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

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 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 ——- 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  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)