Application of data structures and algorithms in Python (with examples)

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Question 1

Now there is a tuple or sequence containing N elements. How to decompress the values in it and assign them at the same time? Give N variables?

Solution

Any sequence (iterable object) can be unpacked and assigned to multiple variables with a simple assignment statement. The premise is that the number of variables and the number of sequence elements must be consistent.

In [3]: p = (4,5) In [4]: x,y = p In [5]: x Out[5]: 4 In [6]: y Out[6]: 5 In [7]: data = ['ACME', 50, 91.1, (2012, 12, 21)] In [8]: name, shares, price, date = data In [9]: name Out[9]: 'ACME' In [10]: shares Out[10]: 50 In [11]: date Out[11]: (2012, 12, 21)

If the number of variables and the number of sequence elements do not match, an exception will be generated.

In [12]: p = (x,5) In [13]: a,b,c = p --------------------------------------------------------------------------- ValueError Traceback (most recent call last)  in () ----> 1 a,b,c = p ValueError: not enough values to unpack (expected 3, got 2)

This unpacking assignment can be used on any iterable object, not just lists or tuples, but also strings, file objects, iterators and generators.

In [14]: A = 'hello' In [15]: a,b,c,d,e = A In [16]: a Out[16]: 'h' In [17]: b Out[17]: 'e' In [18]: c Out[18]: 'l' In [19]: d Out[19]: 'l' In [20]: e Out[20]: 'o' In [21]: a,b,c,d,e Out[21]: ('h', 'e', 'l', 'l', 'o')

For those who only want to decompress part of the sequence and discard some of the values, just use some unnecessary variable names to occupy the sequence elements at the corresponding positions.

In [22]: data = [ 'ACME', 50, 91.1, (2012, 12, 21) ] In [23]: _, shares, price, _ = data In [24]: shares Out[24]: 50 In [25]: price Out[25]: 91.1

Question 2

If the number of elements of an iterable object exceeds the number of variables, a ValueError will be thrown. So how can we extract N elements from this iterable object?

Solution

Python’s asterisk expression can solve this problem. For example, you are studying a course, and at the end of the semester, you want to calculate the average grade of homework assignments, but exclude the first and last grades. If there were only four fractions, you might just go ahead and simply assign them manually, but what if there were 24? At this time, the asterisk expression comes in handy:
In the function call, the matching is simply based on the position of the variable name, but the use of name=value tells Python to still match according to the variable name. These are called keywords parameter. Using*sequenceor**dictin the call allows us to encapsulate any number of position-related or keyword objects in a sequence or dictionary accordingly, and pass them to the function , unpack them into separate, single parameters.

In [26]: def drop_first_last(grades): ....: first,*middle,last = grades ....: return avg(middle)

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