Convert categorical variable into dummy/indicator variables. Data of which to get dummy indicators. 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. If appending prefix, separator/delimiter to use. Or pass a list or dictionary as with prefix. Add a column to indicate NaNs, if False NaNs are ignored. Column names in the DataFrame to be encoded. If columns is None then all the columns with object,
string, or category dtype will be converted.
Whether the dummy-encoded columns should be backed by a SparseArray
[True] or a regular NumPy array [False].
Whether to get k-1 dummies out of k categorical levels by removing the first level.
dtypedtype, default np.uint8Data type for new columns. Only a single dtype is allowed.
ReturnsDataFrameDummy-coded data.
Notes
Reference the user guide for more examples.
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