diff --git "a/docs/views/data/2022-11-5-Numpy\345\237\272\347\241\200\347\237\245\350\257\206.md" "b/docs/views/data/2022-11-5-Numpy\345\237\272\347\241\200\347\237\245\350\257\206.md"
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+---
+layout: post
+title: Numpy基础函数与应用
+date: 2022-11-05
+author: YanY
+categories:
+    - 数据分析部
+tags:
+    - Numpy库
+---
+# 前言
+
+Numpy库是Scipy、Pandas等数据处理或科学计算库的基础，此篇主要针对Numpy库的ndarray对象和与矩阵运算相关函数进行归纳。部分参考资料来自MOOC“python数据分析与展示”课程。
+
+# Numpy的引用
+
+```python
+import Numpy as np
+```
+
+# Numpy数组对象——ndarray对象的属性
+
+```python
+import numpy as np
+
+a = np.array([[0,1,2,3,4],[9,8,7,6,5]])
+
+a.ndim #秩，即轴的数量或维度数
+Out[3]: 2
+
+a.shape #尺度，对于矩阵即n行m列
+Out[4]: (2, 5)
+
+a.size #元素个数，相当于n*m
+Out[5]: 10
+
+a.dtype #元素类型
+Out[6]: dtype('int32')
+
+a.itemsize #每个元素大小，以字节为单位
+Out[7]: 4
+
+```
+
+# 数组的创建
+
+## 1、从列表、元组创建
+
+```python
+x = np.array([1,2])
+
+print(x)
+[1 2]
+
+x = np.array((1,2))
+
+print(x)
+[1 2]
+
+x= np.array([[1,2],(3,4)])
+
+print(x)
+[[1 2]
+ [3 4]]
+ ```
+
+ ## 2、使用函数创建
+
+ ### 基础函数
+
+ ```python
+np.arange(10)   #生成0-(n-1)的向量
+Out[15]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+np.ones((2,3))  #生成元素均为1的矩阵
+Out[16]: 
+array([[1., 1., 1.],
+       [1., 1., 1.]])
+
+np.zeros((2,3))  #生成元素均为0的矩阵
+Out[17]: 
+array([[0., 0., 0.],
+       [0., 0., 0.]])
+
+np.eye(5)   #生成单位矩阵
+Out[19]: 
+array([[1., 0., 0., 0., 0.],
+       [0., 1., 0., 0., 0.],
+       [0., 0., 1., 0., 0.],
+       [0., 0., 0., 1., 0.],
+       [0., 0., 0., 0., 1.]])
+
+np.full((2,3),5)  #生成值均为value的矩阵，value可指定
+Out[20]: 
+array([[5, 5, 5],
+       [5, 5, 5]])
+ ```
+
+### 扩展函数
+
+```python
+a= np.array([[1,2],[3,4]])
+
+ #根据a数组的形状填充数组
+
+np.ones_like(a) 
+Out[22]: 
+array([[1, 1],
+       [1, 1]])
+
+np.zeros_like(a)
+Out[23]: 
+array([[0, 0],
+       [0, 0]])
+
+np.full_like(a,5)
+Out[24]: 
+array([[5, 5],
+       [5, 5]])
+
+
+#linspace函数，给定起始值、结束值、元素个数，等距输出数组
+
+a=np.linspace(1,10,4)
+
+a
+Out[27]: array([ 1.,  4.,  7., 10.])
+
+b= np.linspace(1,10,4,endpoint = False)  #endpoint：规定结束值是否包含在数组中
+
+b
+Out[29]: array([1.  , 3.25, 5.5 , 7.75])
+
+```
+
+### 数组拼接及插入函数
+
+```python
+B= np.array([[1, 2, 3],[2, 3, 4]])
+
+C = np.array( [[ 3,6, 9 ]] )  
+
+D = np.array( [ [ 5 ], [ 10 ] ] )
+
+
+# 列拼接
+
+np.concatenate((B,D),axis=1)  #axis表示在第几个维度进行拼接
+array([[ 1,  2,  3,  5],
+       [ 2,  3,  4, 10]])
+
+#行拼接
+
+np.concatenate((B,C),axis =0)  
+array([[1, 2, 3],
+       [2, 3, 4],
+       [3, 6, 9]])
+
+#append函数：降成一维
+
+np.append(B,C)  
+array([1, 2, 3, 2, 3, 4, 3, 6, 9])
+
+#insert函数
+
+np.insert(B,2,[10,7],axis=1)
+array([[ 1,  2, 10,  3],
+       [ 2,  3,  7,  4]])
+
+np.insert(B,1,[2,2,2],axis=0)
+array([[1, 2, 3],
+       [2, 2, 2],
+       [2, 3, 4]])
+
+np.insert(B,1,[3],axis=1)
+array([[1, 3, 2, 3],
+       [2, 3, 3, 4]])
+
+C = np.insert( B, 1, [3], axis = 0 )
+
+C
+array([[1, 2, 3],
+       [3, 3, 3],
+       [2, 3, 4]])
+
+```
+
+# 数组的维度变换
+
+```python
+
+# reshape函数：生成一个新维度的数组，不改变原数组
+
+a.reshape((3,8))
+Out[3]: 
+array([[1, 1, 1, 1, 1, 1, 1, 1],
+       [1, 1, 1, 1, 1, 1, 1, 1],
+       [1, 1, 1, 1, 1, 1, 1, 1]])
+
+a
+Out[4]: 
+array([[[1, 1, 1, 1],
+        [1, 1, 1, 1],
+        [1, 1, 1, 1]],
+
+       [[1, 1, 1, 1],
+        [1, 1, 1, 1],
+        [1, 1, 1, 1]]])
+
+#resize：对原数组进行操作
+
+a.resize((3,8))
+
+a
+Out[6]: 
+array([[1, 1, 1, 1, 1, 1, 1, 1],
+       [1, 1, 1, 1, 1, 1, 1, 1],
+       [1, 1, 1, 1, 1, 1, 1, 1]])
+
+# flatten： 生成一个一维数组
+
+a.flatten()
+Out[7]: 
+array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+       1, 1])
+
+```
+
+# 数组的类型变换
+
+```python
+
+#astype:改变数组中元素的数据类型
+
+new_a = a.astype(np.float64)
+
+new_a
+Out[10]: 
+array([[1., 1., 1., 1., 1., 1., 1., 1.],
+       [1., 1., 1., 1., 1., 1., 1., 1.],
+       [1., 1., 1., 1., 1., 1., 1., 1.]])
+
+```
+
+# 数组转为列表
+
+```python
+
+#ls = a.tolist()
+
+a.tolist()
+Out[11]: [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]]
+
+```
+
+# 数组的索引和切片
+
+此操作与Python中序列的索引与切片相同，以下针对多维数组的切片进行实例演示，主要理解对各个维度进行索引查值和切片的过程。
+
+```python
+
+a = np.arange(24).reshape((2,3,4))
+
+```
+
+## 索引
+
+```python
+a
+Out[13]: 
+array([[[ 0,  1,  2,  3],
+        [ 4,  5,  6,  7],
+        [ 8,  9, 10, 11]],
+
+       [[12, 13, 14, 15],
+        [16, 17, 18, 19],
+        [20, 21, 22, 23]]])
+
+a[1,2,3]   #最外层索引1，次外层索引2，里层索引3
+Out[14]: 23
+
+a[-1,-2,-3] 
+Out[15]: 17
+
+
+```
+
+## 切片
+
+```python
+
+a[:,1,-3]
+Out[16]: array([ 5, 17])
+
+a[:,1:3,:]
+Out[17]: 
+array([[[ 4,  5,  6,  7],
+        [ 8,  9, 10, 11]],
+
+       [[16, 17, 18, 19],
+        [20, 21, 22, 23]]])
+
+a[:,:,::2]     #步长
+Out[18]: 
+array([[[ 0,  2],
+        [ 4,  6],
+        [ 8, 10]],
+
+       [[12, 14],
+        [16, 18],
+        [20, 22]]])
+
+```
+
+# Numpy一元函数
+ 
+对数组中的每个元素进行运算，如取绝对值、计算平方根、平方、对数、指数、三角函数等。不改变原数组。
+
+```python
+
+#部分函数实例
+
+a
+Out[20]: 
+array([[0, 1, 2, 3, 4],
+       [5, 6, 7, 8, 9]])
+
+np.sqrt(a)
+Out[22]: 
+array([[0.        , 1.        , 1.41421356, 1.73205081, 2.        ],
+       [2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ]])
+
+np.square(a)
+Out[23]: 
+array([[ 0,  1,  4,  9, 16],
+       [25, 36, 49, 64, 81]], dtype=int32)
+
+```
+
+# Numpy二元函数
+
+对两个***相同形状***的数组各元素之间进行运算，如四则运算，次方运算，最大最小值，模运算、比较等。
+
+```python 
+a= np.array([[1,2,3],[4,5,6]])
+
+b= np.array([[4,5,6],[7,8,9]])
+
+a+b
+Out[27]: 
+array([[ 5,  7,  9],
+       [11, 13, 15]])
+
+a*b              #若是矩阵运算，需要与点乘进行区别，此为对位乘法。
+Out[28]: 
+array([[ 4, 10, 18],
+       [28, 40, 54]])
+
+np.maximum(a,b)
+Out[29]: 
+array([[4, 5, 6],
+       [7, 8, 9]])
+
+```
+
+# 与矩阵运算相关的操作
+
+```python
+a
+Out[35]: 
+array([[1, 2, 3],
+       [4, 5, 6]])
+
+b
+Out[36]: 
+array([[0, 1],
+       [2, 3],
+       [4, 5]])
+```
+
+## 点乘
+
+应满足矩阵点乘条件
+
+```python
+a.dot(b)
+Out[37]: 
+array([[16, 22],
+       [34, 49]])
+
+np.dot(a,b)
+Out[38]: 
+array([[16, 22],
+       [34, 49]])
+```
+
+## 求逆
+
+***np.linalg.inv(array)***
+
+实际运用是应先判断是否可逆，否则会报错。
+
+```python
+
+a
+Out[46]: 
+array([[1, 2, 3],
+       [3, 3, 9],
+       [2, 3, 4]])
+
+np.linalg.inv(a)
+Out[47]: 
+array([[-2.50000000e+00,  1.66666667e-01,  1.50000000e+00],
+       [ 1.00000000e+00, -3.33333333e-01,  8.32667268e-17],
+       [ 5.00000000e-01,  1.66666667e-01, -5.00000000e-01]])
+
+```
+
+## 转置
+
+```
+a.T
+Out[48]: 
+array([[1, 3, 2],
+       [2, 3, 3],
+       [3, 9, 4]])
+```
+
+# 参考资料
+
+有关随机数函数、统计函数、梯度函数的学习，详见之后的博客。若有错误之处，望大家指正！
+
+![数组属性](../imgs/2211/10/YYyanyan/Screenshot_20221106_105535_com.netease.edu.ucmooc.jpg)
+
+![数组元素类型（1）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105530_com.netease.edu.ucmooc.jpg)
+
+![数组元素类型（2）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105529_com.netease.edu.ucmooc.jpg)
+
+![数组元素类型（3）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105527_com.netease.edu.ucmooc.jpg)
+
+![数组创建方法——函数创建（1）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105515_com.netease.edu.ucmooc.jpg)
+
+![数组创建方法——函数创建（2）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105512_com.netease.edu.ucmooc.jpg)
+
+![数组创建方法——函数创建（3）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105510_com.netease.edu.ucmooc.jpg)
+
+![数组维度变换](../imgs/2211/10/YYyanyan/Screenshot_20221106_105503_com.netease.edu.ucmooc.jpg)
+
+![一元函数（1）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105453_com.netease.edu.ucmooc.jpg)
+
+![一元函数（2）](../imgs/2211/10/YYyanyan/Screenshot_20221106_105450_com.netease.edu.ucmooc.jpg)
+
+![二元函数](../imgs/2211/10/YYyanyan/Screenshot_20221106_105448_com.netease.edu.ucmooc.jpg)
+
+
diff --git "a/docs/views/data/\345\210\233\345\273\272\344\270\200\347\257\207\346\226\260\347\232\204\345\215\232\345\256\242.md" "b/docs/views/data/\345\210\233\345\273\272\344\270\200\347\257\207\346\226\260\347\232\204\345\215\232\345\256\242.md"
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@@ -0,0 +1,22 @@
+ 创建一篇新的博客
+# Markdown语法学习
+## 标题
+## 段落
+I really like using Markdown.
+
+I think I'll use it to format all of my documents from now on.
+## 强调与斜体
+I just love **bold text**
+
+I just love *Italic*
+
+This text is ***really important***
+## 引用
+> Dorothy followed her through many of the beautiful rooms in her castle.
+>>The Witch bade her clean the pots and kettles and sweep the floor and keep the fire fed with wood.
+## 代码
+    <html>
+      <head>
+      </head>
+    </html>
+
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