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Python Academy · Lesson

Merging, Joining, and Data Cleaning

Combine DataFrames and handle missing values professionally.

pd.merge

pd.merge(left, right, on="key") joins two DataFrames on a common column — similar to SQL JOIN.

import pandas as pd

users = pd.DataFrame({"id":[1,2,3],"name":["A","B","C"]})
orders = pd.DataFrame({"user_id":[1,1,2],"amount":[100,50,200]})
result = pd.merge(users, orders, left_on="id", right_on="user_id")
print(result)

Join Types

Specify how="inner" (default), "left", "right", or "outer" to control which rows are kept.

import pandas as pd

A = pd.DataFrame({"key":["a","b","c"],"val":[1,2,3]})
B = pd.DataFrame({"key":["b","c","d"],"x":[10,20,30]})

print(pd.merge(A, B, on="key", how="left"))   # all of A
print(pd.merge(A, B, on="key", how="outer"))  # all rows

All lessons in this course

  1. Series and DataFrame Fundamentals
  2. Indexing, Filtering, and Boolean Masks
  3. GroupBy, Aggregation, and Pivot Tables
  4. Merging, Joining, and Data Cleaning
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