Python: Visualizing Data with Line Charts

Name: Python: Visualizing Data with Line Charts
Author: Brian Higgins

Learn how to visualize data with line charts.

Preview the first lesson free — get full access to all 3 lessons.

Course: On-Demand

Hello, my name is Brian Higgins, and in these lessons, you will learn to visualize data using line charts. You will learn to layout line graph subplots. You will also learn to create line plots using data from a panda's data frame. In this lesson, I will show you how to create a simple line plot with labels and grids. We'll define our x and y coordinates, and in Python, you can use the numpy library to define an array rather than previously used a list of x and y coordinates. The reason you'll use the numpy array is that it is faster and more compact than the Python list. To do this, we'll import our numpy library as np, and as previously done, we'll import our pyplot library as plt. So to define this array with numpy, you'll set a variable x with your x coordinates, and assign that to np, which is our numpy library, dot array, and you define your array by passing in your list of numbers. So the x array will be the numbers 0 through 9, and then the y array will have variable number of values between 0 and 10. So now that we have our x and y NumPy arrays defined, we can start laying them out on our plot. First we'll add a title, an x-axis label, and a y-axis label to our plot, and we'll call our pi plot functions title, x label, and y label. So we're going to call pi plot's plot function to plot our line with our x and y coordinates, now defined in a numpy array for each of them. We'll call the pyplot title function. We'll call the pyplot xlabel and ylabel functions. And we'll show our line graph. So let me execute these two cells of code. Nothing will be shown from the first because we're just defining values. Next we will plot out our graph here, so we will see our line graph from this next code cell. Okay, so we see our code being shown here in our plot. We have a sample plot title, our x label and y label have now appeared on our graph, along with our line. Next, we can set our font and color for the title, labels, and then we can also add an underlying grid for our line plot. So we'll plot again our NumPy arrays. We will define our fonts for our title and label. So for our font title, we will use the Sans Serif family. We will use the blue color, and we'll give the font a size of 25 pixels. For our label, we will again define the family of sans serif, color will be green, and a size of 15 pixels. We'll add our fontDict parameter to define previously defined fonts. Okay, and these previously defined fonts are our fontTitle and our fontLabel variables just described. So we'll call our pyplot's title function, and we will pass in our fontDict equals the font title that we defined. And for the xLabel and yLabel function calls, We will not only pass in our labels for those axes, but we will also pass in font label as the font dict value for each of them. And then lastly, we will call the call the grid function.

Intermediate Provider : Brian Higgins 3 Lessons · 12m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, you will learn to visualize data with line charts. We’ll show you how to create a single plot line with labels and grids, and you’ll learn to lay out line graph subplots. You will start by creating a simple line plot, then you’ll learn how to generate line plots with styling like titles, x-axis and y-axis labels, and grids. You will also learn how to specify individual fonts for line plot titles and labels.

Then we’ll show you how to lay out line graph subplots. In these graphs, you will be able to lay out the row and column location for each subplot. You’ll use Numpy random integers to create unique lists of hypothetical stock prices in a Pandas DataFrame, then we’ll conclude with how to create line plots using the test stock data from the Pandas DataFrame you created.

What You'll Learn

Create a simple line plot to visualize data
Add titles, x-axis and y-axis labels, and grids to line plots
Specify individual fonts for line plot titles and labels
Lay out line graph subplots by row and column location
Generate hypothetical stock price lists using Numpy random integers in a Pandas DataFrame
Plot line charts using data from a Pandas DataFrame

Key Takeaways

Line charts can be created as single plots with labels and grids or arranged as multiple subplots.
Line plots can be styled with titles, axis labels, grids, and individually specified fonts.
Subplots can be arranged by setting the row and column location for each plot.
Numpy random integers can be used to build hypothetical stock price lists within a Pandas DataFrame.
Line plots can be generated directly from data stored in a Pandas DataFrame.

Frequently Asked Questions

What will I learn in this course?

You will learn to visualize data with line charts, including creating a single plot line with labels and grids, styling plots with titles and axis labels, specifying fonts, laying out line graph subplots, and plotting using a Pandas DataFrame.

What topics do the lessons cover?

The lessons cover Plot Labels and Grids, Creating Subplots, and Plotting With a Pandas DataFrame.

What skills will I gain from this course?

You will build skills in Data Visualization, Graphing, Information Visualization, Line Charts, Scientific Visualization, and Visualization.

How is data prepared for the line plots in this course?

You will use Numpy random integers to create unique lists of hypothetical stock prices in a Pandas DataFrame, then create line plots using that test stock data.