We Keep Coding

I need to speed up my code, Also I need to use += i , for sure:for i in nums: sumof += i, its peivets memory block

I try to convert my argmax into array.index(max(array))
I will appreciate any help.
I need to speed up my code.
This code is only for one picture, I will have around 20 pictures in a min. So that's why I try to make it faster as possible.
import time
import numpy as np
#tab = [0.061,0.001,0.022,0.002,0.015,0.021,0.066,0.005,0.053,0.018,0.011,0.024,0.052,0.021,0.077,0.010,0.045,0.017,0.016,0.007,0.071,0.167,0.052,0.016,0.017,0.004,0.009,0.014,0.001,0.059,0.018,0.008,0.004,0.009,0.009]#tabK = [0.009,0.003,0.002,0.001,0.157,0.003,0.067,0.005,0.006,0.002,0.012,0.005,0.033,0.002,0.007,0.010,0.001,0.008,0.037,0.008,0.377,0.011,0.043,0.009,0.004,0.002,0.013,0.016,0.004,0.005,0.057,0.013,0.023,0.026,0.018]CATEGORIES = ["0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","R","S","T","U","V","W","X","Y","Z"]
#KOLEJNOSC ZNAKOW W TABLICY JEST ODWROCONA
CATEGORIES = ["0","1","2","3","4","5","6","7","8","9",
"A","B","C","D","E","F","G","H","I","J",
"K","L","M","N","O","P","R","S","T","U",
"V","W","X","Y","Z"]
KR887JJ = [[0.002,0.006,0.004,0.045,0.002,0.017,0.006,0.077,0.001,0.035,0.042,0.005,0.004,0.039,0.001,0.002,0.001,0.008,0.058,0.352,0.002,0.007,0.017,0.004,0.007,0.007,0.007,0.004,0.005,0.009,0.089,0.036,0.053,0.041,0.004],[0.003,0.007,0.005,0.075,0.001,0.020,0.006,0.044,0.002,0.035,0.026,0.004,0.004,0.033,0.001,0.001,0.003,0.008,0.049,0.360,0.002,0.007,0.021,0.005,0.009,0.003,0.008,0.007,0.003,0.014,0.092,0.048,0.058,0.031,0.004],[0.002,0.000,0.025,0.012,0.006,0.002,0.001,0.627,0.006,0.021,0.022,0.008,0.004,0.006,0.004,0.033,0.000,0.006,0.011,0.009,0.002,0.002,0.009,0.000,0.002,0.040,0.007,0.005,0.015,0.000,0.035,0.001,0.008,0.015,0.053],[0.056,0.008,0.023,0.038,0.015,0.007,0.050,0.006,0.412,0.004,0.005,0.027,0.011,0.005,0.021,0.007,0.073,0.024,0.012,0.005,0.013,0.005,0.027,0.003,0.015,0.001,0.005,0.074,0.002,0.022,0.005,0.011,0.002,0.001,0.006],[0.025,0.011,0.025,0.034,0.018,0.027,0.090,0.008,0.258,0.006,0.007,0.026,0.016,0.008,0.026,0.011,0.079,0.030,0.026,0.008,0.018,0.011,0.033,0.003,0.016,0.001,0.003,0.106,0.004,0.021,0.012,0.013,0.003,0.005,0.014],[0.048,0.027,0.019,0.002,0.028,0.002,0.008,0.017,0.041,0.014,0.012,0.022,0.031,0.005,0.045,0.100,0.004,0.031,0.033,0.002,0.029,0.006,0.021,0.032,0.008,0.038,0.317,0.007,0.017,0.004,0.018,0.005,0.003,0.004,0.002],[0.013,0.002,0.002,0.000,0.164,0.001,0.060,0.004,0.006,0.002,0.018,0.003,0.035,0.002,0.008,0.008,0.001,0.008,0.028,0.005,0.383,0.013,0.063,0.010,0.004,0.002,0.014,0.016,0.002,0.005,0.048,0.011,0.028,0.017,0.012]]
[[0.004,0.007,0.005,0.042,0.002,0.014,0.011,0.054,0.002,0.032,0.051,0.005,0.005,0.044,0.001,0.002,0.002,0.008,0.056,0.389,0.003,0.008,0.023,0.005,0.009,0.005,0.009,0.006,0.004,0.010,0.070,0.029,0.049,0.031,0.005],[0.005,0.005,0.005,0.034,0.002,0.006,0.005,0.083,0.002,0.062,0.053,0.004,0.006,0.039,0.001,0.002,0.001,0.008,0.055,0.348,0.002,0.005,0.020,0.005,0.011,0.011,0.018,0.004,0.005,0.008,0.086,0.018,0.055,0.020,0.004],[0.001,0.001,0.024,0.009,0.013,0.003,0.002,0.499,0.006,0.011,0.022,0.011,0.007,0.006,0.006,0.048,0.000,0.008,0.013,0.009,0.004,0.002,0.007,0.000,0.002,0.053,0.009,0.007,0.030,0.000,0.030,0.001,0.009,0.021,0.123],[0.039,0.008,0.029,0.041,0.013,0.008,0.054,0.005,0.369,0.004,0.005,0.023,0.011,0.005,0.020,0.005,0.100,0.022,0.012,0.005,0.012,0.007,0.028,0.002,0.015,0.000,0.003,0.102,0.001,0.021,0.006,0.012,0.002,0.002,0.007],[0.031,0.007,0.018,0.032,0.015,0.017,0.075,0.008,0.365,0.005,0.005,0.028,0.015,0.005,0.022,0.011,0.075,0.027,0.014,0.005,0.020,0.006,0.025,0.002,0.014,0.001,0.004,0.099,0.003,0.016,0.008,0.009,0.002,0.002,0.010],[0.046,0.022,0.022,0.002,0.031,0.003,0.008,0.022,0.047,0.011,0.010,0.039,0.035,0.005,0.048,0.110,0.002,0.028,0.031,0.003,0.029,0.006,0.016,0.022,0.007,0.059,0.289,0.005,0.016,0.003,0.013,0.004,0.002,0.004,0.003],[0.019,0.004,0.002,0.000,0.134,0.004,0.088,0.006,0.004,0.003,0.016,0.007,0.037,0.004,0.016,0.014,0.002,0.014,0.026,0.005,0.342,0.019,0.049,0.024,0.004,0.003,0.021,0.009,0.005,0.009,0.049,0.012,0.017,0.026,0.006]]
[[0.002,0.005,0.005,0.046,0.002,0.014,0.008,0.073,0.002,0.023,0.042,0.004,0.007,0.027,0.001,0.003,0.001,0.006,0.047,0.384,0.005,0.006,0.015,0.003,0.006,0.004,0.011,0.006,0.006,0.005,0.088,0.027,0.066,0.040,0.010],[0.002,0.005,0.005,0.039,0.002,0.009,0.005,0.089,0.001,0.036,0.043,0.004,0.006,0.026,0.001,0.002,0.001,0.006,0.051,0.387,0.003,0.005,0.014,0.003,0.007,0.007,0.014,0.004,0.007,0.005,0.090,0.022,0.064,0.030,0.006],[0.002,0.001,0.014,0.011,0.020,0.005,0.004,0.282,0.005,0.012,0.022,0.011,0.009,0.010,0.008,0.041,0.000,0.017,0.030,0.019,0.009,0.003,0.009,0.000,0.004,0.053,0.010,0.016,0.049,0.000,0.043,0.002,0.017,0.037,0.224],[0.028,0.015,0.029,0.039,0.023,0.020,0.097,0.008,0.239,0.004,0.009,0.021,0.019,0.008,0.017,0.008,0.082,0.023,0.025,0.012,0.020,0.010,0.037,0.003,0.016,0.001,0.003,0.110,0.003,0.019,0.012,0.014,0.004,0.005,0.015],[0.037,0.013,0.035,0.053,0.025,0.022,0.057,0.017,0.250,0.005,0.009,0.037,0.025,0.008,0.029,0.013,0.057,0.029,0.029,0.009,0.015,0.009,0.027,0.002,0.020,0.002,0.006,0.092,0.006,0.012,0.011,0.011,0.003,0.004,0.021],[0.036,0.022,0.024,0.003,0.022,0.004,0.011,0.019,0.069,0.014,0.011,0.035,0.045,0.006,0.057,0.096,0.004,0.037,0.030,0.004,0.039,0.007,0.021,0.018,0.010,0.031,0.256,0.013,0.018,0.003,0.018,0.005,0.004,0.006,0.004],[0.015,0.003,0.002,0.000,0.174,0.004,0.085,0.008,0.005,0.002,0.015,0.008,0.028,0.003,0.013,0.016,0.001,0.015,0.029,0.004,0.357,0.013,0.043,0.017,0.003,0.003,0.017,0.010,0.004,0.007,0.037,0.013,0.015,0.020,0.010]]
[[0.005,0.006,0.006,0.055,0.002,0.017,0.009,0.076,0.003,0.035,0.032,0.007,0.005,0.050,0.001,0.002,0.002,0.011,0.050,0.391,0.002,0.005,0.018,0.003,0.012,0.009,0.009,0.005,0.005,0.012,0.068,0.032,0.032,0.017,0.006],[0.009,0.005,0.007,0.064,0.002,0.022,0.009,0.040,0.003,0.030,0.026,0.010,0.006,0.069,0.001,0.001,0.004,0.011,0.040,0.379,0.002,0.009,0.021,0.005,0.017,0.007,0.009,0.005,0.003,0.034,0.063,0.035,0.029,0.017,0.006],[0.001,0.001,0.014,0.011,0.011,0.004,0.003,0.458,0.004,0.012,0.023,0.010,0.007,0.009,0.006,0.039,0.000,0.012,0.016,0.014,0.005,0.003,0.007,0.000,0.002,0.052,0.008,0.009,0.039,0.000,0.036,0.001,0.013,0.032,0.139],[0.042,0.012,0.023,0.029,0.043,0.015,0.111,0.010,0.235,0.004,0.009,0.036,0.023,0.010,0.018,0.010,0.039,0.029,0.024,0.012,0.025,0.009,0.041,0.003,0.019,0.002,0.005,0.084,0.004,0.018,0.013,0.012,0.004,0.005,0.023],[0.030,0.008,0.017,0.033,0.011,0.020,0.103,0.009,0.289,0.004,0.006,0.030,0.022,0.007,0.021,0.011,0.108,0.031,0.013,0.008,0.020,0.006,0.023,0.002,0.018,0.001,0.004,0.102,0.005,0.012,0.007,0.007,0.002,0.003,0.009],[0.034,0.024,0.018,0.003,0.025,0.005,0.008,0.025,0.053,0.010,0.008,0.045,0.036,0.006,0.050,0.119,0.002,0.035,0.028,0.003,0.029,0.005,0.014,0.017,0.007,0.060,0.275,0.006,0.019,0.002,0.013,0.004,0.003,0.006,0.005],[0.016,0.004,0.002,0.000,0.130,0.003,0.080,0.005,0.004,0.003,0.017,0.005,0.038,0.002,0.015,0.014,0.002,0.011,0.027,0.004,0.378,0.016,0.051,0.019,0.004,0.002,0.020,0.012,0.004,0.006,0.040,0.013,0.023,0.020,0.008]]
[[0.006,0.004,0.007,0.052,0.002,0.012,0.005,0.066,0.002,0.043,0.036,0.007,0.007,0.051,0.001,0.001,0.002,0.008,0.037,0.401,0.002,0.008,0.017,0.004,0.013,0.010,0.014,0.004,0.004,0.016,0.077,0.021,0.038,0.018,0.006],[0.001,0.001,0.012,0.008,0.013,0.004,0.002,0.462,0.004,0.011,0.022,0.008,0.006,0.006,0.006,0.052,0.000,0.012,0.018,0.012,0.005,0.002,0.007,0.000,0.002,0.046,0.009,0.009,0.040,0.000,0.039,0.002,0.012,0.031,0.136],[0.004,0.003,0.007,0.042,0.001,0.008,0.005,0.060,0.002,0.062,0.050,0.005,0.004,0.053,0.000,0.001,0.002,0.006,0.033,0.422,0.001,0.008,0.017,0.004,0.011,0.007,0.010,0.003,0.002,0.013,0.065,0.024,0.045,0.017,0.003],[0.029,0.015,0.032,0.058,0.018,0.013,0.067,0.012,0.287,0.006,0.010,0.025,0.016,0.007,0.017,0.009,0.059,0.023,0.026,0.011,0.016,0.006,0.033,0.003,0.017,0.001,0.006,0.118,0.004,0.014,0.009,0.012,0.004,0.003,0.012],[0.056,0.011,0.024,0.034,0.027,0.015,0.065,0.013,0.271,0.006,0.007,0.062,0.028,0.013,0.026,0.014,0.030,0.039,0.027,0.011,0.019,0.006,0.031,0.003,0.027,0.005,0.009,0.061,0.006,0.014,0.011,0.006,0.002,0.004,0.016],[0.041,0.020,0.022,0.002,0.019,0.004,0.007,0.024,0.046,0.016,0.008,0.051,0.036,0.006,0.051,0.109,0.002,0.030,0.027,0.003,0.024,0.005,0.015,0.017,0.008,0.067,0.286,0.005,0.015,0.003,0.014,0.003,0.002,0.005,0.003],[0.014,0.003,0.002,0.000,0.106,0.007,0.079,0.007,0.004,0.003,0.014,0.010,0.041,0.004,0.013,0.011,0.001,0.014,0.031,0.007,0.377,0.022,0.049,0.017,0.004,0.003,0.014,0.009,0.004,0.009,0.050,0.013,0.016,0.034,0.008]]
[[0.008,0.004,0.008,0.064,0.001,0.018,0.009,0.041,0.003,0.043,0.035,0.008,0.005,0.076,0.001,0.001,0.004,0.009,0.037,0.382,0.001,0.011,0.024,0.005,0.015,0.006,0.007,0.005,0.002,0.030,0.057,0.031,0.029,0.017,0.004],[0.002,0.004,0.004,0.044,0.001,0.012,0.005,0.068,0.001,0.043,0.052,0.003,0.005,0.030,0.001,0.002,0.001,0.007,0.041,0.350,0.003,0.007,0.019,0.005,0.007,0.004,0.011,0.005,0.003,0.007,0.099,0.035,0.076,0.038,0.004],[0.001,0.001,0.015,0.011,0.009,0.004,0.002,0.517,0.004,0.012,0.028,0.007,0.005,0.007,0.005,0.035,0.000,0.011,0.014,0.012,0.005,0.003,0.010,0.000,0.002,0.030,0.006,0.009,0.029,0.000,0.039,0.002,0.016,0.034,0.114],[0.026,0.010,0.036,0.064,0.011,0.009,0.052,0.010,0.360,0.004,0.007,0.022,0.015,0.005,0.017,0.006,0.067,0.019,0.017,0.008,0.013,0.006,0.026,0.002,0.015,0.001,0.004,0.121,0.003,0.011,0.006,0.010,0.003,0.002,0.011],[0.044,0.017,0.027,0.037,0.024,0.016,0.079,0.010,0.259,0.006,0.009,0.032,0.023,0.009,0.022,0.012,0.064,0.031,0.026,0.010,0.019,0.007,0.034,0.004,0.021,0.002,0.007,0.086,0.005,0.018,0.011,0.011,0.003,0.003,0.011],[0.033,0.013,0.021,0.002,0.014,0.003,0.006,0.023,0.033,0.015,0.009,0.048,0.040,0.007,0.056,0.098,0.002,0.032,0.023,0.003,0.023,0.006,0.013,0.015,0.009,0.069,0.331,0.005,0.018,0.002,0.012,0.003,0.002,0.005,0.003],[0.017,0.004,0.002,0.000,0.096,0.008,0.095,0.007,0.004,0.003,0.013,0.012,0.045,0.006,0.017,0.013,0.002,0.016,0.029,0.007,0.360,0.023,0.045,0.023,0.005,0.004,0.018,0.008,0.005,0.010,0.042,0.011,0.013,0.032,0.007]]
[[0.007,0.004,0.007,0.082,0.002,0.023,0.009,0.029,0.003,0.024,0.024,0.008,0.006,0.062,0.001,0.001,0.005,0.009,0.032,0.391,0.002,0.012,0.020,0.005,0.016,0.004,0.006,0.006,0.002,0.037,0.061,0.041,0.033,0.019,0.006],[0.004,0.004,0.008,0.078,0.001,0.013,0.005,0.059,0.002,0.044,0.041,0.006,0.005,0.052,0.001,0.001,0.002,0.008,0.036,0.366,0.001,0.010,0.015,0.003,0.011,0.008,0.008,0.005,0.004,0.015,0.074,0.035,0.045,0.023,0.007],[0.001,0.001,0.013,0.010,0.015,0.006,0.004,0.299,0.004,0.011,0.021,0.011,0.009,0.010,0.009,0.044,0.000,0.021,0.023,0.017,0.008,0.004,0.007,0.000,0.003,0.056,0.008,0.014,0.056,0.000,0.040,0.002,0.016,0.039,0.217],[0.021,0.011,0.022,0.060,0.018,0.026,0.090,0.014,0.234,0.005,0.011,0.025,0.016,0.009,0.015,0.011,0.052,0.030,0.029,0.015,0.019,0.007,0.030,0.003,0.016,0.001,0.003,0.134,0.006,0.013,0.011,0.014,0.004,0.005,0.020],[0.031,0.012,0.027,0.036,0.023,0.020,0.053,0.018,0.269,0.006,0.008,0.029,0.036,0.007,0.029,0.021,0.046,0.030,0.028,0.009,0.023,0.007,0.027,0.003,0.020,0.002,0.010,0.098,0.010,0.009,0.016,0.009,0.004,0.005,0.018],[0.036,0.019,0.017,0.002,0.027,0.004,0.009,0.027,0.063,0.012,0.008,0.045,0.035,0.006,0.050,0.140,0.002,0.036,0.030,0.003,0.031,0.004,0.017,0.016,0.007,0.055,0.239,0.007,0.018,0.002,0.014,0.004,0.003,0.006,0.004],[0.018,0.004,0.002,0.000,0.186,0.004,0.107,0.005,0.005,0.003,0.010,0.008,0.041,0.004,0.017,0.018,0.001,0.012,0.034,0.004,0.300,0.014,0.036,0.018,0.004,0.004,0.020,0.010,0.005,0.008,0.049,0.009,0.011,0.020,0.008]]
start = time.time()
for sing in KR887JJ:
print(CATEGORIES[np.argmax(sing)])
#print(CATEGORIES[sing.index(max(sing))])
def list_mean(nums):
sumof = 0
num_of = len(nums)
mean = 0
for i in nums:
sumof += i
mean = sumof / num_of
return float(mean)
end = time.time()
print(f"Runtime of the program is {end - start}")
Also I 100% need this part, for I in nums:
sumof += i. Iteration like this will not take a lot of memory because it counts constantly not ones with big blocked memory peace.
Thanks in advance!

np.argmax isn't slower than array.index(np.max(array)), but the list_mean can be much more efficient: if you want a full python implementation, you can use
sum(nums)/len(nums)
Or even more efficient :
np.mean(nums)
(you already work with arrays)

MORE OR LESS:
import time
import numpy as np
#KOLEJNOSC ZNAKOW W TABLICY JEST ODWROCONA
CATEGORIES = ["0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","R","S","T","U","V","W","X","Y","Z"]
KR887JJ = [[0.002,0.006,0.004,0.045,0.002,0.017,0.006,0.077,0.001,0.035,0.042,0.005,0.004,0.039,0.001,0.002,0.001,0.008,0.058,0.352,0.002,0.007,0.017,0.004,0.007,0.007,0.007,0.004,0.005,0.009,0.089,0.036,0.053,0.041,0.004],[0.003,0.007,0.005,0.075,0.001,0.020,0.006,0.044,0.002,0.035,0.026,0.004,0.004,0.033,0.001,0.001,0.003,0.008,0.049,0.360,0.002,0.007,0.021,0.005,0.009,0.003,0.008,0.007,0.003,0.014,0.092,0.048,0.058,0.031,0.004],[0.002,0.000,0.025,0.012,0.006,0.002,0.001,0.627,0.006,0.021,0.022,0.008,0.004,0.006,0.004,0.033,0.000,0.006,0.011,0.009,0.002,0.002,0.009,0.000,0.002,0.040,0.007,0.005,0.015,0.000,0.035,0.001,0.008,0.015,0.053],[0.056,0.008,0.023,0.038,0.015,0.007,0.050,0.006,0.412,0.004,0.005,0.027,0.011,0.005,0.021,0.007,0.073,0.024,0.012,0.005,0.013,0.005,0.027,0.003,0.015,0.001,0.005,0.074,0.002,0.022,0.005,0.011,0.002,0.001,0.006],[0.025,0.011,0.025,0.034,0.018,0.027,0.090,0.008,0.258,0.006,0.007,0.026,0.016,0.008,0.026,0.011,0.079,0.030,0.026,0.008,0.018,0.011,0.033,0.003,0.016,0.001,0.003,0.106,0.004,0.021,0.012,0.013,0.003,0.005,0.014],[0.048,0.027,0.019,0.002,0.028,0.002,0.008,0.017,0.041,0.014,0.012,0.022,0.031,0.005,0.045,0.100,0.004,0.031,0.033,0.002,0.029,0.006,0.021,0.032,0.008,0.038,0.317,0.007,0.017,0.004,0.018,0.005,0.003,0.004,0.002],[0.013,0.002,0.002,0.000,0.164,0.001,0.060,0.004,0.006,0.002,0.018,0.003,0.035,0.002,0.008,0.008,0.001,0.008,0.028,0.005,0.383,0.013,0.063,0.010,0.004,0.002,0.014,0.016,0.002,0.005,0.048,0.011,0.028,0.017,0.012]]
[[0.004,0.007,0.005,0.042,0.002,0.014,0.011,0.054,0.002,0.032,0.051,0.005,0.005,0.044,0.001,0.002,0.002,0.008,0.056,0.389,0.003,0.008,0.023,0.005,0.009,0.005,0.009,0.006,0.004,0.010,0.070,0.029,0.049,0.031,0.005],[0.005,0.005,0.005,0.034,0.002,0.006,0.005,0.083,0.002,0.062,0.053,0.004,0.006,0.039,0.001,0.002,0.001,0.008,0.055,0.348,0.002,0.005,0.020,0.005,0.011,0.011,0.018,0.004,0.005,0.008,0.086,0.018,0.055,0.020,0.004],[0.001,0.001,0.024,0.009,0.013,0.003,0.002,0.499,0.006,0.011,0.022,0.011,0.007,0.006,0.006,0.048,0.000,0.008,0.013,0.009,0.004,0.002,0.007,0.000,0.002,0.053,0.009,0.007,0.030,0.000,0.030,0.001,0.009,0.021,0.123],[0.039,0.008,0.029,0.041,0.013,0.008,0.054,0.005,0.369,0.004,0.005,0.023,0.011,0.005,0.020,0.005,0.100,0.022,0.012,0.005,0.012,0.007,0.028,0.002,0.015,0.000,0.003,0.102,0.001,0.021,0.006,0.012,0.002,0.002,0.007],[0.031,0.007,0.018,0.032,0.015,0.017,0.075,0.008,0.365,0.005,0.005,0.028,0.015,0.005,0.022,0.011,0.075,0.027,0.014,0.005,0.020,0.006,0.025,0.002,0.014,0.001,0.004,0.099,0.003,0.016,0.008,0.009,0.002,0.002,0.010],[0.046,0.022,0.022,0.002,0.031,0.003,0.008,0.022,0.047,0.011,0.010,0.039,0.035,0.005,0.048,0.110,0.002,0.028,0.031,0.003,0.029,0.006,0.016,0.022,0.007,0.059,0.289,0.005,0.016,0.003,0.013,0.004,0.002,0.004,0.003],[0.019,0.004,0.002,0.000,0.134,0.004,0.088,0.006,0.004,0.003,0.016,0.007,0.037,0.004,0.016,0.014,0.002,0.014,0.026,0.005,0.342,0.019,0.049,0.024,0.004,0.003,0.021,0.009,0.005,0.009,0.049,0.012,0.017,0.026,0.006]]
[[0.002,0.005,0.005,0.046,0.002,0.014,0.008,0.073,0.002,0.023,0.042,0.004,0.007,0.027,0.001,0.003,0.001,0.006,0.047,0.384,0.005,0.006,0.015,0.003,0.006,0.004,0.011,0.006,0.006,0.005,0.088,0.027,0.066,0.040,0.010],[0.002,0.005,0.005,0.039,0.002,0.009,0.005,0.089,0.001,0.036,0.043,0.004,0.006,0.026,0.001,0.002,0.001,0.006,0.051,0.387,0.003,0.005,0.014,0.003,0.007,0.007,0.014,0.004,0.007,0.005,0.090,0.022,0.064,0.030,0.006],[0.002,0.001,0.014,0.011,0.020,0.005,0.004,0.282,0.005,0.012,0.022,0.011,0.009,0.010,0.008,0.041,0.000,0.017,0.030,0.019,0.009,0.003,0.009,0.000,0.004,0.053,0.010,0.016,0.049,0.000,0.043,0.002,0.017,0.037,0.224],[0.028,0.015,0.029,0.039,0.023,0.020,0.097,0.008,0.239,0.004,0.009,0.021,0.019,0.008,0.017,0.008,0.082,0.023,0.025,0.012,0.020,0.010,0.037,0.003,0.016,0.001,0.003,0.110,0.003,0.019,0.012,0.014,0.004,0.005,0.015],[0.037,0.013,0.035,0.053,0.025,0.022,0.057,0.017,0.250,0.005,0.009,0.037,0.025,0.008,0.029,0.013,0.057,0.029,0.029,0.009,0.015,0.009,0.027,0.002,0.020,0.002,0.006,0.092,0.006,0.012,0.011,0.011,0.003,0.004,0.021],[0.036,0.022,0.024,0.003,0.022,0.004,0.011,0.019,0.069,0.014,0.011,0.035,0.045,0.006,0.057,0.096,0.004,0.037,0.030,0.004,0.039,0.007,0.021,0.018,0.010,0.031,0.256,0.013,0.018,0.003,0.018,0.005,0.004,0.006,0.004],[0.015,0.003,0.002,0.000,0.174,0.004,0.085,0.008,0.005,0.002,0.015,0.008,0.028,0.003,0.013,0.016,0.001,0.015,0.029,0.004,0.357,0.013,0.043,0.017,0.003,0.003,0.017,0.010,0.004,0.007,0.037,0.013,0.015,0.020,0.010]]
[[0.005,0.006,0.006,0.055,0.002,0.017,0.009,0.076,0.003,0.035,0.032,0.007,0.005,0.050,0.001,0.002,0.002,0.011,0.050,0.391,0.002,0.005,0.018,0.003,0.012,0.009,0.009,0.005,0.005,0.012,0.068,0.032,0.032,0.017,0.006],[0.009,0.005,0.007,0.064,0.002,0.022,0.009,0.040,0.003,0.030,0.026,0.010,0.006,0.069,0.001,0.001,0.004,0.011,0.040,0.379,0.002,0.009,0.021,0.005,0.017,0.007,0.009,0.005,0.003,0.034,0.063,0.035,0.029,0.017,0.006],[0.001,0.001,0.014,0.011,0.011,0.004,0.003,0.458,0.004,0.012,0.023,0.010,0.007,0.009,0.006,0.039,0.000,0.012,0.016,0.014,0.005,0.003,0.007,0.000,0.002,0.052,0.008,0.009,0.039,0.000,0.036,0.001,0.013,0.032,0.139],[0.042,0.012,0.023,0.029,0.043,0.015,0.111,0.010,0.235,0.004,0.009,0.036,0.023,0.010,0.018,0.010,0.039,0.029,0.024,0.012,0.025,0.009,0.041,0.003,0.019,0.002,0.005,0.084,0.004,0.018,0.013,0.012,0.004,0.005,0.023],[0.030,0.008,0.017,0.033,0.011,0.020,0.103,0.009,0.289,0.004,0.006,0.030,0.022,0.007,0.021,0.011,0.108,0.031,0.013,0.008,0.020,0.006,0.023,0.002,0.018,0.001,0.004,0.102,0.005,0.012,0.007,0.007,0.002,0.003,0.009],[0.034,0.024,0.018,0.003,0.025,0.005,0.008,0.025,0.053,0.010,0.008,0.045,0.036,0.006,0.050,0.119,0.002,0.035,0.028,0.003,0.029,0.005,0.014,0.017,0.007,0.060,0.275,0.006,0.019,0.002,0.013,0.004,0.003,0.006,0.005],[0.016,0.004,0.002,0.000,0.130,0.003,0.080,0.005,0.004,0.003,0.017,0.005,0.038,0.002,0.015,0.014,0.002,0.011,0.027,0.004,0.378,0.016,0.051,0.019,0.004,0.002,0.020,0.012,0.004,0.006,0.040,0.013,0.023,0.020,0.008]]
[[0.006,0.004,0.007,0.052,0.002,0.012,0.005,0.066,0.002,0.043,0.036,0.007,0.007,0.051,0.001,0.001,0.002,0.008,0.037,0.401,0.002,0.008,0.017,0.004,0.013,0.010,0.014,0.004,0.004,0.016,0.077,0.021,0.038,0.018,0.006],[0.001,0.001,0.012,0.008,0.013,0.004,0.002,0.462,0.004,0.011,0.022,0.008,0.006,0.006,0.006,0.052,0.000,0.012,0.018,0.012,0.005,0.002,0.007,0.000,0.002,0.046,0.009,0.009,0.040,0.000,0.039,0.002,0.012,0.031,0.136],[0.004,0.003,0.007,0.042,0.001,0.008,0.005,0.060,0.002,0.062,0.050,0.005,0.004,0.053,0.000,0.001,0.002,0.006,0.033,0.422,0.001,0.008,0.017,0.004,0.011,0.007,0.010,0.003,0.002,0.013,0.065,0.024,0.045,0.017,0.003],[0.029,0.015,0.032,0.058,0.018,0.013,0.067,0.012,0.287,0.006,0.010,0.025,0.016,0.007,0.017,0.009,0.059,0.023,0.026,0.011,0.016,0.006,0.033,0.003,0.017,0.001,0.006,0.118,0.004,0.014,0.009,0.012,0.004,0.003,0.012],[0.056,0.011,0.024,0.034,0.027,0.015,0.065,0.013,0.271,0.006,0.007,0.062,0.028,0.013,0.026,0.014,0.030,0.039,0.027,0.011,0.019,0.006,0.031,0.003,0.027,0.005,0.009,0.061,0.006,0.014,0.011,0.006,0.002,0.004,0.016],[0.041,0.020,0.022,0.002,0.019,0.004,0.007,0.024,0.046,0.016,0.008,0.051,0.036,0.006,0.051,0.109,0.002,0.030,0.027,0.003,0.024,0.005,0.015,0.017,0.008,0.067,0.286,0.005,0.015,0.003,0.014,0.003,0.002,0.005,0.003],[0.014,0.003,0.002,0.000,0.106,0.007,0.079,0.007,0.004,0.003,0.014,0.010,0.041,0.004,0.013,0.011,0.001,0.014,0.031,0.007,0.377,0.022,0.049,0.017,0.004,0.003,0.014,0.009,0.004,0.009,0.050,0.013,0.016,0.034,0.008]]
[[0.008,0.004,0.008,0.064,0.001,0.018,0.009,0.041,0.003,0.043,0.035,0.008,0.005,0.076,0.001,0.001,0.004,0.009,0.037,0.382,0.001,0.011,0.024,0.005,0.015,0.006,0.007,0.005,0.002,0.030,0.057,0.031,0.029,0.017,0.004],[0.002,0.004,0.004,0.044,0.001,0.012,0.005,0.068,0.001,0.043,0.052,0.003,0.005,0.030,0.001,0.002,0.001,0.007,0.041,0.350,0.003,0.007,0.019,0.005,0.007,0.004,0.011,0.005,0.003,0.007,0.099,0.035,0.076,0.038,0.004],[0.001,0.001,0.015,0.011,0.009,0.004,0.002,0.517,0.004,0.012,0.028,0.007,0.005,0.007,0.005,0.035,0.000,0.011,0.014,0.012,0.005,0.003,0.010,0.000,0.002,0.030,0.006,0.009,0.029,0.000,0.039,0.002,0.016,0.034,0.114],[0.026,0.010,0.036,0.064,0.011,0.009,0.052,0.010,0.360,0.004,0.007,0.022,0.015,0.005,0.017,0.006,0.067,0.019,0.017,0.008,0.013,0.006,0.026,0.002,0.015,0.001,0.004,0.121,0.003,0.011,0.006,0.010,0.003,0.002,0.011],[0.044,0.017,0.027,0.037,0.024,0.016,0.079,0.010,0.259,0.006,0.009,0.032,0.023,0.009,0.022,0.012,0.064,0.031,0.026,0.010,0.019,0.007,0.034,0.004,0.021,0.002,0.007,0.086,0.005,0.018,0.011,0.011,0.003,0.003,0.011],[0.033,0.013,0.021,0.002,0.014,0.003,0.006,0.023,0.033,0.015,0.009,0.048,0.040,0.007,0.056,0.098,0.002,0.032,0.023,0.003,0.023,0.006,0.013,0.015,0.009,0.069,0.331,0.005,0.018,0.002,0.012,0.003,0.002,0.005,0.003],[0.017,0.004,0.002,0.000,0.096,0.008,0.095,0.007,0.004,0.003,0.013,0.012,0.045,0.006,0.017,0.013,0.002,0.016,0.029,0.007,0.360,0.023,0.045,0.023,0.005,0.004,0.018,0.008,0.005,0.010,0.042,0.011,0.013,0.032,0.007]]
[[0.007,0.004,0.007,0.082,0.002,0.023,0.009,0.029,0.003,0.024,0.024,0.008,0.006,0.062,0.001,0.001,0.005,0.009,0.032,0.391,0.002,0.012,0.020,0.005,0.016,0.004,0.006,0.006,0.002,0.037,0.061,0.041,0.033,0.019,0.006],[0.004,0.004,0.008,0.078,0.001,0.013,0.005,0.059,0.002,0.044,0.041,0.006,0.005,0.052,0.001,0.001,0.002,0.008,0.036,0.366,0.001,0.010,0.015,0.003,0.011,0.008,0.008,0.005,0.004,0.015,0.074,0.035,0.045,0.023,0.007],[0.001,0.001,0.013,0.010,0.015,0.006,0.004,0.299,0.004,0.011,0.021,0.011,0.009,0.010,0.009,0.044,0.000,0.021,0.023,0.017,0.008,0.004,0.007,0.000,0.003,0.056,0.008,0.014,0.056,0.000,0.040,0.002,0.016,0.039,0.217],[0.021,0.011,0.022,0.060,0.018,0.026,0.090,0.014,0.234,0.005,0.011,0.025,0.016,0.009,0.015,0.011,0.052,0.030,0.029,0.015,0.019,0.007,0.030,0.003,0.016,0.001,0.003,0.134,0.006,0.013,0.011,0.014,0.004,0.005,0.020],[0.031,0.012,0.027,0.036,0.023,0.020,0.053,0.018,0.269,0.006,0.008,0.029,0.036,0.007,0.029,0.021,0.046,0.030,0.028,0.009,0.023,0.007,0.027,0.003,0.020,0.002,0.010,0.098,0.010,0.009,0.016,0.009,0.004,0.005,0.018],[0.036,0.019,0.017,0.002,0.027,0.004,0.009,0.027,0.063,0.012,0.008,0.045,0.035,0.006,0.050,0.140,0.002,0.036,0.030,0.003,0.031,0.004,0.017,0.016,0.007,0.055,0.239,0.007,0.018,0.002,0.014,0.004,0.003,0.006,0.004],[0.018,0.004,0.002,0.000,0.186,0.004,0.107,0.005,0.005,0.003,0.010,0.008,0.041,0.004,0.017,0.018,0.001,0.012,0.034,0.004,0.300,0.014,0.036,0.018,0.004,0.004,0.020,0.010,0.005,0.008,0.049,0.009,0.011,0.020,0.008]]
start = time.time()
for sing in KR887JJ:
print(CATEGORIES[sing.index(max(sing))])
def list_mean(nums):
sumof, len = 0, 0
for i in nums:
sumof += i
#len += 1
return sumof / len
end = time.time()
print(f"Runtime of the program is {end - start}")

Why does my code take 1000x longer to sort lists of 2 than 4

I’m trying to time how long it takes for the selectionsort code to sort a list of length 2^i but for some reason it takes longer to sort 2^1 than 2^2 by a fair margin.
import random
import time
def selectionsort(mylist):
sortedlist=[]
while len(mylist) > 0:
lowest = mylist[0]
for i in mylist:
if i < lowest:
lowest=i
sortedlist.append(lowest)
mylist.remove(lowest)
return sortedlist
mylist = []
ivalues = []
sorttimelist = []
for i in range(2):
ivalues.append(2**i)
for x in range(2**i):
mylist.append(random.random())
start_time=time.perf_counter()
selectionsort(mylist)
end_time=time.perf_counter()
sorttime=end_time-start_time
sorttimelist.append(sorttime)
mylist.clear()
print(sorttimelist)
Using print just to test it’s going correctly.

Testing like this, with a single test iteration and such small data sizes, is meaningless. My guess is that the first test is "warming up" the system and so takes longer. Whichever test you run second will be faster due to this.
I enhanced your code to run each test 10000 times, totaling the individual times. When I do this, the second number is larger than the first every time I run it. Here's the new test code:
sorttimelist = []
for i in range(2):
total_time = 0
for iter in range(10000):
mylist = []
for x in range(2 ** i):
mylist.append(random.random())
start_time = time.perf_counter()
selectionsort(mylist)
end_time = time.perf_counter()
sorttime = end_time - start_time
total_time += sorttime
mylist.clear()
sorttimelist.append(total_time)
print(sorttimelist)
And a few sample results:
[0.0065520769999985184, 0.0096335120000004]
[0.00565655999999945, 0.009094481000001708]
[0.005614095000000513, 0.00950561699999955]

python time results not as expected: time.time() - time.time()

In playing around with the python execution of time, I found an odd behavior when calling time.time() twice within a single statement. There is a very small processing delay in obtaining time.time() during statement execution.
E.g. time.time() - time.time()
If executed immediately in a perfect world, would compute in a result of 0.
However, in real world, this results in a very small number as there is an assumed delay in when the processor executes the first time.time() computation and the next. However, when running this same execution and comparing it to a variable computed in the same way, the results are skewed in one direction.
See the small code snippet below.
This also holds true for very large data sets
import time
counts = 300000
def at_once():
first = 0
second = 0
x = 0
while x < counts:
x += 1
exec_first = time.time() - time.time()
exec_second = time.time() - time.time()
if exec_first > exec_second:
first += 1
else:
second += 1
print('1sts: %s' % first)
print('2nds: %s' % second)
prints:
1sts: 39630
2nds: 260370
Unless I have my logic incorrect, I would expect the results to very close to 50:50, but it does not seem to be the case. Is there anyone who could explain what causes this behavior or point out a potential flaw with the code logic that is making the results skewed in one direction?

Could it be that exec_first == exec_second? Your if-else would add 1 to second in that case.
Try changing you if-else to something like:
if exec_first > exec_second:
first += 1
elif exec_second > exec_first:
second += 1
else:
pass

You assign all of the ties to one category. Try it with a middle ground:
import time
counts = 300000
first = 0
second = 0
same = 0
for _ in range(counts):
exec_first = time.time() - time.time()
exec_second = time.time() - time.time()
if exec_first == exec_second:
same += 1
elif exec_first > exec_second:
first += 1
else:
second += 1
print('1sts: %s' % first)
print('same: %s' % same)
print('2nds: %s' % second)
Output:
$ python3 so.py
1sts: 53099
same: 194616
2nds: 52285
$ python3 so.py
1sts: 57529
same: 186726
2nds: 55745
Also, I'm confused as to why you think that a function call might take 0 time. Every invocation requires at least access to the system clock and copying that value to a temporary location of some sort. This isn't free of overhead on any current computer.

Increment list size based on elapsed time

I am creating a program that will measure the execution times of various sorting algorithms (Selection, Bubble, Merge, and Tree sort).
The list sizes used for the test cases should start at 10,000, and go up by 10,000 for each test until the execution time for the test exceeds 60 seconds.
And that is my issue.
I have this probably very wrong (and ugly) code that I have created (I am currently testing with just the Bubble Sort).
import random
import time
def bubbleSort(a_list):
for passnum in range(len(a_list)-1,0,-1):
for i in range(passnum):
if a_list[i]>a_list[i+1]:
temp = a_list[i]
a_list[i] = a_list[i+1]
a_list[i+1] = temp
a_list = []
for i in range(10000):
a_list.append(random.randrange(0,10000))
start = time.perf_counter()
bubbleSort(a_list)
end = time.perf_counter()
elapsed = end - start
print("{0:.8f}".format(elapsed, "\n"))
print(a_list)
if elapsed <= 60:
for i in range(len(a_list), len(a_list)+10000):
a_list.append(random.randrange(len(a_list)+10000))
start = time.perf_counter()
bubbleSort(a_list)
end = time.perf_counter()
elapsed = end - start
print("{0:.8f}".format(elapsed, "\n"))
print(a_list)
else:
#it'll quit
I'm sorry for the ignorance that is very apparent. So above was my first reaction. Then I came up with this loop:
start = time.perf_counter()
while start <= 60:
for i in range(len(a_list)+10000):
a_list.append(random.randrange(len(a_list)+10000))
bubbleSort(a_list)
end = time.perf_counter()
elapsed = end - start
print("{0:.8f}".format(elapsed, "\n"))
print(a_list)
I would be very grateful if someone can give me a push in the right direction and help me think of the logic behind it. Thank you much in advance.

First, collapse some of the code to improve readability:
Element switch now uses the Python idiom a, b = b, a
Build the list with a comprehension, not a loop
parametrize the list size; increment each time through the loop.
Do you really need 8 decimal places for the execution time?
Code:
import random
import time
def bubbleSort(a_list):
for passnum in range(len(a_list)-1,0,-1):
for i in range(passnum):
if a_list[i] > a_list[i+1]:
a_list[i], a_list[i+1] = a_list[i+1], a_list[i]
elapsed = 0
size = 0
size_inc = 10000
print("Size\tTime")
while elapsed < 60:
# Add 10,000 numbers to the list
size += size_inc
a_list = [random.randrange(0,size) for i in range(size)]
start = time.perf_counter()
bubbleSort(a_list)
end = time.perf_counter()
elapsed = end - start
print(size, "\t{0:.8f}".format(elapsed, "sec.\n"))
Output:
Size Time
10000 12.05934826
20000 47.99201040
30000 111.39582218

Algorithm timing in Python

I want to compute how many times my computer can do counter += 1 in one second. A naive approach is the following:
from time import time
counter = 0
startTime = time()
while time() - startTime < 1:
counter += 1
print counter
The problem is time() - startTime < 1 may be considerably more expensive than counter += 1.
Is there a way to make a less "clean" 1 sec sample of my algorithm?

The usual way to time algorithms is the other way around: Use a fixed number of iterations and measure how long it takes to finish them. The best way to do such timings is the timeit module.
print timeit.timeit("counter += 1", "counter = 0", number=100000000)
Note that timing counter += 1 seems rather pointless, though. What do you want to achieve?

Why don't you infer the time instead? You can run something like:
from datetime import datetime
def operation():
counter = 0
tbeg = datetime.utcnow()
for _ in range(10**6):
counter += 1
td = datetime.utcnow() - tbeg
return (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10**6)/10.0**6
def timer(n):
stack = []
for _ in range(n):
stack.append(operation()) # units of musec/increment
print sum(stack) / len(stack)
if __name__ == "__main__":
timer(10)
and get the average elapsed microseconds per increment; I get 0.09 (most likely very inaccurate). Now, it is a simple operation to infer that if I can make one increment in 0.09 microseconds, then I am able to make about 11258992 in one second.
I think the measurements are very inaccurate, but maybe is a sensible approximation?

I have never worked with the time() library, but according to that code I assume it counts seconds, so what if you do the /sec calculations after ctrl+C happens? It would be something like:
#! /usr/bin/env python
from time import time
import signal
import sys
#The ctrl+C interruption function:
def signal_handler(signal, frame):
counts_per_sec = counter/(time()-startTime)
print counts_per_sec
exit(0)
signal.signal(signal.SIGINT, signal_handler)
counter = 0
startTime = time()
while 1:
counter = counter + 1
Of course, it wont be exact because of the time passed between the last second processed and the interruption signal, but the more time you leave the script running, the more precise it will be :)

Here is my approach
import time
m = 0
timeout = time.time() + 1
while True:
if time.time() > timeout:
break
m = m + 1
print(m)