Data cleaning: extracting numbers out of string array by deleting '.' and ';' ch-CodePudding

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)

CodePudding user response：

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).