Skip to content

matplotlib.md

Latest commit

 

History

History
100 lines (76 loc) · 3.24 KB

matplotlib.md

File metadata and controls

100 lines (76 loc) · 3.24 KB
Raw

Matplotlib — Quick Reference

Matplotlib is a comprehensive Python 2D plotting library that produces publication-quality figures in a variety of formats and interactive environments (scripts, Jupyter notebooks, web application servers, and graphical user interfaces).

Key Concepts

  • Figure: the overall window or page that contains one or more plots.
  • Axes: a single plot area inside a Figure; contains the data, ticks, labels, and legend.
  • Artist: everything you see on the figure (lines, text, patches). Most high-level plotting functions create Artists.
  • pyplot: a stateful, MATLAB-like interface (matplotlib.pyplot) for quick plotting.
  • Object-oriented (OO) API: recommended for complex figures — you explicitly create Figure and Axes objects and call methods on them.

Installation

Install via pip (recommended):

python -m pip install matplotlib

If you use conda:

conda install matplotlib

Simple Examples

Using pyplot (quick):

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

plt.plot(x, y, label='Line')
plt.scatter(x, y, color='red')
plt.title('Simple plot')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.grid(True)
plt.show()

Using the object-oriented API (recommended):

from matplotlib.figure import Figure
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(6,4))
ax.plot(x, y, '-o', label='Series')
ax.set_title('OO API example')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend()
fig.tight_layout()
fig.savefig('figure.png', dpi=300)

Common Plot Types

  • Line plots: plot()
  • Scatter plots: scatter()
  • Bar charts: bar() / barh()
  • Histograms: hist()
  • Boxplots: boxplot()
  • Heatmaps / images: imshow()
  • Pie charts: pie()

Customization and Styles

  • Use rcParams or matplotlib.style.use() to change default styles (e.g., plt.style.use('seaborn')).
  • Title, labels, ticks, and legends are set via Axes methods: set_title, set_xlabel, set_xticks, legend().
  • Annotate with ax.annotate() and add text with ax.text().

Integration

  • Pandas: DataFrame/Series have .plot() which uses Matplotlib under the hood.
  • Seaborn: higher-level statistical plotting built on Matplotlib; use for nicer defaults and statistical plots.
  • Interactive environments: works well in Jupyter with %matplotlib inline or %matplotlib notebook.

Saving Figures

  • fig.savefig('path.png', dpi=300, bbox_inches='tight') supports PNG, PDF, SVG, EPS.

Performance & Tips

  • For many points, prefer scatter() with small markers or use downsampling.
  • Reuse artists (update data) when animating rather than re-creating them for performance.
  • Use constrained_layout or tight_layout() to avoid overlapping labels.

Common Pitfalls

  • Mixing pyplot stateful calls and OO API can lead to confusion; prefer one style per script.
  • Always call plt.close(fig) in scripts that generate many figures to free memory.

Where to Learn More

This file gives a compact overview and examples to get started. For deep dives, consult the official documentation and gallery.