官网:http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html
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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 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 withobject or category 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
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dummies : DataFrame or SparseDataFrame
See also
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
>>> 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
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)