python - 用 Pandas 构建数据框

我正在读取一个Excel文件，其中产品和其他标签（每天，每月等）在同一列中。我想创建一个新列，并将产品名称带到与该产品相关的每一行上。有人可以支持吗？提前致谢！ :)

怎么样：

8HP70
Production/Day
Production/Month
Cum.Production
8HP70X
Production/Day
Production/Month
Cum.Production
8HP75
Production/Day
Production/Month
Cum.Production

**how I expect:**

Column A | Column B

8HP70 | Production/Day
8HP70 | Production/Month
8HP70 | Cum.Production
8HP70X | Production/Day
8HP70X | Production/Month
8HP70X | Cum.Production
8HP75 | Production/Day
8HP75 | Production/Month
8HP75 | Cum.Production

最佳答案

一个如何处理此问题的示例：

import pandas as pd
l = [
    ['8HP70'],
    ['Production/Day'],
    ['Production/Month'],
    ['Cum.Production'],
    ['8HP70X'],
    ['Production/Day'],
    ['Production/Month'],
    ['Cum.Production'],
    ['8HP75'],
    ['Production/Day'],
    ['Production/Month'],
    ['Cum.Production'],
]

df = pd.DataFrame(l, columns=['Column B'])

## repeating product label for every 4 rows
products = df[df['Column B'].index % 4 == 0]

## replicating to a new column
df['Column A'] = products.values.repeat(4)

## removing the product duplication
df = df[df['Column A']!=df['Column B']]

Out[3]:
            Column B Column A
1     Production/Day    8HP70
2   Production/Month    8HP70
3     Cum.Production    8HP70
5     Production/Day   8HP70X
6   Production/Month   8HP70X
7     Cum.Production   8HP70X
9     Production/Day    8HP75
10  Production/Month    8HP75
11    Cum.Production    8HP75

编辑

根据需要进一步添加了一些逻辑。如果在第一个产品标签之前一直有嘈杂的行，我们可以删除，执行逻辑并重新添加（假设我们知道第一个产品标签）：

df = pd.DataFrame(l, columns=['Column B'])


## Identify product starting location
prod_label = '8HP70'

## Get index of where first prod appear
prod_indic = df[df['Column B'] == prod_label].index[0]

## create a temp df only with product info
only_prod_df = df[df.index>=prod_indic].reset_index(drop=True)
products = only_prod_df[only_prod_df['Column B'].index % 4 == 0]

## replicating to a new column
only_prod_df['Column A'] = products.values.repeat(4)

## removing the product duplication
only_prod_df = only_prod_df[only_prod_df['Column A']!=only_prod_df['Column B']]

## append back to noisy rows
final_df = pd.concat([df[df.index<prod_indic], only_prod_df],
                                  axis=0, sort=False, ignore_index=True)

            Column B Column A
0              noise      NaN
1              noise      NaN
2              noise      NaN
3     Production/Day    8HP70
4   Production/Month    8HP70
5     Cum.Production    8HP70
6     Production/Day   8HP70X
7   Production/Month   8HP70X
8     Cum.Production   8HP70X
9     Production/Day    8HP75
10  Production/Month    8HP75
11    Cum.Production    8HP75

同样重要的是要注意，该片段依赖于顺序数字索引。

关于python - 用 Pandas 构建数据框，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/57154645/