根据 Python 中特定列的值替换缺失值

Question

我想根据已提交列的值替换缺失值。

在下面找到我所拥有的：

Year	Country	Submitted	Age12	Age14
2018	CHI	1	267	NaN
2019	CHI	NaN	NaN	NaN
2020	CHI	1	244	203
2018	ALB	1	163	165
2019	ALB	1	NaN	NaN
2020	ALB	1	161	NaN
2018	GER	1	451	381
2019	GER	NaN	NaN	NaN
2020	GER	1	361	321

这是我想要的：

Year	Country	Submitted	Age12	Age14
2018	CHI	1	267	NaN
2019	CHI	NaN	267	NaN
2020	CHI	1	244	203
2018	ALB	1	163	165
2019	ALB	1	NaN	NaN
2020	ALB	1	161	NaN
2018	GER	1	451	381
2019	GER	NaN	451	381
2020	GER	1	361	321

我尝试使用命令 df.fillna(axis=0, method='ffill') 但这将所有值 NaN 替换为之前的值，但这不是我想要的，因为如果“已提交”列值为 1，则某些值应保留为 NaN。

仅当相应的“已提交”值为“NaN”时，我才想更改前一行的值。

谢谢

Answer 1

尝试将 where 与您所做的结合使用：

 df = df.where(~df.Sumbitted.isnull(), df.fillna(axis=0, method='ffill'))

这将仅在 Submitted 为空时替换条目。

Answer 2

您可以使用 np.where

进行条件 ffill()

import numpy as np
(
    df.assign(Age12=np.where(df.Submitted.isna(), df.Age12.ffill(), df.Age12))
    .assign(Age14=np.where(df.Submitted.isna(), df.Age14.ffill(), df.Age14))
)

Answer 3

当Submitted为NaN时可以使用.filter() to select the related columns and put the columns in the list cols. Then, use .mask() to change the values of the selected columns by forward fill using ffill()，如下：

cols = df.filter(like='Age').columns

df[cols] = df[cols].mask(df['Submitted'].isna(), df[cols].ffill())

结果：

print(df)

   Year Country  Submitted  Age12  Age14
0  2018     CHI        1.0  267.0    NaN
1  2019     CHI        NaN  267.0    NaN
2  2020     CHI        1.0  244.0  203.0
3  2018     ALB        1.0  163.0  165.0
4  2019     ALB        1.0    NaN    NaN
5  2020     ALB        1.0  161.0    NaN
6  2018     GER        1.0  451.0  381.0
7  2019     GER        NaN  451.0  381.0
8  2020     GER        1.0  361.0  321.0

Answer 4

我刚刚使用 for 循环来检查和更新数据框中的值

import pandas as pd
new_data = [[2018,'CHI',1,267,30], [2019,'CHI','NaN','NaN','NaN'], [2020,'CHI',1,244,203]]
df = pd.DataFrame(new_data, columns = ['Year','Country','Submitted','Age12','Age14'])
prevValue12 = df.iloc[0]['Age12']
prevValue14 = df.iloc[0]['Age14']
for index, row in df.iterrows():
    if(row['Submitted']=='NaN'):
        df.at[index,'Age12']=prevValue12
        df.at[index,'Age14']=prevValue14
    prevValue12 = row['Age12']
    prevValue14 = row['Age14']
print(df)

输出

Year Country Submitted Age12 Age14
0  2018     CHI         1   267    30
1  2019     CHI       NaN   267    30
2  2020     CHI         1   244   203

根据 Python 中特定列的值替换缺失值

Replace missing values based on value of a specific column in Python

python

pandas

missing-data

fillna

ffill