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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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What is IBM InfoSphere DataStage

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It integrates data across multiple systems using a high-performance parallel framework, and it supports extended metadata management and enterprise connectivity.

IBM InfoSphere

Powerful, scalable ETL platform—supports the collection, integration, and transformation of large volumes of data, with data structures ranging from simple to complex.
  • Support for big data and Hadoop—enables you to directly access big data on a distributed file system, and helps clients more efficiently leverage new data sources by providing JSON support and a new JDBC connector. 
  • Near real-time data integration—as well as connectivity between data sources and applications. 
  • Workload and business rules management—helps you optimize hardware utilization and prioritize mission-critical tasks. 
  • Ease of use—helps improve speed, flexibility, and effectiveness to build, deploy, update and manage your data integration infrastructure. 
  • Rich support for DB2Z and DB2 for z/OS—including data load optimization for DB2Z and balanced optimization for DB2 on z/OS 
  • Ref: IBM

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