Converting Mixed-format .dat To .csv (or Anything Else)

I have a large collection of DAT files that need to be converted (eventually to a unique file type). The DAT's have a mixed amount of whitespace between fields, and the column head

Solution 1:

Treat those header lines in the input file with all the disdain they deserve. (Or, in other words, read them and discard them.)

headers='Year Month Day Hour Minute Direct Diffuse2 D_Global D_IR U_Global U_IR Zenith'withopen ('temp.dat') as input_file:
    withopen ('temp_2.csv', 'w') as output_file:
        output_file.write('"%s"\n'%'","'.join(headers.split()))
        for count, line inenumerate(input_file):
            if count<4: continue
            outLine = ','.join(line.split())
            output_file.write(outLine + '\n')

Solution 2:

This is answer for "Python - download and convert .dat to .csv [duplicate]". I couldn't post there so FYI you can get you exact output from here.

import urllib2
import csv
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
response = urllib2.urlopen(url)
readData = response.read()
strObj = filter(None,readData.splitlines())

strObj = [w.replace('\t', '  ') for w in strObj]

listB = []
for i in strObj:
    listB.append(filter(None,i.split("  ")))
withopen(r'c:/data2.csv','a') as f:
    writer = csv.writer(f)
    writer.writerows(listB)

Solution 3:

It looks like you can combine the header rows dynamically based on a word's position in the line. You can skip the first two lines, and combine the next two. If you do it right, you will be left with an iterator over a file stream that you can use to process the remainder of the data as you wish. You can convert it to a different format, or even import it into a pandas DataFrame directly.

To get the headers:

import re

defget_words_and_positions(line):
    return [(match.start(), match.group()) in re.finditer(r'[\w.]+', line)]

withopen('file.dat') as file:
    iterator = iter(file)
    # Skip two linesnext(iterator)
    next(iterator)
    # Get two header lines
    header = get_words_and_positions(next(iterator)) + \
             get_words_and_positions(next(iterator))
    # Sort by positon
    header.sort()
    # Extract words
    header = [word for pos, word in header]

You can now convert the file to a true CSV, or do something else with it. The important thing here is that you have iterator pointing to the actual data in the file stream, and a bunch of dynamically loaded column headers.

To write the remainder to a CSV file, without having to load the entire thing into memory at once, use csv.writer and the iterator from above:

import csv
 ...
 with ...:
 ...
    withopen('outfile.csv', 'w') as output:
        writer = csv.writer(output)
        writer.writerow(header)
        for line in iterator:
            writer.writerow(re.split(r'\s+', line))

You can combine the nested output with and the outer input with into a single outer block to reduce the nesting levels:

withopen('file.dat') as file, open('outputfile.csv', 'w') as output:
    ....

To read in a pandas DataFrame, you can just pass the file object to pandas.read_csv. Since the file stream is past the headers at this point, it will not give you any issues:

import pandas as pd
...
with ...:
    ...
    df = pd.read_csv(file, sep=r'\s'+, header=None, names=header)