pandas使用get_dummies进行one-hot编码

官网：http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html

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:

data : array-like, Series, or DataFrame

prefix : string, list of strings, or dict of strings, default None

String to append DataFrame column namesPass a list with length equal to the number of columnswhen calling get_dummies on a DataFrame. Alternatively,prefixcan be a dictionary mapping column names to prefixes.

prefix_sep : string, default ‘_’

If appending prefix, separator/delimiter to use. Or pass alist or dictionary as withprefix.

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 withobject orcategory dtype will be converted.

sparse : bool, default False

Whether the dummy columns should be sparse or not. ReturnsSparseDataFrame if data is a Series or if all columns are included.Otherwise returns a DataFrame with some SparseBlocks.

drop_first : bool, default False

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

New in version 0.18.0.

Returns

——-

dummies : DataFrame or SparseDataFrame

See also

Series.str.get_dummies

Examples

>>> import pandas as pd>>> s = pd.Series(list('abca'))

>>> pd.get_dummies(s)   a  b  c0  1  0  01  0  1  02  0  0  13  1  0  0

>>> s1 = ['a', 'b', np.nan]

>>> pd.get_dummies(s1)   a  b0  1  01  0  12  0  0

>>> pd.get_dummies(s1, dummy_na=True)   a  b  NaN0  1  0    01  0  1    02  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_c0  1       1       0       0       1       01  2       0       1       1       0       02  3       1       0       0       0       1

>>> pd.get_dummies(pd.Series(list('abcaa')))   a  b  c0  1  0  01  0  1  02  0  0  13  1  0  04  1  0  0

>>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)   b  c0  0  01  1  02  0  13  0  04  0  0

离散特征的编码分为两种情况：1、离散特征的取值之间没有大小的意义，比如color：[red,blue],那么就使用one-hot编码2、离散特征的取值有大小的意义，比如size:[X,XL,XXL],那么就使用数值的映射{X:1,XL:2,XXL:3}使用pandas可以很方便的对离散型特征进行one-hot编码[python] view plain copy    import pandas as pd      df = pd.DataFrame([                  ['green', 'M', 10.1, 'class1'],                   ['red', 'L', 13.5, 'class2'],                   ['blue', 'XL', 15.3, 'class1']])            df.columns = ['color', 'size', 'prize', 'class label']            size_mapping = {                 'XL': 3,                 'L': 2,                 'M': 1}      df['size'] = df['size'].map(size_mapping)            class_mapping = {label:idx for idx,label in enumerate(set(df['class label']))}      df['class label'] = df['class label'].map(class_mapping)  说明：对于有大小意义的离散特征，直接使用映射就可以了，{'XL':3,'L':2,'M':1}Using the get_dummies will create a new column for every unique string in a certain column:使用get_dummies进行one-hot编码[python] view plain copy    pd.get_dummies(df)