Dataset

This dataset was created by Muhammad Asif

Released under Database: Open Database, Contents: © Original Authors

Contents

According to the data, for the 12 weeks ending July 14, 2024, Uno was the leading refrigerated pizza and pizza kit brand of the United States, after private labels, with sales amounting to over 340,000 units. Panera Bread followed, with sales reaching close to 289,000 units.

In the 12 weeks ending July 14, 2024, the dollar sales of frozen pizza in the United States amounted to 1.5 billion U.S. dollars. Refrigerated pizza and pizza kits followed, with a sales value of close to 123 million dollars.

In the first quarter of 2023, around ** percent of respondents in the United States said that they ordered from quick service pizza restaurants more than once a week. Comparatively, ** percent said that they ordered from pizza QSRs once every couple of weeks and once a month, respectively.

This dataset is designed for SQL analysis exercises, providing comprehensive data on pizza sales, orders, and customer preferences. It includes details on order quantities, pizza types, and the composition of various pizzas. The dataset is ideal for practicing SQL queries, performing revenue analysis, and understanding customer behavior in the pizza industry.

1. order_details.csv Description: Contains details of each pizza order. Columns: order_details_id: Unique identifier for the order detail. order_id: Identifier for the order. pizza_id: Identifier for the pizza type. quantity: Number of pizzas ordered

2. pizza_types.csv Description: Provides information on different types of pizzas available. Columns: pizza_type_id: Unique identifier for the pizza type. name: Name of the pizza. category: Category of the pizza (e.g., Chicken, Vegetarian). ingredients: List of ingredients used in the pizza.

3. Questions.txt Description: Contains various SQL questions for analyzing the dataset. Contents: Basic: Retrieve the total number of orders placed. Calculate the total revenue generated from pizza sales. Identify the highest-priced pizza. Identify the most common pizza size ordered. List the top 5 most ordered pizza types along with their quantities.

Discover the 2026 Market Size of Pizza Restaurants industry in the US, including historical data from 2026 and forecast data up to 2031.

The coronavirus (COVID-19) pandemic resulted in social distancing measures and stay-at-home orders across the globe. This was particularly hard for restaurants, which were often forced to close their doors to sit-down diners. As a result, many restaurants relied heavily on delivery, takeout, and other alternatives to sit-down dining. This included the pizza restaurant industry in the U.S. which, while known for offering delivery and takeout options, still suffered during this crisis. When surveyed, pizza operators, suppliers, and distributors were asked if their pizza restaurants experienced a change in sales - almost ** percent answered that their sales had increased. Meanwhile, almost ** percent stated that sales in their respective restaurants had declined as a result of the pandemic.

I am using the Pizza Corner Sales Dataset to create a Home Page that contains 1) a Pizza stock list Chart and 2) a chart of the top 5 Pizzas sold. Both charts are explained category-wise, including the stock list, Details, and price per Product. Have a look below 👇

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26962338%2F91d893767cce03e04b84adce7725de9c%2FScreenshot%202025-05-23%20102027.png?generation=1747976485481008&alt=media" alt="">

Next, I am creating the Pizza Corner Sales Dashboard. It will include revenue, Orders, Total pizzas sold, average pizzas per order, Daily trends, Monthly Trends, a category-wise pizzas sold list, Etc.. Have a look below 👇

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F26962338%2F134d2923ae3f7e604fdc407c4905b547%2FScreenshot%202025-05-23%20102140.png?generation=1747977617471415&alt=media" alt="">

I hope you like it. Those Dashboards are built using Power BI and SQL to help visualize and analyze Pizza sales Performance based on client requirements.

You will also use this dataset to create more dashboards, so download the dataset below, understand the columns, analyze them, and create good dashboards that you will use in the interview.

This Pizza Corner Sales Make up to 12 relevant features:

1. pizza_id - Unique key identifier that ties the pizza ordered to its details, like size and price

2. order_id - Unique identifier for each order placed by a customer

3. pizza_name_id - Unique key identifier that ties the pizza ordered to its details, like size and price

4. Quantity - Quantity defines, the user ordered for pizzas of the same type and size

5. order_date - Date the order was placed

6. order_time - Time the order was placed

7. unit_price - Price of the pizza in USD

8. total_price - unit_price * quantity

9. pizza_size - Size of the pizza (Small, Medium, Large, X Large, and XX Large)

10. pizza_category - Unique key identifier that ties the pizza ordered types- chicken, classic, supreme, veggie

11. pizza_ingredients - ingredients used in the pizza as shown in the menu (they all include Mozzarella Cheese, even if not specified; and they all include Tomato Sauce, unless another sauce is specified)

12. pizza_name - Name of the pizza as shown in the menu

AUTHOR MESSAGE

If you liked this Notebook, please do upvote. If you have any questions, feel free to comment! 🍕Best Wishes🍕

CREHQ tracks 9,212 Domino's Pizza locations across 5 countries. Licensed dataset: addresses, coordinates, phones, and hours. No AI training.

Description:

This dataset contains two related tables: sales and ingredients. It is designed for projects focusing on sales forecasting, ingredient optimization, and supply chain management for a pizza delivery business. By leveraging historical data, users can build predictive models to minimize ingredient waste, prevent stockouts, and optimize purchasing strategies.

Dataset Details:

1. Sales Table:
- Shape: 48,620 rows × 16 columns - Columns: - pizza_id: Unique identifier for a pizza. - order_id: Unique identifier for a sales order. - pizza_name_id: Identifier linking the pizza to its recipe. - quantity: Number of pizzas sold. - unit_price: Price per unit of pizza. - total_price: Total price for the order. - pizza_size, pizza_category: Size and category of the pizza. - pizza_ingredients: Ingredients used in the pizza. - Year, Month, Day, Hour, Minute, Second: Timestamp details of the order.

2. Ingredients Table: - Shape: 518 rows × 4 columns - Columns: - pizza_name_id: Identifier linking the pizza to its recipe. - pizza_name: Name of the pizza. - pizza_ingredients: List of ingredients in the pizza. - Items_Qty_In_Grams: Quantity of each ingredient in grams.

Use Cases:

• Sales forecasting to predict future demand.
• Ingredient optimization for reducing waste and preventing shortages.
• Supply chain analysis and inventory planning.

Potential Applications:

• Time series analysis and forecasting.
• Machine learning models for predictive analytics.
• Data visualization for understanding sales and ingredient trends.

In the United States between 2022 and 2024, frozen pizza was a very popular meal and its consumer share remained stable throughout the years. In 2024, as much as 87 percent of respondents indicated that they had frozen pizza.

Pizza Market Size is growing with a CAGR of 4.7% in the prediction period & it crosses USD 325.79 Bn by 2033 from USD 236.21 Bn in 2026.

Dataset

This dataset was created by Heba Boudy

Released under MIT

Contents

80 Pizza Pizza locations across 29 US states, extracted from Pizza Pizza's own store locator and refreshed monthly. Includes address, geographic coordinates, phone, and category fields, delivered as CSV and Parquet with a monthly refresh.

Sourced location data for Blaze Pizza: 345 US locations across 40 states, refreshed monthly. This report is generated from Mogean's full Blaze Pizza dataset, available for purchase with address, coordinates, phone, and category fields as CSV and Parquet.

A list of pizza restaurants, 3,500 pizzas, and their menu prices. This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.

This data can be used for various types of analysis for e.g:

• What are the least and most expensive cities for pizza?
• What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
• What is the median price of a large plain pizza across the U.S.?
• Which cities have the most restaurants serving pizza per capita (100,000 residents)?

Domino's Pizza location dataset — 42 locations in New Hampshire. Part of CREHQ's multi-unit intelligence platform covering retail, restaurant, financial services, and healthcare brands. Licensed access via enterprise API or dataset purchase. Training on CREHQ data is not permitted.

Market size, estimate, forecast and CAGR for the Frozen Pizza Market Size, Share & Trends Report 2026-2033.

Simple Pizza

## Overview

Simple Pizza is a dataset for object detection tasks - it contains Simple Pizza Detetor annotations for 216 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Row-level location data for 1 The Original Pizza locations including addresses, coordinates, and store attributes.