Introduction to Matplotlib

📌 What is Matplotlib?

Matplotlib is the most popular data visualization library in Python. It was created by John D. Hunter in 2003 and is now maintained by a large community. It produces publication-quality figures in a variety of formats.

🚀 Why Matplotlib?

• Versatile: Line plots, bar charts, scatter plots, histograms, pie charts, and more.
• Customizable: Full control over every element — colors, labels, fonts, axes, legends.
• Foundation: Seaborn, Pandas plotting, and other libraries are built on top of Matplotlib.
• Export: Save charts as PNG, JPG, SVG, PDF — perfect for reports and papers.

🛠️ Setup

# Install (if not already installed)
# pip install matplotlib

import matplotlib.pyplot as plt
import numpy as np

print("Matplotlib ready! ✅")

Convention: We always import matplotlib.pyplot as plt. This shorthand is the universal standard.

Plotting Basics

📌 The plot() Function

The plot() function draws points (markers) in a diagram. By default, it connects them with a line. It takes two arrays: x-points and y-points.

import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 6])
ypoints = np.array([0, 250])

plt.plot(xpoints, ypoints)
plt.show()

📌 Plotting Without X-Points

If you only supply one array, Matplotlib uses the index (0, 1, 2, 3...) as the x-axis.

ypoints = np.array([3, 8, 1, 10, 5, 7])
plt.plot(ypoints)
plt.show()

📌 Multiple Lines

Call plt.plot() multiple times before plt.show() to draw multiple lines on the same figure.

x = np.array([0, 1, 2, 3])
y1 = np.array([3, 8, 1, 10])
y2 = np.array([6, 2, 7, 11])

plt.plot(x, y1)
plt.plot(x, y2)
plt.show()

Markers & Lines

📌 Markers

Use the marker keyword to emphasize each data point with a symbol.

ypoints = np.array([3, 8, 1, 10])
plt.plot(ypoints, marker = 'o')     # Circle marker
plt.show()

📌 Common Marker Characters

• 'o' — Circle
• '*' — Star
• '.' — Point
• ',' — Pixel
• 'x' — X mark
• '+' — Plus
• 's' — Square
• 'D' — Diamond
• '^' — Triangle up

📌 Format Strings (fmt)

Shortcut syntax: 'marker|line|color'. Example: 'o:r' = circle markers, dotted line, red color.

plt.plot(ypoints, 'o:r')  # Circle, dotted, red
plt.show()

📌 Line Styles

Use linestyle (or ls) to change the line style.

• 'solid' or '-' — Default solid line
• 'dotted' or ':' — Dotted
• 'dashed' or '--' — Dashed
• 'dashdot' or '-.' — Dash-dot

plt.plot(ypoints, linestyle = 'dashed', color = 'hotpink', linewidth = 3)
plt.show()

📌 Line Color

Use color (or c). Accepts named colors ('red'), hex ('#FF5733'), or short form ('r', 'g', 'b').

Labels & Title

📌 Adding Context

Always label your axes and add a title! It makes your graphs readable and professional.

x = np.array([80, 85, 90, 95, 100, 105, 110])
y = np.array([240, 250, 260, 270, 280, 290, 300])

plt.plot(x, y)

plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")
plt.title("Sports Watch Data")

plt.show()

📌 Custom Font Properties

Use a fontdict dictionary to customize fonts.

font_title = {'family':'serif', 'color':'blue', 'size':20}
font_label = {'family':'serif', 'color':'darkred', 'size':15}

plt.title("Sports Watch Data", fontdict = font_title)
plt.xlabel("Average Pulse", fontdict = font_label)
plt.ylabel("Calorie Burnage", fontdict = font_label)

📌 Title Positioning

Use the loc parameter: 'left', 'right', or 'center' (default).

plt.title("My Title", loc = 'left')

Grid

📌 Adding Grid Lines

Use plt.grid() to add grid lines. You can control which axis gets lines.

x = np.array([80, 85, 90, 95, 100])
y = np.array([240, 250, 260, 270, 280])

plt.plot(x, y)
plt.title("Sports Watch Data")
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.grid()  # Add grid
plt.show()

📌 Specify Axis

• plt.grid(axis = 'x') — Only vertical grid lines
• plt.grid(axis = 'y') — Only horizontal grid lines
• plt.grid(axis = 'both') — Both (default)

📌 Grid Line Properties

plt.grid(color = 'green', linestyle = '--', linewidth = 0.5)

Subplots

📌 Display Multiple Plots

The subplot() function takes three arguments: rows, columns, and index (position).

import matplotlib.pyplot as plt
import numpy as np

# Plot 1
x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])

plt.subplot(1, 2, 1)  # 1 row, 2 cols, position 1
plt.plot(x, y)
plt.title("Plot 1")

# Plot 2
x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])

plt.subplot(1, 2, 2)  # 1 row, 2 cols, position 2
plt.plot(x, y)
plt.title("Plot 2")

plt.suptitle("My Subplots")
plt.show()

📌 Super Title

Use plt.suptitle() to add a title for the entire figure (above all subplots).

📌 2x2 Grid Layout

plt.subplot(2, 2, 1)  # Top-left
plt.subplot(2, 2, 2)  # Top-right
plt.subplot(2, 2, 3)  # Bottom-left
plt.subplot(2, 2, 4)  # Bottom-right

Scatter Plots

📌 Compare Two Variables

Scatter plots are used to observe relationships between two numeric variables. Each dot represents one observation.

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78])

plt.scatter(x, y)
plt.xlabel("Car Age")
plt.ylabel("Speed")
plt.title("Car Age vs Speed")
plt.show()

📌 Color, Size & Transparency

Customize with color, s (size), and alpha (transparency).

colors = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
sizes = np.array([20, 50, 100, 200, 500, 1000, 60, 90, 10, 300])

plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='viridis')
plt.colorbar()  # Shows the color scale
plt.show()

📌 Colormaps

Popular colormaps: 'viridis', 'plasma', 'inferno', 'coolwarm', 'RdYlGn'.

Bar Charts

📌 Vertical Bars

Use plt.bar() to draw vertical bar charts. Great for comparing categorical data.

x = np.array(["Apples", "Bananas", "Cherries", "Dates"])
y = np.array([400, 350, 500, 200])

plt.bar(x, y, color = "#4CAF50", width = 0.5)
plt.title("Fruit Sales")
plt.show()

📌 Horizontal Bars

Use plt.barh() for horizontal bars. Use height instead of width.

plt.barh(x, y, color = "hotpink")
plt.show()

📌 Bar Color & Width

• color — Set bar color (name, hex, or short form)
• width — Width of vertical bars (default 0.8)
• height — Height of horizontal bars (default 0.8)

Histograms

📌 Frequency Distribution

A histogram shows frequency of data within specific ranges (bins). It's ideal for understanding the distribution of continuous data.

x = np.random.normal(170, 10, 250)  # 250 values, mean=170, std=10

plt.hist(x, bins=20, color='skyblue', edgecolor='black')
plt.title("Height Distribution")
plt.xlabel("Height (cm)")
plt.ylabel("Frequency")
plt.show()

📌 Key Parameters

• bins — Number of bars/ranges (default: 10)
• color — Fill color
• edgecolor — Border color of each bar
• alpha — Transparency (0-1)

Tip: More bins = more detail. Fewer bins = smoother shape. The "right" number depends on your data size.

Pie Charts

📌 Show Proportions

Pie charts are used to display data as proportions of a whole. Each slice represents a category's share.

y = np.array([35, 25, 25, 15])
mylabels = ["Apples", "Bananas", "Cherries", "Dates"]
mycolors = ["#ff9999", "#ffcc99", "#99ff99", "#66b3ff"]

plt.pie(y, labels=mylabels, colors=mycolors,
        autopct='%1.1f%%', startangle=90)

plt.title("Fruit Popularity")
plt.show()

📌 Key Parameters

• labels — List of labels for each slice
• colors — List of colors for each slice
• autopct — Display percentage on each slice
• startangle — Rotate the start (default: 0)
• explode — Pull out a slice for emphasis
• shadow — Add a shadow (True/False)

📌 Explode a Slice

myexplode = [0.2, 0, 0, 0]  # Pull out Apples
plt.pie(y, labels=mylabels, explode=myexplode, shadow=True)

📌 Legend

plt.legend(title = "Fruits:")

🚀 Real World Projects

🟢 Beginner: Monthly Sales Bar Chart

Goal: Create a bar chart showing monthly sales for a store. Use different colors per bar and add labels.

🟡 Intermediate: Stock Price Line Graph

Goal: Plot daily stock prices over a month. Add grid lines, markers, a title, and axis labels. Overlay a moving average line.

🔴 Advanced: Multi-Metric Dashboard

Goal: Create a 2x2 subplot layout showing 4 different metrics:

1. Line chart — Revenue trend
2. Bar chart — Sales by category
3. Scatter plot — Price vs Quantity
4. Pie chart — Market share

🎯 Matplotlib Mini Task

Goal: Visualize Company Growth.

📋 Requirements:

1. Create two arrays: Year (2020-2025) and Profit (use your own numbers).
2. Plot a line graph with circle markers.
3. Add a Title "Company Profit 2020-2025".
4. Label both axes.
5. Show the grid.
6. Bonus: Add a second line for "Expenses" on the same plot.

Paint with data! 🎨