Data Manipulation

Concept: Combining datasets is a common task in data analysis. In Pandas, the two primary methods are \texttt{merge()} and \texttt{join()}, which allow combining data based on keys or indices, similar to SQL joins. Step 1: {\color{red}Using Merge Operation}
The \texttt{merge()} function combines datasets based on one or more common columns:

• Similar to SQL joins
• Allows inner, left, right, and outer joins

Example: \begin{verbatim} import pandas as pd df1 = pd.DataFrame({'id': [1, 2], 'name': ['A', 'B']}) df2 = pd.DataFrame({'id': [1, 2], 'score': [90, 85]}) merged = pd.merge(df1, df2, on='id', how='inner') \end{verbatim}
Step 2: {\color{red}Types of Merge Joins}

• Inner Join: Common rows only
• Left Join: All rows from left dataset
• Right Join: All rows from right dataset
• Outer Join: All rows from both datasets

Step 3: {\color{red}Using Join Operation}
The \texttt{join()} method combines datasets based on index alignment:

• Default is left join
• Useful when indices represent relationships

Example: \begin{verbatim} df1 = df1.set_index('id') df2 = df2.set_index('id') joined = df1.join(df2) \end{verbatim}
Step 4: {\color{red}Key Differences}

• merge(): Column-based joining, more flexible
• join(): Index-based joining, simpler syntax

Step 5: {\color{red}When to Use Which}

• Use \texttt{merge()} for relational-style joins
• Use \texttt{join()} when working with indexed data