Introduction to Seaborn Relationship Plots Statistical connections between data points are visualized using relationship plots. Visualization is crucial because it helps humans discover trends and patterns in data. Statistical analysis is the process of determining how variables in a dataset…
Seaborn Histograms A histogram represents the distribution of data by forming bins along the data range and plotting a bar chart to show the number of observations in each bin. Seaborn comes with several datasets, some of which we’ve already…
Seaborn Environment Setup In this chapter, we’ll discuss setting up the Seaborn environment. Let’s start with installation and learn how to get started as we go. Installing Seaborn and Getting Started In this section, we’ll walk through the steps for…
Statistical Estimation with Seaborn In most cases, we’re dealing with estimates of the entire distribution of data. But when it comes to estimating central tendency, we need a specific way to summarize the distribution. The mean and median are very…
Introduction to Seaborn Categorical Plots Plots are often used to depict the relationship between two or more variables. These variables can be purely numerical or represent a category, such as a group, class, or division. This article discusses categorical variables…
Visualizing Pairwise Relationships with Seaborn Real-time datasets contain many variables. In such cases, it’s important to analyze the relationships between each variable. Plotting bivariate distributions for (n,2) pairs can be a complex and time-consuming process. To plot multiple pairwise bivariate…
Seaborn Multi-Panel Categorical Plots Categorical data can be visualized using two plot types: the pointplot() function or the higher-level factorplot() function. Factorplot Factorplot draws a categorical plot on a FacetGrid. Using the ‘kind’ parameter, we can choose between boxplot, violinplot,…
Seaborn Function Reference Seaborn is a popular Python visualization library. Unlike other Python visualization libraries, Seaborn includes several predefined datasets. Statistical analysis is the process of determining how variables in a dataset relate to each other and the relationships between…
Introduction to Seaborn Palette Widgets Palette widgets can be categorized into three types: qualitative, continuous, and divergent. In the following chapters, we will learn about and explore each of these in detail. Qualitative Palettes Qualitative palettes are used when a…
Seaborn Kernel Density Estimation Kernel density estimation (KDE) is a method for estimating the probability density function of a continuous random variable. It is used in nonparametric analysis. Setting the hist flag to False in distplot will produce a kernel…