Python Pandas – 从两个 DataFrames 合并并创建笛卡尔积
要合并PandasDataFrame,请使用该merge()函数。通过在函数的“如何”参数下设置,笛卡尔积在两个数据帧上实现,merge()即-
how = “cross”
首先,让我们使用别名导入pandas库-
import pandas as pd
创建DataFrame1-
dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 110, 120] } )
创建DataFrame2
dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Tesla', 'Jaguar'],"Reg_Price": [7000, 8000, 9000] } )
接下来,将DataFrames与“how”参数中的“cross”合并,即笛卡尔积-
mergedRes = pd.merge(dataFrame1, dataFrame2, how ="cross")
示例
以下是代码
import pandas as pd #创建DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Mustang', 'Bentley', 'Jaguar'],"Units": [100, 150, 110, 120] } ) print("DataFrame1 ...\n",dataFrame1) #创建DataFrame2 dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Tesla', 'Jaguar'],"Reg_Price": [7000, 8000, 9000] } ) print("\nDataFrame2 ...\n",dataFrame2) # merge DataFrames with "cross" in "how" parameteri.eCartesian Product mergedRes = pd.merge(dataFrame1, dataFrame2, how ="cross") print("\nMerged dataframe with cartesian product...\n", mergedRes)输出结果
这将产生以下输出-
DataFrame1 ... Car Units 0 BMW 100 1 Mustang 150 2 Bentley 110 3 Jaguar 120 DataFrame2 ... Car Reg_Price 0 BMW 7000 1 Tesla 8000 2 Jaguar 9000 Merged dataframe with cartesian product... Car Units Car_y Reg_Price 0 BMW 100 BMW 7000 1 BMW 100 Tesla 8000 2 BMW 180 Jaguar 9000 3 Mustang 150 BMW 7000 4 Mustang 150 Tesla 8000 5 Mustang 150 Jaguar 9000 6 Bentley 110 BMW 7000 7 Bentley 110 Tesla 8000 8 Bentley 110 Jaguar 9000 9 Jaguar 120 BMW 7000 10 Jaguar 120 Tesla 8000 11 Jaguar 120 Jaguar 9000