Pandas：基于列中的空值拆分数据框

Question

我有一个如下数据框：

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

如何基于性别的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），其值将为gender（df的前三行）

然后将其插入没有性别列（df的后三行）的df2（名称，年龄，高度，日期）

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