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Python-100-Days
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10
Day31-35/31-35.玩转Linux操作系统.md
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@ -230,7 +230,7 @@ Linux系统的命令通常都是如下所示的格式：
[root@iZwz97tbgo9lkabnat2lo8Z ~]# !454
```
> 说明：查看到历史命令之后，可以用`!历史命令编号`来重新执行该命令；通过`history -c`可以清除历史命令。
> **说明**：查看到历史命令之后，可以用`!历史命令编号`来重新执行该命令；通过`history -c`可以清除历史命令。
### 实用程序
@ -308,7 +308,7 @@ Linux系统的命令通常都是如下所示的格式：
...
```
> 说明：上面用到了一个名为`wget`的命令，它是一个网络下载器程序，可以从指定的URL下载资源。
> **说明**：上面用到了一个名为`wget`的命令，它是一个网络下载器程序，可以从指定的URL下载资源。
6. 拷贝/移动文件 - **cp** / **mv**。
@ -350,7 +350,7 @@ Linux系统的命令通常都是如下所示的格式：
52:</script>
...
```
> 说明：`grep`在搜索字符串时可以使用正则表达式，如果需要使用正则表达式可以用`grep -E`或者直接使用`egrep`。
> **说明**：`grep`在搜索字符串时可以使用正则表达式，如果需要使用正则表达式可以用`grep -E`或者直接使用`egrep`。
9. 创建链接和查看链接 - **ln** / **readlink**。
@ -372,7 +372,7 @@ Linux系统的命令通常都是如下所示的格式：
CentOS Linux release 7.4.1708 (Core)
```
> 说明：链接可以分为硬链接和软链接（符号链接）。硬链接可以认为是一个指向文件数据的指针，就像Python中对象的引用计数，每添加一个硬链接，文件的对应链接数就增加1，只有当文件的链接数为0时，文件所对应的存储空间才有可能被其他文件覆盖。我们平常删除文件时其实并没有删除硬盘上的数据，我们删除的只是一个指针，或者说是数据的一条使用记录，所以类似于“文件粉碎机”之类的软件在“粉碎”文件时除了删除文件指针，还会在文件对应的存储区域填入数据来保证文件无法再恢复。软链接类似于Windows系统下的快捷方式，当软链接链接的文件被删除时，软链接也就失效了。
> **说明**：链接可以分为硬链接和软链接（符号链接）。硬链接可以认为是一个指向文件数据的指针，就像Python中对象的引用计数，每添加一个硬链接，文件的对应链接数就增加1，只有当文件的链接数为0时，文件所对应的存储空间才有可能被其他文件覆盖。我们平常删除文件时其实并没有删除硬盘上的数据，我们删除的只是一个指针，或者说是数据的一条使用记录，所以类似于“文件粉碎机”之类的软件在“粉碎”文件时除了删除文件指针，还会在文件对应的存储区域填入数据来保证文件无法再恢复。软链接类似于Windows系统下的快捷方式，当软链接链接的文件被删除时，软链接也就失效了。
10. 压缩/解压缩和归档/解归档 - **gzip** / **gunzip** / **xz**。
@ -429,7 +429,7 @@ Linux系统的命令通常都是如下所示的格式：
[root@iZwz97tbgo9lkabnat2lo8Z ~]# xargs < a.txt > b.txt
```
> 说明：这个命令就像上面演示的那样常在管道（实现进程间通信的一种方式）和重定向（重新指定输入输出的位置）操作中用到，后面的内容中会讲到管道操作和输入输出重定向操作。
> **说明**：这个命令就像上面演示的那样常在管道（实现进程间通信的一种方式）和重定向（重新指定输入输出的位置）操作中用到，后面的内容中会讲到管道操作和输入输出重定向操作。
13. 显示文件或目录 - **basename** / **dirname**。

20
Day41-55/48.前后端分离开发入门.md
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@ -133,21 +133,7 @@ class SubjectMapper(ModelMapper):
<meta charset="UTF-8">
<title>学科信息</title>
<style>
#container {
width: 80%;
margin: 10px auto;
}
#main>dl>dt {
font-size: 1.5em;
font-weight: bold;
}
#main>dl>dd {
font-size: 1.2em;
}
a {
text-decoration: none;
color: darkcyan;
}
/* 此处省略层叠样式表 */
</style>
</head>
<body>
@ -157,7 +143,9 @@ class SubjectMapper(ModelMapper):
<div id="main">
<dl v-for="subject in subjects">
<dt>
<a :href="'/static/html/teachers.html?sno=' + subject.no">{{ subject.name }}</a>
<a :href="'/static/html/teachers.html?sno=' + subject.no">
{{ subject.name }}
</a>
<img v-if="subject.is_hot" src="/static/images/hot-icon-small.png">
</dt>
<dd>{{ subject.intro }}</dd>

46
Day41-55/49.RESTful架构和DRF入门.md
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@ -156,48 +156,14 @@ urlpatterns = [
通过Vue.js渲染页面。
```Python
```HTML
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>老师信息</title>
<style>
#container {
width: 80%;
margin: 10px auto;
}
.teacher {
width: 100%;
margin: 0 auto;
padding: 10px 0;
border-bottom: 1px dashed gray;
overflow: auto;
}
.teacher>div {
float: left;
}
.photo {
height: 140px;
border-radius: 75px;
overflow: hidden;
margin-left: 20px;
}
.info {
width: 75%;
margin-left: 30px;
}
.info div {
clear: both;
margin: 5px 10px;
}
.info span {
margin-right: 25px;
}
a {
text-decoration: none;
color: darkcyan;
}
/* 此处省略掉层叠样式表 */
</style>
</head>
<body>
@ -217,9 +183,11 @@ urlpatterns = [
</div>
<div class="intro">{{ teacher.intro }}</div>
<div class="comment">
<a href="" @click.prevent="praise(teacher)">好评</a>&nbsp;&nbsp;(<strong>{{ teacher.good_count }}</strong>)
<a href="" @click.prevent="praise(teacher)">好评</a>&nbsp;&nbsp;
(<strong>{{ teacher.good_count }}</strong>)
&nbsp;&nbsp;&nbsp;&nbsp;
<a href="" @click.prevent="criticize(teacher)">差评</a>&nbsp;&nbsp;(<strong>{{ teacher.bad_count }}</strong>)
<a href="" @click.prevent="criticize(teacher)">差评</a>&nbsp;&nbsp;
(<strong>{{ teacher.bad_count }}</strong>)
</div>
</div>
</div>
@ -355,7 +323,7 @@ JSON Web Token通常简称为JWT，它是一种开放标准（RFC 7519）。随
2. 在令牌过期之前，无法作废已经颁发的令牌，要解决这个问题，还需要额外的中间层和代码来辅助。
3. JWT是用户的身份令牌，一旦泄露，任何人都可以获得该用户的所有权限。为了降低令牌被盗用后产生的风险，JWT的有效期应该设置得比较短。对于一些比较重要的权限，使用时应通过其他方式再次对用户进行认证，例如短信验证码等。
#### 使用PyJWT生成和验证令牌
#### 使用PyJWT
在Python代码中，可以使用三方库`PyJWT`生成和验证JWT，下面是安装`PyJWT`的命令。

263
Day76-90/code/.ipynb_checkpoints/1-pandas入门-checkpoint.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@ -13,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -22,14 +22,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {
"scrolled": false
},
@ -44,7 +37,7 @@
"dtype: int64"
]
},
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@ -52,30 +45,10 @@
"source": [
"# 创建\n",
"# Series是一维的数据\n",
"s = Series(data = [120,136,128,99],index = ['Math','Python','En','Chinese'])\n",
"s = Series(data=[120,136,128,99], index=['Math','Python','En','Chinese'])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4,)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
@ -84,7 +57,7 @@
{
"data": {
"text/plain": [
"array([120, 136, 128, 99], dtype=int64)"
"(4,)"
]
},
"execution_count": 6,
@ -92,6 +65,26 @@
"output_type": "execute_result"
}
],
"source": [
"s.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([120, 136, 128, 99])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v = s.values\n",
"v"
@ -99,7 +92,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"outputs": [
{
@ -108,7 +101,7 @@
"numpy.ndarray"
]
},
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@ -119,7 +112,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"outputs": [
{
@ -128,7 +121,7 @@
"120.75"
]
},
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -139,7 +132,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"outputs": [
{
@ -148,7 +141,7 @@
"136"
]
},
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@ -159,7 +152,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"outputs": [
{
@ -168,7 +161,7 @@
"15.903353943953666"
]
},
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@ -179,36 +172,33 @@
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Math 14400\n",
"Python 18496\n",
"En 16384\n",
"Chinese 9801\n",
"Math 122\n",
"Python 138\n",
"En 130\n",
"Chinese 101\n",
"dtype: int64"
]
},
"execution_count": 11,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.pow(2)"
"s.add(1)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
@ -238,64 +228,64 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>113</td>\n",
" <td>116</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>19</td>\n",
" <td>145</td>\n",
" <td>a</td>\n",
" <td>109</td>\n",
" <td>120</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>57</td>\n",
" <td>107</td>\n",
" <td>113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>95</td>\n",
" <td>3</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>28</td>\n",
" <td>121</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>141</td>\n",
" <td>85</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h</th>\n",
" <td>124</td>\n",
" <td>b</td>\n",
" <td>54</td>\n",
" <td>39</td>\n",
" <td>10</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i</th>\n",
" <td>c</td>\n",
" <td>97</td>\n",
" <td>22</td>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <td>d</td>\n",
" <td>21</td>\n",
" <td>96</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>e</td>\n",
" <td>23</td>\n",
" <td>145</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <td>f</td>\n",
" <td>80</td>\n",
" <td>35</td>\n",
" <td>17</td>\n",
" <td>62</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>j</th>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>h</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>k</th>\n",
" <td>74</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>i</td>\n",
" <td>132</td>\n",
" <td>51</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <td>j</td>\n",
" <td>95</td>\n",
" <td>143</td>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k</td>\n",
" <td>66</td>\n",
" <td>94</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@ -303,19 +293,19 @@
],
"text/plain": [
" Python En Math\n",
"a 113 116 75\n",
"b 19 145 23\n",
"c 57 107 113\n",
"d 95 3 66\n",
"e 28 121 120\n",
"f 141 85 132\n",
"h 124 39 10\n",
"i 80 35 17\n",
"j 68 99 31\n",
"k 74 12 11"
"a 109 120 23\n",
"b 54 39 54\n",
"c 97 22 106\n",
"d 21 96 3\n",
"e 23 145 147\n",
"f 80 62 83\n",
"h 70 31 134\n",
"i 132 51 115\n",
"j 95 143 111\n",
"k 66 94 7"
]
},
"execution_count": 12,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@ -324,7 +314,7 @@
"# DataFrame是二维的数据\n",
"# excel就非常相似\n",
"# 所有进行数据分析，数据挖掘的工具最基础的结果：行和列，行表示样本，列表示的是属性\n",
"df = DataFrame(data = np.random.randint(0,150,size = (10,3)),index = list('abcdefhijk'),columns=['Python','En','Math'])\n",
"df = DataFrame(data=np.random.randint(0, 150, size=(10, 3)), index=list('abcdefhijk'), columns=['Python', 'En', 'Math'])\n",
"df"
]
},
@ -553,7 +543,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 22,
"metadata": {
"scrolled": true
},
@ -561,50 +551,57 @@
{
"data": {
"text/plain": [
"Python 79.9\n",
"En 76.2\n",
"Math 59.8\n",
"Python 74.7\n",
"En 80.3\n",
"Math 78.3\n",
"dtype: float64"
]
},
"execution_count": 19,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean(axis = 0)"
"df.mean(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 101.333333\n",
"b 62.333333\n",
"c 92.333333\n",
"d 54.666667\n",
"e 89.666667\n",
"f 119.333333\n",
"h 57.666667\n",
"i 44.000000\n",
"j 66.000000\n",
"k 32.333333\n",
"a 84.000000\n",
"b 49.000000\n",
"c 75.000000\n",
"d 40.000000\n",
"e 105.000000\n",
"f 75.000000\n",
"h 78.333333\n",
"i 99.333333\n",
"j 116.333333\n",
"k 55.666667\n",
"dtype: float64"
]
},
"execution_count": 20,
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean(axis = 1)"
"df.mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@ -623,7 +620,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.7"
}
},
"nbformat": 4,

372
Day76-90/code/.ipynb_checkpoints/2-pandas-索引-checkpoint.ipynb Normal file
View File

@ -0,0 +1,372 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import pandas as pd\n",
"\n",
"from pandas import Series, DataFrame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = Series(np.random.randint(0,150,size = 100),index = np.arange(10,110),dtype=np.int16,name = 'Python')\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s[10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s[[10,20]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 切片操作\n",
"s[10:20]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s[::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s[::-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 可以使用pandas为开发者提供方法，去进行检索\n",
"s.loc[10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"s.loc[[10,20]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.loc[10:20]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.loc[::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.loc[::-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# iloc 索引从0开始，数字化自然索引\n",
"s.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.iloc[[0,10]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.iloc[0:20]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s.iloc[::-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# DataFrame是二维，索引大同小异，\n",
"df = DataFrame(data = np.random.randint(0,150,size= (10,3)),index=list('ABCDEFHIJK'),columns=['Python','En','Math'])\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['A']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['Python']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['Python','En']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df['Python':'Math']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['A':'D']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc['Python']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df.loc['A']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc[['A','H']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc['A':'E']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc[::2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc[::-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.iloc['A']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df.iloc[[0,5]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.iloc[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df.iloc[::-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.iloc[::2,1:]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import pandas as pd\n",
"# 数据分析BI-------->人工智能AI\n",
"# 数据分析和数据挖掘一个意思，\n",
"# 工具和软件：Excel 免费版\n",
"# SPSS（一人一年10000）、SAS（一人一年5000）、Matlab 收费\n",
"# R、Python（全方位语言，流行） 免费\n",
"# Python + numpy + scipy + pandas + matplotlib + seaborn + pyEcharts + sklearn + kereas（Tensorflow）+…… \n",
"# 代码，自动化（数据输入----输出结果）\n",
"from pandas import Series,DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"a 63\n",
"b 107\n",
"c 16\n",
"d 35\n",
"e 140\n",
"f 83\n",
"dtype: int32"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 多层索引，行列\n",
"# 单层索引\n",
"s = Series(np.random.randint(0,150,size = 6),index=list('abcdef'))\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"张三 期中 114\n",
" 期末 131\n",
"李四 期中 3\n",
" 期末 63\n",
"王五 期中 107\n",
" 期末 34\n",
"dtype: int32"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 多层索引，两层，三层以上（规则一样）\n",
"s2 = Series(np.random.randint(0,150,size = 6),index = pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]))\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'DataFrame' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-25f058890288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'En'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Math'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_product\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'李四'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'王五'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'期中'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'期末'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined"
]
}
],
"source": [
"df = DataFrame(np.random.randint(0,150,size = (6,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]) )\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Python</th>\n",
" <th>En</th>\n",
" <th>Math</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">张三</th>\n",
" <th rowspan=\"2\" valign=\"top\">期中</th>\n",
" <th>A</th>\n",
" <td>15</td>\n",
" <td>31</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>82</td>\n",
" <td>56</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">期末</th>\n",
" <th>A</th>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">李四</th>\n",
" <th rowspan=\"2\" valign=\"top\">期中</th>\n",
" <th>A</th>\n",
" <td>91</td>\n",
" <td>87</td>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>120</td>\n",
" <td>118</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">期末</th>\n",
" <th>A</th>\n",
" <td>56</td>\n",
" <td>76</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>11</td>\n",
" <td>105</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">王五</th>\n",
" <th rowspan=\"2\" valign=\"top\">期中</th>\n",
" <th>A</th>\n",
" <td>147</td>\n",
" <td>78</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">期末</th>\n",
" <th>A</th>\n",
" <td>49</td>\n",
" <td>45</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>121</td>\n",
" <td>26</td>\n",
" <td>77</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python En Math\n",
"张三 期中 A 15 31 17\n",
" B 82 56 123\n",
" 期末 A 14 2 78\n",
" B 69 50 17\n",
"李四 期中 A 91 87 143\n",
" B 120 118 39\n",
" 期末 A 56 76 55\n",
" B 11 105 121\n",
"王五 期中 A 147 78 1\n",
" B 128 126 146\n",
" 期末 A 49 45 114\n",
" B 121 26 77"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 三层索引\n",
"df3 = DataFrame(np.random.randint(0,150,size = (12,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末'],['A','B']]) )\n",
"\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"73"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 先获取列后获取行\n",
"df['Python']['张三']['期中']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df2 = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
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"<style scoped>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Python</th>\n",
" <th>En</th>\n",
" <th>Math</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>73</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>149</td>\n",
" <td>81</td>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>71</td>\n",
" <td>138</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">王五</th>\n",
" <th>期中</th>\n",
" <td>11</td>\n",
" <td>94</td>\n",
" <td>103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>25</td>\n",
" <td>121</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python En Math\n",
"张三 期中 73 5 25\n",
" 期末 37 36 56\n",
"李四 期中 149 81 142\n",
" 期末 71 138 0\n",
"王五 期中 11 94 103\n",
" 期末 25 121 83"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"73"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 先获取行，后获取列\n",
"df.loc['张三'].loc['期中']['Python']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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" Python En Math\n",
"张三 期中 73 5 25\n",
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}
],
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"df.iloc[[0,1]]"
]
}
],
"metadata": {
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"name": "python3"
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# 分组聚合透视\n",
"# 很多时候属性是相似的\n",
"\n",
"import numpy as np\n",
"\n",
"import pandas as pd\n",
"\n",
"from pandas import Series,DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Hand</th>\n",
" <th>Smoke</th>\n",
" <th>sex</th>\n",
" <th>weight</th>\n",
" <th>IQ</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>right</td>\n",
" <td>yes</td>\n",
" <td>male</td>\n",
" <td>80</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>left</td>\n",
" <td>yes</td>\n",
" <td>female</td>\n",
" <td>50</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>left</td>\n",
" <td>no</td>\n",
" <td>female</td>\n",
" <td>48</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>male</td>\n",
" <td>75</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>right</td>\n",
" <td>yes</td>\n",
" <td>male</td>\n",
" <td>68</td>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>male</td>\n",
" <td>100</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>female</td>\n",
" <td>40</td>\n",
" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>female</td>\n",
" <td>90</td>\n",
" <td>110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>left</td>\n",
" <td>no</td>\n",
" <td>male</td>\n",
" <td>88</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>right</td>\n",
" <td>yes</td>\n",
" <td>female</td>\n",
" <td>76</td>\n",
" <td>160</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Hand Smoke sex weight IQ\n",
"0 right yes male 80 100\n",
"1 left yes female 50 120\n",
"2 left no female 48 90\n",
"3 right no male 75 130\n",
"4 right yes male 68 140\n",
"5 right no male 100 80\n",
"6 right no female 40 94\n",
"7 right no female 90 110\n",
"8 left no male 88 100\n",
"9 right yes female 76 160"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 走右手习惯，是否抽烟，性别，对体重，智商，有一定影响\n",
"\n",
"df = DataFrame({'Hand':['right','left','left','right','right','right','right','right','left','right'],\n",
" 'Smoke':['yes','yes','no','no','yes','no','no','no','no','yes'],\n",
" 'sex':['male','female','female','male','male','male','female','female','male','female'],\n",
" 'weight':[80,50,48,75,68,100,40,90,88,76],\n",
" 'IQ':[100,120,90,130,140,80,94,110,100,160]})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 分组聚合查看规律，某一条件下规律"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>weight</th>\n",
" <th>IQ</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Hand</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>62.0</td>\n",
" <td>103.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>right</th>\n",
" <td>75.6</td>\n",
" <td>116.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight IQ\n",
"Hand \n",
"left 62.0 103.3\n",
"right 75.6 116.3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = df.groupby(by = ['Hand'])[['weight','IQ']].mean().round(1)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Hand</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>right</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight\n",
"Hand \n",
"left 62.0\n",
"right 75.6"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(by = ['Hand'])[['weight']].apply(np.mean).round(1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df2 = df.groupby(by = ['Hand'])[['weight']].transform(np.mean).round(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>weight_mean</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>75.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" weight_mean\n",
"0 75.6\n",
"1 62.0\n",
"2 62.0\n",
"3 75.6\n",
"4 75.6\n",
"5 75.6\n",
"6 75.6\n",
"7 75.6\n",
"8 62.0\n",
"9 75.6"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = df2.add_suffix('_mean')\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Hand</th>\n",
" <th>Smoke</th>\n",
" <th>sex</th>\n",
" <th>weight</th>\n",
" <th>IQ</th>\n",
" <th>weight_mean</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>right</td>\n",
" <td>yes</td>\n",
" <td>male</td>\n",
" <td>80</td>\n",
" <td>100</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>left</td>\n",
" <td>yes</td>\n",
" <td>female</td>\n",
" <td>50</td>\n",
" <td>120</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>left</td>\n",
" <td>no</td>\n",
" <td>female</td>\n",
" <td>48</td>\n",
" <td>90</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>male</td>\n",
" <td>75</td>\n",
" <td>130</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>right</td>\n",
" <td>yes</td>\n",
" <td>male</td>\n",
" <td>68</td>\n",
" <td>140</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>male</td>\n",
" <td>100</td>\n",
" <td>80</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>female</td>\n",
" <td>40</td>\n",
" <td>94</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>right</td>\n",
" <td>no</td>\n",
" <td>female</td>\n",
" <td>90</td>\n",
" <td>110</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>left</td>\n",
" <td>no</td>\n",
" <td>male</td>\n",
" <td>88</td>\n",
" <td>100</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>right</td>\n",
" <td>yes</td>\n",
" <td>female</td>\n",
" <td>76</td>\n",
" <td>160</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Hand Smoke sex weight IQ weight_mean\n",
"0 right yes male 80 100 75.6\n",
"1 left yes female 50 120 62.0\n",
"2 left no female 48 90 62.0\n",
"3 right no male 75 130 75.6\n",
"4 right yes male 68 140 75.6\n",
"5 right no male 100 80 75.6\n",
"6 right no female 40 94 75.6\n",
"7 right no female 90 110 75.6\n",
"8 left no male 88 100 62.0\n",
"9 right yes female 76 160 75.6"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = df.merge(df2,left_index=True,right_index=True)\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Hand\n",
"left ([3, 3], [62.0, 103.3])\n",
"right ([7, 7], [75.6, 116.3])\n",
"dtype: object"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def count(x):\n",
" \n",
" return (x.count(),x.mean().round(1))\n",
"\n",
"df.groupby(by = ['Hand'])[['weight','IQ']].apply(count)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>IQ</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Hand</th>\n",
" <th>sex</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">left</th>\n",
" <th>female</th>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">right</th>\n",
" <th>female</th>\n",
" <td>160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>140</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IQ\n",
"Hand sex \n",
"left female 120\n",
" male 100\n",
"right female 160\n",
" male 140"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(by = ['Hand','sex'])[['IQ']].max()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000019E24051EF0>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = df.groupby(by = ['Hand'])['IQ','weight']\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">IQ</th>\n",
" <th colspan=\"2\" halign=\"left\">weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Hand</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>120</td>\n",
" <td>103.3</td>\n",
" <td>88</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>right</th>\n",
" <td>160</td>\n",
" <td>116.3</td>\n",
" <td>100</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IQ weight \n",
" max mean max mean\n",
"Hand \n",
"left 120 103.3 88 62.0\n",
"right 160 116.3 100 75.6"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.agg(['max','mean']).round(1)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IQ</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Hand</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>120</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>right</th>\n",
" <td>160</td>\n",
" <td>75.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IQ weight\n",
"Hand \n",
"left 120 62.0\n",
"right 160 75.6"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.agg({'IQ':'max','weight':'mean'}).round(1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
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}
},
"nbformat": 4,
"nbformat_minor": 2
}

263
Day76-90/code/1-pandas入门.ipynb
View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@ -13,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -22,14 +22,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {
"scrolled": false
},
@ -44,7 +37,7 @@
"dtype: int64"
]
},
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@ -52,30 +45,10 @@
"source": [
"# 创建\n",
"# Series是一维的数据\n",
"s = Series(data = [120,136,128,99],index = ['Math','Python','En','Chinese'])\n",
"s = Series(data=[120,136,128,99], index=['Math','Python','En','Chinese'])\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4,)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
@ -84,7 +57,7 @@
{
"data": {
"text/plain": [
"array([120, 136, 128, 99], dtype=int64)"
"(4,)"
]
},
"execution_count": 6,
@ -92,6 +65,26 @@
"output_type": "execute_result"
}
],
"source": [
"s.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([120, 136, 128, 99])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v = s.values\n",
"v"
@ -99,7 +92,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"outputs": [
{
@ -108,7 +101,7 @@
"numpy.ndarray"
]
},
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@ -119,7 +112,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"outputs": [
{
@ -128,7 +121,7 @@
"120.75"
]
},
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -139,7 +132,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"outputs": [
{
@ -148,7 +141,7 @@
"136"
]
},
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@ -159,7 +152,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"outputs": [
{
@ -168,7 +161,7 @@
"15.903353943953666"
]
},
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@ -179,36 +172,33 @@
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Math 14400\n",
"Python 18496\n",
"En 16384\n",
"Chinese 9801\n",
"Math 122\n",
"Python 138\n",
"En 130\n",
"Chinese 101\n",
"dtype: int64"
]
},
"execution_count": 11,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.pow(2)"
"s.add(1)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
@ -238,64 +228,64 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>113</td>\n",
" <td>116</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td>19</td>\n",
" <td>145</td>\n",
" <td>a</td>\n",
" <td>109</td>\n",
" <td>120</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>57</td>\n",
" <td>107</td>\n",
" <td>113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>95</td>\n",
" <td>3</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>e</th>\n",
" <td>28</td>\n",
" <td>121</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f</th>\n",
" <td>141</td>\n",
" <td>85</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h</th>\n",
" <td>124</td>\n",
" <td>b</td>\n",
" <td>54</td>\n",
" <td>39</td>\n",
" <td>10</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i</th>\n",
" <td>c</td>\n",
" <td>97</td>\n",
" <td>22</td>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <td>d</td>\n",
" <td>21</td>\n",
" <td>96</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>e</td>\n",
" <td>23</td>\n",
" <td>145</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <td>f</td>\n",
" <td>80</td>\n",
" <td>35</td>\n",
" <td>17</td>\n",
" <td>62</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>j</th>\n",
" <td>68</td>\n",
" <td>99</td>\n",
" <td>h</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>k</th>\n",
" <td>74</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" <td>i</td>\n",
" <td>132</td>\n",
" <td>51</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <td>j</td>\n",
" <td>95</td>\n",
" <td>143</td>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k</td>\n",
" <td>66</td>\n",
" <td>94</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@ -303,19 +293,19 @@
],
"text/plain": [
" Python En Math\n",
"a 113 116 75\n",
"b 19 145 23\n",
"c 57 107 113\n",
"d 95 3 66\n",
"e 28 121 120\n",
"f 141 85 132\n",
"h 124 39 10\n",
"i 80 35 17\n",
"j 68 99 31\n",
"k 74 12 11"
"a 109 120 23\n",
"b 54 39 54\n",
"c 97 22 106\n",
"d 21 96 3\n",
"e 23 145 147\n",
"f 80 62 83\n",
"h 70 31 134\n",
"i 132 51 115\n",
"j 95 143 111\n",
"k 66 94 7"
]
},
"execution_count": 12,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@ -324,7 +314,7 @@
"# DataFrame是二维的数据\n",
"# excel就非常相似\n",
"# 所有进行数据分析，数据挖掘的工具最基础的结果：行和列，行表示样本，列表示的是属性\n",
"df = DataFrame(data = np.random.randint(0,150,size = (10,3)),index = list('abcdefhijk'),columns=['Python','En','Math'])\n",
"df = DataFrame(data=np.random.randint(0, 150, size=(10, 3)), index=list('abcdefhijk'), columns=['Python', 'En', 'Math'])\n",
"df"
]
},
@ -553,7 +543,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 22,
"metadata": {
"scrolled": true
},
@ -561,50 +551,57 @@
{
"data": {
"text/plain": [
"Python 79.9\n",
"En 76.2\n",
"Math 59.8\n",
"Python 74.7\n",
"En 80.3\n",
"Math 78.3\n",
"dtype: float64"
]
},
"execution_count": 19,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean(axis = 0)"
"df.mean(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 101.333333\n",
"b 62.333333\n",
"c 92.333333\n",
"d 54.666667\n",
"e 89.666667\n",
"f 119.333333\n",
"h 57.666667\n",
"i 44.000000\n",
"j 66.000000\n",
"k 32.333333\n",
"a 84.000000\n",
"b 49.000000\n",
"c 75.000000\n",
"d 40.000000\n",
"e 105.000000\n",
"f 75.000000\n",
"h 78.333333\n",
"i 99.333333\n",
"j 116.333333\n",
"k 55.666667\n",
"dtype: float64"
]
},
"execution_count": 20,
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean(axis = 1)"
"df.mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
@ -623,7 +620,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.7"
}
},
"nbformat": 4,

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@ -82,95 +82,21 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Python</th>\n",
" <th>En</th>\n",
" <th>Math</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>73</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>149</td>\n",
" <td>81</td>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>71</td>\n",
" <td>138</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">王五</th>\n",
" <th>期中</th>\n",
" <td>11</td>\n",
" <td>94</td>\n",
" <td>103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>25</td>\n",
" <td>121</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python En Math\n",
"张三 期中 73 5 25\n",
" 期末 37 36 56\n",
"李四 期中 149 81 142\n",
" 期末 71 138 0\n",
"王五 期中 11 94 103\n",
" 期末 25 121 83"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
"ename": "NameError",
"evalue": "name 'DataFrame' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-25f058890288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'En'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Math'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_product\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'李四'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'王五'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'期中'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'期末'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined"
]
}
],
"source": [
@ -560,7 +486,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.7.7"
}
},
"nbformat": 4,

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@ -992,7 +992,7 @@
"name": "python",
"nbconvert_exporter": "python",
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},
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@ -1201,7 +1201,7 @@
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"version": "3.6.5"
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@ -1264,7 +1264,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.7.7"
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},
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@ -869,7 +869,7 @@
"name": "python",
"nbconvert_exporter": "python",
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