CSV Data Reader

Reading and processing CSV (Comma Separated Values) files is a fundamental skill in data analysis and manipulation. This challenge asks you to implement a Python function that reads a CSV file, parses its contents, and returns the data as a list of dictionaries, where each dictionary represents a row and keys are derived from the header row. This is a crucial step in many data processing pipelines.

Problem Description

You are tasked with creating a Python function called read_csv_file that takes the path to a CSV file as input and returns a list of dictionaries. Each dictionary in the list represents a row in the CSV file. The keys of each dictionary should correspond to the column headers in the first row of the CSV file.

Key Requirements:

• Header Row: The first row of the CSV file is assumed to be the header row, containing the column names.
• Comma Delimiter: The CSV file uses a comma (,) as the delimiter between values.
• Data Types: All values should be treated as strings initially. No type conversion is required.
• Error Handling: The function should handle the case where the file does not exist gracefully, returning an empty list in such scenarios.
• Empty Lines: The function should ignore empty lines in the CSV file.

Expected Behavior:

The function should open the CSV file, read its contents line by line, parse each line into a dictionary, and return a list of these dictionaries.

Edge Cases to Consider:

• Empty CSV File: If the CSV file is empty (contains no rows after the header), the function should return an empty list.
• CSV File with Only a Header: If the CSV file contains only a header row and no data rows, the function should return an empty list.
• Lines with Missing Values: If a row has fewer values than the header row, the missing values should be represented as empty strings in the dictionary.
• Lines with Extra Values: If a row has more values than the header row, the extra values should be ignored.
• File Not Found: The function should handle the FileNotFoundError gracefully.

Examples

Example 1:

Input: "data.csv" (where data.csv contains: "name,age,city\nJohn,30,New York\nJane,25,London")
Output: [{'name': 'John', 'age': '30', 'city': 'New York'}, {'name': 'Jane', 'age': '25', 'city': 'London'}]
Explanation: The function reads the header row ("name,age,city") and uses it as keys for the dictionaries.  It then parses each data row into a dictionary with these keys.

Example 2:

Input: "empty.csv" (where empty.csv contains: "header1,header2\n")
Output: []
Explanation: The file contains only a header row, so the function returns an empty list.

Example 3:

Input: "missing_values.csv" (where missing_values.csv contains: "name,age,city\nJohn,30\nJane,25,London,Extra")
Output: [{'name': 'John', 'age': '30', 'city': ''}, {'name': 'Jane', 'age': '25', 'city': 'London'}]
Explanation: The first row has a missing value for 'city', which is represented as an empty string. The second row has an extra value which is ignored.

Constraints

• The CSV file will contain at least one row (header row).
• The maximum number of columns in the CSV file is 100.
• The maximum length of a row in the CSV file is 1000 characters.
• The function must be able to handle CSV files of reasonable size (up to 1MB).
• The function should not use any external libraries other than the built-in csv module.

Notes

• Consider using the csv module for efficient CSV parsing.
• Remember to handle potential FileNotFoundError exceptions.
• Think about how to handle lines with missing or extra values gracefully.
• The order of columns in the header row is important; ensure your dictionaries reflect this order.
• Focus on clarity and readability in your code.