Step 1: Project Setup and Main Function
In this step, we set up our project environment by creating a main Python script. We import necessary modules, define the main function, and establish the overall workflow of our program. The main function acts as the entry point and calls specialized helper functions later in the process. This setup is essential for structuring the code in an organized manner.
#!/usr/bin/env python3
import argparse
def main():
"""
Main function to coordinate file processing.
It parses command line arguments, reads the input file,
processes the file content, and then writes the output.
"""
args = parse_arguments()
text_data = read_file(args.input_file)
result = process_text(text_data)
write_output(args.output_file, result)
if __name__ == "__main__":
main()
In the code above:
• We use the shebang (#!/usr/bin/env python3) for portability.
• argparse is imported to handle command line arguments.
• The main function is defined to execute parsed arguments, read the file, process its contents, and write results.
Step 2: Parsing Command Line Arguments
Next, we want the user to be able to specify the input and output files easily from the command line. Using the argparse module, we create a function to capture these arguments, with default file names provided.
def parse_arguments():
"""
Uses argparse to accept command-line arguments.
Default input is 'input.txt' and default output is 'output.txt'.
"""
parser = argparse.ArgumentParser(description="Process a text file and analyze its content.")
parser.add_argument("--input_file", type=str, default="input.txt",
help="Path to the input text file.")
parser.add_argument("--output_file", type=str, default="output.txt",
help="Path to the output text file where results will be stored.")
return parser.parse_args()
Here, the parser: • Defines two optional arguments: --input_file and --output_file. • Provides default values and help messages, ensuring ease of use by end users.
Step 3: Reading the Input File
After parsing the arguments, our next task is to read the text file provided by the user. We define a function that attempts to open the file and read its content. If an error occurs (for example, if the file does not exist), we print an error message and return an empty string.
def read_file(file_path):
"""
Reads the content of the provided file.
If the file cannot be read, an error is reported.
"""
try:
with open(file_path, "r", encoding="utf-8") as file:
content = file.read()
print(f"Successfully read data from {file_path}.")
return content
except Exception as e:
print(f"Error reading file {file_path}: {e}")
return ""
Key points in this function: • It opens the file using a context manager (the with statement) ensuring proper resource management. • It uses exception handling to catch and report errors. • UTF-8 encoding is used for broader text compatibility.
Step 4: Processing the Text Data
In this part, we process the text data. For our example, we perform a simple word frequency analysis. This step involves tokenizing the text (splitting it into words), converting to lower-case for uniformity, and counting the occurrences of each word using Python’s Counter from the collections module.
import re
from collections import Counter
def process_text(text):
"""
Processes the text by counting the frequency of each word.
It ignores case differences and considers alphanumeric words.
"""
# Use regular expressions to extract words (ignoring punctuation)
words = re.findall(r'\b\w+\b', text.lower())
word_count = Counter(words)
print("Processed text and calculated word frequencies.")
return word_count
Explanation: • The re.findall function extracts words by matching word boundaries. • Converting text to lower-case with text.lower() ensures that ‘Word’ and ‘word’ are counted as the same. • Counter efficiently counts word frequencies and returns a dictionary-like object.
Step 5: Writing the Output File
Finally, we write the processed results (the word frequencies) to an output file. This function iterates over the dictionary of word counts and writes each word along with its count to the specified output file. Proper error handling is included to ensure that any issues during writing are caught and reported.
def write_output(file_path, data):
"""
Writes the processed data (e.g., word counts) to the output file.
Each key-value pair is written on a new line.
"""
try:
with open(file_path, "w", encoding="utf-8") as file:
for word, count in data.items():
file.write(f"{word}: {count}\n")
print(f"Output successfully written to {file_path}.")
except Exception as e:
print(f"Error writing to file {file_path}: {e}")
Highlights: • Like the read_file function, we use a context manager and proper error handling. • The output is formatted so each line contains a word and its frequency, separated by a colon. • UTF-8 encoding is used again to handle different types of characters.
With these five steps, we have a complete, well-structured Python script that reads a text file, processes its content with detailed code implementations and explanations, and then writes the processed results to an output file. This step-by-step approach is ideal for mentoring new developers, as it breaks down complex tasks into manageable pieces with clear commentary.