Data privacy and the protection of people’s identities is important. The video below provides some basic ways to ensure the privacy of individuals when working with data.

There are several ways that an individual’s privacy can be protected when dealing with continuous data. In this post, we will look at how protecting privacy can be accomplished using Python. Libraries We will begin by loading the necessary libraries. Below is the code. The library setup is simple. ...

Data privacy is a major topic among analysts who want to protect people’s information. There are often ethical expectations that personal identifying information is protected. Whenever data is shared, you want to be sure that individual people cannot be identified within a dataset, which can lead to unforeseen consequences. ...

The video below explains how to modify the color of glyphs when using Bokeh. Manipulating the color is another way to convey information about your data to the end user.

Modifying the glyphs in a Bokeh data visualization is another useful tool. The video below explains how to do this

The privacy of individuals in a dataset can be protected through the development of fake data. Using false numbers makes it much more difficult to identify individual people within a dataset. In this post, we will look at how to generate fake numbers and names using Python. Libraries & Data Preparation ...

In this post, we will examine how to manipulate the color of the glyphs in a Bokeh data visualization. We are doing this not necessarily for aesthetic reasons but to convey additional information. Below are the initial libraries that we need. We will load additional libraries as required. pydatasets will ...

The video below shows you how to make multiple plots with Bokeh. This is a valuable tool when you are trying to develop multiple visualizations for comparison purposes.

In the video below, we will take a look at Bokeh tools and tooltips in Python. Tools and tooltips are great options for modifying your data visualization’s interactivity.

In this post, we will examine how to modify the default display of a plot in Bokeh, a library for interactive data visualizations in Python. Below are the initial libraries that we need. The first line of code is where our data comes from. We are using the data() function ...