Series的map方法能够接纳一个函数或带有投射关联的字典型性目标。

应用map是一种完成原素级变换及其别的数据清理工作中的方便快捷方法。

(DataFrame中相匹配的是applymap()函数,自然DataFrame也有apply()函数)

1、词典投射

import pandas as pd
from pandas import Series, DataFrame

data = DataFrame({'food':['bacon','pulled pork','bacon','Pastrami',
   'corned beef','Bacon','pastrami','honey ham','nova lox'],
     'ounces':[4,3,12,6,7.5,8,3,5,6]})
meat_to_animal = {
 'bacon':'pig',
 'pulled pork':'pig',
 'pastrami':'cow',
 'corned beef':'cow',
 'honey ham':'pig',
 'nova lox':'salmon' } 

data['animal'] = data['food'].map(str.lower).map(meat_to_animal) 
data 

data['food'].map(lambda x: meat_to_animal[x.lower()])  

2、运用函数

In [579]: import pandas as pd

In [580]: from pandas import Series, DataFrame

In [581]: index = pd.date_range('2017-08-15', periods=10)

In [582]: ser = Series(list(range(10)), index=index)

In [583]: ser
Out[583]: 
2017-08-15 0
2017-08-16 1
2017-08-17 2
2017-08-18 3
2017-08-19 4
2017-08-20 5
2017-08-21 6
2017-08-22 7
2017-08-23 8
2017-08-24 9
Freq: D, dtype: int64


In [585]: ser.index.map(lambda x: x.day)
Out[585]: Int64Index([15, 16, 17, 18, 19, 20, 21, 22, 23, 24], dtype='int64')

In [586]: ser.index.map(lambda x: x.weekday)
Out[586]: Int64Index([1, 2, 3, 4, 5, 6, 0, 1, 2, 3], dtype='int64')

In [587]: ser.map(lambda x: x 10)
Out[587]: 
2017-08-15 10
2017-08-16 11
2017-08-17 12
2017-08-18 13
2017-08-19 14
2017-08-20 15
2017-08-21 16
2017-08-22 17
2017-08-23 18
2017-08-24 19
Freq: D, dtype: int64

In [588]: def f(x):
  ...:  if x < 5:
  ...:   return True
  ...:  else:
  ...:   return False
  ...:  

In [589]: ser.map(f)
Out[589]: 
2017-08-15  True
2017-08-16  True
2017-08-17  True
2017-08-18  True
2017-08-19  True
2017-08-20 False
2017-08-21 False
2017-08-22 False
2017-08-23 False
2017-08-24 False
Freq: D, dtype: bool

之上这篇对pandas中Series的map函数详细说明便是我共享给大伙儿的所有内容了,期待能给大伙儿一个参照,也期待大伙儿多多的适用。