Data Visualization With SQL — A Brief Guide

Author(s): AlishaS

Originally published on Towards AI.

In data analytics, a picture speaks a thousand words, especially if it’s well presented. By using charts, graphs, and maps, data visualization tells a meaningful story about data in a visual format. This is a vital part of the data analysis process — and a technique (or art form!) that can be applied to any industry.

Let us explore how SQL can be used to visualize data in this article

What does Data Visualization mean?

Data visualization involves presenting data visually. It simplifies the process of identifying trends, patterns, outliers, and correlations in a dataset.

Let’s say you are presented with a spreadsheet where rows and rows of data are displayed. It’s unlikely you’ll be able to identify trends and patterns without delving into the data, and deciphering it requires a lot of work.

If we were to see the same data represented as a bar chart or as a map with colors, this would be very different. Knowing what the data tells you is easier, isn’t it? Have a look at the chart below. How easy it is to depict information —

Data visualization is all about making sense of data. The insight is visibly visible to anyone, making it easy to understand. Well-designed data visualizations tell a story. Data actionability is made possible by this storytelling aspect. Visualizing data bridges the gap between having a lot of data and understanding how to use it to drive decisions and actions.

Importance of Data Visualization

Whatever your profession or business, data visualization can help you deliver data efficiently. In the data visualization process, raw data is analyzed, modeled, and delivered to allow for conclusions to be drawn.

As data scientists develop advanced analytics algorithms, they make it easier to compile essential data into easy-to-understand visualizations. Data visualization benefits every industry that deals with data, which is why it has spread into a wide range of fields.

In every business, information is their most valuable asset. The power of visualization lies in its ability to communicate information efficiently and effectively.

Data visualization facilitates fast, efficient, and universal communication of information. Through this practice, companies can gain insight into what needs to be improved, what impacts customer satisfaction, and how to sell specific products (where and to whom).

Decision-makers, stakeholders, and business owners can predict future sales volumes with a visual representation of data.

Data can be visualized and understood through dashboards, graphs, infographics, maps, charts, videos, slides, etc. Using data visualization to interrelate data enables decision-makers to find better insights and reap the benefits of data visualization, including

1. Visualizing data tells a story about it

Visualizing data can also be used to tell data stories. Using visualization, you can tell a story and lead viewers to an inevitable conclusion while presenting easy-to-understand data facts.

It is critical that this data story has a good beginning, a basic plot, and an ending. An analyst might craft a data visualization detailing the profitability of various products for company executives and then make recommendations on how to deal with losses based on profits and losses.

2. Getting a better understanding of the data

Business stakeholders benefit from analyzing reports, as they can focus on areas that need attention. Analysts gain a better understanding of their business goals through the use of visual mediums.

Through better analysis and better business decisions, companies can increase their profits through a visual representation of data, whether it is a sales report or marketing strategy.

3. Visualizing data gives you a better understanding

Through data visualization, one can gain a deeper understanding of data by putting it in the context of its larger meaning. Based on the overall data picture shows how particular data references fit in.

4. Making decisions more quickly

A visual representation is more easily processed by humans than a tedious tabular report or form. By communicating well with the data, decision-makers can quickly act on new insights, which will accelerate business growth and decision-making

How to visualize data from MySQL database

The prerequisites to visualize the data from MySQL database are

1. My SQL Connector

Client programs can connect to MySQL servers using MySQL Connectors. A MySQL API can be used either to access classic MySQL or the X Protocol to gain low-level access to MySQL resources.

Through connectors and APIs, you can connect to and execute MySQL statements in other languages and environments, such as ODBC, Java (JDBC), C++, Python, Node.js, PHP, Perl, Ruby, and C++. Install the MySQL connector using the following command

pip install mysqlconnector

2. Numpy

In general, NumPy can be used to process arrays. In addition to providing a multidimensional array object, it provides tools for handling these arrays. Scientific computing with Python relies on this python package. The software is free and open-source. Install NumPy using the following command

import numpy as np

3. Matplotlib

In Python, Matplotlib provides the ability to create static, animated, and interactive visualizations. The Matplotlib package generates publication-quality figures across different platforms and hard copies.

This library can be used in Python scripts, scripts in Python/IPython shells, as a toolkit for creating graphical user interfaces, and via various web servers. Install matplotlib by using the command below

pip install matplotlib

Importing matplotlib.pyplot as plt allows us to replace matplotlib.pyplot with plt, which is a collection of functions that make matplotlib work.

import matplotlib.pyplot as plt

The steps to connect a MySQL database to Python are as follows:

1. Importing the MySQL connector we installed earlier is the first step. In order to do so, write:

import mysql.connector

2. Let’s create a variable called database, which will store the result of the connection. This can be accomplished by using the connect() method of the mysql.connector class to connect MySQL to Python:

database=mysql.connector.connect(host="localhost",user="root",password="Your_Password",database="Database_Name")

We have now completed the connection between MySQL and Python. So, we create a variable called mycursor which will store the cursor for the current database since we intend to get information from the database.

When you use a cursor, you can process each row of data returned by a query as you iterate through them.

cursor=mydb.cursor()

The sample Employee database we will use to visualize is shown below

The code to visualize the data is shown below

import mysql.connector
import numpy as np
import matplotlib.pyplot as plt
mydb = mysql.connector.connect(host="localhost",user="root",password="password",
database="Employee")
cursor = mydb.cursor()
cursor.execute("select EmployeeName, EmployeeAge from Employee")
result = cursor.fetchall
EmployeeName = []
EmployeeAge = []
for i in cursor:
 EmployeeName.append(i[0])
 EmployeeAge.append(i[1])
print("Name of Employee = ", EmployeeNames)
print("Age of Students = ",EmployeeAge)
plt.bar(EmployeeName, EmployeeAge)
plt.ylim(0, 5)
plt.xlabel("Name of Employee")
plt.ylabel("Age of Employee")
plt.title("Employee's Information")
plt.show()

Code Explanation:

We now use cursor execute() method to obtain the desired query and store the result by using fetchall to retrieve the result from the query. We are attempting to retrieve Employee Names and Employee Age from the Employee table in the code above. In order to plot their marks in a bar graph, we now store the Employee name and their age in two separate lists.

Output:

Tools for visualizing MySQL data

In order to boost performance, MySQL DBAs use the following data visualization tools for data analytics:

1. My Sql Workbench

With MySQL Workbench, you can develop, administer, design, create and maintain database structures and tables within a single integrated environment

2. Tableau

Tableau has very good compatibility with RDBMSs and is widely used for data visualization. In essence, it is a synonym for Business Intelligence, which aids companies in analyzing and mining data. DBAs can use Tableau to analyze database operations in real-time. Tableau is a versatile MySQL Data Visualization Tool that offers a wide range of visualization figures.

3. Microsoft Power BI

In Power BI, you can combine disparate data sources to create coherent, visually immersive, and interactive insights. Depending on your needs, your data may be in an Excel spreadsheet or in a cloud-based and on-premises hybrid warehouse.

4. Chartio

MySQL DBAs can leverage Chartio’s SQL and interactive query modes to perform advanced data analytics. Chartio’s intuitive interface facilitates analytical insights with quick access to data, data visualization, and analysis insights

Conclusion

In this article, we have explored how to use SQL to visualize the data in a detailed manner. Hope this small piece of information was helpful for you in learning the basics of Data visualization with Python.

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Published via Towards AI