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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' ]) ...
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An effective methodology for BI Implementation

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Given below is an approach that one could follow to ensure an effective implementation of any BI project.
how to implement bi prjectss

Steps in Business Intelligence Project

  1. Requirements elicitation is the most important activity in a BI exercise. We need to very clearly understand the motivation behind the management's need for analytics and their short-term, medium-term and long-term objectives. 
  2. You may conduct a workshop with all stakeholders to understand these requirements. Identifying and involving all stakeholders in the requirements discussion is very important to gain buy-in for the BI initiative throughout the enterprise.
  3. Anyone would like to see a sample of analytics and play around a little to get a sense of the benefit/impact of analytics. Hence developing a quick prototype and showcasing the same to all stakeholders will help you get the necessary impetus for the initiative. 
  4. CAVEAT: Please ensure that the prototype development does not take more than two weeks.

References

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