如何在數據框中將 columnt 類型對象轉換為浮點數

Question

我有以下數據框：

        ID customer Month   Amount
    0   026         201707  31,65
    1   026         201708  31,65
    2   026         201709  31,65
    3   026         201710  31,65
    4   026         201711  31,65

.....

其中“金額”是對象類型。 我想計算每個 ID 的總和和平均金額。 首先，我嘗試將“金額”列從對象轉換為浮點數

df['Amount'] = pd.to_numeric(df['Amount'], errors='coerce')

但是我在“金額”列中的所有值都得到了NaN

        ID customer Month   Amount
    0   026         201707  NaN 
    ....

如何將列對象類型轉換為帶有實數的浮點數並聚合每個客戶的值（總和、平均值、平均值）？

Answer 1

使用Series.str.replace前pd.to_numeric轉換,在. 然后你可以使用groupby.agg

agg_df = (df.assign(Amount = pd.to_numeric(df['Amount'].str.replace(',','.'),
                                           errors = 'coerce'))
            .groupby('ID').Amount.agg(['mean','sum']))
print(agg_df)
#if you want change the type of Amount previously
#df['Amount'] =pd.to_numeric(df['Amount'].str.replace(',','.'),errors = 'coerce')
#agg_df = df.groupby('ID').Amount.agg(['mean','sum']))

     mean    sum
ID              
0   31.65  31.65
1   31.65  31.65
2   31.65  31.65
3   31.65  31.65
4   31.65  31.65

---

如果您想聚合到初始數據幀，請使用GroupBy.transform ：

groups = pd.to_numeric(df['Amount'].str.replace(',','.'),errors = 'coerce').groupby(df['ID'])
#if you want change the type of Amount previously
#df['Amount'] =pd.to_numeric(df['Amount'].str.replace(',','.'),errors = 'coerce')
#groups = df.groupby('ID')['Amount']
df['mean'] = groups.transform('mean')
df['sum'] = groups.transform('sum')
print(df)
   ID  customer   Month Amount   mean    sum  
0   0        26  201707  31,65  31.65  31.65  
1   1        26  201708  31,65  31.65  31.65  
2   2        26  201709  31,65  31.65  31.65  
3   3        26  201710  31,65  31.65  31.65  
4   4        26  201711  31,65  31.65  31.65