Iterate over a list in Python

List is equivalent to arrays in other languages, with the extra benefit of being dynamic in size. In Python, the list is a type of container in Data Structures, which is used to store multiple data at the same time. Unlike Sets, lists in Python are ordered and have a definite count.

There are multiple ways to iterate over a list in Python. Let’s see all the different ways to iterate over a list in Python, and performance comparison between them.

Method #1: Using For loop 

Output: 

1
3
5
7
9

Method #2: For loop and range()
In case we want to use the traditional for loop which iterates from number x to number y.  

Output: 

1
3
5
7
9

Iterating using the index is not recommended if we can iterate over the elements (as done in Method #1). 
  
Method #3: Using while loop 

Output: 

1
3
5
7
9

Method #4: Using list comprehension (Possibly the most concrete way).  

Output: 

1
3
5
7
9

Method #5: Using enumerate()
If we want to convert the list into an iterable list of tuples (or get the index based on a condition check, for example in linear search you might need to save the index of minimum element), you can use the enumerate() function. 

Output: 

0 , 1
1 , 3
2 , 5
3 , 7
4 , 9

Note: Even method #2 can be used to find the index, but method #1 can’t (Unless an extra variable is incremented every iteration) and method #5 gives a concise representation of this indexing. 
  
Method #6: Using numpy
For very large n-dimensional lists (for example an image array), it is sometimes better to use an external library such as numpy. 

Output: 

0
1
2
3
4
5
6
7
8

We can use np.ndenumerate() to mimic the behavior of enumerating. The extra power of NumPy comes from the fact that we can even control the way to visit the elements (Fortran order rather than C order, say :)) but the one caveat is that the np.nditer treats the array as read-only by default, so one must pass extra flags such as op_flags=[‘readwrite’] for it to be able to modify elements.