How do I tokenize a string sentence in NLTK?

I am using nltk, so I want to create my own custom texts just like the default ones on nltk.books. However, I've just got up to the method like

my_text = ['This', 'is', 'my', 'text']

I'd like to discover any way to input my "text" as:

my_text = "This is my text, this is a nice way to input text."

Which method, python's or from nltk allows me to do this. And more important, how can I dismiss punctuation symbols?

This is actually on the main page of nltk.org:

>>> import nltk
>>> sentence = """At eight o'clock on Thursday morning
... Arthur didn't feel very good."""
>>> tokens = nltk.word_tokenize(sentence)
>>> tokens
['At', 'eight', "o'clock", 'on', 'Thursday', 'morning',
'Arthur', 'did', "n't", 'feel', 'very', 'good', '.']

As @PavelAnossov answered, the canonical answer, use the word_tokenize function in nltk:

from nltk import word_tokenize
sent = "This is my text, this is a nice way to input text."
word_tokenize(sent)

---

If your sentence is truly simple enough:

Using the string.punctuation set, remove punctuation then split using the whitespace delimiter:

import string
x = "This is my text, this is a nice way to input text."
y = "".join([i for i in x if not in string.punctuation]).split(" ")
print y