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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' ]) ...

SAP HANA: Top Data Processing Interview Questions

1. How parallel processing is achieved in SAP HANA? The phrase "divide and conquer" (derived from the Latin saying divide et impera) typically is used when a large problem is divided into a number of smaller, easier-to-solve problems. Regarding performance, processing huge amounts of data is a problem that can be solved by splitting the data into smaller chunks of data, which can be processed in parallel. 2.How data portioning will happen in SAP HANA? Although servers that are available today can hold terabytes of data in memory and provide up to eight processors per server with up to 10 cores per processor, the amount of data that is stored in an in-memory database or the computing power that is needed to process such quantities of data might exceed the capacity of a single server. To accommodate the memory and computing power requirements that go beyond the limits of a single server, data can be divided into subsets and placed across a cluster of servers, which forms a d...