CODEWITHSUNDEEP
pythonregexpandas
user9996043
user9996043

Reputation: 229

Regex text to pandas dataframe

I have a text file that contains multiple lines in the format given below:

real    0m0.020s
user    0m0.000s
sys 0m0.000s
Round  1  completed. with matrix size of  1200 x 1200 with threads 8

real    0m0.022s
user    0m0.000s
sys 0m0.001s
Round  2  completed. with matrix size of  1200 x 1200 with threads 8

There are about 500 entries of the this sort(above is an example of 2). I can't seem to figure out how to get them into a pandas dataframe that might look something like this:

Matrix Size    Threads    Round    Real    User    Sys
1200 x 1200    8          1        0.0020  0.0000  0.0000
1200 x 1200    8          2        0.0022  0.0000  0.0001

Is there a way using regex or some other way to convert the test output into a dataframe. Additionally I don't know if I interpreted the times correctly either as they are in 0m(I think 0 minutes) and the 0.02 (I think 0.02 seconds)

Upvotes: 0

Views: 582

Answers (2)

It_is_Chris
It_is_Chris

Reputation: 14063

If you want to solve the problem using only pandas you can use str.split():

# data
s = """real    0m0.020s
user    0m0.000s
sys 0m0.000s
Round  1  completed. with matrix size of  1200 x 1200 with threads 8

real    0m0.022s
user    0m0.000s
sys 0m0.001s
Round  2  completed. with matrix size of  1200 x 1200 with threads 8"""

# str.split on two line breaks for rows then split on the text
df = pd.DataFrame(s.split('\n\n'))[0].str.split('   |real | with |user    |sys |matrix size of  |threads |\n')\
                                  .apply(lambda x: [s for s in x if s]).apply(pd.Series)

# split col 3 on round and completed to get number of rounds
df[3] = df[3].str.strip('Round | completed.')

# rename columns
df.columns = ['real', 'user', 'sys', 'round', 'matrix size', 'threads']

out

       real      user       sys round  matrix size threads
0  0m0.020s  0m0.000s  0m0.000s     1  1200 x 1200       8
1  0m0.022s  0m0.000s  0m0.001s     2  1200 x 1200       8

note that it will be slower gmds' example:

1000 loops, best of 3: 4.42 ms per loop vs 1000 loops, best of 3: 1.84 ms per loop

Upvotes: 1

gmds
gmds

Reputation: 19885

You can use a regex:

import re
import pandas as pd

regex = re.compile(r'real +(\dm\d\.\d+s)\nuser +(\dm\d\.\d+s)\nsys +(\dm\d\.\d+s)\nRound +(\d+).+of +(\d+ x \d+).+threads (\d+)')

df = pd.DataFrame(regex.findall(data), columns=['real', 'user', 'sys', 'round', 'matrix size', 'threads'])

print(df)

Output:

       real      user       sys round  matrix size threads
0  0m0.020s  0m0.000s  0m0.000s     1  1200 x 1200       8
1  0m0.022s  0m0.000s  0m0.001s     2  1200 x 1200       8

Upvotes: 3

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