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Unable to return correct output from created function in JES

I am fairly new to jython and I am inquiring about creating a function that is dealing with a list. Basically what I am trying to do with the below function is create a function that will loop through the entirety of my list, then find the lowest value within that list and return the variable with the lowest number. Though, I keep getting a return of function min at 0x26 everytime I execute the main() I receive the same message but it seems as if the function min at 0x26 will count up ex: 0x27, 0x28... Not sure why this is. As my list only contains integers of minimum 0 to max 99.
Here is the sourcecode:
def min(dataset): #defining a function minimum, with input dataset(the list we are using)..
min = dataset[0]
for num in range(0, len(dataset)):
if dataset[num] < min:
min = dataset(num)
return min
minimum = min(dataset)
print(str(minimum))
Here is the code in its entirety. Though, I currently have a way to find the min/max values in the list. I am looking to move towards a function, as I want to know how to correctly use a function.
def main( ):
dataset = [0]
file = open("D:\numbs.dat", "r")
for line in file: #loop for writing over every line to a storage loc.
num = int(float(line)) #converting int to string
dataset.append(num) #appending the data to 'dataset' list
max = dataset[0] #setting an imaginary max initially
low = dataset[0] #setting an imaginary low initially
for i in range(0, len(dataset)): #for loop to scan thru entire list
if dataset[i] > max: #find highest through scan and replacing each max in var max
max = dataset[i]
if dataset[i] < low: #find lowest through scan and replacing each max in var max
low = dataset[i]
#printNow(dataset) #printing the list in its entirety
#printNow("The maximum is " +str(max)) #print each values of lowest and highest
#printNow("The lowest is " +str(low))
def min(dataset): #defining a function minimum..
min = dataset[0]
for num in range(0, len(dataset)):
if dataset[num] < min:
min = dataset(num)
return min
minimum = min(dataset)
print(str(minimum)) #test to see what output is.
As mentioned above, there is the for loop for finding max/min values. Though I tried doing the same exact thing for the function I am trying to create...
the contents of the numbs.dat can be found here (1001 entries):
70
75
76
49
73
76
52
63
11
25
19
89
17
48
5
48
29
41
23
84
28
39
67
48
97
34
0
24
47
98
0
64
24
51
45
11
37
77
5
54
53
33
91
0
27
0
80
5
11
66
45
57
48
25
72
8
38
29
93
29
58
5
72
36
94
18
92
17
43
82
44
93
10
38
31
52
44
10
50
22
39
71
46
40
33
51
51
57
27
24
40
61
88
87
40
85
91
99
6
3
56
10
85
38
61
91
31
69
39
74
9
17
80
96
49
0
47
68
12
5
6
60
81
51
62
87
70
66
50
30
30
22
45
35
2
39
23
63
35
69
83
84
69
6
54
74
3
29
31
54
45
79
21
74
30
77
77
80
26
63
84
21
58
54
69
2
50
79
90
26
45
29
97
28
57
22
59
2
72
1
92
35
38
2
47
23
52
77
87
34
84
15
84
13
23
93
19
50
99
74
59
4
73
93
29
61
8
45
10
20
15
95
58
43
75
19
61
39
68
47
69
58
88
82
33
30
72
21
74
12
18
0
52
50
62
21
66
26
56
84
16
12
7
45
58
22
26
95
82
6
74
12
16
2
61
58
22
39
0
53
88
79
71
13
54
25
31
93
48
91
90
45
23
54
42
39
78
25
95
58
2
41
61
72
98
91
48
97
93
11
12
1
35
80
81
86
38
70
67
55
55
87
73
79
31
43
97
79
3
51
17
58
70
34
59
61
28
46
13
42
18
0
18
75
75
62
50
62
85
49
83
71
63
32
27
59
42
46
8
13
39
25
13
94
17
48
73
40
31
31
86
23
81
40
92
24
94
67
30
18
74
78
62
89
1
27
95
99
33
53
74
5
84
88
8
52
0
24
21
99
1
74
84
94
29
25
83
93
98
40
21
66
93
28
72
63
77
9
71
18
87
50
77
48
68
88
22
33
16
79
68
69
94
64
5
28
33
22
21
74
44
62
68
47
93
69
9
42
44
87
64
97
42
34
90
70
91
12
18
84
65
23
99
1
55
6
1
23
92
50
96
96
68
27
17
98
42
10
27
26
20
13
94
73
75
12
12
25
33
1
33
67
61
0
98
71
35
75
68
56
45
11
1
69
57
9
15
96
69
2
0
65
44
86
78
97
17
4
81
23
4
43
24
72
70
57
21
91
84
94
40
96
40
78
46
67
6
7
16
49
24
14
12
82
73
60
42
76
62
10
84
49
75
89
43
47
31
68
15
11
32
37
98
72
40
25
69
30
64
60
48
21
11
74
54
24
60
10
96
29
39
53
48
24
68
4
52
12
6
91
15
86
77
65
68
22
91
36
72
82
81
9
77
0
5
83
27
88
17
35
66
76
78
81
19
51
87
66
26
59
65
2
37
37
73
34
98
37
78
92
17
52
62
40
50
84
34
22
25
42
90
19
86
76
68
42
9
89
57
78
64
89
12
34
94
9
77
58
32
27
97
93
79
35
32
75
97
79
65
90
53
43
98
4
99
5
79
38
99
60
78
64
90
2
39
42
52
2
21
77
15
8
87
13
0
4
7
43
76
31
74
16
87
50
73
49
14
35
10
37
91
44
88
71
95
75
98
7
17
23
13
16
77
20
50
50
74
78
58
30
21
74
76
93
5
74
94
83
23
67
18
5
50
47
56
79
26
84
78
48
71
43
41
8
91
23
7
11
96
87
12
42
32
44
99
67
99
64
96
52
19
79
60
66
52
62
17
61
54
24
25
36
4
78
3
94
91
62
65
76
94
2
52
25
61
55
49
88
85
96
5
46
56
48
17
25
3
70
62
3
50
45
47
58
12
41
27
42
90
91
71
53
4
79
47
68
43
87
35
63
10
49
4
81
45
88
80
6
92
47
70
40
7
33
70
61
30
9
55
42
83
26
72
57
77
91
13
15
33
13
62
49
43
65
73
98
59
56
77
62
12
25
33
53
78
73
1
17
44
56
95
10
33
89
33
20
56
69
66
60
53
83
58
43
33
25
21
8
28
65
51
70
53
78
49
30
64
17
76
9
2
32
87
77
39
25
21
66
65
54
81
49
15
27
7
14
4
11
94
9
84
23
13
95
45
67
57
20
3
58
50
97
35
68
47
41
84
59
46
34
19
25
77
29
41
89
80
61
70
40
1
18
32
70
86
76
25
98
99
40
43
92
43
4
70
78
72
71
85
14
84
73
92
60
23
57
44
56
6
96
39
91
63
43
39
71
80
18
93
54
1
4
46
68
93
74
74
88
52
88
55
24
19
92
53
59
1
91
48
47
Let me know what the heck I am doing wrong. Thanks!

#ohGosh welcome to stack overflow. You are almost there with the solution. There are few problems with your program
1) Nums.dat file contains just one line with numbers separated by spaces, not a new line(\n). In order to get the read the numbers from the file do the following
dataset = [] #Create an empty list
file = open("D:\numbs.dat", "r") #Open the file
for line in file:
tempData = line.split(" ") #Returns a list with space used as delimiter
dataset = map(int, tempData) #Convert string data to int
2) Wrong way to get data from a list in the min function
Use
min = dataset[num]
Instead of
min = dataset(num)
Fix this and your program will work. Cheers.

how to multiply multiple columns by another column pandas

I have a Dataframe of 100 Columns and I want to multiply one column ('Count') value with the columns position ranging from 6 to 74. Please tell me how to do that. I have been trying
df = df.ix[0, 6:74].multiply(df["Count"], axis="index")
df = df[df.columns[6:74]]*df["Count"]
None of them is working
The result Dataframe should be of 100 columns with all original columns where columns number 6 to 74 have the multiplied values in all the rows.

Assuming the same dataframe provided by #MaxU
Not easier, but a perspective on how to use other api elements.
pd.DataFrame.update and pd.DataFrame.mul
df.update(df.iloc[:, 3:7].mul(df.Count, 0))
df
0 1 2 3 4 5 6 7 8 9 Count
0 89 38 89 15.366436 1.355862 7.231264 4.971494 12 70 69 0.225977
1 49 1 38 1.004190 1.095480 2.829990 0.273870 57 93 64 0.030430
2 2 53 49 49.749460 50.379200 54.157640 16.373240 22 31 41 0.629740
3 38 44 23 28.437516 73.545300 41.185368 73.545300 19 99 57 0.980604
4 45 2 60 10.093230 4.773825 10.502415 6.274170 43 63 55 0.136395
5 65 97 15 10.375760 57.066680 38.260615 14.915155 68 5 21 0.648485
6 95 90 45 52.776000 16.888320 22.517760 50.664960 76 32 75 0.703680
7 60 31 65 63.242210 2.976104 26.784936 38.689352 72 73 94 0.744026
8 64 96 96 7.505370 37.526850 11.007876 10.007160 68 56 39 0.500358
9 78 54 74 8.409275 25.227825 16.528575 9.569175 97 63 37 0.289975

Demo:
Sample DF:
In [6]: df = pd.DataFrame(np.random.randint(100,size=(10,10))) \
.assign(Count=np.random.rand(10))
In [7]: df
Out[7]:
0 1 2 3 4 5 6 7 8 9 Count
0 89 38 89 68 6 32 22 12 70 69 0.225977
1 49 1 38 33 36 93 9 57 93 64 0.030430
2 2 53 49 79 80 86 26 22 31 41 0.629740
3 38 44 23 29 75 42 75 19 99 57 0.980604
4 45 2 60 74 35 77 46 43 63 55 0.136395
5 65 97 15 16 88 59 23 68 5 21 0.648485
6 95 90 45 75 24 32 72 76 32 75 0.703680
7 60 31 65 85 4 36 52 72 73 94 0.744026
8 64 96 96 15 75 22 20 68 56 39 0.500358
9 78 54 74 29 87 57 33 97 63 37 0.289975
Let's multiply columns 3-6 by df['Count']:
In [8]: df.iloc[:, 3:6+1]
Out[8]:
3 4 5 6
0 68 6 32 22
1 33 36 93 9
2 79 80 86 26
3 29 75 42 75
4 74 35 77 46
5 16 88 59 23
6 75 24 32 72
7 85 4 36 52
8 15 75 22 20
9 29 87 57 33
In [9]: df.iloc[:, 3:6+1] *= df['Count']
In [10]: df
Out[10]:
0 1 2 3 4 5 6 7 8 9 Count
0 89 38 89 66.681065 0.818372 20.751519 15.480964 12 70 69 0.225977
1 49 1 38 32.359929 4.910233 60.309102 6.333122 57 93 64 0.030430
2 2 53 49 77.467708 10.911630 55.769707 18.295685 22 31 41 0.629740
3 38 44 23 28.437513 10.229653 27.236368 52.776014 19 99 57 0.980604
4 45 2 60 72.564688 4.773838 49.933342 32.369289 43 63 55 0.136395
5 65 97 15 15.689662 12.002793 38.260613 16.184644 68 5 21 0.648485
6 95 90 45 73.545292 3.273489 20.751519 50.664974 76 32 75 0.703680
7 60 31 65 83.351331 0.545581 23.345459 36.591370 72 73 94 0.744026
8 64 96 96 14.709058 10.229653 14.266669 14.073604 68 56 39 0.500358
9 78 54 74 28.437513 11.866397 36.963643 23.221446 97 63 37 0.289975

The easiest thing to do here would be to extract the values, multiply, and then assign.
u = df.iloc[0, 6:74].values
v = df[['count']]
df = pd.DataFrame(u * v)

By using combine_first
df.iloc[:, 3:6+1].mul(df['Count'],axis=0).combine_first(df)

You need to concatenate the data frame resulting from multiplication with the remaining columns:
df=pd.concat( [df.iloc[0:6],df.iloc[75:],df.iloc[:,6:74+1].multiply(df['Count'],axis=0)] , axis=1)

Object Similarity Pandas and Scikit Learn

Is there a way to find to find and rank rows in a Pandas Dataframe by their similarity to a row from another Dataframe?

My understanding of your question: you have two data frames, hopfully of the same column count. You want to rate first data frame's, the subject data frame, members by how close, i.e. similar, they are to any of the members of the target data frame.
I am not aware of a built in method.
It is probably not the most efficient way but here is how I'd approach:
#! /usr/bin/python3
import pandas as pd
import numpy as np
import pprint
pp = pprint.PrettyPrinter(indent=4)
# Simulate data
df_subject = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD')) # This is the one we're iterating to check similarity to target.
df_target = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD')) # This is the one we're checking distance to
# This will hold the min dstances.
distances=[]
# Loop to iterate over subject DF
for ix1,subject in df_subject.iterrows():
distances_cur=[]
# Loop to iterate over target DF
for ix2,target in df_target.iterrows():
distances_cur.append(np.linalg.norm(target-subject))
# Get the minimum distance for the subject set member.
distances.append(min(distances_cur))
# Distances to df
distances=pd.DataFrame(distances)
# Normalize.
distances=0.5-(distances-distances.mean(axis=0))/distances.max(axis=0)
# Column index joining, ordering and beautification.
Proximity_Ratings_name='Proximity Ratings'
distances=distances.rename(columns={0: Proximity_Ratings_name})
df_subject=df_subject.join(distances)
pp.pprint(df_subject.sort_values(Proximity_Ratings_name,ascending=False))
It should yeild something like the table below. Higher rating means there's a similar member in the target data frame:
A B C D Proximity Ratings
55 86 21 91 78 0.941537
38 91 31 35 95 0.901638
43 49 89 49 6 0.878030
98 28 98 98 36 0.813685
77 67 23 78 84 0.809324
35 52 16 36 58 0.802223
54 2 25 61 44 0.788591
95 76 3 60 46 0.766896
5 55 39 88 37 0.756049
52 79 71 90 70 0.752520
66 52 27 82 82 0.751353
41 45 67 55 33 0.739919
76 12 93 50 62 0.720323
94 99 84 39 63 0.716123
26 62 6 97 60 0.715081
40 64 50 37 27 0.714042
68 70 21 8 82 0.698824
47 90 54 60 65 0.676680
7 85 95 45 71 0.672036
2 14 68 50 6 0.661113
34 62 63 83 29 0.659322
8 87 90 28 74 0.647873
75 14 61 27 68 0.633370
60 9 91 42 40 0.630030
4 46 46 52 35 0.621792
81 94 19 82 44 0.614510
73 67 27 34 92 0.608137
30 92 64 93 51 0.608137
11 52 25 93 50 0.605770
51 17 48 57 52 0.604984
.. .. .. .. .. ...
64 28 56 0 9 0.397054
18 52 84 36 79 0.396518
99 41 5 32 34 0.388519
27 19 54 43 94 0.382714
92 69 56 73 93 0.382714
59 1 29 46 16 0.374878
58 2 36 8 96 0.362525
69 58 92 16 48 0.361505
31 27 57 80 35 0.349887
10 59 23 47 24 0.345891
96 41 77 76 33 0.345891
78 42 71 87 65 0.344398
93 12 31 6 27 0.329152
23 6 5 10 42 0.320445
14 44 6 43 29 0.319964
6 81 51 44 15 0.311840
3 17 60 13 22 0.293066
70 28 40 22 82 0.251549
36 95 72 35 5 0.249354
49 78 10 30 18 0.242370
17 79 69 57 96 0.225168
46 42 95 86 81 0.224742
84 58 81 59 86 0.221346
9 9 62 8 30 0.211659
72 11 51 74 8 0.159265
90 74 26 80 1 0.138993
20 90 4 6 5 0.117652
50 3 12 5 53 0.077088
42 90 76 42 1 0.075284
45 94 46 88 14 0.054244
Hope I understand correctly. Don't use if performance matters, I'm sure there's an algebraic way to approach this (Multiply matrices) that would run way faster.

nested while loop printing bug

the output I'm getting for looks like this:
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19
20 21 22 23 24 25 26 27 28 29
30 31 32 33 34 35 36 37 38 39
40 41 42 43 44 45 46 47 48 49
50 51 52 53 54 55 56 57 58 59
60 61 62 63 64 65 66 67 68 69
70 71 72 73 74 75 76 77 78 79
80 81 82 83 84 85 86 87 88 89
90 91 92 93 94 95 96 97 98 99
100
I'm trying to get it to display by rows of 10.
row=1
column=1
while int(row) <= 10:
while int(column) <= 10:
print('{:3}'.format(int(row-1)*10+int(column)), end =' ')
column = column +1
if (column%10==0):
print('\n')
column = 1
row = row + 1
This is the code I have for the above result. I have been able to get the desired result with a while loop, but I am unable to get the same result with this while loop.

number = 1
while number <= 100:
print ('{:3}'.format(number), end=' ')
if (number%10==0):
print ("\n")
number = number +1

python index error pop up when I have a longer input

I am solving one sorting problem. I encountered with one problem which has been troubling me for 2 days.
I ran the code for a shorter input, it worked just as what I expected.
However, when I put a much longer input into this program, runtime error emerges.
Here is the code:
row_number, row_length = input().split()
row_number, row_length = int(row_number), int(row_length)
def row_input():
data_input = []
for i in range(0,row_number):
row = list(map(int,input().split()))
data_input.append(row)
return data_input
def sort_data(data):
k = int(input())
sorted_data = []
for row in data:
sorted_data.append(row[k])
sorted_data.sort()
n = 0
while n < row_number:
for m in data:
if sorted_data[n] == m[k]:
print_data(m)
n = n + 1
def print_data(data):
b=''
for n in data:
b= b + str(n).ljust(len(str(n))+1)
print(b)
data = row_input()
sort_data(data)
Here is the short input:
10 3
1 1 1
1 1 2
1 1 3
1 1 4
2 2 5
2 3 6
2 3 7
2 3 8
2 3 9
2 4 0
1
Here is the longer input:
100 10
64 79 18 94 46 81 74 97 71 92
46 24 23 20 68 15 53 93 24 91
17 66 34 64 28 5 55 25 44 96
16 71 80 84 5 79 63 77 69 77
33 77 24 13 58 81 41 36 73 62
93 26 16 55 61 51 39 69 29 45
44 85 1 48 23 59 52 82 50 37
77 74 9 21 35 54 81 57 32 76
82 21 72 49 98 21 77 64 6 63
68 17 93 83 12 43 84 28 96 86
9 16 3 89 38 11 70 25 41 38
49 99 31 19 85 97 80 63 16 69
50 85 80 75 36 48 56 69 63 94
78 80 83 86 92 60 56 90 22 73
69 81 45 9 67 25 82 46 68 82
98 38 23 31 38 83 37 76 69 82
95 48 21 64 25 6 38 96 69 23
44 97 46 54 21 56 65 51 66 34
87 22 27 24 55 48 90 10 8 51
21 6 74 78 8 88 26 63 72 43
64 4 42 20 54 91 2 51 79 40
93 76 52 58 40 78 98 27 53 48
85 23 86 30 91 49 81 4 59 9
88 96 77 95 36 71 7 52 14 20
69 98 21 94 14 35 28 97 3 9
60 47 56 34 35 61 9 44 80 92
4 76 57 28 60 3 46 4 6 17
59 44 88 7 71 60 84 12 91 38
76 57 5 2 25 12 46 62 32 68
14 15 11 1 34 20 54 58 45 38
89 49 16 43 74 51 80 22 88 31
8 98 51 73 32 13 59 12 56 92
36 82 9 63 77 79 77 25 52 91
63 82 58 75 13 20 79 89 55 89
58 37 93 1 29 72 78 95 47 35
90 82 58 60 55 86 82 22 44 94
55 17 51 99 29 92 1 79 96 34
32 78 41 1 24 52 11 80 3 25
30 32 32 71 85 80 63 23 80 97
35 22 11 71 10 48 43 58 31 33
30 98 60 58 28 71 95 28 21 29
74 4 13 99 90 64 28 27 73 4
52 21 52 31 35 82 35 64 21 71
92 85 13 48 5 32 92 70 15 85
47 55 25 80 24 22 19 78 17 43
3 91 71 53 49 39 96 88 59 61
79 26 98 2 95 95 70 38 82 85
69 67 41 11 95 39 20 19 96 36
11 74 48 23 84 49 47 43 27 90
4 28 35 14 70 62 52 94 46 91
72 11 14 82 59 51 93 98 55 79
90 84 84 24 21 81 11 57 27 78
98 97 59 51 89 40 96 35 25 59
73 85 64 17 46 9 79 54 27 15
48 91 7 56 41 6 4 26 96 39
43 22 34 89 52 59 55 52 38 42
10 31 9 8 21 46 29 4 97 4
44 49 78 31 53 29 11 35 46 14
44 39 57 35 9 63 85 5 97 24
9 72 49 50 41 47 23 71 15 45
51 6 98 64 75 35 39 48 2 50
92 22 72 60 96 15 17 4 79 27
90 30 98 28 92 8 83 71 24 62
5 54 86 14 71 96 87 2 58 78
37 61 60 30 46 96 49 58 27 48
14 59 22 35 75 60 55 28 91 85
21 1 85 85 78 67 24 69 22 17
76 61 84 64 33 76 61 10 33 95
71 9 1 32 31 80 69 7 25 59
69 64 78 85 21 88 56 70 92 74
79 12 8 9 54 56 37 44 1 84
6 66 54 5 82 17 41 25 3 71
8 44 63 17 75 43 87 15 85 3
15 42 15 59 38 22 46 27 19 13
54 71 76 93 67 39 46 12 78 46
23 82 71 34 31 61 94 58 10 62
30 8 43 38 7 23 77 38 93 32
32 72 46 59 64 45 14 73 62 72
76 26 47 89 25 73 79 28 60 48
41 58 85 55 29 64 39 84 20 87
24 8 70 16 69 32 17 26 58 16
40 53 40 63 22 37 11 74 7 8
23 4 56 39 27 94 91 72 14 61
41 86 3 29 41 15 99 50 82 84
33 5 22 93 73 86 99 87 26 66
73 25 55 46 69 38 99 14 43 55
43 21 82 30 90 66 6 67 49 25
81 38 65 40 80 7 90 82 33 13
18 45 1 90 53 51 51 96 32 90
32 69 51 22 71 85 80 61 99 23
88 8 41 92 4 25 64 89 30 75
93 85 99 87 67 3 54 16 98 57
33 54 31 83 64 93 3 24 65 81
74 19 15 66 17 14 34 50 57 16
10 30 20 97 32 85 83 89 68 18
46 82 9 14 54 50 55 28 26 96
29 96 3 33 12 52 11 26 19 22
50 81 95 59 76 53 10 9 72 87
25 85 54 43 53 13 52 70 38 76
20 14 30 80 23 43 27 67 42 11
5
Here is the error while running the longer input:
Traceback (most recent call last):
File "solution.py", line 30, in <module>
sort_data(data)
File "solution.py", line 19, in sort_data
if sorted_data[n] == m[k]:
IndexError: list index out of range

The problem is in your sorting logics, because it is highly possible you increment n by more than one in one iteration of the while loop if there are multiple matching rows in the dataset.
The right solution is simpler than you think:
def sort_data(data):
k = int(input())
output = sorted(data, key=lambda row: row[k])
for r in output:
print_data(r)
UPDATE: The smallest dataset on what your algorithm fails is:
2 1
2
1
0
A small modification on your function will stop it from overindexing. The key is to store sorted_data[n] in a variable, and that way it will not try to over index sorted_data when no more output is expected.
def sort_data(data):
k = int(input())
sorted_data = []
for row in data:
sorted_data.append(row[k])
sorted_data.sort()
n = 0
while n < row_number:
key = sorted_data[n]
for m in data:
if key == m[k]:
print_data(m)
n = n + 1
UPDATE:
The sorted function's key parameter is a function, which just selects a value what to sort by. In your case, selects the kth column, which is what you want to sort by.