In this comprehensive guide, we will discuss how to create a Seaborn barplot that can effectively represent your data. Seaborn is a powerful Python data visualization library built on top of Matplotlib that provides an intuitive interface for creating beautiful, informative statistical graphics. By following the steps in this tutorial, you will be able to create a visually appealing and informative Seaborn barplot to showcase your data.

Creating a Basic Barplot

				
					import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = {
    'Category': ['A', 'B', 'C', 'D', 'E'],
    'Values': [23, 45, 56, 22, 12]
}

# Create a bar plot
sns.set_theme(style='whitegrid')
plt.figure(figsize=(10, 6))
ax = sns.barplot(x='Category', y='Values', data=data)

# Add labels and title
plt.xlabel('Category', fontsize=12)
plt.ylabel('Values', fontsize=12)
plt.title('Seaborn Bar Plot Example', fontsize=16)

# Show the plot
plt.show()

Output:

seaborn Distplot

In this example, we first load the “tips” dataset from seaborn’s example data. We then create a distplot using sns.distplot(), passing in the “total_bill” column of the tips dataset. We set kde=False to remove the kernel density estimate and bins=20 to specify the number of bins in the histogram.

Finally, we add labels and a title to the plot using plt.xlabel(), plt.ylabel(), and plt.title(), respectively, and show the plot using plt.show().

Displot multiple variations

By altering the parameters of the distplot() method, it is possible to generate entirely new views. These parameters can be modified to alter color, orientation, and more.

Here’s an example of using subplot() from the pylab module to show four variations of distplot() using different parameters:

				
					import seaborn as sns
import matplotlib.pyplot as plt

# Load example data
iris = sns.load_dataset("iris")

# Create a 2x2 grid of subplots
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))

# Plot a distplot with the default parameters in the top left subplot
sns.distplot(iris["sepal_length"], ax=axs[0, 0])
axs[0, 0].set_title("Default distplot")

# Plot a distplot with a rug plot and a blue color in the top right subplot
sns.distplot(iris["sepal_length"], rug=True, color="blue", ax=axs[0, 1])
axs[0, 1].set_title("Distplot with rug plot")

# Plot a distplot with a kernel density estimate and a green color in the bottom left subplot
sns.distplot(iris["sepal_length"], kde=True, color="green", ax=axs[1, 0])
axs[1, 0].set_title("Distplot with KDE")

# Plot a distplot with both a histogram and kernel density estimate and a red color in the bottom right subplot
sns.distplot(iris["sepal_length"], kde=True, hist=True, color="red", ax=axs[1, 1])
axs[1, 1].set_title("Distplot with both KDE and histogram")

# Increase the margin between the subplots
plt.subplots_adjust(hspace=0.4)

# Show the plot
plt.show()

Output: 

seaborn Distplot

In this example, we create a 2×2 grid of subplots using subplot() and the figsize parameter to specify the size of the figure. We then plot four variations of distplot() using different parameters in each subplot.

In the top left subplot, we use the default parameters for distplot(). In the top right subplot, we add a rug plot and change the color to blue. In the bottom left subplot, we remove the histogram and add a kernel density estimate (KDE), with the color changed to green. In the bottom right subplot, we show both the histogram and the KDE, with the color changed to red.

Moreover, we add the line plt.subplots_adjust(hspace=0.4) after the last sns.distplot() command to increase the vertical space between the subplots. The hspace parameter controls the height ratio between subplots, and its value of 0.4 adds 40% more height between the subplots.

The result is a plot with more vertical space between the subplots containing the two sns.distplot() commands. You can adjust the hspace parameter to your liking to increase or decrease the margin between subplots.

Finally, we set titles for each subplot using set_title() and show the plot using plt.show(). 

Wrap up

To learn more about Seaborn Libarary check out the:
https://seaborn.pydata.org/tutorial.html


Thanks for reading. Happy coding!