Python pandas.map()用法及代码示例

pandas.map()用于映射来自同一列的两个系列的值。为了映射两个系列，第一个系列的最后一列应与第二个系列的索引列相同，并且值也应唯一。

用法:

Series.map(arg, na_action=None)

参数：

arg:function, dict, or Series

na_action:{None, ‘ignore’} If ‘ignore’, propagate NA values, without passing them to the mapping correspondence. na_action checks the NA value and ignores it while mapping in case of ‘ignore’

返回类型：

Pandas Series with same as index as caller

范例1：
在下面的示例中，两个序列由相同的数据组成。 pokemon_names列和pokemon_types索引列相同，因此Pandas.map()与其余两列匹配并返回一个新系列。

Note:
-> 2nd column of caller of map function must be same as index column of passed series.
-> The values of common column must be unique too.

import pandas as pd 
  
#reading csv files 
pokemon_names = pd.read_csv("pokemon.csv", usecols = ["Pokemon"], 
                                                  squeeze = True) 
  
#usecol is used to use selected columns 
#index_col is used to make passed column as index 
pokemon_types = pd.read_csv("pokemon.csv", index_col = "Pokemon", 
                                                  squeeze = True) 
  
#using pandas map function 
new=pokemon_names.map(pokemon_types) 
  
print (new)

输出：

范例2：

此函数仅适用于Series。传递数据帧将导致属性错误。传递不同长度的序列将使输出序列的长度与调用者相同。