使用熊猫将句子拆分为包含不同数量单词的子字符串

Question

My question is related to this past of question of mine: Split text in cells and create additional rows for the tokens .

Let's suppose that I have the following in a DataFrame in pandas :

id  text
1   I am the first document and I am very happy.
2   Here is the second document and it likes playing tennis.
3   This is the third document and it looks very good today.

and I want to split the text of each id in tokens of random number of words (varying between two values eg 1 and 5) so I finally want to have something like the following:

id  text
1   I am the
1   first document
1   and I am very
1   happy
2   Here is
2   the second document and it
2   likes playing
2   tennis
3   This is the third
3   document and
3   looks very
3   very good today

Keep in mind that my dataframe may also have other columns except for these two which should be simply copied at the new dataframe in the same way as id above.

What is the most efficient way to do this?

Answer 1

Define a function to extract chunks in a random fashion using itertools.islice :

from itertools import islice
import random

lo, hi = 3, 5 # change this to whatever
def extract_chunks(it):
    chunks = []
    while True:
        chunk = list(islice(it, random.choice(range(lo, hi+1))))
        if not chunk:
            break
        chunks.append(' '.join(chunk))

    return chunks

Call the function through a list comprehension to ensure least possible overhead, then stack to get your output:

pd.DataFrame([
    extract_chunks(iter(text.split())) for text in df['text']], index=df['id']
).stack()

id   
1   0                    I am the
    1        first document and I
    2              am very happy.
2   0                 Here is the
    1         second document and
    2    it likes playing tennis.
3   0           This is the third
    1       document and it looks
    2            very good today.

You can extend the extract_chunks function to perform tokenisation. Right now, I use a simple splitting on whitespace which you can modify.

---

Note that if you have other columns you don't want to touch, you can do something like a melt ing operation here.

u = pd.DataFrame([
    extract_chunks(iter(text.split())) for text in df['text']])

(pd.concat([df.drop('text', 1), u], axis=1)
   .melt(df.columns.difference(['text'])))