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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' ]) ...

Cloud Computing: Horizontal Vs. Vertical Scaling

The purpose of cloud computing is resource utilization. You can scale up the resources in two ways - vertical and horizontal.

Adding resources, you can do either horizontally and vertically. The advantages and drawbacks you can find in simple words.


Cloud Computing: Horizontal Vs. Vertical Scaling
Scaling

1. Horizontal Scaling


Advantages


  • You can increase workloads in small steps.
  • The upgrade-cost is far less.
  • Scale the system as much as needed.


Drawbacks

  • Dependency on software applications is more for Data distribution and parallel processing.
  • On top of that, fewer software applications exist in the market.


You May Also Like: 9 Top Services AWS Provided



2. Vertical Scaling




Advantages



  • Since it is a single machine, it is easy to manage.
  • On the fly, you can increase workloads.



Drawbacks


  • It is expensive. You need a huge investment.
  • The machine should be powerful to take more workloads - future use.


Below is the List of Resources that You can do both Horizontal and Vertical Scaling



  1. Platform Scaling
  2. Network Scaling
  3. Container Scaling
  4. Database Scaling


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