英文:
Comparing subset of rows in pandas
问题
我在想是否有一种好的方法来比较pandas中的一部分行？
假设我有一个带有以下内容的df：
| id | in_test | value |
|---|---|---|
| 1 | True | 5 |
| 2 | True | 5 |
| 1 | False | 7 |
| 2 | False | 8 |
我想要的结果是id和从in_test从true到false的差异（或百分比变化）的df。
我知道我可以将表旋转然后执行逐行计算，或者创建一个过滤后的df并将其与另一个过滤后的df合并，然后逐行计算。
我在想是否有一种在一行中完成此操作的python方法？可能使用pandas函数？
百分比差异的输出将是：
| id | value |
|---|---|
| 1 | +40% |
| 2 | +60% |
差异的输出将是：
| id | value |
|---|---|
| 1 | 2 |
| 2 | 3 |
英文:
I was wondering if there is nice way to compare a subset of rows in pandas?
let's say I have a df with:
| id | in_test | value |
|---|---|---|
| 1 | True | 5 |
| 2 | True | 5 |
| 1 | False | 7 |
| 2 | False | 8 |
I would like the resulting df with id and difference (or percentage change) from in_test from true to false.
I know I could pivot the table and then perform row wise calculations, or create a filtered df and merge it with another filtered df and then compute it row wise.
I was wondering if there is python way of doing this in one line? With probably a pandas function?
The output for percentage diff would be :
| id | value |
|---|---|
| 1 | +40% |
| 2 | +60% |
The output for diff would be :
| id | value |
|---|---|
| 1 | 2 |
| 2 | 3 |
(or minus -2 & -3 i guess I would have top define some kind of order) )
答案1
得分: 1
按id分组，计算value的差异，然后重置索引
差异
按id分组，计算value的百分比变化，然后重置索引
百分比变化
英文:
df.groupby('id')['value'].apply(lambda x: x.diff().values[1]).reset_index()
difference
df.groupby('id')['value'].apply(lambda x: x.pct_change().values[1] * 100).reset_index()
percentage difference
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