Python学习day18-常用模块之NumPy

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Python学习day18-常用模块之NumPyNumPy模块简介NumPy模块的安装NumPy模块的Ndarray对象NumPy数组的属性NumPy数组的切割，替换和合并切割NumPy数组的替换NumPy数组的合并

Python学习day18-常用模块之NumPy

NumPy模块简介

NumPy是Python的一种开源的数值计算扩展库，支持大量的维度数组和矩阵的运算，此外也针对数组元素提供了大量的数学函数库。

NumPy库的主要有以下几个作用：

1. 与一般的list列表有区别，提供了数组操作，有个非常强大的N维数组对象ndarray，运算以及简单的统计分部和数学模型，比如线性代数，傅里叶变换，随机数生成等。
2. 计算速度非常快，是许多模块的依赖包，比如pandas，matplotlib等，一些高级的框架其结构和NumPy也十分的类似，比如TensorFlow，PyTorch等。

NumPy模块的安装

和其他一些第三方模块一样，NumPy同样可以通过以下几种方式来安装

1. pip工具

   即打开cmd窗口，通过以下语句安装NumPy模块

   python -m pip install NumPy

2. 可以通过pycharm来安装

   路径为File->Settings->Project->Project Interpreter->

点加号，进去上面有搜索框，输入NumPy

点击左下角的安装即可。

NumPy模块的Ndarray对象

Ndarray广义上来说是一个N维数组，但又不单单是一个数组，它其实可以表示为一系列同类数据的集合，其中的每个元素在内存中都有相同存储大小的区域，可以用索引来对其中的数据进行索引和取值。

那么我们现在来创建一个ndarray，其实只需要调用一个array函数即可

NumPy.array(object,dtype = None,copy = True,order = None,subok = False,ndmin = 0)

上述参数说明如下

名称	描述
object	数组或嵌套的数列
dtype	数组元素的数据类型，可选项
copy	是否需要复制，可选项
order	创建数组的样式，C为行方向，F为列方向，A为任意方向（默认）
subok	默认返回一个与基类类型一致的数组
ndmin	指定生成数组的最小维度

我们用实例来证明以上参数：

 

 

 

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1

import NumPy as np

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# 这是一种命名习惯，习惯性将NumPy简化成np

3

​

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# 创建一维的ndarray对象

5

arr = np.array([1, 2, 3])

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print(arr, type(arr))

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​

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# 创建二维的ndarray对象

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print(np.array([[1, 2, 3], [4, 5, 6]]))

10

​

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# 创建三维的ndarray对象

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print(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))

13

# 当然一般不会用NumPy来生成三维数组，太过麻烦，一般会使用TensortFlow或者Pytorch来生成三维数组

14

​

15

​

16

res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 

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dtype=None, copy=True, order=None, subok=False, ndmin=0)

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print(res)

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​

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#[[1 2 3]

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# [4 5 6]

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# [7 8 9]] 

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​

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res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], 

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dtype=str, copy=True, order=None, subok=False, ndmin=0)

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print(res)

27

​

28

#[['1' '2' '3']

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# ['4' '5' '6']

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# ['7' '8' '9']]

31

​

32

res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float64, copy=True, order=None, subok=True, ndmin=0)

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print(res)

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​

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#[[1. 2. 3.]

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# [4. 5. 6.]

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# [7. 8. 9.]]

38

​

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# 以上修改了dtype，可见数组元素的数据类型发生了改变

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res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=None, copy=True, order=None, subok=True, ndmin=3)

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print(res,type(res))

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#[[[1 2 3]

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#  [4 5 6]

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#  [7 8 9]]] <class 'NumPy.ndarray'>

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# 可以看到，我们修改了ndmin为3，即生成的数组最少为三维数组，看外面的括号可以证明

 

 

NumPy数组的属性

上面我们已经成功生成一个NumPy数组，下面我们看一下关于其数组的一些操作和属性。

属性	说明
ndarray.ndim	数组的维数
ndarray.shape	数组维度的大小（元组形式返回，行，列）
ndarray.size	数组元素的个数
ndarray.dtype	数组元素的类型
ndarray.itemsize	数组中每个元素的大小，以字节为单位
ndarray.flags	数组对象的内存信息
ndarray.real	数组内所有元素的实部
ndarray.imag	数组内所有元素的虚部

以下实例为上述属性的结果。

 

 

 

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1

import NumPy as np

2

​

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a = np.arange(123)

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print(a.ndim)# 只有一个维度，即输出结果为1

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b = a.reshape(2,3,4)

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print(b.ndim)# 现在b就有了三个维度

7

​

8

a = np.array([[1,2,3],[3,4,5]])

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print(a.shape)# 输出结果为(2,3)，即2行3列

10

# shape还可以调整数组的大小

11

a = np.array([[1,2,3],[4,5,6]])

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a.shape(3,2)# 即转换成3行2列

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print(a)

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# 结果为：

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[[1 2]

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[3 4]

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[5 6]]

 

 

NumPy数组的切割，替换和合并

切割

NumPy数组的切割与列表有些类似，都是通过索引序号来进行切割，只不过数组是涉及到行和列的切割，但均是从0开始，取头不取尾。

 

 

 

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arr = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])

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print(arr)

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​

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# 取第一行的所有元素

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print(arr[:1, :])

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​

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# 取第一列的所有元素

8

print (arr[:,:1])

9

​

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# 取大于或者小于某个值的元素，即数组可以直接做判断，返回一个数组

11

print(arr[arr>8])

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​

13

# NumPy数组是按照运算符取元素的原理，即通过判断条件会生成一个布尔NumPy数组

14

print(arr>7)

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[[False False False False]

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 [False False False True]

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 [True True True True]]

 

 

NumPy数组的替换

类似于列表元素的替换，并且要注意的是NumPy生成的数组也是一个可变类型，所以对其做操作的时候都是在原数值上操作，要注意，必要时候使用copy来测试实验。

 

 

 

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arr = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])

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print(arr)

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​

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#[[1 2 3 4]

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# [5 6 7 8]

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# [9 10 11 12]]

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​

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# 取第一行所有元素，并让其为0

9

arr1 = arr.copy()

10

arr1[:1,:] = 0

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print(arr1)

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​

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#[[0 0 0 0]

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# [5 6 7 8]

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# [9 10 11 12]]

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​

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# 同样的，也可以通过判断取值，来让符合条件的元素做出改变或赋值

18

arr2 = arr.copy()

19

arr2[arr>5] = 0

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print(arr2)

21

#[[1 2 3 4]

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# [5 0 0 0]

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# [0 0 0 0]]

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​

 

 

NumPy数组的合并

 

 

 

x

 

 

1

arr1 = np.array([[1,2],[3,4],[5,6]])

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print(arr1)

3

​

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#[[1 2]

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# [3 4]

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# [5 6]]

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​

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arr2 = np.array([[7,8],[9,10],[11,12]])

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print(arr2)

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#[[7 8]

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# [9 10]

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# [11 12]]

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​

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# 可以用hstack()的方法合并numpy数组，两个合并数组应有相同数量的行，因为hstack()是按照行合并的，另有vstack()是按照列合并的，即两个数组的列数量应该相等。 

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print(np.hstack((arr1,arr2)))

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#[[ 1  2  7  8]

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# [ 3  4  9 10]

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# [ 5  6 11 12]]

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# 用concatenate也可以合并两个numpy数组，不过需要增加参数，比如axis = 1就是合并两个numpy数组的行

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print(np.concatenate((arr1,arr2),axis = 1))

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#[[ 1  2  7  8]

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# [ 3  4  9 10]

 

 

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# [ 5  6 11 12]]