How to insert pos tags in a dataframe arranged in separate columns in python?

Question

I have POS tagged my input text using TextBlob and exported it into a text file. It gives me three information as POS, Parse Chunker and Deep Parsing. The output of this tagging is in this format: Technique:Plain/NNP/B-NP/O and/CC/I-NP/O . I want this to be arranged in a dataframe in separate columns for each.

This is the code I am using.

 import pandas as pd
 import csv
 from textblob import TextBlob
 with open('report1to8_1.txt', 'r') as myfile:
    report=myfile.read().replace('\n', '')
 out = TextBlob(report).parse()
 tagS = 'taggedop.txt'
 f = open('taggedop.txt', 'w')
 f.write(str(out))
 df = pd.DataFrame(columns=['Words', 'POS', 'Parse chunker','Deep 
 Parsing'])
 df = pd.read_csv('taggedop.txt', sep=' ',error_bad_lines=False, 
 quoting=csv.QUOTE_NONE)   

My expected result is to have a dataframe like this: However, currently I am getting this:

Please Help!!

Answer 1

Try this. The example takes you through putting the data into the correct format so that you are able to create a dataframe. You need to create a list containing lists of your data. This data must be uniformly organised. Then you can create your dataframe. Comment if you need more help

from textblob import TextBlob as blob
import pandas as pd
from string import punctuation

def remove_punctuation(text):
    return ''.join(c for c in text if c not in punctuation)

data = []

text = '''
He an thing rapid these after going drawn or. 
Timed she his law the spoil round defer. 
In surprise concerns informed betrayed he learning is ye. 
Ignorant formerly so ye blessing. He as spoke avoid given downs money on we. 
Of properly carriage shutters ye as wandered up repeated moreover. 
Inquietude attachment if ye an solicitude to. 
Remaining so continued concealed as knowledge happiness. 
Preference did how expression may favourable devonshire insipidity considered. 
An length design regret an hardly barton mr figure.
Those an equal point no years do. Depend warmth fat but her but played. 
Shy and subjects wondered trifling pleasant. 
Prudent cordial comfort do no on colonel as assured chicken. 
Smart mrs day which begin. Snug do sold mr it if such. 
Terminated uncommonly at at estimating. 
Man behaviour met moonlight extremity acuteness direction. '''

text = remove_punctuation(text)
text = text.replace('\n', '')

text = blob(text).parse()
text = text.split(' ')

for tagged_word in text:

    t_word = tagged_word.split('/')
    data.append([t_word[0], t_word[1], t_word[2], t_word[3]])

df = pd.DataFrame(data, columns = ['Words', 'POS', 'Parse Chunker', 'Deep Parsing'] )

Result

Out[18]: 
          Words   POS Parse Chunker Deep Parsing
0            He   PRP          B-NP            O
1            an    DT          I-NP            O
2         thing    NN          I-NP            O
3         rapid    JJ        B-ADJP            O
4         these    DT             O            O
5         after    IN          B-PP        B-PNP
6         going   VBG          B-VP        I-PNP
7         drawn   VBN          I-VP        I-PNP
8            or    CC             O            O
9         Timed   NNP          B-NP            O
10          she   PRP          I-NP            O
11          his  PRP$          I-NP            O
12          law    NN          I-NP            O
13          the    DT             O            O
14        spoil    VB          B-VP            O
15        round    NN          B-NP            O
16        defer    VB          B-VP            O
17           In    IN          B-PP        B-PNP
18     surprise    NN          B-NP        I-PNP
19     concerns   NNS          I-NP        I-PNP
20     informed   VBN          B-VP        I-PNP
21     betrayed   VBN          I-VP        I-PNP
22           he   PRP          B-NP        I-PNP
23     learning   VBG          B-VP        I-PNP
24           is   VBZ          I-VP            O
25           ye   PRP          B-NP            O
26     Ignorant   NNP          I-NP            O
27     formerly    RB          I-NP            O
28           so    RB          I-NP            O
29           ye   PRP          I-NP            O
..          ...   ...           ...          ...
105          no    DT             O            O
106          on    IN          B-PP        B-PNP
107     colonel    NN          B-NP        I-PNP
108          as    IN          B-PP        B-PNP
109     assured   VBN          B-VP        I-PNP
110     chicken    NN          B-NP        I-PNP
111       Smart   NNP          I-NP        I-PNP
112         mrs   NNS          I-NP        I-PNP
113         day    NN          I-NP        I-PNP
114       which   WDT             O            O
115       begin    VB          B-VP            O
116        Snug   NNP          B-NP            O
117          do   VBP          B-VP            O
118        sold   VBN          I-VP            O
119          mr    NN          B-NP            O
120          it   PRP          I-NP            O
121          if    IN          B-PP        B-PNP
122        such    JJ          B-NP        I-PNP
123  Terminated   NNP          I-NP        I-PNP
124  uncommonly    RB        B-ADVP            O
125          at    IN          B-PP        B-PNP
126          at    IN          I-PP        I-PNP
127  estimating   VBG          B-VP        I-PNP
128         Man    NN          B-NP        I-PNP
129   behaviour    NN          I-NP        I-PNP
130         met   VBD          B-VP            O
131   moonlight    NN          B-NP            O
132   extremity    NN          I-NP            O
133   acuteness    NN          I-NP            O
134   direction    NN          I-NP            O

[135 rows x 4 columns]