Python analysis in VS Codeusing pandas and numpy of sales, inventory, and satisfaction CSV files, each in a separate dataframe. Calculate total sales by product and by store. Calculate total revenue by store by multiplying quantity sold by unit price. Generate a statistical summary of sales using the describe() method to obtain the mean, median, and other key metrics. If products are classified by categories, calculate the average sales by store and product category. Use groupby() in Pandas to calculate total sales by store or by product category. Calculate the inventory turnover for each store. This is done by dividing total sales by the available stock of each product. Store the results in a new column within the inventory DataFrame. Filter and display stores with critical inventory levels, i.e., stores where the percentage of products sold is less than 10% of the available stock. Use groupby() and mathematical operations to calculate inventory turnover. Apply filters with Pandas to identify stores with critical levels. Conduct an analysis of customer satisfaction in each store. Relate these data to sales performance. Filter stores with low satisfaction levels (< 60%) and make recommendations to improve the performance of these stores. Use Numpy to perform the following calculations on Median of total sales and the the standard deviation of total sales.

For calculations, convert the Total_Sales column of the Pandas DataFrame to a Numpy array using .to_numpy (or .values if you prefer).

Generate random arrays using the Numpy library to simulate future sales projections.

Use the Numpy randomness module and ensure you set a seed (seed) to obtain reproducible results.