python 03数据分析（numpy简单的使用）

分手后的思念是犯贱 2023-10-08 12:00 34阅读 0赞

#### python 03数据分析 ####

*   *   *  1、图像里的numpy数组
         *   *  1.1、使用jupyter
             *  1.2、导入图片
             *  1.3、查看该图片的各项数据
             *   *  data.shape
                 *  data.dtype
                 *  data.max()、data.min()
             *  1.4、先熟悉一下数组切片
             *  1.5、通过数组改变图片
             *   *  1.5.1、Image.fromarray()
                 *  1.5.2、plt.imshow()
                 *   *  np.transpose
                     *  np.concatenate
         *  2、把数组保存成文件

#### 1、图像里的numpy数组 ####

##### 1.1、使用jupyter #####

这里我在G盘根目录下打开了jupyter notebook  
![在这里插入图片描述][20200114233453686.png]  
新建一个jupyter 文件  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70]  
开始写代码，按Tab有代码提示功能  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 1]  
输入完成可以按住Ctrl+回车，当前内容就载入内存了（运行前你需要执行的内容都要载入内存才能成功运行）  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 2]  
成功载入内存的行会在【】里显示数字，未载入的里面是空的  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 3]  
点击In\[\] 变成蓝色框框后，可以按两下D，删除不需要的行，按  
B可以在当前行下面新建一个行  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 4]

##### 1.2、导入图片 #####

导入图形库  
![在这里插入图片描述][20200114234913762.png]  
如果像我一样提示未安装这个库可以新打开一个cmd安装一下  
![在这里插入图片描述][20200114235024391.png]  
![在这里插入图片描述][20200114235041629.png]  
打开一张图片  
这里我的图片就放在g盘根目录下  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 5]  
查看这张图片的数据  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 6]

##### 1.3、查看该图片的各项数据 #####

我们发现这张图片就是有一个三维数组组成的

> 三维：x，y，颜色（红、绿、蓝、透明度）

> 双击左边的Out\[ \]可以收起  
> ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 7]

###### data.shape ######

查看data的形状可以发现这是一张宽326高302颜色位4的图片  
![在这里插入图片描述][20200115001007383.png]

###### data.dtype ######

是一个无符号的int8类型，也就是最大可以写 2的8次方：256，超出256就溢出了  
![在这里插入图片描述][20200115001622524.png]

###### data.max()、data.min() ######

之所以用uint8类型是因为颜色数值是0~255，所以256够用了  
![在这里插入图片描述][20200115002320424.png]

##### 1.4、先熟悉一下数组切片 #####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 8]

##### 1.5、通过数组改变图片 #####

###### 1.5.1、Image.fromarray() ######

Image.fromarray(data)将array 数组转换成image图片

**改变data**：

data2 = data\[:,:,::-1\] 将颜色数据反序排列  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 9]  
data3 = data\[::-1,:,:\]将x轴反序排列，图片翻转了  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 10]  
data\[::2,::2,:\]在xy轴上隔两个数据切片，图片变小了  
![在这里插入图片描述][20200115003646587.png]  
data\[:,:,\[1,0,2\]\]只改变颜色，把颜色值调换一下位置  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 11]  
data\[:,:,0\] 在红、绿、蓝、透明度，只要红的数值  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 12]

###### 1.5.2、plt.imshow() ######

导入绘图工具matplotlib

import matplotlib.pyplot as plt

显示一个带标尺的图片  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 13]  
变成马赛克  
data\[::5,::5\]每隔五个行列取数据  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 14]

###### np.transpose ######

np.transpose(data,axes = (1,0,2))把xy交换,transpose转置函数![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 15]  
data\[:,:150\]，y只取0到150  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 16]

###### np.concatenate ######

np.concatenate(\[data02,data01\],axis=1)，将data01和data02横向拼接，axis=1数据横向拼接  
x 0 ， y 1 ，颜色 2  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 17]  
这里不能axis=2，因为data竖向拼接后这个数据就不是一个图片数组格式了，如下  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 18]

#### 2、把数组保存成文件 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 19]  
![在这里插入图片描述][20200115172149102.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2FfX2ludF9f_size_16_color_FFFFFF_t_70 20]

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