Question

Install matplotlib. Draw a scatter diagram of these (x, y) pairs: ( (0, 0), (3, 5), (6, 2), (9, 8), (14, 10) ).

Short answer

Install Matplotlib, plot with `plt.scatter(x, y)`, and display using `plt.show()`.

Step-by-step solution

Install Matplotlib

To draw the scatter plot using Python, first ensure that the Matplotlib library is installed. Run the command `pip install matplotlib` in your terminal or command prompt to install it. This will allow you to use the library's plotting functions.

Import Libraries

After installing Matplotlib, open a Python script or Jupyter notebook. Start by importing the necessary modules for plotting. Use: ```python import matplotlib.pyplot as plt ``` This imports the `pyplot` module which contains functions to create various kinds of plots.

Define Data Points

Create two lists to hold the x and y coordinates of the points you want to plot. Define them as follows: ```python x = [0, 3, 6, 9, 14] y = [0, 5, 2, 8, 10] ```

Plot the Scatter Diagram

Use the `scatter` function from Matplotlib to create the scatter plot with the defined `x` and `y` data. This can be done with: ```python plt.scatter(x, y) ```

Customize Plot

Optional customizations can be made to improve the readability of the plot. For example, you can add labels and a title: ```python plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Scatter Plot of Given Points') ```

Display the Plot

Finally, use the `show` function to display the scatter plot: ```python plt.show() ```

Key concepts

Matplotlib Installation

Before you can create stunning visualizations in Python, you need to ensure the right tools are at your disposal. One essential library for data visualization is Matplotlib. Installing it is straightforward and can be done through the command line or terminal using the popular package manager known as pip. Simply type `pip install matplotlib`, and the library will be downloaded and installed on your system.
You can verify that Matplotlib is installed correctly by importing it into a Python environment:

• Start a Python interpreter session.
• Enter `import matplotlib.pyplot as plt`.

If you see no errors, then Matplotlib is ready for use.
This first step empowers you to unlock a wide range of visualization capabilities, including plots and charts that offer insights into the data you work with.

Scatter Plot

A scatter plot is a powerful way to visualize the relationship between two quantities. It shows individual data points on a Cartesian graph, with one variable plotted along the x-axis and another on the y-axis. This type of diagram helps to see patterns, trends, or outliers in a collection of data.
To create a scatter plot, you first need to have a pair of data lists, one for x-values and another for y-values. With Matplotlib installed, use the `scatter` function to visualize these data pairs. For example:

• Define your x-coordinates: `x = [0, 3, 6, 9, 14]`
• Define your y-coordinates: `y = [0, 5, 2, 8, 10]`
• Create a scatter plot: `plt.scatter(x, y)`

Optionally, you can customize this plot by adding labels or a title to provide additional context.
Using the labels and titles helps viewers understand the plot better. After customizing, call `plt.show()` to display the scatter plot on your screen.

Python Plotting Libraries

Python boasts a rich ecosystem of plotting libraries, making it a favorite among data scientists and engineers. Each library offers unique features suitable for different visualization needs. Here's an overview of some of the most popular ones:

• Matplotlib: Widely regarded as the go-to library, Matplotlib is both powerful and flexible. It supports a wide range of plots, including line plots, bar charts, and scatter plots, making it versatile for many applications.


• Seaborn: Built on top of Matplotlib, Seaborn is great for statistical graphs. It simplifies complex visualizations and offers enhanced aesthetic styles by default.


• Plotly: Known for its interactivity, Plotly is web-based and allows you to create interactive plots and dashboards effortlessly.
  
• Bokeh: Focusing on interactive and functional plots, Bokeh is ideal for interactive visualizations that can be easily displayed in web pages.

Choosing the right library depends on your visualization requirements and the ease of use that suits your workflow. Whether you prefer Matplotlib’s depth of features or Plotly’s interactive charm, Python’s plotting libraries provide ample tools for data analysis.