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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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Business Vs Demographic Vs Product Analytics

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List of top analytics areas and their differences



1. Analytics in Business

  • Advertising Analytics
  • Brand Analytics
  • Promotion Analytics
  • Business-to-business marketing Analytics
  • Social Media Analytics
  • Tracking Studies

2. Demographic Analytics

  • Consumer Analytics
  • Concept Testing Data Mining
  • Customer Satisfaction Study Analytics
  • Demographic Analytics
  • Employee Satisfaction Analysis
  • Text Mining
  • Ethnographic Analytics
  • Media Testing
  • Opinion Polling and Predictive Analytics
  • Usage & Attitude Studies
  • Segmentation Analytics
  • Semiotic and Cultural Analysis

3. Product Analytics

  • Packaging and Design Effectiveness Analytics
  • New Product Development
  • Pricing Studies
  • Product Testing
  • Scenario Planning 

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