Pandas Joins: Complete Guide to Merging DataFrames
Pandas provides powerful tools for joining DataFrames. Here’s a comprehensive guide. Merge Types Inner Join import pandas as pd df1 = pd.DataFrame({'key': ['A', 'B'], 'value1': [1, 2]}) df2 = pd.DataFrame({'key': ['B', 'C'], 'value2': [3, 4]}) result = pd.merge(df1, df2, on='key', how='inner') Left Join result = pd.merge(df1, df2, on='key', how='left') Right Join result = pd.merge(df1, df2, on='key', how='right') Outer Join result = pd.merge(df1, df2, on='key', how='outer') Multiple Keys result = pd.merge(df1, df2, on=['key1', 'key2']) Best Practices Choose the right join type Handle missing values Use appropriate keys Check for duplicates Optimize for large datasets Conclusion Master Pandas joins for efficient data manipulation! 📊