Pattern table to Pandas DataFrame

from pattern.en import parse
from pattern.text.tree import WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA, IOB, ROLE, MBSP, Text
import pandas as pd

def sentence2df(sentence, placeholder="-"):
    tags  = [WORD, POS, IOB, CHUNK, ROLE, REL, PNP, ANCHOR, LEMMA]
    tags += [tag for tag in sentence.token if tag not in tags]
    def format(token, tag):
        # Returns the token tag as a string.
        if   tag == WORD   : s = token.string
        elif tag == POS    : s = token.type
        elif tag == IOB    : s = token.chunk and (token.index == token.chunk.start and "B" or "I")
        elif tag == CHUNK  : s = token.chunk and token.chunk.type
        elif tag == ROLE   : s = token.chunk and token.chunk.role
        elif tag == REL    : s = token.chunk and token.chunk.relation and str(token.chunk.relation)
        elif tag == PNP    : s = token.chunk and token.chunk.pnp and token.chunk.pnp.type
        elif tag == ANCHOR : s = token.chunk and token.chunk.anchor_id
        elif tag == LEMMA  : s = token.lemma
        else               : s = token.custom_tags.get(tag)
        return s or placeholder

    columns = [[format(token, tag) for token in sentence] for tag in tags]
    columns[3] = [columns[3][i]+(iob == "I" and " ^" or "") for i, iob in enumerate(columns[2])]
    del columns[2]
    header = ['word', 'tag', 'chunk', 'role', 'id', 'pnp', 'anchor', 'lemma']+tags[9:]

    if not MBSP:
        del columns[6]
        del header[6]

    return pd.DataFrame(
        [[x[i] for x in columns] for i in range(len(columns[0]))],
        columns=header,
    )

>>> string = parse('I want to go to the Restaurant as I am hungry very much')
>>> sentence = Text(string, token=[WORD, POS, CHUNK, PNP])[0]
>>> df = sentence2df(sentence)
>>> print(df)
          word  tag   chunk role id  pnp lemma
0            I  PRP      NP    -  -    -     -
1         want  VBP      VP    -  -    -     -
2           to   TO    VP ^    -  -    -     -
3           go   VB    VP ^    -  -    -     -
4           to   TO       -    -  -    -     -
5          the   DT      NP    -  -    -     -
6   Restaurant  NNP    NP ^    -  -    -     -
7           as   IN      PP    -  -  PNP     -
8            I  PRP      NP    -  -  PNP     -
9           am  VBP      VP    -  -    -     -
10      hungry   JJ    ADJP    -  -    -     -
11        very   RB  ADJP ^    -  -    -     -
12        much   JJ  ADJP ^    -  -    -     -