Matplotlib - Styling

Basic Setup

import numpy as np
import matplotlib.pyplot as plt
 
# If you're running this in a standard .py file (outside Jupyter),
# remember to use plt.show() at the end to display the plot window.

We typically import Matplotlib’s pyplot module under the alias plt. NumPy (np) is used for numerical operations (such as creating arrays of data for plotting).

Below, we define a simple dataset:

x = np.arange(0,10)
y = 2 * x

Legends

To label your plots, you can use label="Some Label" when calling plot() and then invoke ax.legend() to automatically create a legend. For example:

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
 
ax.plot(x, x**2, label="x**2")
ax.plot(x, x**3, label="x**3")
ax.legend()  # Generate the legend
plt.show()

Controlling Legend Location

ax.legend(loc=0) lets Matplotlib choose the “best” location.
Common integer codes for loc:
- loc=1: upper right corner
- loc=2: upper left corner
- loc=3: lower left corner
- loc=4: lower right corner

You can also specify a custom location, for example loc=(1.1, 0.5).

Setting Colors, Line Widths, and Line Types

Matplotlib provides many parameters for customizing your plots:

1. Quick “MATLAB-like” Syntax

fig, ax = plt.subplots()
ax.plot(x, x**2, 'b.-')  # Blue line with dots
ax.plot(x, x**3, 'g--')  # Green dashed line
plt.show()

'b' stands for blue, 'g' for green, etc.
'.-' indicates dotted lines, '--' indicates dashed lines, etc.

2. Using Keyword Arguments (Recommended)

This approach is more explicit and easier to read.

Colors by Name or Hex

fig, ax = plt.subplots()
 
ax.plot(x, x+1, color="blue", alpha=0.5)   # Semi-transparent
ax.plot(x, x+2, color="#8B008B")          # Hex code for dark magenta
ax.plot(x, x+3, color="#FF8C00")          # Hex code for dark orange
 
plt.show()

color accepts named colors or hex codes.
alpha controls transparency (0.0 is fully transparent, 1.0 is fully opaque).

Line Widths

fig, ax = plt.subplots(figsize=(12,6))
 
ax.plot(x, x-1, color="red", linewidth=0.25)
ax.plot(x, x-2, color="red", lw=0.50)
ax.plot(x, x-3, color="red", lw=1)
ax.plot(x, x-4, color="red", lw=10)
 
plt.show()

Use linewidth or its shorthand lw to set line thickness.

Line Styles

fig, ax = plt.subplots(figsize=(12,6))
 
ax.plot(x, x-1, color="green", lw=3, linestyle='-')   # Solid
ax.plot(x, x-2, color="green", lw=3, ls='-.')         # Dash-dot
ax.plot(x, x-3, color="green", lw=3, ls=':')          # Dotted
ax.plot(x, x-4, color="green", lw=3, ls='--')         # Dashed
 
plt.show()

Typical values for linestyle or ls:

'-': Solid
'-.': Dash-dot
':': Dotted
'--': Dashed

Custom Dash Patterns

You can specify dash patterns to define the exact sequence of dashes and spaces:

fig, ax = plt.subplots(figsize=(12,6))
 
lines = ax.plot(x, x+8, color="black", lw=5)
lines[0].set_dashes([10, 10])  # 10 points on, 10 points off
 
plt.show()

The format is [on_1, off_1, on_2, off_2, ...].

Markers

Matplotlib automatically connects data points with lines. To add markers at each data point:

fig, ax = plt.subplots(figsize=(12,6))
 
ax.plot(x, x-1, marker='+', markersize=20)
ax.plot(x, x-2, marker='o', ms=20)            # using ms for marker size
ax.plot(x, x-3, marker='s', ms=20, lw=0)      # no line, just squares
ax.plot(x, x-4, marker='1', ms=20)
 
plt.show()

Customizing Marker Appearance

fig, ax = plt.subplots(figsize=(12,6))
 
ax.plot(x, x, color="black", lw=1, ls='-', 
        marker='s', markersize=20, 
        markerfacecolor="red", 
        markeredgewidth=8, 
        markeredgecolor="blue")
 
plt.show()

markerfacecolor: The inner color of the marker.
markeredgecolor and markeredgewidth: Outline color and thickness.
markersize (ms): Marker size.

🪴LYC