Found 36 Articles for Seaborn

Introduction Data pre-processing is required for producing trustworthy analytical results. Data preparation includes eliminating duplicates, identifying and fixing outliers, normalizing measurements, and filing away categories of information. Popular for its ability to scale features, handle missing data, and encode categorical variables, the Python-based Sklearn toolkit is an essential resource for pre-processing data. With Sklearn, preprocessing data is a breeze, and you have access to trustworthy methodologies for effective data analysis. Data Pre-Processing Techniques Standard Scaling Data can be transformed using standard scaling so that it is normally distributed around zero and one. It ensures that everything is uniform in size. This ... Read More

Data visualization plays a crucial role in understanding and communicating patterns, trends, and insights from data. Python, with its rich ecosystem of libraries, offers powerful tools for creating visually appealing and informative plots. Seaborn, a popular data visualization library built on top of Matplotlib, provides a high-level interface for creating beautiful statistical graphics. In this article, we will explore one of Seaborn's versatile plot types - the strip plot. Strip plots are useful for visualizing the distribution of a continuous variable against a categorical variable. They display individual data points along an axis, making it easy to observe patterns, clusters, ... Read More

Seaborn's 'axes_style()' function allows us to customize the style of plots by modifying various parameters. It returns a dictionary of parameters that control the appearance of the plot elements, such as colors, fonts, gridlines, and more. To view these parameters, we can print the dictionary returned by 'axes_style()'. Importing Required Libraries Before we begin, let's import the necessary libraries. We need to import Seaborn by using the following command. import seaborn as sns Viewing Parameters To view the available parameters in 'axes_style()', we can simply print the dictionary returned by the function. Example When we run the below code, ... Read More

The size of a plot refers to its width and height in units such as inches or centimeters. By adjusting the plot size, we can control how much space it occupies on the screen or in printed media. Seaborn provides several options for modifying the plot size to suit our needs. Seaborn does not have a specific parameter called "figure size" in its API. When using Seaborn to create plots, we can specify the figure size by directly manipulating the Figure object using Matplotlib's functions or parameters. Importing Required Libraries Before proceeding with the approaches, we have to make ... Read More

The axes spines, also known as the axis lines, which are the lines that define the borders or boundaries of a plot's coordinate system. In a two-dimensional plot, there are typically four axes spines such as top, bottom, left, and right. These spines create the framework for the plot and serve as reference lines for the data points. Each spine represents one of the four sides of the plot. The top spine runs horizontally across the top, the bottom spine runs horizontally across the bottom, the left spine runs vertically along the left side, and the right spine runs vertically ... Read More

In Seaborn, the "ticks" figure style is a minimalistic style that removes the background grid lines but retains the tick marks on the axes. This style focuses on presenting the data without any distracting elements and is useful when we want a clean and simple look for our plots. When we set the figure style to "ticks" in Seaborn using the 'sns.set_style()' function, the following changes occur in our plots. Background Grid Lines − The background grid lines are removed, resulting in a blank canvas without any horizontal or vertical lines. This helps to reduce visual clutter and draw ... Read More

In Seaborn, the figure style refers to the overall visual appearance and aesthetics of a plot. Seaborn provides several built-in figure styles that can be used to enhance the look and feel of your visualizations. These figure styles affect various elements such as colors, grid lines, background, fonts, and more. To set the figure style in Seaborn, we can use the sns.set_style() function. In this article we are going to see how to change the figure style to Dark in Seaborn. The following are the steps to be followed. Installing Seaborn Library First we have to ensure that Seaborn is ... Read More

Seaborn is a popular data visualization library in Python that provides a variety of styles to enhance the visual appearance of plots. One of the available styles is "whitegrid, " which offers a white background with grid lines. Changing the figure style in Seaborn is a straightforward process, and it can greatly impact the aesthetics of our visualizations. In this article we are going to see how to change the figure style to white Grid in seaborn. To change the figure style to "whitegrid" in Seaborn, we can follow below steps. Install Seaborn First we should check if we have ... Read More

The "white" figure style in Seaborn is a predefined style that provides a clean and minimalistic appearance to our plots. It is designed to enhance the visual clarity of the plot elements and prioritize the data representation. The purpose of the white figure style is to create visually appealing and easy-to-read plots by emphasizing the data while reducing distractions. Here are the key aspects and benefits of using the "white" figure style in Seaborn. Background Color The white figure style sets the background color of the plot to white, which creates a neutral and unobtrusive background. This helps to draw ... Read More

Seaborn is primarily a data visualization library and does not provide direct methods for performing calculations on data, such as calculating mean or standard deviation. However, Seaborn works seamlessly with the pandas library, which is a powerful data manipulation library in Python. You can use pandas to perform calculations on your data, and then use Seaborn to visualize the calculated results. The mean is a statistical measure that represents the average value of a set of numbers. It is calculated by summing up all the numbers in the set and then dividing the sum by the total count of numbers. ... Read More