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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' ]) ...

Talent Analytics on employees to measure real worth

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Human resource managers are currently embracing talent analytics like never before. Companies are evaluating and analysing raw data to derive valuable insights which are helping them to hire the right talent, retain them as well as help them learn and grow internally. Data to do analytics Talent analytics companies take into account all the data, rather than limited samples, so a full-fledged picture emerges.  It looks for patterns in the data and discovers critical connections that might otherwise go unnoticed. Such data can be related to employees' pre-employment assessments to background checks to social media profiles. How data will gather They also gather data on the characteristics of their most successful employees. When big data is tapped this way, HR managers no longer need to depend on intuitions of interviewers and hiring managers or rely on obsolete hiring tools of yesteryears.  Organisations are also leveraging big data to hire and prom...