Convert categorical variable into dummy/indicator variables.

    Parameters
    ----------
    data : array-like, Series, or DataFrame
        Data of which to get dummy indicators.
    prefix : str, list of str, or dict of str, default None
        String to append DataFrame column names.
        Pass a list with length equal to the number of columns
        when calling get_dummies on a DataFrame. Alternatively, `prefix`
        can be a dictionary mapping column names to prefixes.
    prefix_sep : str, default '_'
        If appending prefix, separator/delimiter to use. Or pass a
        list or dictionary as with `prefix`.
    dummy_na : bool, default False
        Add a column to indicate NaNs, if False NaNs are ignored.
    columns : list-like, default None
        Column names in the DataFrame to be encoded.
        If `columns` is None then all the columns with
        `object` or `category` dtype will be converted.
    sparse : bool, default False
        Whether the dummy-encoded columns should be backed by
        a :class:`SparseArray` (True) or a regular NumPy array (False).
    drop_first : bool, default False
        Whether to get k-1 dummies out of k categorical levels by removing the
        first level.

        .. versionadded:: 0.18.0

    dtype : dtype, default np.uint8
        Data type for new columns. Only a single dtype is allowed.

        .. versionadded:: 0.23.0

    Returns
    -------
    DataFrame
        Dummy-coded data.

    See Also
    --------
    Series.str.get_dummies : Convert Series to dummy codes.

    Examples
    --------
    >>> s = pd.Series(list('abca'))

    >>> pd.get_dummies(s)
       a  b  c
    0  1  0  0
    1  0  1  0
    2  0  0  1
    3  1  0  0

    >>> s1 = ['a', 'b', np.nan]

    >>> pd.get_dummies(s1)
       a  b
    0  1  0
    1  0  1
    2  0  0

    >>> pd.get_dummies(s1, dummy_na=True)
       a  b  NaN
    0  1  0    0
    1  0  1    0
    2  0  0    1

    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
    ...                    'C': [1, 2, 3]})

    >>> pd.get_dummies(df, prefix=['col1', 'col2'])
       C  col1_a  col1_b  col2_a  col2_b  col2_c
    0  1       1       0       0       1       0
    1  2       0       1       1       0       0
    2  3       1       0       0       0       1

    >>> pd.get_dummies(pd.Series(list('abcaa')))
       a  b  c
    0  1  0  0
    1  0  1  0
    2  0  0  1
    3  1  0  0
    4  1  0  0

    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
       b  c
    0  0  0
    1  1  0
    2  0  1
    3  0  0
    4  0  0

    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
         a    b    c
    0  1.0  0.0  0.0
    1  0.0  1.0  0.0
    2  0.0  0.0  1.0

def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
                columns=None, sparse=False, drop_first=False, dtype=None):
    """
    Convert categorical variable into dummy/indicator variables.

    Parameters
    ----------
    data : array-like, Series, or DataFrame
        Data of which to get dummy indicators.
    prefix : str, list of str, or dict of str, default None
        String to append DataFrame column names.
        Pass a list with length equal to the number of columns
        when calling get_dummies on a DataFrame. Alternatively, `prefix`
        can be a dictionary mapping column names to prefixes.
    prefix_sep : str, default '_'
        If appending prefix, separator/delimiter to use. Or pass a
        list or dictionary as with `prefix`.
    dummy_na : bool, default False
        Add a column to indicate NaNs, if False NaNs are ignored.
    columns : list-like, default None
        Column names in the DataFrame to be encoded.
        If `columns` is None then all the columns with
        `object` or `category` dtype will be converted.
    sparse : bool, default False
        Whether the dummy-encoded columns should be backed by
        a :class:`SparseArray` (True) or a regular NumPy array (False).
    drop_first : bool, default False
        Whether to get k-1 dummies out of k categorical levels by removing the
        first level.

        .. versionadded:: 0.18.0

    dtype : dtype, default np.uint8
        Data type for new columns. Only a single dtype is allowed.

        .. versionadded:: 0.23.0

    Returns
    -------
    DataFrame
        Dummy-coded data.

    See Also
    --------
    Series.str.get_dummies : Convert Series to dummy codes.

    Examples
    --------
    >>> s = pd.Series(list('abca'))

    >>> pd.get_dummies(s)
       a  b  c
    0  1  0  0
    1  0  1  0
    2  0  0  1
    3  1  0  0

    >>> s1 = ['a', 'b', np.nan]

    >>> pd.get_dummies(s1)
       a  b
    0  1  0
    1  0  1
    2  0  0

    >>> pd.get_dummies(s1, dummy_na=True)
       a  b  NaN
    0  1  0    0
    1  0  1    0
    2  0  0    1

    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
    ...                    'C': [1, 2, 3]})

    >>> pd.get_dummies(df, prefix=['col1', 'col2'])
       C  col1_a  col1_b  col2_a  col2_b  col2_c
    0  1       1       0       0       1       0
    1  2       0       1       1       0       0
    2  3       1       0       0       0       1

    >>> pd.get_dummies(pd.Series(list('abcaa')))
       a  b  c
    0  1  0  0
    1  0  1  0
    2  0  0  1
    3  1  0  0
    4  1  0  0

    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
       b  c
    0  0  0
    1  1  0
    2  0  1
    3  0  0
    4  0  0

    >>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
         a    b    c
    0  1.0  0.0  0.0
    1  0.0  1.0  0.0
    2  0.0  0.0  1.0
    """
    from pandas.core.reshape.concat import concat
    from itertools import cycle

    dtypes_to_encode = ['object', 'category']

    if isinstance(data, DataFrame):
        # determine columns being encoded
        if columns is None:
            data_to_encode = data.select_dtypes(
                include=dtypes_to_encode)
        else:
            data_to_encode = data[columns]

        # validate prefixes and separator to avoid silently dropping cols
        def check_len(item, name):
            len_msg = ("Length of '{name}' ({len_item}) did not match the "
                       "length of the columns being encoded ({len_enc}).")

            if is_list_like(item):
                if not len(item) == data_to_encode.shape[1]:
                    len_msg = len_msg.format(name=name, len_item=len(item),
                                             len_enc=data_to_encode.shape[1])
                    raise ValueError(len_msg)

        check_len(prefix, 'prefix')
        check_len(prefix_sep, 'prefix_sep')

        if isinstance(prefix, str):
            prefix = cycle([prefix])
        if isinstance(prefix, dict):
            prefix = [prefix[col] for col in data_to_encode.columns]

        if prefix is None:
            prefix = data_to_encode.columns

        # validate separators
        if isinstance(prefix_sep, str):
            prefix_sep = cycle([prefix_sep])
        elif isinstance(prefix_sep, dict):
            prefix_sep = [prefix_sep[col] for col in data_to_encode.columns]

        if data_to_encode.shape == data.shape:
            # Encoding the entire df, do not prepend any dropped columns
            with_dummies = []
        elif columns is not None:
            # Encoding only cols specified in columns. Get all cols not in
            # columns to prepend to result.
            with_dummies = [data.drop(columns, axis=1)]
        else:
            # Encoding only object and category dtype columns. Get remaining
            # columns to prepend to result.
            with_dummies = [data.select_dtypes(exclude=dtypes_to_encode)]

        for (col, pre, sep) in zip(data_to_encode.iteritems(), prefix,
                                   prefix_sep):
            # col is (column_name, column), use just column data here
            dummy = _get_dummies_1d(col[1], prefix=pre, prefix_sep=sep,
                                    dummy_na=dummy_na, sparse=sparse,
                                    drop_first=drop_first, dtype=dtype)
            with_dummies.append(dummy)
        result = concat(with_dummies, axis=1)
    else:
        result = _get_dummies_1d(data, prefix, prefix_sep, dummy_na,
                                 sparse=sparse,
                                 drop_first=drop_first,
                                 dtype=dtype)
    return result