Seaborn Python library

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Seaborn is a popular data visualization library in Python that is built on top of the Matplotlib library. It provides a high-level interface for creating informative and attractive statistical graphics.

Seaborn offers several features that make it useful for data visualization, including:

  1. Easy creation of complex visualizations such as multi-plot grids, heatmaps, and time series.
  2. Built-in themes that can be used to customize the look of visualizations.
  3. Automatic estimation and plotting of common statistical models, such as linear regression and kernel density estimation.
  4. Integration with Pandas data structures for easy data manipulation and plotting.
  5. Support for visualizing categorical data and data with multiple dimensions.

Seaborn is widely used for data exploration, data analysis, and communication of results in scientific research, data journalism, and industry. It is also frequently used in machine learning and artificial intelligence projects to help understand and visualize the data.

The useful functions included in the Seaborn library

Seaborn library in Python includes a wide range of functions for data visualization. Some of the most commonly used functions are:

  1. sns.distplot(): It is used to plot a univariate distribution of observations. It combines a histogram with a kernel density estimate.
  2. sns.scatterplot(): It is used to plot a scatter plot of two variables along with the regression line.
  3. sns.lineplot(): It is used to plot a line plot of one variable against another variable.
  4. sns.barplot(): It is used to plot a bar plot of one categorical variable against another numerical variable.
  5. sns.countplot(): It is used to plot the count of observations in each category of a categorical variable.
  6. sns.boxplot(): It is used to plot a box plot of one variable grouped by another variable.
  7. sns.heatmap(): It is used to plot a heatmap of a correlation matrix or other two-dimensional data.
  8. sns.jointplot(): It is used to plot a joint distribution of two variables with their univariate distributions on the side.
  9. sns.pairplot(): It is used to plot pairwise relationships between multiple variables in a dataset.

These are just some of the most commonly used functions in Seaborn, there are many other functions and customization options available to create a wide range of informative and attractive visualizations.

How to use sns.distplot() included in the Seaborn library

sns.distplot() is a function in Seaborn that can be used to plot a univariate distribution of observations. It combines a histogram with a kernel density estimate plot.

Here is a step-by-step guide on how to use sns.distplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s generate a random dataset of 1000 samples from a normal distribution with a mean of 0 and a standard deviation of 1.
import numpy as np
data = np.random.normal(0, 1, size=1000)
  • Use sns.distplot() to plot the distribution. You can customize the plot by specifying various arguments such as the color, label, and number of bins.
sns.distplot(data, color='blue', bins=20, label='Normal Distribution')
  • Add a legend, axis labels, and a title to the plot using the plt.legend(), plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.legend()
plt.xlabel('Data Values')
plt.ylabel('Density')
plt.title('Distribution of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a distribution using sns.distplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

data = np.random.normal(0, 1, size=1000)
sns.distplot(data, color='blue', bins=20, label='Normal Distribution')
plt.legend()
plt.xlabel('Data Values')
plt.ylabel('Density')
plt.title('Distribution of Data')
plt.show()

This code will generate a plot of the distribution of the data. You can modify the arguments passed to the sns.distplot() function to customize the plot according to your requirements.

How to use sns.lineplot() included in the Seaborn library

sns.lineplot() is a function in Seaborn that can be used to plot a line plot of one variable against another variable. Here is a step-by-step guide on how to use sns.lineplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create a simple dataset of x and y values:
x = [1, 2, 3, 4, 5]
y = [3, 5, 2, 7, 1]
  • Use sns.lineplot() to plot the line plot. You can pass the x and y values as arguments to the function, and customize the plot by specifying various arguments such as the color and label.
sns.lineplot(x=x, y=y, color='blue', label='Line Plot')
  • Add a legend, axis labels, and a title to the plot using the plt.legend(), plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.legend()
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Line Plot of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a line plot using sns.lineplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [3, 5, 2, 7, 1]

sns.lineplot(x=x, y=y, color='blue', label='Line Plot')
plt.legend()
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Line Plot of Data')
plt.show()

This code will generate a plot of the line plot of the x and y values. You can modify the arguments passed to the sns.lineplot() function to customize the plot according to your requirements.

How to use sns.scatterplot() included in the Seaborn library

sns.scatterplot() is a function in Seaborn that can be used to plot a scatter plot of two variables. Here is a step-by-step guide on how to use sns.scatterplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create a simple dataset of x and y values:
x = [1, 2, 3, 4, 5]
y = [3, 5, 2, 7, 1]
  • Use sns.scatterplot() to plot the scatter plot. You can pass the x and y values as arguments to the function, and customize the plot by specifying various arguments such as the color and marker type.
sns.scatterplot(x=x, y=y, color='blue', marker='o')
  • Add axis labels and a title to the plot using the plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Scatter Plot of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a scatter plot using sns.scatterplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [3, 5, 2, 7, 1]

sns.scatterplot(x=x, y=y, color='blue', marker='o')
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Scatter Plot of Data')
plt.show()

This code will generate a plot of the scatter plot of the x and y values. You can modify the arguments passed to the sns.scatterplot() function to customize the plot according to your requirements. For example, you can add a hue parameter to color points by a third variable.

How to use sns.barplot() included in the Seaborn library

sns.barplot() is a function in Seaborn that can be used to plot a bar plot of one variable against another variable. Here is a step-by-step guide on how to use sns.barplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create a simple dataset of x and y values:
x = ['A', 'B', 'C', 'D', 'E']
y = [3, 5, 2, 7, 1]
  • Use sns.barplot() to plot the bar plot. You can pass the x and y values as arguments to the function, and customize the plot by specifying various arguments such as the color and label.
sns.barplot(x=x, y=y, color='blue', label='Bar Plot')
  • Add a legend, axis labels, and a title to the plot using the plt.legend(), plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.legend()
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Bar Plot of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a bar plot using sns.barplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

x = ['A', 'B', 'C', 'D', 'E']
y = [3, 5, 2, 7, 1]

sns.barplot(x=x, y=y, color='blue', label='Bar Plot')
plt.legend()
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Bar Plot of Data')
plt.show()

This code will generate a plot of the bar plot of the x and y values. You can modify the arguments passed to the sns.barplot() function to customize the plot according to your requirements. For example, you can add a hue parameter to create a grouped bar plot or use estimator to apply a function to summarize values.

How to use sns.countplot() included in the Seaborn library

sns.countplot() is a function in Seaborn that can be used to plot a count plot of one categorical variable. Here is a step-by-step guide on how to use sns.countplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create a simple dataset of a categorical variable:
categories = ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B']
  • Use sns.countplot() to plot the count plot. You can pass the categorical variable as an argument to the function, and customize the plot by specifying various arguments such as the color and label.
sns.countplot(x=categories, color='blue', label='Count Plot')
  • Add a legend, axis labels, and a title to the plot using the plt.legend(), plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.legend()
plt.xlabel('Categories')
plt.ylabel('Count')
plt.title('Count Plot of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a count plot using sns.countplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B']

sns.countplot(x=categories, color='blue', label='Count Plot')
plt.legend()
plt.xlabel('Categories')
plt.ylabel('Count')
plt.title('Count Plot of Data')
plt.show()

This code will generate a plot of the count plot of the categorical variable. You can modify the arguments passed to the sns.countplot() function to customize the plot according to your requirements. For example, you can add a hue parameter to create a stacked count plot or change the order of categories using order.

How to use sns.boxplot() included in the Seaborn library

sns.boxplot() is a function in Seaborn that can be used to plot a box plot of one or more variables. Here is a step-by-step guide on how to use sns.boxplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create a simple dataset of a numeric variable:
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  • Use sns.boxplot() to plot the box plot. You can pass the variable as an argument to the function, and customize the plot by specifying various arguments such as the color and label.
sns.boxplot(y=data, color='blue', label='Box Plot')
  • Add a legend, axis labels, and a title to the plot using the plt.legend(), plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.legend()
plt.xlabel('Variable')
plt.ylabel('Values')
plt.title('Box Plot of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a box plot using sns.boxplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

sns.boxplot(y=data, color='blue', label='Box Plot')
plt.legend()
plt.xlabel('Variable')
plt.ylabel('Values')
plt.title('Box Plot of Data')
plt.show()

This code will generate a plot of the box plot of the variable. You can modify the arguments passed to the sns.boxplot() function to customize the plot according to your requirements. For example, you can use the x parameter to plot a box plot of two variables or add a hue parameter to create a grouped box plot.

How to use sns.heatmap() included in the Seaborn library

sns.heatmap() is a function in Seaborn that can be used to plot a heatmap of a matrix or a 2D dataset. Here is a step-by-step guide on how to use sns.heatmap():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create a simple 2D dataset:
data = [[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]]
  • Use sns.heatmap() to plot the heatmap. You can pass the dataset as an argument to the function, and customize the plot by specifying various arguments such as the color map and the axis labels.
sns.heatmap(data, cmap='Blues', annot=True, fmt='d', cbar=False)
  • Add a color bar, axis labels, and a title to the plot using the plt.colorbar(), plt.xlabel(), plt.ylabel(), and plt.title() functions from the Matplotlib library.
plt.colorbar()
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Heatmap of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a heatmap using sns.heatmap() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

data = [[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]]

sns.heatmap(data, cmap='Blues', annot=True, fmt='d', cbar=False)
plt.colorbar()
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Heatmap of Data')
plt.show()

This code will generate a plot of the heatmap of the dataset. You can modify the arguments passed to the sns.heatmap() function to customize the plot according to your requirements. For example, you can use the xticklabels and yticklabels parameters to specify custom labels for the x and y axes or set the annot parameter to False to remove the annotations from the heatmap.

How to use sns.jointplot() included in the Seaborn library

sns.jointplot() is a function in Seaborn that can be used to plot a joint distribution of two variables with their marginal distributions. Here is a step-by-step guide on how to use sns.jointplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s create two arrays of random numbers:
import numpy as np

x = np.random.normal(size=100)
y = np.random.normal(size=100)
  • Use sns.jointplot() to plot the joint distribution. You can pass the two arrays as arguments to the function, and customize the plot by specifying various arguments such as the color and label.
sns.jointplot(x=x, y=y, color='blue', label='Joint Plot')
  • Add a legend and a title to the plot using the plt.legend() and plt.title() functions from the Matplotlib library.
plt.legend()
plt.title('Joint Plot of Data')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a joint distribution using sns.jointplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

x = np.random.normal(size=100)
y = np.random.normal(size=100)

sns.jointplot(x=x, y=y, color='blue', label='Joint Plot')
plt.legend()
plt.title('Joint Plot of Data')
plt.show()

This code will generate a plot of the joint distribution of the two arrays. You can modify the arguments passed to the sns.jointplot() function to customize the plot according to your requirements. For example, you can use the kind parameter to specify the type of plot to be displayed, such as a hexbin plot or a kernel density estimate plot. Additionally, you can use the marginal_kws parameter to pass keyword arguments to the marginal plots, such as the color or label.

How to use sns.pairplot() included in the Seaborn library

sns.pairplot() is a function in Seaborn that can be used to plot pairwise relationships in a dataset. Here is a step-by-step guide on how to use sns.pairplot():

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the iris dataset included in Seaborn:
iris = sns.load_dataset('iris')
  • Use sns.pairplot() to plot the pairwise relationships. You can pass the dataset as an argument to the function, and customize the plot by specifying various arguments such as the color and the diagonal plot type.
sns.pairplot(iris, hue='species', palette='husl', diag_kind='hist')
  • Add a title to the plot using the plt.suptitle() function from the Matplotlib library.
plt.suptitle('Pairwise Relationships in Iris Dataset')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting pairwise relationships using sns.pairplot() would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

iris = sns.load_dataset('iris')

sns.pairplot(iris, hue='species', palette='husl', diag_kind='hist')
plt.suptitle('Pairwise Relationships in Iris Dataset')
plt.show()

This code will generate a plot of the pairwise relationships in the iris dataset. You can modify the arguments passed to the sns.pairplot() function to customize the plot according to your requirements. For example, you can use the vars parameter to select specific variables to plot, or the corner parameter to display only the lower or upper triangle of the plot. Additionally, you can use the plot_kws parameter to pass keyword arguments to the scatter and line plots, such as the color or marker style.

How to use hue parameter included in the Seaborn library

The hue parameter in Seaborn is used to group the data by a categorical variable and plot different colors for each group. It is available in most plotting functions in Seaborn, such as sns.scatterplot(), sns.lineplot(), sns.barplot(), sns.boxplot(), and so on.

Here is an example of how to use the hue parameter in Seaborn with the sns.scatterplot() function:

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the tips dataset included in Seaborn:
tips = sns.load_dataset('tips')
  • Use sns.scatterplot() to plot the data with different colors for each group. You can pass the hue parameter to the function, and specify the name of the categorical variable to group by.
sns.scatterplot(x='total_bill', y='tip', hue='sex', data=tips)
  • Add a legend and a title to the plot using the plt.legend() and plt.title() functions from the Matplotlib library.
plt.legend()
plt.title('Scatter Plot of Tips')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a scatter plot with different colors for each group using the hue parameter in Seaborn would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')

sns.scatterplot(x='total_bill', y='tip', hue='sex', data=tips)
plt.legend()
plt.title('Scatter Plot of Tips')
plt.show()

This code will generate a scatter plot of the tips dataset with different colors for each sex group. You can modify the name of the categorical variable passed to the hue parameter, and customize the plot according to your requirements. For example, you can use the palette parameter to specify the colors used for each group, or the style parameter to display different markers for each group.

How to use xticklabels and yticklabels parameters included in the Seaborn library

The xticklabels and yticklabels parameters in Seaborn are used to customize the labels of the tick marks on the x and y axes of a plot, respectively. These parameters are available in most plotting functions in Seaborn, such as sns.scatterplot(), sns.lineplot(), sns.barplot(), sns.boxplot(), and so on.

Here is an example of how to use the xticklabels and yticklabels parameters in Seaborn with the sns.barplot() function:

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the tips dataset included in Seaborn:
tips = sns.load_dataset('tips')
  • Use sns.barplot() to plot the data and customize the x and y tick labels. You can pass the xticklabels and yticklabels parameters to the function, and specify the list of labels to display on each axis.
sns.barplot(x='day', y='total_bill', data=tips)
plt.xticks(rotation=45, ha='right', fontsize=12)
plt.yticks(fontsize=12)
  • Add a title to the plot using the plt.title() function from the Matplotlib library.
plt.title('Bar Plot of Total Bill by Day')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a bar plot with customized x and y tick labels using the xticklabels and yticklabels parameters in Seaborn would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')

sns.barplot(x='day', y='total_bill', data=tips)
plt.xticks(rotation=45, ha='right', fontsize=12)
plt.yticks(fontsize=12)
plt.title('Bar Plot of Total Bill by Day')
plt.show()

This code will generate a bar plot of the tips dataset with customized x and y tick labels. You can modify the list of labels passed to the xticklabels and yticklabels parameters, and customize the plot according to your requirements. For example, you can use the rotation and ha parameters to rotate and align the x tick labels, or the fontsize parameter to adjust the size of the tick labels.

How to use kind parameter included in the Seaborn library

The kind parameter in Seaborn is used to specify the type of plot you want to create. This parameter is available in many Seaborn plotting functions, such as sns.relplot(), sns.catplot(), and sns.jointplot(). The kind parameter can take various values, each corresponding to a different type of plot.

Here is an example of how to use the kind parameter in Seaborn with the sns.catplot() function:

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the tips dataset included in Seaborn:
tips = sns.load_dataset('tips')
  • Use sns.catplot() to create a categorical plot and specify the kind parameter. You can pass the kind parameter to the function, and specify the type of plot you want to create. For example, you can set kind='bar' to create a bar plot of the data.
sns.catplot(x='day', y='total_bill', data=tips, kind='bar')
  • Add a title to the plot using the plt.title() function from the Matplotlib library.
plt.title('Bar Plot of Total Bill by Day')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a bar plot using the kind parameter in Seaborn would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')

sns.catplot(x='day', y='total_bill', data=tips, kind='box')
plt.title('Bar Plot of Total Bill by Day')
plt.show()

This code will generate a bar plot of the tips dataset. You can modify the kind parameter to create a different type of plot, such as a box plot, violin plot, swarm plot, or point plot. The available values of kind depend on the type of plot you want to create, so make sure to check the Seaborn documentation for the specific function you are using.

How to use marginal_kws parameter included in the Seaborn library

The marginal_kws parameter is included in several Seaborn plotting functions, including sns.jointplot() and sns.pairplot(). This parameter is used to pass additional keyword arguments to the functions that create the marginal plots in these functions.

The marginal plots are the histograms or density plots on the sides of the main plot. By default, Seaborn uses sns.histplot() to create these marginal plots, but you can pass additional arguments to this function using the marginal_kws parameter.

Here is an example of how to use the marginal_kws parameter in Seaborn with the sns.jointplot() function:

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the tips dataset included in Seaborn:
tips = sns.load_dataset('tips')
  • Use sns.jointplot() to create a scatter plot with marginal histograms and specify the marginal_kws parameter. You can pass the marginal_kws parameter to the function, and specify the additional keyword arguments you want to pass to sns.histplot(). For example, you can set marginal_kws={'bins': 20} to create histograms with 20 bins.
sns.jointplot(x='total_bill', y='tip', data=tips, marginal_kws={'bins': 20})
  • Add a title to the plot using the plt.suptitle() function from the Matplotlib library.
plt.suptitle('Scatter Plot with Marginal Histograms')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for plotting a scatter plot with marginal histograms using the marginal_kws parameter in Seaborn would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')

sns.jointplot(x='total_bill', y='tip', data=tips, marginal_kws={'bins': 20})
plt.suptitle('Scatter Plot with Marginal Histograms')
plt.show()

This code will generate a scatter plot of the tips dataset with marginal histograms. You can modify the marginal_kws parameter to pass different arguments to sns.histplot() and create histograms or density plots with different properties, such as the number of bins, the color, or the type of estimator used for the density plot.

How to use vars parameter included in the Seaborn library

The vars parameter is included in several Seaborn plotting functions, such as sns.pairplot() and sns.catplot(). This parameter is used to specify the variables in the dataset that you want to include in the plot.

When you pass a dataset to a Seaborn plotting function, it can contain multiple variables, and the default behavior is to plot all of them. However, you can use the vars parameter to select a subset of the variables to include in the plot.

Here’s an example of how to use the vars parameter in Seaborn:

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the tips dataset included in Seaborn:
tips = sns.load_dataset('tips')
  • Use sns.pairplot() to create a scatter plot matrix with the total_bill, tip, and size variables. You can pass a list of variable names to the vars parameter to select the variables to include in the plot.
sns.pairplot(data=tips, vars=['total_bill', 'tip', 'size'])
  • Add a title to the plot using the plt.suptitle() function from the Matplotlib library.
plt.suptitle('Scatter Plot Matrix with Total Bill, Tip, and Party Size')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for creating a scatter plot matrix with the vars parameter in Seaborn would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')

sns.pairplot(data=tips, vars=['total_bill', 'tip', 'size'])
plt.suptitle('Scatter Plot Matrix with Total Bill, Tip, and Party Size')
plt.show()

This code will generate a scatter plot matrix of the tips dataset with the total_bill, tip, and size variables. You can modify the vars parameter to select different subsets of variables to include in the plot. Note that some Seaborn plotting functions also accept other ways of specifying the variables to include, such as the x and y parameters in sns.catplot().

How to use corner parameter included in the Seaborn library

The corner parameter is included in the sns.pairplot() function in Seaborn. It is used to control the placement of the marginal axes that show the distribution of each variable in the dataset.

By default, corner=False, and the marginal axes are placed on the top and right sides of the plot. However, if you set corner=True, the marginal axes are placed on the bottom and left sides of the plot.

Here’s an example of how to use the corner parameter in Seaborn:

  • Import the necessary libraries:
import seaborn as sns
import matplotlib.pyplot as plt
  • Load or generate the data that you want to plot. For example, let’s load the tips dataset included in Seaborn:
tips = sns.load_dataset('tips')
  • Use sns.pairplot() to create a scatter plot matrix with the total_bill, tip, and size variables. Set corner=True to move the marginal axes to the bottom and left sides of the plot.
sns.pairplot(data=tips, vars=['total_bill', 'tip', 'size'], corner=True)
  • Add a title to the plot using the plt.suptitle() function from the Matplotlib library.
plt.suptitle('Scatter Plot Matrix with Total Bill, Tip, and Party Size')
  • Display the plot using the plt.show() function.
plt.show()

The complete code for creating a scatter plot matrix with the corner parameter in Seaborn would look like this:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset('tips')

sns.pairplot(data=tips, vars=['total_bill', 'tip', 'size'], corner=True)
plt.suptitle('Scatter Plot Matrix with Total Bill, Tip, and Party Size')
plt.show()

This code will generate a scatter plot matrix of the tips dataset with the total_bill, tip, and size variables. The marginal axes will be placed on the bottom and left sides of the plot due to the corner=True parameter. Note that this parameter only works with sns.pairplot() and not with other Seaborn plotting functions.

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