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Add EDA project
Add Olympics EDA project (data and starter notebook).
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‎.gitignore

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*.DS_Store

‎README.md

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## 📊 Python for Data Science
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### ❓ Where to Start!
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🚧 WIP
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To learn data science, the CRISP-DM is a good approach:
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[CRISP-DM methodology](https://www.datasciencecentral.com/userful-r-packages-that-aligns-with-the-crisp-dm-methodology/)
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1. Business/Problem Understanding
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2. 🆕 **Data Understanding:** Start with the EDA Projects in the **Exercises** section below! 🎉
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3. Data Preparation
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4. Modelling
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5. Evaluation
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6. Deployment
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### 📚 Awesome Books
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🚧 WIP
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### 🏋🏽‍♀️ Exercises
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🚧 WIP
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#### 🕵🏻 Exploratory Data Analysis
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1. [Olympic 124 Years Dataset](eda-projects/olympics): Exploring a dataset of the Olympic Games
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##### 🫂 How to contribute?
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- Download the project and try to solve it at your own pace!
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- Ask as many questions as you like in our [discord channel #🐍ds-projects](http://discord.com/invite/mw7xjJ7b7s)
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- Share your final project by creating a Pull Request! 👏
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### 🔗 Tutorials and Resources

‎eda-projects/olympics/Olympics_124_years_Dataset.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "739ce4df",
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"metadata": {},
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"source": [
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"# Exploratory Data Analysis\n",
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"\n",
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"In this notebook, we will analyse the [Olympics 124 years Dataset](https://www.kaggle.com/datasets/nitishsharma01/olympics-124-years-datasettill-2020?resource=download).\n",
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"\n",
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"**This project is inspired by the work of @Vengeance, posted in the** [Data-Centric AI Community](https://tiny.ydata.ai/dcai-community-github).\n",
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"\n",
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"\n",
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"Some of the questions we're looking to answer are:\n",
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"\n",
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"## Basic Analysis\n",
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"- 1. How many events are there in each discipline?\n",
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"- 2. What is the event count over the years?\n",
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"- 3. How is the gender particiption in every olympic games?\n",
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"\n",
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"## Country Analysis\n",
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"- 4. Find the total medals won by each country\n",
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"- 5. What is the total participation from each country until the 2020 olympics?\n",
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"- 6. How has the performance of a particular country changed over different Olympic editions?\n",
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"\n",
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"## Athlete Analysis\n",
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"- 7. Who are the Top Athletes in each discipline from each country?\n",
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"- 8. What is the age distribution of top gold medalists?\n",
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"- 9. What is the age distribution of medalists by medal (gold, silver, bronze)?\n",
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"- 10. How many teams are there from each country?\n",
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"- 11. In which Discipline most medals are won as teams?\n",
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"- 12. Which country is better in which team sport?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "1d2a70e0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "41dde653",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ydata_profiling import ProfileReport"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "5ee98611",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "36fc896704ac40e09954e0be1aa6ffb2",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"model_id": "4c5771e1a87c482281b652b44612eeaf",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a269eafb05c3465f9183ae8149ff8f84",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Export report to file: 0%| | 0/1 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#Read data and generate data profiling report\n",
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"df = pd.read_csv(\"olympics_dataset/Athletes_summer_games.csv\")\n",
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"olympics_report = ProfileReport(df, title='Olympics Dataset')\n",
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"olympics_report.to_file(\"olympics_report.html\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3eeee7ab",
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"metadata": {},
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"source": [
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"## Basic Analysis"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d66216e6",
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"metadata": {},
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"source": [
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"## Country Analysis"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee1e831f",
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"metadata": {},
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"source": [
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"## Athlete Analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c9274e56",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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"nbformat": 4,
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}

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