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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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Best Testing Practices You need for DevOps Projects

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Testing is the critical phase in DevOps. The process of DevOps is to speed up the deployment process. That means there are no shortcuts in testing. Covering most relevant test cases is the main thing the tester has to focus.

DevOps Testing

Requirements 

  1. Good maintainable code
  2. Exhaustive coverage of cases
  3. Training documents to Operations team
  4. Fewer bugs in the bug tracker
  5. Less complex and no redundant code

Testing Activities  

    • The team to use Tools to check the quality of code
    • Style checker helps to correct code style
    • Good design avoids bugs in production
    • Code performance depends on the code-quality
    • Bugs in production say poor testing 

    Tester Roles 

    1. Good quality means zero bugs in production.
    2. Design requirements a base to validate testing results.
    3. Automated test scripts give quick feedback on the quality of code.
    4. Right test cases cover all the functional changes.

    The Bottom Line

    • The DevOps approach is seamless integration between Development and Operations without compromising the Quality.
    • Use test cases in a way to test all possible scenarios.
    • Automate the tasks wherever possible to get the quick reply on quality of source code.

    References

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