Python 将两个不同数据帧中的两列相乘

我有一个熊猫数据框

      df1
           Year    Class     Price    EL
           2024     PC1       $243    Base
           2025     PC1       $215    Base
           2024     PC1       $217    EL_1
           2025     PC1       $255    EL_1
           2024     PC2       $217    Base
           2025     PC2       $232    Base
           2024     PC2       $265    EL_1
           2025     PC2       $215    EL_1

我有另一个数据框

           df2  
              Year    Price_factor
              2024      1
              2025      0.98

我想将df2['Price\u factor']应用于df1['Price']列。我尝试了我的代码，但没有成功

          df3=df1.groupby(['Class','EL'])['Price']*df2['Price_factor]

---

提前感谢您的帮助。

您不需要groupby，而是将两个表合并，然后将列相乘。我必须通过删除$符号并使用.astypefloat将原始价格列转换为浮动，以便能够计算新价格：

import pandas as pdb

# df1 = pd.read_clipboard()
# df2 = pd.read_clipboard()

df3 = df1.merge(df2, how='left', on="Year")
df3['New Price'] = df3['Price'].str[1:].astype(float) *df3['Price_factor']
print(df3)

---

您不需要groupby，而是将两个表合并，然后将列相乘。我必须通过删除$符号并使用.astypefloat将原始价格列转换为浮动，以便能够计算新价格：

import pandas as pdb

# df1 = pd.read_clipboard()
# df2 = pd.read_clipboard()

df3 = df1.merge(df2, how='left', on="Year")
df3['New Price'] = df3['Price'].str[1:].astype(float) *df3['Price_factor']
print(df3)

使用地图

df1['Price_factor'] = df1['Year'].map(df2.set_index('Year')['Price_factor'])
df1['Price_adjusted']= df1['Price'].str.strip('$').astype(int) * df1['Price_factor']
df1

输出：

|    |   Year | Class   | Price   | EL   |   Price_factor |   Price_adjusted |
|----|--------|---------|---------|------|----------------|------------------|
|  0 |   2024 | PC1     | $243    | Base |           1    |           243    |
|  1 |   2025 | PC1     | $215    | Base |           0.98 |           210.7  |
|  2 |   2024 | PC1     | $217    | EL_1 |           1    |           217    |
|  3 |   2025 | PC1     | $255    | EL_1 |           0.98 |           249.9  |
|  4 |   2024 | PC2     | $217    | Base |           1    |           217    |
|  5 |   2025 | PC2     | $232    | Base |           0.98 |           227.36 |
|  6 |   2024 | PC2     | $265    | EL_1 |           1    |           265    |
|  7 |   2025 | PC2     | $215    | EL_1 |           0.98 |           210.7  |

使用地图

df1['Price_factor'] = df1['Year'].map(df2.set_index('Year')['Price_factor'])
df1['Price_adjusted']= df1['Price'].str.strip('$').astype(int) * df1['Price_factor']
df1

输出：

|    |   Year | Class   | Price   | EL   |   Price_factor |   Price_adjusted |
|----|--------|---------|---------|------|----------------|------------------|
|  0 |   2024 | PC1     | $243    | Base |           1    |           243    |
|  1 |   2025 | PC1     | $215    | Base |           0.98 |           210.7  |
|  2 |   2024 | PC1     | $217    | EL_1 |           1    |           217    |
|  3 |   2025 | PC1     | $255    | EL_1 |           0.98 |           249.9  |
|  4 |   2024 | PC2     | $217    | Base |           1    |           217    |
|  5 |   2025 | PC2     | $232    | Base |           0.98 |           227.36 |
|  6 |   2024 | PC2     | $265    | EL_1 |           1    |           265    |
|  7 |   2025 | PC2     | $215    | EL_1 |           0.98 |           210.7  |