bigframes.pandas.get_dummies#

bigframes.pandas.get_dummies(data: DataFrame | Series, prefix: List | dict | str | None = None, prefix_sep: List | dict | str | None = '_', dummy_na: bool = False, columns: List | None = None, drop_first: bool = False, dtype: Any = None) → DataFrame[source]#

Convert categorical variable into dummy/indicator variables.

Each variable is converted in as many 0/1 variables as there are different values. Columns in the output are each named after a value; if the input is a DataFrame, the name of the original variable is prepended to the value.

Examples:

>>> import bigframes.pandas as pd
>>> pd.options.display.progress_bar = None
>>> s = pd.Series(list('abca'))
>>> pd.get_dummies(s)
       a      b      c
0   True  False  False
1  False   True  False
2  False  False   True
3   True  False  False

[4 rows x 3 columns]

>>> s1 = pd.Series(['a', 'b', None])
>>> pd.get_dummies(s1)
       a      b
0   True  False
1  False   True
2  False  False

[3 rows x 2 columns]

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

[3 rows x 3 columns]

>>> 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    True   False   False    True   False
1  2   False    True    True   False   False
2  3    True   False   False   False    True

[3 rows x 6 columns]

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

[5 rows x 3 columns]

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

[5 rows x 2 columns]
Parameters:

  • data (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, list of str, or dict of str, default '_') – Separator/delimiter to use, appended to prefix. 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 only the columns with string dtype will be converted.

  • drop_first (bool, default False) – Whether to get k-1 dummies out of k categorical levels by removing the first level.

  • dtype (dtype, default bool) – Data type for new columns. Only a single dtype is allowed.

Returns:

Dummy-coded data. If data contains other columns than the dummy-coded one(s), these will be prepended, unaltered, to the result.

Return type:

bigframes.pandas.DataFrame

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