![]() ![]() stop refers to the index of the element we should stop just before to finish our slice. start refers to the index of the element which is used as a start of our slice. The full slice syntax is: start:stop:step. So, here is our first example of a slice: 2:7. What if we want to take a sublist from the nums list? This is a snap when using slice: > nums = Those and lots of other cool tricks can be done with slice notation. Or we want to update a bunch of cells at once? Or we want to go on a frenzy and extend a list with an arbitrary number of new cells in any position? What if we want to get a sublist of the list. Slice NotationĪs it was shown, indexing allows you to access/change/delete only a single cell of a list. But assignment and deletion operations are not applicable to immutable sequential types. Read-only indexing operations work perfectly well for all sequential types. ![]() We can also easily delete any element from the list by using indexing and del statement: > basket = ![]() We can freely use positive or negative indexing for assignment. But it’s also possible to change cell content using an assignment operation: > basket = > colors = īefore we used indexing only for accessing the content of a list cell. In negative indexing system -1 corresponds to the last element of the list(value ‘black’), -2 to the penultimate (value ‘white’), and so on. So, instead of using indexes from zero and above, we can use indexes from -1 and below. To address this requirement there is negative indexing. But what if we want to take the last element of a list? Or the penultimate element? In this case, we want to enumerate elements from the tail of a list. This is handy if we use position from the head of a list. Using indexing we can easily get any element by its position. To access an element by its index we need to use square brackets: > colors = That means, the first element(value ‘red’) has an index 0, the second(value ‘green’) has index 1, and so on. Each item in the list has a value(color name) and an index(its position in the list). It allows you to store an enumerated set of items in one place and access an item by its position – index. In Python, list is akin to arrays in other scripting languages(Ruby, JavaScript, PHP). Of course, any sane API should use ::3 with the usual "every 3" semantic.Before discussing slice notation, we need to have a good grasp of indexing for sequential types. This is notably used in Numpy to slice multi-dimensional arrays in any direction. We can then open up slice objects as: s = slice(1, 2, 3) # If you omit any part of the slice notation, it becomes None.Īssert C() = slice(None, None, None)Īssert C() = (slice(None, None, None), 1) # Slice notation generates a slice object. You can also use this notation in your own custom classes to make it do whatever you want class C(object): Now we know the concept, we can easily combine step 1 and step 2 into one consolidated step - for compactness: In : X4 = X We have just selected all the elements as required! :) Consolidate Step 1 (start and end) and Step 2 ("jumping") In code (note the double colons): In : X3 = X2 We can now specify the "jump steps" in both row-wise and column-wise directions (to select elements in a "jumping" way) like this: (read on!) Step 2 - Select elements (with the "jump step" argument) Notice now we've just obtained our subset, with the use of simple start and end indexing technique. Specify the "start index" and "end index" in both row-wise and column-wise directions. ![]() Read on! (We can do this in a 2-step approach) Step 1 - Obtain subset Say for some reason, your boss wants you to select the following elements: Say we have a NumPy matrix that looks like this: In : import numpy as np This example does not cover native Python data structures like List). (Caution: this is a NumPy array specific example with the aim of illustrating the a use case of "double colons" :: for jumping of elements in multiple axes. Step 2 below illustrate the usage of that "double colons" :: in question. This visual example will show you how to a neatly select elements in a NumPy Matrix (2 dimensional array) in a pretty entertaining way (I promise). ![]()
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