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 thereâs 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 isnât 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.
5or'a'(Note that5is 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
NAvalues will be treated asFalse).A
callablefunction 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
NAvalues will be treated asFalse).A
callablefunction 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 []:
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]: np.float64(-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]: np.int64(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
Serieshas 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
.locis strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in aDatetimeIndex. These will raise aTypeError.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:1213, in _LocationIndexer.__getitem__(self, key) 1211 maybe_callable = com.apply_if_callable(key, self.obj) 1212 maybe_callable = self._raise_callable_usage(key, maybe_callable) -> 1213 return self._getitem_axis(maybe_callable, axis=axis) File ~/work/pandas/pandas/pandas/core/indexing.py:1435, in _LocIndexer._getitem_axis(self, key, axis) 1433 if isinstance(key, slice): 1434 self._validate_key(key, axis) -> 1435 return self._get_slice_axis(key, axis=axis) 1436 elif com.is_bool_indexer(key): 1437 return self._getbool_axis(key, axis=axis) File ~/work/pandas/pandas/pandas/core/indexing.py:1467, in _LocIndexer._get_slice_axis(self, slice_obj, axis) 1464 return obj.copy(deep=False) 1466 labels = obj._get_axis(axis) -> 1467 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step) 1469 if isinstance(indexer, slice): 1470 return self.obj._slice(indexer, axis=axis) File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:676, in DatetimeIndex.slice_indexer(self, start, end, step) 668 # GH#33146 if start and end are combinations of str and None and Index is not 669 # monotonic, we can not use Index.slice_indexer because it does not honor the 670 # actual elements, is only searching for start and end 671 if ( 672 check_str_or_none(start) 673 or check_str_or_none(end) 674 or self.is_monotonic_increasing 675 ): --> 676 return Index.slice_indexer(self, start, end, step) 678 mask = np.array(True) 679 in_index = True File ~/work/pandas/pandas/pandas/core/indexes/base.py:6563, in Index.slice_indexer(self, start, end, step) 6512 def slice_indexer( 6513 self, 6514 start: Hashable | None = None, 6515 end: Hashable | None = None, 6516 step: int | None = None, 6517 ) -> slice: 6518 """ 6519 Compute the slice indexer for input labels and step. 6520 (...) 6561 slice(1, 3, None) 6562 """ -> 6563 start_slice, end_slice = self.slice_locs(start, end, step=step) 6565 # return a slice 6566 if not is_scalar(start_slice): File ~/work/pandas/pandas/pandas/core/indexes/base.py:6803, in Index.slice_locs(self, start, end, step) 6801 start_slice = None 6802 if start is not None: -> 6803 start_slice = self.get_slice_bound(start, "left") 6804 if start_slice is None: 6805 start_slice = 0 File ~/work/pandas/pandas/pandas/core/indexes/base.py:6707, in Index.get_slice_bound(self, label, side) 6703 original_label = label 6705 # For datetime indices label may be a string that has to be converted 6706 # to datetime boundary according to its resolution. -> 6707 label = self._maybe_cast_slice_bound(label, side) 6709 # we need to look up the label 6710 try: File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:636, in DatetimeIndex._maybe_cast_slice_bound(self, label, side) 631 if isinstance(label, dt.date) and not isinstance(label, dt.datetime): 632 # Pandas supports slicing with dates, treated as datetimes at midnight. 633 # https://github.com/pandas-dev/pandas/issues/31501 634 label = Timestamp(label).to_pydatetime() --> 636 label = super()._maybe_cast_slice_bound(label, side) 637 self._data._assert_tzawareness_compat(label) 638 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:4077, in Index._raise_invalid_indexer(self, form, key, reraise) 4075 if reraise is not lib.no_default: 4076 raise TypeError(msg) from reraise -> 4077 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]: np.float64(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]: np.float64(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: str
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: str In [69]: s.sort_index().loc[1:6] Out[69]: 2 c 3 b 4 e 5 d dtype: str
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: str
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]: np.float64(-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]: np.float64(-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: str
In [93]: s.iloc[4:10]
Out[93]:
4 e
5 f
dtype: str
In [94]: s.iloc[8:10]
Out[94]: Series([], dtype: str)
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:1741, in _iLocIndexer._get_list_axis(self, key, axis)
1740 try:
-> 1741 return self.obj.take(key, axis=axis)
1742 except IndexError as err:
1743 # re-raise with different error message, e.g. test_getitem_ndarray_3d
File ~/work/pandas/pandas/pandas/core/generic.py:4041, in NDFrame.take(self, indices, axis, **kwargs)
4039 return self.copy(deep=False)
-> 4041 new_data = self._mgr.take(
4042 indices,
4043 axis=self._get_block_manager_axis(axis),
4044 verify=True,
4045 )
4046 return self._constructor_from_mgr(new_data, axes=new_data.axes).__finalize__(
4047 self, method="take"
4048 )
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:1213, in _LocationIndexer.__getitem__(self, key)
1211 maybe_callable = com.apply_if_callable(key, self.obj)
1212 maybe_callable = self._raise_callable_usage(key, maybe_callable)
-> 1213 return self._getitem_axis(maybe_callable, axis=axis)
File ~/work/pandas/pandas/pandas/core/indexing.py:1770, in _iLocIndexer._getitem_axis(self, key, axis)
1768 # a list of integers
1769 elif is_list_like_indexer(key):
-> 1770 return self._get_list_axis(key, axis=axis)
1772 # a single integer
1773 else:
1774 key = item_from_zerodim(key)
File ~/work/pandas/pandas/pandas/core/indexing.py:1744, in _iLocIndexer._get_list_axis(self, key, axis)
1741 return self.obj.take(key, axis=axis)
1742 except IndexError as err:
1743 # re-raise with different error message, e.g. test_getitem_ndarray_3d
-> 1744 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:1206, in _LocationIndexer.__getitem__(self, key)
1204 if self._is_scalar_access(key):
1205 return self.obj._get_value(*key, takeable=self._takeable)
-> 1206 return self._getitem_tuple(key)
1207 else:
1208 # we by definition only have the 0th axis
1209 axis = self.axis or 0
File ~/work/pandas/pandas/pandas/core/indexing.py:1717, in _iLocIndexer._getitem_tuple(self, tup)
1716 def _getitem_tuple(self, tup: tuple):
-> 1717 tup = self._validate_tuple_indexer(tup)
1718 with suppress(IndexingError):
1719 return self._getitem_lowerdim(tup)
File ~/work/pandas/pandas/pandas/core/indexing.py:990, in _LocationIndexer._validate_tuple_indexer(self, key)
988 for i, k in enumerate(key):
989 try:
--> 990 self._validate_key(k, i)
991 except ValueError as err:
992 raise ValueError(
993 f"Location based indexing can only have [{self._valid_types}] types"
994 ) from err
File ~/work/pandas/pandas/pandas/core/indexing.py:1611, in _iLocIndexer._validate_key(self, key, axis)
1609 return
1610 elif is_integer(key):
-> 1611 self._validate_integer(key, axis)
1612 elif isinstance(key, tuple):
1613 # a tuple should already have been caught by this point
1614 # so don't treat a tuple as a valid indexer
1615 raise IndexingError("Too many indexers")
File ~/work/pandas/pandas/pandas/core/indexing.py:1712, in _iLocIndexer._validate_integer(self, key, axis)
1710 len_axis = len(self.obj._get_axis(axis))
1711 if key >= len_axis or key < -len_axis:
-> 1712 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 âAâ 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 6Reindexing#
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:4890, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
4873 @doc(
4874 NDFrame.reindex, # type: ignore[has-type]
4875 klass=_shared_doc_kwargs["klass"],
(...) 4888 tolerance=None,
4889 ) -> Series:
-> 4890 return super().reindex(
4891 index=index,
4892 method=method,
4893 level=level,
4894 fill_value=fill_value,
4895 limit=limit,
4896 tolerance=tolerance,
4897 copy=copy,
4898 )
File ~/work/pandas/pandas/pandas/core/generic.py:5428, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5425 return self._reindex_multi(axes, fill_value)
5427 # perform the reindex on the axes
-> 5428 return self._reindex_axes(
5429 axes, level, limit, tolerance, method, fill_value
5430 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5450, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5447 continue
5449 ax = self._get_axis(a)
-> 5450 new_index, indexer = ax.reindex(
5451 labels, level=level, limit=limit, tolerance=tolerance, method=method
5452 )
5454 axis = self._get_axis_number(a)
5455 obj = obj._reindex_with_indexers(
5456 {axis: [new_index, indexer]},
5457 fill_value=fill_value,
5458 allow_dups=False,
5459 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4207, in Index.reindex(self, target, method, level, limit, tolerance)
4204 raise ValueError("cannot handle a non-unique multi-index!")
4205 elif not self.is_unique:
4206 # GH#42568
-> 4207 raise ValueError("cannot reindex on an axis with duplicate labels")
4208 else:
4209 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:4890, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
4873 @doc(
4874 NDFrame.reindex, # type: ignore[has-type]
4875 klass=_shared_doc_kwargs["klass"],
(...) 4888 tolerance=None,
4889 ) -> Series:
-> 4890 return super().reindex(
4891 index=index,
4892 method=method,
4893 level=level,
4894 fill_value=fill_value,
4895 limit=limit,
4896 tolerance=tolerance,
4897 copy=copy,
4898 )
File ~/work/pandas/pandas/pandas/core/generic.py:5428, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
5425 return self._reindex_multi(axes, fill_value)
5427 # perform the reindex on the axes
-> 5428 return self._reindex_axes(
5429 axes, level, limit, tolerance, method, fill_value
5430 ).__finalize__(self, method="reindex")
File ~/work/pandas/pandas/pandas/core/generic.py:5450, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
5447 continue
5449 ax = self._get_axis(a)
-> 5450 new_index, indexer = ax.reindex(
5451 labels, level=level, limit=limit, tolerance=tolerance, method=method
5452 )
5454 axis = self._get_axis_number(a)
5455 obj = obj._reindex_with_indexers(
5456 {axis: [new_index, indexer]},
5457 fill_value=fill_value,
5458 allow_dups=False,
5459 )
File ~/work/pandas/pandas/pandas/core/indexes/base.py:4207, in Index.reindex(self, target, method, level, limit, tolerance)
4204 raise ValueError("cannot handle a non-unique multi-index!")
4205 elif not self.is_unique:
4206 # GH#42568
-> 4207 raise ValueError("cannot reindex on an axis with duplicate labels")
4208 else:
4209 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=2, weights=example_weights) Out[135]: 5 5 4 4 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=2, weights='weight_column')
Out[139]:
col1 weight_column
0 9 0.5
1 8 0.4
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]:
col2
0 2
1 3
2 4
Finally, one can also set a seed for sampleâs 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 5Fast 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 youâre 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]: np.int64(5) In [156]: df.at[dates[5], 'A'] Out[156]: np.float64(0.1136484096888855) In [157]: df.iat[3, 0] Out[157]: np.float64(-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 DataFrameâs 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 2.543083
3 three x 0.831311
4 two y -0.816973
# equivalent but slower
In [171]: df2[[x.startswith('t') for x in df2['a']]]
Out[171]:
a b c
2 two y 2.543083
3 three x 0.831311
4 two y -0.816973
# Multiple criteria
In [172]: df2[criterion & (df2['b'] == 'x')]
Out[172]:
a b c
3 three x 0.831311
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 0.831311
Warning
While loc supports two kinds of boolean indexing, iloc only supports indexing with a boolean array. 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 donât 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 youâll 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 DataFrameâs 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 NaN NaN -0.250643 -1.350999
2000-01-02 NaN -0.026679 NaN NaN
2000-01-03 NaN -1.112060 NaN -1.281223
2000-01-04 NaN NaN -0.592066 -0.650567
2000-01-05 -0.374599 NaN -1.133167 NaN
2000-01-06 -1.254148 NaN NaN NaN
2000-01-07 -0.524443 -0.712053 -0.267772 NaN
2000-01-08 -0.076848 NaN -1.819296 -1.122503
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 -0.368085 -0.224661 -0.250643 -1.350999
2000-01-02 -0.142692 -0.026679 -1.345835 -0.938848
2000-01-03 -0.509624 -1.112060 -1.648517 -1.281223
2000-01-04 -0.553689 -0.359996 -0.592066 -0.650567
2000-01-05 -0.374599 -0.592071 -1.133167 -0.661259
2000-01-06 -1.254148 -0.627193 -0.411295 -1.282903
2000-01-07 -0.524443 -0.712053 -0.267772 -1.762567
2000-01-08 -0.076848 -2.431230 -1.819296 -1.122503
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 0.368085 0.224661 0.000000 0.000000
2000-01-02 0.142692 0.000000 1.345835 0.938848
2000-01-03 0.509624 0.000000 1.648517 0.000000
2000-01-04 0.553689 0.359996 0.000000 0.000000
2000-01-05 0.000000 0.592071 0.000000 0.661259
2000-01-06 0.000000 0.627193 0.411295 1.282903
2000-01-07 0.000000 0.000000 0.000000 1.762567
2000-01-08 0.000000 2.431230 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 0.368085 0.224661 -0.250643 -1.350999
2000-01-02 3.000000 -0.026679 3.000000 3.000000
2000-01-03 3.000000 -1.112060 3.000000 -1.281223
2000-01-04 3.000000 3.000000 -0.592066 -0.650567
2000-01-05 -0.374599 0.592071 -1.133167 0.661259
2000-01-06 -1.254148 0.627193 0.411295 1.282903
2000-01-07 -0.524443 -0.712053 -0.267772 1.762567
2000-01-08 -0.076848 2.431230 -1.819296 -1.122503
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 0.368085 0.224661 0.368085 0.368085
2000-01-02 0.142692 0.142692 1.345835 0.938848
2000-01-03 0.509624 0.509624 1.648517 0.509624
2000-01-04 0.553689 0.359996 0.553689 0.553689
2000-01-05 -0.374599 0.592071 -0.374599 0.661259
2000-01-06 -1.254148 0.627193 0.411295 1.282903
2000-01-07 -0.524443 -0.524443 -0.524443 1.762567
2000-01-08 -0.076848 2.431230 -0.076848 -0.076848
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 0.368085 0.224661 0.368085 0.368085
2000-01-02 0.142692 0.142692 1.345835 0.938848
2000-01-03 0.509624 0.509624 1.648517 0.509624
2000-01-04 0.553689 0.359996 0.553689 0.553689
2000-01-05 -0.374599 0.592071 -0.374599 0.661259
2000-01-06 -1.254148 0.627193 0.411295 1.282903
2000-01-07 -0.524443 -0.524443 -0.524443 1.762567
2000-01-08 -0.076848 2.431230 -0.076848 -0.076848
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 NaN NaN -0.250643 -1.350999
2000-01-02 NaN -0.026679 NaN NaN
2000-01-03 NaN -1.112060 NaN -1.281223
2000-01-04 NaN NaN -0.592066 -0.650567
2000-01-05 -0.374599 NaN -1.133167 NaN
2000-01-06 -1.254148 NaN NaN NaN
2000-01-07 -0.524443 -0.712053 -0.267772 NaN
2000-01-08 -0.076848 NaN -1.819296 -1.122503
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 âgreenâ when the second column has âZâ. 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 blackThe
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 0.977227 0.727376 0.630865
1 0.076462 0.474453 0.438921
2 0.118680 0.863670 0.138138
3 0.577363 0.686602 0.595307
4 0.564592 0.520630 0.913052
5 0.926075 0.616184 0.078718
6 0.854477 0.898725 0.076404
7 0.523211 0.591538 0.792342
8 0.216974 0.564056 0.397890
9 0.454131 0.915716 0.074315
# pure python
In [233]: df[(df['a'] < df['b']) & (df['b'] < df['c'])]
Out[233]:
a b c
7 0.523211 0.591538 0.792342
# query
In [234]: df.query('(a < b) & (b < c)')
Out[234]:
a b c
7 0.523211 0.591538 0.792342
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 0
1 3 1
2 3 4
3 0 4
4 0 1
5 3 4
6 4 3
7 1 4
8 0 3
9 0 1
In [238]: df.query('a < b and b < c')
Out[238]:
b c
a
2 3 4
If instead you donât 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 2 3
1 9 1
2 3 1
3 3 0
4 5 6
5 5 2
6 7 4
7 0 1
8 2 5
9 0 1
In [241]: df.query('index < b < c')
Out[241]:
b c
0 2 3
4 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
3 3
You can still use the index in a query expression by using the special identifier âindexâ:
In [245]: df.query('index > 2')
Out[245]:
a
a
3 3
4 1
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(['green', 'green', 'red', 'green', 'red', 'red', 'red', 'red',
'green', 'green'], dtype='<U5')
In [250]: foods
Out[250]:
array(['ham', 'ham', 'ham', 'ham', 'ham', '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
green ham -1.087664 -0.883833
ham -1.554827 -0.118953
red ham -1.460084 -0.020351
green ham -0.256125 0.358575
red ham 1.112033 -0.200521
ham -0.508784 -0.327758
ham 0.627056 0.067058
eggs -1.376511 1.162330
green eggs -0.482120 -0.455309
eggs -0.985682 1.383438
In [254]: df.query('color == "red"')
Out[254]:
0 1
color food
red ham -1.460084 -0.020351
ham 1.112033 -0.200521
ham -0.508784 -0.327758
ham 0.627056 0.067058
eggs -1.376511 1.162330
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
green ham -1.087664 -0.883833
ham -1.554827 -0.118953
red ham -1.460084 -0.020351
green ham -0.256125 0.358575
red ham 1.112033 -0.200521
ham -0.508784 -0.327758
ham 0.627056 0.067058
eggs -1.376511 1.162330
green eggs -0.482120 -0.455309
eggs -0.985682 1.383438
In [257]: df.query('ilevel_0 == "red"')
Out[257]:
0 1
red ham -1.460084 -0.020351
ham 1.112033 -0.200521
ham -0.508784 -0.327758
ham 0.627056 0.067058
eggs -1.376511 1.162330
The convention is ilevel_0, which means âindex level 0â 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 youâre 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 0.972314 0.789179 0.293847
1 0.374439 0.739133 0.221186
2 0.900625 0.534438 0.608763
3 0.166933 0.731582 0.965147
4 0.763981 0.372737 0.639792
5 0.702270 0.730804 0.134089
6 0.522758 0.311910 0.656542
7 0.258647 0.655096 0.654920
8 0.452594 0.454307 0.918260
9 0.581556 0.470410 0.417434
In [260]: df2 = pd.DataFrame(np.random.rand(n + 2, 3), columns=df.columns)
In [261]: df2
Out[261]:
a b c
0 0.552021 0.483125 0.807046
1 0.277950 0.213500 0.471524
2 0.501458 0.141708 0.763617
3 0.081639 0.906284 0.480101
4 0.472250 0.380061 0.822149
5 0.459151 0.851196 0.125791
6 0.857816 0.795472 0.527728
7 0.561164 0.945324 0.622249
8 0.511283 0.577675 0.989138
9 0.528050 0.627750 0.652326
10 0.393289 0.103627 0.056786
11 0.749255 0.505533 0.883673
In [262]: expr = '0.0 <= a <= c <= 0.5'
In [263]: map(lambda frame: frame.query(expr), [df, df2])
Out[263]: <map at 0x7f992f140d30>
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 6 2 2
1 2 6 3
2 3 8 2
3 1 7 2
4 5 1 5
5 9 8 0
6 1 5 0
7 4 9 6
8 2 3 0
9 6 5 4
In [266]: df.query('(a < b) & (b < c)')
Out[266]:
Empty DataFrame
Columns: [a, b, c]
Index: []
In [267]: df[(df['a'] < df['b']) & (df['b'] < df['c'])]
Out[267]:
Empty DataFrame
Columns: [a, b, c]
Index: []
Slightly nicer by removing the parentheses (comparison operators bind tighter than & and |):
In [268]: df.query('a < b & b < c')
Out[268]:
Empty DataFrame
Columns: [a, b, c]
Index: []
Use English instead of symbols:
In [269]: df.query('a < b and b < c')
Out[269]:
Empty DataFrame
Columns: [a, b, c]
Index: []
Pretty close to how you might write it on paper:
In [270]: df.query('a < b < c')
Out[270]:
Empty DataFrame
Columns: [a, b, c]
Index: []
The in and not in operators#
query() also supports special use of Pythonâs 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 3
1 a a 0 0
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
6 d b 3 6
7 d b 2 7
8 e c 2 8
9 e c 2 8
10 f c 3 3
11 f c 1 3
In [273]: df.query('a in b')
Out[273]:
a b c d
0 a a 2 3
1 a a 0 0
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
# 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 3
1 a a 0 0
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
In [275]: df.query('a not in b')
Out[275]:
a b c d
6 d b 3 6
7 d b 2 7
8 e c 2 8
9 e c 2 8
10 f c 3 3
11 f c 1 3
# pure Python
In [276]: df[~df['a'].isin(df['b'])]
Out[276]:
a b c d
6 d b 3 6
7 d b 2 7
8 e c 2 8
9 e c 2 8
10 f c 3 3
11 f c 1 3
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 3
2 b a 1 6
4 c b 1 7
5 c b 2 7
# pure Python
In [278]: df[df['b'].isin(df['a']) & (df['c'] < df['d'])]
Out[278]:
a b c d
0 a a 2 3
2 b a 1 6
4 c b 1 7
5 c b 2 7
6 d b 3 6
7 d b 2 7
8 e c 2 8
9 e c 2 8
11 f c 1 3
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.
== 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 3
1 a a 0 0
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
6 d b 3 6
7 d b 2 7
8 e c 2 8
9 e c 2 8
10 f c 3 3
11 f c 1 3
# pure Python
In [280]: df[df['b'].isin(["a", "b", "c"])]
Out[280]:
a b c d
0 a a 2 3
1 a a 0 0
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
6 d b 3 6
7 d b 2 7
8 e c 2 8
9 e c 2 8
10 f c 3 3
11 f c 1 3
In [281]: df.query('c == [1, 2]')
Out[281]:
a b c d
0 a a 2 3
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
7 d b 2 7
8 e c 2 8
9 e c 2 8
11 f c 1 3
In [282]: df.query('c != [1, 2]')
Out[282]:
a b c d
1 a a 0 0
6 d b 3 6
10 f c 3 3
# using in/not in
In [283]: df.query('[1, 2] in c')
Out[283]:
a b c d
0 a a 2 3
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
7 d b 2 7
8 e c 2 8
9 e c 2 8
11 f c 1 3
In [284]: df.query('[1, 2] not in c')
Out[284]:
a b c d
1 a a 0 0
6 d b 3 6
10 f c 3 3
# pure Python
In [285]: df[df['c'].isin([1, 2])]
Out[285]:
a b c d
0 a a 2 3
2 b a 1 6
3 b a 2 2
4 c b 1 7
5 c b 2 7
7 d b 2 7
8 e c 2 8
9 e c 2 8
11 f c 1 3
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
4 0.580078 0.620439 0.434445 False
7 0.260776 0.290751 0.462080 False
9 0.165948 0.902260 0.825007 False
In [289]: df.query('not bools')
Out[289]:
a b c bools
4 0.580078 0.620439 0.434445 False
7 0.260776 0.290751 0.462080 False
9 0.165948 0.902260 0.825007 False
In [290]: df.query('not bools') == df[~df['bools']]
Out[290]:
a b c bools
4 True True True True
7 True True True True
9 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 0.260776 0.290751 0.46208 False
In [294]: longer
Out[294]:
a b c bools
7 0.260776 0.290751 0.46208 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.
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 -1.467157
1 one y 0.113937
2 two x -1.428572
3 two y 0.337109
4 two x 0.052469
5 three x -2.294560
6 four x 2.148507
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 -1.467157
2 two x -1.428572
5 three x -2.294560
6 four x 2.148507
In [304]: df2.drop_duplicates('a', keep='last')
Out[304]:
a b c
1 one y 0.113937
4 two x 0.052469
5 three x -2.294560
6 four x 2.148507
In [305]: df2.drop_duplicates('a', keep=False)
Out[305]:
a b c
5 three x -2.294560
6 four x 2.148507
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 -1.467157
1 one y 0.113937
2 two x -1.428572
3 two y 0.337109
5 three x -2.294560
6 four x 2.148507
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 -0.293144
a 1 -1.596615
b 2 0.149716
c 3 0.173897
b 4 -0.049440
a 5 1.394590
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 -0.293144
b 2 0.149716
c 3 0.173897
In [312]: df3[~df3.index.duplicated(keep='last')]
Out[312]:
a b
c 3 0.173897
b 4 -0.049440
a 5 1.394590
In [313]: df3[~df3.index.duplicated(keep=False)]
Out[313]:
a b
c 3 0.173897
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]: np.int64(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 heterogeneous column types, we subset columns to avoid unnecessary NumPy conversions:
def pd_lookup_het(df, row_labels, col_labels): rows = df.index.get_indexer(row_labels) cols = df.columns.get_indexer(col_labels) sub = df.take(np.unique(cols), axis=1) sub = sub.take(np.unique(rows), axis=0) rows = sub.index.get_indexer(row_labels) values = sub.melt()["value"] cols = sub.columns.get_indexer(col_labels) flat_index = rows + cols * len(sub) result = values[flat_index] return result
For homogeneous column types, it is fastest to skip column subsetting and go directly to NumPy:
def pd_lookup_hom(df, row_labels, col_labels):
rows = df.index.get_indexer(row_labels)
df = df.loc[:, sorted(set(col_labels))]
cols = df.columns.get_indexer(col_labels)
result = df.to_numpy()[rows, cols]
return result
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.
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 [317]: index = pd.Index(['e', 'd', 'a', 'b']) In [318]: index Out[318]: Index(['e', 'd', 'a', 'b'], dtype='str') In [319]: 'd' in index Out[319]: True
or using numbers:
In [320]: index = pd.Index([1, 5, 12]) In [321]: index Out[321]: Index([1, 5, 12], dtype='int64') In [322]: 5 in index Out[322]: 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 [323]: index = pd.Index(['e', 'd', 'a', 'b'], dtype="string") In [324]: index Out[324]: Index(['e', 'd', 'a', 'b'], dtype='string') In [325]: index = pd.Index([1, 5, 12], dtype="int8") In [326]: index Out[326]: Index([1, 5, 12], dtype='int8') In [327]: index = pd.Index([1, 5, 12], dtype="float32") In [328]: index Out[328]: Index([1.0, 5.0, 12.0], dtype='float32')
You can also pass a name to be stored in the index:
In [329]: index = pd.Index(['e', 'd', 'a', 'b'], name='something') In [330]: index.name Out[330]: 'something'
The name, if set, will be shown in the console display:
In [331]: index = pd.Index(list(range(5)), name='rows') In [332]: columns = pd.Index(['A', 'B', 'C'], name='cols') In [333]: df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns) In [334]: df Out[334]: cols A B C rows 0 0.698035 0.631397 0.816412 1 0.709404 -1.193616 -0.263520 2 -0.878602 0.035458 -0.285808 3 -0.957431 2.243279 -1.124957 4 -1.994374 0.050270 0.512794 In [335]: df['A'] Out[335]: rows 0 0.698035 1 0.709404 2 -0.878602 3 -0.957431 4 -1.994374 Name: A, dtype: float64Setting metadata#
Indexes are âmostly immutableâ, 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 [336]: ind = pd.Index([1, 2, 3])
In [337]: ind.rename("apple")
Out[337]: Index([1, 2, 3], dtype='int64', name='apple')
In [338]: ind
Out[338]: Index([1, 2, 3], dtype='int64')
In [339]: ind = ind.set_names(["apple"])
In [340]: ind.name = "bob"
In [341]: ind
Out[341]: Index([1, 2, 3], dtype='int64', name='bob')
set_names, set_levels, and set_codes also take an optional level argument
In [342]: index = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])
In [343]: index
Out[343]:
MultiIndex([(0, 'one'),
(0, 'two'),
(1, 'one'),
(1, 'two'),
(2, 'one'),
(2, 'two')],
names=['first', 'second'])
In [344]: index.levels[1]
Out[344]: Index(['one', 'two'], dtype='str', name='second')
In [345]: index.set_levels(["a", "b"], level=1)
Out[345]:
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 [346]: a = pd.Index(['c', 'b', 'a']) In [347]: b = pd.Index(['c', 'e', 'd']) In [348]: a.difference(b) Out[348]: Index(['a', 'b'], dtype='str')
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 [349]: idx1 = pd.Index([1, 2, 3, 4]) In [350]: idx2 = pd.Index([2, 3, 4, 5]) In [351]: idx1.symmetric_difference(idx2) Out[351]: 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 [352]: idx1 = pd.Index([0, 1, 2]) In [353]: idx2 = pd.Index([0.5, 1.5]) In [354]: idx1.union(idx2) Out[354]: 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 [355]: idx1 = pd.Index([1, np.nan, 3, 4])
In [356]: idx1
Out[356]: Index([1.0, nan, 3.0, 4.0], dtype='float64')
In [357]: idx1.fillna(2)
Out[357]: Index([1.0, 2.0, 3.0, 4.0], dtype='float64')
In [358]: idx2 = pd.DatetimeIndex([pd.Timestamp('2011-01-01'),
.....: pd.NaT,
.....: pd.Timestamp('2011-01-03')])
.....:
In [359]: idx2
Out[359]: DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03'], dtype='datetime64[s]', freq=None)
In [360]: idx2.fillna(pd.Timestamp('2011-01-02'))
Out[360]: 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 youâve 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 [361]: data = pd.DataFrame({'a': ['bar', 'bar', 'foo', 'foo'],
.....: 'b': ['one', 'two', 'one', 'two'],
.....: 'c': ['z', 'y', 'x', 'w'],
.....: 'd': [1., 2., 3, 4]})
.....:
In [362]: data
Out[362]:
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 [363]: indexed1 = data.set_index('c')
In [364]: indexed1
Out[364]:
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 [365]: indexed2 = data.set_index(['a', 'b'])
In [366]: indexed2
Out[366]:
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 [367]: frame = data.set_index('c', drop=False)
In [368]: frame = frame.set_index(['a', 'b'], append=True)
In [369]: frame
Out[369]:
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 [370]: data.set_index('c', drop=False)
Out[370]:
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 DataFrameâs columns and sets a simple integer index. This is the inverse operation of set_index().
In [371]: data
Out[371]:
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 [372]: data.reset_index()
Out[372]:
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 [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
In [374]: frame.reset_index(level=1)
Out[374]:
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 DataFrameâs columns.
You can assign a custom index to the index attribute:
In [375]: df_idx = pd.DataFrame(range(4))
In [376]: df_idx.index = pd.Index([10, 20, 30, 40], name="a")
In [377]: df_idx
Out[377]:
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.
Series Assignment and Index Alignment#When assigning a Series to a DataFrame column, pandas performs automatic alignment based on index labels. This is a fundamental behavior that can be surprising to new users who might expect positional assignment.
Key Points:#Series values are matched to DataFrame rows by index label
Position/order in the Series doesnât matter
Missing index labels result in NaN values
This behavior is consistent across df[col] = series and df.loc[:, col] = series
Examples: .. ipython:: python
import pandas as pd
# Create a DataFrame df = pd.DataFrame({âvaluesâ: [1, 2, 3]}, index=[âxâ, âyâ, âzâ])
# Series with matching indices (different order) s1 = pd.Series([10, 20, 30], index=[âzâ, âxâ, âyâ]) df[âalignedâ] = s1 # Aligns by index, not position print(df)
# Series with partial index match s2 = pd.Series([100, 200], index=[âxâ, âzâ]) df[âpartialâ] = s2 # Missing âyâ gets NaN print(df)
# Series with non-matching indices s3 = pd.Series([1000, 2000], index=[âaâ, âbâ]) df[ânomatchâ] = s3 # All values become NaN print(df)
#Avoiding Confusion: #If you want positional assignment instead of index alignment: # reset the Series index to match DataFrame index df[âs1_valuesâ] = s1.reindex(df.index)
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