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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' ]) ...

Why Amazon Web services AWS Cloud computing is so popular

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Amazon its Cloud computing services started in three stages: S3 (Simple storage service) SQS (Simple Que service) EC2 (Elastic compute cloud) Amazon Web Services was officially revealed to the world on March 13, 2006. On that day, AWS offered the Simple Storage Service, its first service. (As you may imagine, Simple Storage Services was soon shortened to S3.) The idea behind S3 was simple: It could offer the concept of object storage over the web, a setup where anyone could put an object — essentially, any bunch of bytes — into S3. Those bytes may comprise a digital photo or a file backup or a software package or a video or audio recording or a spreadsheet file or — well, you get the idea. S3 was relatively limited when it first started out. Though objects could, admittedly, be written or read from anywhere, they could be stored in only one region: the United States. Moreover, objects could be no larger than 5 gigabytes — not tiny by any means, but certainly smaller than ma...