Bandersnatch-DS-Rampup.md

BandersnatchStarter Project Overview

This document provides an overview of the BandersnatchStarter project, designed for someone with minimal Python experience, primarily familiar with Jupyter notebooks in a data science context. The goal is to help you understand the project’s structure, technical stack, key files, and concepts, along with pointers to resources for learning. The project is a Flask-based web application for working with monster data, creating visualizations, and building machine learning models. It’s structured as a series of sprints to guide you through the development process.

Project Overview

The BandersnatchStarter project is a data science and machine learning application focused on "monster data." It involves setting up a database, creating interactive visualizations, and building a machine learning model. The project is beginner-friendly for those with notebook experience, as it uses familiar Python libraries like pandas and scikit-learn, but introduces web development concepts with Flask and MongoDB.

Project Goals

• Sprint 1: Database Operations - Set up a MongoDB database and manage monster data.
• Sprint 2: Dynamic Visualizations - Create interactive charts using Altair.
• Sprint 3: Machine Learning Model - Build and integrate a predictive model using scikit-learn.

Key Concepts for Beginners

• Flask: A lightweight Python web framework to create web applications. Think of it as a way to turn your Python code into a website.
• MongoDB: A NoSQL database that stores data as JSON-like documents, unlike the tabular data you’re used to in pandas.
• Altair: A Python library for creating interactive visualizations, similar to plotting in notebooks but for web display.
• Scikit-learn: A machine learning library you may have used in notebooks for models like regression or classification.

Project Structure

The repository is organized into folders that align with the sprints:

• / (root): Contains the splash page (main landing page of the web app).
• /data: Stores tabular monster data, likely as CSV files or database collections.
• /view: Contains code for dynamic visualizations (charts/graphs).
• /model: Houses the machine learning model code.
• /app: Likely contains the Flask application code (e.g., main.py).
• Other key files:
  • requirements.txt: Lists Python libraries needed for the project.
  • .env: Stores sensitive data like the MongoDB connection string (not committed to GitHub).
  • install.sh and run.sh: Scripts for macOS/Linux to install dependencies and run the app.

Conceptual Hints

• The root folder contains the Flask app’s entry point (app/main.py), which sets up routes (URLs) for different pages, like the splash page.
• The data folder is where you’ll interact with MongoDB or CSV files, similar to loading data in a notebook with pandas.read_csv().
• The view folder is for visualization code, where you’ll use Altair to create charts, like plotting in notebooks but rendered on a webpage.
• The model folder contains machine learning code, similar to scikit-learn workflows in notebooks (e.g., loading data, training a model, making predictions).

Technical Stack

The project uses the following technologies, with explanations for beginners and links to beginner-friendly resources.

Component	Description	Docs/Guides
Python3	The programming language used for logic. Familiar from notebooks.	Python Docs
Flask	A web framework to create the website. Handles routing (e.g., /home URL) and rendering HTML pages.	Flask Quickstart
Jinja2	A templating engine for Flask to create dynamic HTML pages. Think of it as filling placeholders in HTML with Python data.	Jinja2 Docs
HTML5	Markup language for structuring web pages.	W3Schools HTML
CSS3	Styling for web pages (e.g., colors, layouts).	W3Schools CSS
MongoDB	A NoSQL database for storing monster data as JSON-like documents.	MongoDB Getting Started
Altair	A Python library for creating interactive visualizations, similar to seaborn or matplotlib but web-friendly.	Altair Tutorial
Scikit-learn	A machine learning library for building models (e.g., classification, regression). Familiar from data science notebooks.	Scikit-learn Tutorials
Render.com	A platform for deploying the web app online.	Render Python Guide

Notes for Notebook Users

• Flask vs. Notebooks: In notebooks, you run cells to see outputs. In Flask, you write Python code that responds to web requests (e.g., visiting a URL triggers a function).
• MongoDB vs. pandas: Instead of loading a CSV into a DataFrame, you’ll query MongoDB to get data as JSON, which you can convert to a DataFrame.
• Altair vs. matplotlib: Altair creates interactive charts that work in browsers, unlike static matplotlib plots in notebooks.

Key Files and Their Purpose

Here’s a breakdown of important files (based on typical Flask project structure) and what they do, with hints for understanding their role.

File/Folder	Purpose	Conceptual Hints
app/main.py	The main Flask application file. Defines routes (URLs) and how the app responds to user requests.	Look for @app.route() decorators, which map URLs (e.g., /) to Python functions. Similar to defining functions in a notebook but for web pages.
requirements.txt	Lists Python libraries (e.g., Flask, pymongo, altair) needed to run the project.	Like installing packages in a notebook with !pip install, but done via pip install -r requirements.txt.
.env	Stores sensitive data like MongoDB connection strings.	Keep this file private (not on GitHub). Use the python-dotenv library to load it in your code.
data/	Contains data files (e.g., CSVs) or scripts to interact with MongoDB.	Similar to loading a CSV in a notebook, but you may use pymongo to query MongoDB.
view/	Contains visualization code, likely using Altair.	Think of this as your plotting code in a notebook, but the output is rendered in HTML.
model/	Contains machine learning code, likely using scikit-learn.	Similar to a notebook where you load data, preprocess it, and train a model with fit().
templates/	Folder for HTML templates (used with Jinja2).	These are HTML files with placeholders (e.g., {{ variable }}) filled by Python data.
static/	Folder for CSS, JavaScript, or images.	Like adding styles to a plot, but for the entire webpage.

Example File Insights

• app/main.py: This is the heart of the Flask app. It might look like:

  from flask import Flask, render_template
  app = Flask(__name__)
  @app.route('/')
  def home():
      return render_template('index.html')

  This code sets up a route for the homepage (/) and renders an HTML template.
• data/: Might include a script to load monster data into MongoDB, like:

  from pymongo import MongoClient
  client = MongoClient('mongodb://...')
  db = client['monsters']
  db.collection.insert_one({'name': 'Bandersnatch', 'power': 100})

  This is like adding rows to a DataFrame but in a database.
• view/: Might include an Altair chart, like:

  import altair as alt
  import pandas as pd
  data = pd.DataFrame({'power': [100, 200], 'name': ['Bandersnatch', 'Jabberwock']})
  chart = alt.Chart(data).mark_bar().encode(x='name', y='power')
  chart.save('chart.html')

  This is like plotting in a notebook but saves the chart for web display.

Key Functions/Concepts to Understand

Here are key programming concepts and functions you’ll encounter, with explanations for beginners.

1. Flask Routes (@app.route())

   • What it does: Maps a URL (e.g., /data) to a Python function that returns a webpage or data.
   • Example: A route might fetch monster data from MongoDB and display it in a table.
   • Hint: Think of routes as functions that run when you visit a webpage, like clicking a cell in a notebook.
   • Docs: Flask Routing

2. MongoDB Queries (pymongo)

   • What it does: Retrieves or saves data in MongoDB, similar to filtering a DataFrame.
   • Example: db.collection.find() gets all monster documents, like df[df['power'] > 100].
   • Hint: Use pymongo to connect to MongoDB and query data. Convert results to a DataFrame for familiar manipulation.
   • Docs: PyMongo Tutorial

3. Altair Visualizations (alt.Chart)

   • What it does: Creates interactive charts for the web, like matplotlib but browser-friendly.
   • Example: A bar chart of monster powers, rendered in HTML.
   • Hint: Use pandas DataFrames as input, like in notebooks, and save charts as HTML or JSON.
   • Docs: Altair Basic Example

4. Scikit-learn Models (fit(), predict())

   • What it does: Trains a machine learning model and makes predictions, like in a notebook.
   • Example: A classification model to predict if a monster is "dangerous" based on features.
   • Hint: Load data from MongoDB or CSV, preprocess it with pandas, and use scikit-learn’s familiar API.
   • Docs: Scikit-learn Getting Started

Getting Started Tips

1. Set Up the Environment

   • Follow the README’s instructions to create a virtual environment and install dependencies (pip install -r requirements.txt).
   • If you’re new to virtual environments, think of them as isolated notebooks where libraries don’t conflict with other projects.
   • Resource: Python Virtual Environments

2. Run the App Locally

   • Use python -m app.main (Windows) or ./run.sh (macOS/Linux) to start the app.
   • Visit http://127.0.0.1:5000 in your browser to see the app, like viewing a notebook’s output.
   • Hint: If you get errors, check if all dependencies are installed and the .env file has the correct MongoDB URL.

3. Explore the Code

   • Start with app/main.py to see how routes are defined.
   • Check data/ for database scripts, view/ for visualization code, and model/ for machine learning code.
   • Hint: Treat each folder like a section of a notebook (data loading, plotting, modeling).

4. Work on Sprints

   • Sprint 1: Focus on MongoDB setup and basic queries. Practice inserting and retrieving monster data.
   • Sprint 2: Create simple Altair charts, like bar or scatter plots, using sample data.
   • Sprint 3: Build a basic scikit-learn model, starting with a simple dataset (e.g., monster features like power, speed).
   • Resource: Break each sprint into small tasks, like cells in a notebook, to make it manageable.

Learning Resources for Beginners

• Python Basics: Python for Everybody (free course for beginners).
• Flask for Data Scientists: Flask Tutorial for Beginners (explains Flask with a data science focus).
• MongoDB for Beginners: MongoDB University (free courses on MongoDB basics).
• Altair for Visualizations: Altair Beginner’s Guide (explains data and plotting).
• Scikit-learn for ML: Scikit-learn User Guide (covers common ML tasks).

Stretch Goals for Growth

The README lists stretch goals (e.g., using Plotly instead of Altair, FastAPI instead of Flask). For beginners:

• Focus on the core sprints first.
• If you’re curious, explore one stretch goal, like adding a database reset function, which is similar to clearing and reloading a DataFrame.
• Resource: FastAPI Docs or Plotly Python for stretch goals.

Final Notes

• Treat the project like a notebook: break tasks into small, testable pieces (e.g., load data, plot one chart, train a simple model).
• Use the provided scripts (install.sh, run.sh) to simplify setup, but understand what they do (like running pip install or starting Flask).
• If stuck, check error messages in the terminal, just like debugging a notebook cell, and refer to the linked docs.

Happy coding, and enjoy building your Bandersnatch project!