Tuple pairs, finding minimum using python

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I want to find the minimum of a list of tuples sorting by a given column. I have some data arranged as a list of 2-tuples for example.

data = [ (1, 7.57), (2, 2.1), (3, 1.2), (4, 2.1), (5, 0.01), 
         (6, 0.5), (7, 0.2), (8, 0.6)]

How may I find the min of the dataset by the comparison of the second number in the tuples only?

i.e.

data[0][1] = 7.57
data[1][1] = 2.1

min( data ) = (5, 0.01)

min( data ) returns (1, 7.57), which I accept is correct for the minimum of index 0, but I want minimum of index 1.

In [2]: min(data, key = lambda t: t[1])
Out[2]: (5, 0.01)

or:

In [3]: import operator

In [4]: min(data, key=operator.itemgetter(1))
Out[4]: (5, 0.01)

Using numpy, you can use these commands to get the tuple in list where item is minimum:

The ingredients that make this work are numpy’s advanced array slicing and argsort features.

import numpy as np
#create a python list of tuples and convert it to a numpy ndarray of floats
data = np.array([ (1, 7.57), (2, 2.1), (3, 1.2), 
                  (4, 2.1), (5, 0.01), (6, 0.5), (7, 0.2), (8, 0.6)])

print("data is")
print(data)

#Generate sortIndices from second column
sortIndices = np.argsort(data[:,1])

print("sortIndices using index 1 is:" )
print(sortIndices)
print("The column at index 1 is:")
print(data[:,1])
print("Index 1 put into order using column 1")
print(data[sortIndices,1])
print("The tuples put into order using column 1")
print(data[sortIndices,:])
print("The tuple with minimum value at index 1")
print(data[sortIndices[0],:])
print("The tuple with maximum value at index 1")
print(data[sortIndices[-1],:])

Which prints:

data is
[[ 1.    7.57]
 [ 2.    2.1 ]
 [ 3.    1.2 ]
 [ 4.    2.1 ]
 [ 5.    0.01]
 [ 6.    0.5 ]
 [ 7.    0.2 ]
 [ 8.    0.6 ]]

sortIndices using index 1 is:
[4 6 5 7 2 1 3 0]

The column at index 1 is:
[ 7.57  2.1   1.2   2.1   0.01  0.5   0.2   0.6 ]

Index 1 put into order using column 1
[ 0.01  0.2   0.5   0.6   1.2   2.1   2.1   7.57]

The tuples put into order using column 1
[[ 5.    0.01]
 [ 7.    0.2 ]
 [ 6.    0.5 ]
 [ 8.    0.6 ]
 [ 3.    1.2 ]
 [ 2.    2.1 ]
 [ 4.    2.1 ]
 [ 1.    7.57]]

The tuple with minimum value at index 1
[ 5.    0.01]

The tuple with maximum value at index 1
[ 1.    7.57]

Even though Lev’s answer is correct, I wanted to add the sort Method as well, in case someone is interested in the first n minimas.
One thing to consider is that the min operation’s runtime is O(N) where the sort’s is O(N Log N)

data = [ (1, 7.57), (2, 2.1), (3, 1.2), (4, 2.1), (5, 0.01), (6, 0.5), (7, 0.2), (8, 0.6)]
data.sort(key=lambda x:x[1])
print data

>>> [(5, 0.01), (7, 0.2), (6, 0.5), (8, 0.6), (3, 1.2), (2, 2.1), (4, 2.1), (1, 7.57)]

https://www.ics.uci.edu/~pattis/ICS-33/lectures/complexitypython.txt


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