我想清理我的csv文件中的某个列，当我将它们传递到pandas中时，它会为每行留下一个尾随\r。
54147799,xxx_54147799,xxx_1442517,ALARM_NORMALIZED,xxx,xxx,2023-01-01 00:12:05.168,null,1,NORMAL,xxx/xxx/ATI,xxx,3,ATI System Mode,2,4,-1,-1\r
请帮助我改进我的脚本，使\r不出现。
我的csv文件实际上无法解析到Elasticsearch中，因为\r。第18列预计只接收数字，而不是alpha-numeria。

import pandas as pd
from pathlib import Path
import csv
import os
import re

def format_location(location):
    if location and isinstance(location, str) and re.match(r'^\d+$', location):  # Check if the cell contains only digits
        if len(location) == 3:
            return f"9{location}2"  # Prepend '9' and append '2' to 3-digit numbers
        elif len(location) == 5:
            return location  # Return the original 5-digit value
    return location  # Return the original value for non-numeric or incorrect length values
    
def drop_extra_column(input_directory, intermediate_directory):
    # Check all files in the input directory
    for file in input_directory.iterdir():
        if file.suffix == '.csv':
            try:
                # Read CSV file using Pandas with error handling to warn and skip problematic lines
                with open(file, 'r') as f: pd.read_csv(f)
                df = pd.read_csv(f)

                # Check if DataFrame has more than 18 columns
                if len(df.columns) > 18:
                    # Drop the 19th column (index 18)
                    df.drop(df.columns[18], axis=1, inplace=True)

                    # Define the output file path
                    intermediate_path = intermediate_directory / f"{file.name}"

                    # Write back to the CSV file in the output directory using Pandas
                    df.to_csv(intermediate_path, index=False)
                    print(f"Processed data saved to '{intermediate_path}'")

                else:
                    print(f"No extra column found in '{file.name}'. Skipping this file.")

            except Exception as e:
                print(f"Error processing file '{file.name}': {e}. Skipping this file.")
                continue

def process_csv_files(intermediate_directory, output_directory):
    # Check all files in the input directory
   for file in intermediate_directory.iterdir():
        if file.suffix == '.csv':
            try:
                # Read CSV file using Pandas
                with open(file, 'r') as f: pd.read_csv(f)
                df = pd.read_csv(f)
 
                # Apply formatting to 'location' column only for numeric cells
                if 'location' in df.columns:
                    df['location'] = df['location'].apply(format_location)

                # Define the output file path
                output_path = output_directory / f"{file.name}"

                # Write back to the CSV file in the output directory using Pandas
                df.to_csv(output_path, index=False)
                print(f"Formatted data saved to '{output_path}'")

            except Exception as e:
                print(f"Error processing file '{file.name}': {e}. Skipping this file.")
                continue

# Replace 'input/path/to/directory' and 'output/path/to/directory' with appropriate directory paths
input_csv_directory = Path("C:/Users/ethan/OneDrive/Documents/docker-env/logstash-standard/xxx/2023/01-Jan-23")
intermediate_csv_directory = Path("C:/Users/ethan/OneDrive/Documents/docker-env/logstash-standard/xxx/2023/Test")
output_csv_directory = Path("C:/Users/ethan/OneDrive/Documents/docker-env/logstash-standard/xxx/2023/Test")

# Process CSV files in the input directory and save processed files to the output directory
#drop_extra_column(input_csv_directory, intermediate_csv_directory)

# Process CSV files in the input directory and save processed files to the output directory
process_csv_files(input_csv_directory, output_csv_directory)

#process_csv_files(input_csv_directory, output_csv_directory)

字符串

24/11/23更新包含在with open('my.csv' , 'r') as f: pd.read_csv(f)中

Error processing file 'events2023-01-24.csv': Error tokenizing data. C error: Expected 18 fields in line 6370, saw 19 . Skipping this file.
我的csv的第19列很少有数据，但显然100 k+行中的一些行仍然有数据。我不知道该怎么做，这个问题能解决吗？