数据清洁：通过删除从字符串数组中提取数字＆＃x27;。＆＃x27;和＆＃x27 ;;＆＃x27;人物

发布于 2025-01-23 16:23:19 字数 696 浏览 2 评论 0原文

我有一个大数据集。我试图清洁它。数据看起来像这样：

data= np.array(['0,51\n0,64\n0,76\n0,84\n1,00', 1.36]) #...

我的目标是提取原始数字：

numbers= [51, 64, 76, 84, 100, 136]

我尝试的方法有效，但我认为这不是那么优雅。有更好的方法吗？

import numpy as np
import re

clean= np.array([])

for i in data:
    i = str(i)
    if ',' in i:
        without= i.replace(',', '')
        clean= np.append(clean, without)
    elif '.' in i:
        without= i.replace('.', '')
        clean= np.append(clean, without)

#detect all numbers
numbers= np.array([]) 
for i in clean:
    if type(i) == np.str_:
        a= re.findall(r'\b\d+\b', i)
        numbers= np.append(numbers, a)

原文

I have a big data set what is messed up. I tried to clean it.
The data looks like this:

data= np.array(['0,51\n0,64\n0,76\n0,84\n1,00', 1.36]) #...

My goal is to extract the raw numbers:

numbers= [51, 64, 76, 84, 100, 136]

What I tried worked, but I think it is not that elegant. Is there a better way to do it?

import numpy as np
import re

clean= np.array([])

for i in data:
    i = str(i)
    if ',' in i:
        without= i.replace(',', '')
        clean= np.append(clean, without)
    elif '.' in i:
        without= i.replace('.', '')
        clean= np.append(clean, without)

#detect all numbers
numbers= np.array([]) 
for i in clean:
    if type(i) == np.str_:
        a= re.findall(r'\b\d+\b', i)
        numbers= np.append(numbers, a)

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一身骄傲 2025-01-30 16:23:19

通常，您绝对不应在循环中使用np.append，因为它每次都会重新创建新数组，从而导致二次复杂性效率低下。

除此之外，您还可以使用以下单线来解决您的问题：

result = [int(float(n.replace(',', '.'))*100) for e in data for n in e.split()]

想法是，用。替换基于此的正确整数。您可以使用np.fromiter（结果，dtype = int）将其转换为numpy数组。

Generally, you should never use np.append in a loop since it recreate a new array every time resulting in an inefficient quadratic complexity.

Besides this, you can use the following one-liner to solve your problem:

result = [int(float(n.replace(',', '.'))*100) for e in data for n in e.split()]

The idea is to replace , by a . and then parse the string as a float so to produce the right integer based on this. You can convert it to a numpy array with np.fromiter(result, dtype=int).

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