Скачать или смотреть How to Create or Append Data in a CSV File Using Python Pandas

Learn how to easily manage CSV files in Python with Pandas, allowing you to either create a new CSV file or append data to an existing one without duplicating headers.
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This video is based on the question https://stackoverflow.com/q/73956323/ asked by the user 'Info Rewind' ( https://stackoverflow.com/u/20147022/ ) and on the answer https://stackoverflow.com/a/73956664/ provided by the user 'Jędrzej Wiśniewski' ( https://stackoverflow.com/u/20058780/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Create or Append Data in a CSV File Using Python Pandas

When scraping data from multiple online articles, you may encounter the need to store this data in a CSV file. One common challenge is ensuring that if a CSV file already exists at a specific location, your script will append new data to it without repeating the column headers. In this post, we'll explore a solution using Python's Pandas library to efficiently manage your CSV files.

Understanding the Problem

While scraping, you may:

Want to save data with a consistent format (such as using column headings).

Need to avoid overwriting existing data.

Ensure existing files are not filled with repeated headers, which can cause confusion when analyzing the data.

This results in a need to write code that intelligently checks for the existence of a file and acts accordingly.

Step-by-Step Solution

Below, we break down the steps you can take to create or append data to your CSV file using Python and Pandas.

Required Libraries

First, ensure you have the pandas library installed. If not, you can install it using:

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

Code Explanation

Here’s a refined version of code to manage your CSV file:

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

Code Breakdown

Import Libraries: Start by importing the pandas library to work with data frames.

Sample Data Initialization: You create sample data that you wish to record in your CSV file.

Define Column Names: A list of column names is created for the dataframe.

File Read Attempt:

The try block attempts to open the existing CSV file using pd.read_csv().

If successful, it concatenates the existing data with the new data.

Error Handling:

Using an except block checks for a FileNotFoundError. If the file does not exist, a new DataFrame is created.

Write to CSV: Finally, the combined DataFrame is written to the CSV file using to_csv(), setting index=False to avoid writing row indices to the file.

Conclusion

By following the steps outlined above, you can smoothly manage CSV file creation and data appending in your Python projects. This approach ensures that your scraping efforts are efficiently recorded without any unnecessary duplication in the file's structure. Happy coding!