data-visualisation-techniques-python

Accelerating Data Visualization with Python: Utilizing Matplotlib, Seaborn, Plotly, and Dash. -->source

Up and Running

Create a virtual environment in the root directory

python3 -m venv env
source env/bin/activate

Install Poetry for dependency management

env/bin/pip install -U pip setuptools
env/bin/pip install poetry  

Install project dependencies

poetry install

Jupyter notebook kernel selection: use ./env/bin/python

Contents

matplotlib.ipynb

Basics

• Introduction to Matplotlib
• Creating a Line Plot
• Anatomy of a Figure
• Exploring Figure and Axes Classes
• Quick Plotting with Pyplot
• Object-Oriented Interface
• Adding Annotations
• Drawing Shapes and Lines
• Manipulating Graph Axes

Data Exploration with Iris Dataset

• Iris Dataset Overview
• Loading and Presenting Data
• Pie Chart for Class Distribution
• Modifying Chart Styles
• Scatter Plot Analysis by Petal Features
• 3D Plots for Data Visualization
• 3D Plot of Sepal Lengths
• Box Plot for Feature Value Ranges
• Violin Plot for Feature Distribution
• Bar Chart Comparing Species Features
• Applying Global Styles for Visual Consistency

Image Manipulation

• Integrating Images
• Grayscale vs. RGB Images
• Understanding Color Maps
• Creating Complex Graph Structures
• Generating Color Histograms
• Enhancing Image Resolution
• Saving Graph Images Locally

seaborn.ipynb

Basics

• Introduction to Seaborn
• Figure Level vs. Axes Level Graphs
• Working with Pandas Dataframes
• Modifying Graph Styles

Data Exploration - Titanic Dataset

• Data Presentation
• Loading the Dataset
• Density Plots
• Passenger Analysis
• Fare Analysis
• Survival Odds with Fare
• Passenger Accommodations
• Survival Odds Analysis

Data Exploration - Penguin Species

• Presentation and Loading of the Dataset
• PairGrid Class Usage
• Differentiating Penguin Species
• JointPlot Class and Marginal Plots
• Identifying Penguins with Joint Plots

Data Exploration - Flights

• Presentation and Loading of the Dataset
• Long vs. Wide Data Format
• Evolution of Flight Numbers

plotly.ipynb

Basics

• Introduction to Plotly
• Plotly Express

Data Exploration - Wind

• Polar Charts
• Presentation and Loading of the Dataset
• Wind Direction and Intensity Analysis

Data Exploration - FMRI

• Presentation and Loading of the Dataset
• Brain Activity Analysis for Specific Events

Data Exploration - Stocks

• Presentation and Loading of the Dataset
• Stock Price Evolution Analysis

Data Exploration - Diamonds

• Dataset Loading and Presentation
• Diamond Count by Color, Cut, Clarity
• Common Diamond Features
• Impact of Cut, Color, and Clarity on Price
• Price-affected Continuous Variables
• Relationship Analysis between Carats, Cut, Clarity, and Price

Data Exploration - Carshare

• Working with Maps in Plotly
• Carshare Activity in Montreal

Data Exploration - Car Accidents

• Choropleths
• US Car Accidents by State Analysis

dash_*.ipynb

Basics

• Introduction to Dash
• Elements of a Dash Application

dash_world_bank.ipynb

• Creating the Dash Application
• Dataset Loading and Presentation
• Creating the Application Title
• Creating the Scatterplot
• Adding a Selector for the Scatterplot
• Connecting the Scatterplot Selector with a Callback
• Creating the Map
• Adding a Selector for the Map
• Connecting the Map Selector with a Callback
• Creating the Trend Chart
• Adding a Selector to the Trend Chart
• Connecting the Selector to the Trend Chart
• Creating the Distribution Chart
• Adding a Selector to the Distribution Chart
• Connecting the Selector to the Distribution Chart
• Exploring the Resulting Dashboard

dash_web_traffic.ipynb

• Dashboard Overview
• Creating the Dashboard
• Dataset Loading and Presentation
• Dashboard Styling
• Creating the Dashboard Heading
• First Chart: Evolution of Website Visits
• Second Chart: Sales Funnel
• Third Chart: Proportion of Visits by Category
• Fourth Chart: Visit Distribution by Day and Hour
• Fifth Chart: Visits per Country
• Dashboard Analysis