![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.](https://image.slidesharecdn.com/a9bf73introductiontomatplotlib-241124202633-cffd5fe6/85/a9bf73_Introduction-to-Matplotlib01-pptx-9-320.jpg)
a9bf73_Introduction to Matplotlib01.pptx
- 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).