Exploring and Comparing Data Analysis and File Formats: CSV, Excel, JSON, and Parquet Formats

In this video, I compare four popular data file formats — CSV, Excel (XLSX), JSON, and Parquet — to find out which one is the most efficient for data analysis. Spoiler: It depends on your needs.

Using Python and real benchmarking, I measure:

- File size
- Export time
- Load and analytic time (including unique value calculations)

You'll get clear, side-by-side performance metrics and see which format you should choose depending on your workflow — from spreadsheets to big data. I will also quickly cover some pros and cons of each.

Whether you're a data analyst, researcher, or noob, this breakdown will help you make smarter, faster choices for working with datasets.

Formats tested:

- CSV (.csv)
- Excel (.xlsx)
- JSON (.json)
- Parquet (.parquet)

Given the size and number of scripts, as well as the mysqllite database. I didn't add this to GitHub, however, I can make these available if desired.

TIMELINE
Intro - 0:00
Overview of video - 0:02
Exporting Times and File Size: CSV - 0:37
Exporting Times and File Size: Excel - 1:45
Exporting Times and File Size: JSON - 2:06
Exporting Times and File Size: Parquet - 2:18
Checking out Exporting Times Metrics - 2:32
Pros and Cons/Opening up CSV - 3:06
Pros and Cons/Opening up Excel - 4:37
Pros and Cons/Opening up JSON - 6:08
Pros and Cons/Opening up Parquet - 7:42
Assessing Import and Analytical Times: CSV - 9:15
Assessing Import and Analytical Times: Excel - 9:50
Assessing Import and Analytical Times: JSON - 9:56
Assessing Import and Analytical Times: Parquet - 10:05
Comparing all Metrics - 10:16
Wrapping up - 10:41
Outro - 10:46

Видео Exploring and Comparing Data Analysis and File Formats: CSV, Excel, JSON, and Parquet Formats канала Too Long; Didn't Watch Tutorials