Regression Plots
The regression plots are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses. Regression plots as the name suggests creates a regression line between two parameters and helps to visualize their linear relationships.
Refer to the below article to get detailed information about the regression plots.
there are two main functions that are used to draw linear regression models. These functions are lmplot(), and regplot(), are closely related to each other. They even share their core functionality.
lmplot
lmplot() method can be understood as a function that basically creates a linear model plot. It creates a scatter plot with a linear fit on top of it.
Syntax:
seaborn.lmplot(x, y, data, hue=None, col=None, row=None, **kwargs)
Example:
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt # loading dataset data = sns.load_dataset("tips") sns.lmplot(x='total_bill', y='tip', data=data) plt.show() |
Output:
Refer to the below articles to get detailed information about the lmplot.
Regplot
regplot() method is also similar to lmplot which creates linear regression model.
Syntax:
seaborn.regplot( x, y, data=None, x_estimator=None, **kwargs)
Example:
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt # loading dataset data = sns.load_dataset("tips") sns.regplot(x='total_bill', y='tip', data=data) plt.show() |
Output:
Refer to the below articles to get detailed information about regplot.
Note: The difference between both the function is that regplot accepts the x, y variables in different format including NumPy arrays, Pandas objects, whereas, the lmplot only accepts the value as strings.
Matrix Plots
A matrix plot means plotting matrix data where color coded diagrams shows rows data, column data and values. It can shown using the heatmap and clustermap.
Refer to the below articles to get detailed information about the matrix plots.
Heatmap
Heatmap is defined as a graphical representation of data using colors to visualize the value of the matrix. In this, to represent more common values or higher activities brighter colors basically reddish colors are used and to represent less common or activity values, darker colors are preferred. it can be plotted using the heatmap() function.
Syntax:
seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, **kwargs)
Example:
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt # loading dataset data = sns.load_dataset("tips") # correlation between the different parameters tc = data.corr() sns.heatmap(tc) plt.show() |
Output:
Refer to the below articles to get detailed information about the heatmap.
- Seaborn Heatmap – A comprehensive guide
- How to create a seaborn correlation heatmap in Python?
- How to create a Triangle Correlation Heatmap in seaborn – Python?
- ColorMaps in Seaborn HeatMaps
- How to change the colorbar size of a seaborn heatmap figure in Python?
- How to add a frame to a seaborn heatmap figure in Python?
- How to increase the size of the annotations of a seaborn heatmap in Python?
Clustermap
The clustermap() function of seaborn plots the hierarchically-clustered heatmap of the given matrix dataset. Clustering simply means grouping data based on relationship among the variables in the data.
Syntax:
clustermap(data, *, pivot_kws=None, **kwargs)
Example:
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt # loading dataset data = sns.load_dataset("tips") # correlation between the different parameters tc = data.corr() sns.clustermap(tc) plt.show() |
Output:
Refer to the below articles to get detailed information about clustermap.
Python Seaborn Tutorial
Seaborn is a library mostly used for statistical plotting in Python. It is built on top of Matplotlib and provides beautiful default styles and color palettes to make statistical plots more attractive.
In this tutorial, we will learn about Python Seaborn from basics to advance using a huge dataset of seaborn basics, concepts, and different graphs that can be plotted.
Table Of Content
- Getting Started
- Using Seaborn with Matplotlib
- Customizing Seaborn Plots
- Changing Figure Aesthetic
- Removal of Spines
- Changing the figure Size
- Scaling the plots
- Setting the Style Temporarily
- Color Palette
- Diverging Color Palette
- Sequential Color Palette
- Setting the default Color Palette
- Multiple plots with Seaborn
- Using Matplotlib
- Using Seaborn
- Creating Different Types of Plots
- Relational Plots
- Categorical Plots
- Distribution Plots
- Regression Plots
- More Gaphs in Seaborn
- More Topics on Seaborn