{"id":1251,"date":"2022-06-05T17:27:25","date_gmt":"2022-06-05T09:27:25","guid":{"rendered":"https:\/\/www.dataisfun.cn\/?p=1251"},"modified":"2022-06-05T17:27:27","modified_gmt":"2022-06-05T09:27:27","slug":"python%e5%8f%af%e8%a7%86%e5%8c%96%e5%a4%a7%e5%b1%8f","status":"publish","type":"post","link":"https:\/\/www.dataisfun.cn\/?p=1251","title":{"rendered":"Python\u53ef\u89c6\u5316\u5927\u5c4f"},"content":{"rendered":"\n

\u53ef\u89c6\u5316\u7b97\u662f\u6570\u5206\u4eba\u5458\u7684\u5fc5\u5907\u80fd\u529b\u4e86\uff0c\u5b9e\u9645\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u4e3b\u8981\u5728BI\u7cfb\u7edf\u4e0a\u5b9e\u73b0\u53ef\u89c6\u5316\uff0cBI\u7cfb\u7edf\u53ef\u80fd\u662f\u6280\u672f\u56e2\u961f\u81ea\u5df1\u5f00\u53d1\u7684\u4e5f\u53ef\u80fd\u662f\u91c7\u8d2d\u7b2c\u4e09\u65b9\u7684\u3002\u4f46\u9664BI\u7cfb\u7edf\u4e4b\u5916\uff0c\u6570\u5206\u4eba\u5458\u6700\u597d\u4e5f\u80fd\u719f\u7ec3\u4f7f\u7528Excel\u6216Python\u5b9e\u73b0\u53ef\u89c6\u5316\uff0c\u4ee5\u5907\u4e0d\u65f6\u4e4b\u9700\u3002\u6211\u4eec\u5df2\u7ecf\u8bb2\u8fc7Excel\u62a5\u8868\u53ef\u89c6\u5316<\/a>\u7684\u5b9e\u73b0\uff0c\u672c\u6587\u5c31\u7ee7\u7eed\u603b\u7ed3\u4e0b Python\u53ef\u89c6\u5316\u5927\u5c4f \u7684\u5b9e\u73b0\u3002<\/p>\n\n\n\n


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1. \u5de5\u5177\u51c6\u5907<\/span><\/p>\n\n\n\n

Python\u53ef\u89c6\u5316\u5de5\u5177\u5305\u6709\u5f88\u591a\uff0c\u6211\u4eec\u9009\u7528pyecharts. pyecharts\u662f\u767e\u5ea6\u51fa\u54c1\u7684\uff0c\u5b98\u65b9\u6587\u6863<\/a>\u975e\u5e38\u5b8c\u5584\u4e14\u662f\u4e2d\u6587\u7684\uff0c\u7f51\u7edc\u8d44\u6e90\u4e5f\u975e\u5e38\u591a\uff0c\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u9047\u5230\u95ee\u9898\u65f6\uff0c\u5f88\u5bb9\u6613\u627e\u5230\u89e3\u51b3\u65b9\u6cd5\u3002<\/p>\n\n\n\n

\u786e\u5b9a\u4f7f\u7528\u54ea\u79cd\u5de5\u5177\u5305\u540e\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u5b89\u88c5\u5de5\u5177\u5305\uff0c\u672c\u6587\u4e0d\u505a\u6f14\u793a\uff0c\u8bf7\u81ea\u884c\u5b89\u88c5pyecharts.<\/p>\n\n\n\n


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2. \u4ee3\u7801\u5f00\u53d1<\/span><\/p>\n\n\n\n

# \u5bfc\u5165\u8981\u7528\u7684\u5305\nimport numpy as np\nimport pandas as pd\nfrom pyecharts.charts import Line, Pie, Bar, Timeline, Map, Sankey, Page\nfrom pyecharts import options as opts\nfrom pyecharts.globals import ThemeType\nfrom pyecharts.components import Table\n\n\n# \u53d6\u6570\n# \u5b9e\u9645\u5de5\u4f5c\u4e2d\u662f\u4ece\u6570\u636e\u5e93\u4e2d\u53d6\u6570\uff0c\u8fd9\u91cc\u6211\u4eec\u7b80\u5316\u4e00\u4e0b\uff0c\u76f4\u63a5\u4f2a\u9020\u4e00\u4e9b\u793a\u4f8b\u6570\u636e\ndata = pd.DataFrame()\ndata['date'] = pd.date_range('2022-01-01', periods=90)\ndata['gender'] = np.random.randint(0, 2, size=90)\ndata['product'] = np.random.randint(0, 5, size=90)\ndata['area'] = np.random.randint(0, 26, size=90)\ndata['amount'] = np.random.randint(1000, 2000, size=90)\ndata['cnt'] = np.random.randint(100, 200, size=90)\n\ndata['gender'] = data['gender'].apply(lambda x: '\u7537\u6027' if x == 0 else '\u5973\u6027')\ndata['product'] = data['product'].map({0: '\u4ea7\u54c1A', 1: '\u4ea7\u54c1B', 2: '\u4ea7\u54c1C', 3: '\u4ea7\u54c1D', 4: '\u4ea7\u54c1E'})\narea = ['\u5317\u4eac\u5e02',\n        '\u5929\u6d25\u5e02',\n        '\u4e0a\u6d77\u5e02',\n        '\u91cd\u5e86\u5e02',\n        '\u6cb3\u5317\u7701',\n        '\u5c71\u897f\u7701',\n        '\u8fbd\u5b81\u7701',\n        '\u5409\u6797\u7701',\n        '\u9ed1\u9f99\u6c5f\u7701',\n        '\u6c5f\u82cf\u7701',\n        '\u6d59\u6c5f\u7701',\n        '\u5b89\u5fbd\u7701',\n        '\u798f\u5efa\u7701',\n        '\u6c5f\u897f\u7701',\n        '\u5c71\u4e1c\u7701',\n        '\u6cb3\u5357\u7701',\n        '\u6e56\u5317\u7701',\n        '\u6e56\u5357\u7701',\n        '\u5e7f\u4e1c\u7701',\n        '\u6d77\u5357\u7701',\n        '\u56db\u5ddd\u7701',\n        '\u8d35\u5dde\u7701',\n        '\u4e91\u5357\u7701',\n        '\u9655\u897f\u7701',\n        '\u7518\u8083\u7701',\n        '\u9752\u6d77\u7701']\ndata['area'] = data['area'].apply(lambda x: area[x])\ndata['date_month'] = data['date'].dt.strftime('%Y-%m')\n\n\n# \u67e5\u770b\u6570\u636e\ndata.sample(10)<\/pre>\n\n\n\n
\u6211\u4eec\u4f2a\u9020\u7684\u6570\u636e\u5982\u4e0b\uff1a<\/p>\n\n\n
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\"\"<\/figure>\n<\/div>\n\n\n
\u53d6\u6570\u5b8c\u6210\u540e\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u6309\u9700\u9010\u4e00\u753b\u56fe\uff0c\u5e76\u5c06\u4ee3\u7801\u5c01\u88c5\u4e3a\u51fd\u6570\u4ee5\u4fbf\u540e\u7eed\u8c03\u7528\uff1a<\/p>\n\n\n\n
# \u56fe\u88681-\u6bcf\u65e5\u9500\u552e\u989d-\u6298\u7ebf\u56fe\ndef chart1() -> Line:\n    table1 = data.groupby(by='date')['amount'].sum()\n    table1_x = table1.index.date.tolist()\n    table1_y = table1.values.tolist()\n\n    chart1 = (\n        Line(\n            init_opts=opts.InitOpts(\n                width='700px',\n                height='420px',\n                bg_color='#EDF3F8',\n                chart_id='chart1',\n            )\n        )\n        .add_xaxis(table1_x)\n        .add_yaxis(\n            series_name='',\n            y_axis=table1_y,\n            markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_=\"average\")]),\n        )\n        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n        .set_global_opts(\n            title_opts=opts.TitleOpts('\u6bcf\u65e5\u9500\u552e\u989d'),\n            datazoom_opts=opts.DataZoomOpts(type_='slider')\n        )\n    )\n\n    return chart1<\/pre>\n\n\n\n
# \u56fe\u88682-\u9500\u552e\u989d\u5206\u6027\u522b-\u997c\u56fe\ndef chart2() -> Pie:\n    table2 = data.groupby(by='gender')['amount'].sum()\n    table2_x = table2.index.tolist()\n    table2_y = table2.values.tolist()\n\n    chart2 = (\n        Pie(\n            init_opts=opts.InitOpts(\n                width='700px',\n                height='420px',\n                bg_color='#EDF3F8',\n                chart_id='chart2',\n            )\n        )\n        .add(\n            series_name='\u6027\u522b',\n            data_pair=[list(z) for z in zip(table2_x, table2_y)],\n            radius=[\"50%\", \"70%\"],\n            label_opts=opts.LabelOpts(is_show=True)\n        )\n        .set_series_opts(\n            tooltip_opts=opts.TooltipOpts(\n                trigger=\"item\", formatter=\"{a} <br\/>{b}: {c} ({d}%)\"\n            ),\n            label_opts=opts.LabelOpts(formatter=\"{b}: {d}%\")\n        )\n        .set_global_opts(title_opts=opts.TitleOpts('\u4e0d\u540c\u6027\u522b\u7684\u9500\u552e\u989d\u5360\u6bd4'))\n    )\n\n    return chart2<\/pre>\n\n\n\n
# \u56fe\u88683-\u6bcf\u6708\u9500\u552e\u989d\u5206\u4ea7\u54c1-\u5806\u79ef\u67f1\u5f62\u56fe\ndef chart3() -> Bar:\n    table3 = data.pivot_table(values='amount', index='date_month', columns='product', aggfunc='sum')\n    table3_x = table3.index.tolist()\n    table3_y1 = table3['\u4ea7\u54c1A'].tolist()\n    table3_y2 = table3['\u4ea7\u54c1B'].tolist()\n    table3_y3 = table3['\u4ea7\u54c1C'].tolist()\n    table3_y4 = table3['\u4ea7\u54c1D'].tolist()\n    table3_y5 = table3['\u4ea7\u54c1E'].tolist()\n\n    chart3 = (\n        Bar(\n            init_opts=opts.InitOpts(\n                width='700px',\n                height='420px',\n                bg_color='#EDF3F8',\n                chart_id='chart3',\n            )\n        )\n        .add_xaxis(table3_x)\n        .add_yaxis('\u4ea7\u54c1A', table3_y1, stack='stack0', bar_width=40)\n        .add_yaxis('\u4ea7\u54c1B', table3_y2, stack='stack0', bar_width=40)\n        .add_yaxis('\u4ea7\u54c1C', table3_y3, stack='stack0', bar_width=40)\n        .add_yaxis('\u4ea7\u54c1D', table3_y4, stack='stack0', bar_width=40)\n        .add_yaxis('\u4ea7\u54c1E', table3_y5, stack='stack0', bar_width=40)\n        .set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside'))\n        .set_global_opts(\n            title_opts=opts.TitleOpts(title=\"\u6bcf\u6708\u4e0d\u540c\u4ea7\u54c1\u9500\u552e\u989d\"),\n            legend_opts=opts.LegendOpts(pos_left='right', orient='vertical')\n        )\n    )\n\n    return chart3<\/pre>\n\n\n\n
# \u56fe\u88684-\u6bcf\u6708\u9500\u552e\u989d\u5206\u5730\u533a-\u5730\u56fe\ndef chart4() -> Timeline:\n    data['new_area'] = data['area'].str.replace('\u7701|\u5e02', '', regex=True)  # pyecharts\u4e2d\u7684\u5730\u56fe\u5730\u533a\u540d\u79f0\u4e0d\u5e26\u540e\u7f00\n\n    chart4 = Timeline(\n        init_opts=opts.InitOpts(\n            width='700px',\n            height='420px',\n            bg_color='#EDF3F8',\n            chart_id='chart4',\n        )\n    )\n    for date_month in list(data['date_month'].unique()):\n        table4 = data.loc[data['date_month']==date_month, :].groupby('new_area')['amount'].sum()\n        table4_x = table4.index.tolist()\n        table4_y = table4.values.tolist()\n\n        map_chart = (\n            Map(\n                init_opts=opts.InitOpts(\n                    width='700px',\n                    height='420px',\n                    bg_color='#EDF3F8',\n                )\n            )\n            .add(\n                series_name='\u9500\u552e\u989d',\n                data_pair=[list(z) for z in zip(table4_x, table4_y)],\n                maptype='china',\n                zoom=1,\n            )\n            .set_global_opts(\n                title_opts=opts.TitleOpts('\u6bcf\u6708\u5404\u7701\u4efd\u9500\u552e\u989d'), \n                visualmap_opts=opts.VisualMapOpts(\n                    is_calculable=True,\n                    dimension=0,\n                    pos_left=\"30\",\n                    pos_top=\"center\",\n                    range_text=[\"High\", \"Low\"],\n                    range_color=[\"lightskyblue\", \"yellow\", \"orangered\"],\n                    textstyle_opts=opts.TextStyleOpts(color=\"#000000\"),\n                    min_=min(table4_y),\n                    max_=max(table4_y),\n                ),\n                legend_opts=opts.LegendOpts(is_show=False),\n            )\n        )\n\n        chart4.add(map_chart, date_month)\n\n    return chart4<\/pre>\n\n\n\n
# \u56fe\u88685-\u6bcf\u6708\u9500\u552e\u989d\u53ca\u5e73\u5747\u5355\u4ef7-\u67f1\u7ebf\u7ec4\u5408\u56fe\ndef chart5() -> Bar:\n    table5 = data.groupby(by='date_month')[['amount', 'cnt']].sum()\n    table5['average_price'] = (table5['amount']\/table5['cnt']).round(2)\n    table5_x = table5.index.tolist()\n    table5_y1 = table5['amount'].tolist()\n    table5_y2 = table5['average_price'].tolist()\n\n    chart5 = (\n        Bar(\n            init_opts=opts.InitOpts(\n                width='700px',\n                height='420px',\n                bg_color='#EDF3F8',\n                chart_id='chart5',\n            ),\n        )\n        .add_xaxis(table5_x)\n        .add_yaxis(\n            series_name='\u9500\u552e\u989d',\n            y_axis=table5_y1,\n            yaxis_index=0,\n            color='#91A6DD',\n            bar_width=40,\n            label_opts=opts.LabelOpts(is_show=True, position='inside'),\n        )\n        .extend_axis(yaxis=opts.AxisOpts())\n        .set_global_opts(title_opts=opts.TitleOpts('\u6bcf\u6708\u9500\u552e\u989d\u53ca\u5e73\u5747\u5355\u4ef7'))\n    )\n    chart501 = (\n        Line()\n        .add_xaxis(table5_x)\n        .add_yaxis(\n            series_name='\u5e73\u5747\u5355\u4ef7',\n            y_axis=table5_y2,\n            yaxis_index=1,\n            color='#AD82A4',\n            label_opts=opts.LabelOpts(is_show=True),\n        )\n    )\n    chart5.overlap(chart501)\n\n    return chart5<\/pre>\n\n\n\n
# \u56fe\u88686-\u6bcf\u6708\u5ba2\u7fa4\u8fc1\u79fb-\u6851\u57fa\u56fe\ndef chart6() -> Sankey:\n    # \u5148\u53c2\u8003\u62db\u5546\u94f6\u884c\u7684\u4f1a\u5458\u7b49\u7ea7\u4f2a\u9020\u6837\u4f8b\u6570\u636e\n    table6 = pd.DataFrame({\n        '202201': np.random.randint(0, 10, size=81),\n        '202202': np.random.randint(0, 10, size=81),\n        'cnt': np.random.randint(1000, 10000, size=81)\n    })\n    table6 = pd.DataFrame(table6.groupby(by=['202201', '202202'])['cnt'].sum().reset_index())\n    table6.sort_values(by=['202201', '202202'], inplace=True)\n    table6['202201'] = '202201_M' + table6['202201'].astype('str')  #\u7236\u8282\u70b9\u548c\u5b50\u8282\u70b9\u7684\u503c\u4e0d\u80fd\u76f8\u540c\uff0c\u56e0\u6b64\u52a0\u524d\u7f00\n    table6['202202'] = '202202_M' + table6['202202'].astype('str')  #\u7236\u8282\u70b9\u548c\u5b50\u8282\u70b9\u7684\u503c\u4e0d\u80fd\u76f8\u540c\uff0c\u56e0\u6b64\u52a0\u524d\u7f00\n\n    nodes_content = list(set(table6['202201'].tolist() + table6['202202'].tolist()))\n    nodes_content = sorted(nodes_content)\n    nodes = [{\"name\": x} for x in nodes_content]\n\n    links = [{\"source\": x, \"target\": y, \"value\": z} for x, y, z in zip(table6['202201'], table6['202202'], table6['cnt'])]\n\n    chart6 = (\n        Sankey(\n            init_opts=opts.InitOpts(\n                width='700px',\n                height='420px',\n                theme=ThemeType.WHITE,\n                bg_color='#EDF3F8',\n                chart_id='chart6',\n            )\n        )\n        .add(\n            series_name='',\n            nodes=nodes,\n            links=links,\n            linestyle_opt=opts.LineStyleOpts(opacity=0.2, curve=0.5, color=\"source\"),\n            label_opts=opts.LabelOpts(position=\"right\"),\n            node_width=30,\n            node_gap=10,\n        )\n        .set_global_opts(title_opts=opts.TitleOpts(title='\u4f1a\u5458\u7b49\u7ea7\u8fc1\u79fb\u56fe'))\n    )\n\n    return chart6<\/pre>\n\n\n\n
# \u521b\u5efa\u53ef\u89c6\u5316\u5927\u5c4f\u7684\u6807\u9898\n# pyechats\u6ca1\u6709\u5355\u72ec\u7684\u7ed8\u5236\u6807\u9898\u6a21\u5757\u7684\u4e1c\u897f\uff0c\u6211\u4eec\u501f\u52a9Table\u8868\u683c\u5b9e\u73b0\ndef page_title() -> Table:\n    page_title = (\n        Table()\n        .add(\n            headers=['Python\u53ef\u89c6\u5316\u5927\u5c4f'],\n            rows=[],\n            attributes={\n                'align': 'center',\n                'border': True,\n                'padding': '1px',\n                'style': \"background: #3578AE; color:#FFFFFF; width:1400px; height:50px; font-size:30px\"\n            }\n        )\n    )\n    page_title.chart_id = 'page_title'\n\n    return page_title<\/pre>\n\n\n\n
# \u5c06\u56fe\u8868\u7ec4\u5408\u6210\u53ef\u89c6\u5316\u5927\u5c4f\npage = (\n    Page(\n        page_title='Python\u53ef\u89c6\u5316\u5927\u5c4f',\n        layout=Page.DraggablePageLayout,  # \u62d6\u62fd\u65b9\u5f0f\n        interval=1,\n    )\n    .add(\n        page_title(),  # \u6807\u9898\n        chart1(),  # \u56fe\u88681\n        chart2(),  # \u56fe\u88682\n        chart3(),  # \u56fe\u88683\n        chart4(),  # \u56fe\u88684\n        chart5(),  # \u56fe\u88685\n        chart6(),  # \u56fe\u88686\n    )\n)\npage.render('Python\u53ef\u89c6\u5316\u5927\u5c4f_Demo.html')<\/pre>\n\n\n\n
\u81f3\u6b64\u6211\u4eec\u751f\u6210\u4e86\u53ef\u89c6\u5316\u5927\u5c4f\u7684\u521d\u7248-Python\u53ef\u89c6\u5316\u5927\u5c4f_Demo.html<\/a>\uff0c\u4f46\u8fd8\u9700\u8fdb\u4e00\u6b65\u8c03\u6574\u56fe\u8868\u5e03\u5c40\uff1a<\/p>\n\n\n\n
  1. \u5728\u6d4f\u89c8\u5668\u4e2d\u6253\u5f00 Python\u53ef\u89c6\u5316\u5927\u5c4f_Demo.html<\/li>
  2. \u624b\u52a8\u62d6\u62fd\u5e03\u5c40\u5e76\u4fdd\u5b58\u914d\u7f6e\u5f97\u5230\u914d\u7f6e\u6587\u4ef6 chart_config.json<\/li>
  3. \u6253\u5f00\u914d\u7f6e\u6587\u4ef6 chart_config.json \u5e76\u6309\u9700\u624b\u52a8\u8c03\u6574\u53c2\u6570<\/li><\/ol>\n\n\n\n
    <\/p>\n\n\n\n
    # \u57fa\u4e8e\u65b0\u7684\u914d\u7f6e\u6587\u4ef6\u91cd\u65b0\u751f\u6210\u53ef\u89c6\u5316\u5927\u5c4f\npage_new = page.save_resize_html(\n    'Python\u53ef\u89c6\u5316\u5927\u5c4f_Demo.html',\n    cfg_file='chart_config.json',\n    dest='Python\u53ef\u89c6\u5316\u5927\u5c4f.html',  # \u8be5\u6587\u4ef6\u5373\u662f\u6700\u7ec8\u7684\u53ef\u89c6\u5316\u5927\u5c4f\uff0c\u4f7f\u7528\u6d4f\u89c8\u5668\u6253\u5f00\u5373\u53ef\n)<\/pre>\n\n\n\n
    \u81f3\u6b64\u6211\u4eec\u751f\u6210\u4e86\u53ef\u89c6\u5316\u5927\u5c4f\u7684\u7ec8\u7248-Python\u53ef\u89c6\u5316\u5927\u5c4f.html<\/a>\uff0c\u5c06\u8be5\u6587\u4ef6\u7684\u5185\u7f51\u5730\u5740\u5206\u4eab\u7ed9\u540c\u4e8b\uff0c\u4ed6\u4eec\u5c31\u80fd\u67e5\u770b\u4e86\u3002\u518d\u7ed3\u5408Python\u5b9a\u65f6\u4efb\u52a1\u5b9e\u73b0\u6bcf\u5929\u5b9a\u65f6\u66f4\u65b0\u6570\u636e\uff0c\u8fd9\u4e2a\u53ef\u89c6\u5316\u5927\u5c4f\u5c31\u53ef\u4ee5\u7528\u6765\u76d1\u63a7\u4e1a\u52a1\u6307\u6807\u4e86\u3002<\/p>\n\n\n\n

    <\/p>\n\n\n\n
    3. \u5c0f\u7ed3<\/span><\/p>\n\n\n\n
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