ParcelJS is a web application bundler, it is a compiler for all your code, regardless of the language. The parcel-bundle

As you can see, I gave you the tools that I use a lot as a front-end web developer. Using them will make things easier for you because you don’t have to do these things manually anymore.

As you can see, I gave you the tools that I use a lot as a front-end web developer. Using them will make things easier for you because you don’t have to do these things manually anymore.

Data visualization is a very important part of data science. It is quite useful in exploring and understanding the data. In some cases, visualizations are much better than plain numbers at conveying information.

The hue parameter allows to compare different categories based on the numerical variables. We passed the “Married” column to the hue parameter. Thus, the data points that belong to different categories of this column (single and married) are marked with different colors.

In this post, we will learn about the 8 most commonly used types of data visualizations. I will use Seaborn to create visualizations and also try to explain what kind of information we can infer.

The grocery dataset contains information about customer purchases at grocery stores. The direct marketing dataset contains relevant data of a marketing campaign done via direct mail.

ParcelJS does many tasks for your development process like minifying your files so that their size will be reduced in order to make your application faster and easy to deploy. It is also used for compiling your Sass files and supporting the JavaScript ES6 features so that the browsers can understand that syntax.

AnimateCss is a library that gives you a collection of cross-browser CSS animations that you can use in your sliders, home pages, and other web projects. You can install it with NPM or just by using their CDN in your HTML.

In the previous plot, we saw that married people earn more money than single people in general. Let’s plot the distribution of the “Salary” column for married and single people separately.

In our dataset, we can visualize the number of items purchased over time. First, we will calculate the number of items purchased on each day. The groupby function of pandas will help us do that.

The marketing dataset consists of 1000 observations (i.e. rows) and 10 features (i.e. columns). The focus is on the “AmountSpent” column which indicates how much a customer has spent so far.

It seems like there is a positive correlation between the spent amount and salary which is expected. The more money you have, the more you are likely to spend. We also see that married people are likely to earn and spend more money than single people.

Kde plots are also used to visualize distributions. Instead of using discrete bins like histograms, kde plots smooth the observations with a Gaussian kernel. As a result, a continuous density estimate is produced.

Note: The purpose of this article is to explain different kinds of visualizations. Thus, we will not focus on customizing or editing the plots (e.g. fontsize, labels, colors, and so on)

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