You'll want to use the dependency parse for this. You can see a visualisation of your example sentence using the displaCy visualiser.

You could implement the rules you need a few different ways — much like how there are always multiple ways to write an XPath query, DOM selector, etc.

Something like this should work:

nlp = spacy.load('en')
docs = [nlp(t) for t in text]
for i, doc in enumerate(docs):
    for j, sent in enumerate(doc.sents):
        subjects = [w for w in sent if w.dep_ == 'nsubj']
        for subject in subjects:
            numbers = [w for w in subject.lefts if w.dep_ == 'nummod']
            if len(numbers) == 1:
                print('document.sentence: {}.{}, subject: {}, action: {}, numbers: {}'.format(i, j, subject.text, subject.head.text, numbers[0].text))

For your examples in text you should get:

document.sentence: 0.0, subject: men, action: ran, numbers: 2
document.sentence: 0.0, subject: child, action: ran, numbers: 1
document.sentence: 0.1, subject: people, action: walking, numbers: 3
document.sentence: 1.0, subject: person, action: walking, numbers: One

with open('data.txt', 'r') as f:
    for line in f:
        number = int(line.split(':')[1])
        array.append(number)
print '+'.join(array)

Basically, use the split function to get the number, and then print it as you wish. Please take care of error handling to check if it is integer or not, or if the line has a : or not

Not sure what you mean by shell, but yes you can make a function, and just call a function: