Visualize a dataset and write a data audit

A table of summary statistics rarely reveals every structural problem. A small number of targeted plots makes distributions, temporal structure and anomalous patterns visible before modeling.

measure
explain

You can now

Choose a histogram, line plot, bar chart or scatter plot that matches the question.
Label axes and units and distinguish distributions from time trends.
Use plots to identify outliers, seasonality and unexpected data patterns.
Write a compact audit that separates observations from hypotheses.

Covers

Exploratory plots and data audits

Study, in this order

2 steps

Matplotlib Quick Start Guide
Matplotlib Quick Start Guide

Use the Figure/Axes workflow for labeled plots, legends, histograms and scatter plots.

Done
Bike Sharing Dataset
Bike Sharing Dataset

Use the hourly hour.csv table and variable descriptions; record the dataset citation and CC BY 4.0 attribution.

Done

Focus on

Plot selection based on variable type and question
Axes, labels, units and legends
Demand by hour, weekday and season
Observation versus interpretation in the audit

Reproduce closed-book

Produce three labeled plots for bike demand: distribution of hourly counts, average demand by hour, and average demand by working day or weather condition. Explain one pattern and one limitation for each.

Traps

Do not infer causation from a descriptive plot.
Do not compare quantities with incompatible units or silently change axis scales.
Do not treat an aggregated trend as if every individual observation behaved that way.

Practice

Matplotlib tutorials

Recreate a histogram, a time-oriented line plot and a scatter plot with axis labels and a legend where needed.

Use plots to answer a question; don't spend time styling them.

Self-test

Why is a histogram more suitable than a line plot for a single numeric variable's distribution?
What should an audit say when a plot suggests demand rises during commute hours?

Completion criterion

Stop when

You can create three correctly labeled diagnostic plots and write a short data audit that distinguishes observed patterns from causal claims.

Mark passed, then open 2.1 Only mark this when you can do it without notes.

Leads to

2.1 Frame a prediction task and choose a baseline Follow the practical starter sequence; each unit uses the artifact from the previous step.

Comes after

1.3 Inspect and transform tabular data with pandasFollow the practical starter sequence; each unit uses the artifact from the previous step.

Resources for this unit

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.

Also in this section

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.
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.