离散型特征处理get_dummies()方法
官方文档：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html
get_dummies()：对离散型数据进行one-hot编码
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，如{X:1,XL:2,XXL:3}。
get_dummies()的用法：
参数：
- 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. Alternatively, 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-encoded columns should be be backed by a SparseArray (True) or a regular NumPy array (False).
- drop_first : bool, default False Whether to get k-1 dummies out of categorical levels by removing the first level. New in version 0.18.0.
- dtype : dtype, default np.uint8 Data type for new columns. Only a single dtype is allowed. New in version 0.23.0.
例子（官方文档）：
>>> 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