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# 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
DataFrame | `frame[[colname]]` | `DataFrame` with columns corresponding to `[colname]`
DataFrame | `frame[list_of_colnames]` | `DataFrame` with columns corresponding to `list_of_colnames`

The same principle applies to label-based selection with `.loc`: a list-like of labels preserves the corresponding axis (so `df.loc[:, ["A"]]` returns a `DataFrame`, even when the list contains a single label), while a scalar label reduces the axis when labels on that axis are unique (so `df.loc[:, "A"]` typically returns a `Series`; if `"A"` is duplicated on the axis, a `DataFrame` is returned instead). See Selection by label for details.

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 `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:1342, in _LocationIndexer.__getitem__(self, key)
>        1340 maybe_callable = com.apply_if_callable(key, self.obj)
>        1341 maybe_callable = self._raise_callable_usage(key, maybe_callable)
>     -> 1342 return self._getitem_axis(maybe_callable, axis=axis)

>     File ~/work/pandas/pandas/pandas/core/indexing.py:1914, in _LocIndexer._getitem_axis(self, key, axis)
>        1912 if isinstance(key, slice):
>        1913     self._validate_key(key, axis)
>     -> 1914     return self._get_slice_axis(key, axis=axis)
>        1915 elif com.is_bool_indexer(key):
>        1916     return self._getbool_axis(key, axis=axis)

>     File ~/work/pandas/pandas/pandas/core/indexing.py:1978, in _LocIndexer._get_slice_axis(self, slice_obj, axis)
>        1975     return obj.copy(deep=False)
>        1977 labels = obj._get_axis(axis)
>     -> 1978 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step)
>        1980 if isinstance(indexer, slice):
>        1981     return self.obj._slice(indexer, axis=axis)

>     File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:1338, in DatetimeIndex.slice_indexer(self, start, end, step)
>        1330 # GH#33146 if start and end are combinations of str and None and Index is not
>        1331 # monotonic, we can not use Index.slice_indexer because it does not honor the
>        1332 # actual elements, is only searching for start and end
>        1333 if (
>        1334     check_str_or_none(start)
>        1335     or check_str_or_none(end)
>        1336     or self.is_monotonic_increasing
>        1337 ):
>     -> 1338     return Index.slice_indexer(self, start, end, step)
>        1340 mask = np.array(True)
>        1341 in_index = True

>     File ~/work/pandas/pandas/pandas/core/indexes/base.py:7075, in Index.slice_indexer(self, start, end, step)
>        7024 def slice_indexer(
>        7025     self,
>        7026     start: Hashable | None = None,
>        7027     end: Hashable | None = None,
>        7028     step: int | None = None,
>        7029 ) -> slice:
>        7030     """
>        7031     Compute the slice indexer for input labels and step.
>        7032
>        (...)   7073     slice(1, 3, None)
>        7074     """
>     -> 7075     start_slice, end_slice = self.slice_locs(start, end, step=step)
>        7077     # return a slice
>        7078     if not is_scalar(start_slice):

>     File ~/work/pandas/pandas/pandas/core/indexes/base.py:7352, in Index.slice_locs(self, start, end, step)
>        7350 start_slice = None
>        7351 if start is not None:
>     -> 7352     start_slice = self.get_slice_bound(start, "left")
>        7353 if start_slice is None:
>        7354     start_slice = 0

>     File ~/work/pandas/pandas/pandas/core/indexes/base.py:7238, in Index.get_slice_bound(self, label, side)
>        7234 original_label = label
>        7236 # For datetime indices label may be a string that has to be converted
>        7237 # to datetime boundary according to its resolution.
>     -> 7238 label = self._maybe_cast_slice_bound(label, side)
>        7240 # we need to look up the label
>        7241 try:

>     File ~/work/pandas/pandas/pandas/core/indexes/datetimes.py:1298, in DatetimeIndex._maybe_cast_slice_bound(self, label, side)
>        1289     label = Timestamp(label).to_pydatetime()
>        1290     warnings.warn(
>        1291         # GH#35830 deprecate last remaining inconsistent date treatment
>        1292         "Slicing with a datetime.date object is deprecated. "
>        (...)   1295         stacklevel=find_stack_level(),
>        1296     )
>     -> 1298 label = super()._maybe_cast_slice_bound(label, side)
>        1299 self._data._assert_tzawareness_compat(label)
>        1300 return Timestamp(label)

>     File ~/work/pandas/pandas/pandas/core/indexes/datetimelike.py:629, in DatetimeIndexOpsMixin._maybe_cast_slice_bound(self, label, side)
>         627     return lower if side == "left" else upper
>         628 elif not isinstance(label, self._data._recognized_scalars):
>     --> 629     self._raise_invalid_indexer("slice", label)
>         631 return label

>     File ~/work/pandas/pandas/pandas/core/indexes/base.py:4293, in Index._raise_invalid_indexer(self, form, key, reraise)
>        4291 if reraise is not lib.no_default:
>        4292     raise TypeError(msg) from reraise
>     -> 4293 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.

When using a slice with a step, such as `.loc[start:stop:step]`, note that start and stop are interpreted as labels, while step is applied over the positional index within that label range. This means a stepped slice will behave differently than using the labels `range(start, stop, step)` when the index is not contiguous integers.

For example, in a `Series` with a non-contiguous integer index:

    In [72]: s = pd.Series(range(10), index=[0, 5, 10, 15, 20, 25, 30, 35, 40, 45])

    In [73]: s.loc[10:50:5]              # (10), then skip 5 positions → 35 only
    Out[73]:
    10    2
    35    7
    dtype: int64

    In [74]: s.loc[[10, 15, 20, 25]]     # explicit label selection
    Out[74]:
    10    2
    15    3
    20    4
    25    5
    dtype: int64

The first applies step across positional locations between the start/stop labels. The second selects each label directly.

Similarly, with a string-based index, the behavior is identical:

    In [75]: s = pd.Series(range(10), index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'])

    In [76]: s.loc['b':'i':2]            # Start at 'b' (position 1), stop at 'i' (position 8), step 2 positions → 'b', 'd', 'f', 'h'
    Out[76]:
    b    1
    d    3
    f    5
    h    7
    dtype: int64

    In [77]: s.loc[['b', 'd', 'f', 'h']] # explicit label selection
    Out[77]:
    b    1
    d    3
    f    5
    h    7
    dtype: int64

In both cases, start and stop determine the label boundaries (inclusive), while step skips positions within that range, regardless of the index type.

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

    In [79]: s1
    Out[79]:
    0    0.695775
    2    0.341734
    4    0.959726
    6   -1.110336
    8   -0.619976
    dtype: float64

    In [80]: s1.iloc[:3]
    Out[80]:
    0    0.695775
    2    0.341734
    4    0.959726
    dtype: float64

    In [81]: s1.iloc[3]
    Out[81]: np.float64(-1.110336102891167)

Note that setting works as well:

    In [82]: s1.iloc[:3] = 0

    In [83]: s1
    Out[83]:
    0    0.000000
    2    0.000000
    4    0.000000
    6   -1.110336
    8   -0.619976
    dtype: float64

With a DataFrame:

    In [84]: df1 = pd.DataFrame(np.random.randn(6, 4),
       ....:                    index=list(range(0, 12, 2)),
       ....:                    columns=list(range(0, 8, 2)))
       ....:

    In [85]: df1
    Out[85]:
               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 [86]: df1.iloc[:3]
    Out[86]:
              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 [87]: df1.iloc[1:5, 2:4]
    Out[87]:
              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 [88]: df1.iloc[[1, 3, 5], [1, 3]]
    Out[88]:
               2         6
    2  -0.154951 -2.179861
    6  -0.345352  0.690579
    10 -1.236269 -0.487602

    In [89]: df1.iloc[1:3, :]
    Out[89]:
              0         2         4         6
    2  0.403310 -0.154951  0.301624 -2.179861
    4 -1.369849 -0.954208  1.462696 -1.743161

    In [90]: df1.iloc[:, 1:3]
    Out[90]:
               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 [91]: df1.iloc[1, 1]
    Out[91]: np.float64(-0.1549507744249032)

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

    In [92]: df1.iloc[1]
    Out[92]:
    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 [93]: x = list('abcdef')

    In [94]: x
    Out[94]: ['a', 'b', 'c', 'd', 'e', 'f']

    In [95]: x[4:10]
    Out[95]: ['e', 'f']

    In [96]: x[8:10]
    Out[96]: []

    In [97]: s = pd.Series(x)

    In [98]: s
    Out[98]:
    0    a
    1    b
    2    c
    3    d
    4    e
    5    f
    dtype: str

    In [99]: s.iloc[4:10]
    Out[99]:
    4    e
    5    f
    dtype: str

    In [100]: s.iloc[8:10]
    Out[100]: 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 [101]: dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB'))

    In [102]: dfl
    Out[102]:
              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 [103]: dfl.iloc[:, 2:3]
    Out[103]:
    Empty DataFrame
    Columns: []
    Index: [0, 1, 2, 3, 4]

    In [104]: dfl.iloc[:, 1:3]
    Out[104]:
              B
    0 -2.182937
    1  0.084844
    2  1.519970
    3  0.600178
    4  0.132885

    In [105]: dfl.iloc[4:6]
    Out[105]:
             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 [106]: dfl.iloc[[4, 5, 6]]
    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    File ~/work/pandas/pandas/pandas/core/indexing.py:2394, in _iLocIndexer._get_list_axis(self, key, axis)
       2393 try:
    -> 2394     return self.obj.take(key, axis=axis)
       2395 except IndexError as err:
       2396     # re-raise with different error message, e.g. test_getitem_ndarray_3d

    File ~/work/pandas/pandas/pandas/core/generic.py:4224, in NDFrame.take(self, indices, axis, **kwargs)
       4222             return self.copy(deep=False)
       4223
    -> 4224         new_data = self._mgr.take(
       4225             indices,

    File ~/work/pandas/pandas/pandas/core/internals/managers.py:1091, in BaseBlockManager.take(self, indexer, axis, verify)
       1090 n = self.shape[axis]
    -> 1091 indexer = maybe_convert_indices(indexer, n, verify=verify)
       1093 new_labels = self.axes[axis].take(indexer)

    File ~/work/pandas/pandas/pandas/core/indexers/utils.py:284, in maybe_convert_indices(indices, n, verify)
        283     if mask.any():
    --> 284         raise IndexError("indices are out-of-bounds")
        285 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[106], line 1
    ----> 1 dfl.iloc[[4, 5, 6]]

    File ~/work/pandas/pandas/pandas/core/indexing.py:1342, in _LocationIndexer.__getitem__(self, key)
       1340 maybe_callable = com.apply_if_callable(key, self.obj)
       1341 maybe_callable = self._raise_callable_usage(key, maybe_callable)
    -> 1342 return self._getitem_axis(maybe_callable, axis=axis)

    File ~/work/pandas/pandas/pandas/core/indexing.py:2423, in _iLocIndexer._getitem_axis(self, key, axis)
       2421 # a list of integers
       2422 elif is_list_like_indexer(key):
    -> 2423     return self._get_list_axis(key, axis=axis)
       2425 # a single integer
       2426 else:
       2427     key = item_from_zerodim(key)

    File ~/work/pandas/pandas/pandas/core/indexing.py:2397, in _iLocIndexer._get_list_axis(self, key, axis)
       2394     return self.obj.take(key, axis=axis)
       2395 except IndexError as err:
       2396     # re-raise with different error message, e.g. test_getitem_ndarray_3d
    -> 2397     raise IndexError("positional indexers are out-of-bounds") from err

    IndexError: positional indexers are out-of-bounds

    In [107]: dfl.iloc[:, 4]
    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    Cell In[107], line 1
    ----> 1 dfl.iloc[:, 4]

    File ~/work/pandas/pandas/pandas/core/indexing.py:1335, in _LocationIndexer.__getitem__(self, key)
       1333     if self._is_scalar_access(key):
       1334         return self.obj._get_value(*key, takeable=self._takeable)
    -> 1335     return self._getitem_tuple(key)
       1336 else:
       1337     # we by definition only have the 0th axis
       1338     axis = self.axis or 0

    File ~/work/pandas/pandas/pandas/core/indexing.py:2370, in _iLocIndexer._getitem_tuple(self, tup)
       2369 def _getitem_tuple(self, tup: tuple):
    -> 2370     tup = self._validate_tuple_indexer(tup)
       2371     with suppress(IndexingError):
       2372         return self._getitem_lowerdim(tup)

    File ~/work/pandas/pandas/pandas/core/indexing.py:1124, in _LocationIndexer._validate_tuple_indexer(self, key)
       1122 for i, k in enumerate(key):
       1123     try:
    -> 1124         self._validate_key(k, i)
       1125     except ValueError as err:
       1126         raise ValueError(
       1127             f"Location based indexing can only have [{self._valid_types}] types"
       1128         ) from err

    File ~/work/pandas/pandas/pandas/core/indexing.py:2264, in _iLocIndexer._validate_key(self, key, axis)
       2262     return
       2263 elif is_integer(key):
    -> 2264     self._validate_integer(key, axis)
       2265 elif isinstance(key, tuple):
       2266     # a tuple should already have been caught by this point
       2267     # so don't treat a tuple as a valid indexer
       2268     raise IndexingError("Too many indexers")

    File ~/work/pandas/pandas/pandas/core/indexing.py:2365, in _iLocIndexer._validate_integer(self, key, axis)
       2363 len_axis = len(self.obj._get_axis(axis))
       2364 if key >= len_axis or key < -len_axis:
    -> 2365     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 [108]: df1 = pd.DataFrame(np.random.randn(6, 4),
       .....:                    index=list('abcdef'),
       .....:                    columns=list('ABCD'))
       .....:

    In [109]: df1
    Out[109]:
              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 [110]: df1.loc[lambda df: df['A'] > 0, :]
    Out[110]:
              A         B         C         D
    c  0.299368 -0.863838  0.408204 -1.048089
    e  1.289997  0.082423 -0.055758  0.536580

    In [111]: df1.loc[:, lambda df: ['A', 'B']]
    Out[111]:
              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 [112]: df1.iloc[:, lambda df: [0, 1]]
    Out[112]:
              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 [113]: df1[lambda df: df.columns[0]]
    Out[113]:
    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 [114]: df1['A'].loc[lambda s: s > 0]
    Out[114]:
    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 [115]: bb = pd.read_csv('data/baseball.csv', index_col='id')

    In [116]: (bb.groupby(['year', 'team']).sum(numeric_only=True)
       .....:    .loc[lambda df: df['r'] > 100])
       .....:
    Out[116]:
               stint    g    ab    r    h  ...   ibb   hbp    sh    sf  gidp
    year team                              ...
    2007 CIN       6  379   745  101  203  ...  14.0   1.0   1.0  15.0  18.0
         DET       5  301  1062  162  283  ...   3.0  10.0   4.0   8.0  28.0
         HOU       4  311   926  109  218  ...   3.0   9.0  16.0   6.0  17.0
         LAN      11  413  1021  153  293  ...   8.0   9.0   3.0   8.0  29.0
         NYN      13  622  1854  240  509  ...  24.0  23.0  18.0  15.0  48.0
         SFN       5  482  1305  198  337  ...  51.0   8.0  16.0   6.0  41.0
         TEX       2  198   729  115  200  ...   4.0   5.0   2.0   8.0  16.0
         TOR       4  459  1408  187  378  ...  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 [117]: dfd = pd.DataFrame({'A': [1, 2, 3],
       .....:                     'B': [4, 5, 6]},
       .....:                    index=list('abc'))
       .....:

    In [118]: dfd
    Out[118]:
       A  B
    a  1  4
    b  2  5
    c  3  6

    In [119]: dfd.loc[dfd.index[[0, 2]], 'A']
    Out[119]:
    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 [120]: dfd.iloc[[0, 2], dfd.columns.get_loc('A')]
    Out[120]:
    a    1
    c    3
    Name: A, dtype: int64

For getting multiple indexers, using `.get_indexer`:

    In [121]: dfd.iloc[[0, 2], dfd.columns.get_indexer(['A', 'B'])]
    Out[121]:
       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 [122]: s = pd.Series([1, 2, 3])

    In [123]: s.reindex([1, 2, 3])
    Out[123]:
    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 [124]: labels = [1, 2, 3]

    In [125]: s.loc[s.index.intersection(labels)]
    Out[125]:
    1    2
    2    3
    dtype: int64

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

    In [126]: s = pd.Series(np.arange(4), index=['a', 'a', 'b', 'c'])

    In [127]: labels = ['c', 'd']

    In [128]: s.reindex(labels)
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[128], line 1
    ----> 1 s.reindex(labels)

    File ~/work/pandas/pandas/pandas/core/series.py:5908, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5904         original index entry) unfilled.
       5905
       5906         See the :ref:`user guide <basics.reindexing>` for more.
       5907         """
    -> 5908         return super().reindex(
       5909             index=index,
       5910             method=method,
       5911             level=level,

    File ~/work/pandas/pandas/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5606         if self._needs_reindex_multi(axes, method, level):
       5607             return self._reindex_multi(axes, fill_value)
       5608
       5609         # perform the reindex on the axes
    -> 5610         return self._reindex_axes(
       5611             axes, level, limit, tolerance, method, fill_value
       5612         ).__finalize__(self, method="reindex")

    File ~/work/pandas/pandas/pandas/core/generic.py:5632, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
       5628             if labels is None:
       5629                 continue
       5630
       5631             ax = self._get_axis(a)
    -> 5632             new_index, indexer = ax.reindex(
       5633                 labels, level=level, limit=limit, tolerance=tolerance, method=method
       5634             )
       5635

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:4426, in Index.reindex(self, target, method, level, limit, tolerance)
       4423     raise ValueError("cannot handle a non-unique multi-index!")
       4424 elif not self.is_unique:
       4425     # GH#42568
    -> 4426     raise ValueError("cannot reindex on an axis with duplicate labels")
       4427 else:
       4428     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 [129]: s.loc[s.index.intersection(labels)].reindex(labels)
    Out[129]:
    c    3.0
    d    NaN
    dtype: float64

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

    In [130]: labels = ['a', 'd']

    In [131]: s.loc[s.index.intersection(labels)].reindex(labels)
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[131], line 1
    ----> 1 s.loc[s.index.intersection(labels)].reindex(labels)

    File ~/work/pandas/pandas/pandas/core/series.py:5908, in Series.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)
       5904         original index entry) unfilled.
       5905
       5906         See the :ref:`user guide <basics.reindexing>` for more.
       5907         """
    -> 5908         return super().reindex(
       5909             index=index,
       5910             method=method,
       5911             level=level,

    File ~/work/pandas/pandas/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)
       5606         if self._needs_reindex_multi(axes, method, level):
       5607             return self._reindex_multi(axes, fill_value)
       5608
       5609         # perform the reindex on the axes
    -> 5610         return self._reindex_axes(
       5611             axes, level, limit, tolerance, method, fill_value
       5612         ).__finalize__(self, method="reindex")

    File ~/work/pandas/pandas/pandas/core/generic.py:5632, in NDFrame._reindex_axes(self, axes, level, limit, tolerance, method, fill_value)
       5628             if labels is None:
       5629                 continue
       5630
       5631             ax = self._get_axis(a)
    -> 5632             new_index, indexer = ax.reindex(
       5633                 labels, level=level, limit=limit, tolerance=tolerance, method=method
       5634             )
       5635

    File ~/work/pandas/pandas/pandas/core/indexes/base.py:4426, in Index.reindex(self, target, method, level, limit, tolerance)
       4423     raise ValueError("cannot handle a non-unique multi-index!")
       4424 elif not self.is_unique:
       4425     # GH#42568
    -> 4426     raise ValueError("cannot reindex on an axis with duplicate labels")
       4427 else:
       4428     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 [132]: s = pd.Series([0, 1, 2, 3, 4, 5])

    # When no arguments are passed, returns 1 row.
    In [133]: s.sample()
    Out[133]:
    4    4
    dtype: int64

    # One may specify either a number of rows:
    In [134]: s.sample(n=3)
    Out[134]:
    0    0
    4    4
    1    1
    dtype: int64

    # Or a fraction of the rows:
    In [135]: s.sample(frac=0.5)
    Out[135]:
    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 [136]: s = pd.Series([0, 1, 2, 3, 4, 5])

    # Without replacement (default):
    In [137]: s.sample(n=6, replace=False)
    Out[137]:
    0    0
    1    1
    5    5
    3    3
    2    2
    4    4
    dtype: int64

    # With replacement:
    In [138]: s.sample(n=6, replace=True)
    Out[138]:
    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 [139]: s = pd.Series([0, 1, 2, 3, 4, 5])

    In [140]: example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]

    In [141]: s.sample(n=2, weights=example_weights)
    Out[141]:
    5    5
    4    4
    dtype: int64

    # Weights will be re-normalized automatically
    In [142]: example_weights2 = [0.5, 0, 0, 0, 0, 0]

    In [143]: s.sample(n=1, weights=example_weights2)
    Out[143]:
    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 [144]: df2 = pd.DataFrame({'col1': [9, 8, 7, 6],
       .....:                     'weight_column': [0.5, 0.4, 0.1, 0]})
       .....:

    In [145]: df2.sample(n=2, weights='weight_column')
    Out[145]:
       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 [146]: df3 = pd.DataFrame({'col1': [1, 2, 3], 'col2': [2, 3, 4]})

    In [147]: df3.sample(n=1, axis=1)
    Out[147]:
       col2
    0     2
    1     3
    2     4

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

    # With a given seed, the sample will always draw the same rows.
    In [149]: df4.sample(n=2, random_state=2)
    Out[149]:
       col1  col2
    2     3     4
    1     2     3

    In [150]: df4.sample(n=2, random_state=2)
    Out[150]:
       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 [151]: se = pd.Series([1, 2, 3])

    In [152]: se
    Out[152]:
    0    1
    1    2
    2    3
    dtype: int64

    In [153]: se[5] = 5.

    In [154]: se
    Out[154]:
    0    1
    1    2
    2    3
    5    5
    dtype: int64

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

    In [155]: dfi = pd.DataFrame(np.arange(6).reshape(3, 2),
       .....:                    columns=['A', 'B'])
       .....:

    In [156]: dfi
    Out[156]:
       A  B
    0  0  1
    1  2  3
    2  4  5

    In [157]: dfi.loc[:, 'C'] = dfi.loc[:, 'A']

    In [158]: dfi
    Out[158]:
       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 [159]: dfi.loc[3] = 5

    In [160]: dfi
    Out[160]:
       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 [161]: s.iat[5]
    Out[161]: np.int64(5)

    In [162]: df.at[dates[5], 'A']
    Out[162]: np.float64(0.1136484096888855)

    In [163]: df.iat[3, 0]
    Out[163]: np.float64(-0.7067711336300845)

You can also set using these same indexers.

    In [164]: df.at[dates[5], 'A'] = 7

    In [165]: df.iat[3, 0] = 7

`at` previously could enlarge the object in-place if the indexer was missing, but this behavior is deprecated. Use `.loc` instead for setting values with new keys:

    In [166]: df.loc[dates[-1] + pd.Timedelta('1 day'), 0] = 7

    In [167]: df
    Out[167]:
                       A         B         C         D    0
    2000-01-01 -0.282863  0.469112 -1.509059 -1.135632  NaN
    2000-01-02 -0.173215  1.212112  0.119209 -1.044236  NaN
    2000-01-03 -2.104569 -0.861849 -0.494929  1.071804  NaN
    2000-01-04  7.000000  0.721555 -1.039575  0.271860  NaN
    2000-01-05  0.567020 -0.424972  0.276232 -1.087401  NaN
    2000-01-06  7.000000 -0.673690 -1.478427  0.524988  NaN
    2000-01-07  0.577046  0.404705 -1.715002 -1.039268  NaN
    2000-01-08 -1.157892 -0.370647 -1.344312  0.844885  NaN
    2000-01-09       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 [168]: s = pd.Series(range(-3, 4))

    In [169]: s
    Out[169]:
    0   -3
    1   -2
    2   -1
    3    0
    4    1
    5    2
    6    3
    dtype: int64

    In [170]: s[s > 0]
    Out[170]:
    4    1
    5    2
    6    3
    dtype: int64

    In [171]: s[(s < -1) | (s > 0.5)]
    Out[171]:
    0   -3
    1   -2
    4    1
    5    2
    6    3
    dtype: int64

    In [172]: s[~(s < 0)]
    Out[172]:
    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 [173]: df[df['A'] > 0]
    Out[173]:
                       A         B         C         D   0
    2000-01-04  7.000000  0.721555 -1.039575  0.271860 NaN
    2000-01-05  0.567020 -0.424972  0.276232 -1.087401 NaN
    2000-01-06  7.000000 -0.673690 -1.478427  0.524988 NaN
    2000-01-07  0.577046  0.404705 -1.715002 -1.039268 NaN

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

    In [174]: 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 [175]: criterion = df2['a'].map(lambda x: x.startswith('t'))

    In [176]: df2[criterion]
    Out[176]:
           a  b         c
    2    two  y  2.543083
    3  three  x  0.831311
    4    two  y -0.816973

    # equivalent but slower
    In [177]: df2[[x.startswith('t') for x in df2['a']]]
    Out[177]:
           a  b         c
    2    two  y  2.543083
    3  three  x  0.831311
    4    two  y -0.816973

    # Multiple criteria
    In [178]: df2[criterion & (df2['b'] == 'x')]
    Out[178]:
           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 [179]: df2.loc[criterion & (df2['b'] == 'x'), 'b':'c']
    Out[179]:
       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 [180]: df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
       .....:                   index=list('abc'),
       .....:                   columns=['A', 'B'])
       .....:

    In [181]: s = (df['A'] > 2)

    In [182]: s
    Out[182]:
    a    False
    b     True
    c     True
    Name: A, dtype: bool

    In [183]: df.loc[s, 'B']
    Out[183]:
    b    4
    c    6
    Name: B, dtype: int64

    In [184]: df.iloc[s.values, 1]
    Out[184]:
    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 [185]: s = pd.Series(np.arange(5), index=np.arange(5)[::-1], dtype='int64')

    In [186]: s
    Out[186]:
    4    0
    3    1
    2    2
    1    3
    0    4
    dtype: int64

    In [187]: s.isin([2, 4, 6])
    Out[187]:
    4    False
    3    False
    2     True
    1    False
    0     True
    dtype: bool

    In [188]: s[s.isin([2, 4, 6])]
    Out[188]:
    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 [189]: s[s.index.isin([2, 4, 6])]
    Out[189]:
    4    0
    2    2
    dtype: int64

    # compare it to the following
    In [190]: s.reindex([2, 4, 6])
    Out[190]:
    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 [191]: s_mi = pd.Series(np.arange(6),
       .....:                  index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']]))
       .....:

    In [192]: s_mi
    Out[192]:
    0  a    0
       b    1
       c    2
    1  a    3
       b    4
       c    5
    dtype: int64

    In [193]: s_mi.iloc[s_mi.index.isin([(1, 'a'), (2, 'b'), (0, 'c')])]
    Out[193]:
    0  c    2
    1  a    3
    dtype: int64

    In [194]: s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)]
    Out[194]:
    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 [195]: df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],
       .....:                    'ids2': ['a', 'n', 'c', 'n']})
       .....:

    In [196]: values = ['a', 'b', 1, 3]

    In [197]: df.isin(values)
    Out[197]:
        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 [198]: values = {'ids': ['a', 'b'], 'vals': [1, 3]}

    In [199]: df.isin(values)
    Out[199]:
        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 [200]: values = {'ids': ['a', 'b'], 'vals': [1, 3]}

    In [201]: ~df.isin(values)
    Out[201]:
        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 [202]: values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}

    In [203]: row_mask = df.isin(values).all(axis=1)

    In [204]: df[row_mask]
    Out[204]:
       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 [205]: s[s > 0]
    Out[205]:
    3    1
    2    2
    1    3
    0    4
    dtype: int64

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

    In [206]: s.where(s > 0)
    Out[206]:
    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 [207]: dates = pd.date_range('1/1/2000', periods=8)

    In [208]: df = pd.DataFrame(np.random.randn(8, 4),
       .....:                   index=dates, columns=['A', 'B', 'C', 'D'])
       .....:

    In [209]: df[df < 0]
    Out[209]:
                       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 [210]: df.where(df < 0, -df)
    Out[210]:
                       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 [211]: s2 = s.copy()

    In [212]: s2[s2 < 0] = 0

    In [213]: s2
    Out[213]:
    4    0
    3    1
    2    2
    1    3
    0    4
    dtype: int64

    In [214]: df2 = df.copy()

    In [215]: df2[df2 < 0] = 0

    In [216]: df2
    Out[216]:
                       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 [217]: df.where(df < 0, -df) == np.where(df < 0, df, -df)
    Out[217]:
                   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 [218]: df2 = df.copy()

    In [219]: df2[df2[1:4] > 0] = 3

    In [220]: df2
    Out[220]:
                       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 [221]: df2 = df.copy()

    In [222]: df2.where(df2 > 0, df2['A'], axis='index')
    Out[222]:
                       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 [223]: df2 = df.copy()

    In [224]: df.apply(lambda x, y: x.where(x > 0, y), y=df['A'])
    Out[224]:
                       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 [225]: df3 = pd.DataFrame({'A': [1, 2, 3],
       .....:                     'B': [4, 5, 6],
       .....:                     'C': [7, 8, 9]})
       .....:

    In [226]: df3.where(lambda x: x > 4, lambda x: x + 10)
    Out[226]:
        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 [227]: s.mask(s >= 0)
    Out[227]:
    4   NaN
    3   NaN
    2   NaN
    1   NaN
    0   NaN
    dtype: float64

    In [228]: df.mask(df >= 0)
    Out[228]:
                       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 ‘green’ when the second column has ‘Z’. You can do the following:

    In [229]: df = pd.DataFrame({'col1': list('ABBC'), 'col2': list('ZZXY')})

    In [230]: df['color'] = np.where(df['col2'] == 'Z', 'green', 'red')

    In [231]: df
    Out[231]:
      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 [232]: conditions = [
       .....:     (df['col2'] == 'Z') & (df['col1'] == 'A'),
       .....:     (df['col2'] == 'Z') & (df['col1'] == 'B'),
       .....:     (df['col1'] == 'B')
       .....: ]
       .....:

    In [233]: choices = ['yellow', 'blue', 'purple']

    In [234]: df['color'] = np.select(conditions, choices, default='black')

    In [235]: df
    Out[235]:
      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 [236]: n = 10

    In [237]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))

    In [238]: df
    Out[238]:
              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 [239]: df[(df['a'] < df['b']) & (df['b'] < df['c'])]
    Out[239]:
              a         b         c
    7  0.523211  0.591538  0.792342

    # query
    In [240]: df.query('(a < b) & (b < c)')
    Out[240]:
              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 [241]: df = pd.DataFrame(np.random.randint(n / 2, size=(n, 2)), columns=list('bc'))

    In [242]: df.index.name = 'a'

    In [243]: df
    Out[243]:
       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 [244]: df.query('a < b and b < c')
    Out[244]:
       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 [245]: df = pd.DataFrame(np.random.randint(n, size=(n, 2)), columns=list('bc'))

    In [246]: df
    Out[246]:
       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 [247]: df.query('index < b < c')
    Out[247]:
       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 [248]: df = pd.DataFrame({'a': np.random.randint(5, size=5)})

    In [249]: df.index.name = 'a'

    In [250]: df.query('a > 2')  # uses the column 'a', not the index
    Out[250]:
       a
    a
    3  3

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

    In [251]: df.query('index > 2')
    Out[251]:
       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 [252]: n = 10

    In [253]: colors = np.random.choice(['red', 'green'], size=n)

    In [254]: foods = np.random.choice(['eggs', 'ham'], size=n)

    In [255]: colors
    Out[255]:
    array(['green', 'green', 'red', 'green', 'red', 'red', 'red', 'red',
           'green', 'green'], dtype='<U5')

    In [256]: foods
    Out[256]:
    array(['ham', 'ham', 'ham', 'ham', 'ham', 'ham', 'ham', 'eggs', 'eggs',
           'eggs'], dtype='<U4')

    In [257]: index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food'])

    In [258]: df = pd.DataFrame(np.random.randn(n, 2), index=index)

    In [259]: df
    Out[259]:
                       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 [260]: df.query('color == "red"')
    Out[260]:
                       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 [261]: df.index.names = [None, None]

    In [262]: df
    Out[262]:
                       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 [263]: df.query('ilevel_0 == "red"')
    Out[263]:
                     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 “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 [264]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))

    In [265]: df
    Out[265]:
              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 [266]: df2 = pd.DataFrame(np.random.rand(n + 2, 3), columns=df.columns)

    In [267]: df2
    Out[267]:
               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 [268]: expr = '0.0 <= a <= c <= 0.5'

    In [269]: map(lambda frame: frame.query(expr), [df, df2])
    Out[269]: <map at 0x7f9e43c3af80>

### `query()` Python versus pandas Syntax Comparison

Full numpy-like syntax:

    In [270]: df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc'))

    In [271]: df
    Out[271]:
       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 [272]: df.query('(a < b) & (b < c)')
    Out[272]:
    Empty DataFrame
    Columns: [a, b, c]
    Index: []

    In [273]: df[(df['a'] < df['b']) & (df['b'] < df['c'])]
    Out[273]:
    Empty DataFrame
    Columns: [a, b, c]
    Index: []

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

    In [274]: df.query('a < b & b < c')
    Out[274]:
    Empty DataFrame
    Columns: [a, b, c]
    Index: []

Use English instead of symbols:

    In [275]: df.query('a < b and b < c')
    Out[275]:
    Empty DataFrame
    Columns: [a, b, c]
    Index: []

Pretty close to how you might write it on paper:

    In [276]: df.query('a < b < c')
    Out[276]:
    Empty DataFrame
    Columns: [a, b, c]
    Index: []

### 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 [277]: df = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),
       .....:                    'c': np.random.randint(5, size=12),
       .....:                    'd': np.random.randint(9, size=12)})
       .....:

    In [278]: df
    Out[278]:
        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 [279]: df.query('a in b')
    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

    # How you'd do it in pure Python
    In [280]: df[df['a'].isin(df['b'])]
    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

    In [281]: df.query('a not in b')
    Out[281]:
        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 [282]: df[~df['a'].isin(df['b'])]
    Out[282]:
        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 [283]: df.query('a in b and c < d')
    Out[283]:
       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 [284]: df[df['b'].isin(df['a']) & (df['c'] < df['d'])]
    Out[284]:
        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.

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

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

    In [285]: df.query('b == ["a", "b", "c"]')
    Out[285]:
        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 [286]: df[df['b'].isin(["a", "b", "c"])]
    Out[286]:
        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 [287]: df.query('c == [1, 2]')
    Out[287]:
        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 [288]: df.query('c != [1, 2]')
    Out[288]:
        a  b  c  d
    1   a  a  0  0
    6   d  b  3  6
    10  f  c  3  3

    # using in/not in
    In [289]: df.query('[1, 2] in c')
    Out[289]:
        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 [290]: df.query('[1, 2] not in c')
    Out[290]:
        a  b  c  d
    1   a  a  0  0
    6   d  b  3  6
    10  f  c  3  3

    # pure Python
    In [291]: df[df['c'].isin([1, 2])]
    Out[291]:
        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 [292]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc'))

    In [293]: df['bools'] = np.random.rand(len(df)) > 0.5

    In [294]: df.query('~bools')
    Out[294]:
              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 [295]: df.query('not bools')
    Out[295]:
              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 [296]: df.query('not bools') == df[~df['bools']]
    Out[296]:
          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 [297]: shorter = df.query('a < b < c and (not bools) or bools > 2')

    # equivalent in pure Python
    In [298]: longer = df[(df['a'] < df['b'])
       .....:             & (df['b'] < df['c'])
       .....:             & (~df['bools'])
       .....:             | (df['bools'] > 2)]
       .....:

    In [299]: shorter
    Out[299]:
              a         b        c  bools
    7  0.260776  0.290751  0.46208  False

    In [300]: longer
    Out[300]:
              a         b        c  bools
    7  0.260776  0.290751  0.46208  False

    In [301]: shorter == longer
    Out[301]:
          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 [302]: df = pd.DataFrame(np.random.randn(8, 4),
       .....:                   index=dates, columns=['A', 'B', 'C', 'D'])
       .....:

    In [303]: 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 [304]: 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 [305]: df2
    Out[305]:
           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 [306]: df2.duplicated('a')
    Out[306]:
    0    False
    1     True
    2    False
    3     True
    4     True
    5    False
    6    False
    dtype: bool

    In [307]: df2.duplicated('a', keep='last')
    Out[307]:
    0     True
    1    False
    2     True
    3     True
    4    False
    5    False
    6    False
    dtype: bool

    In [308]: df2.duplicated('a', keep=False)
    Out[308]:
    0     True
    1     True
    2     True
    3     True
    4     True
    5    False
    6    False
    dtype: bool

    In [309]: df2.drop_duplicates('a')
    Out[309]:
           a  b         c
    0    one  x -1.467157
    2    two  x -1.428572
    5  three  x -2.294560
    6   four  x  2.148507

    In [310]: df2.drop_duplicates('a', keep='last')
    Out[310]:
           a  b         c
    1    one  y  0.113937
    4    two  x  0.052469
    5  three  x -2.294560
    6   four  x  2.148507

    In [311]: df2.drop_duplicates('a', keep=False)
    Out[311]:
           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 [312]: df2.duplicated(['a', 'b'])
    Out[312]:
    0    False
    1    False
    2    False
    3    False
    4     True
    5    False
    6    False
    dtype: bool

    In [313]: df2.drop_duplicates(['a', 'b'])
    Out[313]:
           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 [314]: df3 = pd.DataFrame({'a': np.arange(6),
       .....:                     'b': np.random.randn(6)},
       .....:                    index=['a', 'a', 'b', 'c', 'b', 'a'])
       .....:

    In [315]: df3
    Out[315]:
       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 [316]: df3.index.duplicated()
    Out[316]: array([False,  True, False, False,  True,  True])

    In [317]: df3[~df3.index.duplicated()]
    Out[317]:
       a         b
    a  0 -0.293144
    b  2  0.149716
    c  3  0.173897

    In [318]: df3[~df3.index.duplicated(keep='last')]
    Out[318]:
       a         b
    c  3  0.173897
    b  4 -0.049440
    a  5  1.394590

    In [319]: df3[~df3.index.duplicated(keep=False)]
    Out[319]:
       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 [320]: s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])

    In [321]: s.get('a')  # equivalent to s['a']
    Out[321]: np.int64(1)

    In [322]: s.get('x', default=-1)
    Out[322]: -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.

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

    In [324]: index
    Out[324]: Index(['e', 'd', 'a', 'b'], dtype='str')

    In [325]: 'd' in index
    Out[325]: True

or using numbers:

    In [326]: index = pd.Index([1, 5, 12])

    In [327]: index
    Out[327]: Index([1, 5, 12], dtype='int64')

    In [328]: 5 in index
    Out[328]: 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 [329]: index = pd.Index(['e', 'd', 'a', 'b'], dtype="string")

    In [330]: index
    Out[330]: Index(['e', 'd', 'a', 'b'], dtype='string')

    In [331]: index = pd.Index([1, 5, 12], dtype="int8")

    In [332]: index
    Out[332]: Index([1, 5, 12], dtype='int8')

    In [333]: index = pd.Index([1, 5, 12], dtype="float32")

    In [334]: index
    Out[334]: Index([1.0, 5.0, 12.0], dtype='float32')

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

    In [335]: index = pd.Index(['e', 'd', 'a', 'b'], name='something')

    In [336]: index.name
    Out[336]: 'something'

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

    In [337]: index = pd.Index(list(range(5)), name='rows')

    In [338]: columns = pd.Index(['A', 'B', 'C'], name='cols')

    In [339]: df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns)

    In [340]: df
    Out[340]:
    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 [341]: df['A']
    Out[341]:
    rows
    0    0.698035
    1    0.709404
    2   -0.878602
    3   -0.957431
    4   -1.994374
    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 [342]: ind = pd.Index([1, 2, 3])

    In [343]: ind.rename("apple")
    Out[343]: Index([1, 2, 3], dtype='int64', name='apple')

    In [344]: ind
    Out[344]: Index([1, 2, 3], dtype='int64')

    In [345]: ind = ind.set_names(["apple"])

    In [346]: ind.name = "bob"

    In [347]: ind
    Out[347]: Index([1, 2, 3], dtype='int64', name='bob')

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

    In [348]: index = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second'])

    In [349]: index
    Out[349]:
    MultiIndex([(0, 'one'),
                (0, 'two'),
                (1, 'one'),
                (1, 'two'),
                (2, 'one'),
                (2, 'two')],
               names=['first', 'second'])

    In [350]: index.levels[1]
    Out[350]: Index(['one', 'two'], dtype='str', name='second')

    In [351]: index.set_levels(["a", "b"], level=1)
    Out[351]:
    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 [352]: a = pd.Index(['c', 'b', 'a'])

    In [353]: b = pd.Index(['c', 'e', 'd'])

    In [354]: a.difference(b)
    Out[354]: 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 [355]: idx1 = pd.Index([1, 2, 3, 4])

    In [356]: idx2 = pd.Index([2, 3, 4, 5])

    In [357]: idx1.symmetric_difference(idx2)
    Out[357]: 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 [358]: idx1 = pd.Index([0, 1, 2])

    In [359]: idx2 = pd.Index([0.5, 1.5])

    In [360]: idx1.union(idx2)
    Out[360]: 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 [361]: idx1 = pd.Index([1, np.nan, 3, 4])

    In [362]: idx1
    Out[362]: Index([1.0, nan, 3.0, 4.0], dtype='float64')

    In [363]: idx1.fillna(2)
    Out[363]: Index([1.0, 2.0, 3.0, 4.0], dtype='float64')

    In [364]: idx2 = pd.DatetimeIndex([pd.Timestamp('2011-01-01'),
       .....:                          pd.NaT,
       .....:                          pd.Timestamp('2011-01-03')])
       .....:

    In [365]: idx2
    Out[365]:
    DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03'], dtype='datetime64[us]',
                  freq=None)

    In [366]: idx2.fillna(pd.Timestamp('2011-01-02'))
    Out[366]:
    DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'],
                  dtype='datetime64[us]', 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 [367]: data = pd.DataFrame({'a': ['bar', 'bar', 'foo', 'foo'],
       .....:                      'b': ['one', 'two', 'one', 'two'],
       .....:                      'c': ['z', 'y', 'x', 'w'],
       .....:                      'd': [1., 2., 3, 4]})
       .....:

    In [368]: data
    Out[368]:
         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 [369]: indexed1 = data.set_index('c')

    In [370]: indexed1
    Out[370]:
         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 [371]: indexed2 = data.set_index(['a', 'b'])

    In [372]: indexed2
    Out[372]:
             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 [373]: frame = data.set_index('c', drop=False)

    In [374]: frame = frame.set_index(['a', 'b'], append=True)

    In [375]: frame
    Out[375]:
               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 [376]: data.set_index('c', drop=False)
    Out[376]:
         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 [377]: data
    Out[377]:
         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 [378]: data.reset_index()
    Out[378]:
       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 [379]: frame
    Out[379]:
               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 [380]: frame.reset_index(level=1)
    Out[380]:
             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 [381]: df_idx = pd.DataFrame(range(4))

    In [382]: df_idx.index = pd.Index([10, 20, 30, 40], name="a")

    In [383]: df_idx
    Out[383]:
        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 doesn’t matter

- Missing index labels result in NaN values

- This behavior is consistent across df[col] = series and df.loc[:, col] = series

You can think of this as reindexing the Series to the DataFrame index before assignment (for example, `df[col] = series.reindex(df.index)`).

Examples: .. ipython:: python

> import pandas as pd

> # Create a DataFrame df = pd.DataFrame({‘values’: [1, 2, 3]}, index=[‘x’, ‘y’, ‘z’])

> # Series with matching indices (different order) s1 = pd.Series([10, 20, 30], index=[‘z’, ‘x’, ‘y’]) df[‘aligned’] = s1 # Aligns by index, not position print(df)

> # Series with partial index match s2 = pd.Series([100, 200], index=[‘x’, ‘z’]) df[‘partial’] = s2 # Missing ‘y’ gets NaN print(df)

> # Series with non-matching indices s3 = pd.Series([1000, 2000], index=[‘a’, ‘b’]) df[‘nomatch’] = 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[‘s1_values’] = s1.reindex(df.index)

## Column assignment vs. in-place setting

While index alignment is consistent between `df[col] = value` and `df.loc[:, col] = value` (see the previous section), the two forms differ in how they treat the column’s existing dtype:

- `df[col] = value` replaces the column. The new column’s dtype is inferred from `value`, regardless of what dtype the old column had.

- `df.loc[:, col] = value` (and `df.iloc[:, i] = value`) sets the values in place in the existing column. The original dtype is preserved, and the assignment will raise `TypeError` if `value` is not compatible with it.

This is most visible for `ExtensionDtype` columns such as `category`, `Int64`, `boolean`, or `string`, where the replacing form silently drops the extension dtype:

    In [384]: df = pd.DataFrame({"a": pd.Categorical(["x", "y"], categories=["x", "y", "z"])})

    In [385]: df["a"] = "z"          # replaces the column

    In [386]: df.dtypes
    Out[386]:
    a    str
    dtype: object

    In [387]: df = pd.DataFrame({"a": pd.Categorical(["x", "y"], categories=["x", "y", "z"])})

    In [388]: df.loc[:, "a"] = "z"   # sets values in place

    In [389]: df.dtypes
    Out[389]:
    a    category
    dtype: object

The same distinction applies to nullable numeric and boolean dtypes:

    In [390]: df = pd.DataFrame({"a": pd.array([1, 2], dtype="Int64")})

    In [391]: df["a"] = 5

    In [392]: df.dtypes
    Out[392]:
    a    int64
    dtype: object

    In [393]: df = pd.DataFrame({"a": pd.array([1, 2], dtype="Int64")})

    In [394]: df.loc[:, "a"] = 5

    In [395]: df.dtypes
    Out[395]:
    a    Int64
    dtype: object

Use `df.loc[:, col] = value` (or `.iloc`) when you want to update every value of an existing column while keeping its dtype.