删除除逗号以外的所有字符和数字

发布于 2025-01-17 14:40:32 字数 411 浏览 1 评论 0原文

我正在尝试从数据框列中的字符串中删除所有字符，但请保留逗号，但它仍然可以删除包括逗号在内的所有内容。

我知道之前已经问过这个问题，但我尝试了很多答案，并且都删除了逗号。

df[new_text_field_name] = df[new_text_field_name].apply(lambda elem: re.sub(r"(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)|^rt|http.+?", "", str(elem)))

示例文本：

'100％聚酯，纸板（最小30％再生），100％聚丙烯'，

'polyerter，纸板，纸板，聚丙烯'，，

原文

I am trying to remove all the characters from string in the DataFrame column but keep the comma but it still removes everything including the comma.

I know the question has been asked before but I tried many answers and all remove the comma as well.

df[new_text_field_name] = df[new_text_field_name].apply(lambda elem: re.sub(r"(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)|^rt|http.+?", "", str(elem)))

sample text:

'100 % polyester, Paperboard (min. 30% recycled), 100% polypropylene',

the required output:

' polyester, Paperboard , polypropylene',

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叫思念不要吵 2025-01-24 14:40:33

可能的解决方案如下：

# pip install pandas

import pandas as pd
pd.set_option('display.max_colwidth', 200)

# set test data and create dataframe
data = {"text": ['100 % polyester, Paperboard (min. 30% recycled), 100% polypropylene','Polypropylene plastic', '100 % polyester, Paperboard (min. 30% recycled), 100% polypropylene', 'Bamboo, Clear nitrocellulose lacquer', 'Willow, Stain, Solid wood, Polypropylene plastic, Stainless steel, Steel, Galvanized, Steel, 100% polypropylene', 'Banana fibres, Clear lacquer', 'Polypropylene plastic (min. 20% recycled)']}
df = pd.DataFrame(data)

def cleanup(txt):
    re_pattern = re.compile(r"[^a-z, ()]", re.I)
    return re.sub(re_pattern, "", txt).replace("  ", " ").strip()

df['text_cleaned'] = df['text'].apply(cleanup)
df

Possible solution is the following:

# pip install pandas

import pandas as pd
pd.set_option('display.max_colwidth', 200)

# set test data and create dataframe
data = {"text": ['100 % polyester, Paperboard (min. 30% recycled), 100% polypropylene','Polypropylene plastic', '100 % polyester, Paperboard (min. 30% recycled), 100% polypropylene', 'Bamboo, Clear nitrocellulose lacquer', 'Willow, Stain, Solid wood, Polypropylene plastic, Stainless steel, Steel, Galvanized, Steel, 100% polypropylene', 'Banana fibres, Clear lacquer', 'Polypropylene plastic (min. 20% recycled)']}
df = pd.DataFrame(data)

def cleanup(txt):
    re_pattern = re.compile(r"[^a-z, ()]", re.I)
    return re.sub(re_pattern, "", txt).replace("  ", " ").strip()

df['text_cleaned'] = df['text'].apply(cleanup)
df

Returns