数据可视化pyecharts——数据分析（柱状图、折线图、饼图）

安装

首先确保已经安装了pyecharts库，如果没有，可以通过pip install pyecharts进行安装。

柱状图

• 从pyecharts.charts导入Bar，从pyecharts导入options。
• 准备数据（如类别数据x_data和对应的数值数据y_data）。
• 创建Bar对象，通过add_xaxis添加类别数据，add_yaxis添加数据系列，可设置系列名称。
• 可以使用set_global_opts设置全局配置项，如标题等，最后使用render方法生成图表文件。

折线图

• 从pyecharts.charts导入Line。
• 类似柱状图，准备数据后创建Line对象，添加轴数据。
• 能设置线条样式、标记点等个性化配置，再通过全局配置项设置相关属性，最后渲染。

饼图

• 从pyecharts.charts导入Pie。
• 准备数据（通常是数据和对应的标签）。
• 创建Pie对象，通过add方法添加数据系列，可设置半径等参数。
• 同样可配置全局选项，如标题、图例等，完成后进行渲染。

通过这些步骤，可以利用 pyecharts 方便地实现柱状图展示数据对比、折线图展示数据趋势、饼图展示数据占比等数据可视化操作。

数据展示

	2019	2018	2017	2016	2015	2014	2013	2012	2011	2010	2009	2008	2007	2006	2005	2004	2003	2002	2001	2000
北京	5817.1	5785.9176	5430.7875	5081.26	4723.86	4027.1609	3661.1097	3314.934	3006.28	2353.9301	2026.8089	1837.3238	1492.638	1117.1514	919.2098	744.4874	592.5388	533.99	454.1676	344.9968
天津	2410.25	2106.2397	2310.3552	2723.5	2667.11	2390.3518	2079.0716	1760.0201	1455.13	1068.8093	821.9916	675.6186	540.439	417.0479	331.8507	246.18	204.5295	209.7021	163.635	133.6069
河北	3742.67	3513.8643	3233.8332	2849.87	2649.18	2446.6166	2295.6203	2084.2825	1737.77	1331.8547	1067.1231	947.5858	789.1198	620.534	515.7017	407.8273	335.8263	302.3068	283.5023	248.7621
山西	2347.56	2292.6982	1867.0022	1557	1642.35	1820.635	1701.6227	1516.378	1213.43	969.6652	805.8279	748.0047	597.887	583.3752	368.3437	256.3634	186.0547	150.8245	132.7618	114.4762
内蒙古	2059.74	1857.6493	1703.2095	2016.43	1964.48	1843.6736	1720.9843	1552.7453	1356.67	1069.9776	850.8588	650.6764	492.3615	343.3774	277.4553	196.7589	138.7157	112.8546	99.4313	95.032
辽宁	2651.96	2616.0832	2392.7653	2200.49	2127.39	3192.7813	3343.8106	3105.3785	2643.15	2004.8352	1591.2197	1356.0812	1082.6948	817.6718	675.2768	529.6405	447.049	399.6888	370.4387	295.6274
吉林	1116.86	1240.8892	1210.9081	1263.78	1229.35	1203.3843	1156.9616	1041.2514	850.1	602.4092	487.0943	422.7961	320.6892	245.2045	207.152	166.2807	154.0033	131.4885	121.1015	103.8267
黑龙江	1262.64	1282.595	1243.3118	1148.41	1165.88	1301.312	1277.3951	1163.1708	997.55	755.5788	641.6627	578.2773	440.4689	386.844	318.2056	289.42	248.8643	231.8908	213.6398	185.3379
上海	7165.1	7108.148	6642.2638	6406.13	5519.5	4585.5534	4109.5086	3743.7053	3429.83	2873.584	2540.2975	2358.7464	2074.4792	1576.0742	1417.3976	1106.1932	886.2277	719.79	609.4719	485.3777
江苏	8802.36	8630.1605	8171.5315	8121.23	8028.59	7233.1426	6568.464	5860.6884	5148.91	4079.8595	3228.78	2731.4074	2237.7276	1656.682	1322.6753	980.4939	798.1065	643.6966	572.1473	448.3097
浙江	7048	6598.212	5804.3837	5301.98	4809.94	4122.0211	3796.9241	3441.2267	3150.8	2608.4655	2142.5131	1933.389	1649.4981	1298.2044	1066.5964	805.9479	706.5607	566.8522	500.6948	342.7745
安徽	3182.54	3048.6705	2812.4495	2672.79	2454.3	2218.4418	2075.075	1792.7192	1463.56	1149.3952	863.9175	724.6197	543.6973	428.0265	334.017	274.6284	220.7487	200.2154	192.1813	178.7187
福建	3052.72	3007.4087	2809.0332	2654.83	2544.24	2362.2138	2119.4455	1776.1728	1501.51	1151.4923	932.4282	833.4032	699.4577	541.1707	432.6003	333.523	304.7095	272.8867	274.2846	234.1061
江西	2486.51	2373.008	2247.0624	2151.47	2165.74	1881.8315	1621.2358	1371.994	1053.43	778.0922	581.3012	488.6476	389.851	305.5214	252.9236	205.7667	168.167	140.5457	131.979	111.5536
山东	6526.64	6485.3959	6098.6324	5860.18	5529.33	5026.8273	4559.9463	4059.4301	3455.93	2749.3842	2198.6324	1957.0541	1675.398	1356.2526	1073.125	828.3306	713.7877	610.2242	573.1793	463.6788
河南	4041.6	3766.0183	3407.2187	3153.47	3016.05	2739.2556	2415.4482	2040.331	1721.76	1381.3178	1126.0638	1008.9009	862.0804	679.1715	537.6514	428.7799	338.0535	296.7179	267.7459	246.4694
湖北	3388.39	3307.0782	3248.3159	3102.06	3005.53	2566.8953	2191.2221	1823.0532	1526.91	1011.2314	814.8653	710.8492	590.3552	476.0823	375.5217	310.4464	259.7636	243.4403	231.941	214.345
湖南	3006.99	2860.8443	2757.8212	2697.88	2515.43	2262.7859	2030.8758	1782.156	1517.07	1081.6901	847.6178	722.7122	606.5508	477.9274	395.2651	320.6279	268.6469	231.1459	205.4078	177.0403
广东	12651.46	12105.2552	11320.3497	10390.35	9366.78	8065.0758	7081.4655	6229.1804	5514.84	4517.0445	3649.811	3310.3235	2785.8007	2179.4608	1807.2044	1418.5056	1315.5151	1201.6126	1160.5126	910.556
广西	1811.89	1681.4466	1615.1273	1556.27	1515.16	1422.2803	1317.6035	1166.0614	947.72	771.9918	620.9888	518.4245	418.8265	342.5788	283.0359	237.7721	203.6578	186.732	178.6706	147.0539
海南	814.13	752.6673	674.105	637.51	627.7	555.3064	481.014	409.437	340.12	270.9915	178.242	144.8584	108.2935	81.8139	68.6802	57.0358	51.3205	46.2385	43.7656	39.1995
重庆	2134.88	2265.5421	2252.3788	2227.91	2154.83	1922.0159	1693.2438	1703.4885	1488.33	952.0745	655.1701	577.5738	442.7	317.7165	256.8072	200.6241	161.5618	157.8651	106.1243	87.2442
四川	4070.69	3911.0092	3577.9887	3388.85	3355.44	3061.0684	2784.0952	2421.2703	2044.79	1561.6727	1174.5927	1041.6603	850.8606	607.585	479.6635	385.7848	336.5917	291.8746	271.1245	233.863
贵州	1767.36	1726.8516	1613.8377	1561.34	1503.38	1366.6731	1206.4146	1014.0547	773.08	533.7309	416.4761	347.8416	285.1375	226.8157	182.4963	149.2855	124.5552	108.28	99.7494	85.2324
云南	2073.53	1994.3458	1886.1687	1812.29	1808.1	1698.0574	1611.2955	1338.1509	1111.16	871.1875	698.2525	614.0518	486.7146	379.9702	312.649	263.3618	228.9992	206.7594	191.2799	180.745
西藏	222	230.3543	185.8341	155.99	137.13	124.2708	95.0237	86.5827	54.76	36.6473	30.0894	24.8823	20.1412	14.5607	12.0312	10.0188	8.1499	7.3082	6.1108	5.3848
陕西	2287.73	2243.1391	2006.6939	1833.99	2059.95	1890.4044	1748.3305	1600.6862	1500.18	958.2065	735.2704	591.475	475.2398	362.4805	275.3183	214.9586	177.33	150.2934	135.8109	114.9711
甘肃	850.23	871.0537	815.7323	786.97	743.86	672.6698	607.2717	520.3993	450.12	353.5833	286.5898	264.965	190.9107	141.2152	123.5026	104.16	87.6561	76.2432	69.9485	61.2849
青海	282.14	272.887	246.1961	238.51	267.13	251.6759	223.8586	186.4165	151.81	110.2153	87.7381	71.5692	56.7083	42.2437	33.8222	26.996	24.0411	21.0965	19.8241	16.5843
宁夏	423.55	436.5205	417.5888	387.66	373.4	339.8627	308.3376	263.9569	219.98	153.5507	111.5755	95.009	80.0312	61.357	47.7216	37.4677	30.031	26.4714	27.5745	20.8244
新疆	1577.6	1531.4229	1466.5189	1298.95	1330.9	1282.3367	1128.4875	908.9655	720.43	500.5759	388.7848	361.0616	285.86	219.4628	180.3184	155.704	128.2218	116.4724	95.0933	79.0724

 数据引用处理：

# 读取 Excel 文件中的数据
data = pd.read_excel('全国各省财政收入.xlsx', index_col=0)
years = list(data.keys())
citys = list(data.index)
citys = [city.replace('省', '').replace('市', '').replace('自治区', '') for city in citys]

 重新组织数据

# 重新组织数据
total_data = {}
for year in years:temp = list(data[year])total_data[year] = []for i in range(len(temp)):total_data[year].append({"name": citys[i], "value": temp[i]})
print(total_data)

 选取合适代码，进行画图：

对应的代码：

折线图

# 获取指定年份的图表
def get_year_overlap_chart(year: int) -> Line:line = (Line().add_xaxis(xaxis_data=[item["name"] for item in total_data[year]]).add_yaxis(series_name="财政收入",y_axis=[item["value"] for item in total_data[year]],label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="{}年全国各省财政收入".format(year), subtitle="数据来源：全国各省财政收入.xlsx"),tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="shadow"),legend_opts=opts.LegendOpts(selected_map={"财政收入": False,}),))return line# 生成时间轴的图
timeline = Timeline()
for y in years:timeline.add(get_year_overlap_chart(year=y), time_point=str(y))# 设置时间轴自动播放等属性
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("finance_indices_2002.html")

 

柱状图

# 获取指定年份的图表
def get_year_overlap_chart(year: int) -> Bar:bar = (Bar().add_xaxis(xaxis_data=[item["name"] for item in total_data[year]]).add_yaxis(series_name="财政收入",y_axis=[item["value"] for item in total_data[year]],label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="{}年全国各省财政收入".format(year), subtitle="数据来源：全国各省财政收入.xlsx"),tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="shadow"),legend_opts=opts.LegendOpts(selected_map={"财政收入": False,}),))return bar# 生成时间轴的图
timeline = Timeline()for y in years:timeline.add(get_year_overlap_chart(year=y), time_point=str(y))# 设置时间轴自动播放等属性
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("finance_indices_2002.html")
#%%
from pyecharts.charts import Line, Timeline
from pyecharts import options as opts# 获取指定年份的图表
def get_year_overlap_chart(year: int) -> Line:line = (Line().add_xaxis(xaxis_data=[item["name"] for item in total_data[year]]).add_yaxis(series_name="财政收入",y_axis=[item["value"] for item in total_data[year]],label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="{}年全国各省财政收入".format(year), subtitle="数据来源：全国各省财政收入.xlsx"),tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="shadow"),legend_opts=opts.LegendOpts(selected_map={"财政收入": False,}),)

 

饼图

# 获取指定年份的图表
def get_year_overlap_chart(year: int) -> Bar:bar = (Bar().add_xaxis(xaxis_data=[item["name"] for item in total_data[year]]).add_yaxis(series_name="财政收入",y_axis=[item["value"] for item in total_data[year]],label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="{}年全国各省财政收入".format(year), subtitle="数据来源：全国各省财政收入.xlsx"),tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="shadow"),legend_opts=opts.LegendOpts(selected_map={"财政收入": False,}),))return bar# 生成时间轴的图
timeline = Timeline()for y in years:timeline.add(get_year_overlap_chart(year=y), time_point=str(y))# 设置时间轴自动播放等属性
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("finance_indices_2002.html")
#%%
from pyecharts.charts import Line, Timeline
from pyecharts import options as opts# 获取指定年份的图表
def get_year_overlap_chart(year: int) -> Line:line = (Line().add_xaxis(xaxis_data=[item["name"] for item in total_data[year]]).add_yaxis(series_name="财政收入",y_axis=[item["value"] for item in total_data[year]],label_opts=opts.LabelOpts(is_show=False),).set_global_opts(title_opts=opts.TitleOpts(title="{}年全国各省财政收入".format(year), subtitle="数据来源：全国各省财政收入.xlsx"),tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="shadow"),legend_opts=opts.LegendOpts(selected_map={"财政收入": False,}),)