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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...

Python Set Operations Explained: From Theory to Real-Time Applications

set in Python is an unordered collection of unique elements. It is useful when storing distinct values and performing operations like union, intersection, or difference.

Python Set Operations



Real-Time Example: Removing Duplicate Customer Emails in a Marketing Campaign

Imagine you are working on an email marketing campaign for your company. You have a list of customer emails, but some are duplicated. Using a set, you can remove duplicates efficiently before sending emails.

Code Example:


# List of customer emails (some duplicates) customer_emails = [ "alice@example.com", "bob@example.com", "charlie@example.com", "alice@example.com", "david@example.com", "bob@example.com" ] # Convert list to a set to remove duplicates unique_emails = set(customer_emails) # Convert back to a list (if needed) unique_email_list = list(unique_emails) # Print the unique emails print("Unique customer emails:", unique_email_list)

Output:


Unique customer emails: ['alice@example.com', 'david@example.com', 'charlie@example.com', 'bob@example.com']

(Note: The order may vary because sets are unordered.)


Why Use Sets Here?

  1. Fast duplicate removal – Converting a list to a set automatically removes duplicates.
  2. Efficient lookup – Checking if an email exists is faster in a set (O(1) time complexity).
  3. Simpler code – No need for loops or conditional checks to remove duplicates manually.

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