Python 程序在熊猫中执行交叉连接
在熊猫中，有参数在两个数据帧或系列上执行左、右、内或外合并和连接。然而，目前还不可能使用how="cross"参数执行交叉连接来合并或连接两个方法。
交叉连接:
例 1:
上面的例子证明如下
# importing pandas module
import pandas as pd
# Define a dictionary with column A
data1 = {'A': [1, 2]}
# Define another dictionary with column B
data2 = {'B': ['a', 'b', 'c']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data1, index =[0, 1])
# Convert the dictionary into DataFrame
df1 = pd.DataFrame(data2, index =[2, 3, 4])
# Now to perform cross join, we will create
# a key column in both the DataFrames to
# merge on that key.
df['key'] = 1
df1['key'] = 1
# to obtain the cross join we will merge
# on the key and drop it.
result = pd.merge(df, df1, on ='key').drop("key", 1)
result
数据帧 1: 
数据帧 2 : 
输出: 
例 2:
用户和产品的两个数据框交叉连接。
# importing pandas module
import pandas as pd
# Define a dictionary containing user ID
data1 = {'Name': ["Rebecca", "Maryam", "Anita"],
'UserID': [1, 2, 3]}
# Define a dictionary containing product ID
data2 = {'ProductID': ['P1', 'P2', 'P3', 'P4']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data1, index =[0, 1, 2])
# Convert the dictionary into DataFrame
df1 = pd.DataFrame(data2, index =[2, 3, 6, 7])
# Now to perform cross join, we will create
# a key column in both the DataFrames to
# merge on that key.
df['key'] = 1
df1['key'] = 1
# to obtain the cross join we will merge on
# the key and drop it.
result = pd.merge(df, df1, on ='key').drop("key", 1)
result
数据帧 1: 
数据帧 2 : 
输出: 
例 3:
# importing pandas module
import pandas as pd
# Define a dictionary with two columns
data1 = {'col 1': [0, 1],
'col 2': [2, 3]}
# Define another dictionary
data2 = {'col 3': [5, 6],
'col 4': [7, 8]}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data1, index =[0, 1])
# Convert the dictionary into DataFrame
df1 = pd.DataFrame(data2, index =[2, 3])
# Now to perform cross join, we will create
# a key column in both the DataFrames to
# merge on that key.
df['key'] = 1
df1['key'] = 1
# to obtain the cross join we will merge on
# the key and drop it.
result = pd.merge(df, df1, on ='key').drop("key", 1)
result
数据帧 1: 
数据帧 2 : 
输出: 
