Скачать или смотреть How to Resolve FileNotFoundError When Using Pandas read_csv()

Learn how to troubleshoot and fix the `FileNotFoundError` you encounter while using Pandas' `read_csv()` method in Python.
---
This video is based on the question https://stackoverflow.com/q/64400624/ asked by the user 'Linh Bảo' ( https://stackoverflow.com/u/14467025/ ) and on the answer https://stackoverflow.com/a/64400742/ provided by the user 'kiran_raj_r' ( https://stackoverflow.com/u/6506041/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Errors in Pandas read_csv

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Troubleshooting FileNotFoundError in Pandas read_csv()

When working with data in Python, the ability to read CSV files with Pandas is essential. However, many users encounter a common issue: the dreaded FileNotFoundError. This error can result in frustration, especially when you believe you have already downloaded the file. Let's explore the root cause of this error and how to effectively resolve it.

Understanding the FileNotFoundError

When you attempt to read a CSV file using the pd.read_csv() function, Pandas attempts to access the specified file path. If the file cannot be found at the given location, Pandas raises a FileNotFoundError, which often looks something like this:

[[See Video to Reveal this Text or Code Snippet]]

This message indicates that the file you're trying to read is not located in the directory from which your Python script is running.

Common Causes of the FileNotFoundError

Several reasons may lead to this error. Here are the most common causes:

The CSV file and the Python script are not in the same directory.

An incorrect file name or a typo in the file path.

The file extension is not what you believe it to be (e.g., .csv vs .CSV).

The file is located in a subdirectory, and the path is not specified correctly.

How to Resolve the Error

To fix the FileNotFoundError, you must ensure that the file path you provide to pd.read_csv() is correct. Here’s how to do that step by step:

1. Check File Location

Same Directory: If both the Python file and the CSV file are in the same directory, simply using the file name (e.g., "FuelConsumption.csv") should be sufficient.

Different Directory: If the files are not in the same directory, you'll need to provide a correct relative or absolute path to the CSV file.

2. Use Relative or Absolute Paths

Relative Path: If your script is in a different directory but you know the path relative to your script, use something like:

[[See Video to Reveal this Text or Code Snippet]]

Absolute Path: For a complete path that leads directly to the file, use:

[[See Video to Reveal this Text or Code Snippet]]

3. Verify File Name and Extension

Double-check that the file name and extension exactly match the file on disk. Remember that file names can be case-sensitive on some operating systems.

4. Checking for Hidden Characters

If you manually typed the path, ensure that there are no additional spaces or hidden characters in the string.

Conclusion

By following the steps outlined above, you should be able to troubleshoot and resolve the FileNotFoundError when using the pd.read_csv() function in Pandas. If you still encounter issues, verifying your file path and ensuring that the CSV file exists in the specified location are the crucial first steps in debugging this issue.

Now you can confidently read your CSV files in Python and continue your data analysis journey without interruptions!