我做了一些pandas读/写函数，使得我的整数现在由于某种原因变成了浮点数。
Open,5,0,-1,-1
Open,5.0,0.0,-1.0,-1.0
我不确定是哪个函数在我不知情的情况下专门更改了我的数据类型。现在我的数据无法通过elasticsearch解析。
下面是我的代码示例，其中函数发生。

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, output_directory):
    # Check all files in the input directory
    for file in input_directory.iterdir():
        if file.suffix == '.csv':
            try:
                # Open the input CSV file and create an output file in the output directory
                with open(file, 'r', newline='') as input_file, \
                     open(os.path.join(output_directory, f"{file.name}"), 'w', newline='') as output_file:

                    reader = csv.reader(input_file)
                    writer = csv.writer(output_file)

                    for row in reader:
                        # Check if the row has more than 18 columns
                        if len(row) > 18:
                            row.pop(18)  # Remove the 19th column (index 18)

                        writer.writerow(row)

                    # print(f"Processed data saved to '{file.name}'")

            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
                df = pd.read_csv(file)

                # 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

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