地圖值

Question

假設我試圖映射total_cases_age到cases_by_age其中dataframes是：

results_grouped_age = results_grouped[['Make', 'age', 'Test Result', 'Number of Cases']].copy() 
    cases_by_age = results_grouped_age[['Make','age','Test Result','Number of Cases']].groupby(['Make','age','Test Result']).sum().reset_index() 
    total_cases_age = cases_by_age.groupby(['Make','age'])['Number of Cases'].sum() 

不過，而我通常會做：

total_cases_age的

cases_by_age['Total Cases'] = cases_by_age['age'].map(total_cases_age) 

指數實際上是爲'make and age'的組合，這實際上是我想要做的。爲了更容易理解我的問題，假設我有表cases_by_age」

 Make  age  Test Result  Number of Cases 
0 ALFA ROMEO 0-3   ABA    1 
1 ALFA ROMEO 0-3   ABR    NaN 
2 ALFA ROMEO 0-3   F    45 
3 ALFA ROMEO 0-3   P    268 
4 ALFA ROMEO 0-3   PRS    21 
5 ALFA ROMEO 3-5   ABA    NaN 
6 ALFA ROMEO 3-5   ABR    NaN 
7 ALFA ROMEO 3-5   F    159 
8 ALFA ROMEO 3-5   P    720 

而最終的結果應該是這樣的：對於品牌和年齡

 Make  age  Test Result  Number of Cases  Total Cases by Age 
0 ALFA ROMEO 0-3   ABA    1     335 
1 ALFA ROMEO 0-3   ABR    NaN     335 
2 ALFA ROMEO 0-3   F    45     335 
3 ALFA ROMEO 0-3   P    268     335 
4 ALFA ROMEO 0-3   PRS    21     335 
5 ALFA ROMEO 3-5   ABA    NaN     879 
6 ALFA ROMEO 3-5   ABR    NaN     879 
7 ALFA ROMEO 3-5   F    159     879 
8 ALFA ROMEO 3-5   P    720     879 

等等任何幫助將將不勝感激

Answer 1

你可以做一個groupby - sum，其次是左 - merge：

pd.merge(
    df, 
    df['Number of Cases'].groupby(df['age']).sum().reset_index().rename(
     columns={'Number of Cases': 'Total Cases by Age'}), 
    how='left') 

---

例

假設你開始了與

df = pd.DataFrame({ 
    'Make': ['ALPHA ROMEO'] * 3, 
    'age': ['0-3', '0-3', '3-5'], 
    'Number of Cases': [1, 10, 2] 
    }) 
>>> df 
    Make Number of Cases age 
0 ALPHA ROMEO 1 0-3 
1 ALPHA ROMEO 10 0-3 
2 ALPHA ROMEO 2 3-5 

然後groupby - sum給出：

>>> df['Number of Cases'].groupby(df['age']).sum().reset_index().rename(
    columns={'Number of Cases': 'Total Cases by Age'}) 
    age Total Cases by Age 
0 0-3 11 
1 3-5 2 

而且該組合給出了：

>>> pd.merge(
    df, 
    df['Number of Cases'].groupby(df['age']).sum().reset_index().rename(
     columns={'Number of Cases': 'Total Cases by Age'}), 
    how='left') 
    Make Number of Cases age Total Cases by Age 
0 ALPHA ROMEO 1 0-3 11 
1 ALPHA ROMEO 10 0-3 11 
2 ALPHA ROMEO 2 3-5 2