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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' ]) ...

IBM these are analytics solutions offering to different industries

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Every industry has its own particular big data challenges. Banks need to analyze streaming transactions in real time to quickly identify potential fraud. Utility companies need to analyze energy usage data to gain control over demand.  Retailers need to understand the social sentiment around their products and markets to develop more effective campaigns and promotions. Analytics solutions help organizations take control of big data and uncover the insights they need to make the best decisions. IBM has Analytics Solutions in various lines: Banks: Apply analytics to improve customer experiences and operational efficiency, and integrate risk into daily decision making. Communication:Uncover insights about customers, network performance and market trends to make better business decisions. Retail: Build lifetime customer relationships by meeting demands for innovative products while containing costs. Education: Make more informed decisions to improve student perfo...