#!/usr/bin/env python # coding: utf-8 # # pyecharts # # pyecharts is a library to generate charts using Echarts. It simply provides the interface of 28+ kinds of charts between Echarts and Python. https://github.com/pyecharts/pyecharts # # > pip install pyecharts # # > pip install echarts-countries-pypkg # # > pip install echarts-china-provinces-pypkg # # > pip install echarts-china-cities-pypkg # http://nbviewer.jupyter.org/github/pyecharts/pyecharts-users-cases/blob/master/notebook-users-cases/notebook-user-cases.ipynb # # Bar # In[1]: from pyecharts import Bar attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] v1 = [5, 20, 36, 10, 75, 90] v2 = [10, 25, 8, 60, 20, 80] bar = Bar("柱状图数据堆叠示例") bar.add("商家A", attr, v1, is_stack=True) bar.add("商家B", attr, v2, is_stack=True) bar # In[3]: bar = Bar("柱状图示例") bar.add("商家A", attr, v1, is_stack=False) bar.add("商家B", attr, v2, is_stack=False) bar # In[2]: bar = Bar("标记线和标记点示例") bar.add("商家A", attr, v1, mark_point=["average"]) bar.add("商家B", attr, v2, mark_line=["min", "max"]) bar # In[4]: bar = Bar("x 轴和 y 轴交换") bar.add("商家A", attr, v1) bar.add("商家B", attr, v2, is_convert=True) bar # In[5]: import random attr = ["{}天".format(i) for i in range(30)] v1 = [random.randint(1, 30) for _ in range(30)] bar = Bar("Bar - datazoom - slider 示例") bar.add("", attr, v1, is_label_show=True, is_datazoom_show=True) bar # In[6]: attr = ["{}天".format(i) for i in range(20)] v1 = [random.randint(1, 20) for _ in range(20)] bar = Bar("坐标轴标签旋转示例") bar.add("", attr, v1, xaxis_interval=0, xaxis_rotate=30, yaxis_rotate=30) bar # # Bar3D # In[147]: from pyecharts import Bar3D bar3d = Bar3D("3D 柱状图示例", width=1200, height=600) x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a", "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"] y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"] data = [[0, 0, 5], [0, 1, 1], [0, 2, 0], [0, 3, 0], [0, 4, 0], [0, 5, 0], [0, 6, 0], [0, 7, 0], [0, 8, 0],[0, 9, 0], [0, 10, 0], [0, 11, 2], [0, 12, 4], [0, 13, 1], [0, 14, 1], [0, 15, 3], [0, 16, 4], [0, 17, 6], [0, 18, 4], [0, 19, 4], [0, 20, 3], [0, 21, 3], [0, 22, 2], [0, 23, 5], [1, 0, 7], [1, 1, 0], [1, 2, 0], [1, 3, 0], [1, 4, 0], [1, 5, 0], [1, 6, 0], [1, 7, 0], [1, 8, 0], [1, 9, 0], [1, 10, 5], [1, 11, 2], [1, 12, 2], [1, 13, 6], [1, 14, 9], [1, 15, 11], [1, 16, 6], [1, 17, 7], [1, 18, 8], [1, 19, 12], [1, 20, 5], [1, 21, 5], [1, 22, 7], [1, 23, 2], [2, 0, 1], [2, 1, 1], [2, 2, 0], [2, 3, 0], [2, 4, 0], [2, 5, 0], [2, 6, 0], [2, 7, 0], [2, 8, 0], [2, 9, 0], [2, 10, 3], [2, 11, 2], [2, 12, 1], [2, 13, 9], [2, 14, 8], [2, 15, 10], [2, 16, 6], [2, 17, 5], [2, 18, 5], [2, 19, 5], [2, 20, 7], [2, 21, 4], [2, 22, 2], [2, 23, 4], [3, 0, 7], [3, 1, 3], [3, 2, 0], [3, 3, 0], [3, 4, 0], [3, 5, 0], [3, 6, 0], [3, 7, 0], [3, 8, 1], [3, 9, 0], [3, 10, 5], [3, 11, 4], [3, 12, 7], [3, 13, 14], [3, 14, 13], [3, 15, 12], [3, 16, 9], [3, 17, 5], [3, 18, 5], [3, 19, 10], [3, 20, 6], [3, 21, 4], [3, 22, 4], [3, 23, 1], [4, 0, 1], [4, 1, 3], [4, 2, 0], [4, 3, 0], [4, 4, 0], [4, 5, 1], [4, 6, 0], [4, 7, 0], [4, 8, 0], [4, 9, 2], [4, 10, 4], [4, 11, 4], [4, 12, 2], [4, 13, 4], [4, 14, 4], [4, 15, 14], [4, 16, 12], [4, 17, 1], [4, 18, 8], [4, 19, 5], [4, 20, 3], [4, 21, 7], [4, 22, 3], [4, 23, 0], [5, 0, 2], [5, 1, 1], [5, 2, 0], [5, 3, 3], [5, 4, 0], [5, 5, 0], [5, 6, 0], [5, 7, 0], [5, 8, 2], [5, 9, 0], [5, 10, 4], [5, 11, 1], [5, 12, 5], [5, 13, 10], [5, 14, 5], [5, 15, 7], [5, 16, 11], [5, 17, 6], [5, 18, 0], [5, 19, 5], [5, 20, 3], [5, 21, 4], [5, 22, 2], [5, 23, 0], [6, 0, 1], [6, 1, 0], [6, 2, 0], [6, 3, 0], [6, 4, 0], [6, 5, 0], [6, 6, 0], [6, 7, 0], [6, 8, 0], [6, 9, 0], [6, 10, 1], [6, 11, 0], [6, 12, 2], [6, 13, 1], [6, 14, 3], [6, 15, 4], [6, 16, 0], [6, 17, 0], [6, 18, 0], [6, 19, 0], [6, 20, 1], [6, 21, 2], [6, 22, 2], [6, 23, 6]] # In[148]: range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf', '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'] bar3d.add("", x_axis, y_aixs, [[d[1], d[0], d[2]] for d in data], is_visualmap=True, visual_range=[0, 20], visual_range_color=range_color, grid3d_width=200, grid3d_depth=80) bar3d # # EffectScatter # In[8]: from pyecharts import EffectScatter es = EffectScatter("动态散点图各种图形示例") es.add("", [10], [10], symbol_size=20, effect_scale=3.5, effect_period=3, symbol="pin") es.add("", [20], [20], symbol_size=12, effect_scale=4.5, effect_period=4,symbol="rect") es.add("", [30], [30], symbol_size=30, effect_scale=5.5, effect_period=5,symbol="roundRect") es.add("", [40], [40], symbol_size=10, effect_scale=6.5, effect_brushtype='fill',symbol="diamond") es.add("", [50], [50], symbol_size=16, effect_scale=5.5, effect_period=3,symbol="arrow") es.add("", [60], [60], symbol_size=6, effect_scale=2.5, effect_period=3,symbol="triangle") es # # Funnel # In[9]: from pyecharts import Funnel attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] value = [20, 40, 60, 80, 100, 120] funnel = Funnel("漏斗图示例") funnel.add("商品", attr, value, is_label_show=True, label_pos="inside", label_text_color="#fff") funnel # # Gauge # In[10]: from pyecharts import Gauge gauge = Gauge("仪表盘示例") gauge.add("业务指标", "完成率", 66.66) gauge # # Geo # In[145]: from pyecharts import Geo data = [ ("海门", 9),("鄂尔多斯", 12),("招远", 12),("舟山", 12),("齐齐哈尔", 14),("盐城", 15), ("赤峰", 16),("青岛", 18),("乳山", 18),("金昌", 19),("泉州", 21),("莱西", 21), ("日照", 21),("胶南", 22),("南通", 23),("拉萨", 24),("云浮", 24),("梅州", 25), ("文登", 25),("上海", 25),("攀枝花", 25),("威海", 25),("承德", 25),("厦门", 26), ("汕尾", 26),("潮州", 26),("丹东", 27),("太仓", 27),("曲靖", 27),("烟台", 28), ("福州", 29),("瓦房店", 30),("即墨", 30),("抚顺", 31),("玉溪", 31),("张家口", 31), ("阳泉", 31),("莱州", 32),("湖州", 32),("汕头", 32),("昆山", 33),("宁波", 33), ("湛江", 33),("揭阳", 34),("荣成", 34),("连云港", 35),("葫芦岛", 35),("常熟", 36), ("东莞", 36),("河源", 36),("淮安", 36),("泰州", 36),("南宁", 37),("营口", 37), ("惠州", 37),("江阴", 37),("蓬莱", 37),("韶关", 38),("嘉峪关", 38),("广州", 38), ("延安", 38),("太原", 39),("清远", 39),("中山", 39),("昆明", 39),("寿光", 40), ("盘锦", 40),("长治", 41),("深圳", 41),("珠海", 42),("宿迁", 43),("咸阳", 43), ("铜川", 44),("平度", 44),("佛山", 44),("海口", 44),("江门", 45),("章丘", 45), ("肇庆", 46),("大连", 47),("临汾", 47),("吴江", 47),("石嘴山", 49),("沈阳", 50), ("苏州", 50),("茂名", 50),("嘉兴", 51),("长春", 51),("胶州", 52),("银川", 52), ("张家港", 52),("三门峡", 53),("锦州", 54),("南昌", 54),("柳州", 54),("三亚", 54), ("自贡", 56),("吉林", 56),("阳江", 57),("泸州", 57),("西宁", 57),("宜宾", 58), ("呼和浩特", 58),("成都", 58),("大同", 58),("镇江", 59),("桂林", 59),("张家界", 59), ("宜兴", 59),("北海", 60),("西安", 61),("金坛", 62),("东营", 62),("牡丹江", 63), ("遵义", 63),("绍兴", 63),("扬州", 64),("常州", 64),("潍坊", 65),("重庆", 66), ("台州", 67),("南京", 67),("滨州", 70),("贵阳", 71),("无锡", 71),("本溪", 71), ("克拉玛依", 72),("渭南", 72),("马鞍山", 72),("宝鸡", 72),("焦作", 75),("句容", 75), ("北京", 79),("徐州", 79),("衡水", 80),("包头", 80),("绵阳", 80),("乌鲁木齐", 84), ("枣庄", 84),("杭州", 84),("淄博", 85),("鞍山", 86),("溧阳", 86),("库尔勒", 86), ("安阳", 90),("开封", 90),("济南", 92),("德阳", 93),("温州", 95),("九江", 96), ("邯郸", 98),("临安", 99),("兰州", 99),("沧州", 100),("临沂", 103),("南充", 104), ("天津", 105),("富阳", 106),("泰安", 112),("诸暨", 112),("郑州", 113),("哈尔滨", 114), ("聊城", 116),("芜湖", 117),("唐山", 119),("平顶山", 119),("邢台", 119),("德州", 120), ("济宁", 120),("荆州", 127),("宜昌", 130),("义乌", 132),("丽水", 133),("洛阳", 134), ("秦皇岛", 136),("株洲", 143),("石家庄", 147),("莱芜", 148),("常德", 152),("保定", 153), ("湘潭", 154),("金华", 157),("岳阳", 169),("长沙", 175),("衢州", 177),("廊坊", 193), ("菏泽", 194),("合肥", 229),("武汉", 273),("大庆", 279)] # In[146]: geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True) geo # In[12]: geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600, background_color='#404a59') attr, value = geo.cast(data) geo.add("", attr, value, type="heatmap", is_visualmap=True, visual_range=[0, 300], visual_text_color='#fff') geo # # Graph # In[143]: from pyecharts import Graph nodes = [{"name": "结点1", "symbolSize": 10}, {"name": "结点2", "symbolSize": 20}, {"name": "结点3", "symbolSize": 30}, {"name": "结点4", "symbolSize": 40}, {"name": "结点5", "symbolSize": 50}, {"name": "结点6", "symbolSize": 40}, {"name": "结点7", "symbolSize": 30}, {"name": "结点8", "symbolSize": 20}] # In[144]: links = [] for i in nodes: for j in nodes: links.append({"source": i.get('name'), "target": j.get('name')}) graph = Graph("关系图-力引导布局示例") graph.add("", nodes, links, repulsion=8000) graph # In[140]: from pyecharts import Graph nodes = [ { 'name': '乔布斯', 'symbolSize' : 30}, { 'name': '丽萨-乔布斯','symbolSize' : 20}, { 'name': '保罗-乔布斯','symbolSize' : 3}, { 'name': '克拉拉-乔布斯','symbolSize' : 3}, { 'name': '劳伦-鲍威尔','symbolSize' : 7}, { 'name': '史蒂夫-沃兹尼艾克','symbolSize' : 5}, { 'name': '奥巴马','symbolSize' : 8}, { 'name': '比尔-盖茨','symbolSize' : 9}, { 'name': '乔纳森-艾夫','symbolSize' : 4}, { 'name': '蒂姆-库克','symbolSize' : 4}, { 'name': '龙-韦恩','symbolSize' : 1}, ] # In[141]: relationship = [('丽萨-乔布斯', '乔布斯', 1), ('保罗-乔布斯', '乔布斯', 2), ('克拉拉-乔布斯', '乔布斯', 1), ('劳伦-鲍威尔', '乔布斯', 2), ('史蒂夫-沃兹尼艾克', '乔布斯', 3), ('奥巴马', '乔布斯', 1), ('比尔-盖茨', '乔布斯', 6), ('乔纳森-艾夫', '乔布斯', 1), ('蒂姆-库克', '乔布斯', 1), ('龙-韦恩', '乔布斯', 1), ('克拉拉-乔布斯', '保罗-乔布斯', 1), ('奥巴马', '保罗-乔布斯', 1), ('奥巴马', '克拉拉-乔布斯', 1), ('奥巴马', '劳伦-鲍威尔', 1), ('奥巴马', '史蒂夫-沃兹尼艾克', 1), ('比尔-盖茨', '奥巴马', 6), ('比尔-盖茨', '克拉拉-乔布斯', 1), ('蒂姆-库克', '奥巴马', 1)] # In[142]: links = [] for i in relationship: links.append({"source": i[0], "target": i[1]}) graph = Graph("关系图-力引导布局示例2") graph.add("", nodes, links, repulsion=200) graph # In[150]: import networkx as nx from networkx.readwrite import json_graph from pyecharts import Graph g = nx.Graph() g.add_node('G1', name='Gateway 1') g.add_node('N2', name='Node 2') g.add_node('N3', name='Node 3') g.add_edge('G1', 'N2') g.add_edge('G1', 'N3') g_data = json_graph.node_link_data(g) eg = Graph('设备最新拓扑图') eg.add('Devices', nodes=g_data['nodes'], links=g_data['links']) eg # In[156]: ba=nx.barabasi_albert_graph(100,5) g_data = json_graph.node_link_data(ba) eg = Graph('BA Network') eg.add('BA', nodes=g_data['nodes'], links=g_data['links']) eg # In[155]: G=nx.karate_club_graph() g_data = json_graph.node_link_data(G) eg = Graph('karate_club') eg.add('karate_club', nodes=g_data['nodes'], links=g_data['links']) eg # # HeatMap # In[19]: from pyecharts import HeatMap x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a", "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"] y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"] data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)] heatmap = HeatMap() heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True, visual_text_color="#000", visual_orient='horizontal') heatmap # # Kline # In[138]: from pyecharts import Kline v1 = [[2320.26, 2320.26, 2287.3, 2362.94], [2300, 2291.3, 2288.26, 2308.38], [2295.35, 2346.5, 2295.35, 2345.92], [2347.22, 2358.98, 2337.35, 2363.8], [2360.75, 2382.48, 2347.89, 2383.76], [2383.43, 2385.42, 2371.23, 2391.82], [2377.41, 2419.02, 2369.57, 2421.15], [2425.92, 2428.15, 2417.58, 2440.38], [2411, 2433.13, 2403.3, 2437.42], [2432.68, 2334.48, 2427.7, 2441.73], [2430.69, 2418.53, 2394.22, 2433.89], [2416.62, 2432.4, 2414.4, 2443.03], [2441.91, 2421.56, 2418.43, 2444.8], [2420.26, 2382.91, 2373.53, 2427.07], [2383.49, 2397.18, 2370.61, 2397.94], [2378.82, 2325.95, 2309.17, 2378.82], [2322.94, 2314.16, 2308.76, 2330.88], [2320.62, 2325.82, 2315.01, 2338.78], [2313.74, 2293.34, 2289.89, 2340.71], [2297.77, 2313.22, 2292.03, 2324.63], [2322.32, 2365.59, 2308.92, 2366.16], [2364.54, 2359.51, 2330.86, 2369.65], [2332.08, 2273.4, 2259.25, 2333.54], [2274.81, 2326.31, 2270.1, 2328.14], [2333.61, 2347.18, 2321.6, 2351.44], [2340.44, 2324.29, 2304.27, 2352.02], [2326.42, 2318.61, 2314.59, 2333.67], [2314.68, 2310.59, 2296.58, 2320.96], [2309.16, 2286.6, 2264.83, 2333.29], [2282.17, 2263.97, 2253.25, 2286.33], [2255.77, 2270.28, 2253.31, 2276.22]] # In[139]: kline = Kline("K 线图示例") kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1) kline # # Line # In[21]: from pyecharts import Line attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] v1 = [5, 20, 36, 10, 10, 100] v2 = [55, 60, 16, 20, 15, 80] line = Line("折线图示例") line.add("商家A", attr, v1, mark_point=["average"]) line.add("商家B", attr, v2, is_smooth=True, mark_line=["max", "average"]) line # In[22]: line = Line("折线图-面积图示例") line.add("商家A", attr, v1, is_fill=True, line_opacity=0.2, area_opacity=0.4, symbol=None) line.add("商家B", attr, v2, is_fill=True, area_color='#000', area_opacity=0.3, is_smooth=True) line # # Line3D # In[136]: from pyecharts import Line3D import math _data = [] for t in range(0, 25000): _t = t / 1000 x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t) y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t) z = _t + 2.0 * math.sin(75 * _t) _data.append([x, y, z]) # In[137]: range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf', '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'] line3d = Line3D("3D 折线图示例", width=1200, height=600) line3d.add("", _data, is_visualmap=True, visual_range_color=range_color, visual_range=[0, 30], is_grid3d_rotate=True, grid3d_rotate_speed=180) line3d # # Liquid # In[24]: from pyecharts import Liquid liquid = Liquid("水球图示例") liquid.add("Liquid", [0.6, 0.5, 0.4, 0.3], is_liquid_outline_show=False) liquid # # Map # In[25]: from pyecharts import Map value = [155, 10, 66, 78] attr = ["福建", "山东", "北京", "上海"] map = Map("全国地图示例", width=1200, height=600) map.add("", attr, value, maptype='china') map # In[30]: from pyecharts import Map value = [20, 90, 453, 77, 65] attr = ['南京市', '镇江市', '扬州市', '徐州市', '苏州市'] map = Map("江苏省地图示例", width=1200, height=600) map.add("", attr, value, maptype='江苏', is_visualmap=True, visual_text_color='#000') map # In[134]: #https://baike.baidu.com/item/沧州 data = [('运河区', 109.92), ('新华区', 109.47), ('泊头市', 1006.5), ('任丘市', 1023.0), ('黄骅市', 1544.7), ('河间市', 1333.0), ('沧县', 1104.0), ('青县', 968.0), ('东光县', 730.0), ('海兴县', 915.1), ('盐山县', 796.0), ('肃宁县', 525.0), ('南皮县', 794.0), ('吴桥县', 600.0), ('献县', 1191.0), ('孟村回族自治县', 387.0)] # In[135]: districts, areas = zip(*data) map = Map("沧州市图例－各区面积", width=1200, height=600) map.add("", districts, areas, maptype='沧州', is_visualmap=True, visual_range=[min(areas), max(areas)], visual_text_color='#000', is_map_symbol_show=False, is_label_show=True) map # In[111]: value = [1, 100] attr = ['Gujarat', 'Tamil Nadu'] map = Map('India', width=800, height=600) map.add('', attr, value, maptype=u'印度', is_visualmap=True, visual_text_color="#000") map # In[31]: value = [95.1, 23.2, 43.3, 66.4, 88.5] attr= ["China", "Canada", "Brazil", "Russia", "United States"] map = Map("世界地图示例", width=1200, height=600) map.add("", attr, value, maptype="world", is_visualmap=True, visual_text_color='#000') map # # Parallel # In[32]: from pyecharts import Parallel schema = ["data", "AQI", "PM2.5", "PM10", "CO", "NO2"] data = [[1, 91, 45, 125, 0.82, 34], [2, 65, 27, 78, 0.86, 45,], [3, 83, 60, 84, 1.09, 73], [4, 109, 81, 121, 1.28, 68], [5, 106, 77, 114, 1.07, 55], [6, 109, 81, 121, 1.28, 68], [7, 106, 77, 114, 1.07, 55], [8, 89, 65, 78, 0.86, 51, 26], [9, 53, 33, 47, 0.64, 50, 17], [10, 80, 55, 80, 1.01, 75, 24], [11, 117, 81, 124, 1.03, 45]] parallel = Parallel("平行坐标系-默认指示器") parallel.config(schema) parallel.add("parallel", data, is_random=True) parallel # # Pie # In[33]: from pyecharts import Pie attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] v1 = [11, 12, 13, 10, 10, 10] pie = Pie("饼图示例") pie.add("", attr, v1, is_label_show=True) pie # In[34]: attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] v1 = [11, 12, 13, 10, 10, 10] v2 = [19, 21, 32, 20, 20, 33] pie = Pie("饼图-玫瑰图示例", title_pos='center', width=900) pie.add("商品A", attr, v1, center=[25, 50], is_random=True, radius=[30, 75], rosetype='radius') pie.add("商品B", attr, v2, center=[75, 50], is_random=True, radius=[30, 75], rosetype='area', is_legend_show=False, is_label_show=True) pie # # Polar # In[35]: from pyecharts import Polar import random data = [(i, random.randint(1, 100)) for i in range(101)] polar = Polar("极坐标系-散点图示例") polar.add("", data, boundary_gap=False, type='scatter', is_splitline_show=False, area_color=None, is_axisline_show=True) polar # In[36]: radius = ['周一', '周二', '周三', '周四', '周五', '周六', '周日'] polar = Polar("极坐标系-堆叠柱状图示例", width=1200, height=600) polar.add("A", [1, 2, 3, 4, 3, 5, 1], radius_data=radius, type='barRadius', is_stack=True) polar.add("B", [2, 4, 6, 1, 2, 3, 1], radius_data=radius, type='barRadius', is_stack=True) polar.add("C", [1, 2, 3, 4, 1, 2, 5], radius_data=radius, type='barRadius', is_stack=True) polar # # Radar # In[89]: from pyecharts import Radar schema = [ ("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)] v1 = [[4300, 10000, 28000, 35000, 50000, 19000]] v2 = [[5000, 14000, 28000, 31000, 42000, 21000]] radar = Radar() radar.config(schema) radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True) radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False, legend_selectedmode='single') radar # # Scatter # In[5]: help(scatter) # In[2]: from pyecharts import Scatter v1 = [10, 20, 30, 40, 50, 60] v2 = [10, 20, 30, 40, 50, 60] scatter = Scatter("散点图示例") scatter.add("A", v1, v2) #scatter.add("B", v1[::-1], v2) scatter # In[39]: scatter = Scatter("散点图示例") scatter.add("A", ["a", "b", "c", "d", "e", "f"], v2) scatter.add("B", ["a", "b", "c", "d", "e", "f"], v1[::-1], xaxis_type="category") scatter # # Scatter3D # In[40]: from pyecharts import Scatter3D import random data = [[random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)] for _ in range(80)] range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf', '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'] scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600) scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color) scatter3D # # WordCloud # In[41]: from pyecharts import WordCloud name = ['Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World', 'Charter Communications', 'Chick Fil A', 'Planet Fitness', 'Pitch Perfect', 'Express', 'Home', 'Johnny Depp', 'Lena Dunham', 'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham', 'Rita Ora', 'Serena Williams', 'NCAA baseball tournament', 'Point Break'] value = [10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112, 965, 847, 582, 555, 550, 462, 366, 360, 282, 273, 265] wordcloud = WordCloud(width=1300, height=620) wordcloud.add("", name, value, word_size_range=[20, 100]) wordcloud # In[55]: import jieba.analyse import numpy as np with open('../data/gov_reports1954-2017.txt', 'r') as f: reports = f.readlines() txt = reports[-1] tf = jieba.analyse.extract_tags(txt, topK=100, withWeight=True) name, value = np.array(tf).T wordcloud = WordCloud(width=1300, height=620) wordcloud.add("", name, value, word_size_range=[20, 100]) wordcloud # # Grid # In[5]: from pyecharts import Bar, Line, Scatter, EffectScatter, Grid attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] v1 = [5, 20, 36, 10, 75, 90] v2 = [10, 25, 8, 60, 20, 80] bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%") bar.add("商家A", attr, v1, is_stack=True) bar.add("商家B", attr, v2, is_stack=True, legend_pos="80%") # In[6]: line = Line("折线图示例") attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日'] line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"]) line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"], mark_line=["average"], legend_pos="20%") v1 = [5, 20, 36, 10, 75, 90] v2 = [10, 25, 8, 60, 20, 80] scatter = Scatter("散点图示例", title_top="50%", title_pos="65%") scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%") es = EffectScatter("动态散点图示例", title_top="50%") es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6, legend_top="50%", legend_pos="20%") # In[7]: grid = Grid() grid.add(bar, grid_bottom="60%", grid_left="60%") grid.add(line, grid_bottom="60%", grid_right="60%") grid.add(scatter, grid_top="60%", grid_left="60%") grid.add(es, grid_top="60%", grid_right="60%") grid # # Save html files # In[9]: grid.render(path = 'grid.html') #help(grid.render) # # Overlap # In[120]: from pyecharts import Bar, Line, Overlap attr = ['A', 'B', 'C', 'D', 'E', 'F'] v1 = [10, 20, 30, 40, 50, 60] v2 = [38, 28, 58, 48, 78, 68] bar = Bar("Line - Bar 示例") bar.add("bar", attr, v1) line = Line() line.add("line", attr, v2) overlap = Overlap() overlap.add(bar) overlap.add(line) overlap # # Page # In[129]: from pyecharts import Page, Scatter, EffectScatter page = Page() v1 = [10, 20, 30, 40, 50, 60] v2 = [10, 20, 30, 40, 50, 60] scatter = Scatter("散点图示例") scatter.add("A", v1, v2) scatter.add("B", v1[::-1], v2) page.add(scatter) es = EffectScatter("动态散点图示例", title_top="50%") es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6, legend_top="50%", legend_pos="20%") page.add(es) # In[130]: page # # Timeline # In[124]: from pyecharts import Bar, Timeline from random import randint attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"] bar_1 = Bar("2012 年销量", "数据纯属虚构") bar_1.add("春季", attr, [randint(10, 100) for _ in range(6)]) bar_1.add("夏季", attr, [randint(10, 100) for _ in range(6)]) bar_1.add("秋季", attr, [randint(10, 100) for _ in range(6)]) bar_1.add("冬季", attr, [randint(10, 100) for _ in range(6)]) # In[125]: bar_2 = Bar("2013 年销量", "数据纯属虚构") bar_2.add("春季", attr, [randint(10, 100) for _ in range(6)]) bar_2.add("夏季", attr, [randint(10, 100) for _ in range(6)]) bar_2.add("秋季", attr, [randint(10, 100) for _ in range(6)]) bar_2.add("冬季", attr, [randint(10, 100) for _ in range(6)]) bar_3 = Bar("2014 年销量", "数据纯属虚构") bar_3.add("春季", attr, [randint(10, 100) for _ in range(6)]) bar_3.add("夏季", attr, [randint(10, 100) for _ in range(6)]) bar_3.add("秋季", attr, [randint(10, 100) for _ in range(6)]) bar_3.add("冬季", attr, [randint(10, 100) for _ in range(6)]) # In[127]: bar_4 = Bar("2015 年销量", "数据纯属虚构") bar_4.add("春季", attr, [randint(10, 100) for _ in range(6)]) bar_4.add("夏季", attr, [randint(10, 100) for _ in range(6)]) bar_4.add("秋季", attr, [randint(10, 100) for _ in range(6)]) bar_4.add("冬季", attr, [randint(10, 100) for _ in range(6)]) bar_5 = Bar("2016 年销量", "数据纯属虚构") bar_5.add("春季", attr, [randint(10, 100) for _ in range(6)]) bar_5.add("夏季", attr, [randint(10, 100) for _ in range(6)]) bar_5.add("秋季", attr, [randint(10, 100) for _ in range(6)]) bar_5.add("冬季", attr, [randint(10, 100) for _ in range(6)], is_legend_show=True) # In[128]: timeline = Timeline(is_auto_play=True, timeline_bottom=0) timeline.add(bar_1, '2012 年') timeline.add(bar_2, '2013 年') timeline.add(bar_3, '2014 年') timeline.add(bar_4, '2015 年') timeline.add(bar_5, '2016 年') timeline # # More # https://github.com/pyecharts/pyecharts/tree/master/test # In[2]: from pyecharts import ThemeRiver data = [ ['2015/11/08', 10, 'DQ'], ['2015/11/09', 15, 'DQ'], ['2015/11/10', 35, 'DQ'], ['2015/11/14', 7, 'DQ'], ['2015/11/15', 2, 'DQ'], ['2015/11/16', 17, 'DQ'], ['2015/11/17', 33, 'DQ'], ['2015/11/18', 40, 'DQ'], ['2015/11/19', 32, 'DQ'], ['2015/11/20', 26, 'DQ'], ['2015/11/21', 35, 'DQ'], ['2015/11/22', 40, 'DQ'], ['2015/11/23', 32, 'DQ'], ['2015/11/24', 26, 'DQ'], ['2015/11/25', 22, 'DQ'], ['2015/11/08', 35, 'TY'], ['2015/11/09', 36, 'TY'], ['2015/11/10', 37, 'TY'], ['2015/11/11', 22, 'TY'], ['2015/11/12', 24, 'TY'], ['2015/11/13', 26, 'TY'], ['2015/11/14', 34, 'TY'], ['2015/11/15', 21, 'TY'], ['2015/11/16', 18, 'TY'], ['2015/11/17', 45, 'TY'], ['2015/11/18', 32, 'TY'], ['2015/11/19', 35, 'TY'], ['2015/11/20', 30, 'TY'], ['2015/11/21', 28, 'TY'], ['2015/11/22', 27, 'TY'], ['2015/11/23', 26, 'TY'], ['2015/11/24', 15, 'TY'], ['2015/11/25', 30, 'TY'], ['2015/11/26', 35, 'TY'], ['2015/11/27', 42, 'TY'], ['2015/11/28', 42, 'TY'], ['2015/11/08', 21, 'SS'], ['2015/11/09', 25, 'SS'], ['2015/11/10', 27, 'SS'], ['2015/11/11', 23, 'SS'], ['2015/11/12', 24, 'SS'], ['2015/11/13', 21, 'SS'], ['2015/11/14', 35, 'SS'], ['2015/11/15', 39, 'SS'] ] tr = ThemeRiver("主题河流图示例图") tr.add(['DQ', 'TY', 'SS', 'QG', 'SY', 'DD'], data, is_label_show=True) tr # In[163]: from pyecharts import Boxplot boxplot = Boxplot("箱形图") x_axis = ['expr1', 'expr2', 'expr3', 'expr4', 'expr5'] y_axis = [ [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980, 930, 650, 760, 810, 1000, 1000, 960, 960], [960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790, 810, 880, 880, 830, 800, 790, 760, 800], [880, 880, 880, 860, 720, 720, 620, 860, 970, 950, 880, 910, 850, 870, 840, 840, 850, 840, 840, 840], [890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920, 890, 860, 880, 720, 840, 850, 850, 780], [890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870, 810, 740, 810, 940, 950, 800, 810, 870] ] _yaxis = boxplot.prepare_data(y_axis) boxplot.add("boxplot", x_axis, _yaxis) boxplot # In[ ]: