The most common and recommended way to find the length of an array (or list) in Python is using the built-in len() function.
my_array = [1, 2, 3, 4, 5]
length = len(my_array)
print(length) # Output: 5The len() function is efficient (O(1) time complexity), readable, and works with various data structures like lists, strings, tuples, and dictionaries.
Important distinctions based on array type:
Standard Python lists: Use
len(list).Arrays from the
arraymodule: Uselen(array).NumPy arrays: Use
len(array)to get the size of the first dimension (e.g., number of rows), or use.sizefor the total number of elements in the entire array. For multi-dimensional arrays, use.shapeto get the dimensions.
While you can use a for loop with a counter or sum(1 for _ in array) to count elements manually, these methods are less efficient (O(n)) and not recommended for simple length checks. The __len__() method is the underlying mechanism called by len(), but you should always use len() for clarity and best practices.
my_list = [1,2,3,4,5]
len(my_list)
# 5
The same works for tuples:
my_tuple = (1,2,3,4,5)
len(my_tuple)
# 5
And strings, which are really just arrays of characters:
my_string = 'hello world'
len(my_string)
# 11
It was intentionally done this way so that lists, tuples and other container types or iterables didn't all need to explicitly implement a public .length() method, instead you can just check the len() of anything that implements the 'magic' __len__() method.
Sure, this may seem redundant, but length checking implementations can vary considerably, even within the same language. It's not uncommon to see one collection type use a .length() method while another type uses a .length property, while yet another uses .count(). Having a language-level keyword unifies the entry point for all these types. So even objects you may not consider to be lists of elements could still be length-checked. This includes strings, queues, trees, etc.
The functional nature of len() also lends itself well to functional styles of programming.
lengths = map(len, list_of_containers)
Difference between len() and array.length -
Why is getting the length of an array O(1) and not O(n)?
Find length of 2D array Python - Stack Overflow
How do I find the length (or dimensions, size) of a numpy matrix in python? - Stack Overflow
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my_list = [1,2,3,4,5]
len(my_list)
# 5
The same works for tuples:
my_tuple = (1,2,3,4,5)
len(my_tuple)
# 5
And strings, which are really just arrays of characters:
my_string = 'hello world'
len(my_string)
# 11
It was intentionally done this way so that lists, tuples and other container types or iterables didn't all need to explicitly implement a public .length() method, instead you can just check the len() of anything that implements the 'magic' __len__() method.
Sure, this may seem redundant, but length checking implementations can vary considerably, even within the same language. It's not uncommon to see one collection type use a .length() method while another type uses a .length property, while yet another uses .count(). Having a language-level keyword unifies the entry point for all these types. So even objects you may not consider to be lists of elements could still be length-checked. This includes strings, queues, trees, etc.
The functional nature of len() also lends itself well to functional styles of programming.
lengths = map(len, list_of_containers)
The way you take a length of anything for which that makes sense (a list, dictionary, tuple, string, ...) is to call len on it.
l = [1,2,3,4]
s = 'abcde'
len(l) #returns 4
len(s) #returns 5
The reason for the "strange" syntax is that internally python translates len(object) into object.__len__(). This applies to any object. So, if you are defining some class and it makes sense for it to have a length, just define a __len__() method on it and then one can call len on those instances.
I have read that this operation works in constant time. But how is that possible?
Given an array of length n, I would have thought that computing n would require accessing every element. In pseudocode, something like the following:
n = 0
for each item in array:
n = n + 1
return nBut that would clearly be O(n), suggesting that’s not how it actually works.
Can anyone shed light on this?
Like this:
numrows = len(input) # 3 rows in your example
numcols = len(input[0]) # 2 columns in your example
Assuming that all the sublists have the same length (that is, it's not a jagged array).
You can use numpy.shape.
import numpy as np
x = np.array([[1, 2],[3, 4],[5, 6]])
Result:
>>> x
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.shape(x)
(3, 2)
First value in the tuple is number rows = 3; second value in the tuple is number of columns = 2.
shape is a property of both numpy ndarray's and matrices.
A.shape
will return a tuple (m, n), where m is the number of rows, and n is the number of columns.
In fact, the numpy matrix object is built on top of the ndarray object, one of numpy's two fundamental objects (along with a universal function object), so it inherits from ndarray
matrix.size according to the numpy docs returns the Number of elements in the array. Hope that helps.