---
id: 5e9a093a74c4063ca6f7c157
title: Numpy Algebra and Size
challengeType: 11
videoId: XAT97YLOKD8
bilibiliIds:
  aid: 250621433
  bvid: BV1hv41137uM
  cid: 409013128
dashedName: numpy-algebra-and-size
---

# --description--

*Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead.*

More resources:

-  <a href="https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy" target="_blank" rel="noopener noreferrer nofollow">Notebooks on GitHub</a>
-  <a href="https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb" target="_blank" rel="noopener noreferrer nofollow">How to open Notebooks from GitHub using Google Colab.</a>

# --questions--

## --text--

What is the relationship between size of objects (such as lists and datatypes) in memory in Python's standard library and the NumPy library? Knowing this, what are the implications for performance?

## --answers--

Standard Python objects take up much more memory to store than NumPy objects; operations on comparable standard Python and NumPy objects complete in roughly the same time.

---

NumPy objects take up much more memory than standard Python objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.

---

NumPy objects take up much less memory than Standard Python objects; operations on Standard Python objects complete very quickly compared to comparable objects on NumPy Object.

---

Standard Python objects take up more memory than NumPy objects; operations on NumPy objects complete very quickly compared to comparable objects in standard Python.

## --video-solution--

4

