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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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Data Security - Safetly Storing Data In Cloud

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What exactly is the cloud? It is basically the collection of computers on the internet that companies are using to offer their services.  One cloud service that is being offered is a revolutionary storage method for your data. From music files to pictures to sensitive documents, the cloud invisibly backs up your files and folders  and alleviates the potentially endless and costly search for extra storage space. An alternative to buying an external hard drive or deleting old files to make room for new ones, cloud storage is convenient and cost-effective.

It works by storing your files on a server out in the internet somewhere rather than on your local hard drive.  (For a more technical discussion of cloud computing basics, read more here.) 

This allows  you to back up, sync, and access your data across multiple devices as long as they have internet capability.

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