Skip to content
New Workbook
Sign up
Project: Analyzing Online Sports Revenue

Sports clothing and athleisure attire is a huge industry, worth approximately $193 billion in 2021 with a strong growth forecast over the next decade!

In this notebook, you will undertake the role of a product analyst for an online sports clothing company. The company is specifically interested in how it can improve revenue. You will dive into product data such as pricing, reviews, descriptions, and ratings, as well as revenue and website traffic, to produce recommendations for its marketing and sales teams.

You've been provided with five datasets to investigate:

  • info.csv
  • finance.csv
  • reviews.csv
  • traffic.csv
  • brands.csv

The company has asked you to answer the following questions:

What is the volume of products and average revenue for Adidas and Nike products based on price quartiles?

  • Label products priced up to quartile one as "Budget", quartile 2 as "Average", quartile 3 as "Expensive", and quartile 4 as "Elite".
  • Store as a pandas DataFrame called adidas_vs_nike containing the following columns: "brand", "price_label", "num_products", and "mean_revenue".

Do any differences exist between the word count of a product's description and its mean rating?

  • Store the results as a pandas DataFrame called description_lengths containing the following columns: "description_length", "mean_rating", "num_reviews".

How does the volume of products and median revenue vary between clothing and footwear?

  • Create a pandas DataFrame called product_types containing the following columns: "num_clothing_products", "median_clothing_revenue", "num_footwear_products", "median_footwear_revenue".

Data preparation

# Start coding here... 
import pandas as pd

info = pd.read_csv('info.csv')
finance = pd.read_csv('finance.csv')
reviews = pd.read_csv('reviews.csv')
traffic = pd.read_csv('traffic.csv')
brands = pd.read_csv('brands.csv')

display(info.head())
display(finance.head())
display(reviews.head())
display(traffic.head())
display(brands.head())
# merging dataframes and dropping null values
df = info.merge(finance).merge(reviews).merge(traffic).merge(brands).dropna()

# display the merged dataframe
display(df.head())
# create a new column 'price_category' based on the quartiles of the 'listing_price' column
df['price_label'] = pd.qcut(df['listing_price'], q=4, labels=['Budget', 'Average', 'Expensive', 'Elite'])

# display the updated dataframe
display(df.head())

DataFrame 1: adidas_vs_nike

# create a grouped dataframe for each price label per brand
adidas_vs_nike = df.groupby(['brand', 'price_label']).agg(num_products = ('revenue','count'), mean_revenue = ('revenue','mean'))

# round mean, and reset index
adidas_vs_nike['mean_revenue'] = adidas_vs_nike['mean_revenue'].round(decimals=2)
adidas_vs_nike = adidas_vs_nike.reset_index()

# print result
print(adidas_vs_nike)

DataFrame 2: description_lengths

# # create description length 
df['description_length'] = df['description'].str.len()

# # transform description length into bins of 100 words
df['description_length'] = pd.cut(df['description_length'], bins=[0, 100, 200, 300, 400, 500, 600, 700], labels=['100', '200', '300', '400', '500', '600', '700'])

# # print DataFrame head
display(df)
# create description_lengths DataFrame
description_lengths = df.groupby('description_length').agg(
                                mean_rating=('rating', 'mean'),
                                num_reviews=('reviews', 'count')).round(decimals=2)

# reset index
description_lengths = description_lengths.reset_index()

# display DataFrame
display(description_lengths)

DataFrame 3: product_types

# List of footwear keywords
mylist = "shoe*|trainer*|foot*"

# Filter for footwear products
shoes = df[df["description"].str.contains(mylist)]

# Filter for clothing products
clothing = df[~df.isin(shoes["product_id"])]

# Remove null product_id values from clothing DataFrame
clothing.dropna(inplace=True)
# Create product_types DataFrame
product_types = pd.DataFrame({"num_clothing_products": len(clothing), 
                              "median_clothing_revenue": clothing["revenue"].median(), 
                              "num_footwear_products": len(shoes), 
                              "median_footwear_revenue": shoes["revenue"].median()}, 
                              index=[0])

display(product_types)