Line Styles and Colors

Matplotlib provides extensive control over line appearance through style and color parameters.

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Line Style (linestyle / ls)

Common line styles:

Style	Abbreviation	Description
'-'	solid	Solid line (default)
'--'	dashed	Dashed line
':'	dotted	Dotted line
'-.'	dashdot	Dash-dot pattern

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(15, 3))
ax.plot(x, y, linestyle='--')
plt.show()

Short form:

ax.plot(x, y, ls='--')

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Line Width (linewidth / lw)

Control line thickness:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(15, 3))
ax.plot(x, y, linestyle='--', linewidth=10)
plt.show()

Short form:

ax.plot(x, y, ls='--', lw=10)

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Color (color / c)

Specify colors in multiple ways:

Named colors:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y_sin = np.sin(x)
y_cos = np.cos(x)

fig, ax = plt.subplots(figsize=(15, 3))
ax.plot(x, y_sin, color='red')
ax.plot(x, y_cos, color='blue')
plt.show()

Single-letter codes:

ax.plot(x, y_sin, c='r')  # red
ax.plot(x, y_cos, c='b')  # blue

Code	Color
'b'	Blue
'g'	Green
'r'	Red
'c'	Cyan
'm'	Magenta
'y'	Yellow
'k'	Black
'w'	White

Hex codes:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y_sin = np.sin(x)
y_cos = np.cos(x)

fig, ax = plt.subplots(figsize=(15, 3))
ax.plot(x, y_sin, c='#e32b2b')  # Custom red
ax.plot(x, y_cos, c='#3b81f1')  # Custom blue
plt.show()

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Alpha (Transparency)

Control opacity with alpha (0 = transparent, 1 = opaque):

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y_sin = np.sin(x)
y_cos = np.cos(x)

fig, ax = plt.subplots(figsize=(15, 4))
ax.plot(x, y_sin, alpha=0.8)
ax.plot(x, y_cos, alpha=0.2)
plt.show()

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MATLAB-Style Format Strings

Combine style, color, and marker in a single string:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 10)
y = np.sin(x)

plt.plot(x, y, '--*r', ms=20)  # dashed, star markers, red
plt.show()

Format: '[marker][line][color]' or '[line][marker][color]'

Examples:

• '--*r': dashed line, star markers, red
• '-ob': solid line, circle markers, blue
• ':sg': dotted line, square markers, green

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Using Keyword Dictionaries

Pass multiple style options via dictionary:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(15, 3))
ax.plot(x, y, **{'ls': '--', 'lw': 10})
plt.show()

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Complete Example

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y_sin = np.sin(x)
y_cos = np.cos(x)

fig, ax = plt.subplots(figsize=(12, 4))

ax.plot(x, y_sin, 
        linestyle='--', 
        linewidth=2, 
        color='#e74c3c', 
        alpha=0.8,
        label='sin(x)')

ax.plot(x, y_cos, 
        ls=':', 
        lw=3, 
        c='#3498db', 
        alpha=0.8,
        label='cos(x)')

ax.legend()
ax.set_title('Trigonometric Functions')
plt.show()

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Key Takeaways

• Use linestyle or ls for line pattern
• Use linewidth or lw for thickness
• Use color or c for color
• Colors can be names, single letters, or hex codes
• alpha controls transparency (0-1)
• Format strings combine options: '--or'