在Pandas中使用多个数据帧的优雅方法

import pandas as pd

# 创建两个数据帧
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df2 = pd.DataFrame({'A': [3, 4, 5], 'C': ['x', 'y', 'z']})

# 内连接
merged_inner = pd.merge(df1, df2, on='A', how='inner')
print("内连接结果：")
print(merged_inner)

# 左连接
merged_left = pd.merge(df1, df2, on='A', how='left')
print("左连接结果：")
print(merged_left)

# 右连接
merged_right = pd.merge(df1, df2, on='A', how='right')
print("右连接结果：")
print(merged_right)

# 外连接
merged_outer = pd.merge(df1, df2, on='A', how='outer')
print("外连接结果：")
print(merged_outer)

import pandas as pd

# 创建两个数据帧
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})
df2 = pd.DataFrame({'C': ['x', 'y', 'z'], 'D': [4, 5, 6]})

# 按行连接
concatenated_rows = pd.concat([df1, df2], axis=0)
print("按行连接结果：")
print(concatenated_rows)

# 按列连接
concatenated_columns = pd.concat([df1, df2], axis=1)
print("按列连接结果：")
print(concatenated_columns)