Cannot perform contraction using flexible type

Question

I have this dataset:

           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

I want to calculate the sum of 5 games for each player .

This is my code:

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)

I had the error "cannot perform reduction with flexible type". Im really very new to python and I need your help.

thank

Answer 1

Consider using the Pandas module :

import pandas as pd

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

Resulting DataFrame:

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

Amount:

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)

Answer 2

The complication with using numpy is that one has two sources of errors (and documentation for reading), namely python itself, and also numpy.

, , (numpy) .

:

>>> 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

, .

, pandas ( python) R.

---

, "numpy", , , "", "" . numpy, - ( " " ), C.

Pandas, " Python", "R- , Python", "R" - () . R, , Pandas , " ".

Answer 3

, dtypes. , numpy , dtype , :

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)]

. ... [i] nms , nms = a.dtype.names. "", , [i].dtype.kind.

, , , ,

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])

, , , numpy, pandas , .

As a shortcut, I did not mention that you accept “representations” of arrays that are structured by nature, but have the same type. In the above example, the simple way to get the requirements for simple calculations of arrays by row or column is as follows: a copy of the array was made, but not necessarily

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])

Answer 4

You don't need numpy at all, just do the following:

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))