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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：
- 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。
- 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
-
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 -
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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import NumPy as np
# 这是一种命名习惯，习惯性将NumPy简化成np
# 创建一维的ndarray对象
arr = np.array([1, 2, 3])
print(arr, type(arr))
# 创建二维的ndarray对象
print(np.array([[1, 2, 3], [4, 5, 6]]))
# 创建三维的ndarray对象
print(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
# 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
res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
dtype=None, copy=True, order=None, subok=False, ndmin=0)
print(res)
#[[1 2 3]
# [4 5 6]
# [7 8 9]]
res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
dtype=str, copy=True, order=None, subok=False, ndmin=0)
print(res)
#[['1' '2' '3']
# ['4' '5' '6']
# ['7' '8' '9']]
res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float64, copy=True, order=None, subok=True, ndmin=0)
print(res)
#[[1. 2. 3.]
# [4. 5. 6.]
# [7. 8. 9.]]
# 以上修改了dtype，可见数组元素的数据类型发生了改变
res = NumPy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=None, copy=True, order=None, subok=True, ndmin=3)
print(res,type(res))
#[[[1 2 3]
# [4 5 6]
# [7 8 9]]] <class 'NumPy.ndarray'>
# 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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import NumPy as np
a = np.arange(123)
print(a.ndim)# 只有一个维度，即输出结果为1
b = a.reshape(2,3,4)
print(b.ndim)# 现在b就有了三个维度
a = np.array([[1,2,3],[3,4,5]])
print(a.shape)# 输出结果为(2,3)，即2行3列
# shape还可以调整数组的大小
a = np.array([[1,2,3],[4,5,6]])
a.shape(3,2)# 即转换成3行2列
print(a)
# 结果为：
[[1 2]
[3 4]
[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]])
print(arr)
# 取第一行的所有元素
print(arr[:1, :])
# 取第一列的所有元素
print (arr[:,:1])
# 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
print(arr[arr>8])
# 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
print(arr>7)
[[False False False False]
[False False False True]
[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]])
print(arr)
#[[1 2 3 4]
# [5 6 7 8]
# [9 10 11 12]]
# 取第一行所有元素，并让其为0
arr1 = arr.copy()
arr1[:1,:] = 0
print(arr1)
#[[0 0 0 0]
# [5 6 7 8]
# [9 10 11 12]]
# 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
arr2 = arr.copy()
arr2[arr>5] = 0
print(arr2)
#[[1 2 3 4]
# [5 0 0 0]
# [0 0 0 0]]
NumPy数组的合并
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arr1 = np.array([[1,2],[3,4],[5,6]])
print(arr1)
#[[1 2]
# [3 4]
# [5 6]]
arr2 = np.array([[7,8],[9,10],[11,12]])
print(arr2)
#[[7 8]
# [9 10]
# [11 12]]
# 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。
print(np.hstack((arr1,arr2)))
#[[ 1 2 7 8]
# [ 3 4 9 10]
# [ 5 6 11 12]]
# 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
print(np.concatenate((arr1,arr2),axis = 1))
#[[ 1 2 7 8]
# [ 3 4 9 10]
# [ 5 6 11 12]]