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Complete Guide To Visualizer: Python Library for Automating Visualization

Complete Guide To Visualizer: Python Library for Automating Visualization

Visualizer Python

Data Visualization is considered to be one of the best ways to identify any anomaly, outlier, or if data follows a particular pattern. While looking at graphs and plots with our naked eyes we can clearly see what the data is trying to tell us. Data Visualization is the most important part of Story Telling about telling because it backs the story with visualizations that have a greater impact.

Python provides different modules/packages which are used for data visualization. We can create different visualizations like statistical visualizations, 3D visualization, etc. using different python packages and modules like seaborn, matplotlib, bokeh, etc. All python libraries provide us with different processes to create visualizations so each time we use a library we should what syntax to follow and what should be the code for different plots.


Visualizer is a python library that automates the process of visualization. It supports a large variety of graphs and plots which can easily be created using a single line of code. It allows creating visualizations of any individual relationship between multiple columns.

In this article, we will explore what are some of the plots and graphs which we can create using a visualizer.


We will start by installing visualizer using pip install visualizer.

  1. Importing required libraries

In this article, we will be creating visualizations for which we will import visualizer and for the dataset, we will import seaborn to use a default dataset defined in seaborn.

from visualizer import Visualizer

import seaborn as sns

  1. Loading the dataset

We will use seaborn to load the dataset named tips that contain attributes like total_bill, tip, etc. We will store this dataset into a data frame.

df = sns.load_dataset('tips')


Tips Dataset
  1. Creating Visualizations

Now let us start creating different visualizations for different attributes.

  1. Count Plot

Visualizer.create_count_plot(df=df, cat_col="day", annot=True)

Count Plot
  1. Pie Chart

Visualizer.create_pie_plot(df=df, cat_col="sex")

See Also
Lens Data Analysis

Pie Chart
  1. Box Plots

Visualizer.create_box_plot(df=df, num_col="total_bill", cat_col='sex')

Box Plots
  1. Density Plot

Visualizer.create_density_plot(df=df, num_1='tip', num_2='total_bill')

Density Plot
  1. Scatter Plot

Visualizer.create_scatter_plot(df=df, num_1='tip', num_2='total_bill')

Scatter Plot
  1. KDE Plots

Visualizer.create_kde_plot(df=df, num_col='tip')

  1. Ridge Plot

Visualizer.create_ridge_plot(df=df, num_col='total_bill', cat_col='day')


In this article, we saw how we can create different visualizations using visualizer and within a single line of code. All the graphs we created are highly informative and visually appealing. Visualizer is fast and easy to use, it has a variety of plots and graphs which can be used for different purposes as per requirements.

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