feat(商务大数据分析): 添加二手车数据分析任务

- 新增二手车数据源读取和分析任务
- 完成车辆价格分布、销量品牌分布、排放标准分析等9项需求
- 添加数据预处理步骤,包括数据清洗、类型转换等
- 使用pandas进行数据处理和分析
- 新增second_cars_info.csv文件的GBK编码配置
- 更新VCS配置,将项目目录映射到Git版本控制系统
This commit is contained in:
dev_xulongjin 2025-04-20 14:43:44 +08:00
parent 655911b748
commit 38d132913a
4 changed files with 12222 additions and 1 deletions

1
.idea/encodings.xml generated
View File

@ -2,5 +2,6 @@
<project version="4"> <project version="4">
<component name="Encoding"> <component name="Encoding">
<file url="file://$PROJECT_DIR$/商务大数据分析/20250402/data/中国城市人口数据.csv" charset="GBK" /> <file url="file://$PROJECT_DIR$/商务大数据分析/20250402/data/中国城市人口数据.csv" charset="GBK" />
<file url="file://$PROJECT_DIR$/商务大数据分析/20250416/data/second_cars_info.csv" charset="UTF-8" />
</component> </component>
</project> </project>

4
.idea/vcs.xml generated
View File

@ -1,4 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<project version="4"> <project version="4">
<component name="VcsDirectoryMappings" defaultProject="true" /> <component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project> </project>

File diff suppressed because it is too large Load Diff

View File

@ -0,0 +1,936 @@
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": [
"请读取二手车数据源完成以下需求在notebook中运行代码将完整运行结果导出为pdf上传。\n",
"\n",
"1、车辆价格分布情况\n",
"\n",
"2、车辆销量品牌分布\n",
"\n",
"3、排放标准分析\n",
"\n",
"4、车龄分析\n",
"\n",
"5、里程分析\n",
"\n",
"6、折旧价格分析\n",
"\n",
"7、不同品牌新车平均价格对比\n",
"\n",
"8、排放标准与行驶里程的关系\n",
"\n",
"9、车龄与二手车价格的相关性"
],
"id": "2ce470d74c7f8f38"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.419400Z",
"start_time": "2025-04-18T02:17:28.417330Z"
}
},
"cell_type": "code",
"source": "import pandas as pd",
"id": "ca151ea5138a6483",
"outputs": [],
"execution_count": 81
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.463954Z",
"start_time": "2025-04-18T02:17:28.440605Z"
}
},
"cell_type": "code",
"source": [
"car = pd.read_csv('./data/second_cars_info.csv',encoding='gbk')\n",
"car.head(5)"
],
"id": "cafa1492fc572672",
"outputs": [
{
"data": {
"text/plain": [
" Brand Name Boarding_time Km Discharge排放标准 \\\n",
"0 奥迪 奥迪A6L 2006款 2.4 CVT 舒适型 2006年8月 9.00万公里 国3 \n",
"1 奥迪 奥迪A6L 2007款 2.4 CVT 舒适型 2007年1月 8.00万公里 国4 \n",
"2 奥迪 奥迪A6L 2004款 2.4L 技术领先型 2005年5月 15.00万公里 国2 \n",
"3 奥迪 奥迪A8L 2013款 45 TFSI quattro舒适型 2013年10月 4.80万公里 欧4 \n",
"4 奥迪 奥迪A6L 2014款 30 FSI 豪华型 2014年9月 0.81万公里 国4,国5 \n",
"\n",
" Sec_price New_price \n",
"0 6.90 50.89万 \n",
"1 8.88 50.89万 \n",
"2 3.82 54.24万 \n",
"3 44.80 101.06万 \n",
"4 33.19 54.99万 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Brand</th>\n",
" <th>Name</th>\n",
" <th>Boarding_time</th>\n",
" <th>Km</th>\n",
" <th>Discharge排放标准</th>\n",
" <th>Sec_price</th>\n",
" <th>New_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2006款 2.4 CVT 舒适型</td>\n",
" <td>2006年8月</td>\n",
" <td>9.00万公里</td>\n",
" <td>国3</td>\n",
" <td>6.90</td>\n",
" <td>50.89万</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2007款 2.4 CVT 舒适型</td>\n",
" <td>2007年1月</td>\n",
" <td>8.00万公里</td>\n",
" <td>国4</td>\n",
" <td>8.88</td>\n",
" <td>50.89万</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2004款 2.4L 技术领先型</td>\n",
" <td>2005年5月</td>\n",
" <td>15.00万公里</td>\n",
" <td>国2</td>\n",
" <td>3.82</td>\n",
" <td>54.24万</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A8L 2013款 45 TFSI quattro舒适型</td>\n",
" <td>2013年10月</td>\n",
" <td>4.80万公里</td>\n",
" <td>欧4</td>\n",
" <td>44.80</td>\n",
" <td>101.06万</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2014款 30 FSI 豪华型</td>\n",
" <td>2014年9月</td>\n",
" <td>0.81万公里</td>\n",
" <td>国4,国5</td>\n",
" <td>33.19</td>\n",
" <td>54.99万</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 82
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.513253Z",
"start_time": "2025-04-18T02:17:28.491887Z"
}
},
"cell_type": "code",
"source": [
"from decimal import Decimal\n",
"car[\"Km\"] = car[\"Km\"].str.extract(\"(\\d+\\.?\\d+)\",expand = True)\n",
"car[\"New_price\"] = car[\"New_price\"].str.extract(\"(\\d+\\.?\\d+)\",expand = True)\n",
"car[\"New_price\"] = car[\"New_price\"].apply(lambda x : Decimal(x) * 10000)\n",
"# car[\"Sec_price\"] = car[\"Sec_price\"].apply(lambda x : Decimal(str(x)) * 10000)\n",
"# car[\"Km\"] = car[\"Km\"].apply(lambda x : Decimal(str(x)) * 10000)\n",
"car.head(5)"
],
"id": "1094ed7ec64fd6d3",
"outputs": [
{
"data": {
"text/plain": [
" Brand Name Boarding_time Km Discharge排放标准 \\\n",
"0 奥迪 奥迪A6L 2006款 2.4 CVT 舒适型 2006年8月 9.00 国3 \n",
"1 奥迪 奥迪A6L 2007款 2.4 CVT 舒适型 2007年1月 8.00 国4 \n",
"2 奥迪 奥迪A6L 2004款 2.4L 技术领先型 2005年5月 15.00 国2 \n",
"3 奥迪 奥迪A8L 2013款 45 TFSI quattro舒适型 2013年10月 4.80 欧4 \n",
"4 奥迪 奥迪A6L 2014款 30 FSI 豪华型 2014年9月 0.81 国4,国5 \n",
"\n",
" Sec_price New_price \n",
"0 6.90 508900.00 \n",
"1 8.88 508900.00 \n",
"2 3.82 542400.00 \n",
"3 44.80 1010600.00 \n",
"4 33.19 549900.00 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Brand</th>\n",
" <th>Name</th>\n",
" <th>Boarding_time</th>\n",
" <th>Km</th>\n",
" <th>Discharge排放标准</th>\n",
" <th>Sec_price</th>\n",
" <th>New_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2006款 2.4 CVT 舒适型</td>\n",
" <td>2006年8月</td>\n",
" <td>9.00</td>\n",
" <td>国3</td>\n",
" <td>6.90</td>\n",
" <td>508900.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2007款 2.4 CVT 舒适型</td>\n",
" <td>2007年1月</td>\n",
" <td>8.00</td>\n",
" <td>国4</td>\n",
" <td>8.88</td>\n",
" <td>508900.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2004款 2.4L 技术领先型</td>\n",
" <td>2005年5月</td>\n",
" <td>15.00</td>\n",
" <td>国2</td>\n",
" <td>3.82</td>\n",
" <td>542400.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A8L 2013款 45 TFSI quattro舒适型</td>\n",
" <td>2013年10月</td>\n",
" <td>4.80</td>\n",
" <td>欧4</td>\n",
" <td>44.80</td>\n",
" <td>1010600.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2014款 30 FSI 豪华型</td>\n",
" <td>2014年9月</td>\n",
" <td>0.81</td>\n",
" <td>国4,国5</td>\n",
" <td>33.19</td>\n",
" <td>549900.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 83
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.568207Z",
"start_time": "2025-04-18T02:17:28.555681Z"
}
},
"cell_type": "code",
"source": [
"car['Boarding_time'] = pd.to_datetime(car['Boarding_time'].str.replace('年', '-').str.replace('月', ''), errors='coerce', format='%Y-%m')\n",
"car.head(3)"
],
"id": "2df80c752148dcd1",
"outputs": [
{
"data": {
"text/plain": [
" Brand Name Boarding_time Km Discharge排放标准 \\\n",
"0 奥迪 奥迪A6L 2006款 2.4 CVT 舒适型 2006-08-01 9.00 国3 \n",
"1 奥迪 奥迪A6L 2007款 2.4 CVT 舒适型 2007-01-01 8.00 国4 \n",
"2 奥迪 奥迪A6L 2004款 2.4L 技术领先型 2005-05-01 15.00 国2 \n",
"\n",
" Sec_price New_price \n",
"0 6.90 508900.00 \n",
"1 8.88 508900.00 \n",
"2 3.82 542400.00 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Brand</th>\n",
" <th>Name</th>\n",
" <th>Boarding_time</th>\n",
" <th>Km</th>\n",
" <th>Discharge排放标准</th>\n",
" <th>Sec_price</th>\n",
" <th>New_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2006款 2.4 CVT 舒适型</td>\n",
" <td>2006-08-01</td>\n",
" <td>9.00</td>\n",
" <td>国3</td>\n",
" <td>6.90</td>\n",
" <td>508900.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2007款 2.4 CVT 舒适型</td>\n",
" <td>2007-01-01</td>\n",
" <td>8.00</td>\n",
" <td>国4</td>\n",
" <td>8.88</td>\n",
" <td>508900.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>奥迪</td>\n",
" <td>奥迪A6L 2004款 2.4L 技术领先型</td>\n",
" <td>2005-05-01</td>\n",
" <td>15.00</td>\n",
" <td>国2</td>\n",
" <td>3.82</td>\n",
" <td>542400.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 84
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.626329Z",
"start_time": "2025-04-18T02:17:28.622360Z"
}
},
"cell_type": "code",
"source": [
"today = pd.Timestamp('today')\n",
"car['Year'] = today.year - car['Boarding_time'].dt.year\n",
"car['Month'] = today.month - car['Boarding_time'].dt.month\n",
"car['Year'] = car['Year'] + car['Month'] / 12"
],
"id": "adb1e9029cf50440",
"outputs": [],
"execution_count": 85
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.693439Z",
"start_time": "2025-04-18T02:17:28.689606Z"
}
},
"cell_type": "code",
"source": [
"# 1、车辆价格分布情况\n",
"car['Sec_price'].describe()"
],
"id": "192ca8d230bba52c",
"outputs": [
{
"data": {
"text/plain": [
"count 11281.000000\n",
"mean 26.897567\n",
"std 55.451814\n",
"min 0.650000\n",
"25% 5.200000\n",
"50% 10.490000\n",
"75% 24.800000\n",
"max 808.000000\n",
"Name: Sec_price, dtype: float64"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 86
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.759802Z",
"start_time": "2025-04-18T02:17:28.755785Z"
}
},
"cell_type": "code",
"source": [
"# 2、车辆销量品牌分布\n",
"car['Brand'].value_counts()"
],
"id": "84cd0ac0f5397e98",
"outputs": [
{
"data": {
"text/plain": [
"别克 1350\n",
"大众 991\n",
"奔驰 895\n",
"宝马 773\n",
"奥迪 758\n",
" ... \n",
"东风风光 1\n",
"昌河 1\n",
"北汽制造 1\n",
"北京 1\n",
"中欧房车 1\n",
"Name: Brand, Length: 104, dtype: int64"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 87
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.829496Z",
"start_time": "2025-04-18T02:17:28.826138Z"
}
},
"cell_type": "code",
"source": [
"# 3、排放标准分析\n",
"car['Discharge排放标准'].value_counts()"
],
"id": "abfa9e35e2c0e57b",
"outputs": [
{
"data": {
"text/plain": [
"国4 4300\n",
"欧4 1898\n",
"欧5 1201\n",
"国4,国5 848\n",
"国3 798\n",
"国5 683\n",
"欧3 292\n",
"-- 276\n",
"国2 241\n",
"国4,京5 223\n",
"国3,OBD 119\n",
"OBD 90\n",
"国4,OBD 63\n",
"欧4,OBD 62\n",
"OBD,国5 38\n",
"欧6 36\n",
"国4,OBD,国5 31\n",
"国5,京5 22\n",
"欧5,OBD 9\n",
"欧4,欧5 9\n",
"欧5,国4 7\n",
"欧4,国3 7\n",
"国4,OBD,京5 6\n",
"欧4,国4 6\n",
"欧4,国4,OBD 5\n",
"欧3,欧4 2\n",
"欧5,国5 2\n",
"国4,国5,京5 1\n",
"欧1 1\n",
"欧2 1\n",
"京5 1\n",
"国3,国4 1\n",
"欧4,-- 1\n",
"欧5,国4,国5 1\n",
"Name: Discharge排放标准, dtype: int64"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 88
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.890889Z",
"start_time": "2025-04-18T02:17:28.886821Z"
}
},
"cell_type": "code",
"source": [
"# 4、车龄分析\n",
"car['Year'].describe()"
],
"id": "93126f910d7182c6",
"outputs": [
{
"data": {
"text/plain": [
"count 11188.000000\n",
"mean 13.123592\n",
"std 2.991405\n",
"min 7.500000\n",
"25% 10.750000\n",
"50% 13.166667\n",
"75% 15.083333\n",
"max 27.333333\n",
"Name: Year, dtype: float64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 89
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:28.960584Z",
"start_time": "2025-04-18T02:17:28.955583Z"
}
},
"cell_type": "code",
"source": [
"# 5、里程分析\n",
"car['Km'].describe()"
],
"id": "9a5d106b4e15e8a2",
"outputs": [
{
"data": {
"text/plain": [
"count 11184\n",
"unique 968\n",
"top 6.00\n",
"freq 485\n",
"Name: Km, dtype: object"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 90
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:17:29.031125Z",
"start_time": "2025-04-18T02:17:29.024995Z"
}
},
"cell_type": "code",
"source": [
"# 6、折旧价格分析\n",
"# car = car.dropna(subset=['Km'])\n",
"car[['Km', 'Sec_price']].corr()"
],
"id": "52035886c7842d2f",
"outputs": [
{
"data": {
"text/plain": [
" Sec_price\n",
"Sec_price 1.0"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sec_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Sec_price</th>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 91
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:23:06.001261Z",
"start_time": "2025-04-18T02:23:05.987085Z"
}
},
"cell_type": "code",
"source": [
"# 7、不同品牌新车平均价格对比\n",
"car[['Brand', 'New_price']].groupby('Brand').mean()"
],
"id": "568eaf05460ebb68",
"outputs": [
{
"data": {
"text/plain": [
" New_price\n",
"Brand \n",
"DS 2.644188e+05\n",
"GMC 1.423178e+06\n",
"Jeep 4.342455e+05\n",
"MINI 3.076827e+05\n",
"WEY 1.930000e+05\n",
"... ...\n",
"雪佛兰 1.564546e+05\n",
"雪铁龙 1.655019e+05\n",
"雷克萨斯 7.538173e+05\n",
"雷诺 2.770526e+05\n",
"马自达 2.170586e+05\n",
"\n",
"[104 rows x 1 columns]"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>New_price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Brand</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>DS</th>\n",
" <td>2.644188e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GMC</th>\n",
" <td>1.423178e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jeep</th>\n",
" <td>4.342455e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MINI</th>\n",
" <td>3.076827e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WEY</th>\n",
" <td>1.930000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>雪佛兰</th>\n",
" <td>1.564546e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>雪铁龙</th>\n",
" <td>1.655019e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>雷克萨斯</th>\n",
" <td>7.538173e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>雷诺</th>\n",
" <td>2.770526e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>马自达</th>\n",
" <td>2.170586e+05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>104 rows × 1 columns</p>\n",
"</div>"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 96
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:25:53.211176Z",
"start_time": "2025-04-18T02:25:53.179478Z"
}
},
"cell_type": "code",
"source": [
"# 8、排放标准与行驶里程的关系\n",
"car['Km'] = pd.to_numeric(car['Km'], errors='coerce')\n",
"car.groupby('Discharge排放标准')['Km'].mean()"
],
"id": "4af30a35353b2754",
"outputs": [
{
"data": {
"text/plain": [
"Discharge排放标准\n",
"-- 6.302677\n",
"OBD 8.320222\n",
"OBD,国5 3.307105\n",
"京5 2.000000\n",
"国2 11.470539\n",
"国3 8.524411\n",
"国3,OBD 7.691681\n",
"国3,国4 5.000000\n",
"国4 7.075801\n",
"国4,OBD 5.169841\n",
"国4,OBD,京5 5.533333\n",
"国4,OBD,国5 4.262258\n",
"国4,京5 4.808879\n",
"国4,国5 4.026185\n",
"国4,国5,京5 3.400000\n",
"国5 2.302224\n",
"国5,京5 3.365455\n",
"欧1 8.000000\n",
"欧2 12.000000\n",
"欧3 9.064828\n",
"欧3,欧4 8.000000\n",
"欧4 6.729597\n",
"欧4,-- 7.500000\n",
"欧4,OBD 5.134426\n",
"欧4,国3 8.914286\n",
"欧4,国4 6.133333\n",
"欧4,国4,OBD 7.460000\n",
"欧4,欧5 4.120000\n",
"欧5 3.364559\n",
"欧5,OBD 4.911111\n",
"欧5,国4 4.382857\n",
"欧5,国4,国5 7.000000\n",
"欧5,国5 0.650000\n",
"欧6 1.073714\n",
"Name: Km, dtype: float64"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 99
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-18T02:26:08.841815Z",
"start_time": "2025-04-18T02:26:08.835149Z"
}
},
"cell_type": "code",
"source": [
"# 9、车龄与二手车价格的相关性\n",
"car[['Year', 'Sec_price']].corr()"
],
"id": "ab1c177ba107b9bc",
"outputs": [
{
"data": {
"text/plain": [
" Year Sec_price\n",
"Year 1.00000 -0.30768\n",
"Sec_price -0.30768 1.00000"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Sec_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Year</th>\n",
" <td>1.00000</td>\n",
" <td>-0.30768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sec_price</th>\n",
" <td>-0.30768</td>\n",
" <td>1.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 100
}
],
"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
}