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")

👉 Combines using index (not columns)

• Faster for index-based operations


• Equivalent to merge but simpler syntax

🧠 Think: “merge on index”

🔥 Quick Difference Table

✅ When to use what?

• Use concat → when data is already aligned


• Use merge → when you have a common column
  


• Use join → when index is important