我在 pandas 中有一個循環，速度非常慢（十多分鐘）。我試圖用矢量化函數替換它，但不知道該使用什么。有多個記錄具有不同的家庭號碼但具有相同的關系組號碼，如果記錄的家庭號碼與關系組號碼相同，那么我想將該記錄的官員號碼和姓名用于具有該關系組的所有記錄號碼（包括家庭號碼不同的情況）。參見下面的代碼： rg['RG Officer Number'] = pd.np.nan rg['RG Officer Name'] = pd.np.nan for index, row in rg.iterrows(): if row['Relationship Group'] == row['Household Number']: mask = rg['Relationship Group'] == row['Relationship Group'] rg.loc[mask, 'RG Officer Number'] = row['Household Primary Officer Number'] rg.loc[mask, 'RG Officer Name'] = row['Household Primary Officer Name'] 我嘗試了以下操作，但出現錯誤（無法使用單個 bool 來索引 setitem）。我想我完全偏離了軌道。也許這對于向量化函數來說是不可能的，但似乎不應該如此。 mask = row['Relationship Group'] == row['Household Number'] rg.loc[mask, 'RG Officer Number'] = rg.loc['Household Primary Officer Number']您提供的任何幫助將不勝感激。
1 回答
白豬掌柜的
TA貢獻1893條經驗 獲得超10個贊
過濾和合并就可以了。
df = pd.DataFrame({'Household Number':[str(i) for i in range(10)],
'Relationship Number':[str(i) for i in range(5)]*2,
'RG Officer Number':np.random.randint(1,100,10),
'RG Officer Name':['name'+str(i) for i in np.random.randint(1,100,10)]})
df
# Household Number Relationship Number RG Officer Number RG Officer Name
#0 0 0 28 name87
#1 1 1 18 name71
#2 2 2 69 name8
#3 3 3 83 name64
#4 4 4 88 name36
#5 5 0 25 name89
#6 6 1 51 name76
#7 7 2 29 name80
#8 8 3 61 name27
#9 9 4 2 name95
df_filtered = df.loc[df['Household Number'] == df['Relationship Number']]
df_filtered
# Household Number Relationship Number RG Officer Number RG Officer Name
#0 0 0 28 name87
#1 1 1 18 name71
#2 2 2 69 name8
#3 3 3 83 name64
#4 4 4 88 name36
df_merged = pd.merge(left=df,right=df_filtered[['Relationship Number','RG Officer Number','RG Officer Name']],
how='left',
on='Relationship Number',suffixes=('_old','_new'))
這是合并的數據。
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