Matplotlib Get Started
Set up your plotting environment, create your first figure, and learn the anatomy of a Matplotlib chart.
Import Conventions
Import matplotlib.pyplot as plt. When working in notebooks, enable inline displays with %matplotlib inline.
import matplotlib.pyplot as plt
import numpy as npCreate a Basic Figure
Use plt.subplots() to generate a figure and a single axes object.
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(x, y)
ax.set_title("Sine Wave")
ax.set_xlabel("Radians")
ax.set_ylabel("Amplitude")
plt.show()The figsize argument controls figure dimensions in inches.
Figure Anatomy
A figure contains one or more axes, titles, legends, ticks, and spines. Manipulate each via methods on the axes object. For example, adjust tick labels using ax.set_xticklabels().
Saving Figures
Persist figures with fig.savefig(). Provide resolution via the dpi parameter and choose formats like PNG, SVG, or PDF.
fig.savefig("sine-wave.png", dpi=150, bbox_inches="tight")Use vector formats (SVG/PDF) for publications requiring crisp scaling.
Managing Styles
Matplotlib ships with predefined styles. Load them with plt.style.use() to enforce consistent branding.
plt.style.use("seaborn-v0_8-darkgrid")Create custom style sheets and distribute them through your internal packages.
Troubleshooting Backends
If plt.show() displays nothing, verify your backend. Explicitly set a backend for headless servers:
import matplotlib
matplotlib.use("Agg")For cross-team consistency, document backend expectations via info.studygrid@gmail.com.
Next Steps
Explore Pyplot in depth to understand stateful plotting shortcuts and when to prefer object-oriented APIs.
Install and Import
pip install matplotlib
import matplotlib.pyplot as plt # the standard alias
print(plt.matplotlib.__version__)In Jupyter notebooks, charts appear inline automatically. In scripts, you must call plt.show() to display a window.
Try It Yourself
Exercise: Draw a straight line from (0,0) to (6,250).
Show solution
import matplotlib.pyplot as plt
import numpy as np
plt.plot(np.array([0, 6]), np.array([0, 250]))
plt.show()Key Takeaways
- Install with pip; import as
plt. - Call
plt.show()in scripts to render.
📘 Real-World Deep Dive
Knowing <strong>Matplotlib Get Started (Matplotlib)</strong> well is what turns Matplotlib from a curiosity into a daily tool — you'll reach for it in nearly every real project.
Real-Life Scenario
An end-to-end usage of Matplotlib Get Started that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
# Realistic Matplotlib snippet for Matplotlib Get Started
# Replace with data from your own project.
print("Hello from Matplotlib Get Started")Expected Output
(see source)Common mistakes
- Calling
plt.plotwithoutplt.show()in scripts (orplt.savefig) produces no visible output. - Passing mismatched array shapes to
plt.plot(x, y)raises a uselessValueError— verify shapes withx.shapeandy.shapefirst. - Mixing
numpy.float64withpandas.Seriesis OK, but mixingnumpy.datetime64with Pythondatetime.dateneeds an explicit cast. - Treating Matplotlib Get Started as a black box without reading the docs — the API has subtle defaults that bite when you scale.
🚀 Performance & Best Practices
- For large scatter plots use
plt.scatter(..., s=1, rasterised=True)or switch todatashader. - Use the
aggbackend on web services:import matplotlib; matplotlib.use("Agg"). - Plot once and reuse the figure: avoid embedding Matplotlib into tight Python loops without an explicit
clear(). - When working with Matplotlib, prefer vectorised / batched operations over Python loops.
🧪 Try It Yourself
- Reproduce the snippet on a representative slice of your own data.
- Profile the snippet with
cProfileortimeitand find the single biggest improvement. - Generalise the snippet into a small, reusable function you can drop into future projects.