Plotting a Graph with Pyplot Subplot Screen Location

When writing code in a script, it's not sufficient to use the `plot()` function alone to display a graph on the screen. In this case, we're working with the numbers in the `steps_walked` list and plotting a graph using these data as an argument.

To do this, we need to import the `matplotlib.pyplot` module and assign it an alias, commonly referred to as `plt`. We start by importing `matplotlib.pyplot` using the line `import matplotlib.pyplot as plt`.

Once we have imported `matplotlib.pyplot`, we can use its functions to create a graph. In our example, we're defining two lists called `days` and `steps_walked`. We then use two of the functions from `matplotlib.pyplot` to create a graph.

However, if you haven't already installed `Matplotlib`, you'll need to do so before using it. You can install it by typing `pip install matplotlib` in your Terminal or using your IDE's built-in tools. If you're new to installing third-party modules, refer to the section on 'Installing Third-Party Modules' in the Chapter about using NumPy for detailed instructions.

Once installed, `Matplotlib` is a vast library that can be used in many versatile ways. In this chapter, we'll focus on how to plot 2D figures, including using subplots, and when and how to use the two interfaces in `Matplotlib`.

While there's a lot more to learn about `Matplotlib`, understanding its basics is essential for anyone working with data visualization in Python. With a good knowledge of `Matplotlib`, you'll be able to find solutions to plot more advanced figures and translate many of its concepts to other libraries used for data visualization in Python.

In this chapter, we'll cover the basics of data visualization in Python, including how to use `matplotlib.pyplot` to create graphs. Displaying data visually is important not only for those studying the data but also for those presenting it to others. Visualizing data is an essential part of understanding what the data say, as every scientist, data scientist, and anyone working with data will confirm.

As more and more applications of programming using Python involve large amounts of data, learning how to visualize this data effectively is crucial. By the end of this chapter, you'll have a solid understanding of how to use `matplotlib.pyplot` to create graphs and be well on your way to becoming proficient in data visualization with Python.

Pyplot subplot screen location