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Showing posts with the label Digitization

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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' ]) ...

New Directions for Digital Products (1 of 2)

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We already crossed Agriculture, Industrial, Information age. Now we are in digitization age. Many companies investing huge money in digitization. Mphasis - is betting on the digitization of Financial institutions Tech Mahindra - started research on Heath care digitization Infosys - focusing on Automation and artificial intelligence TCS - focussing on Machine learning WIPRO - is focusing on Big data and Hadoop What is digitization What we mean by digital. Digital data is distinguished from analog data in that the datum is represented in discrete, discontinuous values, rather than the continuous, wavelike values of analog. Thus, the digitization of data refers to the conversion of information into binary code, allowing for more efficient transmission and storage of data. A key differentiator of our current age from prior human history is that, as of the last decade, we not only convert data to a digital format, but we also create data in a digital format. Thus, we now h...