数据可视化柱状图多维度（时间，地区，产业）经济指标图表

import pyecharts.options as opts
from pyecharts.charts import Timeline, Bar, Pie"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://www.echartsjs.com/examples/editor.html?c&＃61;mix-timeline-finance目前无法实现的功能:1、暂无
"""
total_data &＃61; {}
name_list &＃61; ["北京","天津","河北","山西","内蒙古","辽宁","吉林","黑龙江","上海","江苏","浙江","安徽","福建","江西","山东","河南","湖北","湖南","广东","广西","海南","重庆","四川","贵州","云南","西藏","陕西","甘肃","青海","宁夏","新疆",
]
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}data_ti &＃61; {2011: [12363.18,5219.24,8483.17,3960.87,5015.89,8158.98,3679.91,4918.09,11142.86,20842.21,14180.23,4975.96,6878.74,3921.2,17370.89,7991.72,7247.02,7539.54,24097.7,3998.33,1148.93,3623.81,7014.04,2781.29,3701.79,322.57,4355.81,1963.79,540.18,861.92,2245.12,],2010: [10600.84,4238.65,7123.77,3412.38,4209.03,6849.37,3111.12,4040.55,9833.51,17131.45,12063.82,4193.69,5850.62,3121.4,14343.14,6607.89,6053.37,6369.27,20711.55,3383.11,953.67,2881.08,6030.41,2177.07,2892.31,274.82,3688.93,1536.5,470.88,702.45,1766.69,],2009: [9179.19,3405.16,6068.31,2886.92,3696.65,5891.25,2756.26,3371.95,8930.85,13629.07,9918.78,3662.15,5048.49,2637.07,11768.18,5700.91,5127.12,5402.81,18052.59,2919.13,748.59,2474.44,5198.8,1885.79,2519.62,240.85,3143.74,1363.27,398.54,563.74,1587.72,],2008: [8375.76,2886.65,5276.04,2759.46,3212.06,5207.72,2412.26,2905.68,7872.23,11888.53,8799.31,3234.64,4346.4,2355.86,10358.64,5099.76,4466.85,4633.67,16321.46,2529.51,643.47,2160.48,4561.69,1652.34,2218.81,218.67,2699.74,1234.21,355.93,475,1421.38,],2007: [7236.15,2250.04,4600.72,2257.99,2467.41,4486.74,2025.44,2493.04,6821.11,9730.91,7613.46,2789.78,3770,1918.95,8620.24,4511.97,3812.34,3835.4,14076.83,2156.76,528.84,1825.21,3881.6,1312.94,1896.78,188.06,2178.2,1037.11,294.91,366.18,1246.89,],2006: [5837.55,1902.31,3895.36,1846.18,1934.35,3798.26,1687.07,2096.35,5508.48,7914.11,6281.86,2390.29,3022.83,1614.65,7187.26,3721.44,3111.98,3229.42,11585.82,1835.12,433.57,1649.2,3319.62,989.38,1557.91,159.76,1806.36,900.16,249.04,294.78,1058.16,],2005: [4854.33,1658.19,3340.54,1611.07,1542.26,3295.45,1413.83,1857.42,4776.2,6612.22,5360.1,2137.77,2551.41,1411.92,5924.74,3181.27,2655.94,2882.88,9772.5,1560.92,377.17,1440.32,2836.73,815.32,1374.62,137.24,1546.59,787.36,213.37,259.49,929.41,],2004: [4092.27,1319.76,2805.47,1375.67,1270,2811.95,1223.64,1657.77,4097.26,5198.03,4584.22,1963.9,2206.02,1225.8,4764.7,2722.4,2292.55,2428.95,8335.3,1361.92,335.3,1229.62,2510.3,661.8,1192.53,123.3,1234.6,688.41,193.7,227.73,833.36,],2003: [3435.95,1150.81,2439.68,1176.65,1000.79,2487.85,1075.48,1467.9,3404.19,4493.31,3890.79,1638.42,1949.91,1043.08,4112.43,2358.86,2003.08,1995.78,7178.94,1178.25,293.85,1081.35,2189.68,558.28,1013.76,96.76,1063.89,589.91,169.81,195.46,753.91,],2002: [2982.57,997.47,2149.75,992.69,811.47,2258.17,958.88,1319.4,3038.9,3891.92,3227.99,1399.02,1765.8,972.73,3700.52,1978.37,1795.93,1780.79,6343.94,1074.85,270.96,956.12,1943.68,480.37,914.5,89.56,963.62,514.83,148.83,171.14,704.5,],
}data_estate &＃61; {2011: [12363.18,5219.24,8483.17,3960.87,5015.89,8158.98,3679.91,4918.09,11142.86,20842.21,14180.23,4975.96,6878.74,3921.2,17370.89,7991.72,7247.02,7539.54,24097.7,3998.33,1148.93,3623.81,7014.04,2781.29,3701.79,322.57,4355.81,1963.79,540.18,861.92,2245.12,],2010: [10600.84,4238.65,7123.77,3412.38,4209.03,6849.37,3111.12,4040.55,9833.51,17131.45,12063.82,4193.69,5850.62,3121.4,14343.14,6607.89,6053.37,6369.27,20711.55,3383.11,953.67,2881.08,6030.41,2177.07,2892.31,274.82,3688.93,1536.5,470.88,702.45,1766.69,],2009: [9179.19,3405.16,6068.31,2886.92,3696.65,5891.25,2756.26,3371.95,8930.85,13629.07,9918.78,3662.15,5048.49,2637.07,11768.18,5700.91,5127.12,5402.81,18052.59,2919.13,748.59,2474.44,5198.8,1885.79,2519.62,240.85,3143.74,1363.27,398.54,563.74,1587.72,],2008: [8375.76,2886.65,5276.04,2759.46,3212.06,5207.72,2412.26,2905.68,7872.23,11888.53,8799.31,3234.64,4346.4,2355.86,10358.64,5099.76,4466.85,4633.67,16321.46,2529.51,643.47,2160.48,4561.69,1652.34,2218.81,218.67,2699.74,1234.21,355.93,475,1421.38,],2007: [7236.15,2250.04,4600.72,2257.99,2467.41,4486.74,2025.44,2493.04,6821.11,9730.91,7613.46,2789.78,3770,1918.95,8620.24,4511.97,3812.34,3835.4,14076.83,2156.76,528.84,1825.21,3881.6,1312.94,1896.78,188.06,2178.2,1037.11,294.91,366.18,1246.89,],2006: [5837.55,1902.31,3895.36,1846.18,1934.35,3798.26,1687.07,2096.35,5508.48,7914.11,6281.86,2390.29,3022.83,1614.65,7187.26,3721.44,3111.98,3229.42,11585.82,1835.12,433.57,1649.2,3319.62,989.38,1557.91,159.76,1806.36,900.16,249.04,294.78,1058.16,],2005: [4854.33,1658.19,3340.54,1611.07,1542.26,3295.45,1413.83,1857.42,4776.2,6612.22,5360.1,2137.77,2551.41,1411.92,5924.74,3181.27,2655.94,2882.88,9772.5,1560.92,377.17,1440.32,2836.73,815.32,1374.62,137.24,1546.59,787.36,213.37,259.49,929.41,],2004: [4092.27,1319.76,2805.47,1375.67,1270,2811.95,1223.64,1657.77,4097.26,5198.03,4584.22,1963.9,2206.02,1225.8,4764.7,2722.4,2292.55,2428.95,8335.3,1361.92,335.3,1229.62,2510.3,661.8,1192.53,123.3,1234.6,688.41,193.7,227.73,833.36,],2003: [3435.95,1150.81,2439.68,1176.65,1000.79,2487.85,1075.48,1467.9,3404.19,4493.31,3890.79,1638.42,1949.91,1043.08,4112.43,2358.86,2003.08,1995.78,7178.94,1178.25,293.85,1081.35,2189.68,558.28,1013.76,96.76,1063.89,589.91,169.81,195.46,753.91,],2002: [2982.57,997.47,2149.75,992.69,811.47,2258.17,958.88,1319.4,3038.9,3891.92,3227.99,1399.02,1765.8,972.73,3700.52,1978.37,1795.93,1780.79,6343.94,1074.85,270.96,956.12,1943.68,480.37,914.5,89.56,963.62,514.83,148.83,171.14,704.5,],
}data_financial &＃61; {2011: [12363.18,5219.24,8483.17,3960.87,5015.89,8158.98,3679.91,4918.09,11142.86,20842.21,14180.23,4975.96,6878.74,3921.2,17370.89,7991.72,7247.02,7539.54,24097.7,3998.33,1148.93,3623.81,7014.04,2781.29,3701.79,322.57,4355.81,1963.79,540.18,861.92,2245.12,],2010: [10600.84,4238.65,7123.77,3412.38,4209.03,6849.37,3111.12,4040.55,9833.51,17131.45,12063.82,4193.69,5850.62,3121.4,14343.14,6607.89,6053.37,6369.27,20711.55,3383.11,953.67,2881.08,6030.41,2177.07,2892.31,274.82,3688.93,1536.5,470.88,702.45,1766.69,],2009: [9179.19,3405.16,6068.31,2886.92,3696.65,5891.25,2756.26,3371.95,8930.85,13629.07,9918.78,3662.15,5048.49,2637.07,11768.18,5700.91,5127.12,5402.81,18052.59,2919.13,748.59,2474.44,5198.8,1885.79,2519.62,240.85,3143.74,1363.27,398.54,563.74,1587.72,],2008: [8375.76,2886.65,5276.04,2759.46,3212.06,5207.72,2412.26,2905.68,7872.23,11888.53,8799.31,3234.64,4346.4,2355.86,10358.64,5099.76,4466.85,4633.67,16321.46,2529.51,643.47,2160.48,4561.69,1652.34,2218.81,218.67,2699.74,1234.21,355.93,475,1421.38,],2007: [7236.15,2250.04,4600.72,2257.99,2467.41,4486.74,2025.44,2493.04,6821.11,9730.91,7613.46,2789.78,3770,1918.95,8620.24,4511.97,3812.34,3835.4,14076.83,2156.76,528.84,1825.21,3881.6,1312.94,1896.78,188.06,2178.2,1037.11,294.91,366.18,1246.89,],2006: [5837.55,1902.31,3895.36,1846.18,1934.35,3798.26,1687.07,2096.35,5508.48,7914.11,6281.86,2390.29,3022.83,1614.65,7187.26,3721.44,3111.98,3229.42,11585.82,1835.12,433.57,1649.2,3319.62,989.38,1557.91,159.76,1806.36,900.16,249.04,294.78,1058.16,],2005: [4854.33,1658.19,3340.54,1611.07,1542.26,3295.45,1413.83,1857.42,4776.2,6612.22,5360.1,2137.77,2551.41,1411.92,5924.74,3181.27,2655.94,2882.88,9772.5,1560.92,377.17,1440.32,2836.73,815.32,1374.62,137.24,1546.59,787.36,213.37,259.49,929.41,],2004: [4092.27,1319.76,2805.47,1375.67,1270,2811.95,1223.64,1657.77,4097.26,5198.03,4584.22,1963.9,2206.02,1225.8,4764.7,2722.4,2292.55,2428.95,8335.3,1361.92,335.3,1229.62,2510.3,661.8,1192.53,123.3,1234.6,688.41,193.7,227.73,833.36,],2003: [3435.95,1150.81,2439.68,1176.65,1000.79,2487.85,1075.48,1467.9,3404.19,4493.31,3890.79,1638.42,1949.91,1043.08,4112.43,2358.86,2003.08,1995.78,7178.94,1178.25,293.85,1081.35,2189.68,558.28,1013.76,96.76,1063.89,589.91,169.81,195.46,753.91,],2002: [2982.57,997.47,2149.75,992.69,811.47,2258.17,958.88,1319.4,3038.9,3891.92,3227.99,1399.02,1765.8,972.73,3700.52,1978.37,1795.93,1780.79,6343.94,1074.85,270.96,956.12,1943.68,480.37,914.5,89.56,963.62,514.83,148.83,171.14,704.5,],
}def format_data(data: dict) -> dict:for year in range(2002, 2012):max_data, sum_data &＃61; 0, 0temp &＃61; data[year]max_data &＃61; max(temp)for i in range(len(temp)):sum_data &＃43;&＃61; temp[i]data[year][i] &＃61; {"name": name_list[i], "value": temp[i]}data[str(year) &＃43; "max"] &＃61; int(max_data / 100) * 100data[str(year) &＃43; "sum"] &＃61; sum_datareturn data# GDP
total_data["dataGDP"] &＃61; format_data(data&＃61;data_gdp)
# 第一产业
total_data["dataPI"] &＃61; format_data(data&＃61;data_pi)
# 第二产业
total_data["dataSI"] &＃61; format_data(data&＃61;data_si)
# 第三产业
total_data["dataTI"] &＃61; format_data(data&＃61;data_ti)
# 房地产
total_data["dataEstate"] &＃61; format_data(data&＃61;data_estate)
# 金融
total_data["dataFinancial"] &＃61; format_data(data&＃61;data_financial)#####################################################################################
# 2002 - 2011 年的数据
def get_year_overlap_chart(year: int) -> Bar:bar &＃61; (Bar().add_xaxis(xaxis_data&＃61;name_list).add_yaxis(series_name&＃61;"GDP",yaxis_data&＃61;total_data["dataGDP"][year],is_selected&＃61;False,label_opts&＃61;opts.LabelOpts(is_show&＃61;False),).add_yaxis(series_name&＃61;"金融",yaxis_data&＃61;total_data["dataFinancial"][year],is_selected&＃61;False,label_opts&＃61;opts.LabelOpts(is_show&＃61;False),).add_yaxis(series_name&＃61;"房地产",yaxis_data&＃61;total_data["dataEstate"][year],is_selected&＃61;False,label_opts&＃61;opts.LabelOpts(is_show&＃61;False),).add_yaxis(series_name&＃61;"第一产业",yaxis_data&＃61;total_data["dataPI"][year],label_opts&＃61;opts.LabelOpts(is_show&＃61;False),).add_yaxis(series_name&＃61;"第二产业",yaxis_data&＃61;total_data["dataSI"][year],label_opts&＃61;opts.LabelOpts(is_show&＃61;False),).add_yaxis(series_name&＃61;"第三产业",yaxis_data&＃61;total_data["dataTI"][year],label_opts&＃61;opts.LabelOpts(is_show&＃61;False),).set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"{}全国宏观经济指标".format(year), subtitle&＃61;"数据来自国家统计局"),tooltip_opts&＃61;opts.TooltipOpts(is_show&＃61;True, trigger&＃61;"axis", axis_pointer_type&＃61;"shadow"),))pie &＃61; (Pie().add(series_name&＃61;"GDP占比",data_pair&＃61;[["第一产业", total_data["dataPI"]["{}sum".format(year)]],["第二产业", total_data["dataSI"]["{}sum".format(year)]],["第三产业", total_data["dataTI"]["{}sum".format(year)]],],center&＃61;["75%", "35%"],radius&＃61;"28%",).set_series_opts(tooltip_opts&＃61;opts.TooltipOpts(is_show&＃61;True, trigger&＃61;"item")))return bar.overlap(pie)# 生成时间轴的图
timeline &＃61; Timeline(init_opts&＃61;opts.InitOpts(width&＃61;"1600px", height&＃61;"800px"))for y in range(2002, 2012):timeline.add(get_year_overlap_chart(year&＃61;y), time_point&＃61;str(y))# 1.0.0 版本的 add_schema 暂时没有补上 return self 所以只能这么写着
timeline.add_schema(is_auto_play&＃61;True, play_interval&＃61;1000)if __name__ &＃61;&＃61; "__main__":from pyecharts.render import make_snapshot# 使用snapshot-selenium 渲染图片from snapshot_selenium import snapshotmake_snapshot(snapshot, timeline.render(), "out.gif")