Python Matplotlib - Real-Time Examples

What is Matplotlib?

Matplotlib is one of the most widely used Python libraries for creating data visualizations. It allows you to represent numerical data using charts, graphs, plots, and figures.

📊 Data Visualization

Convert numerical data into easy-to-understand visual charts and graphs.

📈 Business Analysis

Analyze sales, revenue, expenses, customers, performance, and trends.

📉 Trend Analysis

Identify increases, decreases, patterns, and changes over time.

📋 Reporting

Create visual reports that make business data easier to understand.

1. Installing Matplotlib

Matplotlib can be installed using pip.

Installation

pip install matplotlib

Result: Matplotlib will be installed in your Python environment.

2. Importing Matplotlib

The most commonly used module is matplotlib.pyplot.

Python Code

import matplotlib.pyplot as plt

The alias plt is the standard convention used when working with Matplotlib.

3. Matplotlib Visualization Flow

Input Data

→

Matplotlib

→

Chart / Graph

→

Business Insight

The basic idea is to provide data to Matplotlib, select an appropriate chart type, customize the chart, and display or save the visualization.

4. Basic Line Plot

A line chart is useful for showing changes over time.

Line Plot

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr", "May"]
sales = [120, 150, 180, 170, 220]

plt.plot(months, sales)

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.show()

Output:

5. Adding Markers

Markers make individual data points easier to identify.

Markers

import matplotlib.pyplot as plt


days = ["Mon", "Tue", "Wed", "Thu", "Fri"]
orders = [25, 35, 30, 45, 50]

plt.plot(
days,
orders,
marker="o"
)

plt.title("Daily Orders")
plt.xlabel("Day")
plt.ylabel("Orders")

plt.show()

Output:

6. Line Style and Width

You can customize a line using different styles, widths, and markers.

Line Customization

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr"]
revenue = [50000, 62000, 58000, 75000]

plt.plot(
months,
revenue,
marker="o",
linestyle="--",
linewidth=2
)

plt.title("Monthly Revenue")
plt.xlabel("Month")
plt.ylabel("Revenue")

plt.show()

Output:

7. Bar Chart

Bar charts are useful for comparing categories.

Bar Chart

import matplotlib.pyplot as plt


products = ["Laptop", "Phone", "Tablet", "Monitor"]
sales = [120, 250, 100, 180]

plt.bar(products, sales)

plt.title("Product Sales")
plt.xlabel("Product")
plt.ylabel("Units Sold")

plt.show()

Output:

8. Horizontal Bar Chart

A horizontal bar chart is useful when category names are long.

Horizontal Bar Chart

import matplotlib.pyplot as plt


departments = [
"Information Technology",
"Human Resources",
"Finance",
"Marketing"
]

employees = [45, 20, 25, 30]

plt.barh(departments, employees)

plt.title("Employees by Department")
plt.xlabel("Number of Employees")

plt.show()

Output:

9. Pie Chart

Pie charts can be used to show proportions of a whole.

Pie Chart

import matplotlib.pyplot as plt


categories = [
"Electronics",
"Clothing",
"Groceries",
"Books"
]

sales = [40, 25, 20, 15]

plt.pie(
sales,
labels=categories,
autopct="%1.1f%%"
)

plt.title("Sales Distribution")

plt.show()

Output:

10. Scatter Plot

Scatter plots help identify relationships between two variables.

Scatter Plot

import matplotlib.pyplot as plt


advertising = [10, 20, 30, 40, 50]
sales = [25, 40, 48, 65, 80]

plt.scatter(advertising, sales)

plt.title("Advertising vs Sales")
plt.xlabel("Advertising Spend")
plt.ylabel("Sales")

plt.show()

Output:

Business Use: A company can use a scatter plot to examine whether advertising spending is associated with sales.

11. Histogram

Histograms show the distribution of numerical data.

Histogram

import matplotlib.pyplot as plt


ages = [
22, 25, 28, 30, 31,
35, 36, 38, 40, 42,
45, 47, 50, 52, 55
]

plt.hist(ages, bins=5)

plt.title("Employee Age Distribution")
plt.xlabel("Age")
plt.ylabel("Number of Employees")

plt.show()

Output:

12. Adding Grid Lines

Grid lines make it easier to read and compare values on a chart.

Grid

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr", "May"]
sales = [100, 130, 125, 170, 200]

plt.plot(months, sales, marker="o")

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.grid(True)

plt.show()

Output:

13. Adding Labels to Data Points

The annotate() function can be used to display information near individual points.

Annotations

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr"]
sales = [100, 150, 130, 190]

plt.plot(months, sales, marker="o")

plt.annotate(
"Highest Sales",
xy=("Apr", 190),
xytext=("Mar", 175),
arrowprops={"arrowstyle": "->"}
)

plt.title("Monthly Sales")
plt.show()

Output:

14. Adding Legends

Legends help identify multiple datasets displayed on the same chart.

Legend

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr"]

online_sales = [100, 130, 150, 180]
store_sales = [80, 110, 125, 140]

plt.plot(
months,
online_sales,
marker="o",
label="Online"
)

plt.plot(
months,
store_sales,
marker="o",
label="Store"
)

plt.title("Online vs Store Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.legend()

plt.show()

Output:

15. Multiple Charts with Subplots

Subplots allow multiple charts to be displayed in one figure.

Subplots

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr"]
sales = [100, 130, 150, 180]
expenses = [70, 80, 95, 110]

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].plot(months, sales, marker="o")
axes[0].set_title("Sales")

axes[1].plot(months, expenses, marker="o")
axes[1].set_title("Expenses")

plt.tight_layout()
plt.show()

Output:

16. Figure Size

Use figsize to control the size of a chart.

Figure Size

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr"]
sales = [100, 150, 180, 210]

plt.figure(figsize=(10, 5))

plt.plot(months, sales, marker="o")

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.show()

Output:

17. Saving a Chart as an Image

Charts can be saved as PNG, JPG, PDF, SVG, and other formats.

Save Figure

import matplotlib.pyplot as plt


months = ["Jan", "Feb", "Mar", "Apr"]
sales = [100, 150, 180, 210]

plt.plot(months, sales, marker="o")

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.savefig("monthly_sales.png")

plt.show()

Output:

            ✓ Chart saved as: monthly_sales.png
        

Note: Call savefig() before show() when you want to save the generated figure.

18. Real-Time Example: Monthly Sales Analysis

Suppose a company wants to analyze monthly sales. Matplotlib can convert the sales data into a visual trend.

Month	Sales
January	120000
February	145000
March	135000
April	175000
May	210000

Real-Time Business Example

import matplotlib.pyplot as plt


months = [
"January",
"February",
"March",
"April",
"May"
]

sales = [
120000,
145000,
135000,
175000,
210000
]

plt.figure(figsize=(10, 5))

plt.plot(
months,
sales,
marker="o",
linewidth=2
)

plt.title("Monthly Sales Analysis")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.grid(True)
plt.tight_layout()

plt.show()

Output:

Business Result: Management can visually identify the monthly sales trend and investigate periods where sales changed significantly.

19. Real-Time Example: Employee Performance

An HR department can visualize employee performance scores.

Employee Performance

import matplotlib.pyplot as plt


employees = [
"Amit",
"Priya",
"Rahul",
"Sneha",
"Vikas"
]

performance = [82, 91, 76, 88, 95]

plt.bar(employees, performance)

plt.title("Employee Performance")
plt.xlabel("Employee")
plt.ylabel("Performance Score")

plt.ylim(0, 100)

plt.show()

Output:

Business Result: HR can compare performance scores across employees and use the chart as part of a reporting dashboard.

20. Real-Time Example: Website Traffic

A website team can visualize the number of visitors received each day.

Website Traffic

import matplotlib.pyplot as plt


days = [
"Mon",
"Tue",
"Wed",
"Thu",
"Fri",
"Sat",
"Sun"
]

visitors = [
1200,
1500,
1800,
1700,
2200,
3000,
2800
]

plt.plot(
days,
visitors,
marker="o"
)

plt.title("Website Traffic")
plt.xlabel("Day")
plt.ylabel("Visitors")

plt.grid(True)

plt.show()

Output:

Business Result: The marketing team can identify high-traffic and low-traffic days.

21. Real-Time Example: Product Sales Comparison

A product manager can compare sales across different products.

Product Comparison

import matplotlib.pyplot as plt


products = [
"Laptop",
"Mobile",
"Tablet",
"Headphones",
"Monitor"
]

units_sold = [
150,
420,
180,
350,
220
]

plt.barh(products, units_sold)

plt.title("Product Sales Comparison")
plt.xlabel("Units Sold")
plt.ylabel("Product")

plt.show()

Output:

22. Real-Time Example: Temperature Monitoring

Matplotlib can visualize temperature measurements collected from sensors.

Temperature Monitoring

import matplotlib.pyplot as plt


hours = [
"9 AM",
"10 AM",
"11 AM",
"12 PM",
"1 PM",
"2 PM",
"3 PM"
]

temperature = [
24,
25,
27,
29,
31,
32,
30
]

plt.plot(
hours,
temperature,
marker="o"
)

plt.title("Daily Temperature")
plt.xlabel("Time")
plt.ylabel("Temperature (°C)")

plt.grid(True)

plt.show()

Output:

Real-Time Use: The same approach can be connected to sensor data, CSV files, APIs, or databases to visualize measurements.

23. Real-Time Example: Expense Analysis

A personal finance application can visualize expenses by category.

Expense Distribution (Python / Matplotlib)

import matplotlib.pyplot as plt

categories = [
    "Rent",
    "Food",
    "Travel",
    "Shopping",
    "Bills"
]

expenses = [
    25000,
    8000,
    5000,
    7000,
    4000
]

plt.pie(
    expenses,
    labels=categories,
    autopct="%1.1f%%"
)

plt.title("Monthly Expense Distribution")

plt.show()

Output / Result Preview

24. Real-Time Example: Stock Price Trend

A financial application can plot historical price data to visualize movement over time.

Stock Price Visualization

import matplotlib.pyplot as plt

dates = [
    "Mon",
    "Tue",
    "Wed",
    "Thu",
    "Fri"
]

prices = [
    120,
    125,
    122,
    130,
    135
]

plt.plot(
    dates,
    prices,
    marker="o"
)

plt.title("Stock Price Trend")
plt.xlabel("Day")
plt.ylabel("Price")

plt.grid(True)

plt.show()

Output / Result Preview

Important: A chart shows historical or supplied data. It does not by itself predict future prices.

25. Real-Time Example: Student Marks Analysis

Schools and colleges can visualize student marks across subjects.

Student Marks

import matplotlib.pyplot as plt

subjects = [
    "Python",
    "Java",
    "SQL",
    "Math",
    "English"
]

marks = [
    88,
    82,
    91,
    76,
    85
]

plt.bar(subjects, marks)

plt.title("Student Marks")
plt.xlabel("Subject")
plt.ylabel("Marks")

plt.ylim(0, 100)

plt.show()

Output / Result Preview

26. Real-Time Example: Department Expenses

A company can compare expenses across departments.

Department Expense Analysis

import matplotlib.pyplot as plt

departments = [
    "IT",
    "HR",
    "Finance",
    "Marketing",
    "Operations"
]

expenses = [
    450000,
    180000,
    250000,
    320000,
    500000
]

plt.bar(
    departments,
    expenses
)

plt.title("Department Expenses")
plt.xlabel("Department")
plt.ylabel("Expense")

plt.show()

Output / Result Preview

27. Real-Time Example: Sales vs Expenses

Multiple datasets can be displayed on the same chart for comparison.

Sales vs Expenses

import matplotlib.pyplot as plt

months = [
    "Jan",
    "Feb",
    "Mar",
    "Apr",
    "May"
]

sales = [
    120000,
    150000,
    180000,
    170000,
    220000
]

expenses = [
    80000,
    90000,
    110000,
    100000,
    125000
]

plt.plot(
    months,
    sales,
    marker="o",
    label="Sales"
)

plt.plot(
    months,
    expenses,
    marker="o",
    label="Expenses"
)

plt.title("Sales vs Expenses")
plt.xlabel("Month")
plt.ylabel("Amount")

plt.legend()
plt.grid(True)

plt.show()

Output / Result Preview

Business Use: Comparing multiple series can make financial trends easier to inspect in a single visualization.

28. Working with NumPy and Matplotlib

Matplotlib is commonly used together with NumPy for numerical calculations and visualization.

NumPy + Matplotlib

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.5)
y = x ** 2

plt.plot(x, y)

plt.title("Square Function")
plt.xlabel("X")
plt.ylabel("X²")

plt.grid(True)

plt.show()

Output / Result Preview

29. Working with Pandas and Matplotlib

Pandas DataFrames can be visualized directly using Matplotlib.

Pandas + Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Month": ["Jan", "Feb", "Mar", "Apr"],
    "Sales": [100, 140, 170, 210]
}

df = pd.DataFrame(data)

plt.plot(
    df["Month"],
    df["Sales"],
    marker="o"
)

plt.title("Sales from DataFrame")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.show()

Output / Result Preview

Real-Time Use: This pattern is useful when data is loaded from CSV or Excel files using Pandas and then visualized.

30. Real-Time Example: CSV Sales Visualization

A common business workflow is: load CSV data → analyze it → create a chart.

CSV + Pandas + Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("sales.csv")

plt.plot(
    df["Month"],
    df["Sales"],
    marker="o"
)

plt.title("Monthly Sales")
plt.xlabel("Month")
plt.ylabel("Sales")

plt.grid(True)

plt.show()

Output / Result Preview (Simulated CSV Data)

Workflow: CSV file → Pandas DataFrame → Matplotlib chart.

31. Real-Time Example: E-Commerce Dashboard

An e-commerce company may need to analyze revenue, orders, and customers together.

E-Commerce Analysis

import matplotlib.pyplot as plt

months = [
    "Jan",
    "Feb",
    "Mar",
    "Apr",
    "May",
    "Jun"
]

orders = [
    450,
    520,
    610,
    580,
    720,
    850
]

revenue = [
    180000,
    210000,
    250000,
    240000,
    310000,
    380000
]

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

axes[0].plot(
    months,
    orders,
    marker="o"
)

axes[0].set_title("Monthly Orders")
axes[0].set_xlabel("Month")
axes[0].set_ylabel("Orders")

axes[1].plot(
    months,
    revenue,
    marker="o"
)

axes[1].set_title("Monthly Revenue")
axes[1].set_xlabel("Month")
axes[1].set_ylabel("Revenue")

plt.tight_layout()
plt.show()

Output / Result Preview (Dashboard Subplots)

Business Result: Two related business metrics can be viewed together for easier reporting and analysis.

32. Real-Time Example: Complete Sales Report

The following example combines multiple visualization techniques into a small business sales report.

Complete Sales Report

import matplotlib.pyplot as plt

months = [
    "Jan",
    "Feb",
    "Mar",
    "Apr",
    "May",
    "Jun"
]

sales = [
    120000,
    145000,
    160000,
    150000,
    190000,
    220000
]

expenses = [
    80000,
    90000,
    100000,
    95000,
    115000,
    130000
]

profit = [
    sales[i] - expenses[i]
    for i in range(len(sales))
]

fig, axes = plt.subplots(3, 1, figsize=(10, 12))

axes[0].plot(
    months,
    sales,
    marker="o"
)

axes[0].set_title("Sales Trend")
axes[0].set_ylabel("Sales")

axes[1].plot(
    months,
    expenses,
    marker="o"
)

axes[1].set_title("Expense Trend")
axes[1].set_ylabel("Expenses")

axes[2].bar(
    months,
    profit
)

axes[2].set_title("Monthly Profit")
axes[2].set_xlabel("Month")
axes[2].set_ylabel("Profit")

plt.tight_layout()
plt.show()

Output / Result Preview (Complete Report Subplots)

Result: The report displays sales, expenses, and calculated profit in a single figure.

33. Matplotlib Object-Oriented Approach

For larger applications, the object-oriented API can provide clearer control over figures and axes.

Object-Oriented Plot

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr"]
sales = [100, 140, 170, 210]

fig, ax = plt.subplots(figsize=(9, 5))

ax.plot(
    months,
    sales,
    marker="o"
)

ax.set_title("Monthly Sales")
ax.set_xlabel("Month")
ax.set_ylabel("Sales")

ax.grid(True)

plt.show()

Output / Result Preview

34. Matplotlib Figure and Axes

Object	Purpose
Figure	The overall drawing area.
Axes	The area where a specific plot is drawn.
Axis	Represents x-axis or y-axis scales.
Title	Describes the chart.
Legend	Identifies plotted datasets.

35. Common Matplotlib Functions

Function	Purpose
`plt.plot()`	Create a line plot.
`plt.bar()`	Create a vertical bar chart.
`plt.barh()`	Create a horizontal bar chart.
`plt.scatter()`	Create a scatter plot.
`plt.pie()`	Create a pie chart.
`plt.hist()`	Create a histogram.
`plt.title()`	Add a chart title.
`plt.xlabel()`	Label the x-axis.
`plt.ylabel()`	Label the y-axis.
`plt.legend()`	Display the legend.
`plt.grid()`	Display grid lines.
`plt.show()`	Display the figure.
`plt.savefig()`	Save the figure to a file.
`plt.subplots()`	Create a figure and axes.

36. Choosing the Right Chart

Requirement	Recommended Chart
Show a trend over time	Line chart
Compare categories	Bar chart
Compare long category names	Horizontal bar chart
Show proportions	Pie chart
Show relationship between variables	Scatter plot
Show numerical distribution	Histogram
Compare multiple metrics	Subplots

37. Matplotlib Best Practices

Always give charts meaningful titles.
Use clear x-axis and y-axis labels.
Choose chart types based on the data.
Avoid unnecessary visual clutter.
Use legends when multiple datasets are displayed.
Use tight_layout() when needed.
Save important charts using savefig().
Use readable labels and appropriate figure sizes.
Keep units clear when displaying financial or scientific data.
Validate the source data before creating visualizations.

38. Important Matplotlib Rules

Import Matplotlib using import matplotlib.pyplot as plt.
Use plt.plot() for line charts.
Use plt.bar() for category comparisons.
Use plt.scatter() for relationships between variables.
Use plt.pie() for proportions.
Use plt.hist() for distributions.
Use plt.title() to describe the chart.
Use plt.xlabel() and plt.ylabel() for axes.
Use plt.legend() when multiple datasets are present.
Use plt.grid() when grid lines improve readability.
Use plt.show() to display the chart.
Use plt.savefig() to save visualizations.
Use Pandas or NumPy together with Matplotlib for data analysis workflows.
Use the object-oriented API for larger visualization applications.