Python 模式表到数据帧

我正在使用Python“Pattern.en”包，该包为我提供关于特定句子的主题、对象和其他细节

但我想将这个输出存储到另一个变量或数据帧中，以便进一步处理，但我无法这样做

这方面的任何投入都会有所帮助

下面提到的示例代码仅供参考

from pattern.en import parse
from pattern.en import pprint
import pandas as pd

input = parse('I want to go to the Restaurant as I am hungry very much')
print(input)    
I/PRP/B-NP/O want/VBP/B-VP/O to/TO/I-VP/O go/VB/I-VP/O to/TO/O/O the/DT/B-NP/O Restaurant/NNP/I-NP/O as/IN/B-PP/B-PNP I/PRP/B-NP/I-PNP am/VBP/B-VP/O hungry/JJ/B-ADJP/O very/RB/I-ADJP/O much/JJ/I-ADJP/O

pprint(input)

      WORD   TAG    CHUNK    ROLE   ID     PNP    LEMMA                                                
         I   PRP    NP       -      -      -      -       
      want   VBP    VP       -      -      -      -       
        to   TO     VP ^     -      -      -      -       
        go   VB     VP ^     -      -      -      -       
        to   TO     -        -      -      -      -       
       the   DT     NP       -      -      -      -       
Restaurant   NNP    NP ^     -      -      -      -       
        as   IN     PP       -      -      PNP    -       
         I   PRP    NP       -      -      PNP    -       
        am   VBP    VP       -      -      -      -       
    hungry   JJ     ADJP     -      -      -      -       
      very   RB     ADJP ^   -      -      -      -       
      much   JJ     ADJP ^   -      -      -      -       

请注意print和pprint语句的输出。我试图将它们中的任何一个存储到变量中。如果我可以将pprint语句的输出存储到数据帧中，因为它是以表格格式打印的，那就更好了

但是当我尝试这样做时，我遇到了下面提到的错误

df = pd.DataFrame(input)

ValueError:未正确调用数据帧构造函数

以函数的来源为例，我得出了这个结论

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,
    )

用法

---

看起来很基本，你看过熊猫的资料了吗？您的错误表明您没有正确调用构造函数——事实似乎确实如此。谢谢@Jacob。但我的问题不是如何解决我所犯的错误。它是如何将pattern.en包的输出存储到变量或数据帧中的。如果你对此有任何想法，请告诉我。希望这不是一个基本的问题，如果你认为这不是基本的问题，你可以重新考虑取消否决票。太棒了。你真是太棒了

>>> 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 ^    -  -    -     -