Python merge and run function on data
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WEBSITE DEVELOPMENT & DESIGN ! WORDPRESS ! JAVA ! PHP ! JAVASCRIPT ! GRAPHIC DESIGN | VIDEO | EXCEL VBA & MACRO EXPERT
New Delhi
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Description
Experience Level: Entry
Data set needs merge and run a function.
- Use Jupyter notebook for the report;
- Load pandas, pandas_profiling, seaborn libraries;
- Import bi_product.txt and bi_sales.txt. Decimal character is ».« and delimiter is »;«. Merge tables with pandas;
- Use profile_report() function and describe the overview of your data;
- Create a function function_to_increase(), which will increase the Revenue column randomly between 5-15%. A random function has to be applied to every row HINT : row*(random.randint(5,15)/100+1). Apply function_to_increase() with lambda function and assign the result to a calculated column called Revenue_increased! Show the first 5 rows of Revenue and Revenue_increased!
- Show the sum of units, revenue and revenue_increased for every product category;
- What are the mean and median (Revenue) of every category?
- Calculate the skewness of Revenue.
- Create a chart (catplot) with Revenue across category and segment. Put »segment« in a facet. Use the seaborn package;
- Save notebook as HTML and jupyterlab download file
- Use Jupyter notebook for the report;
- Load pandas, pandas_profiling, seaborn libraries;
- Import bi_product.txt and bi_sales.txt. Decimal character is ».« and delimiter is »;«. Merge tables with pandas;
- Use profile_report() function and describe the overview of your data;
- Create a function function_to_increase(), which will increase the Revenue column randomly between 5-15%. A random function has to be applied to every row HINT : row*(random.randint(5,15)/100+1). Apply function_to_increase() with lambda function and assign the result to a calculated column called Revenue_increased! Show the first 5 rows of Revenue and Revenue_increased!
- Show the sum of units, revenue and revenue_increased for every product category;
- What are the mean and median (Revenue) of every category?
- Calculate the skewness of Revenue.
- Create a chart (catplot) with Revenue across category and segment. Put »segment« in a facet. Use the seaborn package;
- Save notebook as HTML and jupyterlab download file
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