如何使用python有效地填充具有特定价值的"missing time pattern"和"fill them"？

Question

我想从：

中"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：08.00，09.00，10.00，11.00，12.00，13.00，14.00，15.00

+-------------+---------+-------+-------+
| 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 |
+-------------+---------+-------+-------+

并用0填充"丢失"。我该怎么做？

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都是扩展数据框的值

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

与Speed = 0

创建所有可能组合的数据框架

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

conterate两个数据框（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，将其删除并对数据框进行排序

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