Parsing CSV / tab-delimited txt file with Python

Question

I currently have a CSV file which, when opened in Excel, has a total of 5 columns. Only columns A and C are of any significance to me and the data in the remaining columns is irrelevant.

Starting on line 8 and then working in multiples of 7 (ie. lines 8, 15, 22, 29, 36 etc...), I am looking to create a dictionary with Python 2.7 with the information from these fields. The data in column A will be the key (a 6-digit integer) and the data in column C being the respective value for the key. I've tried to highlight this below but the formatting isn't the best:-

    A        B      C          D 1                           CDCDCDCD   2                           VDDBDDB 3 4 5 6 7  DDEFEEF                   FEFEFEFE 8  123456         JONES 9 10 11 12 13 14 15 293849         SMITH 

As per the above, I am looking to extract the value from A7 (DDEFEEF) as a key in my dictionary and "FEFEFEFE" being the respective data and then add another entry to my dictionary, jumping to line 15 with "2938495" being my key and "Smith" being the respective value.

Any suggestions? The source file is a .txt file with entries being tab-delimited. Thanks

Clarification:

Just to clarify, so far, I have tried the below:-

import csv  mydict = {:} f = open("myfile", 'rt') reader = csv.reader(f)     for row in reader:         print row 

The above simply prints out all content though a row at a time. I did try "for row(7) in reader" but this returned an error. I then researched it and had a go at the below but it didn't work neither:

import csv from itertools import islice  entries = csv.reader(open("myfile", 'rb')) mydict = {'key' : 'value'}  for i in xrange(6):     mydict['i(0)] = 'I(2)    # integers representing columns     range = islice(entries,6)     for entry in range:         mydict[entries(0) = entries(2)] # integers representing columns 

Answer 1

Start by turning the text into a list of lists. That will take care of the parsing part:

lol = list(csv.reader(open('text.txt', 'rb'), delimiter='\t')) 

The rest can be done with indexed lookups:

d = dict() key = lol[6][0]      # cell A7 value = lol[6][3]    # cell D7 d[key] = value       # add the entry to the dictionary  ... 

Answer 2

Although there is nothing wrong with the other solutions presented, you could simplify and greatly escalate your solutions by using python's excellent library pandas.

Pandas is a library for handling data in Python, preferred by many Data Scientists.

Pandas has a simplified CSV interface to read and parse files, that can be used to return a list of dictionaries, each containing a single line of the file. The keys will be the column names, and the values will be the ones in each cell.

In your case:

    import pandas      def create_dictionary(filename):         my_data = pandas.DataFrame.from_csv(filename, sep='\t', index_col=False)         # Here you can delete the dataframe columns you don't want!         del my_data['B']         del my_data['D']         # ...         # Now you transform the DataFrame to a list of dictionaries         list_of_dicts = [item for item in my_data.T.to_dict().values()]         return list_of_dicts  # Usage: x = create_dictionary("myfile.csv")