Notebook workflow, Python essentials and reproducible runs

The first objective is to become productive with data, not to master interpreter internals before doing useful work. A notebook is a working laboratory only when its state can be recreated from a clean start.

implement
explain

You can now

Create a small notebook project with a clear data, notebook and output layout.
Use variables, functions, conditionals, loops, lists, dictionaries, imports and basic file paths to inspect a dataset.
Restart a notebook runtime and run every cell from top to bottom without relying on hidden state.
Record the dataset source, package versions and any random seed that affects the result.

Covers

Notebook workflows and reproducible experiments
Python essentials for data work

Study, in this order

3 steps

Python
Kaggle Learn — Python

Complete lessons 1–7: syntax, functions, conditionals, lists, loops, strings/dictionaries and external libraries.

Done
Google Colab FAQ
Google Colab FAQ — free resources and limits

Use a hosted notebook for setup-light practice. Read the free-resource limitations; no GPU availability is guaranteed.

Done
The Python Tutorial
The Python Tutorial

Use as a reference for control flow, data structures, modules, files and exceptions; it assumes some prior programming experience.

Done

Focus on

Python values, variables and expressions
Functions, conditionals, loops, lists and dictionaries
Imports and reading a CSV file
Notebook kernel state, restart-and-run-all and reproducible outputs

Reproduce closed-book

From a blank notebook, import a CSV file, print its shape and column names, compute one summary with a function you wrote, then restart the kernel and reproduce the same output.

Traps

Do not treat notebook cell order as execution order; hidden state can make a broken notebook appear to work.
Do not assume a free hosted notebook provides a stable GPU or unlimited runtime.
Do not install packages repeatedly inside individual cells; keep setup in one documented place.

Practice

Kaggle Python exercises

Redo the list, loop, dictionary and external-library exercises without copying the worked solution.

Keep the practice small; the target is fluency with data-handling syntax, not advanced Python internals.

Use a CPU notebook for this section. A GPU is not needed for CSV inspection, NumPy or pandas work.

Self-test

Why should you restart the kernel and run all notebook cells before treating the result as reproducible?
What is the difference between a Python list and a function?

Completion criterion

Stop when

You can open a fresh notebook, read a CSV, write and call a helper function, inspect a few rows and reproduce the same result after restarting the runtime.

Mark passed, then open 1.2 Only mark this when you can do it without notes.

Leads to

1.2 NumPy arrays: shape, dtype and vectorized operations Follow the practical starter sequence; each unit uses the artifact from the previous step.

Resources for this unit

Kaggle Learn — Python
Kaggle Learnprimarycourse
Complete the course lessons on variables, functions, conditionals, lists, loops, strings, dictionaries and external libraries. Treat it as the practical Python vocabulary needed for the next units.
The Python Tutorial
Python Software Foundationreferencereference
Use as a reference for control flow, data structures, modules, files and exceptions. It assumes some prior programming experience; use Kaggle Learn for the first pass if the syntax is new.
Google Colab FAQ — free resources and limits
Google Researchsupportnotebook-environment
Use a hosted notebook for the first exercises if local setup is a distraction. The free tier has variable, non-guaranteed compute and GPU access; keep every exercise small enough to run on CPU.

Also in this section

NumPy Quickstart Tutorial
NumPyprimarydocumentation
Study array creation, shape, indexing, universal functions, broadcasting and basic operations. Write down shapes and dtypes before changing an array.
Kaggle Learn — Pandas
Kaggle Learnprimarycourse
Complete the short exercises on reading data, selecting, summary functions, grouping, sorting, data types, missing values and combining tables.
pandas Getting Started Tutorials
pandas Development Teamrequireddocumentation
Use the official tutorials as a reference for reading and writing tables, selecting rows and columns, creating columns, summary statistics, joins and time-series handling.
Matplotlib Quick Start Guide
Matplotlibsupportdocumentation
Use the Figure/Axes workflow to make labeled line plots, histograms and scatter plots. Keep plots diagnostic rather than decorative.
Bike Sharing Dataset
UCI Machine Learning Repositoryrequireddataset
Use the hourly hour.csv data for the two linked starter projects. Cite Fanaee-T (2013), DOI 10.24432/C5W894, and the CC BY 4.0 licence. cnt is the target; casual and registered are components of that target and must never be used as input features.