Python: Utilizing Temporal Visualization

Name: Python: Utilizing Temporal Visualization
Author: Brian Higgins

Plot and understand time series data

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Course: On-Demand

Hello, my name is Brian Higgins, and in these lessons, I will show you how to utilize temporal visualization. In this lesson, you will see temporal, or time series, data on a linear graph. Let's draw our time series data on a line graph first. So we're going to draw a line plot showing several time series in this set of code that we're going to go through here. Temporal Data is also known as time series data. This can be thought of as tracking performance over time or looking at data on a timeline. You might track things like population, enrollment, temperature, or stock performance over time. In this lesson, we'll use the Pandas library to load stock data from a file and plot that stock data over time. The Pandas data frame is very useful for reading in time series data from a file and then manipulating it for visualization purposes. So in the code here, we're going to import Panda's data library, and we'll also import the PiPlot library. In the next line of code here, we're going to read in using the pandas read CSV file. The file will be called stocks. csv. And in the date column, we're going to tell pandas to parse those values as dates. So that's what the parseDates equals true parameter is saying there. And then we'll assign this into a stocksDF, standing for stocks data frame. Let's just take a look at what our stocks CSV file looks like. This is an Excel file format CSV. It's a comma separated format. So in this file we'll have a date column with our date data and this is all of the dates in December. So all the stock values for the Apple stock, AAPL, the Tesla stock, TSLA, and the S&P 500, SPY, all again for December of 2022. So the next command we'll call then is the plot function using our stocks data frame. And what we'll do is along the y-axis we'll plot our Apple stock, Tesla and S&P 500. And we're also telling it to use our index, which was our date field. So this use index equals true is just saying make sure you use the date as the index, and that will run along the x-axis. So let's show this plot. OK, so notice the dates along the x-axis. Prices are along the y-axis here, and we also have a legend. The blue is Apple, Orange is Tesla, and green is the S&P 500. Obviously, S&P 500 is much higher. You'll notice the orange line, Tesla, took a dip and then recovered a little bit at the end of December. Next, we'll use Panda's DataFrame and HVPlot to read in stock data again and plot this using a filter widget so we can filter out which line graph we want to see specifically for whatever stock we're looking at. So in this case, we'll import pandas and HV plot pandas.

Intermediate Provider : Brian Higgins 3 Lessons · 17m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons, you will use Pandas to read time series data from an Excel file. Temporal or time series data can be thought of as tracking performance over time or looking at data on a timeline. We’ll show you how to plot multiple time series on the same graph and draw inferences about the data from the graphs. Additionally, you will use the HVPlot library to graph multiple time series and focus on these individually with a filter widget.

We’ll also show you how to use Pandas to calculate performance-based data on hypothetical stock prices, then how to plot the data and draw inferences based on those plots. The performance data calculated will be temporal statistics, like growth (percentage change), rolling mean (7-day average), and cumulative returns. You will also draw box plots using time series data to visualize statistical information on each series.

What You'll Learn

Import and plot time series data from an Excel file using Pandas
Plot multiple time series on the same graph and draw inferences from the graphs
Use the HVPlot library to graph multiple time series and focus on individual series with a filter widget
Calculate performance-based temporal statistics such as growth (percentage change), rolling mean (7-day average), and cumulative returns
Plot calculated performance data with Matplotlib and draw inferences from the plots
Create and read box plots to visualize statistical information on time series data

Key Takeaways

Temporal or time series data tracks performance over time or views data on a timeline.
Pandas can read time series data from an Excel file and calculate performance-based data on hypothetical stock prices.
The HVPlot library lets you graph multiple time series and focus on individual series using a filter widget.
Calculated temporal statistics include growth (percentage change), rolling mean (7-day average), and cumulative returns.
Box plots can be drawn from time series data to visualize statistical information on each series.

Frequently Asked Questions

What will I learn in this course?

You will use Pandas to read time series data from an Excel file, plot multiple time series on the same graph, use the HVPlot library with a filter widget, calculate performance-based temporal statistics, plot the data with Matplotlib, and create and read box plots to draw inferences about the data.

What tools and libraries does this course cover?

The course covers Pandas for reading time series data and calculating performance data, the HVPlot library for graphing multiple time series with a filter widget, and Matplotlib for plotting calculated performance data.

What temporal statistics are calculated in this course?

The course calculates temporal statistics including growth (percentage change), rolling mean (7-day average), and cumulative returns, demonstrated on hypothetical stock prices.

What lessons are included?

The course includes three lessons: Temporal Data With a Line Graph, Plotting Calculated Temporal Data, and Creating a Box Plot to Describe Temporal Data.

What skills will I gain?

You will gain skills in data visualization, scientific visualization, time series, visual analytics, visualization, and the Visualization Toolkit (VTK).