Three different ways to combine data in Pandas — concat, merge, and join.

🔹 1. pd.concat() → Stack or attach data 👉 Used when you want to combine DataFrames along rows or columns 📌 Row-wise (axis=0) combined = pd . concat ([ df1 , df2 ], axis = 0 ) Stacks df2 below df1 Columns should be same (ideally) 🧠 Think: “append rows” 📌 Column-wise (axis=1) combined = pd . concat ([ df1 , df2 ], axis = 1 ) Adds df2 as new columns Works based on index alignment 🧠 Think: “side-by-side” 🔹 2. pd.merge() → Database-style join 👉 Used when you want to combine based on a common column (key) 📌 Default (inner join) merged = pd . merge ( df1 , df2 , on = " common_column " ) Only keeps matching values 📌 Left join merged = pd . merge ( df1 , df2 , how = " left " , on = " common_column " ) Keeps all rows of df1 Matches from df2 (NaN if no match) 📌 Inner join merged = pd . merge ( df1 , df2 , how = " inner " , on = " common_column " ) Same as default Only common rows 🧠 Think: “SQL JOIN using a column” 🔹 3. df.join() → Index-based join joined = df1 . join ( df2 , how = " inner " ) 👉 C