Introduction

As a technical blogger, I’ve come across many scenarios where data extraction and processing are crucial. In this article, we’ll explore how to extract data from a text file with keywords using Python.

Understanding the Problem

The problem at hand is to extract data from a text file that has been extracted as CSV or XLSX earlier. The text file contains keywords that distinguish the data from different sources, such as different batches of experiments. The structure of the data is such that each keyword corresponds to a specific sheet in an Excel workbook.

Background

To tackle this problem, we need to understand how Pandas handles CSV and XLSX files. Pandas is a popular Python library for data manipulation and analysis. It provides a powerful data structure called DataFrame, which can be used to store and manipulate tabular data.

In the provided Stack Overflow post, the user has already extracted the data as CSV using the pandas library. However, they’re facing an issue because their text file doesn’t have sheets like Excel files do.

Solution Overview

To solve this problem, we’ll use the pandas library to read and write Excel files. We’ll also use Python’s built-in open function to read the text file line by line.

Our goal is to create an Excel workbook with separate sheets for each keyword in the text file. Each sheet should contain the relevant data corresponding to that keyword.

Step 1: Reading the Text File

We start by reading the text file using Python’s built-in open function:

token = open('file.txt','r')

This opens the file in read mode ('r') and assigns it to a variable called token.

Next, we read each line of the file using a for loop:

linestoken = token.readlines()

This reads all lines from the file into a list called linestoken.

We then iterate over each line in the list:

for x in linestoken:
    # code here

Each line is split into substrings using the split() function:

resulttoken.append(x.split())

This creates a new list called resulttoken where each element contains the substrings from the corresponding line.

Step 2: Creating an Excel Workbook

To create an Excel workbook, we’ll use the openpyxl library. You can install it using pip:

pip install openpyxl

We import the library and create a new workbook object:

import openpyxl
wb = openpyxl.Workbook()

Next, we iterate over each keyword in the text file:

for x in resulttoken:
    # code here

For each keyword, we create a new sheet in the workbook using the create_sheet() function:

sheet = wb.create_sheet(x[0])

We then select all cells in the sheet and assign them values from the resulttoken list:

for i, row in enumerate(resulttoken):
    for j, value in enumerate(row):
        cell = sheet.cell(row=i+1, column=j+1)
        cell.value = value

This creates a new sheet in the workbook with the same structure as the text file.

Step 3: Writing the Excel Workbook to Disk

Finally, we save the workbook to disk using the save() function:

wb.save('output.xlsx')

This saves the workbook to a file called output.xlsx.

Putting it all Together

Here’s the complete code:

import openpyxl

token = open('file.txt','r')

linestoken = token.readlines()

resulttoken = []
for x in linestoken:
    resulttoken.append(x.split())

wb = openpyxl.Workbook()
for x in resulttoken:
    sheet = wb.create_sheet(x[0])
    for i, row in enumerate(resulttoken):
        for j, value in enumerate(row):
            cell = sheet.cell(row=i+1, column=j+1)
            cell.value = value

token.close()

wb.save('output.xlsx')

This code reads the text file line by line, extracts the keywords and relevant data, creates an Excel workbook with separate sheets for each keyword, and saves it to disk.

Conclusion

In this article, we’ve explored how to extract data from a text file with keywords using Python. We used Pandas to read and write CSV files, but also created an Excel workbook with separate sheets for each keyword using the openpyxl library. This code can be used as a starting point for similar tasks where you need to extract data from a text file and create an Excel workbook with separate sheets.

Troubleshooting

If you encounter any issues while running this code, here are some common problems and their solutions:

• Error: File not found: Make sure that the file.txt file exists in the same directory as the Python script.
• Error: Keyword not found: Check that the keywords in the text file match the corresponding sheet names created by the code.
• Error: Data not extracted correctly: Verify that the data extraction logic is correct and that no errors are being introduced during the iteration process.

Best Practices

When working with large files or datasets, consider the following best practices to optimize performance:

• Use chunksize parameter when reading files in chunks to avoid loading entire files into memory.
• Implement error handling mechanisms such as try-except blocks to catch and handle exceptions gracefully.
• Optimize data structures and algorithms used in the code for efficient processing of large datasets.

Last modified on 2024-11-14