raise if your resulting index is duplicated.

In [124]: labels = ['a', 'd']

In [125]: s.loc[s.index.intersection(labels)].reindex(labels)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[125], line 1
----> 1 s.loc[s.index.intersection(labels)].reindex(labels)

File ~/work/pandas/pandas/pandas/core/series.py:4841, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
   4824 @doc(
   4825     NDFrame.reindex,  # type: ignore[has-type]
   4826     klass=_shared_doc_kwargs["klass"],
   (...)
   4839     tolerance=None,
   4840 ) -> Series:
-> 4841     return super().reindex(
   4842         index=index,
   4843         method=method,
   4844         level=level,
   4845         fill_value=fill_value,
   4846         limit=limit,
   4847         tolerance=tolerance,
   4848         copy=copy,
   4849     )

File ~/work/pandas/pandas/pandas/core/generic.py:5375, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
   5372     return self._reindex_multi(axes, fill_value)
   5374 # perform the reindex on the axes
-> 5375 return self._reindex_axes(
   5376     axes, level, limit, tolerance, method, fill_value
   5377 ).__finalize__(self, method="reindex")

File ~/work/pandas/pandas/pandas/core/generic.py:5397, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
   5394     continue
   5396 ax = self._get_axis(a)
-> 5397 new_index, indexer = ax.reindex(
   5398     labels, level=level, limit=limit, tolerance=tolerance, method=method
   5399 )
   5401 axis = self._get_axis_number(a)
   5402 obj = obj._reindex_with_indexers(
   5403     {axis: [new_index, indexer]},
   5404     fill_value=fill_value,
   5405     allow_dups=False,
   5406 )

File ~/work/pandas/pandas/pandas/core/indexes/base.py:4199, in Index.reindex(self, target, method, level, limit, tolerance)
   4196     raise ValueError("cannot handle a non-unique multi-index!")
   4197 elif not self.is_unique:
   4198     # GH#42568
-> 4199     raise ValueError("cannot reindex on an axis with duplicate labels")
   4200 else:
   4201     indexer, _ = self.get_indexer_non_unique(target)

ValueError: cannot reindex on an axis with duplicate labels

Selecting random samples#

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

In [126]: s = pd.Series([0, 1, 2, 3, 4, 5])

# When no arguments are passed, returns 1 row.
In [127]: s.sample()
Out[127]: 
4    4
dtype: int64

# One may specify either a number of rows:
In [128]: s.sample(n=3)
Out[128]: 
0    0
4    4
1    1
dtype: int64

# Or a fraction of the rows:
In [129]: s.sample(frac=0.5)
Out[129]: 
5    5
3    3
1    1
dtype: int64

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

In [130]: s = pd.Series([0, 1, 2, 3, 4, 5])

# Without replacement (default):
In [131]: s.sample(n=6, replace=False)
Out[131]: 
0    0
1    1
5    5
3    3
2    2
4    4
dtype: int64

# With replacement:
In [132]: s.sample(n=6, replace=True)
Out[132]: 
0    0
4    4
3    3
2    2
4    4
4    4
dtype: int64

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:

In [133]: s = pd.Series([0, 1, 2, 3, 4, 5])

In [134]: example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]

In [135]: s.sample(n=3, weights=example_weights)
Out[135]: 
5    5
4    4
3    3
dtype: int64

# Weights will be re-normalized automatically
In [136]: example_weights2 = [0.5, 0, 0, 0, 0, 0]

In [137]: s.sample(n=1, weights=example_weights2)
Out[137]: 
0    0
dtype: int64

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.

In [138]: df2 = pd.DataFrame({'col1': [9, 8, 7, 6],
   .....:                     'weight_column': [0.5, 0.4, 0.1, 0]})
   .....: 

In [139]: df2.sample(n=3, weights='weight_column')
Out[139]: 
   col1  weight_column
1     8            0.4
0     9            0.5
2     7            0.1

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

In [140]: df3 = pd.DataFrame({'col1': [1, 2, 3], 'col2': [2, 3, 4]})

In [141]: df3.sample(n=1, axis=1)
Out[141]: 
   col1
0     1
1     2
2     3

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.

In [142]: df4 = pd.DataFrame({'col1': [1, 2, 3], 'col2': [2, 3, 4]})

# With a given seed, the sample will always draw the same rows.
In [143]: df4.sample(n=2, random_state=2)
Out[143]: 
   col1  col2
2     3     4
1     2     3

In [144]: df4.sample(n=2, random_state=2)
Out[144]: 
   col1  col2
2     3     4
1     2     3

Setting with enlargement#

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

In the Series case this is effectively an appending operation.

In [145]: se = pd.Series([1, 2, 3])

In [146]: se
Out[146]: 
0    1
1    2
2    3
dtype: int64

In [147]: se[5] = 5.

In [148]: se
Out[148]: 
0    1.0
1    2.0
2    3.0
5    5.0
dtype: float64

A DataFrame can be enlarged on either axis via .loc.

In [149]: dfi = pd.DataFrame(np.arange(6).reshape(3, 2),
   .....:                    columns=['A', 'B'])
   .....: 

In [150]: dfi
Out[150]: 
   A  B
0  0  1
1  2  3
2  4  5

In [151]: dfi.loc[:, 'C'] = dfi.loc[:, 'A']

In [152]: dfi
Out[152]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4

This is like an append operation on the DataFrame.

In [153]: dfi.loc[3] = 5

In [154]: dfi
Out[154]: 
   A  B  C
0  0  1  0
1  2  3  2
2  4  5  4
3  5  5  5

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

In [155]: s.iat[5]
Out[155]: 5

In [156]: df.at[dates[5], 'A']
Out[156]: 0.1136484096888855

In [157]: df.iat[3, 0]
Out[157]: -0.7067711336300845

You can also set using these same indexers.

In [158]: df.at[dates[5], 'E'] = 7

In [159]: df.iat[3, 0] = 7

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

In [160]: df.at[dates[-1] + pd.Timedelta('1 day'), 0] = 7

In [161]: df
Out[161]: 
                   A         B         C         D    E    0
2000-01-01 -0.282863  0.469112 -1.509059 -1.135632  NaN  NaN
2000-01-02 -0.173215  1.212112  0.119209 -1.044236  NaN  NaN
2000-01-03 -2.104569 -0.861849 -0.494929  1.071804  NaN  NaN
2000-01-04  7.000000  0.721555 -1.039575  0.271860  NaN  NaN
2000-01-05  0.567020 -0.424972  0.276232 -1.087401  NaN  NaN
2000-01-06  0.113648 -0.673690 -1.478427  0.524988  7.0  NaN
2000-01-07  0.577046  0.404705 -1.715002 -1.039268  NaN  NaN
2000-01-08 -1.157892 -0.370647 -1.344312  0.844885  NaN  NaN
2000-01-09       NaN       NaN       NaN       NaN  NaN  7.0

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, since by default Python will evaluate an expression such as df['A'] > 2 & df['B'] < 3 as df['A'] > (2 & df['B']) < 3, while the desired evaluation order is (df['A'] > 2) & (df['B'] < 3).

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

In [162]: s = pd.Series(range(-3, 4))

In [163]: s
Out[163]: 
0   -3
1   -2
2   -1
3    0
4    1
5    2
6    3
dtype: int64

In [164]: s[s > 0]
Out[164]: 
4    1
5    2
6    3
dtype: int64

In [165]: s[(s < -1) | (s > 0.5)]
Out[165]: 
0   -3
1   -2
4    1
5    2
6    3
dtype: int64

In [166]: s[~(s < 0)]
Out[166]: 
3    0
4    1
5    2
6    3
dtype: int64

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

In [167]: df[df['A'] > 0]
Out[167]: 
                   A         B         C         D    E   0
2000-01-04  7.000000  0.721555 -1.039575  0.271860  NaN NaN
2000-01-05  0.567020 -0.424972  0.276232 -1.087401  NaN NaN
2000-01-06  0.113648 -0.673690 -1.478427  0.524988  7.0 NaN
2000-01-07  0.577046  0.404705 -1.715002 -1.039268  NaN NaN

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

In [168]: 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'
In [169]: criterion = df2['a'].map(lambda x: x.startswith('t'))

In [170]: df2[criterion]
Out[170]: 
       a  b         c
2    two  y  0.041290
3  three  x  0.361719
4    two  y -0.238075

# equivalent but slower
In [171]: df2[[x.startswith('t') for x in df2['a']]]
Out[171]: 
       a  b         c
2    two  y  0.041290
3  three  x  0.361719
4    two  y -0.238075

# Multiple criteria
In [172]: df2[criterion & (df2['b'] == 'x')]
Out[172]: 
       a  b         c
3  three  x  0.361719

With the choice methods Selection by Label, Selection by Position, and Advanced Indexing you may select along more than one axis using boolean vectors combined with other indexing expressions.

In [173]: df2.loc[criterion & (df2['b'] == 'x'), 'b':'c']
Out[173]: 
   b         c
3  x  0.361719

Warning

iloc supports two kinds of boolean indexing. If the indexer is a boolean Series, an error will be raised. For instance, in the following example, df.iloc[s.values, 1] is ok. The boolean indexer is an array. But df.iloc[s, 1] would raise ValueError.

In [174]: df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
   .....:                   index=list('abc'),
   .....:                   columns=['A', 'B'])
   .....: 

In [175]: s = (df['A'] > 2)

In [176]: s
Out[176]: 
a    False
b     True
c     True
Name: A, dtype: bool

In [177]: df.loc[s, 'B']
Out[177]: 
b    4
c    6
Name: B, dtype: int64

In [178]: df.iloc[s.values, 1]
Out[178]: 
b    4
c    6
Name: B, dtype: int64

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:

In [179]: s = pd.Series(np.arange(5), index=np.arange(5)[::-1], dtype='int64')

In [180]: s
Out[180]: 
4    0
3    1
2    2
1    3
0    4
dtype: int64

In [181]: s.isin([2, 4, 6])
Out[181]: 
4    False
3    False
2     True
1    False
0     True
dtype: bool

In [182]: s[s.isin([2, 4, 6])]
Out[182]: 
2    2
0    4
dtype: int64

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:

In [183]: s[s.index.isin([2, 4, 6])]
Out[183]: 
4    0
2    2
dtype: int64

# compare it to the following
In [184]: s.reindex([2, 4, 6])
Out[184]: 
2    2.0
4    0.0
6    NaN
dtype: float64

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

In [185]: s_mi = pd.Series(np.arange(6),
   .....:                  index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]))
   .....: 

In [186]: s_mi
Out[186]: 
0  a    0
   b    1
   c    2
1  a    3
   b    4
   c    5
dtype: int64

In [187]: s_mi.iloc[s_mi.index.isin([(1, 'a'), (2, 'b'), (0, 'c')])]
Out[187]: 
0  c    2
1  a    3
dtype: int64

In [188]: s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)]
Out[188]: 
0  a    0
   c    2
1  a    3
   c    5
dtype: int64

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.

In [189]: df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
   .....:                    'ids2': ['a', 'n', 'c', 'n']})
   .....: 

In [190]: values = ['a', 'b', 1, 3]

In [191]: df.isin(values)
Out[191]: 
    vals    ids   ids2
0   True   True   True
1  False   True  False
2   True  False  False
3  False  False  False

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.

In [192]: values = {'ids': ['a', 'b'], 'vals': [1, 3]}

In [193]: df.isin(values)
Out[193]: 
    vals    ids   ids2
0   True   True  False
1  False   True  False
2   True  False  False
3  False  False  False

To return the DataFrame of booleans where the values are not in the original DataFrame, use the ~ operator:

In [194]: values = {'ids': ['a', 'b'], 'vals': [1, 3]}

In [195]: ~df.isin(values)
Out[195]: 
    vals    ids  ids2
0  False  False  True
1   True  False  True
2  False   True  True
3   True   True  True

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:

In [196]: values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}

In [197]: row_mask = df.isin(values).all(axis=1)

In [198]: df[row_mask]
Out[198]: 
   vals ids ids2
0     1   a    a

The 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:

In [199]: s[s > 0]
Out[199]: 
3    1
2    2
1    3
0    4
dtype: int64

To return a Series of the same shape as the original:

In [200]: s.where(s > 0)
Out[200]: 
4    NaN
3    1.0
2    2.0
1    3.0
0    4.0
dtype: float64

Selecting values from a DataFrame with a boolean criterion now also preserves input data shape. where is used under the hood as the implementation. The code below is equivalent to df.where(df < 0).

In [201]: dates = pd.date_range('1/1/2000', periods=8)

In [202]: df = pd.DataFrame(np.random.randn(8, 4),
   .....:                   index=dates, columns=['A', 'B', 'C', 'D'])
   .....: 

In [203]: df[df < 0]
Out[203]: 
                   A         B         C         D
2000-01-01 -2.104139 -1.309525       NaN       NaN
2000-01-02 -0.352480       NaN -1.192319       NaN
2000-01-03 -0.864883       NaN -0.227870       NaN
2000-01-04       NaN -1.222082       NaN -1.233203
2000-01-05       NaN -0.605656 -1.169184       NaN
2000-01-06       NaN -0.948458       NaN -0.684718
2000-01-07 -2.670153 -0.114722       NaN -0.048048
2000-01-08       NaN       NaN -0.048788 -0.808838

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

In [204]: df.where(df < 0, -df)
Out[204]: 
                   A         B         C         D
2000-01-01 -2.104139 -1.309525 -0.485855 -0.245166
2000-01-02 -0.352480 -0.390389 -1.192319 -1.655824
2000-01-03 -0.864883 -0.299674 -0.227870 -0.281059
2000-01-04 -0.846958 -1.222082 -0.600705 -1.233203
2000-01-05 -0.669692 -0.605656 -1.169184 -0.342416
2000-01-06 -0.868584 -0.948458 -2.297780 -0.684718
2000-01-07 -2.670153 -0.114722 -0.168904 -0.048048
2000-01-08 -0.801196 -1.392071 -0.048788 -0.808838

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

In [205]: s2 = s.copy()

In [206]: s2[s2 < 0] = 0

In [207]: s2
Out[207]: 
4    0
3    1
2    2
1    3
0    4
dtype: int64

In [208]: df2 = df.copy()

In [209]: df2[df2 < 0] = 0

In [210]: df2
Out[210]: 
                   A         B         C         D
2000-01-01  0.000000  0.000000  0.485855  0.245166
2000-01-02  0.000000  0.390389  0.000000  1.655824
2000-01-03  0.000000  0.299674  0.000000  0.281059
2000-01-04  0.846958  0.000000  0.600705  0.000000
2000-01-05  0.669692  0.000000  0.000000  0.342416
2000-01-06  0.868584  0.000000  2.297780  0.000000
2000-01-07  0.000000  0.000000  0.168904  0.000000
2000-01-08  0.801196  1.392071  0.000000  0.000000

where returns a modified copy of the data.

Note

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

In [211]: df.where(df < 0, -df) == np.where(df < 0, df, -df)
Out[211]: 
               A     B     C     D
2000-01-01  True  True  True  True
2000-01-02  True  True  True  True
2000-01-03  True  True  True  True
2000-01-04  True  True  True  True
2000-01-05  True  True  True  True
2000-01-06  True  True  True  True
2000-01-07  True  True  True  True
2000-01-08  True  True  True  True

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 .loc (but on the contents rather than the axis labels).

In [212]: df2 = df.copy()

In [213]: df2[df2[1:4] > 0] = 3

In [214]: df2
Out[214]: 
                   A         B         C         D
2000-01-01 -2.104139 -1.309525  0.485855  0.245166
2000-01-02 -0.352480  3.000000 -1.192319  3.000000
2000-01-03 -0.864883  3.000000 -0.227870  3.000000
2000-01-04  3.000000 -1.222082  3.000000 -1.233203
2000-01-05  0.669692 -0.605656 -1.169184  0.342416
2000-01-06  0.868584 -0.948458  2.297780 -0.684718
2000-01-07 -2.670153 -0.114722  0.168904 -0.048048
2000-01-08  0.801196  1.392071 -0.048788 -0.808838

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

In [215]: df2 = df.copy()

In [216]: df2.where(df2 > 0, df2['A'], axis='index')
Out[216]: 
                   A         B         C         D
2000-01-01 -2.104139 -2.104139  0.485855  0.245166
2000-01-02 -0.352480  0.390389 -0.352480  1.655824
2000-01-03 -0.864883  0.299674 -0.864883  0.281059
2000-01-04  0.846958  0.846958  0.600705  0.846958
2000-01-05  0.669692  0.669692  0.669692  0.342416
2000-01-06  0.868584  0.868584  2.297780  0.868584
2000-01-07 -2.670153 -2.670153  0.168904 -2.670153
2000-01-08  0.801196  1.392071  0.801196  0.801196

This is equivalent to (but faster than) the following.

In [217]: df2 = df.copy()

In [218]: df.apply(lambda x, y: x.where(x > 0, y), y=df['A'])
Out[218]: 
                   A         B         C         D
2000-01-01 -2.104139 -2.104139  0.485855  0.245166
2000-01-02 -0.352480  0.390389 -0.352480  1.655824
2000-01-03 -0.864883  0.299674 -0.864883  0.281059
2000-01-04  0.846958  0.846958  0.600705  0.846958
2000-01-05  0.669692  0.669692  0.669692  0.342416
2000-01-06  0.868584  0.868584  2.297780  0.868584
2000-01-07 -2.670153 -2.670153  0.168904 -2.670153
2000-01-08  0.801196  1.392071  0.801196  0.801196

where can accept a callable as condition and other arguments. The function must be with one argument (the calling Series or DataFrame) and that returns valid output as condition and other argument.

In [219]: df3 = pd.DataFrame({'A': [1, 2, 3],
   .....:                     'B': [4, 5, 6],
   .....:                     'C': [7, 8, 9]})
   .....: 

In [220]: df3.where(lambda x: x > 4, lambda x: x + 10)
Out[220]: 
    A   B  C
0  11  14  7
1  12   5  8
2  13   6  9

Mask#

mask() is the inverse boolean operation of where.

In [221]: s.mask(s >= 0)
Out[221]: 
4   NaN
3   NaN
2   NaN
1   NaN
0   NaN
dtype: float64

In [222]: df.mask(df >= 0)
Out[222]: 
                   A         B         C         D
2000-01-01 -2.104139 -1.309525       NaN       NaN
2000-01-02 -0.352480       NaN -1.192319       NaN
2000-01-03 -0.864883       NaN -0.227870       NaN
2000-01-04       NaN -1.222082       NaN -1.233203
2000-01-05       NaN -0.605656 -1.169184       NaN
2000-01-06       NaN -0.948458       NaN -0.684718
2000-01-07 -2.670153 -0.114722       NaN -0.048048
2000-01-08       NaN       NaN -0.048788 -0.808838

Setting with enlargement conditionally using numpy()#

An alternative to where() is to use numpy.where(). Combined with setting a new column, you can use it to enlarge a DataFrame where the values are determined conditionally.

Consider you have two choices to choose from in the following DataFrame. And you want to set a new column color to ‘green’ when the second column has ‘Z’. You can do the following:

In [223]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')})

In [224]: df['color'] = np.where(df['col2'] == 'Z', 'green', 'red')

In [225]: df
Out[225]: 
  col1 col2  color
0    A    Z  green
1    B    Z  green
2    B    X    red
3    C    Y    red

If you have multiple conditions, you can use numpy.select() to achieve that. Say corresponding to three conditions there are three choice of colors, with a fourth color as a fallback, you can do the following.

In [226]: conditions = [
   .....:     (df['col2'] == 'Z') & (df['col1'] == 'A'),
   .....:     (df['col2'] == 'Z') & (df['col1'] == 'B'),
   .....:     (df['col1'] == 'B')
   .....: ]
   .....: 

In [227]: choices = ['yellow', 'blue', 'purple']

In [228]: df['color'] = np.select(conditions, choices, default='black')

In [229]: df
Out[229]: 
  col1 col2   color
0    A    Z  yellow
1    B    Z    blue
2    B    X  purple
3    C    Y   black

The query() Method#

DataFrame objects have a 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:

In [230]: n = 10

In [231]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))

In [232]: df
Out[232]: 
          a         b         c
0  0.438921  0.118680  0.863670
1  0.138138  0.577363  0.686602
2  0.595307  0.564592  0.520630
3  0.913052  0.926075  0.616184
4  0.078718  0.854477  0.898725
5  0.076404  0.523211  0.591538
6  0.792342  0.216974  0.564056
7  0.397890  0.454131  0.915716
8  0.074315  0.437913  0.019794
9  0.559209  0.502065  0.026437

# pure python
In [233]: df[(df['a'] < df['b']) & (df['b'] < df['c'])]
Out[233]: 
          a         b         c
1  0.138138  0.577363  0.686602
4  0.078718  0.854477  0.898725
5  0.076404  0.523211  0.591538
7  0.397890  0.454131  0.915716

# query
In [234]: df.query('(a < b) & (b < c)')
Out[234]: 
          a         b         c
1  0.138138  0.577363  0.686602
4  0.078718  0.854477  0.898725
5  0.076404  0.523211  0.591538
7  0.397890  0.454131  0.915716

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

In [235]: df = pd.DataFrame(np.random.randint(n / 2, size=(n, 2)), columns=list('bc'))

In [236]: df.index.name = 'a'

In [237]: df
Out[237]: 
   b  c
a      
0  0  4
1  0  1
2  3  4
3  4  3
4  1  4
5  0  3
6  0  1
7  3  4
8  2  3
9  1  1

In [238]: df.query('a < b and b < c')
Out[238]: 
   b  c
a      
2  3  4

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

In [239]: df = pd.DataFrame(np.random.randint(n, size=(n, 2)), columns=list('bc'))

In [240]: df
Out[240]: 
   b  c
0  3  1
1  3  0
2  5  6
3  5  2
4  7  4
5  0  1
6  2  5
7  0  1
8  6  0
9  7  9

In [241]: df.query('index < b < c')
Out[241]: 
   b  c
2  5  6

Note

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

In [242]: df = pd.DataFrame({'a': np.random.randint(5, size=5)})

In [243]: df.index.name = 'a'

In [244]: df.query('a > 2')  # uses the column 'a', not the index
Out[244]: 
   a
a   
1  3
3  3

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

In [245]: df.query('index > 2')
Out[245]: 
   a
a   
3  3
4  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.

MultiIndex query() Syntax#

You can also use the levels of a DataFrame with a MultiIndex as if they were columns in the frame:

In [246]: n = 10

In [247]: colors = np.random.choice(['red', 'green'], size=n)

In [248]: foods = np.random.choice(['eggs', 'ham'], size=n)

In [249]: colors
Out[249]: 
array(['red', 'red', 'red', 'green', 'green', 'green', 'green', 'green',
       'green', 'green'], dtype='<U5')

In [250]: foods
Out[250]: 
array(['ham', 'ham', 'eggs', 'eggs', 'eggs', 'ham', 'ham', 'eggs', 'eggs',
       'eggs'], dtype='<U4')

In [251]: index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food'])

In [252]: df = pd.DataFrame(np.random.randn(n, 2), index=index)

In [253]: df
Out[253]: 
                   0         1
color food                    
red   ham   0.194889 -0.381994
      ham   0.318587  2.089075
      eggs -0.728293 -0.090255
green eggs -0.748199  1.318931
      eggs -2.029766  0.792652
      ham   0.461007 -0.542749
      ham  -0.305384 -0.479195
      eggs  0.095031 -0.270099
      eggs -0.707140 -0.773882
      eggs  0.229453  0.304418

In [254]: df.query('color == "red"')
Out[254]: 
                   0         1
color food                    
red   ham   0.194889 -0.381994
      ham   0.318587  2.089075
      eggs -0.728293 -0.090255

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

In [255]: df.index.names = [None, None]

In [256]: df
Out[256]: 
                   0         1
red   ham   0.194889 -0.381994
      ham   0.318587  2.089075
      eggs -0.728293 -0.090255
green eggs -0.748199  1.318931
      eggs -2.029766  0.792652
      ham   0.461007 -0.542749
      ham  -0.305384 -0.479195
      eggs  0.095031 -0.270099
      eggs -0.707140 -0.773882
      eggs  0.229453  0.304418

In [257]: df.query('ilevel_0 == "red"')
Out[257]: 
                 0         1
red ham   0.194889 -0.381994
    ham   0.318587  2.089075
    eggs -0.728293 -0.090255

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

query() Use Cases#

A use case for query() is when you have a collection of 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

In [258]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))

In [259]: df
Out[259]: 
          a         b         c
0  0.224283  0.736107  0.139168
1  0.302827  0.657803  0.713897
2  0.611185  0.136624  0.984960
3  0.195246  0.123436  0.627712
4  0.618673  0.371660  0.047902
5  0.480088  0.062993  0.185760
6  0.568018  0.483467  0.445289
7  0.309040  0.274580  0.587101
8  0.258993  0.477769  0.370255
9  0.550459  0.840870  0.304611

In [260]: df2 = pd.DataFrame(np.random.rand(n + 2, 3), columns=df.columns)

In [261]: df2
Out[261]: 
           a         b         c
0   0.357579  0.229800  0.596001
1   0.309059  0.957923  0.965663
2   0.123102  0.336914  0.318616
3   0.526506  0.323321  0.860813
4   0.518736  0.486514  0.384724
5   0.190804  0.505723  0.614533
6   0.891939  0.623977  0.676639
7   0.480559  0.378528  0.460858
8   0.420223  0.136404  0.141295
9   0.732206  0.419540  0.604675
10  0.604466  0.848974  0.896165
11  0.589168  0.920046  0.732716

In [262]: expr = '0.0 <= a <= c <= 0.5'

In [263]: map(lambda frame: frame.query(expr), [df, df2])
Out[263]: <map at 0x7f0bc15a9150>

query() Python versus pandas Syntax Comparison#

Full numpy-like syntax:

In [264]: df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc'))

In [265]: df
Out[265]: 
   a  b  c
0  7  8  9
1  1  0  7
2  2  7  2
3  6  2  2
4  2  6  3
5  3  8  2
6  1  7  2
7  5  1  5
8  9  8  0
9  1  5  0

In [266]: df.query('(a < b) & (b < c)')
Out[266]: 
   a  b  c
0  7  8  9

In [267]: df[(df['a'] < df['b']) & (df['b'] < df['c'])]
Out[267]: 
   a  b  c
0  7  8  9

Slightly nicer by removing the parentheses (comparison operators bind tighter than & and |):

In [268]: df.query('a < b & b < c')
Out[268]: 
   a  b  c
0  7  8  9

Use English instead of symbols:

In [269]: df.query('a < b and b < c')
Out[269]: 
   a  b  c
0  7  8  9

Pretty close to how you might write it on paper:

In [270]: df.query('a < b < c')
Out[270]: 
   a  b  c
0  7  8  9

The in and not in operators#

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.

# get all rows where columns "a" and "b" have overlapping values
In [271]: df = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),
   .....:                    'c': np.random.randint(5, size=12),
   .....:                    'd': np.random.randint(9, size=12)})
   .....: 

In [272]: df
Out[272]: 
    a  b  c  d
0   a  a  2  6
1   a  a  4  7
2   b  a  1  6
3   b  a  2  1
4   c  b  3  6
5   c  b  0  2
6   d  b  3  3
7   d  b  2  1
8   e  c  4  3
9   e  c  2  0
10  f  c  0  6
11  f  c  1  2

In [273]: df.query('a in b')
Out[273]: 
   a  b  c  d
0  a  a  2  6
1  a  a  4  7
2  b  a  1  6
3  b  a  2  1
4  c  b  3  6
5  c  b  0  2

# How you'd do it in pure Python
In [274]: df[df['a'].isin(df['b'])]
Out[274]: 
   a  b  c  d
0  a  a  2  6
1  a  a  4  7
2  b  a  1  6
3  b  a  2  1
4  c  b  3  6
5  c  b  0  2

In [275]: df.query('a not in b')
Out[275]: 
    a  b  c  d
6   d  b  3  3
7   d  b  2  1
8   e  c  4  3
9   e  c  2  0
10  f  c  0  6
11  f  c  1  2

# pure Python
In [276]: df[~df['a'].isin(df['b'])]
Out[276]: 
    a  b  c  d
6   d  b  3  3
7   d  b  2  1
8   e  c  4  3
9   e  c  2  0
10  f  c  0  6
11  f  c  1  2

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

# rows where cols a and b have overlapping values
# and col c's values are less than col d's
In [277]: df.query('a in b and c < d')
Out[277]: 
   a  b  c  d
0  a  a  2  6
1  a  a  4  7
2  b  a  1  6
4  c  b  3  6
5  c  b  0  2

# pure Python
In [278]: df[df['b'].isin(df['a']) & (df['c'] < df['d'])]
Out[278]: 
    a  b  c  d
0   a  a  2  6
1   a  a  4  7
2   b  a  1  6
4   c  b  3  6
5   c  b  0  2
10  f  c  0  6
11  f  c  1  2

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.

In [279]: df.query('b == ["a", "b", "c"]')
Out[279]: 
    a  b  c  d
0   a  a  2  6
1   a  a  4  7
2   b  a  1  6
3   b  a  2  1
4   c  b  3  6
5   c  b  0  2
6   d  b  3  3
7   d  b  2  1
8   e  c  4  3
9   e  c  2  0
10  f  c  0  6
11  f  c  1  2

# pure Python
In [280]: df[df['b'].isin(["a", "b", "c"])]
Out[280]: 
    a  b  c  d
0   a  a  2  6
1   a  a  4  7
2   b  a  1  6
3   b  a  2  1
4   c  b  3  6
5   c  b  0  2
6   d  b  3  3
7   d  b  2  1
8   e  c  4  3
9   e  c  2  0
10  f  c  0  6
11  f  c  1  2

In [281]: df.query('c == [1, 2]')
Out[281]: 
    a  b  c  d
0   a  a  2  6
2   b  a  1  6
3   b  a  2  1
7   d  b  2  1
9   e  c  2  0
11  f  c  1  2

In [282]: df.query('c != [1, 2]')
Out[282]: 
    a  b  c  d
1   a  a  4  7
4   c  b  3  6
5   c  b  0  2
6   d  b  3  3
8   e  c  4  3
10  f  c  0  6

# using in/not in
In [283]: df.query('[1, 2] in c')
Out[283]: 
    a  b  c  d
0   a  a  2  6
2   b  a  1  6
3   b  a  2  1
7   d  b  2  1
9   e  c  2  0
11  f  c  1  2

In [284]: df.query('[1, 2] not in c')
Out[284]: 
    a  b  c  d
1   a  a  4  7
4   c  b  3  6
5   c  b  0  2
6   d  b  3  3
8   e  c  4  3
10  f  c  0  6

# pure Python
In [285]: df[df['c'].isin([1, 2])]
Out[285]: 
    a  b  c  d
0   a  a  2  6
2   b  a  1  6
3   b  a  2  1
7   d  b  2  1
9   e  c  2  0
11  f  c  1  2

Boolean operators#

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

In [286]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))

In [287]: df['bools'] = np.random.rand(len(df)) > 0.5

In [288]: df.query('~bools')
Out[288]: 
          a         b         c  bools
2  0.697753  0.212799  0.329209  False
7  0.275396  0.691034  0.826619  False
8  0.190649  0.558748  0.262467  False

In [289]: df.query('not bools')
Out[289]: 
          a         b         c  bools
2  0.697753  0.212799  0.329209  False
7  0.275396  0.691034  0.826619  False
8  0.190649  0.558748  0.262467  False

In [290]: df.query('not bools') == df[~df['bools']]
Out[290]: 
      a     b     c  bools
2  True  True  True   True
7  True  True  True   True
8  True  True  True   True

Of course, expressions can be arbitrarily complex too:

# short query syntax
In [291]: shorter = df.query('a < b < c and (not bools) or bools > 2')

# equivalent in pure Python
In [292]: longer = df[(df['a'] < df['b'])
   .....:             & (df['b'] < df['c'])
   .....:             & (~df['bools'])
   .....:             | (df['bools'] > 2)]
   .....: 

In [293]: shorter
Out[293]: 
          a         b         c  bools
7  0.275396  0.691034  0.826619  False

In [294]: longer
Out[294]: 
          a         b         c  bools
7  0.275396  0.691034  0.826619  False

In [295]: shorter == longer
Out[295]: 
      a     b     c  bools
7  True  True  True   True

Performance of query()#

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

../_images/query-perf.png

You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 100,000 rows.

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

In [296]: df = pd.DataFrame(np.random.randn(8, 4),
   .....:                   index=dates, columns=['A', 'B', 'C', 'D'])
   .....: 

In [297]: 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.

In [298]: df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'two', 'two', 'three', 'four'],
   .....:                     'b': ['x', 'y', 'x', 'y', 'x', 'x', 'x'],
   .....:                     'c': np.random.randn(7)})
   .....: 

In [299]: df2
Out[299]: 
       a  b         c
0    one  x -1.067137
1    one  y  0.309500
2    two  x -0.211056
3    two  y -1.842023
4    two  x -0.390820
5  three  x -1.964475
6   four  x  1.298329

In [300]: df2.duplicated('a')
Out[300]: 
0    False
1     True
2    False
3     True
4     True
5    False
6    False
dtype: bool

In [301]: df2.duplicated('a', keep='last')
Out[301]: 
0     True
1    False
2     True
3     True
4    False
5    False
6    False
dtype: bool

In [302]: df2.duplicated('a', keep=False)
Out[302]: 
0     True
1     True
2     True
3     True
4     True
5    False
6    False
dtype: bool

In [303]: df2.drop_duplicates('a')
Out[303]: 
       a  b         c
0    one  x -1.067137
2    two  x -0.211056
5  three  x -1.964475
6   four  x  1.298329

In [304]: df2.drop_duplicates('a', keep='last')
Out[304]: 
       a  b         c
1    one  y  0.309500
4    two  x -0.390820
5  three  x -1.964475
6   four  x  1.298329

In [305]: df2.drop_duplicates('a', keep=False)
Out[305]: 
       a  b         c
5  three  x -1.964475
6   four  x  1.298329

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

In [306]: df2.duplicated(['a', 'b'])
Out[306]: 
0    False
1    False
2    False
3    False
4     True
5    False
6    False
dtype: bool

In [307]: df2.drop_duplicates(['a', 'b'])
Out[307]: 
       a  b         c
0    one  x -1.067137
1    one  y  0.309500
2    two  x -0.211056
3    two  y -1.842023
5  three  x -1.964475
6   four  x  1.298329

To drop duplicates by index value, use Index.duplicated then perform slicing. The same set of options are available for the keep parameter.

In [308]: df3 = pd.DataFrame({'a': np.arange(6),
   .....:                     'b': np.random.randn(6)},
   .....:                    index=['a', 'a', 'b', 'c', 'b', 'a'])
   .....: 

In [309]: df3
Out[309]: 
   a         b
a  0  1.440455
a  1  2.456086
b  2  1.038402
c  3 -0.894409
b  4  0.683536
a  5  3.082764

In [310]: df3.index.duplicated()
Out[310]: array([False,  True, False, False,  True,  True])

In [311]: df3[~df3.index.duplicated()]
Out[311]: 
   a         b
a  0  1.440455
b  2  1.038402
c  3 -0.894409

In [312]: df3[~df3.index.duplicated(keep='last')]
Out[312]: 
   a         b
c  3 -0.894409
b  4  0.683536
a  5  3.082764

In [313]: df3[~df3.index.duplicated(keep=False)]
Out[313]: 
   a         b
c  3 -0.894409

Dictionary-like get() method#

Each of Series or DataFrame have a get method which can return a default value.

In [314]: s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])

In [315]: s.get('a')  # equivalent to s['a']
Out[315]: 1

In [316]: s.get('x', default=-1)
Out[316]: -1

Looking up values by index/column labels#

Sometimes you want to extract a set of values given a sequence of row labels and column labels, this can be achieved by pandas.factorize and NumPy indexing. For instance:

In [317]: df = pd.DataFrame({'col': ["A", "A", "B", "B"],
   .....:                    'A': [80, 23, np.nan, 22],
   .....:                    'B': [80, 55, 76, 67]})
   .....: 

In [318]: df
Out[318]: 
  col     A   B
0   A  80.0  80
1   A  23.0  55
2   B   NaN  76
3   B  22.0  67

In [319]: idx, cols = pd.factorize(df['col'])

In [320]: df.reindex(cols, axis=1).to_numpy()[np.arange(len(df)), idx]
Out[320]: array([80., 23., 76., 67.])

Formerly this could be achieved with the dedicated DataFrame.lookup method which was deprecated in version 1.2.0 and removed in version 2.0.0.

Index objects#

The pandas Index class and its subclasses can be viewed as implementing an ordered multiset. Duplicates are allowed.

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

In [321]: index = pd.Index(['e', 'd', 'a', 'b'])

In [322]: index
Out[322]: Index(['e', 'd', 'a', 'b'], dtype='object')

In [323]: 'd' in index
Out[323]: True

or using numbers:

In [324]: index = pd.Index([1, 5, 12])

In [325]: index
Out[325]: Index([1, 5, 12], dtype='int64')

In [326]: 5 in index
Out[326]: True

If no dtype is given, Index tries to infer the dtype from the data. It is also possible to give an explicit dtype when instantiating an Index:

In [327]: index = pd.Index(['e', 'd', 'a', 'b'], dtype="string")

In [328]: index
Out[328]: Index(['e', 'd', 'a', 'b'], dtype='string')

In [329]: index = pd.Index([1, 5, 12], dtype="int8")

In [330]: index
Out[330]: Index([1, 5, 12], dtype='int8')

In [331]: index = pd.Index([1, 5, 12], dtype="float32")

In [332]: index
Out[332]: Index([1.0, 5.0, 12.0], dtype='float32')

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

In [333]: index = pd.Index(['e', 'd', 'a', 'b'], name='something')

In [334]: index.name
Out[334]: 'something'

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

In [335]: index = pd.Index(list(range(5)), name='rows')

In [336]: columns = pd.Index(['A', 'B', 'C'], name='cols')

In [337]: df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns)

In [338]: df
Out[338]: 
cols         A         B         C
rows                              
0     1.295989 -1.051694  1.340429
1    -2.366110  0.428241  0.387275
2     0.433306  0.929548  0.278094
3     2.154730 -0.315628  0.264223
4     1.126818  1.132290 -0.353310

In [339]: df['A']
Out[339]: 
rows
0    1.295989
1   -2.366110
2    0.433306
3    2.154730
4    1.126818
Name: A, dtype: float64

Setting metadata#

Indexes are “mostly immutable”, but it is possible to set and change their name attribute. You can use the rename, set_names to set these attributes directly, and they default to returning a copy.

See Advanced Indexing for usage of MultiIndexes.

In [340]: ind = pd.Index([1, 2, 3])

In [341]: ind.rename("apple")
Out[341]: Index([1, 2, 3], dtype='int64', name='apple')

In [342]: ind
Out[342]: Index([1, 2, 3], dtype='int64')

In [343]: ind = ind.set_names(["apple"])

In [344]: ind.name = "bob"

In [345]: ind
Out[345]: Index([1, 2, 3], dtype='int64', name='bob')

set_names, set_levels, and set_codes also take an optional level argument

In [346]: index = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])

In [347]: index
Out[347]: 
MultiIndex([(0, 'one'),
            (0, 'two'),
            (1, 'one'),
            (1, 'two'),
            (2, 'one'),
            (2, 'two')],
           names=['first', 'second'])

In [348]: index.levels[1]
Out[348]: Index(['one', 'two'], dtype='object', name='second')

In [349]: index.set_levels(["a", "b"], level=1)
Out[349]: 
MultiIndex([(0, 'a'),
            (0, 'b'),
            (1, 'a'),
            (1, 'b'),
            (2, 'a'),
            (2, 'b')],
           names=['first', 'second'])

Set operations on Index objects#

The two main operations are union and intersection. Difference is provided via the .difference() method.

In [350]: a = pd.Index(['c', 'b', 'a'])

In [351]: b = pd.Index(['c', 'e', 'd'])

In [352]: a.difference(b)
Out[352]: Index(['a', 'b'], dtype='object')

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

In [353]: idx1 = pd.Index([1, 2, 3, 4])

In [354]: idx2 = pd.Index([2, 3, 4, 5])

In [355]: idx1.symmetric_difference(idx2)
Out[355]: Index([1, 5], dtype='int64')

Note

The resulting index from a set operation will be sorted in ascending order.

When performing Index.union() between indexes with different dtypes, the indexes must be cast to a common dtype. Typically, though not always, this is object dtype. The exception is when performing a union between integer and float data. In this case, the integer values are converted to float

In [356]: idx1 = pd.Index([0, 1, 2])

In [357]: idx2 = pd.Index([0.5, 1.5])

In [358]: idx1.union(idx2)
Out[358]: Index([0.0, 0.5, 1.0, 1.5, 2.0], dtype='float64')

Missing values#

Important

Even though Index can hold missing values (NaN), it should be avoided if you do not want any unexpected results. For example, some operations exclude missing values implicitly.

Index.fillna fills missing values with specified scalar value.

In [359]: idx1 = pd.Index([1, np.nan, 3, 4])

In [360]: idx1
Out[360]: Index([1.0, nan, 3.0, 4.0], dtype='float64')

In [361]: idx1.fillna(2)
Out[361]: Index([1.0, 2.0, 3.0, 4.0], dtype='float64')

In [362]: idx2 = pd.DatetimeIndex([pd.Timestamp('2011-01-01'),
   .....:                          pd.NaT,
   .....:                          pd.Timestamp('2011-01-03')])
   .....: 

In [363]: idx2
Out[363]: DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03'], dtype='datetime64[s]', freq=None)

In [364]: idx2.fillna(pd.Timestamp('2011-01-02'))
Out[364]: DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], dtype='datetime64[s]', freq=None)

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, re-indexed DataFrame:

In [365]: data = pd.DataFrame({'a': ['bar', 'bar', 'foo', 'foo'],
   .....:                      'b': ['one', 'two', 'one', 'two'],
   .....:                      'c': ['z', 'y', 'x', 'w'],
   .....:                      'd': [1., 2., 3, 4]})
   .....: 

In [366]: data
Out[366]: 
     a    b  c    d
0  bar  one  z  1.0
1  bar  two  y  2.0
2  foo  one  x  3.0
3  foo  two  w  4.0

In [367]: indexed1 = data.set_index('c')

In [368]: indexed1
Out[368]: 
     a    b    d
c               
z  bar  one  1.0
y  bar  two  2.0
x  foo  one  3.0
w  foo  two  4.0

In [369]: indexed2 = data.set_index(['a', 'b'])

In [370]: indexed2
Out[370]: 
         c    d
a   b          
bar one  z  1.0
    two  y  2.0
foo one  x  3.0
    two  w  4.0

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

In [371]: frame = data.set_index('c', drop=False)

In [372]: frame = frame.set_index(['a', 'b'], append=True)

In [373]: frame
Out[373]: 
           c    d
c a   b          
z bar one  z  1.0
y bar two  y  2.0
x foo one  x  3.0
w foo two  w  4.0

Other options in set_index allow you not drop the index columns.

In [374]: data.set_index('c', drop=False)
Out[374]: 
     a    b  c    d
c                  
z  bar  one  z  1.0
y  bar  two  y  2.0
x  foo  one  x  3.0
w  foo  two  w  4.0

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 of set_index().

In [375]: data
Out[375]: 
     a    b  c    d
0  bar  one  z  1.0
1  bar  two  y  2.0
2  foo  one  x  3.0
3  foo  two  w  4.0

In [376]: data.reset_index()
Out[376]: 
   index    a    b  c    d
0      0  bar  one  z  1.0
1      1  bar  two  y  2.0
2      2  foo  one  x  3.0
3      3  foo  two  w  4.0

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

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

In [377]: frame
Out[377]: 
           c    d
c a   b          
z bar one  z  1.0
y bar two  y  2.0
x foo one  x  3.0
w foo two  w  4.0

In [378]: frame.reset_index(level=1)
Out[378]: 
         a  c    d
c b               
z one  bar  z  1.0
y two  bar  y  2.0
x one  foo  x  3.0
w two  foo  w  4.0

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

Adding an ad hoc index#

You can assign a custom index to the index attribute:

In [379]: df_idx = pd.DataFrame(range(4))

In [380]: df_idx.index = pd.Index([10, 20, 30, 40], name="a")

In [381]: df_idx
Out[381]: 
    0
a    
10  0
20  1
30  2
40  3

Why does assignment fail when using chained indexing?#

Copy-on-Write is the new default with pandas 3.0. This means that chained indexing will never work. See this section for more context.