原始的 Python list 虽然很好用，但是不具备能够“整体”进行数学运算的性质，并且速度也不够快（按照视频上的说法），而 Numpy.array 恰好可以弥补这些缺陷。
初步应用就是“整体数学运算”和“subset（取子集、随机访问）”。
1、如何构造一个 Numpy array
# Create list baseball
baseball = [180, 215, 210, 210, 188, 176, 209, 200] # Import the numpy package as np
import numpy as np # Create a numpy array from baseball: np_baseball
np_baseball = np.array(baseball) # Print out type of np_baseball
print(type(np_baseball))
2、利用 Numpy 进行整体数学运算
example - 1：
# height is available as a regular list # Import numpy
import numpy as np # Create a numpy array from height: np_height
np_height = np.array(height) # Print out np_height
print(np_height) # Convert np_height to m: np_height_m
np_height_m = np_height * 0.0254 # Print np_height_m
print(np_height_m)
example - 2：
# height and weight are available as a regular lists # Import numpy
import numpy as np # Create array from height with correct units: np_height_m
np_height_m = np.array(height) * 0.0254 # Create array from weight with correct units: np_weight_kg
np_weight_kg = np.array(weight) * 0.453592 # Calculate the BMI: bmi
bmi = np_weight_kg / np_height_m ** 2 # Print out bmi
print(bmi)
3、Subset of Numpy array
# height and weight are available as a regular lists # Import numpy
import numpy as np # Calculate the BMI: bmi
np_height_m = np.array(height) * 0.0254
np_weight_kg = np.array(weight) * 0.453592
bmi = np_weight_kg / np_height_m ** 2 # Create the light array
light = bmi < 21 # Print out light
print(light) # Print out BMIs of all baseball players whose BMI is below 21
print(bmi[light])
这种取子集的方式整体上看起来很自然，但是让我不解的是：为什么 bmi < 21 不直接返回一个子集呢？稍微思考了一下，bmi < 21 本身也是一个类似与 np_array1 < np_array2 的整体数学运算，返回值显然必须是一个布尔型的 np_array3
另外，我发现直接把一个布尔数组放进“[ ]”中取子集本身也非常巧妙、自然。
虽然 NumPy Array 很有“个性”，但是仍具备很多和 Python list 一样的共性：
# height and weight are available as a regular lists # Import numpy
import numpy as np # Store weight and height lists as numpy arrays
np_weight = np.array(weight)
np_height = np.array(height) # Print out the weight at index 50
print(np_weight[50]) # Print out sub-array of np_height: index 100 up to and including index 110
print(np_height[100:111])
4、Numpy 的副作用（NumPy Side Effects）
First of all, numpy arrays cannot contain elements with different types. If you try to build such a list, some of the elements' types are changed to end up with a homogeneous list. This is known as type coercion.
Second, the typical arithmetic operators, such as +, -, * and / have a different meaning for regular Python lists and numpy arrays.