Python Data Visualization for Beginners (CPD Accredited)

Explore core visualization concepts and aesthetics to map data effectively in Python, distinguishing continuous and discrete data, and applying essentials like position, shape, size, color, and line attributes across libraries.

Differentiate continuous and discrete data and classify as quantitative or qualitative to select effective visualizations. Explore examples like height, price, time, hair color, and Pandas data frames.

Explore color scales, including discrete, sequential, and diverging, and discover small multiples, trellis charts, and accessibility considerations for color-deficient vision in Python visualizations.

Discover the history and landscape of matplotlib, from its 2003 origins inspired by MATLAB to its foundation for pandas and seaborn.

Explore the two interfaces for generating visualizations: the functional pyplot interface and the object-oriented approach. Use the object-oriented workflow to create figures and axes, update histograms, labels, and titles.

Examine the EPA Fuel Economy CSV, a messy real-world dataset with 24,000 values from 2000–2020, including make, model, year, mpg, and emissions, plus figure versus axis concepts.

Launch a jupyter notebook in a conda environment on Windows and learn how matplotlib's figure and axes relate, using the Amazon ebook Excel and EPA fuel economy CSV.

Rename the notebook, and import pandas, numpy, and matplotlib; set up pathlib paths. Read the EPA fuel economy csv, inspect with info and head, and ticker customization.

Learn to create a simple histogram of combined fuel economy in a Jupyter notebook using matplotlib, compare state-based and object-oriented interfaces, and customize axes and title.

Set up a Python data visualization notebook, import pandas, numpy, and matplotlib, configure paths and data from EPA fuel economy CSV, inspect data with info, and begin plotting histograms.

Create a figure with two axes, plot two histograms and a box plot, and customize labels, figure size, and layout to compare distributions clearly.

save images in multiple formats with map plot lib, including png with transparent background, svg, jpeg, and pdf, while setting dpi to 80 and layout options.

Plot the EPA fuel economy data as a line chart of average highway mileage by year, using numpy and pandas, and improve readability with x ticks and a major formatter.

Create bar plots with map plot lib using the same format as line charts, adjust x ticks to display years, and try a horizontal bar chart with bar H.

learn to create a scatter plot of fuel cost versus engine displacement with alpha transparency and color by cylinders, including axis labels, currency formatting, and a 3500 target line.

Explore how map plot lib styles quickly improve visuals by applying presets like ggplot, using the style context manager with a scatterplot, and experimenting with colors, grids, fonts, and sizes.

Create a complex customized visualization with two side-by-side plots: a year vs fuel cost scatter with a trend line and annotated average, plus a histogram annotated with the average.

Learn to configure and customize map plot lib plots with two axes, scatter, line, and histogram plots; label axes, apply currency formatting, add annotated lines, and save as transparent svg.

Explore the pros and cons of map plot lib as a robust option to create almost any plot type, with extensive documentation and examples, while avoiding old state based APIs.

Learn how pandas visualizations built on map plot lib enable quick plotting during data wrangling; compare the plot method with specialized histogram and box plot APIs.

Learn how to plot data using pandas visualization APIs, creating histograms, box plots, density and area plots, and customizing plots with matplotlib for annual fuel efficiency data.

Learn to create and customize bar, horizontal bar, line, box, histogram, density, area, scatter, hexbin, and pie plots using the map plot lib API and pandas hist and box plots.

Explore four advanced pandas visualizations—scatter matrix, Andrew's curves, parallel coordinates, and rad viz report—to reveal relationships in high-dimensional car data.

Use pandas for basic exploratory analysis and matplotlib customization when needed; review scatter matrix and plots, including Andrew's curves, parallel coordinates, and radial plots for high-dimensional data, noting non-interactive defaults.

Discover seaborn, a mature statistical visualization library built on matplotlib that operates on whole dataframes and integrates with pandas, offering a declarative API for quick, dataframe-level visualizations and statistical summaries.

Install and import Seabourn, explore style and theming options, then start with figure-level plots—relational, distribution, and category plots—before customizing at the axes level.

Introduce a preprocessed dataset for Seaborn visuals, with a drive column, simplified vehicle type, transmission as automatic or manual, fuel types (gas, diesel, electric, other), and year ranges (2011-2020, 2000-2010).

Learn to use seaborn displot to visualize fuel economy data in a pandas dataframe, experiment with histogram, kde, ecdf, and rug plots, and compare seaborn facet grid with matplotlib axes.

Explore the Seaborn catplot family to visualize distributions and means, including strip plots, box plots, violin plots, bar plots with cylinder-based averages and error bars, and point plots.

Explore how the relplot in seaborn visualizes relationships by plotting cylinders against combined fuel economy, using scatter and line plots with hue for automatic versus manual transmissions.

Explore seaborn displot and relplot with EPA fuel economy data, adjusting size, aspect, and hue by year or date range, and facet by columns or rows to compare fuel cost.

Explore the catplot API to generate box, point, and bar plots for fuel economy across date ranges and fuels, plus heat maps, pair plots, and joint plots for insights.

Transform data with pandas cross tab to build a mean fuel economy grid, then render a heat map with annotations and color palettes to reveal outliers and trends.

Explore cat plots, box plots, and pair plots to compare fuel economy across date ranges and fuel types, including electric vs fossil fuels, with joint plots and regression lines.

Master seaborn customization using the theming API for global style, the axis level API for detailed axis tweaks, and facet grid methods for multi-plot layouts in exploratory analysis.

Altair provides a wrapper API to Python and pandas data frames for visuals with minimal code. Altair uses Vega and Vega Lite to provide a declarative grammar for interactive plots.

Use Altair to visualize data with cat plots, box plots, and point plots across date ranges and fuel types, and learn the installation of Altair packages and API styles.

Explore cat plots, box plots, and point plots in Altair to compare fuel economy across date ranges and fuel types, and learn to set up Altair with Conda.

Explore additional Altair plots to master the api, including fuel type summaries with bar plots and box plots, and learn to treat year as ordinal or categorical for accurate visuals.

Explore Altair's longhand API to customize histograms with binning and x–y encodings, color by cylinders, and reveal variable types like ordinal, quantitative, nominal in scatterplots of displacement and fuel cost.

Explore Altair's long form vs shorthand, using Altair's alt dot x and x values, including type and aggregate functions like sum, to move from quick exploratory analysis to granular visualization control.

Explore data types—quantitative, ordinal, nominal, and temporal—and see how Altair uses them to shape colors, axes, and bar charts, guided by pandas type inference.

Explore interactive Altair visualizations by creating a scatterplot with a tooltip for make, model, and year, then concatenate charts horizontally or vertically for combined visuals.

Explore faceting and layering in Altair to create grid of charts by class summary, combine bars with a mean line and annotated text for clear business insights.

Consolidate multiple charts in Altair using horizontal and vertical concatenation, layered charts, and faceting. Apply these techniques to an Amazon books dataset to visualize reviews, price, and genre.

Identify the top 20 authors by reviews and visualize their time-based distribution with a circle chart sized by reviews, colored by author, using Pandas and Altair.

Explore how Altair builds bubble charts with year on the x-axis, author on the y-axis, bubble size by reviews, and color by author for exploratory analysis in Python.

Explore Plotly, a free open-source data visualization library built on pandas data frames, with a high-level express API and low-level graph objects API, offering interactive visuals and kaleido image saving.

Create histograms from a data frame and explore distributions with interactive visuals, including zoom, pan, and legend filtering, and export SVGs while comparing distributions with graph objects and plotly express.

Explore customization options in Python data visualization by using templates and color palettes. Reverse palettes with an underscore r and apply qualitative and sequential colors to histograms and heat maps.

Create box plots and strip plots to visualize fuel cost by class summary. Discover hover details, outliers, histograms with marginal plots, and annotations for the average cost.

Learn to build and customize plots with the Plot Express API, including scatter plots and scatter matrices, hover data, color, layout, and facets.

Create and visualize a data frame of yearly average fuel economy and number of cars, using line, area, and bar charts by class summary to compare trends over time.

Demonstrates building diverse visualizations in Plot Express, including scatter plots with trend lines (OLS/LOESS), tree maps, sunburst plots, heatmaps, and a density map using average by year by class.

Facet histograms by date range and drive with Plot Express, wrap to three columns, adjust bins, and build box plots of fuel cost by fuel type summary while updating annotations.

Create a customized strip plot of estimated fuel cost by class, annotate an outlier around 1150 and a highlighted inefficiency range from 3500 to 4000, and export as an SVG.

Annotate and customize a strip plot, adjusting the range and y-axis for clarity. Add an annotation and a vertical rectangle to highlight insights for sharing with Plot Li Express.

Explore Streamlit for building interactive dashboards in Python, learn installation with pip, native plots, and how to combine with pandas dataframes and other visualization backends.

Launch a basic Python app that reads data, builds a histogram with colors by class, and serves the plot via a built-in web server using stream lit.

Learn to run a Streamlit app locally on port 8501 and build interactive visualizations with widgets and a caching decorator. Explore layout options like the sidebar, columns, and container.

Build an interactive streamlit app that caches data and exposes a multiselect for makes and a year range slider. Visualize with Plot Express and Altair, updating dynamically.

Learn to filter data with user inputs from a year range and make list, build a filtered data frame, and compute the average fuel economy using the metric function.

Create and compare interactive charts by adding a histogram and an Altair tick plot, filtering data with user input, and displaying results as a live data frame.

Learn to control layout with the Streamline sidebar by using a multi-select for makes (default top five or all) and a year range slider, built on a cache-loaded data frame.

Apply range slider filters and a makes filter in a Streamlit app, then display a histogram and Altair chart in the sidebar, updating visualizations in real time.

compare the pros and cons of streamline for adding interactivity to visualizations, using existing libraries, and a flexible, out-of-the-box display with open source and commercial options.

Discover Dash, a powerful tool for building interactive dashboards and visualizations in Python, using Flask, React, and pandas data frames; get started with installation and a simple app.

Create your first dash app in a Python file by importing pandas and dash, loading the EPA fuel economy file, and rendering a histogram with a layout.

Learn how to run a dash app from the command line using Python, view a simple histogram on a local server, and explore the app layout and debug information.

Explore dash components: layout HTML/CSS controls page appearance, core components for inputs and graphs, and callbacks for interactivity; build pure HTML layouts with external stylesheets, markdown, and live reload.

Learn to add interactivity to a histogram in a dash app by filtering a data frame with a multi-select fuel type dropdown and updating the graph via callbacks.

Interactively explore a histogram in a Python data visualization app that updates as you use a dropdown and multi-select, with Dash callbacks handling the figure update.

Build a full featured dash app with a histogram, a scatterplot, a year range slider, and transmission type filters, selecting items in the scatterplot to display in a data table.

Build an interactive data dashboard with a callback that filters data by year range and transmission, handles selected data, updates histogram and scatterplot, and refreshes the data table.

Run a full dash app to explore a histogram and a scatterplot with real-time updates, a year range slider, transmission type selection boxes, and a data table with lasso.

Dash enables building highly interactive dashboards using Python, Flask, and React; customize with HTML and CSS, load data with pandas, and run a local server for interactive visualizations.

Review key concepts and course objectives for Python data visualization, compare libraries, install them, and apply exploratory and explanatory analysis using notebooks.

Explore core Python data visualization tools—Matplotlib, Seaborn, Altair, Plotly, Streamlit, and Dash—focusing on plotting fundamentals, high-level vs. object-oriented interfaces, and how to combine libraries for interactive dashboards.

Develop a practical data visualization workflow using seaborn for quick exploratory analysis, switch between seaborn and plotly for visuals, and use streamlined for interactive, shareable analyses.