Pokémon Data Analysis

Introduction

This repository contains a detailed analysis of Pokémon data, exploring various aspects such as the relationship between legendary status and performance, type-based statistical strengths, and generational trends in Pokémon stats. The analysis is performed using Python and presented in a Google Colab Notebook format.

Overview

The analysis covers the following key areas:

• Relationship Between Legendary Status and Total Performance: Evaluates whether legendary Pokémon have higher average total stats compared to non-legendary Pokémon.
• Stats Based on Pokémon Types: Investigates the average stats of different Pokémon types to identify strengths and weaknesses.
• Stats Distribution Across Generations: Analyzes the trends in Pokémon stats across different generations, highlighting notable findings such as Generation 2 having the lowest average total stats and Generation 4 having the highest.

Repository Contents

• pokemon_analysis.ipynb: The Google Colab Notebook containing the full analysis, including data visualization and insights.
• Pokemon.csv: The dataset used for the analysis, containing information about various Pokémon, their stats, types, and other attributes.
• README.md: This file, providing an overview of the project and instructions for usage.

Getting Started

To view and run the analysis on Google Colab, follow these steps:

Prerequisites

Ensure you have a Google account to access Google Colab.

Usage

1. Open the Google Colab Notebook:

   • Go to the following URL to open the notebook in Google Colab: Open in Colab.

2. Run the Notebook:

   • Once the notebook is open in Google Colab, you can run each cell sequentially to execute the analysis.
   • Make sure to upload the Pokemon.csv file to the Colab environment if it's not already accessible from a URL.

Example Code for Loading Data in Colab

If the dataset is hosted online, you can use the following code snippet to load it directly into the notebook:

import pandas as pd

# Load the dataset from a URL
url = 'https://path/to/Pokemon.csv'
df = pd.read_csv(url)

# Display the first few rows of the dataframe
df.head()

Insights

Key insights from the analysis include:

• Legendary Pokémon generally have higher total stats, supporting their perceived superiority.
• Dragon type Pokémon exhibit the highest average stats, indicating their overall strength.
• Generational analysis reveals that Generation 4 Pokémon have the highest average total stats, while Generation 2 Pokémon have the lowest.