NumPy
Python 入门
Hugo Bowne-Anderson
Data Scientist at DataCamp
列表回顾
功能强大
数值的集合
存储不同类型的数据
更改、添加、删除
数据科学的需求
集合上的数学运算
速度
示意图
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'
解决方案： NumPy
Numeric Python
Python 列表的替代方案： NumPy 数组
对整个数组进行计算
简单快捷
安装
- 在终端中：
pip3 install numpy
- 在终端中：
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])
对比
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：注意事项
np.array([1.0, "is", True])
array(['1.0', 'is', 'True'], dtype='<U32')
- NumPy 数组：仅包含一种类型
NumPy：注意事项
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])
- 不同类型：行为也不同！
NumPy 子集化
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])
动手练一练！
Python 入门
