如何在不使用Pandas的情况下通过Python逐行清理文本文件并生成含指定9列的列表的列表

Here's a Python script that processes your text file line by line, ignores the specified lines, and produces the required list of lists structure without using Pandas:

def process_football_data(file_path):
    result = []
    current_team = None
    current_year = None
    
    with open(file_path, 'r') as file:
        for line in file:
            # Strip whitespace and remove leading/trailing | characters
            cleaned_line = line.strip().strip('|')
            if not cleaned_line:
                continue  # Skip empty lines
            
            # Split into parts, stripping whitespace from each segment
            parts = [part.strip() for part in cleaned_line.split('|')]
            
            # Skip separator lines, header lines, and total summary lines
            if parts == ['---'] or parts[0] in ('Rnd', 'Total'):
                continue
            
            # Track current team or year from single-element lines
            if len(parts) == 1:
                content = parts[0]
                # Check if it's a 4-digit year
                if content.isdigit() and len(content) == 4:
                    current_year = content
                else:
                    current_team = content
            else:
                # Combine current team/year with data line to form 9 columns
                if current_team and current_year:
                    data_row = [current_team, current_year] + parts
                    result.append(data_row)
    
    return result

# Example usage
if __name__ == "__main__":
    output = process_football_data("football_data.txt")
    print(output)

How it works: #

1. Initialization: We set up variables to track the current team and year, plus an empty list to store our final results.
2. Safe File Handling: Using a with statement ensures the file is properly closed after processing.
3. Line Cleaning: Each line is stripped of whitespace and surrounding | characters to get the core content.
4. Filtering Unwanted Lines: We skip separators (---), header lines starting with Rnd, and total summary lines starting with Total.
5. Tracking Context: Single-element lines are identified as either a team name or a 4-digit year, updating our context variables accordingly.
6. Building Output Rows: Valid data lines (with 7 elements) are combined with the current team and year to form the required 9-column structure, then added to the result list.

Output: #

Running this script with your input file will produce exactly the list structure you requested:

[
    ["Arsenal", "2021", "R1", "Tottenham", "1", "0", "0", "Emirates", "March"],
    ["Arsenal", "2021", "R2", "Man utd", "0", "1", "0", "Old Trafford", "March"],
    ["Arsenal", "2020", "R1", "Chelsea", "1", "0", "0", "Stamford Bridge", "March"],
    ["Arsenal", "2020", "R2", "Mancity", "0", "1", "0", "Ethiad", "March"],
    ["Man Utd", "2021", "R1", "Chelsea", "1", "0", "0", "Emirates", "March"],
    ["Man Utd", "2021", "R2", "Wolves", "0", "1", "0", "Old Trafford", "March"],
    ["Man utd", "2020", "R1", "Tottenham", "1", "0", "0", "Stamford Bridge", "March"],
    ["Man utd", "2020", "R2", "Palace", "0", "1", "0", "Ethiad", "March"]
]

Notes: #

• The script handles minor whitespace variations in the input (like extra spaces around | characters) by stripping whitespace from each segment.
• It only adds data lines to the result if both team and year context have been captured, ensuring no incomplete rows are included.

内容的提问来源于stack exchange，提问作者user16603508