SQL allows you to start with a dataset which is structured into tables. Notice how the Pandas syntax remains almost unaltered as complexity increases, whereas the SQL syntax becomes more complex to read.Īnother way to think about differences between Python and SQL is that SQL provides the setup and Python takes you from there. nlargest(20, columns=’ Hotel_Count ‘).tail(10) SELECT City FROM hotels_by_city ORDER BY Hotel_Count DESC LIMIT 10 OFFSET 10 Let’s add a layer of complexity, so we now need to determine using this new dataset table, which is the next ten hotels, after the top ten. You can start to see that the Pandas syntax is becoming easier to grasp and write, as the examples of the data being retrieved, become a bit more complicated. SELECT City FROM hotels_by_city ORDER BY Hotel_Count DESC LIMIT 10 With this new dataset, let’s say we want to order results by Hotel _Count and only select the top ten cities with the largest count. Let’s say we did some preliminary data querying, and now have a dataset table called hotels_by_city, that contains the number of hotels per city, which looks as follows: City Examples when Python is easier to use than SQL However, things start to become more interesting when we need to do more complex queries. NameĪs we can see, so far, both are quite similar. SELECT Name FROM hotels WHERE City = ‘ Paris ‘ Let’s compare how we could query this dataset using SQL versus Python using Pandas. This table could include thousands or millions of rows (entries), each one representing a hotel. This is a small sample dataset of the information contained within the table. Say we have the following database table with hotel information called hotels. Let’s take a look at an example using SQL: Use SQL to retrieve the essential data required for the analysis, and then use specialized Python libraries to process it. This leads to the following question: When should you use Python vs. Python is particularly well suited for structured (tabular) data which can be fetched using SQL and then require farther manipulation, which might be challenging to achieve using SQL alone. Python, on the other hand, has a well-known data analysis Library called Pandas, which has been specially designed for data analysis and manipulation.
Types of higher-level data manipulation that are common within data science, such as statistical analysis, regression tests, and time-series data manipulation, are is very difficult to achieve using SQL exclusively. However, SQL isn’t designed for manipulating or transforming data into other formats. Combining data from multiple tables is a key strength.
SQL is good at allowing you as a developer, to seamlessly join (or merge) several data together. SQL is designed to query and extract data from tables within a database. Python’s set of libraries include everything from data visualization to statistical analysis, making it convenient for developers to jump into data analysis and begin identifying patterns. With a vast set of helper libraries and associated platforms, Python is an excellent programming language for quick, and iterative data exploration. It is in this maze of data that Python’s broad toolset of libraries shine. Data exists within CSV files, plain text, and on the web, as well as in many other different formats. Nowadays, data comes in many shapes and formats, not necessarily synonymous with relational databases any longer. Even back then, Structured Query Language, or SQL, was the go-to language when you needed to gain quick insight on some data, fetch records, and then draw preliminary conclusions that might, eventually, lead to a report or to writing an application.
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Python vs SQL – What’s the Difference?Īpproximately twenty years ago, there were only a handful of programming languages that a software engineer would need to know well. I write about coding, the internet, and social impact. Faculty at Columbia University where I teach Digital Literacy. Chris Castiglione Follow Teacher at One Month.
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