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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' ]) ...
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Top 10 SCALA Quiz Questions for Programmers

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Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results.

Scala Quiz

But you can also rely on it for large mission-critical systems, as many companies, including Twitter, LinkedIn, or Intel does.


To some, Scala feels like a scripting language. Its syntax is a concise and low ceremony; its types get out of the way because the compiler can infer them. There are a REPL and IDE worksheets for quick feedback.

Developers like it so much that Scala won the ScriptBowl contest at the 2012 JavaOne conference. At the same time, Scala is the preferred workhorse language for many mission-critical server systems.

The generated code is on a par with Java’s and its precise typing means that many problems are caught at compile-time rather than after deployment.


At the root, the language’s scalability is the result of a careful integration of object-oriented and functional language concepts.(Ref-what is Scala).

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