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Showing posts with the label DevOps learning tips

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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' ]) ...

How Does DevOps Speeds up Delivery

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The DevOps is basically a Software development system and most popular in Cloud computing. On-device software and storage may not be pass just yet, but it’s getting there, as more and more computing processes are taking place in the cloud. How Does DevOps Speeds up Delivery Many companies are also preferring distributed computing, which allows multiple computers to network with one another to solve a task (and, unlike the cloud, gives companies control over the actual hardware involved). Many of the skills involved with cloud computing and distributed computing are related, so the two are often grouped together: Skills to learn include SQL for database management, programming languages like Ruby, JavaScript, and Python for building cloud-based applications, and DevOps (a software development system popular in the cloud computing field). Specific in-demand programs include Microsoft Azure and Amazon Web Services. Also Read: 2017 Fast Growing IT Skills For instance, Agile, Scrum, C...