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Line Plots

Visualize trends over time or ordered sequences

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Anatomy of a matplotlib Figure0%

Explanation

Line plots connect data points in order — ideal for time series and continuous data.

python

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots() x = [2019, 2020, 2021, 2022, 2023] y = [100, 115, 108, 130, 145]

ax.plot(x, y, color='blue', linewidth=2, marker='o', markersize=6, label='Revenue') ax.set_title('Annual Revenue') ax.set_xlabel('Year') ax.set_ylabel('Revenue ($K)') ax.legend() plt.show() ```

Key parameters:

color — line color ('red', '#FF5500', 'C0')
linewidth / lw — line thickness
linestyle / ls — '--' dashed, ':' dotted, '-.' dash-dot
marker — point markers: 'o' circle, 's' square, '^' triangle
label — for the legend (call ax.legend() to show it)

Multiple lines:

python ax.plot(x, y1, label='Product A') ax.plot(x, y2, label='Product B') ax.legend()

Examples

Comparing two trends

Each call to ax.plot() adds a line to the same axes

import matplotlib.pyplot as plt

years = [2019,2020,2021,2022,2023]
sales_a = [100,115,108,130,145]
sales_b = [80,95,120,115,135]

fig, ax = plt.subplots(figsize=(8,4))
ax.plot(years, sales_a, marker='o', label='Product A')
ax.plot(years, sales_b, marker='s', linestyle='--', label='Product B')
ax.set_title('Sales Comparison')
ax.legend()
plt.tight_layout()
plt.show()

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