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Showing posts with the label SAS career options

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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' ]) ...

SAS career these are different job roles

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What is SAS? Originating in 1966 at North Carolina State University, SAS is a proprietary 4th generation programming language specifically designed for data analysis. Data analysis includes data management, ETL, descriptive statistics, plots and graphics, inferential statistics, data mining, forecasting, etc.. In addition to the SAS programming language, SAS Institute, its parent company, also sells solutions built on top of the SAS language. Many people assumptions about SAS: Some people confuse SAS with the common database query language called “SQL”, thinking that both languages manage data in some way. While SAS may be used for database querying (reading/writing data to databases), this is only a tiny fraction of SAS’s capabilities. To give some scope, the entire SQL language is available in SAS as but one “procedure” (think of a procedure as a bundle of functionality). SKILL Set: What It Is: Pronounced “sass,” this software helps workers perform a variety of tasks, in...