Data visualization lets you to better understand and communicate that data.
I've written a number of articles on introductory Data Visualization in the Python and Julia languages.
The first is an introduction to data visualization with Python and Pandas: Plotting with Pandas: an Introduction to Data Visualization.
It covers the following:
- Importing the appropriate libraries
- Getting data about the weather in London
- Produce a first Pandas visualization using the plot() method
- Find out how different types of charts are created
- Plotting simple charts: line charts, bar charts, pie charts and scatter diagrams
- Plotting statistical Pandas charts— spotting unusual events
- Box Plots — Showing the range of data
- Histograms
- Changing the number of bins to focus on the outliers — Just how often is it really, really wet?
- Pandas plot utilities — multiple plots and saving images
You don’t need to be an expert in Python to be able to follow this tutorial, but a little exposure to programming in Python would be good and a basic idea of what a Pandas DataFrames is, would be helpful.
The tutorial explores the data visualization capabilities of Pandas, starting with the basics — line graphs, bar charts and pie
charts — and then looking at the more statistical views with
histograms and box plots. Finally, we see how we can create multiple
plot in one chart and how we save charts as images, so we can utilize
them in our own reports, documents and web pages.
Go to the tutorial here: Plotting with Pandas: an Introduction to Data Visualization