Data Visualization with Matplotlib

Numbers are harder to interpret than graphs. Matplotlib is the foundation of almost all data visualization libraries in Python, allowing you to plot line charts, bar graphs, histograms, and scatter plots.

---

19.3.1 Installing Matplotlib

Open your terminal and install Matplotlib:

pip install matplotlib

---

19.3.2 Plotting a Basic Chart

Let us plot the temperature changes over a week:

import matplotlib.pyplot as plt

# 1. Define Data
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
temperatures = [22, 24, 21, 25, 27, 28, 26]

# 2. Create the Plot
# marker='o' adds circular dots at the data coordinates
plt.plot(days, temperatures, marker='o', color='purple', linestyle='-', label='Temp')

# 3. Add Labels, Legend, and Title
plt.xlabel("Days of the Week")
plt.ylabel("Temperature (°C)")
plt.title("Weekly Temperature Trend")
plt.legend() # Displays the 'Temp' label block

# 4. Save and Show
# Always save the chart before calling plt.show()
plt.savefig("temp_chart.png")
plt.show()

---

19.3.3 Customizing Your Charts

Professional reports require clean layouts. Here are some options you can configure:

• Grid: Add grid lines for background coordinate reference.

  plt.grid(True, alpha=0.3) # alpha adjusts transparency (0 to 1)

• Rotation: Rotate X-axis coordinate labels if they are long.

  plt.xticks(rotation=45)

• Multiple Lines: Plot multiple lines on the same canvas.

  plt.plot(days, min_temps, color='blue', label='Min')
  plt.plot(days, max_temps, color='red', label='Max')