how numpy arrays are faster than python list

tutorial: why numpy arrays are faster than python lists

numpy is a powerful library in python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. one of the most significant advantages of numpy arrays over python lists is performance. in this tutorial, we'll explore why numpy arrays are generally faster and more efficient than python lists, and we'll provide code examples to illustrate this point.

key differences between numpy arrays and python lists

1. **homogeneous vs. heterogeneous data types**:
- **python lists**: can contain elements of different data types (e.g., integers, floats, strings).
- **numpy arrays**: require elements to be of the same data type, which allows for more optimized memory usage and faster processing.

2. **memory layout**:
- **python lists**: store references to objects, leading to overhead in memory usage.
- **numpy arrays**: store data in a contiguous block of memory, which can be more efficient for computational operations.

3. **vectorized operations**:
- **python lists**: operations on lists require explicit loops, which can be slow.
- **numpy arrays**: support vectorized operations, allowing for element-wise operations without explicit loops, which is much faster.

performance comparison: code example

let's compare the performance of numpy arrays and python lists using a simple numerical operation: adding two collections of numbers.

step 1: import libraries

step 2: create data

let's create two collections of numbers, one as a python list and the other as a numpy array.

step 3: define a function to add elements

we'll define two functions: one for adding the lists and another for adding the numpy arrays.

step 4: measure execution time

now, let's measure the execution time for both operations.

expected output

when you run the above code, you should see a significant difference in execution tim ...

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