pandas.get_dummies()

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

Convert categorical variable into dummy/indicator variables

Parameters:

Parameters:	data : array-like, Series, or DataFrame prefix : string, list of strings, or dict of strings, 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. Alternativly, `prefix` can be a dictionary mapping column names to prefixes. prefix_sep : string, 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 columns should be sparse or not. Returns SparseDataFrame if `data` is a Series or if all columns are included. Otherwise returns a DataFrame with some SparseBlocks. New in version 0.16.1. 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. Returns ??- dummies : DataFrame or SparseDataFrame

data : array-like, Series, or DataFrame

prefix : string, list of strings, or dict of strings, 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. Alternativly, prefix can be a dictionary mapping column names to prefixes.

prefix_sep : string, 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 columns should be sparse or not. Returns SparseDataFrame if data is a Series or if all columns are included. Otherwise returns a DataFrame with some SparseBlocks.

New in version 0.16.1.

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.

Returns

??-

dummies : DataFrame or SparseDataFrame

Examples

>>> import pandas as pd
>>> 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

1	`>>> 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

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.get_dummies.html

doc_Pandas

2025-01-10 15:47:30

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