import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

df = pd.read_csv('/work/largest_us_retailers.csv') df.head()

Companyobject

Salesint64

Walmart US

658119

Kroger

115037

Costco

90048

Home Depot

83976

Walgreen Boots

78924

Business-related questions

1. What is the average sales excluding the dominant company?

df_w_excluded = df[df.Company != 'Walmart US'] ventas_promedio = df_w_excluded['Sales'].mean() sns.barplot(data=df_w_excluded, x = 'Sales', y = 'Company') plt.title('Top 5 retailers') plt.xlabel('Company') plt.ylabel('Sales') ventas_promedio

Here is the average sales for all companies excluding the dominant one, which is Walmart: 46.602 millions of dollars.

2. How much money in sales did most companies make?

median = df_w_excluded.Sales.median() sns.histplot(data= df_w_excluded, x='Sales') plt.title('Company sales') plt.xlabel('Sales') median

Excluding Walmart, companies made 34694 millions of dollars.

3. How many stores do most companies have?

median = df_w_excluded.Stores.median() sns.histplot(data= df_w_excluded, x='Stores') plt.title('Company stores') plt.xlabel('Number of stores') median

Some companies have many stores, but the median is 1800, excluding Walmart.

4. Is the number of stores related to the number of sales? In other words, if a company has more stores, will it have more money from sales?

sns.lineplot(data=df, x='Stores', y='Sales') sns.scatterplot(data=df, x='Stores', y='Sales') plt.title('Store-related sales')

There is no relationship between sales and the number of stores.

5. What is the range between sales?

sales_range = df_w_excluded.Sales.max() - df_w_excluded.Sales.min() sales_range

Range is 90.445 millions of dollars, excluding Walmart.

6. What are the 5 companies that have the most physical stores? Which of them are among the 5 companies that had the most sales?

#Companies with more stores df_most_stores = df.sort_values('Stores',ascending=0) df_most_stores_5 = df_most_stores[0:5] #Companies that sold the most df_most_sales = df.sort_values('Sales', ascending=0) df_most_sales_5 = df_most_sales[0:5] #We chart the stores sns.barplot(data=df_most_stores_5,x='Stores',y='Company')

#We chart sales sns.barplot(data=df_most_sales_5,x='Sales',y='Company')

In the first graph, we find Dollar Tree with the largest number of stores and Walmart with the fewest number of stores within the 5 companies that have the most stores.

In the second graph, we find Walmart with the highest number of sales and Walgreen with the lowest number of sales.

The first graph indicates that Walgreen Boots has more stores and sales than all of those in the second graph, while Walmart has the highest number of sales but the fewest number of stores in our top 5.

II. Optional questions

7. Which category of company generated the most sales?

df_categories = sns.barplot(data=df_w_excluded, x='Sales', y='Category')

Excluding Walmart, Warehouse Club was the bestseller.

df_categories = sns.barplot(data=df, x='Sales', y='Category')

Meanwhile, if we count Walmart, we realize that it is an outlier and that the Supercenters category has the highest number of sales.