我希望計算兩個時間點之間每個人的心理健康分數的變化。
每個用戶都有一個姓名，以及來自 3 個不同時間點的心理健康評分。我想計算時間點 3 和 1 之間心理健康評分的變化
下面是我開始的 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
這是所需的輸出：
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
有誰知道如何做到這一點？
uj5u.com熱心網友回復：
您可以使用shift()和np.where()
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
uj5u.com熱心網友回復：
嘗試使用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
uj5u.com熱心網友回復：
正如評論中已經說明的那樣，您可以使用groupby您的資料框User并計算差異Mental Health Score
我在這里放了一段代碼來演示
def _overall_change(scores):
return scores.iloc[-1] - scores.iloc[0]
person = df.groupby('User')['Score'].agg(_overall_change)
uj5u.com熱心網友回復：
使用groupby和merge：
g = df.sort_values(by='Timepoint').groupby('User')['Mental Health Score']
s = pd.concat({3: g.last()-g.first()})
# User
# 3 Bill 10
# Sam 0
# Wiz 5
# Name: Mental Health Score, dtype: int64
df.merge(s, left_on=['Timepoint', 'User'], right_index=True, how='left')
輸出：
User Timepoint Mental Health Score_x Mental Health Score_y
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
uj5u.com熱心網友回復：
這是另一種可能的解決方案：
import pandas as pd
def calculate_change(mhs):
mhs = list(mhs)
return mhs[-1] - mhs[0]
df = df.sort_values(["User", "Timepoint"])
diff = df.groupby('User')['Mental Health Score'].agg(calculate_change)
df = pd.merge(df, diff, how='left', left_on='User', right_index=True)
df.columns = ['User', 'Timepoint', 'Mental Health Score', 'Change']
df['Change'] = df['Change'].loc[df['Timepoint']==3]
print(df)
輸出
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
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