Line Graphs & Trends in Matplotlib — Baby Steps

Keyword: matplotlib line plot • Difficulty: Beginner • Author: Champak Roy

Line graphs are the go-to chart for showing change over time and trends. In this lesson you'll learn simple line plots, multiple series, markers & styles, smoothing / moving averages (trend lines), and how to annotate important points. Try examples live below using Python (Pyodide)!

💡 Step-by-Step Tutorial

1) Import Matplotlib

import matplotlib.pyplot as plt

2) Prepare your data

months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug']
sales = [120, 130, 115, 150, 170, 165, 180, 190]

3) Draw your first line chart

plt.plot(months, sales)
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

4) Multiple series & styles

plt.plot(months, sales, marker='o', linestyle='-', label='This Year')
plt.plot(months, [100,110,120,130,140,150,160,170], marker='s', linestyle='--', label='Last Year')
plt.legend()

5) Markers, colors and grids

plt.plot(months, sales, color='#2b9bd3', marker='o', linewidth=2)
plt.grid(alpha=0.3)

6) Trend line — moving average (simple)

import numpy as np
window = 3
movavg = np.convolve(sales, np.ones(window)/window, mode='valid')
# plot movavg aligned to the middle of the window
plt.plot(months[window-1:], movavg, color='orange', label='3-month MA')

7) Annotate points

plt.annotate('Peak', xy=('Aug', 190), xytext=('Jun',200),
             arrowprops=dict(arrowstyle='->'))

💬 Tip: In the Pyodide practice section below, use matplotlib.use('Agg') and save your figure with plt.savefig() to display it in-browser.

📊 Sample static output: sample line chart

🧪 Practice: Run Matplotlib with Pyodide

Click Preload Pyodide once to install matplotlib and numpy. Then press Run & Render to display your chart as an image!

# Example: Line plot with two series and a 3-month moving average
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import io, base64

months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug']
sales = np.array([120,130,115,150,170,165,180,190])
last_year = np.array([100,110,120,130,140,150,160,170])

plt.figure(figsize=(7,3.5))
plt.plot(months, sales, marker='o', linestyle='-', linewidth=2, color='#2b9bd3', label='This Year')
plt.plot(months, last_year, marker='s', linestyle='--', linewidth=1.6, color='#7fbbe0', label='Last Year')

# moving average (3-month simple)
window = 3
movavg = np.convolve(sales, np.ones(window)/window, mode='valid')
# align x positions for movavg (place it starting at month index window-1)
x_mov = months[window-1:]
plt.plot(x_mov, movavg, color='#f39c12', linewidth=2, label='3-month MA')

# annotate peak
max_idx = int(np.argmax(sales))
plt.annotate('Peak', xy=(months[max_idx], sales[max_idx]), xytext=(months[max_idx-2], sales[max_idx]+15),
             arrowprops=dict(arrowstyle='->', color='gray'))

plt.title('Monthly Sales & Trend')
plt.xlabel('Month'); plt.ylabel('Sales')
plt.legend()
plt.grid(alpha=0.25)

buf = io.BytesIO()
plt.tight_layout()
plt.savefig(buf, format='png')
buf.seek(0)
base64.b64encode(buf.read()).decode('ascii')

Idle

Console output:

Rendered Chart

No chart yet — run code to render a PNG here.

🧠 Tips & Notes

Use plt.plot() for continuous data — ideal for time series and trends.
Plot multiple series to compare (this year vs last year) — use label and plt.legend().
Use moving averages or smoothing to emphasise trend over noise.
Annotate important points with plt.annotate() to call out peaks or events.
If Pyodide fails to load: refresh, try Incognito, or follow the site instructions to preload Pyodide in the theme.