pandas.get_dummies — pandas 1.0.5 documentation (original) (raw)

pandas. get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None) → ’DataFrame’[source]¶

Convert categorical variable into dummy/indicator variables.

Parameters

dataarray-like, Series, or DataFrame

Data of which to get dummy indicators.

prefixstr, 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, prefixcan be a dictionary mapping column names to prefixes.

prefix_sepstr, default ‘_’

If appending prefix, separator/delimiter to use. Or pass a list or dictionary as with prefix.

dummy_nabool, default False

Add a column to indicate NaNs, if False NaNs are ignored.

columnslist-like, default None

Column names in the DataFrame to be encoded. If columns is None then all the columns withobject or category dtype will be converted.

sparsebool, default False

Whether the dummy-encoded columns should be backed by a SparseArray (True) or a regular NumPy array (False).

drop_firstbool, default False

Whether to get k-1 dummies out of k categorical levels by removing the first level.

dtypedtype, 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.

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