Python3.5Pandas模块缺失值处理和层次索引实例详解-创新互联

本文实例讲述了Python3.5 Pandas模块缺失值处理和层次索引。分享给大家供大家参考，具体如下：

1、pandas缺失值处理

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

df3 = DataFrame([
  ["Tom",np.nan,456.67,"M"],
  ["Merry",34,345.56,np.nan],
  [np.nan,np.nan,np.nan,np.nan],
  ["John",23,np.nan,"M"],
  ["Joe",18,385.12,"F"]
],columns = ["name","age","salary","gender"])

print(df3)
print("=======判断NaN值=======")
print(df3.isnull())
print("=======判断非NaN值=======")
print(df3.notnull())
print("=======删除包含NaN值的行=======")
print(df3.dropna())
print("=======删除全部为NaN值的行=======")
print(df3.dropna(how="all"))

df3.ix[2,0] = "Gerry"    #修改第2行第0列的值
print(df3)

print("=======删除包含NaN值的列=======")
print(df3.dropna(axis=1))