A **histogram (aka histplot)** is a classic visualization tool that
represents the distribution of one or more variables by counting the
number of observations that fall within discrete bins.

To initialize a histplot based on the ``pandas`` DataFrame, we need to input at least 2 parameters: `x/y` (the column in which values will be used to create a histplot) and `data` (the DataFrame containing the data).

# Importing libraries needed
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Reading a file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c5b4ea8f-8a30-439f-9625-ddf2effbd9ac/example6.csv')

# Creating a histplot
sns.histplot(x = 'value', data = df)

# Showing the plot
plt.show()

We can change the histogram binning intervals by using ``binwidth=n`` as an argument in the plot function, ``n`` - is the width of one column:

# Importing the libraries needed
import seaborn as sns
import matplotlib.pyplot
import pandas as pd

# Reading a file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c5b4ea8f-8a30-439f-9625-ddf2effbd9ac/example6.csv')

# Creating a histplot
sns.histplot(x = 'value', data = df, binwidth = 2)

# Showing the plot
plt.show()

Together we will make the first dive into visualization based on a seaborn library. We will learn how to build simple plots and how to customize them. You will cope faster with tasks if you know something about pandas. Do not hesitate, and let's go!

In this section, we will learn about the seaborn library and build our first plot! We will also learn about the friendship between the pandas and the Seaborn.

We continue learning plots with Seaborn.\n\nYou will learn some more new plots and even build 3-variable plot!

Plots seem so boring without customization...\nIn this section, we will learn how to make our plots perfectly styled!!

Histplot

First Dive into seaborn Visualization

Histplot