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feat(learn): NumPy Algebra and Size: Size and Efficiency (#38725)

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* NumPy Algebra and Size: Size and Efficiency Question

* Update curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-course/numpy-algebra-and-size.english.md

Co-authored-by: Eric Leung <eric@erictleung.com>

Co-authored-by: Eric Leung <eric@erictleung.com>
This commit is contained in:
Ray Johnson
2020-05-12 04:35:57 -05:00
committed by Mrugesh Mohapatra
parent c30fd2aa8f
commit add7feb7a5
1 changed files with 6 additions and 6 deletions
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curriculum/challenges/english/08-data-analysis-with-python/data-analysis-with-python-course/numpy-algebra-and-size.english.md
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@@ -14,13 +14,13 @@ videoId: XAT97YLOKD8
```yml
question:
text: Question
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:
- one
- two
- three
solution: 3
- 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.
solution: 4
```
</section>