Game1    Game2   Game3   Game4     Game5

Player1       2        6        5       2        2

Player2       6        4        1       8        4

Player3       8        3        2       1        5

Player4       4        9        4       7        9

import csv
f=open('Games','rb')
f=csv.reader(f,delimiter=';')
lst=list(f)
lst
import numpy as np
myarray = np.asarray(lst)
x=myarray[1,1:] #First player
y=np.sum(x)

import pandas as pd

df = pd.read_csv('/path/to.file.csv', sep=';')

In [196]: df
Out[196]:
         Game1  Game2  Game3  Game4  Game5
Player1      2      6      5      2      2
Player2      6      4      1      8      4
Player3      8      3      2      1      5
Player4      4      9      4      7      9

In [197]: df.sum(axis=1)
Out[197]:
Player1    17
Player2    23
Player3    19
Player4    33
dtype: int64

In [198]: df.sum(1).values
Out[198]: array([17, 23, 19, 33], dtype=int64)

>>> import numpy as np
>>> a = np.array([(1,2), (4,5)],  dtype=[('Game 1', '<f8'), ('Game 2', '<f8')])
>>> a.sum()
TypeError: cannot perform reduce with flexible type

>>> import numpy as np
>>> a = np.array([(1,2), (4,5)],  dtype=[('Game 1', '<f8'), ('Game 2', '<f8')])
>>> a["Game 1"].sum()
5.0

import csv

## Data-file
data =
'''
       , Game1, Game2,   Game3,   Game4,   Game5
Player1,  2,    6,       5,       2,     2
Player2,  6,      4 ,      1,       8,      4
Player3,  8,     3 ,      2,    1,     5
Player4,  4,  9 ,   4,     7,    9
'''

# Write data to file
with open('data.csv', 'w') as FILE:
    FILE.write(data)

print("Raw data:")
print(data)

# 1) Read the data-file (and strip away spaces), the result is data by column:
with open('data.csv','rb') as FILE:
  raw = [ [ item.strip() for item in line]
                      for line in list(csv.reader(FILE,delimiter=',')) if line]

print("Data after Read:")
print(raw)

# 2) Convert numerical data to integers ("float" would also work)
for (i, line) in enumerate(raw[1:], 1):
    for (j, item) in enumerate(line[1:], 1):
        raw[i][j] = int(item)

print("Data after conversion:")
print(raw)

# 3) Use the data...
print("Use the data")
for i in range(1, len(raw)):
  print("Sum for Player %d: %d" %(i, sum(raw[i][1:])) )

for i in range(1, len(raw)):
  print("Total points in Game %d: %d" %(i, sum(list(zip(*raw))[i][1:])) )

Raw data:

       , Game1, Game2,   Game3,   Game4,   Game5
Player1,  2,    6,       5,       2,     2
Player2,  6,      4 ,      1,       8,      4
Player3,  8,     3 ,      2,    1,     5
Player4,  4,  9 ,   4,     7,    9

Data after Read:
[['', 'Game1', 'Game2', 'Game3', 'Game4', 'Game5'], ['Player1', '2', '6', '5', '2', '2'], ['Player2', '6', '4', '1', '8', '4'], ['Player3', '8', '3', '2', '1', '5'], ['Player4', '4', '9', '4', '7', '9']]
Data after conversion:
[['', 'Game1', 'Game2', 'Game3', 'Game4', 'Game5'], ['Player1', 2, 6, 5, 2, 2], ['Player2', 6, 4, 1, 8, 4], ['Player3', 8, 3, 2, 1, 5], ['Player4', 4, 9, 4, 7, 9]]
Use the data
Sum for Player 1: 17
Sum for Player 2: 23
Sum for Player 3: 19
Sum for Player 4: 33
Total points in Game 1: 20
Total points in Game 2: 22
Total points in Game 3: 12
Total points in Game 4: 18

dt = [('Game1', '<i4'), ('Game2', '<i4'), ('Game3', '<i4'),
      ('Game4', '<i4'), ('Game5', '<i4')]
a = np.array([(2, 6, 5, 2, 2),
              (6, 4, 1, 8, 4),
              (8, 3, 2, 1, 5),
              (4, 9, 4, 7, 9)], dtype= dt)

nms = a.dtype.names
by_col = [(i, a[i].sum()) for i in nms if a[i].dtype.kind in ('i', 'f')]
by_col
[('Game1', 20), ('Game2', 22), ('Game3', 12), ('Game4', 18), ('Game5', 20)]

by_row = [("player {}".format(i), sum(a[i])) for i in range(a.shape[0])]
by_row
[('player 0', 17), ('player 1', 23), ('player 2', 19), ('player 3', 33)]

a[0].sum()  # massive failure
....snip out huge error stuff...
TypeError: cannot perform reduce with flexible type
# whereas, this works....
sum(a[0])   # use list/tuple summation

b = np.array([list(a[i]) for i in range(a.shape[0])])
b
array([[2, 6, 5, 2, 2],
       [6, 4, 1, 8, 4],
       [8, 3, 2, 1, 5],
       [4, 9, 4, 7, 9]])

b.sum(axis=0)
array([20, 22, 12, 18, 20])

b.sum(axis=1)
array([17, 23, 19, 33])

b = a.view(np.int32).reshape(len(a), -1)
b
array([[2, 6, 5, 2, 2],
       [6, 4, 1, 8, 4],
       [8, 3, 2, 1, 5],
       [4, 9, 4, 7, 9]])
b.dtype
dtype('int32')

b.sum(axis=0)
array([20, 22, 12, 18, 20])

b.sum(axis=1)
array([17, 23, 19, 33])

import csv
from collections import OrderedDict

with open('games') as f:
    reader = csv.reader(f, delimiter=';')
    data = list(reader)

sums = OrderedDict()
for row in data[1:]:
    player, games = row[0], row[1:]
    sums[player] = sum(map(int, games))