LeetCode 2882 Drop Duplicate Rows in Python | Pandas Tutorial for Beginners
Solve LeetCode 2882 "Drop Duplicate Rows" in Python with this beginner-friendly Pandas tutorial! This problem asks you to remove duplicate rows from a DataFrame based on the `email` column, keeping the first occurrence. We’ll use `pandas`’ `drop_duplicates()` method to do this in one line, perfect for data cleaning practice. Great for Python learners, data science beginners, or anyone prepping for coding interviews with Pandas!
🔍 *What You'll Learn:*
Understanding LeetCode 2882’s requirements
Using `pandas`’ `drop_duplicates()` to remove duplicates
Keeping the first occurrence with `keep='first'`
Testing with example customer data
💻 *Code Used in This Video:*
import pandas as pd
def dropDuplicateEmails(customers: pd.DataFrame) - pd.DataFrame:
return customers.drop_duplicates(subset='email', keep='first')
Test case
data = [[1, "Ella", "[email protected]"], [2, "David", "[email protected]"], [3, "Zachary", "[email protected]"], [4, "Alice", "[email protected]"]]
df = pd.DataFrame(data, columns=["customer_id", "name", "email"])
result = dropDuplicateEmails(df)
print(result)
Output:
customer_id name email
0 1 Ella [email protected]
1 2 David [email protected]
2 3 Zachary [email protected]
Test with no duplicates
data_no_duplicates = [[1, "Frank", "[email protected]"], [2, "Grace", "[email protected]"]]
df_no_duplicates = pd.DataFrame(data_no_duplicates, columns=["customer_id", "name", "email"])
result_no_duplicates = dropDuplicateEmails(df_no_duplicates)
print(result_no_duplicates)
Output:
customer_id name email
0 1 Frank [email protected]
1 2 Grace [email protected]
🌟 *Why Solve LeetCode 2882?*
This problem teaches you how to clean data with `pandas`—a key skill for data science and coding interviews! We’ll show how `drop_duplicates(subset='email', keep='first')` removes duplicate emails, keeping the first row. It’s a great way to learn data cleaning, with a time complexity of O(n) where n is the number of rows. Master this, and you’ll be ready for more advanced Pandas challenges!
📚 *Who’s This For?*
Python beginners learning Pandas
Data science enthusiasts working with DataFrames
Coders prepping for data-focused interviews
👍 Like, subscribe, and comment: What Pandas problem should we solve next? Next up: Pandas sorting—stay tuned!
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