- Executors
-
Learn
Learn Python Runnable examples, NumPy, pandas, and more Learn AINeural networks, ML and LLMs from scratch Learn C & C++ Pointers, memory, STL, and modern C++ Learn C#C# basics, OOP, LINQ, async, and .NET exercises Learn MatplotlibLine, bar, scatter and pie charts with live visualizers Python Algorithms Search, sorting, recursion, and visualizers O(n) C & C++ Algorithms Compare algorithms implemented in C and C++
- ChallengesNew
- For TeachersNew
Intermediate Matplotlib
Control axes and ticks, annotate what matters, show spread and uncertainty, stack data, plot pandas DataFrames and save figures for reports.
Start the path →- Lesson Axis Limits, Ticks, and Log Scale Set axis limits, choose tick positions and labels, and switch to a log scale with set_yscale.
- Lesson Annotations, Text, and Reference Lines Point at important values with ax.annotate, add notes with ax.text and mark targets with axhline.
- Lesson Box Plots and Violin Plots Compare the spread of several groups with ax.boxplot and ax.violinplot.
- Lesson Stacked Bars and Area Charts Show how parts add up over time with stacked bars (bottom=) and ax.stackplot.
- Lesson Error Bars and Uncertainty Show measurement uncertainty with ax.errorbar and a shaded band with fill_between.
- Lesson Plotting pandas DataFrames Turn a pandas DataFrame into a chart with df.plot and customize it with Matplotlib.
- Lesson Saving Figures: savefig, DPI, and Size Save charts as PNG or SVG with fig.savefig and control their size, resolution and margins.