使用 pandas/python 從字符串列表中刪除空格

Question

我有一個 dataframe，其中一列值是字符串列表。 這里要讀取的文件結構：

[
    {
        "key1":"value1 ",
        "key2":"2",
        "key3":["a","b  2 "," exp  white   space 210"],
    },
    {
        "key1":"value1 ",
        "key2":"2",
        "key3":[],
    },

]

如果每個項目不止一個空白，我需要刪除所有空白。 預計 output：

[
    {
        "key1":"value1",
        "key2":"2",
        "key3":["a","b2","exp white space 210"],
    },
    {
        "key1":"value1",
        "key2":"2",
        "key3":[],
    }
]

注意：我有一些值在某些行中是空的，例如"key3":[]

Answer 1

如果我理解正確的話，您的某些 dataframe 單元格具有list type值。

file_name.json內容如下：

[
    {
        "key1": "value1 ",
        "key2": "2",
        "key3": ["a", "b  2 ", " exp  white   space 210"]
    }, 
    {
        "key1": "value1 ",
        "key2": "2",
        "key3": []
    }
]

在這種情況下可能的解決方案如下：

import pandas as pd
import re

df = pd.read_json("file_name.json")


def cleanup_data(value):
    if value and type(value) is list:
        return [re.sub(r'\s+', ' ', x.strip()) for x in value]
    elif value and type(value) is str:
        return re.sub(r'\s+', ' ', value.strip())
    else:
        return value

# apply cleanup function to all cells in dataframe
df = df.applymap(cleanup_data)

df

退貨

     key1  key2                           key3
0  value1     2  [a, b 2, exp white space 210]
1  value1     2                             []

Answer 2

如果我理解正確的話：

df = pd.read_json('''{
    "key1":"value1 ",
    "key2":"value2",
    "key3":["a","b   "," exp  white   space "],
    "key2":" value2"
}''')

df = df.apply(lambda col: col.str.strip().str.replace(r'\s+', ' ', regex=True))

Output：

>>> df
     key1    key2             key3
0  value1  value2                a
1  value1  value2                b
2  value1  value2  exp white space

>>> df.to_numpy()
array([['value1', 'value2', 'a'],
       ['value1', 'value2', 'b'],
       ['value1', 'value2', 'exp white space']], dtype=object)