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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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Excel: 10 Key Topics You Need to Learn

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The below-listed topics help you get a solid footing in Excel Analytics. Just practice these 10 topics step by step and by completing all, you will be an expert in Excel.

Topics you need to learn in Excel
10 Top Excel Topics

  1. Tables in Excel 
  2. Grabbing data from external sources 
  3. Cleaning data with functions 
  4. Working with Pivot tables 
  5. Writing Formulae for Pivot tables 
  6. Pivot Charts 
  7. How to use database functions 
  8. How to use statistics 
  9. Inferential Statistics 
  10. Descriptive statistics

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