The following will read everything into a dictionary keyed by player name. The value associated with each player is itself a dictionary acting as a record with named fields associated with the items converted to a format suitable for further processing.

info = {}
with open('scoring_info.txt') as input_file:
    for line in input_file:
        player, stats, outcome, date = (
            item.strip() for item in line.split('-', 3))
        stats = dict(zip(('kills', 'deaths', 'assists'),
                          map(int, stats.split('/'))))
        date = tuple(map(int, date.split('-')))
        info[player] = dict(zip(('stats', 'outcome', 'date'),
                                (stats, outcome, date)))

print('info:')
for player, record in info.items():
    print('  player %r:' % player)
    for field, value in record.items():
        print('    %s: %s' % (field, value))

# sample usage
player = 'Fizz'
print('\n%s had %s kills in the game' % (player, info[player]['stats']['kills']))

Output:

info:
  player 'Shyvana':
    date: (2012, 11, 22)
    outcome: Loss
    stats: {'assists': 5, 'kills': 12, 'deaths': 4}
  player 'Miss Fortune':
    date: (2012, 11, 22)
    outcome: Win
    stats: {'assists': 3, 'kills': 12, 'deaths': 4}
  player 'Fizz':
    date: (2012, 11, 22)
    outcome: Win
    stats: {'assists': 5, 'kills': 12, 'deaths': 4}

Fizz had 12 kills in the game

Alternatively, rather than holding most of the data in dictionaries, which can make nested-field access a little awkward — info[player]['stats']['kills'] — you could instead use a little more advanced "generic" class to hold them, which will let you write info2[player].stats.kills instead.

To illustrate, here's almost the same thing using a class I've named Struct because it's somewhat like the C language's struct data type:

class Struct(object):
    """ Generic container object """
    def __init__(self, **kwds): # keyword args define attribute names and values
        self.__dict__.update(**kwds)

info2 = {}
with open('scoring_info.txt') as input_file:
    for line in input_file:
        player, stats, outcome, date = (
            item.strip() for item in line.split('-', 3))
        stats = dict(zip(('kills', 'deaths', 'assists'),
                          map(int, stats.split('/'))))
        victory = (outcome.lower() == 'win') # change to boolean T/F
        date = dict(zip(('year','month','day'), map(int, date.split('-'))))
        info2[player] = Struct(champ_name=player, stats=Struct(**stats),
                               victory=victory, date=Struct(**date))
print('info2:')
for rec in info2.values():
    print('  player %r:' % rec.champ_name)
    print('    stats: kills=%s, deaths=%s, assists=%s' % (
          rec.stats.kills, rec.stats.deaths, rec.stats.assists))
    print('    victorious: %s' % rec.victory)
    print('    date: %d-%02d-%02d' % (rec.date.year, rec.date.month, rec.date.day))

# sample usage
player = 'Fizz'
print('\n%s had %s kills in the game' % (player, info2[player].stats.kills))

Output:

info2:
  player 'Shyvana':
    stats: kills=12, deaths=4, assists=5
    victorious: False
    date: 2012-11-22
  player 'Miss Fortune':
    stats: kills=12, deaths=4, assists=3
    victorious: True
    date: 2012-11-22
  player 'Fizz':
    stats: kills=12, deaths=4, assists=5
    victorious: True
    date: 2012-11-22

Fizz had 12 kills in the game

first of all you'll need to open the text file in read mode. I'd suggest using a context manager like so:

with open('path/to/your/file.txt', 'r') as file:
    for line in file.readlines():
       # do something with the line (it is a string)

as for managing the info you could build some intermediate structure, for example a dictionary or a list of dictionaries, and then translate that into a CSV file with the csv module.

you could for example split the file whenever there is a blank line, maybe like this:

with open('Downloads/test.txt', 'r') as f:
     my_list = list() # this will be the final list
     entry = dict()   # this contains each user info as a dict
     for line in f.readlines():
         if line.strip() == "":  # if line is empty start a new dict
             my_list.append(entry) # and append the old one to the list
             entry = dict()
         else:                   # otherwise split the line and create new dict
             line_items = line.split(r'  ')
             print(line_items)
             entry[line_items[0]] = line_items[1]
 
 print(my_list)

this code won't work because your text is not formatted in a consistent way: you need to find a way to make the split between "title" and "content" (like "first name" and "bob") in a consistent way. I suggest maybe looking at regex and fixing the txt file by making spacing more consistent.

You can possibly use a combination of Named Entity Recognition and Syntactical Analysis - while the word Edwin is certainly propping up, imagine a situation where the name is Edward Philip Martel. NER detects each word as a separate entities (hence 3 different entities) - thus, you will anyways have to string them together based on some logic. Further, in the case of multiple names being present, it can get harder to disambiguate (e.g. John & Ramsey dined at Winterfell).

This is where the analysis of the sentence syntax would also help (assuming that the end user enters a relatively coherent and proper sentence - if slang and short forms of text are used, even the Stanford NLP can help upto a certain extent only).

One way of leveraging on syntax analysis / parsing and NER is in the following examples -

 1. User: Edwin is my name.
 2. User: I am Edwin.
 3. User: My name is Edwin.

In each of the cases (as is generically the case as well), the Entity Name (Proper Noun / Noun) is associated in close proximity to a Verb. Hence, if you first parse the sentence to determine verbs and then apply NER to surrounding (+/- 1 or 2) words, you may have a relatively decent way to resolve the problem. This solution would depend primarily on the syntax rules you create to identify NERs as well as the window around the verbs.

split should work just fine so long you ignore the header in your file

processes = []

with open("file.txt", "r") as f:
    lines = f.readlines()

    # Loop through all lines, ignoring header.
    # Add last element to list (i.e. the process name)
    for l in lines[1:]:
        processes.append(l.split()[-1])

print processes

Result:

['dns.exe', 'lsass.exe', 'svchost.exe', 'lsass.exe', 'System']