NumPy
Introduction to Python
Hugo Bowne-Anderson
Data Scientist at DataCamp
Lists Recap
Powerful
Collection of values
Hold different types
Change, add, remove
Need for Data Science
Mathematical operations over collections
Speed
Illustration
height = [1.73, 1.68, 1.71, 1.89, 1.79]
height
[1.73, 1.68, 1.71, 1.89, 1.79]
weight = [65.4, 59.2, 63.6, 88.4, 68.7]
weight
[65.4, 59.2, 63.6, 88.4, 68.7]
weight / height ** 2
TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int'
Solution: NumPy
Numeric Python
Alternative to Python List: NumPy Array
Calculations over entire arrays
Easy and Fast
Installation
- In the terminal:
pip3 install numpy
- In the terminal:
NumPy
import numpy as npnp_height = np.array(height) np_height
array([1.73, 1.68, 1.71, 1.89, 1.79])
np_weight = np.array(weight)
np_weight
array([65.4, 59.2, 63.6, 88.4, 68.7])
bmi = np_weight / np_height ** 2
bmi
array([21.85171573, 20.97505669, 21.75028214, 24.7473475 , 21.44127836])
Comparison
height = [1.73, 1.68, 1.71, 1.89, 1.79]
weight = [65.4, 59.2, 63.6, 88.4, 68.7]
weight / height ** 2
TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int'
np_height = np.array(height)
np_weight = np.array(weight)
np_weight / np_height ** 2
array([21.85171573, 20.97505669, 21.75028214, 24.7473475 , 21.44127836])
NumPy: remarks
np.array([1.0, "is", True])
array(['1.0', 'is', 'True'], dtype='<U32')
- NumPy arrays: contain only one type
NumPy: remarks
python_list = [1, 2, 3]
numpy_array = np.array([1, 2, 3])
python_list + python_list
[1, 2, 3, 1, 2, 3]
numpy_array + numpy_array
array([2, 4, 6])
- Different types: different behavior!
NumPy Subsetting
bmi
array([21.85171573, 20.97505669, 21.75028214, 24.7473475 , 21.44127836])
bmi[1]
20.975
bmi > 23
array([False, False, False, True, False])
bmi[bmi > 23]
array([24.7473475])
Let's practice!
Introduction to Python
