Dict to Excel Without External Library - Python

In this guide, I will show you how to convert a Python dictionary into an Excel file without using any external libraries.

Background

Excel files are widely used for data storage and analysis. Python provides several libraries like pandas, openpyxl, and xlwt that simplify the conversion of a dictionary to an Excel file. However, if you want to achieve this without using any external library, you need to rely on the built-in csv module and some custom logic.

Approach

To convert a dictionary to an Excel file in Python without any external library, we will follow these steps:

1. Import the necessary modules.
2. Define a function called dict_to_excel that takes a dictionary as input and writes its contents into an Excel file.
3. Initialize the Excel file and create a writer object using the csv module.
4. Write the column headers to the Excel file.
5. Iterate over the dictionary and write each key-value pair as a row in the Excel file.
6. Save and close the Excel file.

Here is the code snippet that demonstrates the above approach:

import csv

def dict_to_excel(dictionary, filename):
    with open(filename, 'w', newline='') as file:
        writer = csv.writer(file, delimiter='\t')
        
        # Write column headers
        writer.writerow(['Key', 'Value'])
        
        # Write dictionary entries
        for key, value in dictionary.items():
            writer.writerow([key, value])

        print("Dictionary successfully converted to Excel.")

# Example usage
sample_dict = {'Name': 'John', 'Age': 30, 'Country': 'USA'}
dict_to_excel(sample_dict, 'output.txt')

Make sure to replace 'output.txt' with the desired filename and extension.

Output

After running the above code, you will find a file named 'output.txt' in the current directory. This file will contain the dictionary contents in a tab-separated format.

Key|Value ---|----- Name|John Age|30 Country|USA

Conclusion

In this guide, we have explored how to convert a dictionary to an Excel file without using any external libraries in Python. Although this method provides a basic solution, it may lack some advanced features provided by external libraries. If you require more advanced functionality, consider using libraries like pandas or openpyxl.