August 2016
Beginner to intermediate
352 pages
9h 20m
English
Content preview from Foundations for Analytics with Python
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Chapter 2. Comma-Separated Values (CSV) Files
The comma-separated values (CSV) file format is a very simple way of storing and sharing data. CSV files hold data tables as plain text; each cell of the table (or spreadsheet) is just a number or string. One of the principal advantages of a CSV file compared to an Excel file is that there are many programs capable of storing, transferring, and processing plain-text files; on the other hand, there are fewer that are capable of handling Excel files. Any spreadsheet program, word processor, or simple text editor can handle plain-text files, but not all of them can handle Excel files. While Excel is an incredibly powerful tool, when you work with Excel files, you’re basically limited to the tasks that Excel can perform. CSV files give you the freedom to send your data to the right tool for the job you want to do—or to build your own tools using Python!
You do lose some of Excel’s features when you work with CSV files: whereas every cell of an Excel spreadsheet has a defined “type” (number, text, currency, date, etc.), cells of CSV files are just raw data. Thankfully, Python is pretty clever about recognizing different data types, as we’ve seen in Chapter 1. Another trade-off with using CSV files is that they don’t store formulas, only data. However, by separating the data storage (CSV file) and data processing (Python script), you make it easier to apply your processing to different datasets. It’s also easier to find—and harder to propagate!—errors ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.