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]

Why is the time function working differently in both these cases?

I am solving Problem 14 of Project Euler and I wrote 2 programs, one which is optimised and the other which is not. I've even imported the time module to calculate the time taken, but it's not working properly. it works fine in the unoptimised code:
import time
start = time.time()
def collatz(n):
chain=1
while(n>1):
chain+=1
if(n%2==0):
n/=2
else:
n = 3*n+1
return chain
maxChain = 0
num=0
counter = 10**6
while(counter>13):
coll = collatz(counter)
if(coll > maxChain):
maxChain = coll
num = counter
counter-=1
end = time.time()
print("Time taken:",end-start)
print(start+', '+ end)
the output is:
Time taken: 47.83728861808777
1591290440.8452923, 1591290488.682581
But in my other code:
import time
start = time.time()
dict = {n:0 for n in range(1,10**6)}
dict[1], dict[2] = 1,2
for i in range(3,10**6):
counter = 0
start = i
while(i > 1):
#Have we already encountered this sequence?
if(i < start):
dict[start] = counter + dict[i]
break
if(i%2==0):
i/=2
else:
i = 3*i+1
counter += 1
end = time.time()
print('Time taken:',end-start)
print(start+', '+end)
the output is:
Time taken: 1590290651.4527032
999999, 1591290650.4527032
The start time in the second program is 999999 while the end time is fine. this problem doesn't occur in the first program, I don't know why this is happening?

Translated from comment:
You can see in the second version of the code you shadow/reuse the variable start, using it for a counter. Thus the 999999 in your output, and the strange results.
Renaming it to anything else will fix you right up =)

python - printing something in a for loop every n seconds

I have a for loop that iterates over a number and performs some simple calculations. I am trying to figure out how to print out ( or log to file) the current value of 'val' every .5 to 1 second with out having to pause or sleep during the loop. Here is a super simple example
val_list = []
for i in xrange(iterations):
val = (i*(1/2)) * pi
val2 = np.linalg.pinv(val)
# print or write to log val2 after every half second (or 1 second)
val_list.append(val2)

Just use time.time to capture the time before starting, then check how long it's been after you calculate val2:
import time
val_list = []
prev_time = time.time()
for i in xrange(iterations):
val = (i*(1/2)) * pi
val2 = np.linalg.pinv(val)
# print or write to log val2 after every half second (or 1 second)
dt = time.time() - prev_time
if dt > 1:
# print or write to log here
prev_time = time.time()
val_list.append(val2)

You can use time.time():
from time import time as t
val_list = []
nowTime = t()
for i in xrange(iterations):
val = (i*(1/2)) * pi
val2 = np.linalg.pinv(val)
curTime = t()
if curTime - nowTime >= 0.5:
#Do your stuff
nowTime = curTime
val_list.append(val2)

You can achieve this using Threads.
Here's a documentation on how to utilize Threads : https://docs.python.org/3/library/threading.html ( If you're using Python2.7 then change the 3 in the url to a 2 )
Here's a link which is similar to what you want and should also point you in the right direction : Python threading.timer - repeat function every 'n' seconds
Basically you have to create a Thread that will only execute ever n number of seconds. On each iteration it will print the value. The above link should suffice for that. Good luck !

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)