# pandas.get_dummies

`pandas.``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 `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.

  > New in version 0.18.0.

  dtype : dtype, default np.uint8

  > Data type for new columns. Only a single dtype is allowed.

  > New in version 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

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