<jupyter_start><jupyter_text>Formula to calculate bin width for each histogram<jupyter_code>## calculating bin width for servinsp1
Q1 = np.quantile(data[0], 0.25)
Q3 = np.quantile(data[0], 0.75)
IQR = Q3 - Q1
cube = np.cbrt(len(data[0]))
bin_width = (IQR/cube * 2)
print(bin_width)<jupyter_output>3.024350683293212<jupyter_text>Formula to count number of occurances in each bin<jupyter_code>servinsp1_array
bin_values = 0
bin_width_start = 0
bin_width_end = bin_width
for i in range (0,26):
    for data_value in servinsp1_array:
        if(data_value <= bin_width_end and data_value > bin_width_start):
            bin_values = bin_values + 1
    print('bin {} from {} to {} is {} '.format(i,bin_width_start,bin_width_end,bin_values))
    bin_values = 0
    bin_width_start = bin_width_end
    bin_width_end = bin_width_end + bin_width<jupyter_output>bin 0 from 0 to 3.024350683293212 is 61 
bin 1 from 3.024350683293212 to 6.048701366586424 is 62 
bin 2 from 6.048701366586424 to 9.073052049879635 is 50 
bin 3 from 9.073052049879635 to 12.097402733172848 is 34 
bin 4 from 12.097402733172848 to 15.12175341646606 is 28 
bin 5 from 15.12175341646606 to 18.14610409975927 is 13 
bin 6 from 18.14610409975927 to 21.17045478305248 is 15 
bin 7 from 21.17045478305248 to 24.19480546634569 is 13 
bin 8 from 24.19480546634569 to 27.219156149638902 is 9 
bin 9 from 27.219156149638902 to 30.243506832932113 is 3 
bin 10 from 30.243506832932113 to 33.26785751622533 is 3 
bin 11 from 33.26785751622533 to 36.29220819951854 is 1 
bin 12 from 36.29220819951854 to 39.316558882811755 is 4 
bin 13 from 39.316558882811755 to 42.34090956610497 is 1 
bin 14 from 42.34090956610497 to 45.36526024939818 is 0 
bin 15 from 45.36526024939818 to 48.3896109326914 is 0 
bin 16 from 48.3896109326914 to 51.41396161598461 is 1 
bin 17 from 51.41396161598461 to 54.4383122[...]
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