参照:https://blog.csdn.net/weixin_38168620/article/details/79596819
import pandas as pd import numpy as np from numpy import nan as NaN
填充缺失数据
fillna()是最主要的处理方式了。
df1=pd.DataFrame([[1,2,3],[NaN,NaN,2],[NaN,NaN,NaN],[8,8,NaN]]) df1
代码结果: 0 1 2 0 1.0 2.0 3.0 1 NaN NaN 2.0 2 NaN NaN NaN 3 8.0 8.0 NaN 用常数填充: df1.fillna(100) 1 代码结果: 0 1 2 0 1.0 2.0 3.0 1 100.0 100.0 2.0 2 100.0 100.0 100.0 3 8.0 8.0 100.0 通过字典填充不同的常数: df1.fillna({0:10,1:20,2:30}) 1 代码结果: 0 1 2 0 1.0 2.0 3.0 1 10.0 20.0 2.0 2 10.0 20.0 30.0 3 8.0 8.0 30.0 传入inplace=True直接修改原对象: df1.fillna(0,inplace=True) df1 1 2 代码结果: 0 1 2 0 1.0 2.0 3.0 1 0.0 0.0 2.0 2 0.0 0.0 0.0 3 8.0 8.0 0.0 传入method=” “改变插值方式: df2=pd.DataFrame(np.random.randint(0,10,(5,5))) df2.iloc[1:4,3]=NaN;df2.iloc[2:4,4]=NaN df2 1 2 3 代码结果: 0 1 2 3 4 0 6 6 2 4.0 1.0 1 4 7 0 NaN 5.0 2 6 5 5 NaN NaN 3 1 9 9 NaN NaN 4 4 8 1 5.0 9.0 df2.fillna(method='ffill')#用前面的值来填充 1 代码结果: 0 1 2 3 4 0 6 6 2 4.0 1.0 1 4 7 0 4.0 5.0 2 6 5 5 4.0 5.0 3 1 9 9 4.0 5.0 4 4 8 1 5.0 9.0 传入limit=” “限制填充个数: df2.fillna(method='bfill',limit=2) 1 代码结果: 0 1 2 3 4 0 6 6 2 4.0 1.0 1 4 7 0 NaN 5.0 2 6 5 5 5.0 9.0 3 1 9 9 5.0 9.0 4 4 8 1 5.0 9.0 传入axis=” “修改填充方向: df2.fillna(method="ffill",limit=1,axis=1) 1 代码结果: 0 1 2 3 4 0 6.0 6.0 2.0 4.0 1.0 1 4.0 7.0 0.0 0.0 5.0 2 6.0 5.0 5.0 5.0 NaN 3 1.0 9.0 9.0 9.0 NaN 4 4.0 8.0 1.0 5.0 9.0标签:5.0,8.0,1.0,4.0,df,NaN,fillna,2.0 From: https://www.cnblogs.com/Li-JT/p/16714754.html