The document provides a comprehensive introduction to matplotlib, a Python library used for creating various types of plots including line graphs, bar plots, scatter plots, and histograms. It covers basics like importing pyplot, customizing plots, adding multiple lines, creating subplots, and exploring advanced techniques for better data visualization. Additionally, it includes mini-projects to apply the concepts learned, such as visualizing sales data and temperature variations.

1. Introduction to Matplotlib
Introduction to Artificial Intelligence

2. Introduction to Matplotlib
• Matplotlib is a widely-used Python library for creating a variety of
static, animated, and interactive plots.
• It helps visualize data, which makes understanding trends, patterns,
and comparisons much easier.
• Example: Importing Matplotlib
import matplotlib.pyplot as plt
pyplot is a sub-module of Matplotlib. We use plt as a shorthand alias,
which is standard and widely used in tutorials and documentation.

3. Basics of Plotting with pyplot
• The plot() function in pyplot creates line graphs. This function accepts
lists (or arrays) for the x and y coordinates.
• x and y represent the data points.
• xlabel, ylabel, and title add labels to the axes and title the plot.
• Task: Modify the data in x and y lists
to see how the plot changes.

4. Customizing Line Style and Colors
• Customizing the appearance of the line can make plots visually
appealing and help differentiate between multiple data sets.
• color='red' changes the line color to red.
• linestyle='--' uses a dashed line style.
• marker='o' adds circular markers at each data point.
• Try different colors ('blue', 'green'), line styles ('-', ':'), and markers
('x', '*') to explore customization.

5. Adding Multiple Lines to the Same Plot
• we can use multiple lines if we want to compare multiple data series
in a single plot.
• label parameter names each line; plt.legend() displays the legend,
making it easier to distinguish between the series.
• Task: Add a third line with their own
data and customize its appearance.

6. Bar Plots
• Bar plots display data in rectangular bars, ideal for comparing
categories.
• plt.bar() creates a bar plot, with categories on the x-axis and values as
heights of the bars.
• Task: Change the values list to represent different data (e.g., scores
or population).

7. Scatter Plots
• Scatter plots are used to show relationships between two variables.
• plt.scatter() plots each point individually, useful for analyzing data
distribution.
• Task: Use different colors and markers to represent data from two
groups.

8. Histograms
• Histograms are useful for showing data distribution, especially with
large datasets.
• np.random.normal() generates 100 random data points.
• plt.hist() creates the histogram, where bins=10 means 10 intervals.
• Task: Change bins to see how it affects the granularity of the
histogram.

9. Using Subplots for Multiple Plots in One
Figure
• Subplots allow multiple plots in a single figure, useful for comparing
related data.
• fig, axs = plt.subplots(1, 2) creates a 1x2 grid.
• axs[0] and axs[1] refer to the first and second subplot.
• Task: Add a third subplot showing a bar plot.

10. Advanced Customization Techniques
• Advanced techniques make plots more informative and visually
engaging.
• linewidth=2 adjusts line thickness.
• grid=True adds a background grid.
• xlim and ylim set the range for x and y axes.
• Task: Experiment with different xlim
and ylim values to see how it changes
plot focus.

11. MINI PROJECTS

12. Sales Comparison Plot
• plot weekly sales data for two products, customize it with labels, title,
and legend.

13. Temperature Variation Plot
• Visualize temperature changes over a week using a line plot and a
scatter plot in subplots.

14. Monthly Expenses Bar Chart
• Visualize monthly expenses in different categories (like groceries, rent,
entertainment, and utilities) using a bar chart.

15. Survey Data Pie Chart
• Create a pie chart to display survey results (Visualize survey data from
a poll about favourite programming languages, showing each
language’s share.)

16. Sales Trend Comparison Line Plot
• Compare monthly sales trends for two products (Plot the sales of two
products over 12 months to see trends and seasonal effects.)

17. Population Distribution Histogram
• Display population distribution across age groups (Plot a histogram of
ages within a community to show distribution.)

18. Weather Data Subplots
• Create subplots to display monthly average temperature and rainfall
(Use subplots to plot monthly temperature and rainfall for a city).