├── loan.csv
├── order-14.1.csv
├── order-14.3.csv
├── train-pivot.csv
├── .idea
    ├── encodings.xml
    ├── vcs.xml
    ├── modules.xml
    ├── misc.xml
    ├── 项目.iml
    └── workspace.xml
├── order.csv
├── README.md
├── 自动化.ipynb
├── .ipynb_checkpoints
    ├── 自动化-checkpoint.ipynb
    ├── Supermarket-checkpoint.ipynb
    └── Bank-checkpoint.ipynb
├── Supermarket.ipynb
└── Bank.ipynb


/loan.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Andchenn/Analysis_Item/HEAD/loan.csv


--------------------------------------------------------------------------------
/order-14.1.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Andchenn/Analysis_Item/HEAD/order-14.1.csv


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/order-14.3.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Andchenn/Analysis_Item/HEAD/order-14.3.csv


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/train-pivot.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Andchenn/Analysis_Item/HEAD/train-pivot.csv


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/.idea/encodings.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="Encoding" addBOMForNewFiles="with NO BOM" />
4 | </project>


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/.idea/vcs.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="VcsDirectoryMappings">
4 |     <mapping directory="$PROJECT_DIR$" vcs="Git" />
5 |   </component>
6 | </project>


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/order.csv:
--------------------------------------------------------------------------------
1 | ,本月累计,上月同期,去年同期,环比,同比
2 | 销售额,10412.78007,9940.97291,8596.313470000001,0.04746086366711566,0.21130762696581828
3 | 客流量,343.0,315.0,262.0,0.0888888888888888,0.30916030534351147
4 | 客单价,30.357959387755105,31.55864415873016,32.810356755725195,-0.038046145611832505,-0.0747446114721737
5 | 


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/.idea/modules.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="ProjectModuleManager">
4 |     <modules>
5 |       <module fileurl="file://$PROJECT_DIR$/.idea/项目.iml" filepath="$PROJECT_DIR$/.idea/项目.iml" />
6 |     </modules>
7 |   </component>
8 | </project>


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/.idea/misc.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="JavaScriptSettings">
4 |     <option name="languageLevel" value="ES6" />
5 |   </component>
6 |   <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK" />
7 | </project>


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/README.md:
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 1 | # 数据分析小项目
 2 | 
 3 | #### 前言
 4 | 
 5 | Hi! 这里有三个小项目分别是利用 Python 实现报表自动化、某连锁超市、某银行数据分析，展示了问题分解、数据清洗、数据分析与可视化的过程。
 6 | 
 7 | #### 如下：
 8 | 
 9 | 1.利用 Python 实现报表自动化
10 | 
11 | + 为什么要进行报表自动化
12 | 
13 | + 什么样的报表适合自动化
14 | 
15 | + 如何实现报表自动化
16 | 
17 | 
18 | 2.假如你是某连锁超市的数据分析师
19 | 
20 | + 哪些类别的商品比较畅销
21 | 
22 | + 哪些商品比较畅销
23 | 
24 | + 不同门店的销售额占比
25 | 
26 | + 哪些时间段是超市的客流高峰期
27 | 
28 | 3. 假如你是某银行的数据分析师
29 | 
30 | + 是不是人收入越高的坏账率越低
31 | 
32 | + 年龄和坏账率有什么关系
33 | 
34 | + 家庭人口数量和坏账率有什么关系


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/.idea/项目.iml:
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 28 |     <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
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 30 |   <component name="IdeDocumentHistory">
 31 |     <option name="CHANGED_PATHS">
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 45 |     <navigator proportions="" version="1">
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 49 |       <pane id="ProjectPane">
 50 |         <subPane>
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 52 |             <path>
 53 |               <item name="项目" type="b2602c69:ProjectViewProjectNode" />
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 60 |       <pane id="Scope" />
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 65 |     <property name="last_opened_file_path" value="$PROJECT_DIR$" />
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 68 |   </component>
 69 |   <component name="RunDashboard">
 70 |     <option name="ruleStates">
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 72 |         <RuleState>
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 76 |           <option name="name" value="StatusDashboardGroupingRule" />
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 81 |   <component name="TaskManager">
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 83 |       <changelist id="7364c3c8-1191-4384-811f-5eac1a0a150c" name="Default Changelist" comment="" />
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 90 |     <servers />
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101 |       <window_info anchor="bottom" id="Docker" show_stripe_button="false" />
102 |       <window_info anchor="bottom" id="Database Changes" />
103 |       <window_info anchor="bottom" id="Version Control" />
104 |       <window_info active="true" anchor="bottom" id="Terminal" visible="true" weight="0.32951945" />
105 |       <window_info anchor="bottom" id="Python Console" />
106 |       <window_info anchor="bottom" id="Event Log" side_tool="true" />
107 |       <window_info anchor="bottom" id="Message" order="0" />
108 |       <window_info anchor="bottom" id="Find" order="1" />
109 |       <window_info anchor="bottom" id="Run" order="2" />
110 |       <window_info anchor="bottom" id="Debug" order="3" weight="0.4" />
111 |       <window_info anchor="bottom" id="Cvs" order="4" weight="0.25" />
112 |       <window_info anchor="bottom" id="Inspection" order="5" weight="0.4" />
113 |       <window_info anchor="bottom" id="TODO" order="6" />
114 |       <window_info anchor="right" id="Mongo Explorer" />
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126 |     <entry file="file://$PROJECT_DIR$/README.md">
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130 |             <caret line="4" column="72" selection-start-line="4" selection-start-column="4" selection-end-line="4" selection-end-column="72" />
131 |           </first_editor>
132 |           <second_editor />
133 |         </state>
134 |       </provider>
135 |     </entry>
136 |   </component>
137 | </project>


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/自动化.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 25,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "<class 'pandas.core.frame.DataFrame'>\n",
 13 |       "RangeIndex: 3744 entries, 0 to 3743\n",
 14 |       "Data columns (total 7 columns):\n",
 15 |       "商品ID    3478 non-null float64\n",
 16 |       "类别ID    3478 non-null float64\n",
 17 |       "门店编号    3478 non-null object\n",
 18 |       "单价      3478 non-null float64\n",
 19 |       "销量      3478 non-null float64\n",
 20 |       "成交时间    3478 non-null datetime64[ns]\n",
 21 |       "订单ID    3478 non-null object\n",
 22 |       "dtypes: datetime64[ns](1), float64(4), object(2)\n",
 23 |       "memory usage: 204.8+ KB\n"
 24 |      ]
 25 |     }
 26 |    ],
 27 |    "source": [
 28 |     "import pandas as pd\n",
 29 |     "from datetime import datetime\n",
 30 |     "\n",
 31 |     "data=pd.read_csv(\"order-14.1.csv\",parse_dates=[\"成交时间\"],encoding='gbk')\n",
 32 |     "data.head()\n",
 33 |     "# print(data.head(5))\n",
 34 |     "# 查看源数据类型\n",
 35 |     "data.info()"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": 17,
 41 |    "metadata": {},
 42 |    "outputs": [
 43 |     {
 44 |      "name": "stdout",
 45 |      "output_type": "stream",
 46 |      "text": [
 47 |       "本月销售额为：10412.78,客流量为：343,客单价为：30.36\n"
 48 |      ]
 49 |     }
 50 |    ],
 51 |    "source": [
 52 |     "# 计算本月的相关的指标\n",
 53 |     "This_month=data[(data[\"成交时间\"]>=datetime(2018,2,1))&(data[\"成交时间\"]<=datetime(2018,2,28))]\n",
 54 |     "# 销售额计算\n",
 55 |     "sales_1=(This_month[\"销量\"]*This_month['单价']).sum()\n",
 56 |     "# 客流量计算\n",
 57 |     "traffic_1=This_month[\"订单ID\"].drop_duplicates().count()\n",
 58 |     "# 客单价计算\n",
 59 |     "s_t_1=sales_1/traffic_1\n",
 60 |     "print(\"本月销售额为：{:.2f},客流量为：{},客单价为：{:.2f}\".format(sales_1,traffic_1,s_t_1))\n"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": 19,
 66 |    "metadata": {},
 67 |    "outputs": [
 68 |     {
 69 |      "name": "stdout",
 70 |      "output_type": "stream",
 71 |      "text": [
 72 |       "本月销售额为：9940.97,客流量为：315,客单价为：31.56\n"
 73 |      ]
 74 |     }
 75 |    ],
 76 |    "source": [
 77 |     "# 计算上月相关指标\n",
 78 |     "last_month=data[(data[\"成交时间\"]>=datetime(2018,1,1))&(data[\"成交时间\"]<=datetime(2018,1,31))]\n",
 79 |     "\n",
 80 |     "# 销售额计算\n",
 81 |     "sales_2=(last_month[\"销量\"]*last_month['单价']).sum()\n",
 82 |     "# 客流量计算\n",
 83 |     "traffic_2=last_month[\"订单ID\"].drop_duplicates().count()\n",
 84 |     "# 客单价计算\n",
 85 |     "s_t_2=sales_2/traffic_2\n",
 86 |     "print(\"本月销售额为：{:.2f},客流量为：{},客单价为：{:.2f}\".format(sales_2,traffic_2,s_t_2))\n"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 20,
 92 |    "metadata": {},
 93 |    "outputs": [
 94 |     {
 95 |      "name": "stdout",
 96 |      "output_type": "stream",
 97 |      "text": [
 98 |       "本月销售额为：8596.31,客流量为：262,客单价为：32.81\n"
 99 |      ]
100 |     }
101 |    ],
102 |    "source": [
103 |     "# 计算去年同期相关指标\n",
104 |     "same_month=data[(data[\"成交时间\"]>=datetime(2017,2,1))&(data[\"成交时间\"]<=datetime(2017,2,28))]\n",
105 |     "\n",
106 |     "sales_3=(same_month[\"销量\"]*same_month[\"单价\"]).sum()\n",
107 |     "\n",
108 |     "traffic_3=same_month[\"订单ID\"].drop_duplicates().count()\n",
109 |     "s_t_3=sales_3/traffic_3\n",
110 |     "print(\"本月销售额为：{:.2f},客流量为：{},客单价为：{:.2f}\".format(sales_3,traffic_3,s_t_3))\n"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 29,
116 |    "metadata": {},
117 |    "outputs": [
118 |     {
119 |      "name": "stdout",
120 |      "output_type": "stream",
121 |      "text": [
122 |       "10412.78007 343 30.357959387755105\n",
123 |       "9940.97291 315 31.55864415873016\n",
124 |       "8596.313470000001 262 32.810356755725195\n"
125 |      ]
126 |     }
127 |    ],
128 |    "source": [
129 |     "# 利用函数提高编码效率\n",
130 |     "def get_month_data(data):\n",
131 |     "    sale=(data[\"销量\"]*data[\"单价\"]).sum()\n",
132 |     "    traffic=data[\"订单ID\"].drop_duplicates().count()\n",
133 |     "    s_t=sale/traffic\n",
134 |     "    return (sale,traffic,s_t)\n",
135 |     "\n",
136 |     "# 本月相关指数\n",
137 |     "sales_1,traffic_1,s_t_1=get_month_data(This_month)\n",
138 |     "print(sales_1,traffic_1,s_t_1)\n",
139 |     "\n",
140 |     "# 上月相关指数\n",
141 |     "sales_2,traffic_2,s_t_2=get_month_data(last_month)\n",
142 |     "print(sales_2,traffic_2,s_t_2)\n",
143 |     "\n",
144 |     "# 去年同期相关指数\n",
145 |     "sales_3,traffic_3,s_t_3=get_month_data(same_month)\n",
146 |     "print(sales_3,traffic_3,s_t_3)\n"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 36,
152 |    "metadata": {},
153 |    "outputs": [
154 |     {
155 |      "name": "stdout",
156 |      "output_type": "stream",
157 |      "text": [
158 |       "             本月累计         上月同期         去年同期\n",
159 |       "销售额  10412.780070  9940.972910  8596.313470\n",
160 |       "客流量    343.000000   315.000000   262.000000\n",
161 |       "客单价     30.357959    31.558644    32.810357\n",
162 |       "             本月累计         上月同期         去年同期        环比        同比\n",
163 |       "销售额  10412.780070  9940.972910  8596.313470  0.047461  0.211308\n",
164 |       "客流量    343.000000   315.000000   262.000000  0.088889  0.309160\n",
165 |       "客单价     30.357959    31.558644    32.810357 -0.038046 -0.074745\n"
166 |      ]
167 |     }
168 |    ],
169 |    "source": [
170 |     "report=pd.DataFrame([[sales_1,sales_2,sales_3],[traffic_1,traffic_2,traffic_3],[s_t_1,s_t_2,s_t_3]],columns=[\"本月累计\",\"上月同期\",\"去年同期\"],index=[\"销售额\",\"客流量\",\"客单价\"])\n",
171 |     "print(report)\n",
172 |     "# 添加同比和环比字段\n",
173 |     "report[\"环比\"]=report[\"本月累计\"]/report[\"上月同期\"]-1\n",
174 |     "\n",
175 |     "report[\"同比\"]=report[\"本月累计\"]/report[\"去年同期\"]-1\n",
176 |     "\n",
177 |     "print(report)"
178 |    ]
179 |   },
180 |   {
181 |    "cell_type": "code",
182 |    "execution_count": 37,
183 |    "metadata": {},
184 |    "outputs": [],
185 |    "source": [
186 |     "# 将结果导出本地\n",
187 |     "report.to_csv(\"order.csv\",encoding=\"utf-8-sig\")"
188 |    ]
189 |   }
190 |  ],
191 |  "metadata": {
192 |   "kernelspec": {
193 |    "display_name": "Python 3",
194 |    "language": "python",
195 |    "name": "python3"
196 |   },
197 |   "language_info": {
198 |    "codemirror_mode": {
199 |     "name": "ipython",
200 |     "version": 3
201 |    },
202 |    "file_extension": ".py",
203 |    "mimetype": "text/x-python",
204 |    "name": "python",
205 |    "nbconvert_exporter": "python",
206 |    "pygments_lexer": "ipython3",
207 |    "version": "3.7.3"
208 |   },
209 |   "toc": {
210 |    "base_numbering": 1,
211 |    "nav_menu": {},
212 |    "number_sections": true,
213 |    "sideBar": true,
214 |    "skip_h1_title": false,
215 |    "title_cell": "Table of Contents",
216 |    "title_sidebar": "Contents",
217 |    "toc_cell": false,
218 |    "toc_position": {},
219 |    "toc_section_display": true,
220 |    "toc_window_display": false
221 |   }
222 |  },
223 |  "nbformat": 4,
224 |  "nbformat_minor": 2
225 | }
226 | 


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/.ipynb_checkpoints/自动化-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 25,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "<class 'pandas.core.frame.DataFrame'>\n",
 13 |       "RangeIndex: 3744 entries, 0 to 3743\n",
 14 |       "Data columns (total 7 columns):\n",
 15 |       "商品ID    3478 non-null float64\n",
 16 |       "类别ID    3478 non-null float64\n",
 17 |       "门店编号    3478 non-null object\n",
 18 |       "单价      3478 non-null float64\n",
 19 |       "销量      3478 non-null float64\n",
 20 |       "成交时间    3478 non-null datetime64[ns]\n",
 21 |       "订单ID    3478 non-null object\n",
 22 |       "dtypes: datetime64[ns](1), float64(4), object(2)\n",
 23 |       "memory usage: 204.8+ KB\n"
 24 |      ]
 25 |     }
 26 |    ],
 27 |    "source": [
 28 |     "import pandas as pd\n",
 29 |     "from datetime import datetime\n",
 30 |     "\n",
 31 |     "data=pd.read_csv(\"order-14.1.csv\",parse_dates=[\"成交时间\"],encoding='gbk')\n",
 32 |     "data.head()\n",
 33 |     "# print(data.head(5))\n",
 34 |     "# 查看源数据类型\n",
 35 |     "data.info()"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": 17,
 41 |    "metadata": {},
 42 |    "outputs": [
 43 |     {
 44 |      "name": "stdout",
 45 |      "output_type": "stream",
 46 |      "text": [
 47 |       "本月销售额为：10412.78,客流量为：343,客单价为：30.36\n"
 48 |      ]
 49 |     }
 50 |    ],
 51 |    "source": [
 52 |     "# 计算本月的相关的指标\n",
 53 |     "This_month=data[(data[\"成交时间\"]>=datetime(2018,2,1))&(data[\"成交时间\"]<=datetime(2018,2,28))]\n",
 54 |     "# 销售额计算\n",
 55 |     "sales_1=(This_month[\"销量\"]*This_month['单价']).sum()\n",
 56 |     "# 客流量计算\n",
 57 |     "traffic_1=This_month[\"订单ID\"].drop_duplicates().count()\n",
 58 |     "# 客单价计算\n",
 59 |     "s_t_1=sales_1/traffic_1\n",
 60 |     "print(\"本月销售额为：{:.2f},客流量为：{},客单价为：{:.2f}\".format(sales_1,traffic_1,s_t_1))\n"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": 19,
 66 |    "metadata": {},
 67 |    "outputs": [
 68 |     {
 69 |      "name": "stdout",
 70 |      "output_type": "stream",
 71 |      "text": [
 72 |       "本月销售额为：9940.97,客流量为：315,客单价为：31.56\n"
 73 |      ]
 74 |     }
 75 |    ],
 76 |    "source": [
 77 |     "# 计算上月相关指标\n",
 78 |     "last_month=data[(data[\"成交时间\"]>=datetime(2018,1,1))&(data[\"成交时间\"]<=datetime(2018,1,31))]\n",
 79 |     "\n",
 80 |     "# 销售额计算\n",
 81 |     "sales_2=(last_month[\"销量\"]*last_month['单价']).sum()\n",
 82 |     "# 客流量计算\n",
 83 |     "traffic_2=last_month[\"订单ID\"].drop_duplicates().count()\n",
 84 |     "# 客单价计算\n",
 85 |     "s_t_2=sales_2/traffic_2\n",
 86 |     "print(\"本月销售额为：{:.2f},客流量为：{},客单价为：{:.2f}\".format(sales_2,traffic_2,s_t_2))\n"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 20,
 92 |    "metadata": {},
 93 |    "outputs": [
 94 |     {
 95 |      "name": "stdout",
 96 |      "output_type": "stream",
 97 |      "text": [
 98 |       "本月销售额为：8596.31,客流量为：262,客单价为：32.81\n"
 99 |      ]
100 |     }
101 |    ],
102 |    "source": [
103 |     "# 计算去年同期相关指标\n",
104 |     "same_month=data[(data[\"成交时间\"]>=datetime(2017,2,1))&(data[\"成交时间\"]<=datetime(2017,2,28))]\n",
105 |     "\n",
106 |     "sales_3=(same_month[\"销量\"]*same_month[\"单价\"]).sum()\n",
107 |     "\n",
108 |     "traffic_3=same_month[\"订单ID\"].drop_duplicates().count()\n",
109 |     "s_t_3=sales_3/traffic_3\n",
110 |     "print(\"本月销售额为：{:.2f},客流量为：{},客单价为：{:.2f}\".format(sales_3,traffic_3,s_t_3))\n"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 29,
116 |    "metadata": {},
117 |    "outputs": [
118 |     {
119 |      "name": "stdout",
120 |      "output_type": "stream",
121 |      "text": [
122 |       "10412.78007 343 30.357959387755105\n",
123 |       "9940.97291 315 31.55864415873016\n",
124 |       "8596.313470000001 262 32.810356755725195\n"
125 |      ]
126 |     }
127 |    ],
128 |    "source": [
129 |     "# 利用函数提高编码效率\n",
130 |     "def get_month_data(data):\n",
131 |     "    sale=(data[\"销量\"]*data[\"单价\"]).sum()\n",
132 |     "    traffic=data[\"订单ID\"].drop_duplicates().count()\n",
133 |     "    s_t=sale/traffic\n",
134 |     "    return (sale,traffic,s_t)\n",
135 |     "\n",
136 |     "# 本月相关指数\n",
137 |     "sales_1,traffic_1,s_t_1=get_month_data(This_month)\n",
138 |     "print(sales_1,traffic_1,s_t_1)\n",
139 |     "\n",
140 |     "# 上月相关指数\n",
141 |     "sales_2,traffic_2,s_t_2=get_month_data(last_month)\n",
142 |     "print(sales_2,traffic_2,s_t_2)\n",
143 |     "\n",
144 |     "# 去年同期相关指数\n",
145 |     "sales_3,traffic_3,s_t_3=get_month_data(same_month)\n",
146 |     "print(sales_3,traffic_3,s_t_3)\n"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 36,
152 |    "metadata": {},
153 |    "outputs": [
154 |     {
155 |      "name": "stdout",
156 |      "output_type": "stream",
157 |      "text": [
158 |       "             本月累计         上月同期         去年同期\n",
159 |       "销售额  10412.780070  9940.972910  8596.313470\n",
160 |       "客流量    343.000000   315.000000   262.000000\n",
161 |       "客单价     30.357959    31.558644    32.810357\n",
162 |       "             本月累计         上月同期         去年同期        环比        同比\n",
163 |       "销售额  10412.780070  9940.972910  8596.313470  0.047461  0.211308\n",
164 |       "客流量    343.000000   315.000000   262.000000  0.088889  0.309160\n",
165 |       "客单价     30.357959    31.558644    32.810357 -0.038046 -0.074745\n"
166 |      ]
167 |     }
168 |    ],
169 |    "source": [
170 |     "report=pd.DataFrame([[sales_1,sales_2,sales_3],[traffic_1,traffic_2,traffic_3],[s_t_1,s_t_2,s_t_3]],columns=[\"本月累计\",\"上月同期\",\"去年同期\"],index=[\"销售额\",\"客流量\",\"客单价\"])\n",
171 |     "print(report)\n",
172 |     "# 添加同比和环比字段\n",
173 |     "report[\"环比\"]=report[\"本月累计\"]/report[\"上月同期\"]-1\n",
174 |     "\n",
175 |     "report[\"同比\"]=report[\"本月累计\"]/report[\"去年同期\"]-1\n",
176 |     "\n",
177 |     "print(report)"
178 |    ]
179 |   },
180 |   {
181 |    "cell_type": "code",
182 |    "execution_count": 37,
183 |    "metadata": {},
184 |    "outputs": [],
185 |    "source": [
186 |     "# 将结果导出本地\n",
187 |     "report.to_csv(\"order.csv\",encoding=\"utf-8-sig\")"
188 |    ]
189 |   }
190 |  ],
191 |  "metadata": {
192 |   "kernelspec": {
193 |    "display_name": "Python 3",
194 |    "language": "python",
195 |    "name": "python3"
196 |   },
197 |   "language_info": {
198 |    "codemirror_mode": {
199 |     "name": "ipython",
200 |     "version": 3
201 |    },
202 |    "file_extension": ".py",
203 |    "mimetype": "text/x-python",
204 |    "name": "python",
205 |    "nbconvert_exporter": "python",
206 |    "pygments_lexer": "ipython3",
207 |    "version": "3.7.3"
208 |   },
209 |   "toc": {
210 |    "base_numbering": 1,
211 |    "nav_menu": {},
212 |    "number_sections": true,
213 |    "sideBar": true,
214 |    "skip_h1_title": false,
215 |    "title_cell": "Table of Contents",
216 |    "title_sidebar": "Contents",
217 |    "toc_cell": false,
218 |    "toc_position": {},
219 |    "toc_section_display": true,
220 |    "toc_window_display": false
221 |   }
222 |  },
223 |  "nbformat": 4,
224 |  "nbformat_minor": 2
225 | }
226 | 


--------------------------------------------------------------------------------
/Supermarket.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 14,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "data": {
 10 |       "text/html": [
 11 |        "<div>\n",
 12 |        "<style scoped>\n",
 13 |        "    .dataframe tbody tr th:only-of-type {\n",
 14 |        "        vertical-align: middle;\n",
 15 |        "    }\n",
 16 |        "\n",
 17 |        "    .dataframe tbody tr th {\n",
 18 |        "        vertical-align: top;\n",
 19 |        "    }\n",
 20 |        "\n",
 21 |        "    .dataframe thead th {\n",
 22 |        "        text-align: right;\n",
 23 |        "    }\n",
 24 |        "</style>\n",
 25 |        "<table border=\"1\" class=\"dataframe\">\n",
 26 |        "  <thead>\n",
 27 |        "    <tr style=\"text-align: right;\">\n",
 28 |        "      <th></th>\n",
 29 |        "      <th>商品ID</th>\n",
 30 |        "      <th>类别ID</th>\n",
 31 |        "      <th>门店编号</th>\n",
 32 |        "      <th>单价</th>\n",
 33 |        "      <th>销量</th>\n",
 34 |        "      <th>成交时间</th>\n",
 35 |        "      <th>订单ID</th>\n",
 36 |        "    </tr>\n",
 37 |        "  </thead>\n",
 38 |        "  <tbody>\n",
 39 |        "    <tr>\n",
 40 |        "      <th>0</th>\n",
 41 |        "      <td>30006206</td>\n",
 42 |        "      <td>915000003</td>\n",
 43 |        "      <td>CDNL</td>\n",
 44 |        "      <td>25.23</td>\n",
 45 |        "      <td>0.328</td>\n",
 46 |        "      <td>2017-01-03 09:56:00</td>\n",
 47 |        "      <td>20170103CDLG000210052759</td>\n",
 48 |        "    </tr>\n",
 49 |        "    <tr>\n",
 50 |        "      <th>1</th>\n",
 51 |        "      <td>30163281</td>\n",
 52 |        "      <td>914010000</td>\n",
 53 |        "      <td>CDNL</td>\n",
 54 |        "      <td>2.00</td>\n",
 55 |        "      <td>2.000</td>\n",
 56 |        "      <td>2017-01-03 09:56:00</td>\n",
 57 |        "      <td>20170103CDLG000210052759</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>2</th>\n",
 61 |        "      <td>30200518</td>\n",
 62 |        "      <td>922000000</td>\n",
 63 |        "      <td>CDNL</td>\n",
 64 |        "      <td>19.62</td>\n",
 65 |        "      <td>0.230</td>\n",
 66 |        "      <td>2017-01-03 09:56:00</td>\n",
 67 |        "      <td>20170103CDLG000210052759</td>\n",
 68 |        "    </tr>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>3</th>\n",
 71 |        "      <td>29989105</td>\n",
 72 |        "      <td>922000000</td>\n",
 73 |        "      <td>CDNL</td>\n",
 74 |        "      <td>2.80</td>\n",
 75 |        "      <td>2.044</td>\n",
 76 |        "      <td>2017-01-03 09:56:00</td>\n",
 77 |        "      <td>20170103CDLG000210052759</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>4</th>\n",
 81 |        "      <td>30179558</td>\n",
 82 |        "      <td>915000100</td>\n",
 83 |        "      <td>CDNL</td>\n",
 84 |        "      <td>47.41</td>\n",
 85 |        "      <td>0.226</td>\n",
 86 |        "      <td>2017-01-03 09:56:00</td>\n",
 87 |        "      <td>20170103CDLG000210052759</td>\n",
 88 |        "    </tr>\n",
 89 |        "  </tbody>\n",
 90 |        "</table>\n",
 91 |        "</div>"
 92 |       ],
 93 |       "text/plain": [
 94 |        "       商品ID       类别ID  门店编号     单价     销量                成交时间  \\\n",
 95 |        "0  30006206  915000003  CDNL  25.23  0.328 2017-01-03 09:56:00   \n",
 96 |        "1  30163281  914010000  CDNL   2.00  2.000 2017-01-03 09:56:00   \n",
 97 |        "2  30200518  922000000  CDNL  19.62  0.230 2017-01-03 09:56:00   \n",
 98 |        "3  29989105  922000000  CDNL   2.80  2.044 2017-01-03 09:56:00   \n",
 99 |        "4  30179558  915000100  CDNL  47.41  0.226 2017-01-03 09:56:00   \n",
100 |        "\n",
101 |        "                       订单ID  \n",
102 |        "0  20170103CDLG000210052759  \n",
103 |        "1  20170103CDLG000210052759  \n",
104 |        "2  20170103CDLG000210052759  \n",
105 |        "3  20170103CDLG000210052759  \n",
106 |        "4  20170103CDLG000210052759  "
107 |       ]
108 |      },
109 |      "execution_count": 14,
110 |      "metadata": {},
111 |      "output_type": "execute_result"
112 |     }
113 |    ],
114 |    "source": [
115 |     "import pandas as pd\n",
116 |     "from datetime import datetime\n",
117 |     "# 导入数据源\n",
118 |     "data=pd.read_csv(\"order-14.3.csv\",parse_dates=[\"成交时间\"],encoding='gbk')\n",
119 |     "data.head()\n",
120 |     "# data.shape"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 20,
126 |    "metadata": {},
127 |    "outputs": [
128 |     {
129 |      "data": {
130 |       "text/html": [
131 |        "<div>\n",
132 |        "<style scoped>\n",
133 |        "    .dataframe tbody tr th:only-of-type {\n",
134 |        "        vertical-align: middle;\n",
135 |        "    }\n",
136 |        "\n",
137 |        "    .dataframe tbody tr th {\n",
138 |        "        vertical-align: top;\n",
139 |        "    }\n",
140 |        "\n",
141 |        "    .dataframe thead th {\n",
142 |        "        text-align: right;\n",
143 |        "    }\n",
144 |        "</style>\n",
145 |        "<table border=\"1\" class=\"dataframe\">\n",
146 |        "  <thead>\n",
147 |        "    <tr style=\"text-align: right;\">\n",
148 |        "      <th></th>\n",
149 |        "      <th>类别ID</th>\n",
150 |        "      <th>销量</th>\n",
151 |        "    </tr>\n",
152 |        "  </thead>\n",
153 |        "  <tbody>\n",
154 |        "    <tr>\n",
155 |        "      <th>240</th>\n",
156 |        "      <td>922000003</td>\n",
157 |        "      <td>425.328</td>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <th>239</th>\n",
161 |        "      <td>922000002</td>\n",
162 |        "      <td>206.424</td>\n",
163 |        "    </tr>\n",
164 |        "    <tr>\n",
165 |        "      <th>251</th>\n",
166 |        "      <td>923000006</td>\n",
167 |        "      <td>190.294</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>216</th>\n",
171 |        "      <td>915030104</td>\n",
172 |        "      <td>175.059</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>238</th>\n",
176 |        "      <td>922000001</td>\n",
177 |        "      <td>121.355</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>367</th>\n",
181 |        "      <td>960000000</td>\n",
182 |        "      <td>121.000</td>\n",
183 |        "    </tr>\n",
184 |        "    <tr>\n",
185 |        "      <th>234</th>\n",
186 |        "      <td>920090000</td>\n",
187 |        "      <td>111.565</td>\n",
188 |        "    </tr>\n",
189 |        "    <tr>\n",
190 |        "      <th>249</th>\n",
191 |        "      <td>923000002</td>\n",
192 |        "      <td>91.847</td>\n",
193 |        "    </tr>\n",
194 |        "    <tr>\n",
195 |        "      <th>237</th>\n",
196 |        "      <td>922000000</td>\n",
197 |        "      <td>86.395</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>247</th>\n",
201 |        "      <td>923000000</td>\n",
202 |        "      <td>85.845</td>\n",
203 |        "    </tr>\n",
204 |        "  </tbody>\n",
205 |        "</table>\n",
206 |        "</div>"
207 |       ],
208 |       "text/plain": [
209 |        "          类别ID       销量\n",
210 |        "240  922000003  425.328\n",
211 |        "239  922000002  206.424\n",
212 |        "251  923000006  190.294\n",
213 |        "216  915030104  175.059\n",
214 |        "238  922000001  121.355\n",
215 |        "367  960000000  121.000\n",
216 |        "234  920090000  111.565\n",
217 |        "249  923000002   91.847\n",
218 |        "237  922000000   86.395\n",
219 |        "247  923000000   85.845"
220 |       ]
221 |      },
222 |      "execution_count": 20,
223 |      "metadata": {},
224 |      "output_type": "execute_result"
225 |     }
226 |    ],
227 |    "source": [
228 |     "# 哪些类别的商品比较畅销\n",
229 |     "# ascending=False 降序\n",
230 |     "data.groupby(\"类别ID\")[\"销量\"].sum().reset_index().sort_values(by=\"销量\",ascending=False).head(10)"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 22,
236 |    "metadata": {},
237 |    "outputs": [
238 |     {
239 |      "data": {
240 |       "text/html": [
241 |        "<div>\n",
242 |        "<style scoped>\n",
243 |        "    .dataframe tbody tr th:only-of-type {\n",
244 |        "        vertical-align: middle;\n",
245 |        "    }\n",
246 |        "\n",
247 |        "    .dataframe tbody tr th {\n",
248 |        "        vertical-align: top;\n",
249 |        "    }\n",
250 |        "\n",
251 |        "    .dataframe thead th {\n",
252 |        "        text-align: right;\n",
253 |        "    }\n",
254 |        "</style>\n",
255 |        "<table border=\"1\" class=\"dataframe\">\n",
256 |        "  <thead>\n",
257 |        "    <tr style=\"text-align: right;\">\n",
258 |        "      <th></th>\n",
259 |        "      <th>商品ID</th>\n",
260 |        "      <th>销量</th>\n",
261 |        "    </tr>\n",
262 |        "  </thead>\n",
263 |        "  <tbody>\n",
264 |        "    <tr>\n",
265 |        "      <th>8</th>\n",
266 |        "      <td>29989059</td>\n",
267 |        "      <td>391.549</td>\n",
268 |        "    </tr>\n",
269 |        "    <tr>\n",
270 |        "      <th>18</th>\n",
271 |        "      <td>29989072</td>\n",
272 |        "      <td>102.876</td>\n",
273 |        "    </tr>\n",
274 |        "    <tr>\n",
275 |        "      <th>469</th>\n",
276 |        "      <td>30022232</td>\n",
277 |        "      <td>101.000</td>\n",
278 |        "    </tr>\n",
279 |        "    <tr>\n",
280 |        "      <th>523</th>\n",
281 |        "      <td>30031960</td>\n",
282 |        "      <td>99.998</td>\n",
283 |        "    </tr>\n",
284 |        "    <tr>\n",
285 |        "      <th>57</th>\n",
286 |        "      <td>29989157</td>\n",
287 |        "      <td>72.453</td>\n",
288 |        "    </tr>\n",
289 |        "    <tr>\n",
290 |        "      <th>476</th>\n",
291 |        "      <td>30023041</td>\n",
292 |        "      <td>64.416</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>505</th>\n",
296 |        "      <td>30026255</td>\n",
297 |        "      <td>62.375</td>\n",
298 |        "    </tr>\n",
299 |        "    <tr>\n",
300 |        "      <th>7</th>\n",
301 |        "      <td>29989058</td>\n",
302 |        "      <td>56.052</td>\n",
303 |        "    </tr>\n",
304 |        "    <tr>\n",
305 |        "      <th>510</th>\n",
306 |        "      <td>30027007</td>\n",
307 |        "      <td>48.757</td>\n",
308 |        "    </tr>\n",
309 |        "    <tr>\n",
310 |        "      <th>903</th>\n",
311 |        "      <td>30171264</td>\n",
312 |        "      <td>45.000</td>\n",
313 |        "    </tr>\n",
314 |        "  </tbody>\n",
315 |        "</table>\n",
316 |        "</div>"
317 |       ],
318 |       "text/plain": [
319 |        "         商品ID       销量\n",
320 |        "8    29989059  391.549\n",
321 |        "18   29989072  102.876\n",
322 |        "469  30022232  101.000\n",
323 |        "523  30031960   99.998\n",
324 |        "57   29989157   72.453\n",
325 |        "476  30023041   64.416\n",
326 |        "505  30026255   62.375\n",
327 |        "7    29989058   56.052\n",
328 |        "510  30027007   48.757\n",
329 |        "903  30171264   45.000"
330 |       ]
331 |      },
332 |      "execution_count": 22,
333 |      "metadata": {},
334 |      "output_type": "execute_result"
335 |     }
336 |    ],
337 |    "source": [
338 |     "# 哪些商品比较畅销\n",
339 |     "pd.pivot_table(data,index=\"商品ID\",values=\"销量\",aggfunc=\"sum\").reset_index().sort_values(by=\"销量\",ascending=False).head(10)"
340 |    ]
341 |   },
342 |   {
343 |    "cell_type": "code",
344 |    "execution_count": 25,
345 |    "metadata": {},
346 |    "outputs": [
347 |     {
348 |      "data": {
349 |       "text/plain": [
350 |        "门店编号\n",
351 |        "CDLG    10908.82612\n",
352 |        "CDNL     8059.47867\n",
353 |        "CDXL     9981.76166\n",
354 |        "Name: 销售额, dtype: float64"
355 |       ]
356 |      },
357 |      "execution_count": 25,
358 |      "metadata": {},
359 |      "output_type": "execute_result"
360 |     }
361 |    ],
362 |    "source": [
363 |     "# 不同门店的销售额占比\n",
364 |     "data[\"销售额\"]=data[\"销量\"]*data[\"单价\"]\n",
365 |     "data.groupby(\"门店编号\")[\"销售额\"].sum()\n"
366 |    ]
367 |   },
368 |   {
369 |    "cell_type": "code",
370 |    "execution_count": 29,
371 |    "metadata": {},
372 |    "outputs": [
373 |     {
374 |      "data": {
375 |       "text/plain": [
376 |        "门店编号\n",
377 |        "CDLG    0.376815\n",
378 |        "CDNL    0.278392\n",
379 |        "CDXL    0.344792\n",
380 |        "Name: 销售额, dtype: float64"
381 |       ]
382 |      },
383 |      "execution_count": 29,
384 |      "metadata": {},
385 |      "output_type": "execute_result"
386 |     }
387 |    ],
388 |    "source": [
389 |     "data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()"
390 |    ]
391 |   },
392 |   {
393 |    "cell_type": "code",
394 |    "execution_count": 30,
395 |    "metadata": {},
396 |    "outputs": [
397 |     {
398 |      "data": {
399 |       "text/plain": [
400 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8f0cfeef0>"
401 |       ]
402 |      },
403 |      "execution_count": 30,
404 |      "metadata": {},
405 |      "output_type": "execute_result"
406 |     },
407 |     {
408 |      "data": {
409 |       "image/png": 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\n",
410 |       "text/plain": [
411 |        "<Figure size 432x288 with 1 Axes>"
412 |       ]
413 |      },
414 |      "metadata": {},
415 |      "output_type": "display_data"
416 |     }
417 |    ],
418 |    "source": [
419 |     "import matplotlib as mpl\n",
420 |     "\n",
421 |     "mpl.rcParams[\"font.family\"]=\"SimHei\"\n",
422 |     "mpl.rcParams[\"axes.unicode_minus\"]=False\n",
423 |     "(data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()).plot.pie()"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "code",
428 |    "execution_count": 37,
429 |    "metadata": {},
430 |    "outputs": [
431 |     {
432 |      "data": {
433 |       "text/plain": [
434 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ee9ce898>"
435 |       ]
436 |      },
437 |      "execution_count": 37,
438 |      "metadata": {},
439 |      "output_type": "execute_result"
440 |     },
441 |     {
442 |      "data": {
443 |       "image/png": 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\n",
444 |       "text/plain": [
445 |        "<Figure size 432x288 with 1 Axes>"
446 |       ]
447 |      },
448 |      "metadata": {
449 |       "needs_background": "light"
450 |      },
451 |      "output_type": "display_data"
452 |     }
453 |    ],
454 |    "source": [
455 |     "# 哪个时间段是超市的客流高封期\n",
456 |     "# 利用自定义时间格式函数strftime提取小时数\n",
457 |     "data[\"小时\"]=data[\"成交时间\"].map(lambda x:int(x.strftime(\"%H\")))\n",
458 |     "# 对小时和订单去重\n",
459 |     "traffic=data[[\"小时\",\"订单ID\"]].drop_duplicates()\n",
460 |     "# 求每小时的客流量\n",
461 |     "traffic.groupby(\"小时\")[\"订单ID\"].count().plot()"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": null,
467 |    "metadata": {},
468 |    "outputs": [],
469 |    "source": []
470 |   }
471 |  ],
472 |  "metadata": {
473 |   "kernelspec": {
474 |    "display_name": "Python 3",
475 |    "language": "python",
476 |    "name": "python3"
477 |   },
478 |   "language_info": {
479 |    "codemirror_mode": {
480 |     "name": "ipython",
481 |     "version": 3
482 |    },
483 |    "file_extension": ".py",
484 |    "mimetype": "text/x-python",
485 |    "name": "python",
486 |    "nbconvert_exporter": "python",
487 |    "pygments_lexer": "ipython3",
488 |    "version": "3.7.3"
489 |   },
490 |   "toc": {
491 |    "base_numbering": 1,
492 |    "nav_menu": {},
493 |    "number_sections": true,
494 |    "sideBar": true,
495 |    "skip_h1_title": false,
496 |    "title_cell": "Table of Contents",
497 |    "title_sidebar": "Contents",
498 |    "toc_cell": false,
499 |    "toc_position": {},
500 |    "toc_section_display": true,
501 |    "toc_window_display": false
502 |   }
503 |  },
504 |  "nbformat": 4,
505 |  "nbformat_minor": 2
506 | }
507 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Supermarket-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 14,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "data": {
 10 |       "text/html": [
 11 |        "<div>\n",
 12 |        "<style scoped>\n",
 13 |        "    .dataframe tbody tr th:only-of-type {\n",
 14 |        "        vertical-align: middle;\n",
 15 |        "    }\n",
 16 |        "\n",
 17 |        "    .dataframe tbody tr th {\n",
 18 |        "        vertical-align: top;\n",
 19 |        "    }\n",
 20 |        "\n",
 21 |        "    .dataframe thead th {\n",
 22 |        "        text-align: right;\n",
 23 |        "    }\n",
 24 |        "</style>\n",
 25 |        "<table border=\"1\" class=\"dataframe\">\n",
 26 |        "  <thead>\n",
 27 |        "    <tr style=\"text-align: right;\">\n",
 28 |        "      <th></th>\n",
 29 |        "      <th>商品ID</th>\n",
 30 |        "      <th>类别ID</th>\n",
 31 |        "      <th>门店编号</th>\n",
 32 |        "      <th>单价</th>\n",
 33 |        "      <th>销量</th>\n",
 34 |        "      <th>成交时间</th>\n",
 35 |        "      <th>订单ID</th>\n",
 36 |        "    </tr>\n",
 37 |        "  </thead>\n",
 38 |        "  <tbody>\n",
 39 |        "    <tr>\n",
 40 |        "      <th>0</th>\n",
 41 |        "      <td>30006206</td>\n",
 42 |        "      <td>915000003</td>\n",
 43 |        "      <td>CDNL</td>\n",
 44 |        "      <td>25.23</td>\n",
 45 |        "      <td>0.328</td>\n",
 46 |        "      <td>2017-01-03 09:56:00</td>\n",
 47 |        "      <td>20170103CDLG000210052759</td>\n",
 48 |        "    </tr>\n",
 49 |        "    <tr>\n",
 50 |        "      <th>1</th>\n",
 51 |        "      <td>30163281</td>\n",
 52 |        "      <td>914010000</td>\n",
 53 |        "      <td>CDNL</td>\n",
 54 |        "      <td>2.00</td>\n",
 55 |        "      <td>2.000</td>\n",
 56 |        "      <td>2017-01-03 09:56:00</td>\n",
 57 |        "      <td>20170103CDLG000210052759</td>\n",
 58 |        "    </tr>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>2</th>\n",
 61 |        "      <td>30200518</td>\n",
 62 |        "      <td>922000000</td>\n",
 63 |        "      <td>CDNL</td>\n",
 64 |        "      <td>19.62</td>\n",
 65 |        "      <td>0.230</td>\n",
 66 |        "      <td>2017-01-03 09:56:00</td>\n",
 67 |        "      <td>20170103CDLG000210052759</td>\n",
 68 |        "    </tr>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>3</th>\n",
 71 |        "      <td>29989105</td>\n",
 72 |        "      <td>922000000</td>\n",
 73 |        "      <td>CDNL</td>\n",
 74 |        "      <td>2.80</td>\n",
 75 |        "      <td>2.044</td>\n",
 76 |        "      <td>2017-01-03 09:56:00</td>\n",
 77 |        "      <td>20170103CDLG000210052759</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>4</th>\n",
 81 |        "      <td>30179558</td>\n",
 82 |        "      <td>915000100</td>\n",
 83 |        "      <td>CDNL</td>\n",
 84 |        "      <td>47.41</td>\n",
 85 |        "      <td>0.226</td>\n",
 86 |        "      <td>2017-01-03 09:56:00</td>\n",
 87 |        "      <td>20170103CDLG000210052759</td>\n",
 88 |        "    </tr>\n",
 89 |        "  </tbody>\n",
 90 |        "</table>\n",
 91 |        "</div>"
 92 |       ],
 93 |       "text/plain": [
 94 |        "       商品ID       类别ID  门店编号     单价     销量                成交时间  \\\n",
 95 |        "0  30006206  915000003  CDNL  25.23  0.328 2017-01-03 09:56:00   \n",
 96 |        "1  30163281  914010000  CDNL   2.00  2.000 2017-01-03 09:56:00   \n",
 97 |        "2  30200518  922000000  CDNL  19.62  0.230 2017-01-03 09:56:00   \n",
 98 |        "3  29989105  922000000  CDNL   2.80  2.044 2017-01-03 09:56:00   \n",
 99 |        "4  30179558  915000100  CDNL  47.41  0.226 2017-01-03 09:56:00   \n",
100 |        "\n",
101 |        "                       订单ID  \n",
102 |        "0  20170103CDLG000210052759  \n",
103 |        "1  20170103CDLG000210052759  \n",
104 |        "2  20170103CDLG000210052759  \n",
105 |        "3  20170103CDLG000210052759  \n",
106 |        "4  20170103CDLG000210052759  "
107 |       ]
108 |      },
109 |      "execution_count": 14,
110 |      "metadata": {},
111 |      "output_type": "execute_result"
112 |     }
113 |    ],
114 |    "source": [
115 |     "import pandas as pd\n",
116 |     "from datetime import datetime\n",
117 |     "# 导入数据源\n",
118 |     "data=pd.read_csv(\"order-14.3.csv\",parse_dates=[\"成交时间\"],encoding='gbk')\n",
119 |     "data.head()\n",
120 |     "# data.shape"
121 |    ]
122 |   },
123 |   {
124 |    "cell_type": "code",
125 |    "execution_count": 20,
126 |    "metadata": {},
127 |    "outputs": [
128 |     {
129 |      "data": {
130 |       "text/html": [
131 |        "<div>\n",
132 |        "<style scoped>\n",
133 |        "    .dataframe tbody tr th:only-of-type {\n",
134 |        "        vertical-align: middle;\n",
135 |        "    }\n",
136 |        "\n",
137 |        "    .dataframe tbody tr th {\n",
138 |        "        vertical-align: top;\n",
139 |        "    }\n",
140 |        "\n",
141 |        "    .dataframe thead th {\n",
142 |        "        text-align: right;\n",
143 |        "    }\n",
144 |        "</style>\n",
145 |        "<table border=\"1\" class=\"dataframe\">\n",
146 |        "  <thead>\n",
147 |        "    <tr style=\"text-align: right;\">\n",
148 |        "      <th></th>\n",
149 |        "      <th>类别ID</th>\n",
150 |        "      <th>销量</th>\n",
151 |        "    </tr>\n",
152 |        "  </thead>\n",
153 |        "  <tbody>\n",
154 |        "    <tr>\n",
155 |        "      <th>240</th>\n",
156 |        "      <td>922000003</td>\n",
157 |        "      <td>425.328</td>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <th>239</th>\n",
161 |        "      <td>922000002</td>\n",
162 |        "      <td>206.424</td>\n",
163 |        "    </tr>\n",
164 |        "    <tr>\n",
165 |        "      <th>251</th>\n",
166 |        "      <td>923000006</td>\n",
167 |        "      <td>190.294</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>216</th>\n",
171 |        "      <td>915030104</td>\n",
172 |        "      <td>175.059</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>238</th>\n",
176 |        "      <td>922000001</td>\n",
177 |        "      <td>121.355</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>367</th>\n",
181 |        "      <td>960000000</td>\n",
182 |        "      <td>121.000</td>\n",
183 |        "    </tr>\n",
184 |        "    <tr>\n",
185 |        "      <th>234</th>\n",
186 |        "      <td>920090000</td>\n",
187 |        "      <td>111.565</td>\n",
188 |        "    </tr>\n",
189 |        "    <tr>\n",
190 |        "      <th>249</th>\n",
191 |        "      <td>923000002</td>\n",
192 |        "      <td>91.847</td>\n",
193 |        "    </tr>\n",
194 |        "    <tr>\n",
195 |        "      <th>237</th>\n",
196 |        "      <td>922000000</td>\n",
197 |        "      <td>86.395</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>247</th>\n",
201 |        "      <td>923000000</td>\n",
202 |        "      <td>85.845</td>\n",
203 |        "    </tr>\n",
204 |        "  </tbody>\n",
205 |        "</table>\n",
206 |        "</div>"
207 |       ],
208 |       "text/plain": [
209 |        "          类别ID       销量\n",
210 |        "240  922000003  425.328\n",
211 |        "239  922000002  206.424\n",
212 |        "251  923000006  190.294\n",
213 |        "216  915030104  175.059\n",
214 |        "238  922000001  121.355\n",
215 |        "367  960000000  121.000\n",
216 |        "234  920090000  111.565\n",
217 |        "249  923000002   91.847\n",
218 |        "237  922000000   86.395\n",
219 |        "247  923000000   85.845"
220 |       ]
221 |      },
222 |      "execution_count": 20,
223 |      "metadata": {},
224 |      "output_type": "execute_result"
225 |     }
226 |    ],
227 |    "source": [
228 |     "# 哪些类别的商品比较畅销\n",
229 |     "# ascending=False 降序\n",
230 |     "data.groupby(\"类别ID\")[\"销量\"].sum().reset_index().sort_values(by=\"销量\",ascending=False).head(10)"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 22,
236 |    "metadata": {},
237 |    "outputs": [
238 |     {
239 |      "data": {
240 |       "text/html": [
241 |        "<div>\n",
242 |        "<style scoped>\n",
243 |        "    .dataframe tbody tr th:only-of-type {\n",
244 |        "        vertical-align: middle;\n",
245 |        "    }\n",
246 |        "\n",
247 |        "    .dataframe tbody tr th {\n",
248 |        "        vertical-align: top;\n",
249 |        "    }\n",
250 |        "\n",
251 |        "    .dataframe thead th {\n",
252 |        "        text-align: right;\n",
253 |        "    }\n",
254 |        "</style>\n",
255 |        "<table border=\"1\" class=\"dataframe\">\n",
256 |        "  <thead>\n",
257 |        "    <tr style=\"text-align: right;\">\n",
258 |        "      <th></th>\n",
259 |        "      <th>商品ID</th>\n",
260 |        "      <th>销量</th>\n",
261 |        "    </tr>\n",
262 |        "  </thead>\n",
263 |        "  <tbody>\n",
264 |        "    <tr>\n",
265 |        "      <th>8</th>\n",
266 |        "      <td>29989059</td>\n",
267 |        "      <td>391.549</td>\n",
268 |        "    </tr>\n",
269 |        "    <tr>\n",
270 |        "      <th>18</th>\n",
271 |        "      <td>29989072</td>\n",
272 |        "      <td>102.876</td>\n",
273 |        "    </tr>\n",
274 |        "    <tr>\n",
275 |        "      <th>469</th>\n",
276 |        "      <td>30022232</td>\n",
277 |        "      <td>101.000</td>\n",
278 |        "    </tr>\n",
279 |        "    <tr>\n",
280 |        "      <th>523</th>\n",
281 |        "      <td>30031960</td>\n",
282 |        "      <td>99.998</td>\n",
283 |        "    </tr>\n",
284 |        "    <tr>\n",
285 |        "      <th>57</th>\n",
286 |        "      <td>29989157</td>\n",
287 |        "      <td>72.453</td>\n",
288 |        "    </tr>\n",
289 |        "    <tr>\n",
290 |        "      <th>476</th>\n",
291 |        "      <td>30023041</td>\n",
292 |        "      <td>64.416</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>505</th>\n",
296 |        "      <td>30026255</td>\n",
297 |        "      <td>62.375</td>\n",
298 |        "    </tr>\n",
299 |        "    <tr>\n",
300 |        "      <th>7</th>\n",
301 |        "      <td>29989058</td>\n",
302 |        "      <td>56.052</td>\n",
303 |        "    </tr>\n",
304 |        "    <tr>\n",
305 |        "      <th>510</th>\n",
306 |        "      <td>30027007</td>\n",
307 |        "      <td>48.757</td>\n",
308 |        "    </tr>\n",
309 |        "    <tr>\n",
310 |        "      <th>903</th>\n",
311 |        "      <td>30171264</td>\n",
312 |        "      <td>45.000</td>\n",
313 |        "    </tr>\n",
314 |        "  </tbody>\n",
315 |        "</table>\n",
316 |        "</div>"
317 |       ],
318 |       "text/plain": [
319 |        "         商品ID       销量\n",
320 |        "8    29989059  391.549\n",
321 |        "18   29989072  102.876\n",
322 |        "469  30022232  101.000\n",
323 |        "523  30031960   99.998\n",
324 |        "57   29989157   72.453\n",
325 |        "476  30023041   64.416\n",
326 |        "505  30026255   62.375\n",
327 |        "7    29989058   56.052\n",
328 |        "510  30027007   48.757\n",
329 |        "903  30171264   45.000"
330 |       ]
331 |      },
332 |      "execution_count": 22,
333 |      "metadata": {},
334 |      "output_type": "execute_result"
335 |     }
336 |    ],
337 |    "source": [
338 |     "# 哪些商品比较畅销\n",
339 |     "pd.pivot_table(data,index=\"商品ID\",values=\"销量\",aggfunc=\"sum\").reset_index().sort_values(by=\"销量\",ascending=False).head(10)"
340 |    ]
341 |   },
342 |   {
343 |    "cell_type": "code",
344 |    "execution_count": 25,
345 |    "metadata": {},
346 |    "outputs": [
347 |     {
348 |      "data": {
349 |       "text/plain": [
350 |        "门店编号\n",
351 |        "CDLG    10908.82612\n",
352 |        "CDNL     8059.47867\n",
353 |        "CDXL     9981.76166\n",
354 |        "Name: 销售额, dtype: float64"
355 |       ]
356 |      },
357 |      "execution_count": 25,
358 |      "metadata": {},
359 |      "output_type": "execute_result"
360 |     }
361 |    ],
362 |    "source": [
363 |     "# 不同门店的销售额占比\n",
364 |     "data[\"销售额\"]=data[\"销量\"]*data[\"单价\"]\n",
365 |     "data.groupby(\"门店编号\")[\"销售额\"].sum()\n"
366 |    ]
367 |   },
368 |   {
369 |    "cell_type": "code",
370 |    "execution_count": 29,
371 |    "metadata": {},
372 |    "outputs": [
373 |     {
374 |      "data": {
375 |       "text/plain": [
376 |        "门店编号\n",
377 |        "CDLG    0.376815\n",
378 |        "CDNL    0.278392\n",
379 |        "CDXL    0.344792\n",
380 |        "Name: 销售额, dtype: float64"
381 |       ]
382 |      },
383 |      "execution_count": 29,
384 |      "metadata": {},
385 |      "output_type": "execute_result"
386 |     }
387 |    ],
388 |    "source": [
389 |     "data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()"
390 |    ]
391 |   },
392 |   {
393 |    "cell_type": "code",
394 |    "execution_count": 30,
395 |    "metadata": {},
396 |    "outputs": [
397 |     {
398 |      "data": {
399 |       "text/plain": [
400 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8f0cfeef0>"
401 |       ]
402 |      },
403 |      "execution_count": 30,
404 |      "metadata": {},
405 |      "output_type": "execute_result"
406 |     },
407 |     {
408 |      "data": {
409 |       "image/png": 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\n",
410 |       "text/plain": [
411 |        "<Figure size 432x288 with 1 Axes>"
412 |       ]
413 |      },
414 |      "metadata": {},
415 |      "output_type": "display_data"
416 |     }
417 |    ],
418 |    "source": [
419 |     "import matplotlib as mpl\n",
420 |     "\n",
421 |     "mpl.rcParams[\"font.family\"]=\"SimHei\"\n",
422 |     "mpl.rcParams[\"axes.unicode_minus\"]=False\n",
423 |     "(data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()).plot.pie()"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "code",
428 |    "execution_count": 37,
429 |    "metadata": {},
430 |    "outputs": [
431 |     {
432 |      "data": {
433 |       "text/plain": [
434 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ee9ce898>"
435 |       ]
436 |      },
437 |      "execution_count": 37,
438 |      "metadata": {},
439 |      "output_type": "execute_result"
440 |     },
441 |     {
442 |      "data": {
443 |       "image/png": 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\n",
444 |       "text/plain": [
445 |        "<Figure size 432x288 with 1 Axes>"
446 |       ]
447 |      },
448 |      "metadata": {
449 |       "needs_background": "light"
450 |      },
451 |      "output_type": "display_data"
452 |     }
453 |    ],
454 |    "source": [
455 |     "# 哪个时间段是超市的客流高封期\n",
456 |     "# 利用自定义时间格式函数strftime提取小时数\n",
457 |     "data[\"小时\"]=data[\"成交时间\"].map(lambda x:int(x.strftime(\"%H\")))\n",
458 |     "# 对小时和订单去重\n",
459 |     "traffic=data[[\"小时\",\"订单ID\"]].drop_duplicates()\n",
460 |     "# 求每小时的客流量\n",
461 |     "traffic.groupby(\"小时\")[\"订单ID\"].count().plot()"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": null,
467 |    "metadata": {},
468 |    "outputs": [],
469 |    "source": []
470 |   }
471 |  ],
472 |  "metadata": {
473 |   "kernelspec": {
474 |    "display_name": "Python 3",
475 |    "language": "python",
476 |    "name": "python3"
477 |   },
478 |   "language_info": {
479 |    "codemirror_mode": {
480 |     "name": "ipython",
481 |     "version": 3
482 |    },
483 |    "file_extension": ".py",
484 |    "mimetype": "text/x-python",
485 |    "name": "python",
486 |    "nbconvert_exporter": "python",
487 |    "pygments_lexer": "ipython3",
488 |    "version": "3.7.3"
489 |   },
490 |   "toc": {
491 |    "base_numbering": 1,
492 |    "nav_menu": {},
493 |    "number_sections": true,
494 |    "sideBar": true,
495 |    "skip_h1_title": false,
496 |    "title_cell": "Table of Contents",
497 |    "title_sidebar": "Contents",
498 |    "toc_cell": false,
499 |    "toc_position": {},
500 |    "toc_section_display": true,
501 |    "toc_window_display": false
502 |   }
503 |  },
504 |  "nbformat": 4,
505 |  "nbformat_minor": 2
506 | }
507 | 


--------------------------------------------------------------------------------
/Bank.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 8,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "<class 'pandas.core.frame.DataFrame'>\n",
 13 |       "RangeIndex: 150000 entries, 0 to 149999\n",
 14 |       "Data columns (total 6 columns):\n",
 15 |       "用户ID    150000 non-null int64\n",
 16 |       "好坏客户    150000 non-null int64\n",
 17 |       "年龄      150000 non-null int64\n",
 18 |       "负债率     150000 non-null float64\n",
 19 |       "月收入     120269 non-null float64\n",
 20 |       "家属数量    146076 non-null float64\n",
 21 |       "dtypes: float64(3), int64(3)\n",
 22 |       "memory usage: 6.9 MB\n"
 23 |      ]
 24 |     }
 25 |    ],
 26 |    "source": [
 27 |     "import pandas as pd\n",
 28 |     "from datetime import datetime\n",
 29 |     "\n",
 30 |     "data=pd.read_csv(\"loan.csv\",encoding=\"gbk\")\n",
 31 |     "data.info()\n",
 32 |     "# print(data)"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 12,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "name": "stdout",
 42 |      "output_type": "stream",
 43 |      "text": [
 44 |       "<class 'pandas.core.frame.DataFrame'>\n",
 45 |       "RangeIndex: 150000 entries, 0 to 149999\n",
 46 |       "Data columns (total 6 columns):\n",
 47 |       "用户ID    150000 non-null int64\n",
 48 |       "好坏客户    150000 non-null int64\n",
 49 |       "年龄      150000 non-null int64\n",
 50 |       "负债率     150000 non-null float64\n",
 51 |       "月收入     150000 non-null float64\n",
 52 |       "家属数量    146076 non-null float64\n",
 53 |       "dtypes: float64(3), int64(3)\n",
 54 |       "memory usage: 6.9 MB\n",
 55 |       "None\n",
 56 |       "          用户ID  好坏客户  年龄          负债率           月收入  家属数量\n",
 57 |       "0            1     1  45     0.802982   9120.000000   2.0\n",
 58 |       "1            2     0  40     0.121876   2600.000000   1.0\n",
 59 |       "2            3     0  38     0.085113   3042.000000   0.0\n",
 60 |       "3            4     0  30     0.036050   3300.000000   0.0\n",
 61 |       "4            5     0  49     0.024926  63588.000000   0.0\n",
 62 |       "5            6     0  74     0.375607   3500.000000   1.0\n",
 63 |       "6            7     0  57  5710.000000   6670.221237   0.0\n",
 64 |       "7            8     0  39     0.209940   3500.000000   0.0\n",
 65 |       "8            9     0  27    46.000000   6670.221237   NaN\n",
 66 |       "9           10     0  57     0.606291  23684.000000   2.0\n",
 67 |       "10          11     0  30     0.309476   2500.000000   0.0\n",
 68 |       "11          12     0  51     0.531529   6501.000000   2.0\n",
 69 |       "12          13     0  46     0.298354  12454.000000   2.0\n",
 70 |       "13          14     1  40     0.382965  13700.000000   2.0\n",
 71 |       "14          15     0  76   477.000000      0.000000   0.0\n",
 72 |       "15          16     0  64     0.209892  11362.000000   2.0\n",
 73 |       "16          17     0  78  2058.000000   6670.221237   0.0\n",
 74 |       "17          18     0  53     0.188274   8800.000000   0.0\n",
 75 |       "18          19     0  43     0.527888   3280.000000   2.0\n",
 76 |       "19          20     0  25     0.065868    333.000000   0.0\n",
 77 |       "20          21     0  43     0.430046  12300.000000   0.0\n",
 78 |       "21          22     1  38     0.475841   3000.000000   2.0\n",
 79 |       "22          23     0  39     0.241104   2500.000000   0.0\n",
 80 |       "23          24     0  32     0.085512   7916.000000   0.0\n",
 81 |       "24          25     0  58     0.241622   2416.000000   0.0\n",
 82 |       "25          26     1  50     1.595253   4676.000000   1.0\n",
 83 |       "26          27     0  58     0.097672   8333.000000   0.0\n",
 84 |       "27          28     0  69     0.042383   2500.000000   1.0\n",
 85 |       "28          29     0  24     0.011761   3400.000000   0.0\n",
 86 |       "29          30     0  58     0.436103   5500.000000   0.0\n",
 87 |       "...        ...   ...  ..          ...           ...   ...\n",
 88 |       "149970  149971     0  58     0.253855  15500.000000   2.0\n",
 89 |       "149971  149972     0  83     0.013997   5000.000000   0.0\n",
 90 |       "149972  149973     0  42     0.008638   6945.000000   1.0\n",
 91 |       "149973  149974     0  44     0.494819   5500.000000   1.0\n",
 92 |       "149974  149975     0  61     0.603479   5000.000000   0.0\n",
 93 |       "149975  149976     0  58  2716.000000   6670.221237   0.0\n",
 94 |       "149976  149977     0  76    60.000000   6670.221237   0.0\n",
 95 |       "149977  149978     0  29   349.000000   6670.221237   0.0\n",
 96 |       "149978  149979     0  52     0.259496   2500.000000   0.0\n",
 97 |       "149979  149980     1  55     0.057235   8700.000000   0.0\n",
 98 |       "149980  149981     0  64     0.254976   5525.000000   0.0\n",
 99 |       "149981  149982     0  43     0.121752   6849.000000   4.0\n",
100 |       "149982  149983     0  37     0.250272   2760.000000   3.0\n",
101 |       "149983  149984     0  82     0.000800   5000.000000   0.0\n",
102 |       "149984  149985     0  84    25.000000   6670.221237   0.0\n",
103 |       "149985  149986     0  26     0.324962   1950.000000   0.0\n",
104 |       "149986  149987     0  49     0.080384   5000.000000   1.0\n",
105 |       "149987  149988     0  28     0.055692   3249.000000   0.0\n",
106 |       "149988  149989     0  31     0.347924   7515.000000   0.0\n",
107 |       "149989  149990     0  62     0.001408   9233.000000   3.0\n",
108 |       "149990  149991     0  46     0.609779   4335.000000   2.0\n",
109 |       "149991  149992     0  59     0.477658  10316.000000   0.0\n",
110 |       "149992  149993     0  50  4132.000000   6670.221237   3.0\n",
111 |       "149993  149994     0  22     0.000000    820.000000   0.0\n",
112 |       "149994  149995     0  50     0.404293   3400.000000   0.0\n",
113 |       "149995  149996     0  74     0.225131   2100.000000   0.0\n",
114 |       "149996  149997     0  44     0.716562   5584.000000   2.0\n",
115 |       "149997  149998     0  58  3870.000000   6670.221237   0.0\n",
116 |       "149998  149999     0  30     0.000000   5716.000000   0.0\n",
117 |       "149999  150000     0  64     0.249908   8158.000000   0.0\n",
118 |       "\n",
119 |       "[150000 rows x 6 columns]\n"
120 |      ]
121 |     }
122 |    ],
123 |    "source": [
124 |     "# 是不是收入越高的人坏账率越低\n",
125 |     "data=data.fillna({\"月收入\":data[\"月收入\"].mean()})\n",
126 |     "print(data.info())\n",
127 |     "print(data)"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": null,
133 |    "metadata": {},
134 |    "outputs": [],
135 |    "source": []
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": 11,
140 |    "metadata": {},
141 |    "outputs": [
142 |     {
143 |      "name": "stdout",
144 |      "output_type": "stream",
145 |      "text": [
146 |       "0           (5000.0, 10000.0]\n",
147 |       "1               (0.0, 5000.0]\n",
148 |       "2               (0.0, 5000.0]\n",
149 |       "3               (0.0, 5000.0]\n",
150 |       "4         (20000.0, 100000.0]\n",
151 |       "5               (0.0, 5000.0]\n",
152 |       "6           (5000.0, 10000.0]\n",
153 |       "7               (0.0, 5000.0]\n",
154 |       "8           (5000.0, 10000.0]\n",
155 |       "9         (20000.0, 100000.0]\n",
156 |       "10              (0.0, 5000.0]\n",
157 |       "11          (5000.0, 10000.0]\n",
158 |       "12         (10000.0, 15000.0]\n",
159 |       "13         (10000.0, 15000.0]\n",
160 |       "14                        NaN\n",
161 |       "15         (10000.0, 15000.0]\n",
162 |       "16          (5000.0, 10000.0]\n",
163 |       "17          (5000.0, 10000.0]\n",
164 |       "18              (0.0, 5000.0]\n",
165 |       "19              (0.0, 5000.0]\n",
166 |       "20         (10000.0, 15000.0]\n",
167 |       "21              (0.0, 5000.0]\n",
168 |       "22              (0.0, 5000.0]\n",
169 |       "23          (5000.0, 10000.0]\n",
170 |       "24              (0.0, 5000.0]\n",
171 |       "25              (0.0, 5000.0]\n",
172 |       "26          (5000.0, 10000.0]\n",
173 |       "27              (0.0, 5000.0]\n",
174 |       "28              (0.0, 5000.0]\n",
175 |       "29          (5000.0, 10000.0]\n",
176 |       "                 ...         \n",
177 |       "149970     (15000.0, 20000.0]\n",
178 |       "149971          (0.0, 5000.0]\n",
179 |       "149972      (5000.0, 10000.0]\n",
180 |       "149973      (5000.0, 10000.0]\n",
181 |       "149974          (0.0, 5000.0]\n",
182 |       "149975      (5000.0, 10000.0]\n",
183 |       "149976      (5000.0, 10000.0]\n",
184 |       "149977      (5000.0, 10000.0]\n",
185 |       "149978          (0.0, 5000.0]\n",
186 |       "149979      (5000.0, 10000.0]\n",
187 |       "149980      (5000.0, 10000.0]\n",
188 |       "149981      (5000.0, 10000.0]\n",
189 |       "149982          (0.0, 5000.0]\n",
190 |       "149983          (0.0, 5000.0]\n",
191 |       "149984      (5000.0, 10000.0]\n",
192 |       "149985          (0.0, 5000.0]\n",
193 |       "149986          (0.0, 5000.0]\n",
194 |       "149987          (0.0, 5000.0]\n",
195 |       "149988      (5000.0, 10000.0]\n",
196 |       "149989      (5000.0, 10000.0]\n",
197 |       "149990          (0.0, 5000.0]\n",
198 |       "149991     (10000.0, 15000.0]\n",
199 |       "149992      (5000.0, 10000.0]\n",
200 |       "149993          (0.0, 5000.0]\n",
201 |       "149994          (0.0, 5000.0]\n",
202 |       "149995          (0.0, 5000.0]\n",
203 |       "149996      (5000.0, 10000.0]\n",
204 |       "149997      (5000.0, 10000.0]\n",
205 |       "149998      (5000.0, 10000.0]\n",
206 |       "149999      (5000.0, 10000.0]\n",
207 |       "Name: 月收入, Length: 150000, dtype: category\n",
208 |       "Categories (5, interval[int64]): [(0, 5000] < (5000, 10000] < (10000, 15000] < (15000, 20000] < (20000, 100000]]\n"
209 |      ]
210 |     }
211 |    ],
212 |    "source": [
213 |     "cut_bins=[0,5000,10000,15000,20000,100000]\n",
214 |     "income_cut=pd.cut(data[\"月收入\"],cut_bins)\n",
215 |     "print(income_cut)"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": 15,
221 |    "metadata": {},
222 |    "outputs": [
223 |     {
224 |      "name": "stdout",
225 |      "output_type": "stream",
226 |      "text": [
227 |       "月收入\n",
228 |       "(0, 5000]          0.087543\n",
229 |       "(5000, 10000]      0.058308\n",
230 |       "(10000, 15000]     0.041964\n",
231 |       "(15000, 20000]     0.041811\n",
232 |       "(20000, 100000]    0.053615\n",
233 |       "Name: 好坏客户, dtype: float64\n"
234 |      ]
235 |     },
236 |     {
237 |      "data": {
238 |       "text/plain": [
239 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ce4ab588>"
240 |       ]
241 |      },
242 |      "execution_count": 15,
243 |      "metadata": {},
244 |      "output_type": "execute_result"
245 |     },
246 |     {
247 |      "data": {
248 |       "image/png": 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\n",
249 |       "text/plain": [
250 |        "<Figure size 432x288 with 1 Axes>"
251 |       ]
252 |      },
253 |      "metadata": {
254 |       "needs_background": "light"
255 |      },
256 |      "output_type": "display_data"
257 |     }
258 |    ],
259 |    "source": [
260 |     "import matplotlib.pyplot as plt\n",
261 |     "\n",
262 |     "all_income_user=data[\"好坏客户\"].groupby(income_cut).count()\n",
263 |     "bad_income_user=data[\"好坏客户\"].groupby(income_cut).sum()\n",
264 |     "bad_rate=bad_income_user/all_income_user\n",
265 |     "print(bad_rate)\n",
266 |     "\n",
267 |     "# 绘制月收入与坏账率关系图\n",
268 |     "bad_rate.plot.bar()"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 21,
274 |    "metadata": {},
275 |    "outputs": [
276 |     {
277 |      "name": "stdout",
278 |      "output_type": "stream",
279 |      "text": [
280 |       "年龄\n",
281 |       "(-0.109, 18.167]    0.000000\n",
282 |       "(18.167, 36.333]    0.110124\n",
283 |       "(36.333, 54.5]      0.081645\n",
284 |       "(54.5, 72.667]      0.041719\n",
285 |       "(72.667, 90.833]    0.021585\n",
286 |       "(90.833, 109.0]     0.022495\n",
287 |       "Name: 好坏客户, dtype: float64\n"
288 |      ]
289 |     },
290 |     {
291 |      "data": {
292 |       "text/plain": [
293 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f19c7cfe710>"
294 |       ]
295 |      },
296 |      "execution_count": 21,
297 |      "metadata": {},
298 |      "output_type": "execute_result"
299 |     },
300 |     {
301 |      "data": {
302 |       "image/png": 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\n",
303 |       "text/plain": [
304 |        "<Figure size 432x288 with 1 Axes>"
305 |       ]
306 |      },
307 |      "metadata": {
308 |       "needs_background": "light"
309 |      },
310 |      "output_type": "display_data"
311 |     }
312 |    ],
313 |    "source": [
314 |     "# 年龄和坏账率有什么关系\n",
315 |     "age_cut=pd.cut(data[\"年龄\"],6)\n",
316 |     "all_age_user=data[\"好坏客户\"].groupby(age_cut).count()\n",
317 |     "bad_age_user=data[\"好坏客户\"].groupby(age_cut).sum()\n",
318 |     "bad_rate=bad_age_user/all_age_user\n",
319 |     "print(bad_rate)\n",
320 |     "\n",
321 |     "bad_rate.plot.bar()"
322 |    ]
323 |   },
324 |   {
325 |    "cell_type": "code",
326 |    "execution_count": 23,
327 |    "metadata": {},
328 |    "outputs": [
329 |     {
330 |      "name": "stdout",
331 |      "output_type": "stream",
332 |      "text": [
333 |       "家属数量\n",
334 |       "0.0     0.058629\n",
335 |       "1.0     0.073529\n",
336 |       "2.0     0.081139\n",
337 |       "3.0     0.088263\n",
338 |       "4.0     0.103774\n",
339 |       "5.0     0.091153\n",
340 |       "6.0     0.151899\n",
341 |       "7.0     0.098039\n",
342 |       "8.0     0.083333\n",
343 |       "9.0     0.000000\n",
344 |       "10.0    0.000000\n",
345 |       "13.0    0.000000\n",
346 |       "20.0    0.000000\n",
347 |       "Name: 好坏客户, dtype: float64\n"
348 |      ]
349 |     }
350 |    ],
351 |    "source": [
352 |     "# 家庭人口数量和坏账率有什么关系\n",
353 |     "all_age_user=data.groupby(\"家属数量\")[\"好坏客户\"].count()\n",
354 |     "bad_age_user=data.groupby(\"家属数量\")[\"好坏客户\"].sum()\n",
355 |     "bad_rate=bad_age_user/all_age_user\n",
356 |     "print(bad_rate)"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": 25,
362 |    "metadata": {},
363 |    "outputs": [
364 |     {
365 |      "data": {
366 |       "text/plain": [
367 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ce71a1d0>"
368 |       ]
369 |      },
370 |      "execution_count": 25,
371 |      "metadata": {},
372 |      "output_type": "execute_result"
373 |     },
374 |     {
375 |      "data": {
376 |       "image/png": 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\n",
377 |       "text/plain": [
378 |        "<Figure size 432x288 with 1 Axes>"
379 |       ]
380 |      },
381 |      "metadata": {
382 |       "needs_background": "light"
383 |      },
384 |      "output_type": "display_data"
385 |     }
386 |    ],
387 |    "source": [
388 |     "bad_rate.plot()"
389 |    ]
390 |   },
391 |   {
392 |    "cell_type": "code",
393 |    "execution_count": null,
394 |    "metadata": {},
395 |    "outputs": [],
396 |    "source": []
397 |   }
398 |  ],
399 |  "metadata": {
400 |   "kernelspec": {
401 |    "display_name": "Python 3",
402 |    "language": "python",
403 |    "name": "python3"
404 |   },
405 |   "language_info": {
406 |    "codemirror_mode": {
407 |     "name": "ipython",
408 |     "version": 3
409 |    },
410 |    "file_extension": ".py",
411 |    "mimetype": "text/x-python",
412 |    "name": "python",
413 |    "nbconvert_exporter": "python",
414 |    "pygments_lexer": "ipython3",
415 |    "version": "3.7.3"
416 |   },
417 |   "toc": {
418 |    "base_numbering": 1,
419 |    "nav_menu": {},
420 |    "number_sections": true,
421 |    "sideBar": true,
422 |    "skip_h1_title": false,
423 |    "title_cell": "Table of Contents",
424 |    "title_sidebar": "Contents",
425 |    "toc_cell": false,
426 |    "toc_position": {},
427 |    "toc_section_display": true,
428 |    "toc_window_display": false
429 |   }
430 |  },
431 |  "nbformat": 4,
432 |  "nbformat_minor": 2
433 | }
434 | 


--------------------------------------------------------------------------------
/.ipynb_checkpoints/Bank-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 8,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "<class 'pandas.core.frame.DataFrame'>\n",
 13 |       "RangeIndex: 150000 entries, 0 to 149999\n",
 14 |       "Data columns (total 6 columns):\n",
 15 |       "用户ID    150000 non-null int64\n",
 16 |       "好坏客户    150000 non-null int64\n",
 17 |       "年龄      150000 non-null int64\n",
 18 |       "负债率     150000 non-null float64\n",
 19 |       "月收入     120269 non-null float64\n",
 20 |       "家属数量    146076 non-null float64\n",
 21 |       "dtypes: float64(3), int64(3)\n",
 22 |       "memory usage: 6.9 MB\n"
 23 |      ]
 24 |     }
 25 |    ],
 26 |    "source": [
 27 |     "import pandas as pd\n",
 28 |     "from datetime import datetime\n",
 29 |     "\n",
 30 |     "data=pd.read_csv(\"loan.csv\",encoding=\"gbk\")\n",
 31 |     "data.info()\n",
 32 |     "# print(data)"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 12,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "name": "stdout",
 42 |      "output_type": "stream",
 43 |      "text": [
 44 |       "<class 'pandas.core.frame.DataFrame'>\n",
 45 |       "RangeIndex: 150000 entries, 0 to 149999\n",
 46 |       "Data columns (total 6 columns):\n",
 47 |       "用户ID    150000 non-null int64\n",
 48 |       "好坏客户    150000 non-null int64\n",
 49 |       "年龄      150000 non-null int64\n",
 50 |       "负债率     150000 non-null float64\n",
 51 |       "月收入     150000 non-null float64\n",
 52 |       "家属数量    146076 non-null float64\n",
 53 |       "dtypes: float64(3), int64(3)\n",
 54 |       "memory usage: 6.9 MB\n",
 55 |       "None\n",
 56 |       "          用户ID  好坏客户  年龄          负债率           月收入  家属数量\n",
 57 |       "0            1     1  45     0.802982   9120.000000   2.0\n",
 58 |       "1            2     0  40     0.121876   2600.000000   1.0\n",
 59 |       "2            3     0  38     0.085113   3042.000000   0.0\n",
 60 |       "3            4     0  30     0.036050   3300.000000   0.0\n",
 61 |       "4            5     0  49     0.024926  63588.000000   0.0\n",
 62 |       "5            6     0  74     0.375607   3500.000000   1.0\n",
 63 |       "6            7     0  57  5710.000000   6670.221237   0.0\n",
 64 |       "7            8     0  39     0.209940   3500.000000   0.0\n",
 65 |       "8            9     0  27    46.000000   6670.221237   NaN\n",
 66 |       "9           10     0  57     0.606291  23684.000000   2.0\n",
 67 |       "10          11     0  30     0.309476   2500.000000   0.0\n",
 68 |       "11          12     0  51     0.531529   6501.000000   2.0\n",
 69 |       "12          13     0  46     0.298354  12454.000000   2.0\n",
 70 |       "13          14     1  40     0.382965  13700.000000   2.0\n",
 71 |       "14          15     0  76   477.000000      0.000000   0.0\n",
 72 |       "15          16     0  64     0.209892  11362.000000   2.0\n",
 73 |       "16          17     0  78  2058.000000   6670.221237   0.0\n",
 74 |       "17          18     0  53     0.188274   8800.000000   0.0\n",
 75 |       "18          19     0  43     0.527888   3280.000000   2.0\n",
 76 |       "19          20     0  25     0.065868    333.000000   0.0\n",
 77 |       "20          21     0  43     0.430046  12300.000000   0.0\n",
 78 |       "21          22     1  38     0.475841   3000.000000   2.0\n",
 79 |       "22          23     0  39     0.241104   2500.000000   0.0\n",
 80 |       "23          24     0  32     0.085512   7916.000000   0.0\n",
 81 |       "24          25     0  58     0.241622   2416.000000   0.0\n",
 82 |       "25          26     1  50     1.595253   4676.000000   1.0\n",
 83 |       "26          27     0  58     0.097672   8333.000000   0.0\n",
 84 |       "27          28     0  69     0.042383   2500.000000   1.0\n",
 85 |       "28          29     0  24     0.011761   3400.000000   0.0\n",
 86 |       "29          30     0  58     0.436103   5500.000000   0.0\n",
 87 |       "...        ...   ...  ..          ...           ...   ...\n",
 88 |       "149970  149971     0  58     0.253855  15500.000000   2.0\n",
 89 |       "149971  149972     0  83     0.013997   5000.000000   0.0\n",
 90 |       "149972  149973     0  42     0.008638   6945.000000   1.0\n",
 91 |       "149973  149974     0  44     0.494819   5500.000000   1.0\n",
 92 |       "149974  149975     0  61     0.603479   5000.000000   0.0\n",
 93 |       "149975  149976     0  58  2716.000000   6670.221237   0.0\n",
 94 |       "149976  149977     0  76    60.000000   6670.221237   0.0\n",
 95 |       "149977  149978     0  29   349.000000   6670.221237   0.0\n",
 96 |       "149978  149979     0  52     0.259496   2500.000000   0.0\n",
 97 |       "149979  149980     1  55     0.057235   8700.000000   0.0\n",
 98 |       "149980  149981     0  64     0.254976   5525.000000   0.0\n",
 99 |       "149981  149982     0  43     0.121752   6849.000000   4.0\n",
100 |       "149982  149983     0  37     0.250272   2760.000000   3.0\n",
101 |       "149983  149984     0  82     0.000800   5000.000000   0.0\n",
102 |       "149984  149985     0  84    25.000000   6670.221237   0.0\n",
103 |       "149985  149986     0  26     0.324962   1950.000000   0.0\n",
104 |       "149986  149987     0  49     0.080384   5000.000000   1.0\n",
105 |       "149987  149988     0  28     0.055692   3249.000000   0.0\n",
106 |       "149988  149989     0  31     0.347924   7515.000000   0.0\n",
107 |       "149989  149990     0  62     0.001408   9233.000000   3.0\n",
108 |       "149990  149991     0  46     0.609779   4335.000000   2.0\n",
109 |       "149991  149992     0  59     0.477658  10316.000000   0.0\n",
110 |       "149992  149993     0  50  4132.000000   6670.221237   3.0\n",
111 |       "149993  149994     0  22     0.000000    820.000000   0.0\n",
112 |       "149994  149995     0  50     0.404293   3400.000000   0.0\n",
113 |       "149995  149996     0  74     0.225131   2100.000000   0.0\n",
114 |       "149996  149997     0  44     0.716562   5584.000000   2.0\n",
115 |       "149997  149998     0  58  3870.000000   6670.221237   0.0\n",
116 |       "149998  149999     0  30     0.000000   5716.000000   0.0\n",
117 |       "149999  150000     0  64     0.249908   8158.000000   0.0\n",
118 |       "\n",
119 |       "[150000 rows x 6 columns]\n"
120 |      ]
121 |     }
122 |    ],
123 |    "source": [
124 |     "# 是不是收入越高的人坏账率越低\n",
125 |     "data=data.fillna({\"月收入\":data[\"月收入\"].mean()})\n",
126 |     "print(data.info())\n",
127 |     "print(data)"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": null,
133 |    "metadata": {},
134 |    "outputs": [],
135 |    "source": []
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": 11,
140 |    "metadata": {},
141 |    "outputs": [
142 |     {
143 |      "name": "stdout",
144 |      "output_type": "stream",
145 |      "text": [
146 |       "0           (5000.0, 10000.0]\n",
147 |       "1               (0.0, 5000.0]\n",
148 |       "2               (0.0, 5000.0]\n",
149 |       "3               (0.0, 5000.0]\n",
150 |       "4         (20000.0, 100000.0]\n",
151 |       "5               (0.0, 5000.0]\n",
152 |       "6           (5000.0, 10000.0]\n",
153 |       "7               (0.0, 5000.0]\n",
154 |       "8           (5000.0, 10000.0]\n",
155 |       "9         (20000.0, 100000.0]\n",
156 |       "10              (0.0, 5000.0]\n",
157 |       "11          (5000.0, 10000.0]\n",
158 |       "12         (10000.0, 15000.0]\n",
159 |       "13         (10000.0, 15000.0]\n",
160 |       "14                        NaN\n",
161 |       "15         (10000.0, 15000.0]\n",
162 |       "16          (5000.0, 10000.0]\n",
163 |       "17          (5000.0, 10000.0]\n",
164 |       "18              (0.0, 5000.0]\n",
165 |       "19              (0.0, 5000.0]\n",
166 |       "20         (10000.0, 15000.0]\n",
167 |       "21              (0.0, 5000.0]\n",
168 |       "22              (0.0, 5000.0]\n",
169 |       "23          (5000.0, 10000.0]\n",
170 |       "24              (0.0, 5000.0]\n",
171 |       "25              (0.0, 5000.0]\n",
172 |       "26          (5000.0, 10000.0]\n",
173 |       "27              (0.0, 5000.0]\n",
174 |       "28              (0.0, 5000.0]\n",
175 |       "29          (5000.0, 10000.0]\n",
176 |       "                 ...         \n",
177 |       "149970     (15000.0, 20000.0]\n",
178 |       "149971          (0.0, 5000.0]\n",
179 |       "149972      (5000.0, 10000.0]\n",
180 |       "149973      (5000.0, 10000.0]\n",
181 |       "149974          (0.0, 5000.0]\n",
182 |       "149975      (5000.0, 10000.0]\n",
183 |       "149976      (5000.0, 10000.0]\n",
184 |       "149977      (5000.0, 10000.0]\n",
185 |       "149978          (0.0, 5000.0]\n",
186 |       "149979      (5000.0, 10000.0]\n",
187 |       "149980      (5000.0, 10000.0]\n",
188 |       "149981      (5000.0, 10000.0]\n",
189 |       "149982          (0.0, 5000.0]\n",
190 |       "149983          (0.0, 5000.0]\n",
191 |       "149984      (5000.0, 10000.0]\n",
192 |       "149985          (0.0, 5000.0]\n",
193 |       "149986          (0.0, 5000.0]\n",
194 |       "149987          (0.0, 5000.0]\n",
195 |       "149988      (5000.0, 10000.0]\n",
196 |       "149989      (5000.0, 10000.0]\n",
197 |       "149990          (0.0, 5000.0]\n",
198 |       "149991     (10000.0, 15000.0]\n",
199 |       "149992      (5000.0, 10000.0]\n",
200 |       "149993          (0.0, 5000.0]\n",
201 |       "149994          (0.0, 5000.0]\n",
202 |       "149995          (0.0, 5000.0]\n",
203 |       "149996      (5000.0, 10000.0]\n",
204 |       "149997      (5000.0, 10000.0]\n",
205 |       "149998      (5000.0, 10000.0]\n",
206 |       "149999      (5000.0, 10000.0]\n",
207 |       "Name: 月收入, Length: 150000, dtype: category\n",
208 |       "Categories (5, interval[int64]): [(0, 5000] < (5000, 10000] < (10000, 15000] < (15000, 20000] < (20000, 100000]]\n"
209 |      ]
210 |     }
211 |    ],
212 |    "source": [
213 |     "cut_bins=[0,5000,10000,15000,20000,100000]\n",
214 |     "income_cut=pd.cut(data[\"月收入\"],cut_bins)\n",
215 |     "print(income_cut)"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": 15,
221 |    "metadata": {},
222 |    "outputs": [
223 |     {
224 |      "name": "stdout",
225 |      "output_type": "stream",
226 |      "text": [
227 |       "月收入\n",
228 |       "(0, 5000]          0.087543\n",
229 |       "(5000, 10000]      0.058308\n",
230 |       "(10000, 15000]     0.041964\n",
231 |       "(15000, 20000]     0.041811\n",
232 |       "(20000, 100000]    0.053615\n",
233 |       "Name: 好坏客户, dtype: float64\n"
234 |      ]
235 |     },
236 |     {
237 |      "data": {
238 |       "text/plain": [
239 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ce4ab588>"
240 |       ]
241 |      },
242 |      "execution_count": 15,
243 |      "metadata": {},
244 |      "output_type": "execute_result"
245 |     },
246 |     {
247 |      "data": {
248 |       "image/png": 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\n",
249 |       "text/plain": [
250 |        "<Figure size 432x288 with 1 Axes>"
251 |       ]
252 |      },
253 |      "metadata": {
254 |       "needs_background": "light"
255 |      },
256 |      "output_type": "display_data"
257 |     }
258 |    ],
259 |    "source": [
260 |     "import matplotlib.pyplot as plt\n",
261 |     "\n",
262 |     "all_income_user=data[\"好坏客户\"].groupby(income_cut).count()\n",
263 |     "bad_income_user=data[\"好坏客户\"].groupby(income_cut).sum()\n",
264 |     "bad_rate=bad_income_user/all_income_user\n",
265 |     "print(bad_rate)\n",
266 |     "\n",
267 |     "# 绘制月收入与坏账率关系图\n",
268 |     "bad_rate.plot.bar()"
269 |    ]
270 |   },
271 |   {
272 |    "cell_type": "code",
273 |    "execution_count": 21,
274 |    "metadata": {},
275 |    "outputs": [
276 |     {
277 |      "name": "stdout",
278 |      "output_type": "stream",
279 |      "text": [
280 |       "年龄\n",
281 |       "(-0.109, 18.167]    0.000000\n",
282 |       "(18.167, 36.333]    0.110124\n",
283 |       "(36.333, 54.5]      0.081645\n",
284 |       "(54.5, 72.667]      0.041719\n",
285 |       "(72.667, 90.833]    0.021585\n",
286 |       "(90.833, 109.0]     0.022495\n",
287 |       "Name: 好坏客户, dtype: float64\n"
288 |      ]
289 |     },
290 |     {
291 |      "data": {
292 |       "text/plain": [
293 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f19c7cfe710>"
294 |       ]
295 |      },
296 |      "execution_count": 21,
297 |      "metadata": {},
298 |      "output_type": "execute_result"
299 |     },
300 |     {
301 |      "data": {
302 |       "image/png": 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\n",
303 |       "text/plain": [
304 |        "<Figure size 432x288 with 1 Axes>"
305 |       ]
306 |      },
307 |      "metadata": {
308 |       "needs_background": "light"
309 |      },
310 |      "output_type": "display_data"
311 |     }
312 |    ],
313 |    "source": [
314 |     "# 年龄和坏账率有什么关系\n",
315 |     "age_cut=pd.cut(data[\"年龄\"],6)\n",
316 |     "all_age_user=data[\"好坏客户\"].groupby(age_cut).count()\n",
317 |     "bad_age_user=data[\"好坏客户\"].groupby(age_cut).sum()\n",
318 |     "bad_rate=bad_age_user/all_age_user\n",
319 |     "print(bad_rate)\n",
320 |     "\n",
321 |     "bad_rate.plot.bar()"
322 |    ]
323 |   },
324 |   {
325 |    "cell_type": "code",
326 |    "execution_count": 23,
327 |    "metadata": {},
328 |    "outputs": [
329 |     {
330 |      "name": "stdout",
331 |      "output_type": "stream",
332 |      "text": [
333 |       "家属数量\n",
334 |       "0.0     0.058629\n",
335 |       "1.0     0.073529\n",
336 |       "2.0     0.081139\n",
337 |       "3.0     0.088263\n",
338 |       "4.0     0.103774\n",
339 |       "5.0     0.091153\n",
340 |       "6.0     0.151899\n",
341 |       "7.0     0.098039\n",
342 |       "8.0     0.083333\n",
343 |       "9.0     0.000000\n",
344 |       "10.0    0.000000\n",
345 |       "13.0    0.000000\n",
346 |       "20.0    0.000000\n",
347 |       "Name: 好坏客户, dtype: float64\n"
348 |      ]
349 |     }
350 |    ],
351 |    "source": [
352 |     "# 家庭人口数量和坏账率有什么关系\n",
353 |     "all_age_user=data.groupby(\"家属数量\")[\"好坏客户\"].count()\n",
354 |     "bad_age_user=data.groupby(\"家属数量\")[\"好坏客户\"].sum()\n",
355 |     "bad_rate=bad_age_user/all_age_user\n",
356 |     "print(bad_rate)"
357 |    ]
358 |   },
359 |   {
360 |    "cell_type": "code",
361 |    "execution_count": 25,
362 |    "metadata": {},
363 |    "outputs": [
364 |     {
365 |      "data": {
366 |       "text/plain": [
367 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ce71a1d0>"
368 |       ]
369 |      },
370 |      "execution_count": 25,
371 |      "metadata": {},
372 |      "output_type": "execute_result"
373 |     },
374 |     {
375 |      "data": {
376 |       "image/png": 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\n",
377 |       "text/plain": [
378 |        "<Figure size 432x288 with 1 Axes>"
379 |       ]
380 |      },
381 |      "metadata": {
382 |       "needs_background": "light"
383 |      },
384 |      "output_type": "display_data"
385 |     }
386 |    ],
387 |    "source": [
388 |     "bad_rate.plot()"
389 |    ]
390 |   },
391 |   {
392 |    "cell_type": "code",
393 |    "execution_count": null,
394 |    "metadata": {},
395 |    "outputs": [],
396 |    "source": []
397 |   }
398 |  ],
399 |  "metadata": {
400 |   "kernelspec": {
401 |    "display_name": "Python 3",
402 |    "language": "python",
403 |    "name": "python3"
404 |   },
405 |   "language_info": {
406 |    "codemirror_mode": {
407 |     "name": "ipython",
408 |     "version": 3
409 |    },
410 |    "file_extension": ".py",
411 |    "mimetype": "text/x-python",
412 |    "name": "python",
413 |    "nbconvert_exporter": "python",
414 |    "pygments_lexer": "ipython3",
415 |    "version": "3.7.3"
416 |   },
417 |   "toc": {
418 |    "base_numbering": 1,
419 |    "nav_menu": {},
420 |    "number_sections": true,
421 |    "sideBar": true,
422 |    "skip_h1_title": false,
423 |    "title_cell": "Table of Contents",
424 |    "title_sidebar": "Contents",
425 |    "toc_cell": false,
426 |    "toc_position": {},
427 |    "toc_section_display": true,
428 |    "toc_window_display": false
429 |   }
430 |  },
431 |  "nbformat": 4,
432 |  "nbformat_minor": 2
433 | }
434 | 


--------------------------------------------------------------------------------