Exploration and Manipulation of Data Using Python, R, and Structured Query Language (SQL)

In the realm of data analysis, three powerful tools stand out: Python, R, and SQL. Each has its unique strengths, making them essential in various data science environments.

This article aims to shed light on the similarities and differences between these tools, using basic-level operations as examples. For comparison purposes, we'll use a dataset that contains information about items sold in different stores.

SQL, often used for data querying and handling large datasets in databases, employs the function to find the number of items sold in each store. For instance, to find the number of items sold in store 3, one would write:

On the other hand, Python's Pandas uses the function to achieve the same result:

Data.table, a popular R package, uses the option for aggregation, similar to SQL's function:

When it comes to filtering based on strings, SQL uses the keyword, while Pandas and Data.table both use the function of the str accessor.

Sorting results is another common operation. In SQL, one can use the clause with the keyword to sort results in descending order. For example:

Data.table uses the function to sort results, changing the default behavior of sorting in ascending order by adding a minus sign:

Pandas sorts results using the function, with the parameter to change the sort order:

Finding the average price of items for each store id is another task where these tools differ slightly. SQL selects and aggregates the price column while grouping by the store id column:

Pandas applies the group by function followed by the aggregate function (mean):

Data.table applies the aggregation and specifies the grouping column while selecting the columns:

To find the price of the most expensive item in store 3, SQL selects and applies the function while filtering by the store id:

Pandas first applies the filter and selects the column of interest followed by the function:

Data.table filters similarly to Pandas but the aggregation is similar to SQL syntax, with the difference that the aggregation function is specified with a dot in some cases but without a dot in others:

In summary, while these tools share many similarities, they each have unique ways of handling data analysis tasks. Understanding these differences can help data analysts choose the right tool for the job, especially in environments like Databricks, where they support scalable, collaborative data work. Mastering Python, R, and SQL is crucial for automating workflows, conducting complex analyses, and producing reproducible results.