如何使用python有效地填充“缺失时间模式”和“填充它们”特定值？

Question

我想“扩展”我的行：

+-------------+---------+-------+-------+
| Week Number | Weekday | Time  | Speed |
+-------------+---------+-------+-------+
|           1 | Monday  | 09.00 |     2 |
|           1 | Monday  | 12.00 |     2 |
|           1 | Monday  | 14.00 |     2 |
|           1 | Monday  | 15.00 |     1 |
|           1 | Tuesday | 08.00 |     4 |
|           1 | Tuesday | 10.00 |     2 |
|           1 | Tuesday | 11.00 |     3 |
|           1 | Tuesday | 13.00 |     2 |
+-------------+---------+-------+-------+

每天进入以下模式：08.00,9.00.00,10.00,11.00,12.00,13.00,14.00,15.00

+-------------+---------+-------+-------+
| Week Number | Weekday | Time  | Speed |
+-------------+---------+-------+-------+
|           1 | Monday  | 08.00 |     0 |
|           1 | Monday  | 09.00 |     2 |
|           1 | Monday  | 10.00 |     0 |
|           1 | Monday  | 11.00 |     0 |
|           1 | Monday  | 12.00 |     2 |
|           1 | Monday  | 13.00 |     0 |
|           1 | Monday  | 14.00 |     2 |
|           1 | Monday  | 15.00 |     1 |
|           1 | Tuesday | 08.00 |     4 |
|           1 | Tuesday | 09.00 |     0 |
|           1 | Tuesday | 10.00 |     2 |
|           1 | Tuesday | 11.00 |     3 |
|           1 | Tuesday | 12.00 |     0 |
|           1 | Tuesday | 13.00 |     3 |
|           1 | Tuesday | 14.00 |     0 |
|           1 | Tuesday | 15.00 |     0 |
+-------------+---------+-------+-------+

用0补充“缺失”。我怎么能这样做？

我正在使用python 3.6和pandas库。

Answer 1

import pandas as pd
df = pd.DataFrame({'Week Number': 1, 'Weekday': ['Monday'] * 4 + ['Tuesday'] * 4, 'Time':['09.00', '12.00', '14.00', '15.00'] * 2,
                  'Speed': [2, 4] * 4})

假设times，days和week_nums都是扩展DataFrame的值

times = ['08.00', '09.00', '10.00', '11.00', '12.00', '13.00', '14.00', '15.00']
days = ['Monday', 'Tuesday']
week_nums = [1]

使用Speed = 0创建所有可能组合的DataFrame

from itertools import product
df_combinations = pd.DataFrame(list(product(, days, times, [0])), columns=['Week Number', 'Weekday', 'Time', 'Speed'])

Concat两个数据帧（df_combinations必须是重复删除的第二个！）

df_new = pd.concat([df, df_combinations], ignore_index=True, sort=False)

创建重复的二进制掩码，删除它们并对数据帧进行排序

df_new = df_new[~df_new.duplicated(subset=['Week Number', 'Weekday', 'Time'], keep='first')]
df_new.sort_values(['Week Number', 'Weekday', 'Time'])