Pandas：根据列中的空值拆分数据框

Question

我有一个如下所示的数据框：

data = [['lynda', 10,'F',125,'5/21/2018'],['tom', np.nan,'M',135,'7/21/2018'], ['nick', 15,'F',99,'6/21/2018'], ['juli', 14,np.nan,120,'1/21/2018'],['juli', 19,np.nan,140,'10/21/2018'],['juli', 18,np.nan,170,'9/21/2018']]
df = pd.DataFrame(data, columns = ['Name', 'Age','Gender','Height','Date'])

df

如何根据性别的 np.NaN 值转换数据框？

我希望将原始数据帧 df 拆分为 df1(Name,Age,Gender,Height,Date) ，其中包含性别值（df 的前 3 行）

AND 其中df2(Name,Age,Height,Date)不会有性别列（df 的最后 3 行）

Answer 1

这是一种方法：

import pandas as pd
import numpy as np


data = [['lynda', 10,'F',125,'5/21/2018'],['tom', np.nan,'M',135,'7/21/2018'], ['nick', 15,'F',99,'6/21/2018'], ['juli', 14,np.nan,120,'1/21/2018'],['juli', 19,np.nan,140,'10/21/2018'],['juli', 18,np.nan,170,'9/21/2018']]
df = pd.DataFrame(data, columns = ['Name', 'Age','Gender','Height','Date'])

df2 = df[df['Gender'].notnull()].drop("Gender", axis=1)
print(df2)

输出：

    Name   Age  Height       Date
0  lynda  10.0     125  5/21/2018
1    tom   NaN     135  7/21/2018
2   nick  15.0      99  6/21/2018