What are the different plots available in the Seaborn?

Seaborn is a powerful data visualization library in Python that builds on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. It offers a variety of plot types to explore and visualize data effectively. The below are the different plots available in Seaborn which helps the user to visualize the data.

Scatter Plot

A scatter plot is used to display the relationship between two numeric variables. In seaborn we have a function namely scatterplot() which creates a scatter plot with optional additional features such as coloring and marker size based on other variables.

Line Plot

A line plot shows the trend or change in a variable over time or any other continuous dimension. Seaborn's 'lineplot()' function can create line plots with multiple lines for different groups or categories.

Bar Plot

A bar plot compares categorical variables and shows the distribution or count of each category. Seaborn's 'barplot()' function creates vertical or horizontal bar plots, and it can also display confidence intervals.

Histogram

A histogram visualizes the distribution of a single variable by dividing it into bins and showing the frequency or density of values in each bin. Seaborn's 'histplot()' function can create histograms with additional features like kernel density estimation.

Box Plot

A box plot displays the distribution of a numeric variable across different categories. Seaborn's 'boxplot()' function creates box plots with whiskers representing the range of values, a box indicating the interquartile range, and points for potential outliers.

Violin Plot

A violin plot combines a box plot with a kernel density plot to show the distribution of a numeric variable across different categories. Seaborn's 'violinplot()' function creates violin plots, providing a more detailed view of the data distribution.

Heatmap

A heatmap represents the relationship between two categorical variables using colors. Seaborn's 'heatmap()' function creates heatmaps, with color intensity indicating the relationship between the variables.

Pair Plot

A pair plot displays pairwise relationships between multiple variables in a dataset. Seaborn's 'pairplot()' function creates a grid of scatter plots for each combination of variables, along with distributions on the diagonal.

Facet Grid

A facet grid allows you to create multiple plots, each representing a subset of the data based on categorical variables. Seaborn's 'FacetGrid' class enables the creation of customized grids of subplots using the 'map()' function to apply different plot types to each subset.

Regression Plot

A regression plot visualizes the relationship between two variables, with a regression line representing the best-fit relationship. Seaborn's 'regplot()' and 'lmplot()' functions create regression plots with additional features like confidence intervals and polynomial regression.

Count Plot

A count plot displays the count of observations in each category of a categorical variable. Seaborn's 'countplot()' function creates count plots, making it easy to compare the distribution of categories.

Joint Plot

A joint plot combines two different plot types to visualize the relationship between two variables, including scatter plots, histograms, kernel density plots, and more. Seaborn's 'jointplot()' function creates joint plots with additional features like marginal distributions.

KDE Plot

A KDE is abbreviated as Kernel Density Estimate plot visualizes the probability density function of a continuous variable. Seaborn's 'kdeplot()' function creates KDE plots, showing the estimated distribution of the variable.

Swarm Plot

A swarm plot displays the distribution of a categorical variable with points, avoiding overlap by adjusting their positions. Seaborn's 'swarmplot()' function creates swarm plots, providing a more detailed view of the data distribution.

Factor Plot

A factor plot displays the relationship between two variables using different plot types based on the variable types. Seaborn's 'factorplot()' function creates factor plots, allowing for easy visualization of relationships with different categorical and numerical variables.