pandas使用两个数据帧向量化函数

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})

def add_values(row):
    return row['A'] + row['C']

df_result = df1.apply(add_values, axis=1)

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})
mapping = {1: 'One', 2: 'Two', 3: 'Three'}

df_result = df1['A'].map(mapping)

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})

def square_value(value):
    return value ** 2

df_result = df1.applymap(square_value)

df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12]})

def max_value(x, y):
    return max(x, y)

df_result = df1.combine(df2, max_value)