I have a text file from amazon, containing the following info:
# user item time rating review text (the header is added by me for explanation, not in the text file disjiad123 TYh23hs9 13160032 5 I love this phone as it is easy to use hjf2329ccc TGjsk123 14423321 3 Suck restaurant
As you see, the data is separated by space and there are different number of columns in each row. However, so it is the text content. Here is the code I have tried:
pd.read_csv(filename, sep = " ", header = None, names = ["user","item","time","rating", "review"], usecols = ["user", "item", "rating"])#I'd like to skip the text review part
And such an error occurs:
ValueError: Passed header names mismatches usecols
When I tried to read all the columns:
pd.read_csv(filename, sep = " ", header = None)
And the error this time is:
Error tokenizing data. C error: Expected 229 fields in line 3, saw 320
And given the review text is so long in many rows , the method of adding header names for each column in this question can not work.
I wonder how to read the csv file if I want to keep the review text and skip them respectively. Thank you in advance!
The problem has been solved by Martin Evans perfectly. But now I am playing with another data set with similar but different format. Now the order of the data is converse:
# review text user item time rating (the header is added by me for explanation, not in the text file I love this phone as it is easy to used isjiad123 TYh23hs9 13160032 5 Suck restaurant hjf2329ccc TGjsk123 14423321 3
Do you have any idea to read it properly? It would be appreciated for any help!
DictReader could also be used as follows to create a list of rows. This could then be imported as a frame in pandas:
import pandas as pd import csv rows =  csv_header = ['user', 'item', 'time', 'rating', 'review'] frame_header = ['user', 'item', 'rating', 'review'] with open('input.csv', 'rb') as f_input: for row in csv.DictReader(f_input, delimiter=' ', fieldnames=csv_header[:-1], restkey=csv_header[-1], skipinitialspace=True): try: rows.append([row['user'], row['item'], row['rating'], ' '.join(row['review'])]) except KeyError, e: rows.append([row['user'], row['item'], row['rating'], ' ']) frame = pd.DataFrame(rows, columns=frame_header) print frame
This would display the following:
user item rating review 0 disjiad123 TYh23hs9 5 I love this phone as it is easy to use 1 hjf2329ccc TGjsk123 3 Suck restaurant
If the review appears at the start of the row, then one approach would be to parse the line in reverse as follows:
import pandas as pd import csv rows =  frame_header = ['rating', 'time', 'item', 'user', 'review'] with open('input.csv', 'rb') as f_input: for row in f_input: cols = [col[::-1] for col in row[::-1][2:].split(' ') if len(col)] rows.append(cols[:4] + [' '.join(cols[4:][::-1])]) frame = pd.DataFrame(rows, columns=frame_header) print frame
This would display:
rating time item user \ 0 5 13160032 TYh23hs9 isjiad123 1 3 14423321 TGjsk123 hjf2329ccc review 0 I love this phone as it is easy to used 1 Suck restaurant
row[::-1] is used to reverse the text of the whole line, the
[2:] skips over the line ending which is now at the start of the line. Each line is then split on spaces. A list comprehension then re-reverses each split entry. Finally
rows is appended to first by taking the fixed 5 column entries (now at the start). The remaining entries are then joined back together with a space and added as the final column.
The benefit of this approach is that it does not rely on your input data being in an exactly fixed width format, and you don't have to worry if the column widths being used change over time.