Python: Creating Interactive Data with Plots

Name: Python: Creating Interactive Data with Plots
Author: Brian Higgins

Creating interactive plots using HVPlot.

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Course: On-Demand

Hello, my name is Brian Higgins, and in these lessons, you will learn how to use HVPlot, you will explore the toolbars widgets, and you will learn how to add a filter widget. In this lesson, I will show you how to create interactive data with plots. Python data analysis uses the Pandas data library heavily. HVPlot provides a high-level plotting API built on HoloViews that provides a general and consistent API for plotting. HVPlot can be used with pandas data frames to create interactive plots. We'll be doing that in this lesson. First, we'll import the pandas library, the numpy library, and HVPlots pandas library. Let's do that first in our first code cell. Next, we'll create a pandas stock price data frame, which we will use for interactive plotting. First, we'll generate dates for the first quarter or the first 120 days of 2022. So our start date will be January 1st of 2022. The periods that we'll pass in are 120, standing for 120 days. To do this, we'll call the pandas date range function and put the result in a variable called dates. Next, we'll use NumPy's randint function, passing in a low value of 100 and a high value of 1, 000, along with a size, meaning number of days, of 120. We will place the random list of stock prices into our price underscore data variable. Next we will call the pandas data frame function to create our pandas data frame called DF. And we'll pass into that a column header called date, which will have our dates list values associated with it. And we'll have another column header called TSLA, standing for fictional Tesla stock prices that we assigned in price underscore data variable above. The next call will be using the data frames head function, which essentially will just get you the first five rows of the data frame that we created to give you a feel for what the data looks like. Notice we've created a column called date, which has the first five days of 2022 in it, followed by the TSLA or Tesla stock price column, which has our random stock prices for the first five days of January 2022. Next, we'll use this data frame to plot using HV plot. And our data frame again contains stock prices for our stock Tesla, for this example. HV plot can generate plots from Panda's data frames. So that's why it's handy for the lesson that we're doing here. So what we'll do is use our DF data frame, already created, and call the HVplot function with it, passing in our x-axis, naming that date. So our x-axis will be the date column from our data frame for our plot that we generate. Let's run this code. Notice the plot that's been generated.

Intermediate Provider : Brian Higgins 3 Lessons · 14m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, you will learn to create interactive plots using HVPlot. The Python HVPlot library is a high-level plotting API built on HoloViews, which provides a general and consistent API for plotting data. We’ll show you how to use HVPlot and how the various toolbar widgets work, with functions including pan, reset, box zoom, wheel zoom, save, and hover. These features will allow you to move the graph, zoom in or out of the graph, and get data on a point of interest in the graph.

We’ll also show you how the save feature lets you save a graph image to disk for later use. For these graphs, you will use the Python Numpy library to generate random hypothetical data to plot out. Then you’ll learn how to add and use a filter widget on your HVPlot, which will allow you to show the line graph for the list of data you filter on.

What You'll Learn

Create interactive line plots using the Python HVPlot library
Use HVPlot toolbar widgets including pan, reset, box zoom, wheel zoom, save, and hover
Generate random hypothetical data to plot using the Python NumPy library
Add and use a filter widget to focus a line graph on selected data
Save a graph image to disk for later use with the save feature

Key Takeaways

HVPlot is a high-level plotting API built on HoloViews that provides a general and consistent API for plotting data.
The toolbar widgets let you move the graph, zoom in or out, and get data on a point of interest, including pan, reset, box zoom, wheel zoom, save, and hover functions.
The save feature lets you save a graph image to disk for later use.
The Python NumPy library is used to generate random hypothetical data to plot.
A filter widget added to an HVPlot lets you show the line graph for the list of data you filter on.

Frequently Asked Questions

What will I learn in this course?

You will learn to create interactive line plots using HVPlot, use each toolbar widget attached to an HVPlot graph, and add a filter widget to a line graph to focus in on data.

What is HVPlot?

The Python HVPlot library is a high-level plotting API built on HoloViews, which provides a general and consistent API for plotting data.

Which toolbar widgets does the course cover?

The course covers toolbar widgets with functions including pan, reset, box zoom, wheel zoom, save, and hover, which let you move the graph, zoom in or out, and get data on a point of interest.

What Python library is used to generate the data for the plots?

The course uses the Python NumPy library to generate random hypothetical data to plot out.

What topics do the lessons cover?

The lessons cover creating an interactive line plot using HVPlot, exploring toolbar widgets, and adding a filter widget.