如何将列表保存到保留空行的数据框？

Question

I'm trying to extract subject-verb-object triplets and then attach an ID. I am using a loop so my list of extracted triplets keeping the results for the rows were no triplet was found. So it looks like:

[]
[trump,carried,energy]
[]
[clinton,doesn't,trust]

When I print mylist it looks as expected.

However when I try and create a dataframe from mylist I get an error caused by the empty rows

`IndexError: list index out of range`.

I tried to include an if statement to avoid this but the problem is the same. I also tried using reindex instead but the df2 came out empty.

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import spacy
import textacy
import csv, string, re
import numpy as np
import pandas as pd

#Import csv file with pre-processing already carried out
import pandas as pd
df = pd.read_csv("pre-processed_file_1.csv", sep=",")

#Prepare dataframe to be relevant columns and unicode
df1 = df[['text_1', 'id']].copy()
import StringIO
s = StringIO.StringIO()
tweets = df1.to_csv(encoding='utf-8');
nlp = spacy.load('en')

count = 0;
df2 = pd.DataFrame();
for row in df1.iterrows():
  doc = nlp(unicode(row));
  text_ext = textacy.extract.subject_verb_object_triples(doc);
  tweetID = df['id'].tolist();
  mylist = list(text_ext)
  count = count + 1;
  if (mylist):
        df2 = df2.append(mylist, ignore_index=True)
  else:
        df2 = df2.append('0','0','0')

Any help would be very appreciated. Thank you!

Answer 1

You're supposed to pass a DataFrame-shaped object to append . Passing the raw data doesn't work. So df2=df2.append([['0','0','0']],ignore_index=True)

You can also wrap your processing in a function process_row , then do df2 = pd.DataFrame([process_row(row) for row in df1.iterrows()]) . Note that while append won't work with empty rows, the DataFrame constructor just fills them in with None . If you want empty rows to be ['0','0','0'] , you have several options:

-Have your processing function return ['0','0','0'] for empty rows
-Change the list comprehension to [process_row(row) if process_row(row) else ['0','0','0'] for row in df1.iterrows()]
-Do df2=df2.fillna('0')