From 655911b74834b9271b077fb3d163404d5c8f139f Mon Sep 17 00:00:00 2001 From: dev_xulongjin Date: Mon, 14 Apr 2025 16:06:13 +0800 Subject: [PATCH] =?UTF-8?q?chore(project):=20=E5=88=9D=E5=A7=8B=E5=8C=96?= =?UTF-8?q?=E9=A1=B9=E7=9B=AE=E7=BB=93=E6=9E=84=E5=92=8C=E9=85=8D=E7=BD=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加 .idea 目录和相关配置文件，设置项目忽略文件、编码、模块管理等 - 创建商务大数据分析目录和子目录，准备数据和任务笔记本 - 添加示例数据文件：中国城市人口数据.csv - 创建任务笔记本文件，进行数据处理和分析示例 --- .idea/.gitignore | 8 + .idea/encodings.xml | 6 + .idea/gcc-project-py-25-2.iml | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 6 + .idea/modules.xml | 8 + .idea/vcs.xml | 4 + .../data/中国城市人口数据.csv | 23 + .../data/某地区房屋销售数据 (1).csv | 27369 ++++++++++++++++ 商务大数据分析/20250402/task.ipynb | 699 + 商务大数据分析/20250402/task2.ipynb | 478 + 商务大数据分析/20250402/task3.ipynb | 464 + .../data/healthcare-dataset-age_abs.xlsx | Bin 0 -> 110592 bytes .../data/healthcare-dataset-stroke.xlsx | Bin 0 -> 284160 bytes 商务大数据分析/20250408/task.ipynb | 902 + 15 files changed, 29981 insertions(+) create mode 100644 .idea/.gitignore create mode 100644 .idea/encodings.xml create mode 100644 .idea/gcc-project-py-25-2.iml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/vcs.xml create mode 100644 商务大数据分析/20250402/data/中国城市人口数据.csv create mode 100644 商务大数据分析/20250402/data/某地区房屋销售数据 (1).csv create mode 100644 商务大数据分析/20250402/task.ipynb create mode 100644 商务大数据分析/20250402/task2.ipynb create mode 100644 商务大数据分析/20250402/task3.ipynb create mode 100644 商务大数据分析/20250408/data/healthcare-dataset-age_abs.xlsx create mode 100644 商务大数据分析/20250408/data/healthcare-dataset-stroke.xlsx create mode 100644 商务大数据分析/20250408/task.ipynb diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files 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" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 126 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/商务大数据分析/20250402/task2.ipynb b/商务大数据分析/20250402/task2.ipynb new file mode 100644 index 0000000..7cfc989 --- /dev/null +++ b/商务大数据分析/20250402/task2.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-02T07:57:56.382179Z", + "start_time": "2025-04-02T07:57:55.984261Z" + } + }, + "cell_type": "code", + "source": "import pandas as pd", + "id": "3244cf38b10be81b", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-02T08:00:15.267189Z", + "start_time": "2025-04-02T08:00:15.229542Z" + } + }, + "cell_type": "code", + "source": [ + "data = pd.read_csv('data/某地区房屋销售数据 (1).csv', encoding='gbk')\n", + "data['new_postcode'] = data['地区邮编'].apply(lambda x: str(x)[:2])\n", + "data.head(3)" + ], + "id": "d973cf9fe6ac90a6", + "outputs": [ + { + "data": { + "text/plain": [ + " 房屋出售时间 地区邮编 房屋价格 房屋类型 配套房间数 new_postcode\n", + "0 2010/1/4 0:00 2615 435000 house 3 26\n", + "1 2010/1/5 0:00 2904 712000 house 4 29\n", + "2 2010/1/6 0:00 2617 435000 house 4 26" + ], + "text/html": [ + "
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房屋出售时间地区邮编房屋价格房屋类型配套房间数new_postcode
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房屋类型houseunit
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" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-02T08:04:41.035870Z", + "start_time": "2025-04-02T08:04:41.012959Z" + } + }, + "cell_type": "code", + "source": [ + "# 3、不同类型房产和不同房间数的房价之间的比较，使用pivot_table函数\n", + "data.pivot_table(values='房屋价格', index='房屋类型', columns='配套房间数', aggfunc='mean')" + ], + "id": "4ed9b36daea1c503", + "outputs": [ + { + "data": { + "text/plain": [ + "配套房间数 0 1 2 3 \\\n", + "房屋类型 \n", + "house 677394.736842 353634.269663 489555.889339 560117.683516 \n", + "unit 330850.000000 336570.325391 432502.153116 594535.982287 \n", + "\n", + "配套房间数 4 5 \n", + "房屋类型 \n", + "house 730667.024375 9.290297e+05 \n", + "unit 641736.842105 1.146333e+06 " + ], + "text/html": [ + "
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配套房间数012345
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" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/商务大数据分析/20250402/task3.ipynb b/商务大数据分析/20250402/task3.ipynb new file mode 100644 index 0000000..321f6b5 --- /dev/null +++ b/商务大数据分析/20250402/task3.ipynb @@ -0,0 +1,464 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-04-08T10:57:29.263616Z", + "start_time": "2025-04-08T10:57:28.865194Z" + } + }, + "source": "import pandas as pd", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-08T10:59:11.991479Z", + "start_time": "2025-04-08T10:59:11.985778Z" + } + }, + "cell_type": "code", + "source": [ + "data = pd.read_csv('data/中国城市人口数据.csv',encoding=\"GBK\")\n", + "data.head(5)" + ], + "id": "c3fd933261d1f7fb", + "outputs": [ + { + "data": { + "text/plain": [ + " 省份 2020年人口（万人） 2019年人口（万人）\n", + "0 河北省 7461 7447\n", + "1 山西省 3492 3497\n", + "2 辽宁省 4259 4277\n", + "3 吉林省 2407 2448\n", + "4 江苏省 8475 8469" + ], + "text/html": [ + "
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省份2020年人口（万人）2019年人口（万人）compare
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编号性别高血压是否结婚工作类型居住类型体重指数吸烟史中风
09046男否是私人城市36.6以前吸烟是
151676女否是私营企业农村NaN从不吸烟是
231112男否是私人农村32.5从不吸烟是
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编号年龄平均血糖
0904667.0228.69
15167661.0202.21
23111280.0105.92
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编号性别高血压是否结婚工作类型居住类型体重指数吸烟史中风年龄平均血糖
09046男否是私人城市36.6以前吸烟是67.0228.69
151676女否是私营企业农村NaN从不吸烟是61.0202.21
231112男否是私人农村32.5从不吸烟是80.0105.92
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编号性别高血压是否结婚工作类型居住类型体重指数吸烟史中风年龄平均血糖
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