Pandas中数据表合并的几种实现方法

import pandas as pd  

# 创建示例 DataFrame  
df1 = pd.DataFrame({  
    'key': ['A', 'B', 'C'],  
    'value1': [1, 2, 3]  
})  

df2 = pd.DataFrame({  
    'key': ['B', 'C', 'D'],  
    'value2': [4, 5, 6]  
})  

# 合并 DataFrame  
result = pd.merge(df1, df2, on='key', how='inner')  # inner join  
print(result)

# 外连接  
result_outer = pd.merge(df1, df2, on='key', how='outer')  
print(result_outer)  

# 左连接  
result_left = pd.merge(df1, df2, on='key', how='left')  
print(result_left)  

# 右连接  
result_right = pd.merge(df1, df2, on='key', how='right')  
print(result_right)

df3 = pd.DataFrame({  
    'key1': ['A', 'B', 'C'],  
    'key2': ['W', 'X', 'Y'],  
    'value1': [1, 2, 3]  
})  

df4 = pd.DataFrame({  
    'key1': ['A', 'B', 'D'],  
    'key2': ['W', 'X', 'Z'],  
    'value2': [4, 5, 6]  
})  

result_multi_key = pd.merge(df3, df4, on=['key1', 'key2'], how='inner')  
print(result_multi_key)

df5 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})  
df6 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})  

# 按行连接（纵向）  
result_concat = pd.concat([df5, df6], axis=0)  
print(result_concat)  

# 按列连接（横向）  
result_concat_columns = pd.concat([df5, df6], axis=1)  
print(result_concat_columns)

result_concat_ignore_index = pd.concat([df5, df6], axis=0, ignore_index=True)  
print(result_concat_ignore_index)

df7 = pd.DataFrame({  
    'value1': [1, 2, 3]},  
    index=['A', 'B', 'C']  
)  

df8 = pd.DataFrame({  
    'value2': [4, 5]},  
    index=['B', 'C']  
)  

# 基于索引连接  
result_join = df7.join(df8, how='inner')  
print(result_join)