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{
"cell_type": "markdown",
"id": "97982f4b-b3d0-47fe-b6e8-a1a327d03d93",
"metadata": {},
"source": [
"## 深入浅出pandas-2"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "834d7d19-4015-4048-a1a6-2b8c756f0115",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
"plt.rcParams['axes.unicode_minus'] = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "56becc00-f1c7-4f81-86d1-7f7b23fd65d4",
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "markdown",
"id": "c81bfe94-906c-4e13-b321-0e6c397aea46",
"metadata": {},
"source": [
"### 数据透视\n",
"\n",
"1. 数据聚合（指标统计）\n",
"2. 排序和头部值\n",
"3. 透视表和交叉表"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7836ce8b-1e1d-40e5-b01c-c228ebf60928",
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>销售日期</th>\n",
" <th>销售区域</th>\n",
" <th>销售渠道</th>\n",
" <th>销售订单</th>\n",
" <th>品牌</th>\n",
" <th>售价</th>\n",
" <th>销售数量</th>\n",
" <th>直接成本</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>拼多多</td>\n",
" <td>182894-455</td>\n",
" <td>八匹马</td>\n",
" <td>99</td>\n",
" <td>83</td>\n",
" <td>3351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>抖音</td>\n",
" <td>205635-402</td>\n",
" <td>八匹马</td>\n",
" <td>219</td>\n",
" <td>29</td>\n",
" <td>1016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>205654-021</td>\n",
" <td>八匹马</td>\n",
" <td>169</td>\n",
" <td>85</td>\n",
" <td>6320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>205654-519</td>\n",
" <td>八匹马</td>\n",
" <td>169</td>\n",
" <td>14</td>\n",
" <td>485</td>\n",
" </tr>\n",
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" <th>4</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>377781-010</td>\n",
" <td>皮皮虾</td>\n",
" <td>249</td>\n",
" <td>61</td>\n",
" <td>2452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" 销售日期 销售区域 销售渠道 销售订单 品牌 售价 销售数量 直接成本\n",
"0 2020-01-01 上海 拼多多 182894-455 八匹马 99 83 3351\n",
"1 2020-01-01 上海 抖音 205635-402 八匹马 219 29 1016\n",
"2 2020-01-01 上海 天猫 205654-021 八匹马 169 85 6320\n",
"3 2020-01-01 上海 天猫 205654-519 八匹马 169 14 485\n",
"4 2020-01-01 上海 天猫 377781-010 皮皮虾 249 61 2452"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_df = pd.read_excel('res/2020年销售数据.xlsx', sheet_name='data')\n",
"sales_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ca3b2020-9470-4e49-a3ca-066dc9a437b3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1945 entries, 0 to 1944\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 销售日期 1945 non-null datetime64[ns]\n",
" 1 销售区域 1945 non-null object \n",
" 2 销售渠道 1945 non-null object \n",
" 3 销售订单 1945 non-null object \n",
" 4 品牌 1945 non-null object \n",
" 5 售价 1945 non-null int64 \n",
" 6 销售数量 1945 non-null int64 \n",
" 7 直接成本 1945 non-null int64 \n",
"dtypes: datetime64[ns](1), int64(3), object(4)\n",
"memory usage: 121.7+ KB\n"
]
}
],
"source": [
"sales_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "224ae45b-172a-48bc-8df0-7d6c7099529a",
"metadata": {},
"outputs": [
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"data": {
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" <th>销售订单</th>\n",
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" <th>销售数量</th>\n",
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" <tr>\n",
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" <td>上海</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>205654-519</td>\n",
" <td>八匹马</td>\n",
" <td>169</td>\n",
" <td>14</td>\n",
" <td>485</td>\n",
" <td>2366</td>\n",
" <td>1881</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>377781-010</td>\n",
" <td>皮皮虾</td>\n",
" <td>249</td>\n",
" <td>61</td>\n",
" <td>2452</td>\n",
" <td>15189</td>\n",
" <td>12737</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 销售日期 销售区域 销售渠道 销售订单 品牌 售价 销售数量 直接成本 销售额 毛利润 月份\n",
"0 2020-01-01 上海 拼多多 182894-455 八匹马 99 83 3351 8217 4866 1\n",
"1 2020-01-01 上海 抖音 205635-402 八匹马 219 29 1016 6351 5335 1\n",
"2 2020-01-01 上海 天猫 205654-021 八匹马 169 85 6320 14365 8045 1\n",
"3 2020-01-01 上海 天猫 205654-519 八匹马 169 14 485 2366 1881 1\n",
"4 2020-01-01 上海 天猫 377781-010 皮皮虾 249 61 2452 15189 12737 1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 添加销售额、毛利润、月份列\n",
"sales_df['销售额'] = sales_df.售价 * sales_df.销售数量\n",
"sales_df['毛利润'] = sales_df.销售额 - sales_df.直接成本\n",
"sales_df['月份'] = sales_df.销售日期.dt.month\n",
"sales_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c8878bfd-64de-4a6a-9ae3-36e9af40d45e",
"metadata": {},
"outputs": [],
"source": [
"def make_tag(price):\n",
" if price < 300:\n",
" return '低端'\n",
" elif price < 800:\n",
" return '中端'\n",
" return '高端'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4cba4262-b952-4fce-9b07-fed9c5f2a2e3",
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th>销售日期</th>\n",
" <th>销售区域</th>\n",
" <th>销售渠道</th>\n",
" <th>销售订单</th>\n",
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" <th>销售数量</th>\n",
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" <th>3</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>205654-519</td>\n",
" <td>八匹马</td>\n",
" <td>169</td>\n",
" <td>14</td>\n",
" <td>485</td>\n",
" <td>2366</td>\n",
" <td>1881</td>\n",
" <td>1</td>\n",
" <td>低端</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-01-01</td>\n",
" <td>上海</td>\n",
" <td>天猫</td>\n",
" <td>377781-010</td>\n",
" <td>皮皮虾</td>\n",
" <td>249</td>\n",
" <td>61</td>\n",
" <td>2452</td>\n",
" <td>15189</td>\n",
" <td>12737</td>\n",
" <td>1</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 销售日期 销售区域 销售渠道 销售订单 品牌 售价 销售数量 直接成本 销售额 毛利润 月份 价位\n",
"0 2020-01-01 上海 拼多多 182894-455 八匹马 99 83 3351 8217 4866 1 低端\n",
"1 2020-01-01 上海 抖音 205635-402 八匹马 219 29 1016 6351 5335 1 低端\n",
"2 2020-01-01 上海 天猫 205654-021 八匹马 169 85 6320 14365 8045 1 低端\n",
"3 2020-01-01 上海 天猫 205654-519 八匹马 169 14 485 2366 1881 1 低端\n",
"4 2020-01-01 上海 天猫 377781-010 皮皮虾 249 61 2452 15189 12737 1 低端"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 根据商品的价格添加价位标签\n",
"sales_df['价位'] = sales_df.售价.apply(make_tag)\n",
"sales_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b2a2b472-55fc-4d35-8530-445e8ffbf800",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"销售额: 39772087元\n",
"毛利润: 27926715元\n",
"销售数量: 107403件\n",
"毛利率: 70.22%\n"
]
}
],
"source": [
"# 统计北极星指标\n",
"GMV, profit, quantity = sales_df[['销售额', '毛利润', '销售数量']].sum()\n",
"print(f'销售额: {GMV}元')\n",
"print(f'毛利润: {profit}元')\n",
"print(f'销售数量: {quantity}件')\n",
"print(f'毛利率: {profit / GMV:.2%}')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5b6ac225-6834-462f-a5b7-8be8297b66d0",
"metadata": {},
"outputs": [
{
"data": {
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" <th>10</th>\n",
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],
"text/plain": [
" 销售额 毛利润\n",
"月份 \n",
"1 5409855 3845162\n",
"2 4608455 3190039\n",
"3 4164972 2934584\n",
"4 3996770 2882592\n",
"5 3239005 2272902\n",
"6 2817936 1897643\n",
"7 3501304 2449313\n",
"8 2948189 2071782\n",
"9 2632960 1849277\n",
"10 2375385 1671442\n",
"11 2385283 1691646\n",
"12 1691973 1170333"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 统计每个月的销售额和毛利润\n",
"temp1 = sales_df.groupby('月份')[['销售额', '毛利润']].agg('sum')\n",
"temp1"
]
},
{
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" 毛利润 销售额\n",
"月份 \n",
"1 3845162 5409855\n",
"2 3190039 4608455\n",
"3 2934584 4164972\n",
"4 2882592 3996770\n",
"5 2272902 3239005\n",
"6 1897643 2817936\n",
"7 2449313 3501304\n",
"8 2071782 2948189\n",
"9 1849277 2632960\n",
"10 1671442 2375385\n",
"11 1691646 2385283\n",
"12 1170333 1691973"
]
},
"execution_count": 10,
"metadata": {},
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}
],
"source": [
"# 使用透视表统计每个月的销售额和毛利润\n",
"pd.pivot_table(\n",
" sales_df,\n",
" index='月份',\n",
" values=['销售额', '毛利润'],\n",
" aggfunc='sum'\n",
")"
]
},
{
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"text/plain": [
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"source": [
"# 绘制折线图\n",
"temp1.plot(\n",
" kind='line',\n",
" figsize=(10, 5),\n",
" y=['销售额', '毛利润'], # 放到纵轴上的数据\n",
" xlabel='', # 横轴的标签\n",
" ylabel='销售额和毛利润', # 纵轴的标签\n",
" marker='^', # 标记点符号\n",
")\n",
"# plt.fill_between(np.arange(1, 13), temp1.销售额, where=temp1.销售额 >= 3e6, facecolor='red', alpha=0.25)\n",
"# plt.fill_between(np.arange(1, 13), temp1.销售额, where=temp1.销售额 < 3e6, facecolor='green', alpha=0.25)\n",
"# 定制纵轴的取值范围\n",
"plt.ylim(0, 6e6)\n",
"# 定制横轴的刻度\n",
"plt.xticks(np.arange(1, 13), labels=[f'{x}月' for x in range(1, 13)])\n",
"# 定制标题\n",
"plt.title('2020年月度销售额和毛利润', fontdict={'fontsize': 22, 'color': 'navy'})\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "389db27d-2b11-47ea-a8c6-660fcc39b863",
"metadata": {},
"outputs": [
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"plt.cm.RdYlBu_r"
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},
{
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"id": "621a2487-2d15-450d-9f69-df8089616bd9",
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" <use xlink:href=\"#SimHei-65\" x=\"50\"/>\n",
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"Q 900 -400 625 -300 \n",
"Q 1000 50 1225 512 \n",
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"z\n",
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" <use xlink:href=\"#SimHei-31\"/>\n",
" <use xlink:href=\"#SimHei-6708\" x=\"50\"/>\n",
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" <g id=\"ytick_2\">\n",
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" <path d=\"M 34.2 297.229091 \n",
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" <use xlink:href=\"#SimHei-33\"/>\n",
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" <g id=\"ytick_4\">\n",
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"L 1875 1200 \n",
"L 200 1200 \n",
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"L 2450 1675 \n",
"L 2975 1675 \n",
"L 2975 1200 \n",
"z\n",
"M 1875 1675 \n",
"L 1875 3525 \n",
"L 750 1675 \n",
"L 1875 1675 \n",
"z\n",
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" <use xlink:href=\"#SimHei-34\"/>\n",
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" <g id=\"ytick_6\">\n",
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" <g id=\"line2d_28\">\n",
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" <g id=\"ytick_7\">\n",
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" <g id=\"line2d_30\">\n",
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" <g id=\"text_16\">\n",
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],
"source": [
"# 绘制横线图\n",
"mu = temp1.销售额.mean()\n",
"temp1['diff'] = temp1.销售额 - mu\n",
"temp1['colors'] = temp1.销售额.map(lambda x: 'green' if x > mu else 'red')\n",
"\n",
"plt.figure(figsize=(8, 6), dpi=200)\n",
"plt.hlines(y=temp1.index, xmin=0, xmax=temp1['diff'], color=temp1.colors, alpha=0.6, linewidth=6)\n",
"plt.yticks(np.arange(1, 13), labels=[f'{x}月' for x in np.arange(1, 13)])\n",
"# 定制网格线\n",
"plt.grid(linestyle='--', linewidth=0.4, alpha=0.5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "810936a1-f6c5-43e9-9eda-b9fe8014ba9f",
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"source": [
"# 各品牌对销售额贡献占比\n",
"temp2 = sales_df.groupby('品牌')['销售额'].sum()\n",
"temp2.plot(\n",
" kind='pie',\n",
" ylabel='',\n",
" autopct='%.2f%%', # 自动计算并显示百分比\n",
" pctdistance=0.82, # 百分比标签到圆心的距离\n",
" wedgeprops=dict(width=0.35, edgecolor='w'), # 定制环状饼图\n",
" explode=[0.1, 0, 0, 0, 0], # 分离饼图\n",
")\n",
"plt.show()"
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},
{
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"id": "1465d545-14e8-435d-9b12-651df3bab9ea",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>广东</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>388180</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>469390</td>\n",
" <td>365191</td>\n",
" <td>0</td>\n",
" <td>395188</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>江苏</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>537079</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>841032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>710962</td>\n",
" <td>0</td>\n",
" <td>215307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>浙江</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248354</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>439508</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>福建</th>\n",
" <td>1852496</td>\n",
" <td>1111141</td>\n",
" <td>1106579</td>\n",
" <td>830207</td>\n",
" <td>1036351</td>\n",
" <td>816100</td>\n",
" <td>577283</td>\n",
" <td>658627</td>\n",
" <td>769999</td>\n",
" <td>580707</td>\n",
" <td>486258</td>\n",
" <td>352479</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"月份 1 2 3 4 5 6 7 8 \\\n",
"销售区域 \n",
"上海 1679125 1689527 1061193 1082187 841199 785404 863906 734937 \n",
"北京 1878234 1807787 1360666 1205989 807300 1216432 1219083 645727 \n",
"安徽 0 0 0 341308 554155 0 0 0 \n",
"广东 0 0 388180 0 0 0 0 469390 \n",
"江苏 0 0 0 537079 0 0 841032 0 \n",
"浙江 0 0 248354 0 0 0 0 439508 \n",
"福建 1852496 1111141 1106579 830207 1036351 816100 577283 658627 \n",
"\n",
"月份 9 10 11 12 \n",
"销售区域 \n",
"上海 1107693 412108 825169 528041 \n",
"北京 390077 671608 678668 596146 \n",
"安徽 0 0 0 0 \n",
"广东 365191 0 395188 0 \n",
"江苏 0 710962 0 215307 \n",
"浙江 0 0 0 0 \n",
"福建 769999 580707 486258 352479 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 各销售区域每个月的销售额\n",
"temp3 = sales_df.groupby(['销售区域', '月份'], as_index=False)[['销售额']].sum()\n",
"# pivot - 将行旋转到列上（窄表 ----> 宽表）\n",
"# melt - 将列旋转到行上（宽表 ----> 窄表）\n",
"temp3.pivot(index='销售区域', columns='月份', values='销售额').fillna(0).astype('i8')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1adf23bb-7120-416c-846e-1098aac2e1f4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>月份</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" <th>12</th>\n",
" <th>总计</th>\n",
" </tr>\n",
" <tr>\n",
" <th>销售区域</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>上海</th>\n",
" <td>1679125</td>\n",
" <td>1689527</td>\n",
" <td>1061193</td>\n",
" <td>1082187</td>\n",
" <td>841199</td>\n",
" <td>785404</td>\n",
" <td>863906</td>\n",
" <td>734937</td>\n",
" <td>1107693</td>\n",
" <td>412108</td>\n",
" <td>825169</td>\n",
" <td>528041</td>\n",
" <td>11610489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>北京</th>\n",
" <td>1878234</td>\n",
" <td>1807787</td>\n",
" <td>1360666</td>\n",
" <td>1205989</td>\n",
" <td>807300</td>\n",
" <td>1216432</td>\n",
" <td>1219083</td>\n",
" <td>645727</td>\n",
" <td>390077</td>\n",
" <td>671608</td>\n",
" <td>678668</td>\n",
" <td>596146</td>\n",
" <td>12477717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>安徽</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>341308</td>\n",
" <td>554155</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>895463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>广东</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>388180</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>469390</td>\n",
" <td>365191</td>\n",
" <td>0</td>\n",
" <td>395188</td>\n",
" <td>0</td>\n",
" <td>1617949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>江苏</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>537079</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>841032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>710962</td>\n",
" <td>0</td>\n",
" <td>215307</td>\n",
" <td>2304380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>浙江</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248354</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>439508</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>687862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>福建</th>\n",
" <td>1852496</td>\n",
" <td>1111141</td>\n",
" <td>1106579</td>\n",
" <td>830207</td>\n",
" <td>1036351</td>\n",
" <td>816100</td>\n",
" <td>577283</td>\n",
" <td>658627</td>\n",
" <td>769999</td>\n",
" <td>580707</td>\n",
" <td>486258</td>\n",
" <td>352479</td>\n",
" <td>10178227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>总计</th>\n",
" <td>5409855</td>\n",
" <td>4608455</td>\n",
" <td>4164972</td>\n",
" <td>3996770</td>\n",
" <td>3239005</td>\n",
" <td>2817936</td>\n",
" <td>3501304</td>\n",
" <td>2948189</td>\n",
" <td>2632960</td>\n",
" <td>2375385</td>\n",
" <td>2385283</td>\n",
" <td>1691973</td>\n",
" <td>39772087</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"月份 1 2 3 4 5 6 7 8 \\\n",
"销售区域 \n",
"上海 1679125 1689527 1061193 1082187 841199 785404 863906 734937 \n",
"北京 1878234 1807787 1360666 1205989 807300 1216432 1219083 645727 \n",
"安徽 0 0 0 341308 554155 0 0 0 \n",
"广东 0 0 388180 0 0 0 0 469390 \n",
"江苏 0 0 0 537079 0 0 841032 0 \n",
"浙江 0 0 248354 0 0 0 0 439508 \n",
"福建 1852496 1111141 1106579 830207 1036351 816100 577283 658627 \n",
"总计 5409855 4608455 4164972 3996770 3239005 2817936 3501304 2948189 \n",
"\n",
"月份 9 10 11 12 总计 \n",
"销售区域 \n",
"上海 1107693 412108 825169 528041 11610489 \n",
"北京 390077 671608 678668 596146 12477717 \n",
"安徽 0 0 0 0 895463 \n",
"广东 365191 0 395188 0 1617949 \n",
"江苏 0 710962 0 215307 2304380 \n",
"浙江 0 0 0 0 687862 \n",
"福建 769999 580707 486258 352479 10178227 \n",
"总计 2632960 2375385 2385283 1691973 39772087 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 创建透视表\n",
"pd.pivot_table(\n",
" sales_df,\n",
" index='销售区域',\n",
" columns='月份',\n",
" values='销售额',\n",
" aggfunc='sum',\n",
" fill_value=0,\n",
" margins=True,\n",
" margins_name='总计'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "fdf54c9a-64c3-453d-bc5b-533fce7b25ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1945 entries, 0 to 1944\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 销售日期 1945 non-null datetime64[ns]\n",
" 1 销售区域 1945 non-null object \n",
" 2 销售渠道 1945 non-null object \n",
" 3 销售订单 1945 non-null object \n",
" 4 品牌 1945 non-null object \n",
" 5 售价 1945 non-null int64 \n",
" 6 销售数量 1945 non-null int64 \n",
" 7 直接成本 1945 non-null int64 \n",
" 8 销售额 1945 non-null int64 \n",
" 9 毛利润 1945 non-null int64 \n",
" 10 月份 1945 non-null int32 \n",
" 11 价位 1945 non-null category \n",
"dtypes: category(1), datetime64[ns](1), int32(1), int64(5), object(4)\n",
"memory usage: 161.7+ KB\n"
]
}
],
"source": [
"# 将价位字段处理成category类型并指定排序的顺序\n",
"sales_df['价位'] = sales_df.价位.astype('category').cat.reorder_categories(['高端', '中端', '低端'])\n",
"sales_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6e706575-3260-4b37-99fe-696db4f6fe7b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>月份</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
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" <th>10</th>\n",
" <th>11</th>\n",
" <th>12</th>\n",
" </tr>\n",
" <tr>\n",
" <th>价位</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>高端</th>\n",
" <td>1157</td>\n",
" <td>897</td>\n",
" <td>642</td>\n",
" <td>685</td>\n",
" <td>490</td>\n",
" <td>378</td>\n",
" <td>541</td>\n",
" <td>528</td>\n",
" <td>210</td>\n",
" <td>113</td>\n",
" <td>93</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>中端</th>\n",
" <td>6518</td>\n",
" <td>4963</td>\n",
" <td>5469</td>\n",
" <td>5155</td>\n",
" <td>4170</td>\n",
" <td>3679</td>\n",
" <td>4361</td>\n",
" <td>3680</td>\n",
" <td>3412</td>\n",
" <td>3104</td>\n",
" <td>3225</td>\n",
" <td>2048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>低端</th>\n",
" <td>5220</td>\n",
" <td>5525</td>\n",
" <td>5127</td>\n",
" <td>4430</td>\n",
" <td>4625</td>\n",
" <td>3947</td>\n",
" <td>4184</td>\n",
" <td>3233</td>\n",
" <td>3618</td>\n",
" <td>4008</td>\n",
" <td>4199</td>\n",
" <td>3642</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"月份 1 2 3 4 5 6 7 8 9 10 11 12\n",
"价位 \n",
"高端 1157 897 642 685 490 378 541 528 210 113 93 127\n",
"中端 6518 4963 5469 5155 4170 3679 4361 3680 3412 3104 3225 2048\n",
"低端 5220 5525 5127 4430 4625 3947 4184 3233 3618 4008 4199 3642"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 统计每个月各种价位产品的销量\n",
"temp4 = sales_df.pivot_table(\n",
" index='价位',\n",
" columns='月份',\n",
" values='销售数量',\n",
" observed=False,\n",
" fill_value=0,\n",
" aggfunc='sum'\n",
")\n",
"temp4"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "12527025-64f5-4a0a-9bf5-6bdbc4b6f93b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th>月份</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
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" <th></th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>高端</th>\n",
" <td>1157</td>\n",
" <td>897</td>\n",
" <td>642</td>\n",
" <td>685</td>\n",
" <td>490</td>\n",
" <td>378</td>\n",
" <td>541</td>\n",
" <td>528</td>\n",
" <td>210</td>\n",
" <td>113</td>\n",
" <td>93</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>中端</th>\n",
" <td>6518</td>\n",
" <td>4963</td>\n",
" <td>5469</td>\n",
" <td>5155</td>\n",
" <td>4170</td>\n",
" <td>3679</td>\n",
" <td>4361</td>\n",
" <td>3680</td>\n",
" <td>3412</td>\n",
" <td>3104</td>\n",
" <td>3225</td>\n",
" <td>2048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>低端</th>\n",
" <td>5220</td>\n",
" <td>5525</td>\n",
" <td>5127</td>\n",
" <td>4430</td>\n",
" <td>4625</td>\n",
" <td>3947</td>\n",
" <td>4184</td>\n",
" <td>3233</td>\n",
" <td>3618</td>\n",
" <td>4008</td>\n",
" <td>4199</td>\n",
" <td>3642</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"月份 1 2 3 4 5 6 7 8 9 10 11 12\n",
"价位 \n",
"高端 1157 897 642 685 490 378 541 528 210 113 93 127\n",
"中端 6518 4963 5469 5155 4170 3679 4361 3680 3412 3104 3225 2048\n",
"低端 5220 5525 5127 4430 4625 3947 4184 3233 3618 4008 4199 3642"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 交叉表\n",
"pd.crosstab(\n",
" index=sales_df.价位,\n",
" columns=sales_df.月份,\n",
" values=sales_df.销售数量,\n",
" aggfunc='sum'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "d99ecd49-2669-493c-ae04-974afa71dc4d",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>人格</th>\n",
" <th>𝛂</th>\n",
" <th>𝛃</th>\n",
" <th>𝛄</th>\n",
" </tr>\n",
" <tr>\n",
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" <tr>\n",
" <th>A</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AB</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>O</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"人格 𝛂 𝛃 𝛄\n",
"血型 \n",
"A 2 1 1\n",
"AB 1 1 0\n",
"B 4 1 1\n",
"O 5 0 3"
]
},
"execution_count": 21,
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}
],
"source": [
"blood_types = np.array(['B', 'A', 'O', 'O', 'AB', 'B', 'O', 'B', 'AB', 'A', 'A', 'O', 'B', 'O', 'O', 'O', 'O', 'A', 'B', 'B'])\n",
"personality_types = np.array(['𝛃', '𝛂', '𝛂', '𝛂', '𝛃', '𝛂', '𝛄', '𝛄', '𝛂', '𝛄', '𝛃', '𝛂', '𝛂', '𝛂', '𝛄', '𝛄', '𝛂', '𝛂', '𝛂', '𝛂'])\n",
"\n",
"# 创建交叉表\n",
"pd.crosstab(\n",
" index=blood_types,\n",
" columns=personality_types,\n",
" rownames=['血型'],\n",
" colnames=['人格'],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3cba2e9e-8335-4081-a253-5ab6d08d8559",
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"source": [
"# 绘制堆叠柱状图\n",
"temp4.T.plot(\n",
" figsize=(10, 4),\n",
" kind='bar',\n",
" width=0.6,\n",
" xlabel='',\n",
" ylabel='销售数量',\n",
" stacked=True\n",
")\n",
"plt.xticks(rotation=0)\n",
"plt.show()"
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},
{
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"id": "9eba1698-d8a5-4b66-b617-fc68072a670e",
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"价位 高端 中端 低端\n",
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"source": [
"# 让每一项数据除以对应月份的销售数量之和\n",
"temp5 = temp4.T.divide(temp4.sum(), axis=0)\n",
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" <use xlink:href=\"#SimHei-35\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <use xlink:href=\"#SimHei-33\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-34\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_29\">\n",
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" <use xlink:href=\"#SimHei-39\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_30\">\n",
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" <use xlink:href=\"#SimHei-34\"/>\n",
" <use xlink:href=\"#SimHei-35\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-39\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-38\" x=\"50\"/>\n",
" <use xlink:href=\"#SimHei-2e\" x=\"100\"/>\n",
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" <use xlink:href=\"#SimHei-30\" x=\"200\"/>\n",
" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_33\">\n",
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" <use xlink:href=\"#SimHei-36\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_34\">\n",
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" <g transform=\"translate(383.240741 163.967139) scale(0.08 -0.08)\">\n",
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" <use xlink:href=\"#SimHei-39\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-34\" x=\"150\"/>\n",
" <use xlink:href=\"#SimHei-36\" x=\"200\"/>\n",
" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g transform=\"translate(383.240741 65.860325) scale(0.08 -0.08)\">\n",
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" <use xlink:href=\"#SimHei-33\" x=\"50\"/>\n",
" <use xlink:href=\"#SimHei-2e\" x=\"100\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_36\">\n",
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" <use xlink:href=\"#SimHei-37\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_37\">\n",
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" <g transform=\"translate(429.356444 72.749579) scale(0.08 -0.08)\">\n",
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" <use xlink:href=\"#SimHei-39\" x=\"50\"/>\n",
" <use xlink:href=\"#SimHei-2e\" x=\"100\"/>\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_38\">\n",
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" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_39\">\n",
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" <g transform=\"translate(475.472146 78.559345) scale(0.08 -0.08)\">\n",
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" <use xlink:href=\"#SimHei-37\" x=\"200\"/>\n",
" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_40\">\n",
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" <g transform=\"translate(521.587849 183.260485) scale(0.08 -0.08)\">\n",
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" <use xlink:href=\"#SimHei-2e\" x=\"100\"/>\n",
" <use xlink:href=\"#SimHei-39\" x=\"150\"/>\n",
" <use xlink:href=\"#SimHei-30\" x=\"200\"/>\n",
" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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" <g id=\"text_41\">\n",
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" <g transform=\"translate(521.587849 78.966963) scale(0.08 -0.08)\">\n",
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" <use xlink:href=\"#SimHei-35\" x=\"50\"/>\n",
" <use xlink:href=\"#SimHei-2e\" x=\"100\"/>\n",
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" <use xlink:href=\"#SimHei-36\" x=\"200\"/>\n",
" <use xlink:href=\"#SimHei-25\" x=\"250\"/>\n",
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"# 绘制百分比堆叠柱状图\n",
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},
{
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"metadata": {},
"source": [
"### 作业：招聘岗位数据分析\n",
"\n",
"1. 统计出城市、招聘信息、招聘岗位的数量和平均月薪。\n",
"2. 统计每个城市的岗位数量从高到低排序。\n",
"3. 统计每个城市的平均薪资从高到低排序。\n",
"4. 统计招聘岗位对学历要求的占比。\n",
"5. 统计招聘岗位对工作年限的要求占比。\n",
"6. 分析薪资跟学历和工作年限的关系。"
]
},
{
"cell_type": "code",
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"城市数量: 9\n",
"信息数量: 6487\n",
"岗位数量: 22611\n",
"平均薪资: 17.0\n"
]
}
],
"source": [
"# 统计北极星指标\n",
"city_count = jobs_df['city'].nunique()\n",
"info_count = jobs_df['company_name'].count()\n",
"post_count = jobs_df['pos_count'].sum()\n",
"salary_avg = jobs_df['salary'].mean().round(1)\n",
"print(f'城市数量: {city_count}')\n",
"print(f'信息数量: {info_count}')\n",
"print(f'岗位数量: {post_count}')\n",
"print(f'平均薪资: {salary_avg}')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "cb9fc057-df56-4f01-a08e-ab3427dca467",
"metadata": {},
"outputs": [
{
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" </tr>\n",
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" <th>北京</th>\n",
" <td>3009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>杭州</th>\n",
" <td>2732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>广州</th>\n",
" <td>2560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>南京</th>\n",
" <td>2345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>武汉</th>\n",
" <td>2031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>成都</th>\n",
" <td>1913</td>\n",
" </tr>\n",
" <tr>\n",
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],
"text/plain": [
" pos_count\n",
"city \n",
"上海 3158\n",
"深圳 3061\n",
"北京 3009\n",
"杭州 2732\n",
"广州 2560\n",
"南京 2345\n",
"武汉 2031\n",
"成都 1913\n",
"西安 1802"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 统计每个城市的岗位数量从高到低排序\n",
"jobs_df.groupby('city')[['pos_count']].sum().sort_values(by='pos_count', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "430af08e-2ce2-4f4f-9d64-df2d09c7c9f3",
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" pos_count\n",
"city \n",
"上海 3158\n",
"深圳 3061\n",
"北京 3009\n",
"杭州 2732\n",
"广州 2560\n",
"南京 2345\n",
"武汉 2031\n",
"成都 1913\n",
"西安 1802"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(\n",
" jobs_df,\n",
" index='city',\n",
" values='pos_count',\n",
" aggfunc='sum'\n",
").sort_values(by='pos_count', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "bcd4aeca-bb90-42e6-b45c-6cdea2c76271",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>salary</th>\n",
" </tr>\n",
" <tr>\n",
" <th>city</th>\n",
" <th></th>\n",
" </tr>\n",
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" <th>北京</th>\n",
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" <th>深圳</th>\n",
" <td>20.3</td>\n",
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" <tr>\n",
" <th>杭州</th>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>广州</th>\n",
" <td>14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>南京</th>\n",
" <td>14.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>成都</th>\n",
" <td>12.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>武汉</th>\n",
" <td>11.8</td>\n",
" </tr>\n",
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],
"text/plain": [
" salary\n",
"city \n",
"北京 23.6\n",
"深圳 20.3\n",
"上海 19.8\n",
"杭州 18.9\n",
"广州 14.4\n",
"南京 14.2\n",
"成都 12.6\n",
"武汉 11.8\n",
"西安 10.5"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jobs_df.groupby('city')[['salary']].mean().round(1).sort_values(by='salary', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "6e93f5c8-da3e-4064-ac77-748eb3167504",
"metadata": {},
"outputs": [
{
"data": {
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" <th>深圳</th>\n",
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],
"text/plain": [
" salary\n",
"city \n",
"北京 23.6\n",
"深圳 20.3\n",
"上海 19.8\n",
"杭州 18.9\n",
"广州 14.4\n",
"南京 14.2\n",
"成都 12.6\n",
"武汉 11.8\n",
"西安 10.5"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 统计每个城市的平均薪资从高到低排序\n",
"pd.pivot_table(\n",
" jobs_df,\n",
" index='city',\n",
" values='salary',\n",
" aggfunc='mean'\n",
").round(1).sort_values(by='salary', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "7ae067bf-2981-49f2-a224-28a5223dc21f",
"metadata": {},
"outputs": [],
"source": [
"jobs_df['edu'] = jobs_df.edu.astype('category').cat.reorder_categories(['学历不限', '大专', '本科', '研究生'])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "a87fab8f-18af-4c0b-9546-63d79778b4d8",
"metadata": {},
"outputs": [
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"source": [
"# 统计招聘岗位对学历要求占比\n",
"pd.pivot_table(\n",
" jobs_df,\n",
" index='edu',\n",
" values='pos_count',\n",
" aggfunc='sum',\n",
" observed=True\n",
").plot(\n",
" kind='pie',\n",
" ylabel='',\n",
" subplots=True,\n",
" legend=False,\n",
" autopct='%.2f%%',\n",
" pctdistance=0.85,\n",
" wedgeprops={'width': 0.35}\n",
")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "15202818-48a8-40bb-8701-47bc1b481f80",
"metadata": {},
"outputs": [],
"source": [
"jobs_df['year'] = jobs_df.year.astype('category').cat.reorder_categories(['应届生', '1年以内', '经验不限', '1-3年', '3-5年', '5年以上'])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "6d965229-36d5-4c38-850c-3300726f208a",
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" <use xlink:href=\"#SimHei-33\" x=\"100\"/>\n",
" <use xlink:href=\"#SimHei-5e74\" x=\"150\"/>\n",
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" </g>\n",
" <g id=\"text_8\">\n",
" <!-- 24.32% -->\n",
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" <use xlink:href=\"#SimHei-34\" x=\"50\"/>\n",
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" </g>\n",
" <g id=\"text_12\">\n",
" <!-- 14.43% -->\n",
" <g transform=\"translate(206.593868 182.658142) scale(0.1 -0.1)\">\n",
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" <use xlink:href=\"#SimHei-34\" x=\"50\"/>\n",
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],
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 统计招聘岗位对工作年限要求绘制饼图\n",
"pd.pivot_table(\n",
" jobs_df,\n",
" index='year',\n",
" values='pos_count',\n",
" aggfunc='sum',\n",
" observed=True\n",
").plot(\n",
" kind='pie',\n",
" y='pos_count',\n",
" ylabel='',\n",
" legend=False,\n",
" autopct='%.2f%%',\n",
" pctdistance=0.85,\n",
" wedgeprops={'width': 0.35}\n",
")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "9c7ab419-0bc4-428f-be9b-6d4d617b6663",
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>year</th>\n",
" <th>应届生</th>\n",
" <th>1年以内</th>\n",
" <th>经验不限</th>\n",
" <th>1-3年</th>\n",
" <th>3-5年</th>\n",
" <th>5年以上</th>\n",
" </tr>\n",
" <tr>\n",
" <th>edu</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>学历不限</th>\n",
" <td>9.2</td>\n",
" <td>8.2</td>\n",
" <td>10.9</td>\n",
" <td>10.9</td>\n",
" <td>16.4</td>\n",
" <td>21.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>大专</th>\n",
" <td>7.2</td>\n",
" <td>8.0</td>\n",
" <td>8.4</td>\n",
" <td>9.4</td>\n",
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" <th>本科</th>\n",
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" <td>13.0</td>\n",
" <td>13.2</td>\n",
" <td>20.0</td>\n",
" <td>25.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>研究生</th>\n",
" <td>8.3</td>\n",
" <td>12.7</td>\n",
" <td>15.2</td>\n",
" <td>17.1</td>\n",
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" <td>30.5</td>\n",
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],
"text/plain": [
"year 应届生 1年以内 经验不限 1-3年 3-5年 5年以上\n",
"edu \n",
"学历不限 9.2 8.2 10.9 10.9 16.4 21.1\n",
"大专 7.2 8.0 8.4 9.4 14.3 22.5\n",
"本科 7.3 9.6 13.0 13.2 20.0 25.5\n",
"研究生 8.3 12.7 15.2 17.1 24.8 30.5"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 统计不同学历和工作年限平均薪资\n",
"temp6 = pd.pivot_table(\n",
" jobs_df,\n",
" index='edu',\n",
" columns='year',\n",
" values='salary',\n",
" observed=False,\n",
" fill_value=0\n",
").round(1)\n",
"temp6"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "d8492b17-5ae8-47f0-a058-ab303a6087a9",
"metadata": {},
"outputs": [
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}
],
"source": [
"# 绘制热力图\n",
"plt.imshow(temp6, cmap='Reds')\n",
"plt.xticks(np.arange(6), labels=temp6.columns)\n",
"plt.yticks(np.arange(4), labels=temp6.index)\n",
"\n",
"for i in range(temp6.index.size):\n",
" for j in range(temp6.columns.size):\n",
" value = temp6.iat[i, j]\n",
" color = 'w' if value > salary_avg else 'k'\n",
" plt.text(j, i, value, ha='center', va='center', color=color)\n",
"\n",
"# 定制颜色条\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "a3eb8cd0-fe07-43a4-9514-8a38c08ca081",
"metadata": {},
"outputs": [],
"source": [
"# %pip install seaborn"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "f6287994-0865-432a-9932-92b05b5bf7e8",
"metadata": {},
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" <use xlink:href=\"#SimHei-7ecf\"/>\n",
" <use xlink:href=\"#SimHei-9a8c\" x=\"100\"/>\n",
" <use xlink:href=\"#SimHei-4e0d\" x=\"200\"/>\n",
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],
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"sns.heatmap(temp6, cmap='Reds', annot=True)\n",
"plt.xlabel('')\n",
"plt.ylabel('')\n",
"plt.yticks(rotation=0)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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