Requirements I Am Providing A Text File Containing 58,112 Wo

Requirementsi Am Providing A Text File Containing 58112 Words You Ca

Requirements I am providing a text file containing 58,112 words. You can download using the link: words.txt using the right-click "Save Link As". Place this file into the directory where you will have your pa05.py program. This way your code can easily find the text file with no extra work on your end. Write a function named getListContain(s,ifile,ofile) function that takes the string s (not just a single character) as the input and extracts all words that contain that string and writes them into a new file "ofile" in the same order. Run this program with different inputs, making sure that it works. Write a function named getListCount(n,ifile,ofile) function that takes the positive integer (1,2,3,..) and extracts all words that has exactly n letters, abd writes them into a new file "ofile" in the same order. Run this program with different inputs, making sure that it works.

Paper For Above instruction

Implementing Text File Word Extraction Functions in Python

The task involves developing Python functions to process a large text file containing 58,112 words, named "words.txt". The main objectives are to create functions that extract specific subsets of words based on user-defined criteria and write these subsets into new files, maintaining the original order. The functions to be implemented are getListContain and getListCount. These functions will facilitate efficient text processing, an essential skill in data analysis, natural language processing, and information retrieval.

Firstly, the getListContain(s, ifile, ofile) function searches for all words within the input file ifile that contain a specific substring s. It then writes all matching words sequentially into an output file ofile. This is particularly useful for tasks such as identifying words with specific patterns or substrings, which have applications in search algorithms, linguistic analysis, and pattern recognition.

Secondly, the getListCount(n, ifile, ofile) function filters words by their length, extracting only those with exactly n characters. It writes these words into ofile in the same order they appear in the original text. This function can be used for statistical language studies, word length distributions, and custom text analyses where word length is a parameter of interest.

Implementing these functions necessitates careful handling of file I/O operations. The process involves opening the large word list, reading and parsing each word, applying the specified filtering logic, and writing the results into new files. It is essential to process the data efficiently to avoid excessive memory usage, especially given the large size of the input file.

Below is an example implementation of the getListContain and getListCount functions in Python, with appropriate considerations for file handling and processing efficiency.

Implementation of the functions

def getListContain(s, ifile, ofile):
"""
Extracts all words from 'ifile' that contain the substring 's' and writes
them into 'ofile' in the same order.
"""
with open(ifile, 'r') as infile, open(ofile, 'w') as outfile:
for line in infile:
for word in line.strip().split():
if s in word:
outfile.write(word + '\n')
def getListCount(n, ifile, ofile):
"""
Extracts all words from 'ifile' that have exactly 'n' characters,
writes them into 'ofile' in the same order.
"""
with open(ifile, 'r') as infile, open(ofile, 'w') as outfile:
for line in infile:
for word in line.strip().split():
if len(word) == n:
outfile.write(word + '\n')

To ensure the robustness and correctness of these functions, run them with different input parameters. For example, use s='test' in getListContain to find words containing "test", or n=5 in getListCount to find all five-letter words. Verify that the output files contain the correct filtered words, maintaining the original order.

In addition, handle potential case sensitivity issues by normalizing case if needed, for example, converting all words to lowercase before comparison, depending on application requirements.

Advantages and Applications

These functions highlight essential techniques in text processing, allowing users to quickly filter large datasets for targeted searches. They are fundamental in building search engines, linguistic tools, and data preprocessing pipelines. This example demonstrates how Python's file handling capabilities combined with simple filtering logic can efficiently process big data in real-world applications.

Furthermore, optimizing such functions for performance, perhaps through buffered reading or utilizing specialized libraries, can improve scalability, making them suitable for even larger datasets or real-time processing scenarios.

Conclusion

Developing these functions enhances proficiency in file I/O operations and string manipulation in Python. They serve as practical tools in various data analysis and natural language processing tasks. Proper testing and validation ensure that the functions perform accurately across different input parameters, making them reliable components in text processing workflows.

References

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