问题

# 我目前正在从SQL中提取数据，希望计算具有相同标识符的数据点之间的最大差异。

import pandas as pd

df1 = pd.read_csv('SQLdata.csv')

# 这个数据框的示例数据如下：

Year,Case
2015,1A
2016,1A
2017,1A
2015,2F
2018,2F

# 我希望编写一个脚本，为每个案例添加一个新列，该列表示最小(Year)和最大(Year)之间的差异。类似于这样：

Year,Case,YearDifference
2015,1A,2
2016,1A,2
2017,1A,2
2015,2F,3
2018,2F,3

我尝试编写了一个for循环，但在尝试为每个案例执行特定操作时遇到了问题。

<details>
<summary>英文:</summary>

I&#39;m currently taking data from SQL in hopes of calculating the biggest difference between data points with the same identifiers.

import pandas as pd

df1 = pd.read_csv('SQLdata.csv')

Sample data for this df looks like

Year,Case
2015,1A
2016,1A
2017,1A
2015,2F
2018,2F

I'm hoping to write a script that will add a new column for the difference between the min(Year) and max(Year) for each case. Something like this:

Year,Case,YearDifference
2015,1A,2
2016,1A,2
2017,1A,2
2015,2F,3
2018,2F,3

I've tried to write a for loop, but got stuck when trying to do it specific for each case.

</details>


# 答案1
**得分**: 0

你可以尝试使用`groupby`和`transform`来计算每个分组的最大日期减去最小日期：

```python
grp = df.groupby('Case')['Year']
df['Diff'] = grp.transform(max).sub(grp.transform(min))

你也可以尝试使用groupby.agg来获取每个分组的最小和最大年份，然后将差异映射回Case：

df['Diff'] = df['Case'].map(df.groupby('Case')['Year'].agg([min, max]).diff(axis=1)['max'])

输出结果如下：

   Year Case  Diff
0  2015   1A     2
1  2016   1A     2
2  2017   1A     2
3  2015   2F     3
4  2018   2F     3

grp = df.groupby('Case')['Year']
df['Diff'] = grp.transform(max).sub(grp.transform(min))

df['Diff'] = df['Case'].map(df.groupby('Case')['Year'].agg([min, max])
                            .diff(axis=1)['max'])

   Year Case  Diff
0  2015   1A     2
1  2016   1A     2
2  2017   1A     2
3  2015   2F     3
4  2018   2F     3