from __future__ import division, print_function
from collections import Counter
from capstone.util.c4uci import load_instance

FILENAME = 'datasets/connect-4.data'

outcomes = []

with open(FILENAME) as f:
    for i, line in enumerate(f, 1):
        _, outcome = load_instance(line)
        outcomes.append(outcome)
        if i % 1000 == 0:
            print(i)

counter = Counter(outcomes)
print('\n---------')
print(' Results')
print('---------\n')
print('total: {}'.format(len(outcomes)))
for outcome in ['win', 'loss', 'draw']:
    print('{outcome}: {count} ({pct:.2f}%)'.format(
        outcome=outcome,
        count=counter[outcome],
        pct=((counter[outcome] / len(outcomes)) * 100)
    ))

from __future__ import division, print_function
import pandas as pd
from sklearn.linear_model import LinearRegression
from capstone.game import Connect4 as C4
from capstone.util import print_header

FILENAME = 'datasets/connect-4.data'

def column_name(i):
    if i == 42:
        return 'outcome'
    row = chr(ord('a') + (i // C4.ROWS))
    col = (i % C4.ROWS) + 1
    return '{row}{col}'.format(row=row, col=col)

column_names = [column_name(i) for i in range(43)]
df = pd.read_csv(FILENAME, header=None, names=column_names)
outcomes = df.loc[:, 'outcome']

print_header('Dataset')
print(df, end='\n\n')

print_header('Number of instances')
print(df.shape[0], end='\n\n')

print_header('Outcomes')
print(outcomes.value_counts(), end='\n\n')

print_header('Normalized Outcomes')
print(outcomes.value_counts(normalize=True))

Use pandas dataframes for UCI C4 dataset