Python Seaborn Tutorial

02 Mar, 2022
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 AestheticRemoval of SpinesChanging the figure SizeScaling the plotsSetting the Style Temporarily
Changing Figure Aesthetic
Removal of Spines
Changing the figure Size
Scaling the plots
Setting the Style Temporarily
Color Palette Diverging Color PaletteSequential Color PaletteSetting the default Color Palette
Diverging Color Palette
Sequential Color Palette
Setting the default Color Palette
Multiple plots with Seaborn Using MatplotlibUsing Seaborn
Using Matplotlib
Using Seaborn
Creating Different Types of Plots Relational PlotsCategorical PlotsDistribution PlotsRegression Plots
Relational Plots
Categorical Plots
Distribution Plots
Regression Plots
More Gaphs in Seaborn
More Topics on Seaborn
 
Recent articles on Seaborn !!
 
First of all, let us install Seaborn. Seaborn can be installed using the pip. Type the below command in the terminal.
 
pip install seaborn
 
In the terminal, it will look like this – 
 
After the installation is completed you will get a successfully installed message at the end of the terminal as shown below.
 
 
Note: Seaborn has the following dependencies – 
 
Python 2.7 or 3.4+
numpy
scipy
pandas
matplotlib
After the installation let us see an example of a simple plot using Seaborn. We will be plotting a simple line plot using the iris dataset. Iris dataset contains five columns such as Petal Length, Petal Width, Sepal Length, Sepal Width and Species Type. Iris is a flowering plant, the researchers have measured various features of the different iris flowers and recorded them digitally.
 
Example:
 
Python3
# importing packages import seaborn as sns   # loading dataset data = sns.load_dataset("iris")   # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)
Output:
In the above example, a simple line plot is created using the lineplot() method. Do not worry about these functions as we will be discussing them in detail in the below sections. Now after going through a simple example let us see a brief introduction about the Seaborn. Refer to the below articles to get detailed information about the same.
Introduction to Seaborn – Python
Plotting graph using Seaborn
In the introduction, you must have read that Seaborn is built on the top of Matplotlib. It means that Seaborn can be used with Matplotlib. 
Using both Matplotlib and Seaborn together is a very simple process. We just have to invoke the Seaborn Plotting function as normal, and then we can use Matplotlib’s customization function.
Example 1: We will be using the above example and will add the title to the plot using the Matplotlib.
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")   # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)   # setting the title using Matplotlibplt.title('Title using Matplotlib Function')  plt.show()
  
Output:
 
 
Example 2: Setting the xlim and ylim
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")   # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)   # setting the x limit of the plotplt.xlim(5)  plt.show()
  
Output:
 
 
Seaborn comes with some customized themes and a high-level interface for customizing the looks of the graphs. Consider the above example where the default of the Seaborn is used. It still looks nice and pretty but we can customize the graph according to our own needs. So let’s see the styling of plots in detail.
 
set_style() method is used to set the aesthetic of the plot. It means it affects things like the color of the axes, whether the grid is active or not, or other aesthetic elements. There are five themes available in Seaborn.
 
darkgrid
whitegrid
dark
white
ticks
Syntax:
 
set_style(style=None, rc=None)
Example: Using the dark theme
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")   # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)   # changing the theme to darksns.set_style("dark")plt.show()
  
Output:
 
 
Spines are the lines noting the data boundaries and connecting the axis tick marks. It can be removed using the despine() method.
 
Syntax:
 
sns.despine(left = True)
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")   # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)   # Removing the spinessns.despine()plt.show()
  
Output:
 
 
The figure size can be changed using the figure() method of Matplotlib. figure() method creates a new figure of the specified size passed in the figsize parameter.
 
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")   # changing the figure sizeplt.figure(figsize = (2, 4))  # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)   # Removing the spinessns.despine()  plt.show()
  
Output:
 
 
It can be done using the set_context() method. It allows us to override default parameters. This affects things like the size of the labels, lines, and other elements of the plot, but not the overall style. The base context is “notebook”, and the other contexts are “paper”, “talk”, and “poster”. font_scale sets the font size.
 
Syntax:
 
set_context(context=None, font_scale=1, rc=None)
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")   # draw lineplot sns.lineplot(x="sepal_length", y="sepal_width", data=data)   # Setting the scale of the plotsns.set_context("paper")  plt.show()
  
Output:
 
 
axes_style() method is used to set the style temporarily. It is used along with the with statement.
 
Syntax:
 
axes_style(style=None, rc=None)
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt   # loading dataset data = sns.load_dataset("iris")     def plot():    sns.lineplot(x="sepal_length", y="sepal_width", data=data)   with sns.axes_style('darkgrid'):          # Adding the subplot    plt.subplot(211)    plot()      plt.subplot(212)plot()
  
Output:
 
 
Refer to the below article for detailed information about styling Seaborn Plot.
 
Seaborn | Style And Color
 
Colormaps are used to visualize plots effectively and easily. One might use different sorts of colormaps for different kinds of plots. color_palette() method is used to give colors to the plot. Another function palplot() is used to deal with the color palettes and plots the color palette as a horizontal array.
 
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt  # current colot palettepalette = sns.color_palette()  # plots the color palette as a# horizontal arraysns.palplot(palette)  plt.show()
  
Output:
 
This type of color palette uses two different colors where each color depicts different points ranging from a common point in either direction. Consider a range of -10 to 10 so the value from -10 to 0 takes one color and values from 0 to 10 take another.
 
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt  # current colot palettepalette = sns.color_palette('PiYG', 11)  # diverging color palettesns.palplot(palette)  plt.show()
  
Output:
 
In the above example, we have used an in-built diverging color palette which shows 11 different points of color. The color on the left shows pink color and color on the right shows green color.
 
A sequential palette is used where the distribution ranges from a lower value to a higher value. To do this add the character ‘s’ to the color passed in the color palette.
 
Example: 
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt  # current colot palettepalette = sns.color_palette('Greens', 11)  # sequential color palettesns.palplot(palette)  plt.show()
  
Output:
 
 
set_palette() method is used to set the default color palette for all the plots. The arguments for both color_palette() and set_palette() is same. set_palette() changes the default matplotlib parameters.
 
Example:
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt  # loading dataset data = sns.load_dataset("iris")   def plot():    sns.lineplot(x="sepal_length", y="sepal_width", data=data)  # setting the default color palettesns.set_palette('vlag')plt.subplot(211)  # plotting with the color palette# as vlagplot()  # setting another default color palettesns.set_palette('Accent')plt.subplot(212)plot()  plt.show()
  
Output:
 
 
Refer to the below article to get detailed information about the color palette.
 
Seaborn – Color Palette
 
You might have seen multiple plots in the above examples and some of you might have got confused. Don’t worry we will cover multiple plots in this section. Multiple plots in Seaborn can also be created using the Matplotlib as well as Seaborn also provides some functions for the same.
 
Matplotlib provides various functions for plotting subplots. Some of them are add_axes(), subplot(), and subplot2grid(). Let’s see an example of each function for better understanding.
 
Example 1: Using add_axes() method
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt    # loading datasetdata = sns.load_dataset("iris")  def graph():    sns.lineplot(x="sepal_length", y="sepal_width", data=data)  # Creating a new figure with width = 5 inches# and height = 4 inchesfig = plt.figure(figsize =(5, 4))   # Creating first axes for the figureax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])   # plotting the graphgraph()  # Creating second axes for the figureax2 = fig.add_axes([0.5, 0.5, 0.3, 0.3])  # plotting the graphgraph()  plt.show()
  
Output:
 
 
Example 2: Using subplot() method
 
Python3
# importing packages import seaborn as sns import matplotlib.pyplot as plt  # loading dataset data = sns.load_dataset("iris")   def graph():    sns.lineplot(x="sepal_length", y="sepal_width", data=data)  # Adding the subplot at the specified# grid positionplt.subplot(121)graph()  # Adding the subplot at the specified# grid positionplt.subplot(122)graph()  plt.show()
  
Output:
 
 
Example 3: Using subplot2grid() method
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  def graph():    sns.lineplot(x="sepal_length", y="sepal_width", data=data)  # adding the subplotsaxes1 = plt.subplot2grid (  (7, 1), (0, 0), rowspan = 2,  colspan = 1) graph()  axes2 = plt.subplot2grid (  (7, 1), (2, 0), rowspan = 2, colspan = 1) graph()    axes3 = plt.subplot2grid (  (7, 1), (4, 0), rowspan = 2, colspan = 1)graph()
  
Output:
 
 
Seaborn also provides some functions for plotting multiple plots. Let’s see them in detail
 
Method 1: Using FacetGrid() method
 
FacetGrid class helps in visualizing distribution of one variable as well as the relationship between multiple variables separately within subsets of your dataset using multiple panels.
A FacetGrid can be drawn with up to three dimensions ? row, col, and hue. The first two have obvious correspondence with the resulting array of axes; think of the hue variable as a third dimension along a depth axis, where different levels are plotted with different colors.
FacetGrid object takes a dataframe as input and the names of the variables that will form the row, column, or hue dimensions of the grid. The variables should be categorical and the data at each level of the variable will be used for a facet along that axis.
 
Syntax:
 
seaborn.FacetGrid( data, \*\*kwargs)
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  plot = sns.FacetGrid(data, col="species")plot.map(plt.plot, "sepal_width")  plt.show()
  
Output:
 
 
Method 2: Using PairGrid() method
 
Subplot grid for plotting pairwise relationships in a dataset.
This class maps each variable in a dataset onto a column and row in a grid of multiple axes. Different axes-level plotting functions can be used to draw bivariate plots in the upper and lower triangles, and the marginal distribution of each variable can be shown on the diagonal.
It can also represent an additional level of conventionalization with the hue parameter, which plots different subsets of data in different colors. This uses color to resolve elements on a third dimension, but only draws subsets on top of each other and will not tailor the hue parameter for the specific visualization the way that axes-level functions that accept hue will.
 
Syntax:
 
seaborn.PairGrid( data, \*\*kwargs)
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("flights")  plot = sns.PairGrid(data)plot.map(plt.plot)  plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about the multiple plots
 
Python – seaborn.FacetGrid() method
Python – seaborn.PairGrid() method
 
 
Relational plots are used for visualizing the statistical relationship between the data points. Visualization is necessary because it allows the human to see trends and patterns in the data. The process of understanding how the variables in the dataset relate each other and their relationships are termed as Statistical analysis. Refer to the below articles for detailed information.
 
Relational plots in Seaborn – Part I
Relational plots in Seaborn – Part II
There are different types of Relational Plots. We will discuss each of them in detail –
 
This function provides us the access to some other different axes-level functions which shows the relationships between two variables with semantic mappings of subsets. It is plotted using the relplot() method.
 
Syntax:
 
seaborn.relplot(x=None, y=None, data=None, **kwargs) 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  # creating the relplotsns.relplot(x='sepal_width', y='species', data=data)  plt.show()
  
Output:
 
The scatter plot is a mainstay of statistical visualization. It depicts the joint distribution of two variables using a cloud of points, where each point represents an observation in the dataset. This depiction allows the eye to infer a substantial amount of information about whether there is any meaningful relationship between them. It is plotted using the scatterplot() method.
 
Syntax:
 
seaborn.scatterplot(x=None, y=None, data=None, **kwargs)
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.scatterplot(x='sepal_length', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about Scatter plot.
 
Scatterplot using Seaborn in Python
Visualizing Relationship between variables with scatter plots in Seaborn
How To Make Scatter Plot with Regression Line using Seaborn in Python?
Scatter Plot with Marginal Histograms in Python with Seaborn
 
For certain datasets, you may want to consider changes as a function of time in one variable, or as a similarly continuous variable. In this case, drawing a line-plot is a better option. It is plotted using the lineplot() method.
 
Syntax:
 
seaborn.lineplot(x=None, y=None, data=None, **kwargs)
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.lineplot(x='sepal_length', y='species', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about line plot.
 
seaborn.lineplot() method in Python
Data Visualization with Seaborn Line Plot
Creating A Time Series Plot With Seaborn And Pandas
How to Make a Time Series Plot with Rolling Average in Python?
 
 
Categorical Plots are used where we have to visualize relationship between two numerical values. A more specialized approach can be used if one of the main variable is categorical which means such variables that take on a fixed and limited number of possible values.
 
Refer to the below articles to get detailed information.
 
Categorical Plots
 
 There are various types of categorical plots let’s discuss each one them in detail.
 
 
A barplot is basically used to aggregate the categorical data according to some methods and by default its the mean. It can also be understood as a visualization of the group by action. To use this plot we choose a categorical column for the x axis and a numerical column for the y axis and we see that it creates a plot taking a mean per categorical column. It can be created using the barplot() method.
 
Syntax:
 
barplot([x, y, hue, data, order, hue_order, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.barplot(x='species', y='sepal_length', data=data)plt.show()
  
Output:
 
 
Refer to the below article to get detailed information about the topic.
 
Seaborn.barplot() method in Python
Barplot using seaborn in Python
Seaborn – Sort Bars in Barplot
 
A countplot basically counts the categories and returns a count of their occurrences. It is one of the most simple plots provided by the seaborn library. It can be created using the countplot() method.
 
Syntax:
 
countplot([x, y, hue, data, order, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.countplot(x='species', data=data)plt.show()
  
Output:
 
 
Refer to the below articles t get detailed information about the count plot.
 
Countplot using seaborn in Python
 
A boxplot is sometimes known as the box and whisker plot.It shows the distribution of the quantitative data that represents the comparisons between variables. boxplot shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution i.e. the dots indicating the presence of outliers. It is created using the boxplot() method.
 
Syntax:
 
boxplot([x, y, hue, data, order, hue_order, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.boxplot(x='species', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about box plot.
 
Boxplot using Seaborn in Python
Horizontal Boxplots with Seaborn in Python
How To Use Seaborn Color Palette to Color Boxplot?
Seaborn – Coloring Boxplots with Palettes
How to Show Mean on Boxplot using Seaborn in Python?
Sort Boxplot by Mean with Seaborn in Python
How To Manually Order Boxplot in Seaborn?
Grouped Boxplots in Python with Seaborn
Horizontal Boxplots with Points using Seaborn in Python
How to Make Boxplots with Data Points using Seaborn in Python?
Box plot visualization with Pandas and Seaborn
It is similar to the boxplot except that it provides a higher, more advanced visualization and uses the kernel density estimation to give a better description about the data distribution. It is created using the violinplot() method.
 
Syntax:
 
violinplot([x, y, hue, data, order, ...]
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.violinplot(x='species', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about violin plot.
 
Violinplot using Seaborn in Python
How to Make Horizontal Violin Plot with Seaborn in Python?
Make Violinplot with data points using Seaborn
How To Make Violinpot with data points in Seaborn?
How to Make Grouped Violinplot with Seaborn in Python?
 
It basically creates a scatter plot based on the category. It is created using the stripplot() method.
 
Syntax:
 
stripplot([x, y, hue, data, order, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.stripplot(x='species', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to detailed information about strip plot.
 
Stripplot using Seaborn in Python
Swarmplot is very similar to the stripplot except the fact that the points are adjusted so that they do not overlap.Some people also like combining the idea of a violin plot and a stripplot to form this plot. One drawback to using swarmplot is that sometimes they dont scale well to really large numbers and takes a lot of computation to arrange them. So in case we want to visualize a swarmplot properly we can plot it on top of a violinplot. It is plotted using the swarmplot() method.
 
Syntax:
 
swarmplot([x, y, hue, data, order, ...])
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.swarmplot(x='species', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about swarmplot.
 
Python – seaborn.swarmplot() method
Swarmplot using Seaborn in Python
 
Factorplot is the most general of all these plots and provides a parameter called kind to choose the kind of plot we want thus saving us from the trouble of writing these plots separately. The kind parameter can be bar, violin, swarm etc. It is plotted using the factorplot() method.
 
Syntax:
 
sns.factorplot([x, y, hue, data, row, col, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.factorplot(x='species', y='sepal_width', data=data)plt.show()
  
 
Refer to the below articles to get dtailed information about the factor plot.
 
Python – seaborn.factorplot() method
Plotting different types of plots using Factor plot in seaborn
Distribution Plots are used for examining univariate and bivariate distributions meaning such distributions that involve one variable or two discrete variables.
 
Refer to the below article to get detailed informaon about the distribution plots.
 
Distribution Plots
 
 There are various types of distribution plots let’s discuss each one them in detail.
 
A histogram is basically used to represent data provided in a form of some groups.It is accurate method for the graphical representation of numerical data distribution. It can be plotted using the histplot() function.
 
Syntax:
 
histplot(data=None, *, x=None, y=None, hue=None,  **kwargs)
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.histplot(x='species', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about histplot.
 
How to Make Histograms with Density Plots with Seaborn histplot?
How to Add Outline or Edge Color to Histogram in Seaborn?
Scatter Plot with Marginal Histograms in Python with Seaborn
 
Distplot is used basically for univariant set of observations and visualizes it through a histogram i.e. only one observation and hence we choose one particular column of the dataset. It is potted using the distplot() method.
 
Syntax:
 
distplot(a[, bins, hist, kde, rug, fit, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.distplot(data['sepal_width'])plt.show()
  
Output:
 
Jointplot is used to draw a plot of two variables with bivariate and univariate graphs. It basically combines two different plots. It is plotted using the jointplot() method.
 
Syntax:
 
jointplot(x, y[, data, kind, stat_func, ...])
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.jointplot(x='species', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about the topic.
 
Python – seaborn.jointplot() method
 
Pairplot represents pairwise relation across the entire dataframe and supports an additional argument called hue for categorical separation. What it does basically is create a jointplot between every possible numerical column and takes a while if the dataframe is really huge. It is plotted using the pairplot() method.
 
Syntax:
 
pairplot(data[, hue, hue_order, palette, ...])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.pairplot(data=data, hue='species')plt.show()
  
Output:
 
 
Refer to the below articles to get detailed information about the pairplot.
 
Python – seaborn.pairplot() method
Data visualization with Pairplot Seaborn and Pandas
 
Rugplot plots datapoints in an array as sticks on an axis.Just like a distplot it takes a single column. Instead of drawing a histogram it creates dashes all across the plot. If you compare it with the joinplot you can see that what a jointplot does is that it counts the dashes and shows it as bins. It is plotted using the rugplot() method.
 
Syntax:
 
rugplot(a[, height, axis, ax])
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.rugplot(data=data)plt.show()
  
Output:
 
KDE Plot described as Kernel Density Estimate is used for visualizing the Probability Density of a continuous variable. It depicts the probability density at different values in a continuous variable. We can also plot a single graph for multiple samples which helps in more efficient data visualization.
 
Syntax:
 
seaborn.kdeplot(x=None, *, y=None, vertical=False, palette=None, **kwargs)
 
Example:
 
Python3
# importing packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = sns.load_dataset("iris")  sns.kdeplot(x='sepal_length', y='sepal_width', data=data)plt.show()
  
Output:
 
 
Refer to the below articles to getdetailed information about the topic.
 
Seaborn Kdeplot – A Comprehensive Guide
KDE Plot Visualization with Pandas and Seaborn
 
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.
 
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() 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 packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = 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.
 
Python – seaborn.lmplot() method
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 packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = 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.
 
Python – seaborn.regplot() method
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.
 
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.
 
Matrix plots
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 packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = 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?
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 packagesimport seaborn as snsimport matplotlib.pyplot as plt  # loading datasetdata = 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.
 
Hierarchically-clustered Heatmap in Python with Seaborn Clustermap
Exploring Correlation in Python
 
Seaborn – Bubble Plot
Python – seaborn.residplot() method
Python – seaborn.boxenplot() method
Python – seaborn.pointplot() method
Python Seaborn – Catplot
How to Make Countplot or barplot with Seaborn Catplot?
How To Make Grouped Boxplot with Seaborn Catplot?
Python Seaborn – Strip plot illustration using Catplot
How To Make Simple Facet Plots with Seaborn Catplot in Python?
How To Make Ridgeline plot in Python with Seaborn?
 
Change Axis Labels, Set Title and Figure Size to Plots with Seaborn
How To Place Legend Outside the Plot with Seaborn in Python?
How to Plot a Confidence Interval in Python?
Creating A Time Series Plot With Seaborn And Pandas
How to Make a Time Series Plot with Rolling Average in Python?
How To Add Regression Line Per Group with Seaborn in Python?
Data Visualization with Python Seaborn and Pandas
Data Visualization in Python using Matplotlib and Seaborn
Visualizing ML DataSet Through Seaborn Plots and Matplotlib
 
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