Summary  
This chapter covers how to use Seaborn’s countplot function to visualize the frequency of categorical values by passing a list or column and then displaying the resulting bar plot with matplotlib’s plt.show().

General domain of usage  
Exploratory data analysis and visualization of categorical distributions.

A **countplot** is a plot that takes a categorical list and returns columns that represent the number of list entries for each category.

To initialize a countplot, we need to input at least 1 parameter: `x` (the column in which values will be counted). 

> Note
>
> We may also use `y` instead of `x` to change the orientation of the plot.

As the ``seaborn`` is based on the ``matplotlib``, we may use a special ``matplotlib.pyplot`` library and its function ``plt.show()`` to show the plot:

```python
# Importing the matplotlib.pyplot 
import matplotlib.pyplot as plt

# Showing the plot
plt.show()
```

# Importing the Seaborn 
import seaborn as sns
# Importing the matplotlib.pyplot
import matplotlib.pyplot as plt

# Data list
data_list = ['apple', 'fish', 'milk', 'milk', 'apple', 'apple', 'milk', 'fish', 'fish']

# Creating the countplot
sns.countplot(x = data_list)

# 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!!

Countplot

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