Working With SQL Versus Pandas (Part 1) Plus Practice Problems - Selecting, filtering, and sorting data
Selecting, filtering, and sorting data
The Python Pandas library and SQL are both popular tools for manipulating data and have a lot of overlap in their functionality. So, what better way to improve your skill at both than to put Pandas and SQL head-to-head by working through the same coding problems for each?
This is the first of a series of articles I will write to help you directly compare Pandas and SQL. My goal is to help you:
For the examples here I will use the infamous Titanic dataset that can be found on Kaggle. I encourage you to look at it while going through this article so you can follow along. At the end, I will present some practice problems for you to work through on your own based on the specific functionality discussed in this article.
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
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