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Showing posts with the label alternative to ibm mainframe

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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' ]) ...

Data Analytics Tutorial for COBOL Programmers

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Mainframe developers look for an alternative IT course to grow in their careers.  I have explained in this post how can they use their business knowledge. Data analytics tutorial  is a top an alternative for COBOL programmers . What is Data Analytics The field of data science is evolving into one of the fastest-growing and most in-demand fields in the world.  Organizations across industries are looking to make sense of the data they can now collect from new technologies – from predicting the next hot product to determining the risk of an infectious disease outbreak. Demand and Opportunity According to The New York Times, data science “promises to revolutionize industries from business to government, health care to academia.” As data accumulates, organizations are hiring individuals with the expertise to find meaning in the numbers and drive positive business decisions based on what they learn. It is estimated that by 2018, 4 million to 5 million jo...