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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 analysis report these are example queries to use on final data

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ApplyAnalytics@twitter The role of data analysis will come into picture, once you have cleaned and filter the raw unstructured data. The next stage is called analysis. Your success of data analysis project is based preparing highly informative final report. Tip:  What could you investigate with data To prepare analysis report, you need to ask some intelligent questions. These are example questions you can use. Based on your questions, you  need to prepare SQL queries to get the desired report or dashboard from your final data or cleaned data.  The report or dashboard should be such that it should improve client business. Let us use some case study on world bank data, what are the questions come into mind:  How much (in USD) is spent on healthcare in total in each country?  How much (in USD) is spent per capita in each country?  In which country is the most spent per person?  In which country is the least spe...