Merging of Data Objects

In real projects, related data is often split across multiple files or tables - student details in one file, their marks in another. Merging lets you combine such DataFrames based on a common column, just like a join in SQL.

A Basic Merge

import pandas as pd

students = pd.DataFrame({
    "StudentID": [1, 2, 3],
    "Name": ["Abhay", "Priya", "Rohit"]
})

marks = pd.DataFrame({
    "StudentID": [1, 2, 3],
    "Score": [85, 92, 78]
})

merged = pd.merge(students, marks, on="StudentID")
print(merged)
#    StudentID   Name  Score
# 0          1  Abhay     85
# 1          2  Priya     92
# 2          3  Rohit     78

Merging on Differently Named Columns

merged2 = pd.merge(students, marks, left_on="StudentID", right_on="StudentID")

# If the key columns have different names:
merged3 = pd.merge(df_a, df_b, left_on="ID", right_on="StudentID")

Merging on Multiple Columns

result = pd.merge(df_a, df_b, on=["StudentID", "Year"])

Coming Up Next

Next, you'll learn about concatenation - stacking DataFrames together instead of joining them on a key.