import pandas as pd
from collections import Counter
import numpy as np
from io import StringIO
New historical extraction data
data = “””02/01/2024 01 50 38 42 29 18
04/01/2024 02 02 44 36 24 89
05/01/2024 03 68 13 19 71 69
08/01/2024 04 86 87 21 76 47
09/01/2024 05 25 67 44 28 19
11/01/2024 06 89 82 72 02 26
12/01/2024 07 31 81 34 32 08
13/01/2024 08 75 21 35 01 50
16/01/2024 09 40 27 45 07 84
18/01/2024 10 01 51 88 50 03
19/01/2024 11 31 67 79 78 17
20/01/2024 12 79 84 10 61 51
23/01/2024 13 09 13 80 19 21
25/01/2024 14 44 40 70 56 29
26/01/2024 15 39 74 47 75 05
27/01/2024 16 24 04 29 18 77
30/01/2024 17 84 40 35 47 50
01/02/2024 18 90 65 57 21 85
02/02/2024 19 45 60 06 10 53
03/02/2024 20 27 23 59 21 55
06/02/2024 21 60 74 68 75 66
08/02/2024 22 36 64 47 45 80
09/02/2024 23 29 74 53 88 78
10/02/2024 24 28 70 31 46 59
13/02/2024 25 83 86 56 60 33
15/02/2024 26 87 02 34 19 30
16/02/2024 27 29 22 31 61 57
17/02/2024 28 72 71 48 21 29
20/02/2024 29 12 25 58 47 72
22/02/2024 30 63 73 69 23 28
23/02/2024 31 20 15 28 80 63
24/02/2024 32 07 02 80 25 78
27/02/2024 33 04 45 25 52 30
29/02/2024 34 05 55 46 01 20
01/03/2024 35 41 78 80 34 74
02/03/2024 36 82 76 57 09 74
05/03/2024 37 42 05 46 30 03
07/03/2024 38 14 30 45 51 56
08/03/2024 39 86 19 45 08 06
09/03/2024 40 45 51 12 66 65
12/03/2024 41 33 38 13 88 75
14/03/2024 42 30 83 61 69 20
15/03/2024 43 87 35 45 48 73
16/03/2024 44 09 59 12 08 63
19/03/2024 45 46 35 19 13 30
21/03/2024 46 36 84 15 70 86
22/03/2024 47 50 45 43 51 25
23/03/2024 48 29 07 32 36 45
26/03/2024 49 13 70 25 68 47
28/03/2024 50 64 36 37 02 04
29/03/2024 51 86 15 42 84 81
30/03/2024 52 56 50 01 90 86
02/04/2024 53 34 39 33 15 64
04/04/2024 54 59 55 07 75 64
05/04/2024 55 11 85 42 01 04
06/04/2024 56 49 16 01 25 15
09/04/2024 57 42 23 13 39 43
11/04/2024 58 83 65 43 82 04
12/04/2024 59 02 35 16 78 76
13/04/2024 60 30 22 07 06 10
16/04/2024 61 42 80 16 39 65
18/04/2024 62 67 65 03 87 63
19/04/2024 63 56 21 01 90 03
20/04/2024 64 86 44 19 31 72
23/04/2024 65 33 14 86 02 62
26/04/2024 66 38 09 83 18 20
27/04/2024 67 60 62 43 58 38
29/04/2024 68 45 68 85 44 01
30/04/2024 69 67 01 87 84 47
02/05/2024 70 10 23 48 01 87
03/05/2024 71 69 50 27 65 06
04/05/2024 72 39 88 84 01 67
07/05/2024 73 43 74 20 02 11
09/05/2024 74 29 34 75 82 18
10/05/2024 75 68 52 49 22 48
11/05/2024 76 24 54 06 42 02
14/05/2024 77 62 83 20 56 85
16/05/2024 78 75 31 29 78 15
17/05/2024 79 13 07 23 24 38
18/05/2024 80 89 01 35 54 38
21/05/2024 81 53 65 02 72 32
23/05/2024 82 36 49 76 51 54
24/05/2024 83 13 86 72 71 32
25/05/2024 84 44 56 70 54 29
28/05/2024 85 11 01 81 07 12
30/05/2024 86 29 48 75 49 10
31/05/2024 87 32 54 68 64 86
01/06/2024 88 49 08 82 79 84
04/06/2024 89 31 49 27 86 81
06/06/2024 90 12 27 33 64 89
07/06/2024 91 69 14 53 63 65
08/06/2024 92 12 20 90 26 30
11/06/2024 93 03 78 18 20 61
13/06/2024 94 69 80 84 59 24
14/06/2024 95 82 56 79 62 31
15/06/2024 96 39 42 79 30 28
18/06/2024 97 56 83 43 48 74
20/06/2024 98 56 80 36 11 31
21/06/2024 99 74 84 71 49 03
22/06/2024 100 51 71 61 54 55
25/06/2024 101 60 62 78 22 30
27/06/2024 102 62 66 09 69 16
28/06/2024 103 46 73 87 52 05
29/06/2024 104 59 30 85 82 39
02/07/2024 105 14 48 57 47 80
04/07/2024 106 76 12 47 19 41
05/07/2024 107 65 23 89 37 34
06/07/2024 108 05 16 32 69 27
09/07/2024 109 15 40 90 62 17
11/07/2024 110 71 11 54 87 12
12/07/2024 111 42 31 17 38 23
13/07/2024 112 40 54 44 72 14
16/07/2024 113 60 50 17 12 84
18/07/2024 114 01 49 31 05 67
19/07/2024 115 89 86 76 21 45
20/07/2024 116 28 04 69 51 83
23/07/2024 117 01 70 20 90 41
25/07/2024 118 57 10 20 69 44
26/07/2024 119 50 35 74 61 06
27/07/2024 120 68 80 63 13 55
30/07/2024 121 84 46 42 45 02
01/08/2024 122 74 42 38 64 60
02/08/2024 123 03 66 29 11 33
03/08/2024 124 69 73 43 70 33
06/08/2024 125 29 05 35 02 12
08/08/2024 126 87 61 68 22 13
09/08/2024 127 42 34 14 51 58
10/08/2024 128 24 22 35 21 31
13/08/2024 129 89 69 62 81 63
16/08/2024 130 69 59 10 25 31
17/08/2024 131 60 78 45 88 54
19/08/2024 132 87 45 30 77 23
20/08/2024 133 43 23 31 41 57
22/08/2024 134 35 89 22 72 54
23/08/2024 135 18 60 67 83 15
24/08/2024 136 17 63 13 74 47
27/08/2024 137 72 80 86 18 70
29/08/2024 138 76 47 33 28 16
30/08/2024 139 66 58 77 16 88
31/08/2024 140 28 21 22 61 10
03/09/2024 141 34 28 68 90 31
05/09/2024 142 14 02 13 69 75
06/09/2024 143 15 06 07 30 71
07/09/2024 144 32 62 36 71 72
10/09/2024 145 64 77 88 73 45
12/09/2024 146 11 07 60 02 26
13/09/2024 147 79 13 26 66 17
14/09/2024 148 43 37 50 05 72
17/09/2024 149 38 09 89 76 64
19/09/2024 150 57 71 73 52 74
20/09/2024 151 85 56 06 80 34
21/09/2024 152 68 16 42 76 87
24/09/2024 153 34 64 90 02 55
26/09/2024 154 88 37 68 17 13
27/09/2024 155 53 48 78 66 80
28/09/2024 156 49 67 26 29 68
01/10/2024 157 62 21 29 07 32″””
Create a DataFrame from the new data
df = pd.read_csv(StringIO(data), sep=’\s+’, header=None, names=[‘Date’, ‘Extraction’, ‘N1’, ‘N2’, ‘N3’, ‘N4’, ‘N5’])
List of all drawn numbers
all_numbers = []
for col in [‘N1’, ‘N2’, ‘N3’, ‘N4’, ‘N5’]:
all_numbers.extend(df[col].tolist())
Calculate the frequency of each number
number_counts = Counter(all_numbers)
Function to calculate the score for overdue numbers
def punteggio_ritardatari(numero, df):
last_extraction = df[df.apply(lambda row: numero in row[2:], axis=1)].index.max()
if np.isnan(last_extraction):
return 1.0 # Number not present
else:
return 1 / (len(df) – last_extraction)
Function to calculate the score for frequent numbers
def punteggio_frequenti(numero, number_counts, total_numbers):
return number_counts[numero] / total_numbers
Calculate scores for each number
punteggi = {}
total_numbers = len(all_numbers)
for numero in range(1, 91): # Assuming numbers are from 1 to 90
punteggi[numero] = (punteggio_ritardatari(numero, df) + punteggio_frequenti(numero, number_counts, total_numbers)) / 2 # Simple average of scores
Sort numbers based on scores
numeri_ordinati = sorted(punteggi, key=punteggi.get, reverse=True)
dieci_numeri_probabili = numeri_ordinati[:10]
print(f”I 10 numeri più probabili sono: {dieci_numeri_probabili}”) modifica questo script immettendo queste estrazioni
02/01/2024 01 86 84 52 15 44
04/01/2024 02 49 03 78 79 14
05/01/2024 03 51 58 29 14 22
08/01/2024 04 65 56 10 72 23
09/01/2024 05 74 63 15 28 04
11/01/2024 06 33 81 66 90 56
12/01/2024 07 68 86 14 59 49
13/01/2024 08 88 12 30 23 54
16/01/2024 09 01 45 66 51 48
18/01/2024 10 26 82 58 04 67
19/01/2024 11 53 75 30 46 10
20/01/2024 12 40 42 24 56 50
23/01/2024 13 04 81 32 33 65
25/01/2024 14 76 54 86 74 80
26/01/2024 15 03 35 87 18 28
27/01/2024 16 33 27 02 45 58
30/01/2024 17 18 44 17 48 51
01/02/2024 18 35 48 68 85 39
02/02/2024 19 85 30 77 56 81
03/02/2024 20 34 32 44 66 13
06/02/2024 21 58 85 77 80 73
08/02/2024 22 58 53 30 90 67
09/02/2024 23 90 36 89 75 55
10/02/2024 24 67 24 38 84 13
13/02/2024 25 45 41 89 60 43
15/02/2024 26 41 55 68 65 51
16/02/2024 27 13 06 46 25 31
17/02/2024 28 09 80 01 14 54
20/02/2024 29 44 47 11 34 32
22/02/2024 30 23 28 50 49 51
23/02/2024 31 69 06 88 35 74
24/02/2024 32 27 33 09 46 66
27/02/2024 33 45 83 86 82 51
29/02/2024 34 74 26 33 50 80
01/03/2024 35 43 05 87 74 12
02/03/2024 36 40 71 87 51 56
05/03/2024 37 45 48 62 69 03
07/03/2024 38 56 23 60 41 63
08/03/2024 39 55 32 75 66 27
09/03/2024 40 27 32 70 76 43
12/03/2024 41 23 64 13 81 47
14/03/2024 42 51 85 18 09 73
15/03/2024 43 51 61 54 62 60
16/03/2024 44 13 73 41 43 17
19/03/2024 45 89 85 58 49 08
21/03/2024 46 37 70 86 57 16
22/03/2024 47 25 29 45 51 46
23/03/2024 48 59 81 56 75 58
26/03/2024 49 28 30 54 70 88
28/03/2024 50 66 27 44 90 17
29/03/2024 51 19 51 13 37 08
30/03/2024 52 04 03 18 08 22
02/04/2024 53 58 82 16 61 25
04/04/2024 54 38 26 40 90 41
05/04/2024 55 60 87 30 31 62
06/04/2024 56 72 50 79 70 35
09/04/2024 57 14 05 53 86 17
11/04/2024 58 19 90 88 25 08
12/04/2024 59 55 77 76 02 90
13/04/2024 60 15 90 28 24 51
16/04/2024 61 58 22 11 86 40
18/04/2024 62 85 90 19 67 78
19/04/2024 63 05 56 68 27 09
20/04/2024 64 38 70 31 33 26
23/04/2024 65 61 22 44 19 26
26/04/2024 66 76 24 78 30 40
27/04/2024 67 88 70 85 38 50
29/04/2024 68 76 52 03 22 16
30/04/2024 69 31 56 40 53 50
02/05/2024 70 71 12 04 27 01
03/05/2024 71 60 05 42 75 84
04/05/2024 72 36 30 70 06 41
07/05/2024 73 75 19 18 74 69
09/05/2024 74 16 74 36 55 64
10/05/2024 75 12 33 80 62 75
11/05/2024 76 12 33 75 30 68
14/05/2024 77 09 90 41 32 33
16/05/2024 78 78 09 14 17 85
17/05/2024 79 35 80 90 76 73
18/05/2024 80 84 58 25 64 57
21/05/2024 81 40 80 14 19 13
23/05/2024 82 74 28 44 03 78
24/05/2024 83 28 76 34 11 44
25/05/2024 84 61 46 70 36 65
28/05/2024 85 37 24 82 75 31
30/05/2024 86 31 49 43 18 11
31/05/2024 87 01 24 19 14 63
01/06/2024 88 10 52 67 34 43
04/06/2024 89 84 69 21 57 45
06/06/2024 90 61 20 75 17 02
07/06/2024 91 65 63 41 31 09
08/06/2024 92 77 82 60 10 42
11/06/2024 93 06 01 16 12 20
13/06/2024 94 43 01 79 86 53
14/06/2024 95 20 69 71 19 43
15/06/2024 96 40 61 78 34 54
18/06/2024 97 34 61 32 02 60
20/06/2024 98 63 48 26 84 42
21/06/2024 99 73 41 43 31 17
22/06/2024 100 11 13 15 53 23
25/06/2024 101 23 47 55 60 08
27/06/2024 102 70 28 80 62 18
28/06/2024 103 52 02 38 04 71
29/06/2024 104 46 79 29 14 25
02/07/2024 105 81 37 67 83 64
04/07/2024 106 56 71 51 76 45
05/07/2024 107 65 31 84 86 19
06/07/2024 108 31 78 54 53 52
09/07/2024 109 28 19 84 05 62
11/07/2024 110 73 38 61 26 20
12/07/2024 111 54 07 64 65 63
13/07/2024 112 45 44 76 83 77
16/07/2024 113 10 48 70 07 38
18/07/2024 114 12 67 59 20 48
19/07/2024 115 67 44 30 31 63
20/07/2024 116 69 17 49 75 52
23/07/2024 117 37 09 76 51 61
25/07/2024 118 36 03 30 57 56
26/07/2024 119 14 76 62 50 83
27/07/2024 120 57 10 34 04 02
30/07/2024 121 52 61 29 08 19
01/08/2024 122 35 72 84 33 05
02/08/2024 123 81 28 47 29 20
03/08/2024 124 15 38 41 37 27
06/08/2024 125 83 41 03 11 07
08/08/2024 126 13 35 56 79 75
09/08/2024 127 45 36 89 20 90
10/08/2024 128 39 02 78 36 43
13/08/2024 129 46 37 81 62 18
16/08/2024 130 67 59 84 65 70
17/08/2024 131 75 67 47 53 12
19/08/2024 132 81 59 86 31 04
20/08/2024 133 02 07 31 80 46
22/08/2024 134 85 10 42 29 39
23/08/2024 135 27 71 52 66 21
24/08/2024 136 69 57 01 55 45
27/08/2024 137 09 52 73 43 48
29/08/2024 138 17 03 12 50 63
30/08/2024 139 51 50 10 05 70
31/08/2024 140 46 36 17 49 28
03/09/2024 141 15 14 86 67 09
05/09/2024 142 58 86 08 23 73
06/09/2024 143 08 46 01 32 39
07/09/2024 144 09 31 29 42 55
10/09/2024 145 80 69 07 88 33
12/09/2024 146 59 77 85 37 84
13/09/2024 147 39 35 23 36 81
14/09/2024 148 09 08 51 59 45
17/09/2024 149 34 75 36 29 73
19/09/2024 150 03 14 23 53 06
20/09/2024 151 30 44 68 03 11
21/09/2024 152 11 31 19 67 25
24/09/2024 153 45 53 71 48 38
26/09/2024 154 72 75 01 39 71
27/09/2024 155 09 29 10 43 59
28/09/2024 156 37 01 54 79 15
01/10/2024 157 29 68 31 66 50
To modify your script to include the new extraction data, we’ll replace the existing data string with the new one provided. Here’s how the modified script will look:
import pandas as pd
from collections import Counter
import numpy as np
from io import StringIO
# New historical extraction data
data = """02/01/2024 01 86 84 52 15 44
04/01/2024 02 49 03 78 79 14
05/01/2024 03 51 58 29 14 22
08/01/2024 04 65 56 10 72 23
09/01/2024 05 74 63 15 28 04
11/01/2024 06 33 81 66 90 56
12/01/2024 07 68 86 14 59 49
13/01/2024 08 88 12 30 23 54
16/01/2024 09 01 45 66 51 48
18/01/2024 10 26 82 58 04 67
19/01/2024 11 53 75 30 46 10
20/01/2024 12 40 42 24 56 50
23/01/2024 13 04 81 32 33 65
25/01/2024 14 76 54 86 74 80
26/01/2024 15 03 35 87 18 28
27/01/2024 16 33 27 02 45 58
30/01/2024 17 18 44 17 48 51
01/02/2024 18 35 48 68 85 39
02/02/2024 19 85 30 77 56 81
03/02/2024 20 34 32 44 66 13
06/02/2024 21 58 85 77 80 73
08/02/2024 22 58 53 30 90 67
09/02/2024 23 90 36 89 75 55
10/02/2024 24 67 24 38 84 13
13/02/2024 25 45 41 89 60 43
15/02/2024 26 41 55 68 65 51
16/02/2024 27 13 06 46 25 31
17/02/2024 28 09 80 01 14 54
20/02/2024 29 44 47 11 34 32
22/02/2024 30 23 28 50 49 51
23/02/2024 31 69 06 88 35 74
24/02/2024 32 27 33 09 46 66
27/02/2024 33 45 83 86 82 51
29/02/2024 34 74 26 33 50 80
01/03/2024 35 43 05 87 74 12
02/03/2024 36 40 71 87 51 56
05/03/2024 37 45 48 62 69 03
07/03/2024 38 56 23 60 41 63
08/03/2024 39 55 32 75 66 27
09/03/2024 40 27 32 70 76 43
12/03/2024 41 23 64 13 81 47
14/03/2024 42 51 85 18 09 73
15/03/2024 43 51 61 54 62 60
16/03/2024 44 13 73 41 43 17
19/03/2024 45 89 85 58 49 08
21/03/2024 46 37 70 86 57 16
22/03/2024 47 25 29 45 51 46
23/03/2024 48 59 81 56 75 58
26/03/2024 49 28 30 54 70 88
28/03/2024 50 66 27 44 90 17
29/03/2024 51 19 51 13 37 08
30/03/2024 52 04 03 18 08 22
02/04/2024 53 58 82 16 61 25
04/04/2024 54 38 26 40 90 41
05/04/2024 55 60 87 30 31 62
06/04/2024 56 72 50 79 70 35
09/04/2024 57 14 05 53 86 17
11/04/2024 58 19 90 88 25 08
12/04/2024 59 55 77 76 02 90
13/04/2024 60 15 90 28 24 51
16/04/2024 61 58 22 11 86 40
18/04/2024 62 85 90 19 67 78
19/04/2024 63 05 56 68 27 09
20/04/2024 64 38 70 31 33 26
23/04/2024 65 61 22 44 19 26
26/04/2024 66 76 24 78 30 40
27/04/2024 67 88 70 85 38 50
29/04/2024 68 76 52 03 22 16
30/04/2024 69 31 56 40 53 50
02/05/2024 70 71 12 04 27 01
03/05/2024 71 60 05 42 75 84
04/05/2024 72 36 30 70 06 41
07/05/2024 73 75 19 18 74 69
09/05/2024 74 16 74 36 55 64
10/05/2024 75 12 33 80 62 75
11/05/2024 76 12 33 75 30 68
14/05/2024 77 09 90 41 32 33
16/05/2024 78 78 09 14 17 85
17/05/2024 79 35 80 90 76 73
18/05/2024 80 84 58 25 64 57
21/05/2024 81 40 80 14 19 13
23/05/2024 82 74 28 44 03 78
24/05/2024 83 28 76 34 11 44
25/05/2024 84 61 46 70 36 65
28/05/2024 85 37 24 82 75 31
30/05/2024 86 31 49 43 18 11
31/05/2024 87 01 24 19 14 63
01/06/2024 88 10 52 67 34 43
04/06/2024 89 84 69 21 57 45
06/06/2024 90 61 20 75 17 02
07/06/2024 91 65 63 41 31 09
08/06/2024 92 77 82 60 10 42
11/06/2024 93 06 01 16 12 20
13/06/2024 94 43 01 79 86 53
14/06/2024 95 20 69 71 19 43
15/06/2024 96 40 61 78 34 54
18/06/2024 97 34 61 32 02 60
20/06/2024 98 63 48 26 84 42
21/06/2024 99 73 41 43 31 17
22/06/2024 100 11 13 15 53 23
25/06/2024 101 23 47 55 60 08
27/06/2024 102 70 28 80 62 18
28/06/2024 103 52 02 38 04 71
29/06/2024 104 46 79 29 14 25
02/07/2024 105 81 37 67 83 64
04/07/2024 106 56 71 51 76 45
05/07/2024 107 65 31 84 86 19
06/07/2024 108 31 78 54 53 52
09/07/2024 109 28 19 84 05 62
11/07/2024 110 73 38 61 26 20
12/07/2024 111 54 07 64 65 63
13/07/2024 112 45 44 76 83 77
16/07/2024 113 10 48 70 07 38
18/07/2024 114 12 67 59 20 48
19/07/2024 115 67 44 30 31 63
20/07/2024 116 69 17 49 75 52
23/07/2024 117 37 09 76 51 61
25/07/2024 118 36 03 30 57 56
26/07/2024 119 14 76 62 50 83
27/07/2024 120 57 10 34 04 02
30/07/2024 121 52 61 29 08 19
01/08/2024 122 35 72 84 33 05
02/08/2024 123 81 28 47 29 20
03/08/2024 124 15 38 41 37 27
06/08/2024 125 83 41 03 11 07
08/08/2024 126 13 35 56 79 75
09/08/2024 127 45 36 89 20 90
10/08/2024 128 39 02 78 36 43
13/08/2024 129 46 37 81 62 18
16/08/2024 130 67 59 84 65 70
17/08/2024 131 75 67 47 53 12
19/08/2024 132 81 59 86 31 04
20/08/2024 133 02 07 31 80 46
22/08/2024 134 85 10 42 29 39
23/08/2024 135 27 71 52 66 21
24/08/2024 136 69 57 01 55 45
27/08/2024 137 09 52 73 43 48
29/08/2024 138 17 03 12 50 63
30/08/2024 139 51 50 10 05 70
31/08/2024 140 46 36 17 49 28
03/09/2024 141 15 14 86 67 09
05/09/2024 142 58 86 08 23 73
06/09/2024 143 08 46 01 32 39
07/09/2024 144 09 31 29 42 55
10/09/2024 145 80 69 07 88 33
12/09/2024 146 59 77 85 37 84
13/09/2024 147 39 35 23 36 81
14/09/2024 148 09 08 51 59 45
17/09/2024 149 34 75 36 29 73
19/09/2024 150 03 14 23 53 06
20/09/2024 151 30 44 68 03 11
21/09/2024 152 11 31 19 67 25
24/09/2024 153 45 53 71 48 38
26/09/2024 154 72 75 01 39 71
27/09/2024 155 09 29 10 43 59
28/09/2024 156 37 01 54 79 15
01/10/2024 157 29 68 31 66 50"""
# Create a DataFrame from the new data
df = pd.read_csv(StringIO(data), sep='\s+', header=None, names=['Date', 'Extraction', 'N1', 'N2', 'N3', 'N4', 'N5'])
# List of all drawn numbers
all_numbers = []
for col in ['N1', 'N2', 'N3', 'N4', 'N5']:
all_numbers.extend(df[col].tolist())
# Calculate the frequency of each number
number_counts = Counter(all_numbers)
# Function to calculate the score for overdue numbers
def punteggio_ritardatari(numero, df):
last_extraction = df[df.apply(lambda row: numero in row[2:], axis=1)].index.max()
if np.isnan(last_extraction):
return 1.0 # Number not present
else:
return 1 / (len(df) - last_extraction)
# Function to calculate the score for frequent numbers
def punteggio_frequenti(numero, number_counts, total_numbers):
return number_counts[numero] / total_numbers
# Calculate scores for each number
punteggi = {}
total_numbers = len(all_numbers)
for numero in range(1, 91): # Assuming numbers are from 1 to 90
punteggi[numero] = (punteggio_ritardatari(numero, df) + punteggio_frequenti(numero, number_counts, total_numbers)) / 2 # Simple average of scores
# Sort numbers based on scores
numeri_ordinati = sorted(punteggi, key=punteggi.get, reverse=True)
dieci_numeri_probabili = numeri_ordinati[:10]
print(f"I 10 numeri più probabili sono: {dieci_numeri_probabili}")
Explanation of Changes:
- Data Replacement: The previous extraction data was replaced with the new extraction data you provided.
- Data Processing: The rest of the script remains unchanged, continuing to process the new data in the same way as before.
This adjusted script will now compute the ten most probable numbers based on the newly provided extraction data. Simply run the script in your Python environment to see the results.
#chats