使用 Python 将文本 csv 文件拆分为另一个 csv 文件，其中包含文本表示的变量

您需要拆分文本，将所需的部分分成不同的变量，然后您可以根据需要进行处理。试一试，然后根据需要进行修改：

line = "380865 2022-11-20 Seriously considerin switching to a rival bank.."

sp = line.split(" ")
id, date, text = sp[0], sp[1], " ".join(sp[2:])

print(id)
print(date)
print(text)

由于您的文本没有简单的结构（它包含用于分隔的空格和其中一个字段内），因此我共享此代码以防它对您有所帮助。我已经在代码本身中包含了解释每个步骤的注释，如果它们还不够，请不要犹豫！

首先，您需要安装和模块：pandasregex

pip install pandas
pip install regex

import regex as re
import pandas as pd 


def split_line(line):
    # We split the text by date (element with common structure 
    # in all entries YYYYY-MM-DD) using regex.

    date_pattern = r"[0-9]{4}\-[0-9]{2}\-[0-9]{2}"

    # We search the fields `customer_id` and `comments` by
    # splitting the text with date pattern
    customer_id, comments = re.split(date_pattern, line)

    # We search the date number using the regex search
    date = re.search(date_pattern, line).group(0)

    return {
        "customer_id": customer_id.strip(),
        "date": date.strip(),
        "comments": comments.strip()
    }
    


if __name__ == "__main__":

    # If you have the text as a python variable of type docstring
    text = """"customer_id date comments
    216604 2022-08-22 Overal, this bank is satisfactory,
    259276 2022-11-23 Easy to find zhe bank ' s branches
    380865 2022-11-20 Seriously considerin switching to a rival bank
    """
    all_lines = text.split("\n")[1:]

    # If you have the text as a .txt file 
    # with open("path/to/txt/file", "r") as f:
    #     all_lines = f.readlines()[1:]

    # Note that we index the text lines from [1:] to remove the header 
    
    all_parsed_lanes = []
    for line in all_lines:
        
        #We measure the length of the line, eliminating spaces with .strip() 
        #to verify that it is not an empty line. 

        if len(line.strip()) > 0:
            extracted_fields = split_line(line)
            all_parsed_lanes.append(extracted_fields)

    # We convert the list of dictionaries into a ordered and redeable
    # dataframe using pandas module.
    df = pd.DataFrame(all_parsed_lanes)
    print(df)

返回为输出：

  customer_id        date                                        comments
0      216604  2022-08-22              Overall, this bank is satisfactory,
1      259276  2022-11-23              Easy to find zhe bank ' s branches
2      380865  2022-11-20  Seriously considering switching to a rival bank

你的问题很模糊。下次请问你已经尝试过什么，你的具体目标是什么。例如，您希望将输出作为嵌套列表还是字典。这是一个适用于您的特定问题的代码。但通常这样的文件应该有一些分隔符来区分哪个值属于哪个列。

该代码首先读取文件的行，并在空格处拆分。这将创建一个列表，其中前两个值是您的 ID 和日期。然后，列表的其余部分将再次加入注释。

data = []
filename = "yourfile.txt"
with open(filename) as f:
    header = f.readline()[:-1]
    header = header.split(" ")
    data.append(header)
    for line in f.readlines():
        line = line[:-1].split(" ")
        v1 = line[0]
        v2 = line[1]
        v3 = " ".join(line[2:])
        data.append([v1, v2, v3])

第二个块将带有制表符的文件保存为分隔符。这也可以更改为分号。

filename = "output.csv"
with open(filename, "w") as f:
    for line in data:
        for val in line:
            f.write(val)
            f.write("\t")
        f.write("\n")