Project: Analyzing Online Sports Revenue

#Title: Data Analysis of Online Sports Revenue and Product Types

#Summary: This code conducts an extensive data analysis of online sports retail data. It begins by merging multiple datasets related to brands, finance, product information, and reviews. The analysis includes categorizing products into price quartiles, computing statistics for product descriptions, distinguishing between footwear and clothing products, and summarizing the data by product types. The code provides insights into revenue, product types, and their characteristics in the online sports retail industry.

import pandas as pd

# Read data from CSV files into DataFrames
brands = pd.read_csv("brands.csv") 
finance = pd.read_csv("finance.csv")
info = pd.read_csv("info.csv")
reviews = pd.read_csv("reviews.csv")

# Merge the DataFrames based on the "product_id" using outer joins
online_sports_revenue = brands.merge(finance,
                                     on='product_id',
                                     how='outer')
online_sports_revenue = online_sports_revenue.merge(info,
                                                    on='product_id',
                                                    how='outer')
online_sports_revenue = online_sports_revenue.merge(reviews,
                                                    on='product_id',
                                                    how='outer')

# Remove rows with missing values
online_sports_revenue.dropna(inplace=True)

# Define quartile labels
quartile_labels = ['Budget', 'Average', 'Expensive', 'Elite']

# Create a new column "price_label" based on "listing_price" quartiles
online_sports_revenue['price_label'] = pd.qcut(
    online_sports_revenue['listing_price'],
    q=4,
    labels=quartile_labels)

# Group data by "brand" and "price_label," calculating counts and mean revenue
adidas_vs_nike = online_sports_revenue.groupby(
    ['brand', 'price_label'],
    as_index=False).agg(num_products=('price_label',
                                      'count'),
                        mean_revenue=('revenue',
                                      'mean')).round(2).reset_index(drop=True)

# Calculate the maximum description length
max_description_length = max(online_sports_revenue['description'].str.len())

# Calculate description lengths
online_sports_revenue['description_length'] = online_sports_revenue['description'].str.len()

# Define bins and labels for "description_length"
lengthes = [0, 100, 200, 300, 400, 500, 600, 700]
labels = ['100', '200', '300', '400', '500', '600', '700']

# Apply binning to "description_length" column
online_sports_revenue['description_length'] = pd.cut(
    online_sports_revenue['description_length'],
    bins=lengthes,
    labels=labels)

# Group by "description_length" and calculate mean rating and number of reviews
description_lengths = online_sports_revenue.groupby(
    'description_length',
    as_index=False).agg(mean_rating=('rating',
                                     'mean'),
                        num_reviews=('reviews',
                                     'count')).round(2)

# Define a regex pattern for filtering footwear products in "description"
footwear_list = 'shoe*|trainer*|foot*'

# Create DataFrames for footwear and clothing products
shoes = online_sports_revenue[online_sports_revenue['description'].str.contains(footwear_list)]
clothing = online_sports_revenue[~online_sports_revenue.isin(shoes['product_id'])]

# Remove rows with missing values in the clothing DataFrame
clothing.dropna(inplace=True)

# Create a summary DataFrame with product type statistics
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])

print(product_types)

print(product_types.columns.tolist())