NumPy |
Python Methods and Functions Examples :
Input: ([`a`,` apple`], [`b`,` ball`]) Output: [`a`,` apple`, `b`,` ball`] Input: ([1, `sam`, 75], [2,` bob `, 39], [3,` Kate`, 87]) Output: [1, `sam`, 75, 2,` bob`, 39, 3, `Kate`, 87] Approach # 1: Using reduce()
reduce () — classic list operation, used to apply a specific function passed in its argument to all elements of the list In this case, we used the add function of the operator module which simply adds the given list arguments to an empty list.
|
from functools import reduce import operator def unpackTuple (tup): return ( reduce (operator.add, tup))
tup = ([ `a` , `apple` ], [ ` b` , `ball` ]) print (unpackTuple (tup)) |
Exit :
[`a`,` apple`, `b`,` ball`]
Approach # 2 Using Numpy [Alternative Approach # 1]
| from functools import reduce import numpy def unpackTuple (tup): print ( reduce (numpy.append, tup))
tup = ( [ `a` , ` apple` ], [ ` b` , `ball` ]) unpackTuple (tup) |
Exit :
[`a`` apple` `b`` ball`]
Approach # 3 Usage itertools.chain(*iterables)
itertools.chain (* iterables) creates an iterator that returns items from the first iteration until it is exhausted , then moves on to the next iteration until all iterations are exhausted. This makes our job much easier because we can simply add each iteration to an empty list and return it.
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from itertools import chain def unpackTuple (tup): res = [] for i in chain ( * tup): res.append (i) print (res)
tup = ([ ` a` , `apple` ], [ `b` , ` ball` ]) unpackTuple (tup) |
Exit :
[`a`,` apple`, `b`,` ball`]
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