Read log file with pandas

Question

I have a log file that I tried to read in pandas with read_csv or read_table. I've got this example of results:

0    date=2015-09-17    time=21:05:35     duration=0    etc...

on 1 column.

I would like to split each row, take the names (like date, time, ...) and convert them to columns so I would get:

          date           time     duration   ...
  0    2015-09-17      21:05:35      0              

Thank you !

Answer 1

I know this is an old post, but I came across this same problem and found a solution. The error Expected n fields in line n, saw n is probably due to each row having different number of columns. This method is also not good if the ordering of columns are different for each row. I wrote a sample code here which converts your log into json and then to pandas Dataframe.

import pandas as pd
import json

path='log_sample.log'

log_data=open(path,'r')
result={}
i=0
for line in log_data:
    columns = line.split('') #or w/e you're delimiter/separator is
    data={}
    for c in columns:
        key = c.split('=')[0]
        value=c.split('=')[1]
        data[key]=value
    result[i]=data
    i+=1
j=json.dumps(result)

df=pd.read_json(j, orient='index')

Answer 2

----- Editing answer to account for inconsistent spacing:

Not sure what the pythonic approach should be, but here's a method that could work.

Using OP's data sample as an example:

0    date=2015-09-17    time=21:05:35     duration=0
1    date=2015-09-17    time=21:05:36     duration=0
2    date=2015-09-17    time=21:05:37     duration=0
3    date=2015-09-17    time=21:05:38     duration=0
4    date=2015-09-17    time=21:05:39     duration=0
5    date=2015-09-17    time=21:05:40     duration=0

I loop through each line and split at the equals sign, then grab the desired text:

import pandas as pd

log_data  = open('log_sample.txt', 'r')
split_list = []

for line in log_data:
    thing1 = line.split('=')
    #print(thing1)
    date = thing1[1][:10]
    time = thing1[2][:8]
    dur = thing1[3]

    split_list.append([date, time, dur])

df = pd.DataFrame(split_list, columns=['date', 'time', 'duration'])
df

----- First Answer:

As @jezrael mentions in the comments, you can leverage the "sep" argument within read_csv.

pd.read_csv('test.txt', sep=r'\\t', engine='python') #[1]

See:

• [1] python pandas read_csv not recognizing \t in tab delimited file
• https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html