Скачать или смотреть How to Separate Frequencies in a Text File Using Python

Discover how to transform frequency values in a text file from a compressed format (like `4*7895`) into expanded format (like `7895 7895 7895 7895`) using Python scripting.
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Transforming Frequency Values in Text Files with Python

When dealing with large datasets, especially in text files, you might encounter values represented as multiplication terms, for example, 4*7895 which indicates that the number 7895 should be repeated four times. This can make processing data cumbersome, especially if you're aiming for clarity or need to analyze the values further.

In this guide, we’ll explore a simple and effective way to convert these frequency expressions into a straightforward list of values using Python.

Understanding the Problem

Let's consider an example input data contained in a file named OUT2.txt:

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

In this file, values might be given as:

2*8.17997 meaning the value 8.17997 should appear twice as 8.17997 8.17997.

4*33.33 meaning 33.33 should appear four times as 33.33 33.33 33.33 33.33.

The goal is to transform this data into a flat format, where the values appear directly next to each other:

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

The Solution

By utilizing Python, we can create a script that reads the input file, processes each line to convert the frequency expressions into their expanded forms, and then writes the result into a new output file.

Step-by-Step Breakdown

Read the Input File: Start by reading the contents of the file into a list of lines.

Process Each Line: For each line, split the string into parts based on spaces, to handle each segment individually.

Handle Frequency Expressions: For each part of the split line:

If it doesn’t contain *, add it directly to a list.

If it does contain *, split it further to extract the multiplier and the value. Use a loop to add the value to the list as many times as indicated by the multiplier.

Writing to Output File: Finally, join the values in the list with spaces and write them back out to a new text file.

Sample Python Code

Here’s a concise implementation of the above logic:

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

Running the Script

Make sure you save the code in a .py file (for example, expand_frequencies.py).

Place your OUT2.txt file in the same directory as the Python script.

Execute the script using Python:

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

Upon running, you will generate OUT.3.txt, where your numbers are cleanly formatted as required.

Conclusion

By following these steps, you can easily convert frequency notations into a more comprehensible format, enabling better data analysis and manipulation. This simple Python script is an excellent tool for anyone working with text data files containing such patterns. Happy coding!