SQL vs Python: Difference Between SQL and Python

When we talk about the world’s leading programming languages, their are some programming language to learn which are and will be in demand in the future. SQL and Python are well-placed on the top of the list. As a beginner, it can be daunting to make sense of the pros and cons on your own. So, in this article, we have addressed the SQL vs. Python debate to guide you better. 

About two decades ago, software engineers had limited options when it came to programming languages. Structured Query Language or SQL was the most sought-after language for quick data insights, fetching records, and drawing conclusions. Then, data existed predominantly in relational databases, and SQL proved to be an excellent resource while writing reports and applications. 

Today, data comes in many shapes and forms, such as plain text, CSV files, on the web, and many other formats. This is where Python comes into the picture, boasting of its vast set of libraries and associated platforms. Over the years, it has emerged as a widely preferred programming language for fast and iterative data exploration. Moreover, its diverse toolkit allows developers to conduct visualization and statistical analysis and further identify patterns in the available data. 

Benefits of using SQL and Python

Be it SQL or Python, each language comes with its own set of advantages. SQL has been designed to query and extract data. One of its main strengths includes merging data from multiple tables within a database. However, you cannot use SQL exclusively for performing higher-level data manipulations and transformations like regression tests, time series, etc. Python’s specialized library, Pandas, facilitates such data analysis. Therefore, you can use SQL to fetch data and further manipulate the structured data using Python. 

Now that we have discussed how these languages can complement each other, let us move on to the question of SQL vs. Python

Must Read: Python Interview Questions

Differences between SQL and Python

The fundamental difference is that SQL is a query language primarily used for accessing and extracting data, whereas Python is a general-purpose programming language that enables experimentation with the data. So, which language should you prefer to learn? Let us consider an example to compare their application.

Suppose you have a database table called “eateries” containing information on several restaurants. This table could have millions of entries, each representing a different restaurant. Every row would include details like Name, City, Address, Prices, Rating, Contact Information, etc. Now, let us see how we could query this dataset using SQL vs. Python

In SQL, you can retrieve all the restaurants with the statement SELECT* FROM eateries. Similarly, to retrieve the first five restaurants, you can use SELECT* FROM eateries LIMIT 5. You can also get the names of all the restaurants located in Mumbai with the SQL statement SELECT Name FROM eateries WHERE City = ‘Mumbai’

On the other hand, you can retrieve the same information in Python using Pandas by executing the following statements:

All restaurants: eateries

First five restaurants: eateries.head(5)

Restaurants in Mumbai: eateries[eateries.City = = ‘Mumbai’].Name

In the above example, you will observe that the two languages are quite similar. But things start to change as we implement more complex queries. 

Adding layers of complexity

After some preliminary data querying, you may want to order the results. Let’s say you have a dataset table eateries_by_city comprising the number of restaurants per city, where Mumbai has 510 restaurants, Delhi has 420 restaurants, and so on. Here, the figures are given under the head Eatery_Count. Now, with this new dataset, we want to select the top 10 cities having the largest count. 

SQL:

SELECT City FROM eateries_by_city ORDER BY Eatery_Count DESC LIMIT 10

Python using Pandas:

eateries_by_city.nlargest(10, columns=’Eatery_Count‘)

How will the structure of these statements change if you need to determine the next ten restaurants? you can learn more on python pandas tutorial which will clear the idea of this concept.

SQL:

SELECT City FROM eateries_by_city ORDER BY Eatery_Count DESC LIMIT 10 OFFSET 10

Python:

eateries_by_city.nlargest(20,columns=’Eatery_Count‘).tail(10)

As the queries become more complicated, you will notice that the SQL syntax becomes harder to read as compared to the Python syntax, which remains relatively unaltered. 

History, usage, and types of SQL

SQL is a brainchild of Donald D. Chamberlin and Raymond F. Boyce, who developed the database management system at IBM in 1974. In the early 1970s, Dr. E.F. Codd (popularly known as the Father of Database Management Systems) had published his ideas about relational databases in a paper, which led IBM to create a group for prototype development. Codd had proposed a language called the DSL/Alpha, which went through multiple stages of simplification and refinement to be finally renamed as SQL. 

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Coming to the present day, nearly every website uses an SQL database for its back-end development. And SQL is the standard language that expresses your need to the database. We have compiled the different types of SQL databases below.

  • MySQL: This database contains tables that link together in some way. Pronounced as “My Sequel,” it is the default setting for all WordPress installations.
  • PostgreSQL: Often referred to as Postgres, this type of database is advisable for use by more advanced developers. You can think of it as a durable version of MySQL. 
  • SQLite: Being a portable and compact SQL database, it is best suited for testing simple applications.

Read more: Top 7 Reasons to Start Learning SQL Today

History, usage, and types of Python

Python finds its origin in the 1980s when ABC, a language to teach programming, was first introduced. In the late 1980s, Guido Van Rossum from the Netherlands began his work to derive a language with better code readability. And hence, Python was created in 1991 and named after the famous British TV series “Monty Python’s Flying Circus” that he was watching at the time. 

Today, high-tech companies in the world are using Python for data science and exploration. Some examples include industry leaders like Google, Dropbox, and YouTube. Now, coming to the types of Python, Python 2 and Python 3 are the two most common versions. Python 3 was believed to take over Python 2 by 2020, but Python 2 is still an established part of the software in many companies. However, the Python 2 version may lose its security features and bug fixes in the near future, so it makes sense to brush up your Python 3 skills.  

SQL or Python: Which one should you use?

SQL commands are simpler and narrower vis-a-vis Python commands. More often than not, they form a combination of JOINS, aggregate functions, and subqueries functions.  

As for Python, the programming commands are like an assortment of a Lego set, where each piece has a specific purpose. The libraries consist of specialized bits that help you build something in that particular niche. For example, Pandas are used for data analysis, Scikit-learn for machine learning, PyPDF2 for PDF manipulation, SciPy for numerical routines, and Numpy for mathematical operations and scientific computing. 

Relational database management systems used in many corporate applications call for having prior knowledge of SQL. It provides a structured route to get the desired information. Conversely, Python offers more readability and portability, assisting the development of just about anything with the right tools and libraries. 

Know more: Top 5 Python Modules You Should Know

Which language should you learn first?

Let us first recap what each of the languages brings to the table. SQL is a standard query language for data retrieval, and Python is a widely recognized scripting language for building desktop and web applications. So, which of these two languages is the best place to start?

Typically, SQL is believed to be the first step in the learning ladder as it is an essential tool for summoning relevant information from relational databases. Also, it is easy to grasp as it reads like English. So, having a reasonable understanding of this language sets you up for Python. Once you can write a query to join two tables, apply the same logic to rewrite code in Python using the Pandas library. 

With a solid foundation of the two languages, you will be all set to undertake various functions like back-end development, data analysis, scientific computing, artificial intelligence, and so on. 

Summing up

Weighing in from the above arguments, we can see that SQL is applicable in relational databases with only a few exceptions. But it can still be a powerful tool for beginners. Over the years, many new features have been incorporated in SQL to improve its object-oriented functionality. 

Python is a versatile and dynamic programming language having multiple applications. The broad scope can be attributed to its extensive collection of python libraries for data science, each of them serving a distinct purpose.

By becoming adept in these two languages, you will be one step closer to landing a lucrative job. Some of the job profiles include Software Engineer, DevOps Engineer, Data Scientist, and many machine learning and AI-related roles. Companies like IBM, NASA, Walt Disney, Google, and Yahoo! Maps regularly hire professionals who possess superior Python skills. 

With this, we have covered the different aspects of SQL vs. Python. As you start your learning journey, you will now have a much more precise approach. The coding community is always abuzz with new and exciting things, and having a conceptual base allows you to adapt seamlessly and shine!

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