补充DataFrame中缺失的Date-Sex配对的数值字段值为0

初始DataFrame #

先构造你的初始DataFrame：

import pandas as pd
from pandas import Timestamp

data = [
    {'Date': Timestamp('2023-01-01 00:00:00'),'Sex':'M', 'Value':11, 'Target':5, 'A':48},
    {'Date': Timestamp('2023-01-01 00:00:00'),'Sex':'F', 'Value':25, 'Target':7, 'A':20},
    {'Date': Timestamp('2023-01-10 00:00:00'),'Sex':'M', 'Value':45, 'Target':6, 'A':20},
    {'Date': Timestamp('2023-01-10 00:00:00'),'Sex':'F', 'Value':5, 'Target':2, 'A':16},
    {'Date': Timestamp('2023-01-20 00:00:00'),'Sex':'M', 'Value':10, 'Target':8, 'A':30},
    {'Date': Timestamp('2023-01-20 00:00:00'),'Sex':'M', 'Value':1, 'Target':18, 'A':3}
]

df = pd.DataFrame(data)

初始数据内容：

Date Sex  Value  Target   A
0 2023-01-01   M     11       5  48
1 2023-01-01   F     25       7  20
2 2023-01-10   M     45       6  20
3 2023-01-10   F      5       2  16
4 2023-01-20   M     10       8  30
5 2023-01-20   M      1      18   3

补全缺失记录的操作 #

构造缺失的行数据，再合并到原DataFrame中：

# 生成缺失的目标记录
missing_row = pd.DataFrame({
    'Date': [Timestamp('2023-01-20 00:00:00')],
    'Sex': ['F'],
    'Value': [0],
    'Target': [0],
    'A': [0]
})

# 合并数据并重置索引
df_complete = pd.concat([df, missing_row], ignore_index=True)

最终结果 #

执行后得到的完整DataFrame：

Date Sex  Value  Target   A
0 2023-01-01   M     11       5  48
1 2023-01-01   F     25       7  20
2 2023-01-10   M     45       6  20
3 2023-01-10   F      5       2  16
4 2023-01-20   M     10       8  30
5 2023-01-20   M      1      18   3
6 2023-01-20   F      0       0   0

内容的提问来源于stack exchange，提问作者Parvez Alam