Matplotlib Mastery: Python Data Visualization Unleashed

Name: Matplotlib Mastery: Python Data Visualization Unleashed
Rating: 3.5 (10 reviews)

Master data visualization with Matplotlib. Unleash your data storytelling skills and create stunning visualizations.

Created byEDUCBA Bridging the Gap

Last updated 3/2024

English

English [Auto],

What you'll learn

Introduction to Matplotlib and fundamental graph types like line, bar, scatter, and pie charts. Annotation, customization, and styling for effective data reps
Advanced features like working with text, layout customization, and creating complex plots.
In-depth understanding of legends, layout customization, and working with GridSpec.
Constrained layout, padding, and advanced GridSpec usage for precise figure layouts.
Advanced topics such as Path Tutorial, Path Effect Guide, and Color Tutorials for intricate data visualizations.
Transformation, color customization, and creating custom color maps.
Annotation techniques, text properties, and layout design for sophisticated visualizations.
Installation of necessary software and inline functions.
Practical application through plotting line graphs, histograms, bar graphs, scatter plots, and pie charts.
In-depth analysis using box plots and real-world scenario-based visualizations.
This course empowers students with a comprehensive understanding of Matplotlib, enabling them to create impactful data visualizations and analyze complex data

Course content

4 sections • 107 lectures • 14h 51m total length

Introduction to Matplolip9:47
Learn to install numpy and matplotlib with Python's command prompt, then create basic plots using matplotlib.pyplot to visualize data with lines, axis labels, a title, and a legend.
Simple Graphs6:29
Learn to plot with the pay plot interface in matplotlib, using plt and np, while avoiding pylab to prevent namespace pollution. Build reusable plots with clear, explicit code.
Simple Graphs Continue5:55
Create and customize dot plots with matplotlib, using plt.plot for single and multi-argument data, automatic x values, and format strings for colors and markers, including axis ranges and numpy arrays.
More on Line Graphs9:25
Manage multiple figures and axes in Matplotlib, using current axis and figure, subplots, and figure lifecycle. Control line segment rendering and performance with interactive mode and path.simplify, path.simplify_threshold.
Bar Graph6:49
Generate data with numpy, plot it with matplotlib, and tune rendering with rcParams such as path.simplify_threshold and path.chunk_size.
Scatter Graph8:17
Explore how to plot with matplotlib, customize styles, and speed up large data visualizations using fast style, ggplot, and dark_background while adding titles and axis labels.
Using Text10:38
Explore matplotlib essentials by adding legends and grid lines, customizing plots with labels, titles, and ggplot style, and building bar charts, scatter plots, and numpy-driven workflows.
Annotation in Graph4:32
Master annotation in Matplotlib with text and arrows using annotate, and place labels at data coordinates. Load CSV data with NumPy loadtxt and create labeled plots.
Basic of Pyplot6:44
Plot four subplots of the same data using different y-axis scales—linear, log, symmetric log, and logit—while tuning grids, minor formatters, and subplot layout.
Basic of Pyplot Text8:46
Explore how to plot with keyword strings in matplotlib using data dictionaries, create scatter and bar plots with categorical variables, and annotate with text, labels, and histograms.
Basic Bar and Fill6:25
Learn to create 3D visualizations with axis 3D in matplotlib, plotting 3D scatter and bar diagrams, and arranging multiple subplots with labeled x, y, and z axes.
Complex Fill Demo6:39
Explore complex fill demos in Matplotlib by building simple horizontal bar charts and multi-curve fills with color and transparency, including axis labeling, tick customization, and grid options.
Custom Dashed Lines and Bar Charts11:51
Master matplotlib by building custom dashed lines and bar charts, using the line object, dash definitions, legends, scatter plots, and labeled bars with error bars.
Inch and cms and Color Bars10:54
Create and attach color bars to color-mapped data with a color mapper, using blues for positive data and reds for negative data across subplots; demonstrate inches and centimeters unit usage.
Demo Image11:58
Explore image visualization in matplotlib with an image demo, using imshow to display arrays, color maps, and sample data from matplotlib, while applying numpy operations.
Pcolormesh and Pathpatch Demo10:13
Demonstrates pcolormesh with normalization and a color map to draw levels, and shows contour and contourf usage, then builds path and path patch objects to visualize points and connections.
Creating Streamplot9:57
Explore creating stream plots in matplotlib to visualize 2d vector fields, showing color variation along streamlines, adjustable density, varying line width, and control over starting points, masks, and NaN values.
Creating Streamplot Continue4:53
Create and customize a stream plot by defining seed points, plotting with x, y, u, v, applying a mask, and adding color mapping and a color bar.
Eillpise Demo8:27
Explore eclipse plots with matplotlib, including random ellipses, rotated variants, and a basic pie chart with labels, explode, auto pct, shadow, and a 90-degree start angle.
Eillpise Demo Continue15:49
Explore matplotlib visualizations with eclipse demos, creating multiple eclipses and rotated variants. Learn to craft pie charts with labels, explode effects, and start angles, and to display tables alongside charts.
Pie Chart8:01
Explore interactive data visualization with Matplotlib by using sliders, buttons, and radio buttons to control a sine wave's frequency, amplitude, color, and axis properties, with real-time updates.
Table Demo5:11
Learn to create date plots in matplotlib with date tick locators and formatters. Control major and minor ticks and format x-axis dates from Yahoo data for clear visualization.
Log Demo and Polar Demo8:55
Explore creating log and polar plots with Matplotlib, including semi-log and log-log scales, polar line plots, and customized legends with NumPy data.
Customizing Image6:41
Learn to load image data with numpy, and display it using matplotlib.pyplot. Apply color maps, color bars, and histograms to analyze and adjust image data, including subplots and titles.
Customizing Image Continue6:44
Explore the plot life cycle by building a sample data set, creating a bar chart with company sales, and applying x tick label rotation and layout tweaks for clear visualization.
Customizing Plot9:35
Learn to customize Matplotlib plots by adding and formatting labels, rotating x tick labels, setting axis titles, adjusting figure size, applying currency formatting, and annotating data points.
Customizing Styles10:31
Save plots in various formats with fig.savefig, exploring dpi and file type options. Customize visuals with styles, rc params, and context managers, and build figures with lines and histograms.

Introduction to Matplotlib Intermediate2:06
Master Matplotlib, a Python library for 2D graphs and plots. Use the plot module to control line styles, fonts, and axis formatting, from basics through intermediate and advanced levels.
Simple Working with Legend9:20
Explore how to customize the Matplotlib axis, patches, and tick properties, including tick label colors, rotations, and grid lines, and create flexible legends with custom handles.
Simple Working with Legends Continue6:27
Explore constructing custom legend handles using matplotlib lines with markers, then customize legend placement with loc and bbox_to_anchor, including multiple legends across subplots.
More on Legends Part 111:08
Explore legend handlers in Matplotlib and learn how to map each legend key to a custom or built-in handler using a handler map.
More on Legends Part 26:41
Create a custom ellipse legend patch by defining a HandlerEllipse class and mapping it in a legend handler to render an elliptical symbol instead of a rectangle.
Basic Customizing Figure Layout10:39
Explore styling with cycler to control colors and line styles, including default rc parameters and axis-specific prop cycles. Learn grid specs and subplots for flexible figure layouts.
Advance Customizing Figure Layout6:00
Explore advanced figure layout with grid specs to create and arrange multiple subplots, spanning rows and columns, using add_subplot and add_grid_spec in Matplotlib.
More on Customizing Figure Layout12:36
Explore customizing figure layouts with gridspec to create subplots of different widths and heights using width and height ratios, constraint layout, and annotate grid specs.
More Examples4:33
Explore advanced grid specs layouts in Matplotlib, adjusting subplot layout with GridSpec and left-right margins, while comparing constraint layout and tight layout behavior.
Complex Nested Grid spec9:17
Learn to create complex nested grids with gridspec and subplot specifications, building figures from outer 4x4 to inner 3x3 grids and customizing spines.
Constrained Layout Guide8:00
Use constraint layout to automatically fit subplots and decorations like legends and color bars within figure, preserving the requested layout; activate it before adding axes via subplots or rc params.
Constrained Layout Guide Continue7:15
Master constrained layout to create accurate color bars with Matplotlib, and learn to assign color bars to multiple axes using dictionaries, norm, and cmap for consistent scalar maps.
Padding8:25
Master advanced matplotlib layouts with constraint layout, color mesh plots and color bars, subtitles, and legends, while managing space across subplots and saving figures.
Spacing5:58
Explore spacing and layout techniques in matplotlib, using legends, subplots with constraint layout, and padding settings to control edge and inter-plot distances.
Use with Grid Spec6:23
Adjust subplot spacing using w space and h space as fractions of the figure, and tune padding with constraint layout to position color bars and tick labels precisely.
More on Grid spec8:42
Explore advanced grid specifications in matplotlib, using gridspec and subgridspec to arrange multiple subplots, and customize titles, labels, and colorbar across complex layouts.
Examples on Grid Spec9:01
Manually set axis positions with constraint layout in Matplotlib, using a figure and insets in data space, and learn to execute layout while noting subplot limitations and backend differences.
Examples on Grid Spec Continue5:42
Tight Layout Guide Basic8:23
Tight Layout Guide Advance6:08
Learn to prevent subplot overlap by using tight layout and figure rc parameters, automating spacing with tight_layout and grid specifications for multi-subplot figures.

Introduction to Matplotlib Advance Level3:27
Explore advanced Matplotlib topics from shadows and transformations to color maps, text, annotations, and 3D tools, including LaTeX rendering, axis grid, and custom box styles.
Path Tutorial11:02
Master matplotlib's path tutorial by creating paths with move to, line to, and curve codes, then render path patches and compound paths for histograms and bars.
More on Path Tutorial5:17
Construct a compound path for rectangles using move to, line to, and close poly with defined vertices. Visualize the result as a histogram created with a bar patch in Matplotlib.
Path Effect Guide10:17
Explore matplotlib path effects guide to apply draw-stage path effects to artists, including text and lines. Learn to create normal, shadow, and path patch effects to make elements stand out.
Transformation Level 18:56
Explore Matplotlib's transformation framework across data, axis, figure, and display coordinates. Learn to create and invert custom transforms and apply them to data points and user interface events.
Transformation Level 1 and Example5:42
Explore axis coordinates and data coordinates, learn how transformations update display points, place text with fixed axis coordinates, and build multi-panel figures with labeled subplots and fixed shapes.
Transformation Level 2 and Example12:02
Explore blended transformations in Matplotlib to plot data and axis coordinates, create horizontal spans across the x axis, and render patches and circles with offset and shadow effects.
Colors Tutorial10:40
explore matplotlib color formats, including rgb(a), hex strings, grayscale values, and color names, and examine how the c color cycle and xkcd colors map to rgb in plots.
Customized Colorbars10:04
Discover how to build standalone and customized color bars in matplotlib, using color maps, norms, orientation, and extend options for continuous and discrete intervals with labeled ticks.
Creating Colormaps Basic8:11
Learn to create and manipulate colormaps in matplotlib, using listed color maps and linear segmented maps, and generate custom palettes from numpy arrays with interpolation.
Creating Colormaps Advance10:19
Create and merge color maps in matplotlib, build a brown-to-white gradient, and construct a linear segmented color map from a c dict with red, green, and blue anchors.
Logarithmic and Symmetric Logarithmic9:56
Explore mapping data to color maps with non-linear normalization using logarithmic norm and symmetric logarithmic norm in matplotlib, with examples of linear versus logarithmic scales and color bars.
Power-Law and Discrete bounds9:05
Explore power-law color normalization with colors.power_norm (gamma=0.5) and when to apply it, and implement discrete bounds with colors.boundary_norm for even and uneven boundaries using color meshes and colorbars.
Two Linear Ranges6:21
Explore custom normalization with linear ranges in Matplotlib, implementing a midpoint normalization to map negative and positive data with separate linear scales, and visualize using color mesh and colorbars.
Choosing Colormaps Overview9:53
Explore how to choose color maps in matplotlib, using built-in and external options, and apply perceptual uniformity across sequential, diverging, cyclic, and quantitative classes.
Classes of Colormaps11:48
Explore cyclic, quantitative, and miscellaneous colormaps in matplotlib, comparing monotonic behavior, symmetry, and practical examples like jet, ocean, terrain, and pastel palettes.
Lightness of Matplotlib Colormaps5:47
Explore the lightness values of matplotlib color maps by configuring rc params, categorizing maps (perceptually uniform, sequential, diverging, cyclic, qualitative, miscellaneous), and plotting their lightness with rgb conversions.
Lightness of Matplotlib Colormaps Continue8:11
Plot color map values for each category and vary scatter marker colors to illustrate the lightness of matplotlib colormaps, including sequential, diverging, qualitative, cyclic, and miscellaneous.
Basic Text Command11:19
Learn how to add and customize text in Matplotlib plots using text, annotate, titles, and axis labels, with font properties, Unicode, math expressions, and styling options.
Legends and Annotations12:15
Master font properties and axis labels in matplotlib, using Times New Roman serif in italic or bold, then tune titles and date ticks with auto date locator and formatters.
Text Properties8:18
Explore how to control text properties and layout in Matplotlib, using horizontal and vertical alignment, font properties, colors, and transforms to position text around shapes.
Layouts4:11
Master Matplotlib text layouts by adjusting x.text alignment, horizontal and vertical alignments, rotation and transform, and customize fonts, color, and size via rc params for math text.
Basic Annotation10:03
Master matplotlib annotations, from basic text placement on the axis to advanced features like arrows, text boxes, and connection patches. Learn polar annotations and text positioning with the annotate method.
Annotation Polar7:43
Explore annotation techniques in matplotlib, including text annotations, arrows, and fancy box patches, then learn to create and customize multiple subplots with annotated visuals.
Fancy Demo7:22
Master advanced annotations in Matplotlib using patches, annotate, and text to connect points with arrows. The Fancy Demo covers figure setup, subplots, and text annotations with arrows.
Connectionstyle Demo9:31
Explore matplotlib connection styles by plotting multiple arc and radius configurations, annotating points with customized arrow styles and transforms to visualize dynamic connections.
Using Connection Patch12:16
Demonstrate building fancy matplotlib patches, including connection patches with arc styles, and anchoring text and circles in an anchored drawing area for annotated visualizations.
Zoom Effect Between Axes7:07
Connect between subplots with annotation using ConnectionPatch and annotate in Matplotlib. Explore axis zoom effects with Bbox patch and connector patch to keep connections intact when zooming.
Simple Example6:22
Learn how to render mathematical expressions in matplotlib using Tex markup inside dollar signs, including subscripts, superscripts, fractions, nesting, and left and right brackets for the whole expression in titles.
Simple Example Continue6:14
Explore constructing radicals with slash or square root in Matplotlib captions, and customize math text fonts and fonts for roman or calligraphy styles while rendering with plt.show.
Saving Multipage PDF Files7:13
Export multi-page PDFs from Matplotlib using the PGF backend and LaTeX, after installing LaTeX. Customize text fonts via rcParams, selecting serif, sans-serif, monospace, and other families in your figures.
Modifying Parameters8:42
Learn how to customize matplotlib visualizations by adjusting rc params for fonts and packages, enable tex rendering, and configure preamble and pdf text settings for precise data visuals.
Text Rendering with LaTex7:52
Enable LaTeX text rendering in matplotlib with rcParams text.use_tex and backends like HG, PS, or PDF, and explore Unicode and math expressions with consistent fonts.
Simple Axes Grid8:25
Explore the access grid toolkit for displaying multiple images in matplotlib, using image grid, rgb axis, and access divider to adjust axis positions and synchronize colorbars with the master axis.
Parasite Axes7:58
Explore parasite axes in matplotlib, where a parasite axis shares the host axis location using twin x or twin y and maps data coordinates for the top or right axes.
Anchored Artists6:26
Explore anchored artists in matplotlib, anchoring text, circles, and ellipses to axis boxes and canvas coordinates using offset boxes and transforms, with examples of a size bar, and labeled figures.
RGB Axes7:35
Master RGB axis in matplotlib to display RGB composite images, share axes, and fit the layout into a rectangle using a sample data workflow.
Simple Axes Artist9:41
Explore floating axis features in matplotlib to create customized subplots with grid helper curve linear, a rotated view, and controlled boundaries using transforms and fixed locators.
Axes Artist with Parasite Axes12:02
Create a parasite axis in matplotlib, attach a secondary axis to a floating axis, customize patches and z order, and render bar and scatter plots across multiple subplots.
Floating Axis Demo Part 19:31
Floating Axis Demo Part 212:02
Axes Artist Demo13:47
Explore the axis artist demo to rotate and align tick labels, set axis direction, and create floating axes using mpl_toolkits.axis_artist, grid helpers, and polar transformations.
Line 3D8:19
demonstrate 3d plotting with matplotlib's mplot3d toolkit, creating a 3d line, a 3d scatter plot, and a 3d wireframe to visualize parametric curves and random data.
Bar 3D8:10
Explore creating 3d bar diagrams with matplotlib, importing axes3d, setting projection to 3d, and customizing colors, x/y/z labels, ticks, and 3d text annotations.

Introduction to Project2:11
Master a project-based approach to e-commerce data analysis with matplotlib, from installing Anaconda and matplotlib to loading, preprocessing, and exploring datasets.
Installation of Software's5:05
Install and set up Anaconda, launch Jupyter Notebook, and implement matplotlib with a CSV dataset of over 7000 electronic products from data.world.
Installation of Anaconda and Code7:59
Install matplotlib in an Anaconda environment using pip, conda, or Anaconda Navigator. Verify the installation and explore integration with numpy and pandas for data visualization.
Inline Function11:12
Master inline matplotlib to render graphs in the same code file, and learn pandas data preparation by loading csv files, cleaning null values, and assessing data via info and shape.
Unique Value10:03
Identify unique values in each column using the unique function, drop columns with many nulls, then remove duplicates to reveal 835 unique IDs across 24 columns.
Prices Condition8:47
Encode complex string IDs to integers using sklearn's preprocessing label encoder, apply to id and keys, verify with df.head, and explore unique values with to_list for price condition and availability.
Understanding Basics of Graph11:00
Explore the basics of graphs with matplotlib by creating figures and axes, adjusting figure size and subplots, and applying set title, set x limit, set y limit, and labels.
Data Visualization7:07
Explore how to create figures and subplots with matplotlib, switch between axis setter methods and pyplot calls, and label x/y axes and titles for clear data visualizations.
Plotting of Line Graph6:17
Plot a line graph in matplotlib by mapping the maximum amount (x) to product categories (y), set axis labels for electronics, and adjust rcParams to display prices.
Plotting of Histogram11:08
Learn to create histograms and single-column plots with matplotlib, customize color, marker, and line style using fmt, and build multi-line graphs to compare availability frequencies.
Plotting of Histogram Continue8:51
Explore plotting histograms with Matplotlib, range and bins, label axes, adjust orientation and color, use histogram types such as step and step filled, tune x and y ticks, save figures.
Plotting of Bar Graph9:59
Customize histograms and bar charts in matplotlib by adjusting colors, rotation, and font sizes, and use axis labels and scatter plots to explore product availability and prices.
Plotting of Scatter Plot4:56
Explore how to visualize relationships between two price columns with scatter plots, using plt.scatter, axis labels, markers, colors, and legends, and compare with a basic pie chart for proportions.
Plotting of Pie Graph6:08
Learn to create a pie chart from a data frame by identifying unique labels, counting sizes, calculating percentages, and configuring colors and explode parameters for clarity.
Plotting of Pie Graph Continue5:10
Demonstrate how to plot and customize a pie graph in Matplotlib, using explode to offset slices, adjust starting angle, set line width, add shadows, and show a legend and title.
Plotting of Boxplot5:04
Create and interpret a box plot with the five number summary (minimum, first quartile, median, third quartile, maximum) using Matplotlib, and explore line, histogram, bar, scatter, and pie charts.

Requirements

There are a few things that you should be supposed to know before you can start learning about MatPlotLib. The very first thing is, you should know python fundamental. As MatPlotLib is a python library, you are supposed to know how does python works so that you can bring this library in use while developing a program in python. If you are already working as a python developer, you might find it very easy to learn python while if you are a beginner, you will need to give some time practicing it so that you can understand everything perfectly.

Description

Welcome to "Matplotlib Mastery for Python Data Visualization," a comprehensive course designed to empower you with the skills needed to create compelling visualizations using Matplotlib in Python. This course caters to participants ranging from beginners to advanced users, offering a step-by-step journey through the intricacies of Matplotlib, a powerful and versatile plotting library.

Course Overview:

Matplotlib is a go-to library for data visualization in Python, and this course is crafted to provide you with a deep understanding of its features. Whether you're a data scientist, analyst, or anyone working with data, mastering Matplotlib will enhance your ability to convey insights effectively.

What You'll Learn:

Basics for Beginners: Understand the foundational elements of Matplotlib, including simple and line graphs, bar graphs, and scatter plots. Learn to annotate, customize layouts, and work with Pyplot effectively.
Intermediate Techniques: Dive into more advanced topics, including legends, complex layouts, and constrained layouts. Enhance your visualization skills with nested grids and gain mastery over customizing figure layouts.
Advanced Concepts: Explore path tutorials, color customization, and advanced transformations. Understand colormap creation, logarithmic scales, and power-law transformations. Delve into text properties, annotations, and layout intricacies.
Practical Case Study: Apply your Matplotlib skills to a real-world scenario with an E-commerce Data Analysis case study. Learn how to preprocess data and create various visualizations, providing valuable insights for decision-making.

Why Take This Course:

Hands-On Learning: Engage in practical exercises and a real-world case study to reinforce your learning.
Comprehensive Curriculum: Cover Matplotlib from the basics to advanced techniques, ensuring a holistic understanding of the library.
Expert Guidance: Benefit from expert insights and guidance to navigate the nuances of data visualization effectively.

Join us on this journey to master Matplotlib and elevate your data visualization skills. Let's transform raw data into meaningful insights that drive informed decision-making. Get ready to unlock the full potential of Matplotlib!

Section 1: Matplotlib for Python Data Visualization - Beginners

In this introductory section, participants will delve into the fundamentals of Matplotlib for Python data visualization. Starting with the basics, such as simple graphs and line graphs, the course progresses to cover more advanced visualizations like bar graphs, scatter plots, and various annotation techniques. Additionally, participants will gain insights into customizing images and styles using Pyplot, along with exploring the intricacies of layout customization.

Section 2: Matplotlib for Python Data Visualization - Intermediate

Building on the foundational knowledge acquired in the beginners' section, the intermediate segment focuses on refining visualization skills. Participants will learn to work with legends effectively, customize figure layouts, and use advanced techniques like constrained layout and grid specifications. This section empowers learners with more complex and nested grid layouts, providing a comprehensive understanding of layout manipulation.

Section 3: Matplotlib for Python Data Visualization - Advanced

The advanced level of Matplotlib mastery introduces participants to sophisticated concepts and techniques. Starting with path tutorials and effects, the section progresses to cover transformations, color customization, and colormap creation. Participants will delve into logarithmic scales, power-law transformations, and advanced color mapping. The section concludes with in-depth exploration of text properties, annotations, layouts, and various annotation styles.

Section 4: Matplotlib Case Study - E-commerce Data Analysis

In this practical case study, participants will apply their Matplotlib skills to analyze E-commerce data. The project encompasses installation procedures, data preprocessing, and an extensive exploration of various visualizations. From line graphs and histograms to bar graphs and scatter plots, participants will gain hands-on experience in data analysis and visualization. The case study aims to provide a real-world application of Matplotlib for effective data interpretation and decision-making.

Who this course is for:

Data Scientists and Analysts: Gain advanced visualization techniques to present insights effectively.
Python Developers: Expand your skill set with a focus on Matplotlib for data representation.
Students and Researchers: Learn practical applications for data visualization in research and academia.
Business Professionals: Understand how to interpret and communicate data trends visually.
Whether you are a beginner or have some experience in Python, this course provides valuable insights for leveraging Matplotlib in various domains.

Matplotlib Mastery: Python Data Visualization Unleashed

What you'll learn

Explore related topics

Course content

Matplotlib for Python Data Visualization - Beginners27 lectures • 3hr 50min

Matplotlib for Python Data Visualization - Intermediate20 lectures • 2hr 33min

Matplotlib for Python Data Visualization - Advanced44 lectures • 6hr 27min

Matplotlib Case Study - E-commerce Data Analysis16 lectures • 2hr 1min

Requirements

Description

Who this course is for: