This project explores the evolution of Pokémon games' popularity using Python and integrates data from the VGChartz dataset and PokeAPI. By leveraging programming, analytics techniques, and Object-Oriented Programming (OOP) principles, this project aims to analyze Pokémon games' sales figures across different platforms and explore related data to uncover the franchise's enduring success.
The objective of this project is to:
- Analyze the global sales data of Pokémon games using the VGChartz dataset.
- Examine prominent Pokémon-specific base stats through the PokeAPI to explore their impact on the games' success.
- Demonstrate skills in Python programming, data visualization techniques, and modular code design via OOP principles.
- Python Libraries:
pandas,NumPy,matplotlib,seaborn,requests,Beautiful Soup,python-pptx,Pillow - Visualization: Python (matplotlib, seaborn) with integrated images from Tableau.
- Object-Oriented Programming: Encapsulation of functionality into reusable classes (e.g., PokeAPI).
This analysis will quantitatively explore the relationship between global sales of Pokémon games and factors such as platform availability, regional sales trends, and franchise timeline. The idea is to investigate whether games released during periods of heightened franchise interest or on widely adopted platforms exhibit higher global sales.
This exploration will qualitatively examine how intrinsic Pokémon characteristics might contribute to the appeal and popularity of the games they are featured in. The analysis will consider how Pokémon with pivotal roles in the storyline may influence player engagement and game reception.
To run this project locally, you must have the following installed:
- Python 3.8 or later
- VS Code (with the Python extension installed)
- Required Python Libraries: These can be installed using
pipwithin VS Code's terminal or using the "Python: Select Interpreter" command to select your Python environment and then installing packages via the Python Environments window.
Clone the repository to your local machine using the following command:
git clone https://github.com/BasirS/poke-analytics_projectOpen the project folder in VS Code.
- VS Code will usually prompt you to select a Python interpreter. If it doesn't, use the "Python: Select Interpreter" command (Ctrl+Shift+P or Cmd+Shift+P).
- VS Code can create a virtual environment for you automatically. If you choose an interpreter that is not already in a virtual environment, VS Code will offer to create one. This is the recommended approach.
- If you choose to create a virtual environment yourself, run
python3 -m venv .venvin the VS Code terminal, and then select that environment as the interpreter.
Open a terminal in VS Code (View > Terminal) and navigate to the project directory, then install the required libraries from the requirements.txt file:
pip install -r requirements.txtOpen the Jupyter Notebooks (.ipynb files) in VS Code. You should be able to run the cells directly within the editor.
PokeAPI is freely accessible, and no API key is required. You can directly make requests to its endpoints to retrieve Pokémon data.
- An example API Endpoint for Bulbasaur:
https://pokeapi.co/api/v2/pokemon/bulbasaurOR
https://pokeapi.co/api/v2/pokemon/1- For more information on endpoints and data structures, visit the PokeAPI Documentation here.
If you use additional APIs for your project which require keys, make sure to store them in a file named config.py in the following format:
OTHER_API_KEY = "your_other_api_key_here"- Source: The VGChartz dataset provides global sales data for video games, including Pokémon titles.
- Purpose: This dataset serves as the foundation for analyzing Pokémon game sales and identifying trends across regions and platforms.
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Purpose: PokeAPI offers comprehensive data on individual Pokémon, including stats, types, and abilities.
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Usage: In this project, PokeAPI is used to retrieve and visualize the base stats of selected Pokémon.
For example, starter Pokémon are an important part of the player experience, and their design can be a significant factor in a game's initial appeal.
- The bar chart offers a comprehensive look at Pokémon's global sales across its entire game library, showing how the franchise has stayed relevant for decades. From the groundbreaking success of Red/Blue (31.37 million) to modern hits like Sword/Shield (15.24 million), it’s clear that Pokémon thrives by adapting to new platforms while maintaining its core appeal. Mainline titles dominate, but spin-offs like Mystery Dungeon and remakes such as HeartGold/SoulSilver demonstrate how nostalgia and experimentation keep the fanbase engaged.
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Now, the second bar chart showcases the global sales data for the top 10 best-selling Pokémon games. It highlights the enormous popularity of Pokémon Red/Blue, leading the list with an impressive 31.37 million units sold, followed by Pokémon Gold/Silver at 23.10 million units. This significant gap indicates how the first-generation games established the foundation for the franchise's enduring success. Factors such as the novelty of the Pokémon concept, the introduction of link cable trading, and the nostalgia associated with these titles have contributed to their success.
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Other high-ranking entries like Pokémon Diamond/Pearl (18.36 million) and Ruby/Sapphire (15.85 million) reflect how recurring improvements, new mechanics, and fresh regions continued to engage audiences over the years. The consistent sales starting from Pokemon Black/Pokemon White and across newer titles suggest that nostalgia (Ruby/Saphire -> Omega Ruby/Alpha Sapphire), along with creative storylines, played a critical role in maintaining Pokémon’s dominance in the gaming industry.
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The line chart analyzes global sales trends across various platforms, illustrating the important role hardware plays in the franchise's success. The Game Boy (GB) is evidently seen as the most successful platform, generating close to 80 million units in sales, reflecting its broad market penetration and compatibility with multiple Pokémon titles. Similarly, platforms like the Nintendo DS (DS) and Game Boy Advance (GBA) sustain the trend of high sales during their respective peaks.
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However, the chart also reveals dips in sales for platforms like the GameCube (GC) and Mobile, emphasizing the importance of accessible and popular hardware in driving game success. The spike in sales for the Nintendo Switch (NS) highlights the impact of modern hybrid gaming experiences, overall reflecting Pokémon’s ability to adapt and thrive with changing player preferences, which ultimately increased their revenue.
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This Tableau visualization is a pie chart that provides an overview of Pokémon game sales across various platforms and release years. From the visualization, it's obvious that earlier platforms like the Game Boy (GB) and Game Boy Advance (GBA) played a foundational role in establishing Pokémon as a blockbuster franchise, with combined sales exceeding 79 million units (79M) and 49 million units (49M), respectively. These platforms represent the franchise's formative years, where games like Pokémon Red, Blue, and Emerald capitalized on the novelty of portable gaming and simple yet immersive mechanics.
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In contrast, newer platforms like the Nintendo Switch (NS) and 3DS reflect Pokémon’s ability to stay relevant by evolving with technology. The Nintendo Switch alone boasts sales of over 25 million units (25.04M) for Pokémon titles in 2022, which correlates with its versatility as both a handheld and docked console. Similarly, the 3DS saw sales spikes during its peak years in 2013 (14.35M) and 2016 (15.17M), coinciding with the release of highly anticipated titles like Pokémon X/Y and Pokémon Sun/Moon.
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The timeline reveals that platform adoption and hardware capabilities heavily influence game sales. For example, the GameCube (GC) and Nintendo 64 (N64) had lower contributions to Pokémon sales due to their limited portable appeal, compared to handheld-focused devices that align better with the franchise's design philosophy. Additionally, the dominance of mobile platforms in 2016 (with the release of Pokémon GO - not included in the data) speaks for itself on how Pokémon successfully leveraged emerging trends in casual and mobile gaming to expand its audience.
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Following up with the above one is another Tableau visualization—a donut chart, which depicts the significance of the most popular platforms of all time, accounting for a notable share of the total 329.6 million (M) global sales. The DS (76.7M), for example, demonstrates the success of innovative features like dual screens and Wi-Fi connectivity, which were instrumental in the popularity of games like Pokémon Diamond/Pearl and Black/White.
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The transition to the 3DS (66.6M) showcases a natural progression, with Nintendo capitalizing on backward compatibility and improved hardware, ensuring a seamless shift in player loyalty. Similarly, the GBA (49.0M) served as a crucial platform for Pokémon's evolution, introducing fan-favorite titles like Ruby/Sapphire while setting the stage for later advancements.
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The Nintendo Switch (58.3M) marks a new era of Pokémon games, offering HD visuals and open-world exploration that appeals to both nostalgic fans and newer audiences. This aligns with broader gaming trends, where hybrid consoles appeal to diverse player preferences, from casual to competitive.
Two of the most iconic Pokémon, Pikachu and Charizard, have played prominent roles in the franchise's popularity, particularly within the beloved Kanto region. Their distinct designs and in-game characteristics have undoubtedly contributed to their appeal and the success of the games they feature in.
Let's Go Pikachu - The Face of Pokémon
Game Boy Charizard - The Most Popular Pokemon
The Kanto region, featured in the original Pokémon Red, Blue, and Yellow (as well as their remakes), remains one of the most popular and recognizable settings in the Pokémon world. The choice of starter Pokémon in these games: Bulbasaur, Charmander (evolving into Charizard), or Squirtle, is a key early-game decision that significantly influences the player's experience.
- The selection of Pikachu as the starter Pokémon in Pokémon Yellow, a special edition released shortly after Red and Blue, further cemented its status as a mascot for the franchise. This game remarkably followed the anime's storyline, where Ash Ketchum begins his journey with Pikachu. This close tie-in with the anime, a major driver of Pokémon's popularity, likely contributed to Yellow's commercial success. While Pikachu's base stats, as seen in the individual visualization, are generally balanced with a focus on Speed, its unique gameplay mechanics in Yellow, such as following the player on the overworld and having unique interactions, added a new layer of engagement that distinguished it from previous titles.
- Charizard, the final evolution of Charmander, is a powerful Fire/Flying-type Pokémon with a striking design. Its high Attack, Special Attack, and Speed stats, as demonstrated in the individual visualization, make it a formidable opponent in battles, contributing to its popularity among players. The desire to train and evolve Charmander into the powerful Charizard likely drove player engagement in Pokémon Red and Blue, contributing to their commercial success. The fact that Charizard is the final evolution of Charmander, which is a popular fire type pokémon in the Kanto region, makes it very appealing to players.
- The comparison of Pikachu and Charizard's stats, as visualized in the combined bar chart, highlights their different strengths. Charizard boasts significantly higher Attack, Special Attack, and Special Defense, solidifying its role as a powerful offensive and special wall Pokémon. While Pikachu excels in Speed, its other stats are considerably lower than Charizard's. Charizard's power fantasy is definitely a part of why it's so popular till today. This difference in stats further reaffirms their distinct roles within the games and contributed to their individual appeal to different player preferences.
I found myself stuck in the frustrating role of a "digital janitor" spending countless days trying to clean data scraped from VGChartz. After 3–4 grueling days of trial and error, I had to wave the white flag and accept that I couldn’t make the scraped data usable. As a last resort, I turned to an outdated Kaggle dataset from 2016—yes, a dataset that’s 8 years old! To bring it closer to 2023, I manually updated the dataset using information directly from the VGChartz website and the Nintendo Financial Report Results website. It was an exhausting and time-consuming process, but that was the only way left for me to succeed in an analysis–if any. Click here if you want to witness how the lack of proper training in data cleaning during my coursework turned me into such a novice when dealing with even a relatively small dataset. This experience made me painfully aware of how much more I need to learn to handle real-world big data challenges effectively.
- By analyzing game sales data and Pokémon traits, this project identified key drivers of the franchise's success. Sales trends clearly correlate with platform popularity and strategic release timing, often coinciding with new generations or franchise milestones, indicating the importance of marketing and franchise momentum.
- Additionally, exploring iconic Pokémon like Pikachu and Charizard highlighted how design and in-game mechanics enhance player engagement. While challenges with data cleaning were significant, they provided an invaluable self-realization for future projects.
- Overall, this analysis offered a glimpse into the franchise's sustained popularity and areas for deeper exploration.
I believe I successfully addressed all the key points I initially set out to explore, combining my lifelong passion for Pokémon with the data analysis skills I honed in my Programming for Analytics course. That said, I can’t help but feel there’s so much more I could’ve uncovered. I wish I had the time to dive deeper into connecting the sales data with the vast array of Pokémon traits, making the analysis even more thorough and nuanced. Despite this, I’m proud of the work and research I’ve done here. If time constraints weren’t a factor, I would have absolutely taken it further, creating a more comprehensive connection between Pokémon traits and their lasting success.
To share my findings and insights, I created a presentation, which summarizes my findings on Pokémon's success:
Any ideas on how I can further enhance this analysis? Feel free to contribute or share ideas via GitHub :)