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”

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📌 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”

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🔹 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

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📌 Left join

merged = pd.merge(df1, df2, how="left", on="common_column")

• Keeps all rows of df1
• Matches from df2 (NaN if no match)

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📌 Inner join

merged = pd.merge(df1, df2, how="inner", on="common_column")

• Same as default
• Only common rows

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🧠 Think: “SQL JOIN using a column”

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🔹 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”

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🔥 Quick Difference Table

Method	Based On	Use Case
concat	index/axis	stacking data
merge	column (key)	SQL-style joins
join	index	quick index-based combine

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✅ 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