Finding the max and min in a tuple of tuples

Question

I’m new to python and having some problems finding the minimum and maximum values for a tuple of tuples. I need them to normalise my data. So, basically, I have a list that is a row of 13 numbers, each representing something. Each number makes a column in a list, and I need the max and min for each column. I tried indexing/iterating through but keep getting an error of

max_j = max(j)

TypeError: 'float' object is not iterable

any help would be appreciated!

The code is (assuming data_set_tup is a tuple of tuples, eg ((1,3,4,5,6,7,…),(5,6,7,3,6,73,2…)…(3,4,5,6,3,2,2…)) I also want to make a new list using the normalised values.

normal_list = []

for i in data_set_tup:

    for j in i[1:]: # first column doesn't need to be normalised
        max_j = max(j)
        min_j = min(j)
        normal_j = (j-min_j)/(max_j-min_j)
        normal_list.append(normal_j)
    normal_tup = tuple(normal_list)

Asked By: user1782742

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Source

Answer 1

You can transpose rows to columns and vice versa with zip(*...). (Use list(zip(*...)) in Python 3)

cols = zip(*data_set_tup)
normal_cols = [cols[0]] # first column doesn't need to be normalised
for j in cols[1:]:
    max_j = max(j)
    min_j = min(j)
    normal_cols.append(tuple((k-min_j)/(max_j-min_j) for k in j)

normal_list = zip(*normal_cols)

Answered By: Janne Karila

Answer 2

This really sounds like a job for the non-builtin numpy module, or maybe the pandas module, depending on your needs.

Adding an extra dependency on your application should not be done lightly, but if you do a lot of work on matrix-like data, then your code will likely be both faster and more readable if you use one of the above modules throughout your application.

I do not recommend converting a list of lists to a numpy array and back again just to get this single result — it’s better to use the pure python method of Jannes answer. Also, seeing that you’re a python beginner, numpy may be overkill right now. But I think your question deserves an answer pointing out that this is an option.

Here’s a step-by-step console illustration of how this would work in numpy:

>>> import numpy as np
>>> a = np.array([[1,3,4,5,6],[5,6,7,3,6],[3,4,5,6,3]], dtype=float)
>>> a
array([[ 1.,  3.,  4.,  5.,  6.],
       [ 5.,  6.,  7.,  3.,  6.],
       [ 3.,  4.,  5.,  6.,  3.]])
>>> min = np.min(a, axis=0)
>>> min
array([1, 3, 4, 3, 3])
>>> max = np.max(a, axis=0)
>>> max
array([5, 6, 7, 6, 6])
>>> normalized = (a - min) / (max - min) 
>>> normalized
array([[ 0.        ,  0.        ,  0.        ,  0.66666667,  1.        ],
       [ 1.        ,  1.        ,  1.        ,  0.        ,  1.        ],
       [ 0.5       ,  0.33333333,  0.33333333,  1.        ,  0.        ]])

So in actual code:

import numpy as np

def normalize_by_column(a):
    min = np.min(a, axis=0)
    max = np.max(a, axis=0)
    return (a - min) / (max - min)

Answered By: Lauritz V. Thaulow

Answer 3

We have nested_tuple = ((1, 2, 3), (4, 5, 6), (7, 8, 9)).
First of all we need to normalize it. Pythonic way:

flat_tuple = [x for row in nested_tuple for x in row]

Output: [1, 2, 3, 4, 5, 6, 7, 8, 9] # it's a list

Move it to tuple: tuple(flat_tuple), get max value: max(flat_tuple), get min value: min(flat_tuple)

Answered By: Vadim Balaban

Finding the max and min in a tuple of tuples

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