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- apply 用在dataframe上,用于对row或者column进行计算
- applymap: 作用在dataframe的每一个元素上
- map (其实是python自带的)用于series上,是元素级别的操作,map 跟apply 功能类似,用法差不多
# encoding: utf-8
import pandas as pd
data=pd.DataFrame({'user_id':['Adff','B','C','D'],'score':[13,23,57,89]})
print(data)
# apply 用在dataframe上,用于对row或者column进行计算
data['score2']=data['score'].apply(lambda x:x+6)
print(data)
# applymap: 作用在dataframe的每一个元素上
data2=data.ix[:,['score','score2']]
data3=data2.applymap(lambda x:x+1)
print(data3)
# map (其实是python自带的)用于series上,是元素级别的操作,map 跟apply 功能类似,用法差不多
data['length']=data['user_id'].map(lambda x:len(x))
data['length2']=data['user_id'].apply(lambda x:len(x))
print(data)
运行结果
score user_id
0 13 Adff
1 23 B
2 57 C
3 89 D
score user_id score2
0 13 Adff 19
1 23 B 29
2 57 C 63
3 89 D 95
score score2
0 14 20
1 24 30
2 58 64
3 90 96
score user_id score2 length length2
0 13 Adff 19 4 4
1 23 B 29 1 1
2 57 C 63 1 1
3 89 D 95 1 1
Process finished with exit code 0