Kanaries · Knowledge Base
Practical tutorials, references, and fixes in this topic.
Use plt.fill_between / ax.fill_between to shade areas between curves, highlight conditions with where=, build confidence bands, and fix ValueError when filtering index labels with isin().
Move a Matplotlib legend outside the axes with bbox_to_anchor and loc. Place it right, left, or below the plot, shrink axes when needed, and export with bbox_inches='tight' so nothing is clipped.
Quick Matplotlib savefig reference: fix cut-off labels with bbox_inches='tight', set DPI/formats, and avoid blank figures after plt.show().
Master Matplotlib colormaps for data visualization. Learn built-in colormaps, custom colormaps, diverging vs sequential, colorbar customization, and choosing the right colormap.
Master matplotlib pie charts with practical examples covering labels, colors, explode, autopct, donut charts, nested pies, and professional customization techniques.
Learn how to create bar charts in Matplotlib with grouped bars, stacked bars, horizontal bars, custom colors, and labels. Master plt.bar() with examples.
Learn how to add, position, and customize legends in Matplotlib. Master legend placement, styling, multiple legends, and handling many entries with practical examples.
Learn how to create scatter plots in Matplotlib with color mapping, size encoding, annotations, and multiple datasets. Master plt.scatter() with practical examples.
Learn how to create multi-panel figures in Matplotlib using plt.subplots(), GridSpec, and subplot2grid. Master layouts, shared axes, and spacing control.
Learn how to create histograms with matplotlib in Python. Master plt.hist() with bins, density, color, stacked histograms, and customization options.
Use annotate, text, and bar_label to highlight peaks, explain bars, and keep labels readable with offsets, arrows, and clipping in Matplotlib.
Control tick frequency, rotation, and formatting for numbers and dates in Matplotlib using locators and formatters, with ready-to-use recipes and a quick tool map.
Learn when to use twinx/twiny and when to reach for secondary_yaxis, with conversion-safe examples, legends, and scaling tips for dual-axis plots in Matplotlib.
5 ready to go examples of matplilib cmaps/colormaps, which you can learn or directly copy to modify.
Explore 10 different types of histograms in Matplotlib, including basic, colored, normalized, cumulative, and more. Learn how to create each type with code snippets you can copy.
How to use matplotlib's vlines function to enhance your data visualizations with vertical lines. Explore chart examples and code snippets you can directly copy to use.
A complete, updated 2025 guide to fixing the 'No Module Named Matplotlib' error in Python — covering all causes and providing clear, actionable solutions.
Learn all practical ways to set and adjust figure sizes in Matplotlib—figsize, rcParams, subplots, Pandas integration, centimeters, defaults, and more. Includes examples and troubleshooting.
Explore the power of Matplotlib in Python for image processing. Learn to modify, crop, rotate, and visualize images in new ways. Discover the world of data visualization through images.
Explore the world of custom colormaps in Matplotlib. Understand, modify, and create your own colormaps for enriched data visualization. Perfect for Python enthusiasts and data scientists!
An in-depth guide on how to diagnose and fix Matplotlib syntax errors in your Python code. We also discuss the SyntaxError: invalid syntax %matplotlib inline error and its potential solution. Learn alternative methods using PyGWalker.
Explore the power of Matplotlib for data visualization in Python. Learn about the .savefig() method, customizing charts, and more with this easy-to-understand guide.
Dive into the common issues surrounding 'matplotlib.pyplot not resolved from source', and explore the alternative tool, PyGWalker, for seamless data visualization.
Discover the art of data visualization with Matplotlib Stylesheets in Python. Learn to customize styles, choose the right theme and boost your graphs with these effective tips.
Dive into the world of data visualization with our comprehensive tutorial on creating animations using the Matplotlib library in Python. From line plot animations to 3D visualizations, we cover it all. Start animating your data today!
A detailed guide on how to plot multiple lines in a single chart using the versatile Python library, Matplotlib.
In-depth guide on how to fix the common error 'module 'matplotlib' has no attribute 'plot' in Python's Matplotlib library.
Learn how to create rich, interactive plots in Python using Matplotlib. This detailed guide provides you with hands-on examples to help you master interactive plotting.
In-depth guide to resolve the issue: AttributeError in matplotlib.cbook, specifically for 'iterable'. Optimizing your Python coding with NetworkX and Matplotlib.
A comprehensive guide to removing axes in Matplotlib, packed with examples, to give you a better understanding of how to declutter your visualizations.
Discover how to create multiple plots on the same figure in Matplotlib, enhancing data visualization and plot readability.
Learn how to use PyPlot Figure from Matplotlib's powerful plotting library for creating stunning visualizations in Python. Find step-by-step tutorials and resources to master PyPlot Figure. Download our free guide now.
Learn how to create a basic time series plot with Matplotlib in Python. Customize tick markers and labels, work with dates on the horizontal axis, and add minor tick marks for a more detailed view of your data.
Your complete guide to solving the 'module matplotlib has no attribute plot' error in Python, covering both installation and syntax issues with detailed examples.
Explore detailed solutions to the error 'matplotlib is currently using agg, a non-GUI backend,' ensuring smooth data visualization in Python.
Unleash the power of Matplotlib and PyGWalker for data visualization in Python. Create eye-catching plots, customize, and save them with ease.
Learn what %matplotlib inline does, when to use it, and how it works in Jupyter Notebooks. A beginner-friendly guide to Matplotlib's inline backend.