Multiple Condition Indexing
Lesson 15 of 18 in Coddy's Numpy Fundamentals course.
We learned how to apply conditions, but sometimes we want to apply multiple conditions at the same time.
Python ways to combine different conditions are: and or not
In numpy it is different.
We use:
| Python | Numpy |
| and | & |
| or | | |
| not | ~ |
Note: Every condition must be inside the parenthesis (). look at the examples to see what we mean.
Example 1
Retrieve all elements that are bigger than 5 and smaller than 10
ary = np.array([1, 2, 3, 5, 8, 3, 9, 10, 2])
ary[(ary > 5) & (ary < 10)]
>>> [8, 9]Example 2
Retrieve all elements that are smaller than 3 or bigger than 7
ary = np.array([1, 5, 2, 3, 8, 7])
ary[(ary < 3) | (ary > 7)]
>>> [1, 2, 8]Example 3
Retrieve all elements that are not equal to 5
ary = np.array([1, 5, 2])
ary[~(ary == 5)]
>>> [1, 2]
Test yourselfThis lesson includes a short quiz. Start the lesson to answer it and track your progress.
Test yourselfThis lesson includes a short quiz. Start the lesson to answer it and track your progress.
Test yourselfThis lesson includes a short quiz. Start the lesson to answer it and track your progress.
Challenge
EasyCreate a function called condition_master that receives a python list and returns all the elements that are smaller or equal to 0 or bigger than 5 and not equal to 10.
Return the result as a python list
- To convert a numpy array to a python list use:
str(list(ary))
Try it yourself
import numpy as np
def condition_master(lst):
# Complete here
All lessons in Numpy Fundamentals
2N-Dimensional Array Creation
Higher DimensionUnderstanding ShapesPopulate with Fixed ValuesNumpy TypesRangeReshape3Indexing Numpy Array
Basic IndexingSlicingArray IndexingDimensional IndexingBoolean IndexingMultiple Condition Indexing