Concatenate, Merge & Join
Pandas Basics
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Published Sep 29 2025, updated Oct 24 2025
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PandasPython
Concatenate (pd.concat)
Think of this as stacking DataFrames either vertically (rows) or horizontally (columns).
Syntax:
pd.concat(objs, axis=0, join="outer", ignore_index=False, keys=None)
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Key Parameters
objs→ list/tuple of DataFrames.axis=0→ stack rows (default).axis=1→ stack columns (side by side).ignore_index=True→ reindex result.join→ how to handle mismatched columns ('outer','inner').
Example
import pandas as pd
df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df2 = pd.DataFrame({"A": [5, 6], "B": [7, 8]})
pd.concat([df1, df2], ignore_index=True)
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Output:
A B
0 1 3
1 2 4
2 5 7
3 6 8
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Merge (pd.merge)
This is like SQL joins (inner, left, right, outer). It combines DataFrames based on common keys/columns.
Syntax:
pd.merge(left, right, how="inner", on=None, left_on=None, right_on=None)
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Key Parameters
on→ column(s) to join on (must exist in both).left_on,right_on→ join on different column names.how→ type of join:"inner"→ only matching rows"left"→ keep all from left"right"→ keep all from right"outer"→ union of keys
Example:
df1 = pd.DataFrame({"ID": [1, 2, 3], "Name": ["Alice", "Bob", "Charlie"]})
df2 = pd.DataFrame({"ID": [2, 3, 4], "Salary": [50000, 60000, 70000]})
pd.merge(df1, df2, on="ID", how="inner")
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Output:
ID Name Salary
0 2 Bob 50000
1 3 Charlie 60000
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Join (df.join)
A convenience method for combining DataFrames by index (or by a key column).
Syntax:
df1.join(df2, how="left", on=None, lsuffix="", rsuffix="")
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Key Parameters
on→ column in calling DataFrame to use as join key (if not index).lsuffix,rsuffix→ handle overlapping column names.how→ join type (same as merge).
Example:
df1 = pd.DataFrame({"Name": ["Alice", "Bob"], "ID": [1, 2]}).set_index("ID")
df2 = pd.DataFrame({"Salary": [50000, 60000]}, index=[1, 2])
df1.join(df2)
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Output:
Name Salary
ID
1 Alice 50000
2 Bob 60000
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