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Showing posts with the label industrial iot

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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' ]) ...

Industrial IoT what GE says to improve Productivity

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GE is once a top company in Heavy Engineering. This is to say items related to Thermal Power plants, Turbines, and maintenance. GE had always believed that since it knew the materials and the physics of its jet engines and medical scanners, no one could best it in understanding those machines. GE Industrial Internet  The aim is it should not share its data to third parties.    GE sets up its own IoT center.    GE is in IoT mood.    GE can improve operational efficiency by studying data from its machines like situated India and Russia. This is just an example.  GE is Targetting for Predictive Maintenace Improves industrial productivity Based on criticality productivity will zoom if maintenance carried in-time.