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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

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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. 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 Platform Scaling Network Scaling Container Scaling...