Matplotlib Basics for Plotting and Visualizing Data
Summary
This tutorial introduces Matplotlib as a Python library for visualizing data and walks through common chart types and layout tools. Examples show how to plot one or more series, customize line colors and markers, create scatter plots with variable point size and color, and display histograms and bar charts. It also demonstrates filled contour plots and three dimensional surfaces.
The later sections cover subplot grids, mixed chart layouts, and adding titles, axis labels, and legends. The examples use NumPy arrays and generated data, so they teach plotting mechanics rather than financial analysis. The document provides no trading strategy, market data, performance evaluation, or guidance on whether a visualization is statistically appropriate for a particular research question.
Key ideas
- Matplotlib works with NumPy arrays to create common data visualizations.
- Line styles, markers, colors, labels, and legends help distinguish and explain plotted series.
- Scatter plots can encode additional values through point size and color.
- Histograms, bar charts, contour plots, and three dimensional surfaces support different views of data.
- Subplots arrange multiple charts in a shared figure.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.