How to Read the Header of a CSV File in Python

Reading and manipulating data is a crucial skill for any Python programmer, especially when dealing with big data. CSV (Comma Separated Values) files are a common data source that you’re likely to encounter.

They are simple to understand and read as they are basically just a series of values separated by commas. Python, with its powerful and versatile libraries, provides amazing tools to read and work with CSV files. This tutorial will guide you on how to read the header of a CSV file in Python.

Step 1: Prepare the CSV file

Firstly, you need to prepare a CSV file that you want to read. This tutorial will use an example file named ‘sample.csv’ with the following content:

Name,Age,Location
John,23,New York
Emma,25,Los Angeles
David,27,Chicago
Sophia,22,San Francisco

Step 2: Import necessary Python Libraries

Python’s built-in csv module along with the os library will be used to read the CSV file. Use the import statement to include these libraries in your Python program as shown below:

Step 3: Open the CSV file

We now need to open the CSV file. Python’s built-in open() function will be used, which opens the file and returns it as a file object. We will open the file in read mode, denoted by ‘r’.

Step 4: Read the CSV file

We will use the reader function of the csv module to read the CSV file. As an argument, this function takes the file object we just opened.

Step 5: Extract and print the header

After opening the file we need to extract the header. The header of the csv file is simply the first row. We can extract it using Python’s built-in next function, which returns the next row of the reader’s iterable object. After getting the header, print it using Python’s built-in print function.

The full code

Here is the complete Python code for this task.

Output:

['Name', 'Age', 'Location']

Conclusion

In this tutorial, we learned how to read the header of a CSV file in Python using the csv module and the open() function. Understanding how to work with CSV files is space-saving, simple, and beneficial in dealing with large volumes of data. We hope this tutorial assists you in your Python programming journey. Happy coding!