Python: Overview of Data Visualization and Library of Choice

Name: Python: Overview of Data Visualization and Library of Choice
Author: Brian Higgins

Learn how to plot line graphs using Matplotlib

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Course: On-Demand

Hello. Welcome to Knowledge City's course on Python for Data Visualization. My name is Brian Higgins, and in these lessons, you will get an overview of data visualization using the Python Matplotlib library. In this lesson, we will draw our first plot. Before we begin, it will be helpful to have some definitions. Data visualization is the representation of information in the form of a chart, diagram, picture, etc. To make data easier for someone to understand and pull insights from. Data visualization is used mainly to explore, monitor, and explain. In exploring data, one is looking for patterns and insights in the data. When monitoring data, one is checking the performance of something. Data visualization is often used to explain why something occurs with simple graph or chart. Matplotlib is an open source Python library, which is most commonly used for data visualization and line charts. Pyplot is the Matplotlib sub-module we will use in this lesson to draw our line charts. A line chart connects a series of data points using a line. It's also known as a line graph or a line plot. The tool we'll be using in this lesson is JupyterLab. It runs Jupyter notebook files which allow documentation and Python code cells to be interspersed to best explain the coding logic being performed. The .ipynb file suffix indicates that the file itself is a Jupyter notebook file. So for instance, testing.ipynb is a sample file name for a Jupyter notebook file. Let's start looking at some of our code cells for this lesson. This first code cell, highlighted on the bottom of the screen, will basically import the matplotlib library and then print out its version. Let's execute this code. Notice we printed out the version of matplotlib 3.5.1. Normally we'll use this just as a sanity check or a check to make sure that we're using the proper version that we expect to be using of this library. In the next coding cell, we import the pyplot submodule of the matplotlib library and give it the alias PLT. Let's execute this code cell. Since we're just importing a library here, nothing will show as an output. Next, we're going to draw our simple line plot. We're going to draw a line between two points with the x-y coordinates 1, 0 and 13, 51 where the x-coordinates are 1 and the 13 and the y- coordinates are the 0 and the 51. In our coding cell, we define our x-coordinates and y-coordinates in a list. So our x-coordinates list and our y-coordinates lists have the values that we specified above. In the next command, we're going to call the pyplot function plot, passing in the x-coordinates and the y-coordinates lists. Let's run this code cell to generate our first line plot. Notice what appears is the address of this line plot that we created along with our plot itself, graphing the line between points 1, 0 on the bottom and 13, 51 on the top. Lastly, we'll call our pyplot plot function with our coordinates, and then we'll call the pyplot show function, which will essentially refresh our graph without showing the address. Normally, when we show a line graph, we don't want to see the address. So this show function eliminates that. Okay, notice at the bottom here, you don't see the address anymore, you simply just see the plot.

Intermediate Provider : Brian Higgins 3 Lessons · 15m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

These lessons will show you how to plot points using x and y coordinates. Then you will draw a line between the points. You will also learn to draw multiple lines on the same plot. The line plots can be enhanced by adding styling. With these line plots you will learn how to add a plot title, labels for the x and y axes, and labels for each plot line. You will also see how to add a legend containing each of the plot line labels. Additionally, you will be able to add an underlying grid pattern to your plot to make it easier to read and to locate plot points within the graph. Point markers are formatted, different styles can be used for lines (dotted, dashed, etc), and plot lines can be assigned colors. These lessons will use the Matplotlib library Pyplot submodule to accomplish most of these tasks.

What You'll Learn

Plot points using x and y coordinates and draw a line between them
Create your first line plot using the Matplotlib library's Pyplot submodule
Plot multiple lines on the same graph
Style line plots with titles, axis labels, line labels, and a legend
Add an underlying grid pattern to make plots easier to read
Format point markers, apply line styles (dotted, dashed), and assign colors to plot lines

Key Takeaways

The course teaches plotting points with x and y coordinates and drawing lines between them.
Multiple lines can be drawn on the same plot, and line plots can be enhanced with styling.
Line plots can include a plot title, x and y axis labels, labels for each plot line, and a legend of those labels.
An underlying grid pattern can be added to a plot to make it easier to read and to locate plot points.
The lessons use the Matplotlib library's Pyplot submodule to accomplish most of these tasks.

Frequently Asked Questions

What library does this course use?

The lessons use the Matplotlib library's Pyplot submodule to accomplish most of the tasks.

What will I be able to do after taking this course?

You will become proficient in using the Matplotlib library to create line plots, style line plots, and plot multiple lines on the same graph.

What styling options does the course cover?

It covers adding a plot title, x and y axis labels, line labels, and a legend; adding a grid pattern; formatting point markers; using different line styles such as dotted and dashed; and assigning colors to plot lines.

What lessons are included?

The course includes three lessons: First Plot, Plot Styling, and Multi-line Plots.

What skills does this course build?

It builds skills in Data Visualization, Graphing, Information Visualization, Matplotlib (Python Package), Scientific Visualization, and Visualization.