计算分数的变化并增加新的栏熊猫

Question

我期待着计算两个时间点之间每个人的心理健康分数的变化。

每个用户都有一个名字，以及3个不同时间点的心理健康评分。我想计算一下心理健康分数在3点到1点之间的变化。

下面是我从下面开始的df示例：

User   Timepoint   Mental Health Score
Bill       1               5
Bill       2               10
Bill       3               15
Wiz        1               10
Wiz        2               10
Wiz        3               15
Sam        1               5
Sam        2               5
Sam        3               5

这是所需的输出：

User   Timepoint   Mental Health Score      Change in Mental Health (TP1 and 3) 
Bill       1               5                               
Bill       2               10
Bill       3               15                              10
Wiz        1               10
Wiz        2               10
Wiz        3               15                              5
Sam        1               5
Sam        2               5
Sam        3               5                               0

有人知道怎么做吗？

Answer 1

您可以使用shift()和np.where()完成这一任务。

df['Change in Mental Health (TP1 and 3)'] = df['Mental Health Score'] - df['Mental Health Score'].shift(2)
df['Change in Mental Health (TP1 and 3)'] = np.where(df['Timepoint'] != 3, 0, df['Change in Mental Health (TP1 and 3)']).astype(int)
df

Answer 2

尝试使用groupby和where

#sort by Timepoint if needed
#df = df.sort_values("Timepoint")

changes = df.groupby("User")["Mental Health Score"].transform('last')-df.groupby("User")["Mental Health Score"].transform('first')
df["Change"] = changes.where(df["Timepoint"].eq(3))

>>> df
   User  Timepoint  Mental Health Score  Change
0  Bill          1                    5     NaN
1  Bill          2                   10     NaN
2  Bill          3                   15    10.0
3   Wiz          1                   10     NaN
4   Wiz          2                   10     NaN
5   Wiz          3                   15     5.0
6   Sam          1                    5     NaN
7   Sam          2                    5     NaN
8   Sam          3                    5     0.0

Answer 3

正如注释中已经指出的，您可以在groupby上User上计算数据，并在Mental Health Score上计算差异。

我在这里放了一段代码来演示

def _overall_change(scores):
  return scores.iloc[-1] - scores.iloc[0]

person = df.groupby('User')['Score'].agg(_overall_change)