The csv.reader function accepts lots of different arguments in order to parse different formatting standards in a CSV file. These rows are stored as lists, without us having to ever create a single list ourselves. When we iterate over the reader, we end up iterating over the rows in the CSV file. # Īs you can see, the reader automatically organizes the data into rows. (We assume that you are executing Python in the same folder where the people.csv file is.) We can use it by opening the CSV file (with the open() function), then passing the file handle created by the open() function to the csv.reader function. The most basic function for reading a CSV file is csv.reader. We simply need to import this module and we’ll have access to tons of functionality related to loading data from CSV files and writing it back into them. (Side note: If you haven’t heard about the open() function, we recommend you read our article on how to write to a file in Python.) This is certainly a valid approach – we encourage you to try it out as a coding challenge – but we can do even better.ĬSV files are so ubiquitous that the Python development team went ahead and created the csv module for us. You may be tempted to use the open() function to read the contents of the file, split the line at each column separator, and finally put it into a data structure like a list or dictionary. Okay, we have a basic idea of what a CSV file is. Keep an eye out to make sure that you don’t inadvertently modify your data! Some spreadsheet programs (like Excel) include a CSV import functionality that allows you to specify column delimiters and data types, among other parameters. Opening CSV files as spreadsheets may lead to unexpected results – for example, the software might think that the commas are numerical separators instead of column delimiters. If they weren’t enclosed in quotation symbols, they would be considered numbers and their leading zeros would be discarded upon loading the data. That’s much better! Also, note how the values in the id column were interpreted as text. The data in people.csv, as displayed in a spreadsheet. The data inside the CSV file is easier to understand if we open it in a spreadsheet like Excel, Google Sheets or LibreOffice Calc: Since the commas do not necessarily align, it is a bit difficult to visualize each column in plain text. Quotation symbols may be used to encapsulate text, as seen in the id column. After the first comma comes the column id, then age, and so on. The first column (under the header label name and just before the first comma in each line) stores the name of each person. We will use this file in the examples to come.Īs you can see, the columns are defined by commas. Here’s what it looks like if we open it with a text editor: Let’s work with an example CSV file named people.csv. The very first line is often the table’s header, which contains a description of the data in each column. On the other hand, rows are simply delimited by lines in the file. The name CSV stands for comma-separated values, meaning that the table’s columns are delimited by commas. In a nutshell, a CSV file is a plain-text file that represents a table. But for now, let’s get started! The Structure of a CSV File After reading this article, you’ll want to check out our course on writing and reading CSV files in Python to solidify your knowledge. Then, we will learn to use Python’s built-in csv module to read and write CSV files quickly and effectively. We’ll start by learning what CSV files actually are. In this article, we will learn about CSV files and how to use Python to work with them. (Think of a table in an article or an Excel spreadsheet.) CSV files are one of the most common types of tables used by data scientists, but they can be daunting if you don’t know exactly how they work or how to use them alongside Python. You have probably seen tabular data before: it’s simply rows and columns containing some data. What are CSV files, and how can you use Python alongside them? Learn how to use the csv module to read and work with CSV files in Python.
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