In a world full of data, managing it all together in one place is crucial. And, it is a very tedious task even to imagine the picture in your head. Every application or software you use, every job you do, and everything you use online produce data. We produce about 2.5 quintillion bytes of data every day! That’s a considerable number even to picture.
Now, when it comes to data, we must be very careful about how we handle and analyze it. To make this cumbersome process easygoing, we have different programming options that help us simplify the knot. Python and SQL are prominent tools, which have everything to do with data analysis. Before getting started with the need for SQL and Python, let’s first understand their basic meaning and how they differ from each other.
What Is Python?
Python is a general-purpose scripting language used for a wide range of applications. It covers the area of recommendation algorithms to self-automation in cars. Using significant indentation makes this programming language easy to read.
Python is also preferred as a general-purpose programming language because of its popularity. Some prominent use cases are – web development, software development, back-end development, automation, software testing and prototyping, and routine tasks. Moreover, it was found to be the developers’ second most popular programming language in 2021.
Another reason that marks its credibility is the ease of learning. From a simple scientist or journalist to a seasoned developer, anyone can learn and implement Python into his/her life. So, how is Python related to data?
Python has a huge role in the world of data sciences as it is an excellent tool for analysing complex data in simpler terms. Python consists of numerous libraries that help discover and explore data, such as TensorFlow, Pyglet and Pandas which are made for statistical and mathematical analysis.
Most SQL tasks are made simpler for developers, thanks to Python libraries. The SQL base is further explored by Python, which displays the results in real time. Now, let’s understand the concept of SQL and its relation with Python.
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What is SQL?
SQL or Structured Query Language is also a programming language that gives space to the developers to manage or retrieve the database or even create one of their own. The different elements that come along with SQL are – tables, rows and columns that together help organise the data in one place.
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Some of the prominent applications of SQL could be the production of data insights, data analysis, retrieving records from databases and other things along the same lines. Other daily-use applications of databases are – banking, social media and music software. Besides that, SQL can also be seen as a base from which other data can be spun-off (by Python) for deeper analysis.
But what sets SQL and Python apart? Let’s find out.
What Makes the Difference Between the Two?
Looking at the two general programming languages, you won’t find much of a difference. But as we go deeper into it, we will find out the measures by which they differ from one another.
- SQL is like an initial step where developers have to create data from their database. This way, they help themselves simplify things by converting the data into a legible or usable format. Next comes exploring and analysing the data in-depth, and that’s where Python comes into play.
- SQL cannot be used for tasks such as data manipulation, trend lines and statistical analysis, which might look like a little barrier in SQL. Although, despite the complications, it is still one of the most used languages for complex operations.
- Python acts as a helping hand for SQL in which a deeper analysis and manipulation can be done, which is not common with SQL. Therefore, Python is the component of SQL that together streamline data analytics.
- Python is for someone who wants to go deeper into the analysis part of data. It uncovers the core as you go in depth. It gives you room to familiarise yourself with the data that doesn’t seem alien to you. Here, SQL can help the developers with familiarity with the content. If they know SQL and Python, then attaching them to the workflow can give them remarkable outcomes.
What is the Correct Time to Use SQL and Python?
Despite some overlap, programmers tend to prefer Python when dealing with more general software systems and SQL when dealing with data directly. Choosing the right language depends on your task. The different entries which may be helpful in selecting the language are –
- Demographic information
- Performance information
- Budgetary information
Depending on the goal, the data scientist may use a simpler SQL query. For more complicated calculations, the data scientist can use Python and SQL for the first query, but if additional analysis is required, then they may use Python. Such data can be processed, analysed or experimented with using SQL functions. Because SQL isn’t capable of handling computations well, it can be tedious or inefficient when processing data. Python is far more adaptable and appropriate for using the retrieved data.
Python or SQL – Which one is Easier and Better?
One common question that comes to our mind is, ‘SQL or Python, which is better? Or ‘should I learn SQL or Python first’?
Let’s take a step back and go back to the days when SQL was a go-to for most developers. Whenever they had to gain records, get insights into the data or find out the preliminary outcomes, SQL was the main tool for them. But as the data format changed (for example, CSV files, plain texts, etc.), people understood the requirement and value of Python, and it became prominent. Both languages are equally important in their ways. However, which one should be learned first?
If we scrutinise the pattern, then we will see that SQL is the base of data analysis. It is the initial process of the database. Therefore, learning SQL before Python makes sense, even if it might seem cumbersome and lengthy. Python can enhance the skills and knowledge of an SQL analyst. As a result, analysts become more dexterous with data and can easily integrate Panda libraries into their workflow.
Also Read: What are the Hottest Trends in Python
