1.pyecharts安装:
pip install pyecharts2.pyecharts入门:
# 1.导入模块
from pyecharts.charts import Line# 2.创建Line对象
line = Line()
# 添加数据
line.add_xaxis(["中国", "美国", "印度"])
line.add_yaxis("GDP数据", [30, 20, 5])# 3.生成html文件
# render(文件名) 默认文件名:render.html
line.render("line.html")3.Python与Json数据的相互转换:
json.dumps(data) : 把python数据转化为了 json数据
json.loads(data) : 把josn数据转化为了 python数据
# 导入json模块
import json# 准备符合格式json格式要求的python数据
data = [{"name": "老王", "age": 16}, {"name": "张三", "age": 20}]# 通过 json.dumps(data) 方法把python数据转化为了 json数据
data = json.dumps(data)# 通过 json.loads(data) 方法把josn数据转化为了 python数据
data = json.loads(data)4.pyecharts常用配置项:
- 折线图相关配置项:
| 配置项 | 作用 | 代码实例 |
| init_opts | 对折线图初始化设置宽高 | init_opts=opts.InitOpts(width="1600px", height="800px") |
| .add_xaxis | 添加x轴数据 | .add_xaxis(列表) |
| .add_yaxis | 添加y轴数据 |
.set_global_opts全局配置选项:
| 配置项 | 作用 | 代码实例 |
| title_opts | 设置图标题和位置 | title_opts=opts.TitleOpts(title="标题", pos_left="center" |
| yaxis_opts | y轴配置项 | yaxis_opts=opts.AxisOpts(name="累计确诊人数") |
| xaxis_opts | x轴配置项 | xaxis_opts=opts.AxisOpts(name="时间") |
| legend_opts | 图例配置项 | legend_opts=opts.LegendOpts(pos_left='70%') |
-
.set_series_opts系列配置选项:
| 配置项 | 作用 | 代码实例 |
| label_opts | 标签配置项 | label_opts=opts.LabelOpts(position="right") |
| textstyle_opts | 文字样式配置项 | textstyle_opts=opts.TextStyleOpts(font_size=12) |
| linestyle_opts | 线样式配置项 | linestyle_opts=opts.LineStyleOpts(width=2) |
- .
add_yaxis相关配置选项:
| 配置项 | 作用 | 代码实例 |
| series_name | 设置图例名称 | series_name="美国确诊人数" |
| y_axis | 输入y轴数据 | y_axis=["列表"] |
| symbol_size | 设置点的大小 | symbol_size=10 |
| label_opts | 标签设置项：不显示标签 | label_opts=opts.LabelOpts(is_show=False) |
| linestyle_opts | 线条宽度和样式 | linestyle_opts=opts.LineStyleOpts(width=2) |
5.Faker自动生成模拟数据:
Faker.choose() : 自动生成名称
Faker.values() : 自动生成数据
from pyecharts.faker import Faker# 自动生成名称 ['衬衫', '毛衣', '领带', '裤子', '风衣', '高跟鞋', '袜子']
Faker.choose()# 自动生成数据 [148, 22, 71, 75, 95, 140, 140]
Faker.values()6.Timeline时间线轮播多图:
from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.charts import Timeline
import pyecharts.options as opts# Faker.choose():自动生成名称,如: ['衬衫', '毛衣', '领带', '裤子', '风衣', '高跟鞋', '袜子']
# Faker.values():自动生成数据,如:[148, 22, 71, 75, 95, 140, 140]
x_data = Faker.choose()# 创建 Timeline
t = Timeline()
for i in range(2000, 2022):bar = (Bar(init_opts=opts.InitOpts(width="1200px", height="600px")).add_xaxis(x_data).add_yaxis("各国GDP", Faker.values()).set_global_opts(title_opts=opts.TitleOpts(title=f"{i}年各国GDP柱状图", ), ).set_series_opts(label_opts=opts.LabelOpts(position="right")).reversal_axis() # x轴反转)t.add(bar, f"{i}年")# 配置 Timeline 播放选项
t.add_schema(play_interval=500, # 设置时间间隔is_auto_play=True, # 是否自动播放is_loop_play=False, # 是否循环播放# is_timeline_show=False # 是否显示时间轴
)t.render("bar.html")7.折线图示例:
from pyecharts.charts import Line
import pyecharts.options as optsyd_date=['4.7', '4.8', '4.9', '4.10', '4.11', '4.12', '4.13', '4.14', '4.15', '4.16', '4.17', '4.18', '4.19', '4.20', '4.21', '4.22', '4.23', '4.24', '4.25', '4.26', '4.27', '4.28', '4.29', '4.30', '5.1', '5.2', '5.3', '5.4', '5.5', '5.6', '5.7', '5.8', '5.9', '5.10', '5.11', '5.12', '5.13', '5.14', '5.15', '5.16', '5.17', '5.18', '5.19', '5.20', '5.21', '5.22', '5.23', '5.24', '5.25', '5.26', '5.27', '5.28', '5.29', '5.30', '5.31', '6.1', '6.2', '6.3', '6.4', '6.5', '6.6', '6.7', '6.8', '6.9', '6.10', '6.11', '6.12', '6.13', '6.14', '6.15', '6.16', '6.17', '6.18', '6.19', '6.20', '6.21', '6.22', '6.23', '6.24', '6.25', '6.26', '6.27', '6.28', '6.29', '6.30', '7.1', '7.2', '7.3', '7.4', '7.5', '7.6', '7.7', '7.8', '7.9', '7.10', '7.11', '7.12', '7.13', '7.14', '7.15', '7.16', '7.17', '7.18', '7.19', '7.20', '7.21', '7.22', '7.23', '7.24', '7.25', '7.26', '7.27', '7.28', '7.29', '7.30', '7.31', '8.1', '8.2', '8.3', '8.4', '8.5', '8.6', '8.7', '8.8', '8.9', '8.10', '8.11', '8.12', '8.13', '8.14', '8.15', '8.16', '8.17', '8.18', '8.19', '8.20', '8.21', '8.22', '8.23', '8.24', '8.25', '8.26', '8.27', '8.28', '8.29', '8.30', '8.31', '9.1', '9.2', '9.3', '9.4', '9.5', '9.6', '9.7', '9.8', '9.9', '9.10', '9.11', '9.12', '9.13', '9.14', '9.15', '9.16', '9.17', '9.18', '9.19', '9.20', '9.21', '9.22', '9.23', '9.24', '9.25', '9.26', '9.27', '9.28', '9.29', '9.30', '10.1', '10.2', '10.3', '10.4', '10.5', '10.6', '10.7', '10.8', '10.9', '10.10', '10.11', '10.12', '10.13', '10.14', '10.15', '10.16', '10.17', '10.18', '10.19', '10.20', '10.21', '10.22', '10.23', '10.24', '10.25', '10.26', '10.27', '10.28', '10.29', '10.30', '10.31', '11.1', '11.2', '11.3', '11.4', '11.5', '11.6', '11.7', '11.8', '11.9', '11.10', '11.11', '11.12', '11.13', '11.14', '11.15', '11.16', '11.17', '11.18', '11.19', '11.20', '11.21', '11.22', '11.23', '11.24', '11.25', '11.26', '11.27', '11.28', '11.29', '11.30', '12.1', '12.2', '12.3', '12.4', '12.5', '12.6', '12.7', '12.8', '12.9', '12.10', '12.11', '12.12', '12.13', '12.14', '12.15', '12.16', '12.17', '12.18', '12.19', '12.20', '12.21', '12.22', '12.23', '12.24', '12.25', '12.26', '12.27', '12.28', '12.29', '12.30', '12.31', '1.1', '1.2', '1.3', '1.4', '1.5', '1.6', '1.7', '1.8', '1.9', '1.10', '1.11', '1.12', '1.13', '1.14', '1.15', '1.16', '1.17', '1.18', '1.19', '1.20', '1.21', '1.22', '1.23', '1.24', '1.25', '1.26', '1.27', '1.28', '1.29', '1.30', '1.31', '2.1', '2.2', '2.3', '2.4', '2.5', '2.6', '2.7', '2.8', '2.9', '2.10', '2.11', '2.12', '2.13', '2.14', '2.15', '2.16', '2.17', '2.18', '2.19', '2.20', '2.21', '2.22', '2.23', '2.24', '2.25', '2.26', '2.27', '2.28', '3.1', '3.2', '3.3', '3.4', '3.5', '3.6', '3.7', '3.8', '3.9', '3.10', '3.11', '3.12', '3.13', '3.14', '3.15', '3.16', '3.17', '3.18', '3.19', '3.20', '3.21', '3.22', '3.23', '3.24', '3.25', '3.26', '3.27', '3.28', '3.29', '3.30', '3.31', '4.1', '4.2', '4.3', '4.4', '4.5', '4.6']
yd_count=[5480, 5916, 6725, 7600, 8446, 9205, 10453, 11487, 12322, 13430, 14352, 15722, 17615, 18539, 20080, 21370, 23039, 24447, 26283, 27890, 29451, 31332, 33062, 34862, 37257, 39699, 42505, 46437, 49400, 52987, 56351, 59693, 62808, 67161, 70768, 74243, 78055, 81997, 85784, 90648, 95698, 100161, 103292, 107819, 114478, 124073, 130506, 137608, 141228, 150313, 154820, 163120, 172359, 180621, 189765, 194837, 202860, 214664, 224215, 233576, 243733, 254340, 264143, 273443, 284754, 297001, 305951, 317368, 324482, 336185, 347821, 359506, 371734, 385276, 400724, 421765, 430708, 449613, 465553, 481179, 501864, 527738, 548154, 562457, 574926, 593703, 612486, 633381, 664488, 687760, 712920, 739646, 760761, 790649, 818647, 847575, 871499, 898680, 933450, 959993, 1001863, 1036497, 1055932, 1106135, 1127281, 1171446, 1220433, 1263336, 1323471, 1383172, 1424202, 1466059, 1529653, 1579240, 1601070, 1690546, 1749771, 1780268, 1830949, 1901334, 1958592, 2021407, 2057816, 2129154, 2199101, 2244435, 2322755, 2372318, 2431558, 2506247, 2530943, 2634256, 2684314, 2732218, 2814157, 2873173, 2925337, 3038013, 3079925, 3149759, 3211848, 3286512, 3377908, 3454513, 3477250, 3583807, 3649639, 3715931, 3810625, 3904508, 3993412, 4092550, 4160493, 4236961, 4313129, 4417550, 4494389, 4606149, 4688470, 4788593, 4878042, 4963097, 5060818, 5141905, 5228478, 5323907, 5417274, 5517601, 5580286, 5669610, 5765744, 5843349, 5915753, 6041638, 6087454, 6156722, 6245404, 6323247, 6438968, 6509916, 6573678, 6650456, 6724380, 6764710, 6841813, 6946598, 6997852, 7063955, 7160805, 7205923, 7275588, 7349290, 7416538, 7475572, 7536769, 7574167, 7644979, 7701365, 7727289, 7781746, 7829226, 7873664, 7918102, 7974963, 8006340, 8070589, 8094636, 8178645, 8208774, 8250951, 8305267, 8352518, 8380734, 8439389, 8478689, 8531420, 8576689, 8601937, 8659513, 8690621, 8751254, 8790760, 8837037, 8867857, 8886987, 8925467, 8974910, 9021020, 9065301, 9129003, 9170825, 9193982, 9245108, 9291068, 9308751, 9393039, 9432075, 9463254, 9484506, 9523678, 9556881, 9593688, 9625289, 9667084, 9689302, 9714308, 9756610, 9780486, 9814064, 9844322, 9881357, 9902262, 9925062, 9933997, 9966966, 9987949, 10015973, 10047131, 10067196, 10094801, 10111256, 10141215, 10157903, 10178592, 10208725, 10224797, 10237117, 10260618, 10282624, 10293028, 10310778, 10337069, 10345118, 10369514, 10388018, 10405097, 10426407, 10448134, 10460179, 10473696, 10485420, 10497470, 10525452, 10540365, 10556184, 10566720, 10572672, 10582647, 10606215, 10619603, 10634414, 10645580, 10661138, 10672035, 10689202, 10690279, 10702730, 10720971, 10740309, 10750224, 10764177, 10768991, 10788136, 10799024, 10805790, 10816147, 10831279, 10846028, 10848045, 10859057, 10878758, 10880794, 10894638, 10910589, 10919616, 10931492, 10942948, 10956182, 10969230, 10989668, 10997821, 11008665, 11028114, 11043925, 11056933, 11077957, 11094249, 11102946, 11121186, 11136452, 11152127, 11171166, 11190651, 11204179, 11228288, 11241990, 11260750, 11266216, 11287543, 11327129, 11353712, 11382610, 11404279, 11410769, 11473015, 11509345, 11551980, 11590373, 11607548, 11682440, 11726364, 11780157, 11794407, 11860672, 11965931, 11990353, 12089876, 12110693, 12203953, 12229790, 12319836, 12476468, 12508609, 12625146, 12732968]
rb_date=['4.7', '4.8', '4.9', '4.10', '4.11', '4.12', '4.13', '4.14', '4.15', '4.16', '4.17', '4.18', '4.19', '4.20', '4.21', '4.22', '4.23', '4.24', '4.25', '4.26', '4.27', '4.28', '4.29', '4.30', '5.1', '5.2', '5.3', '5.4', '5.5', '5.6', '5.7', '5.8', '5.9', '5.10', '5.11', '5.12', '5.13', '5.14', '5.15', '5.16', '5.17', '5.18', '5.19', '5.20', '5.21', '5.22', '5.23', '5.24', '5.25', '5.26', '5.27', '5.28', '5.29', '5.30', '5.31', '6.1', '6.2', '6.3', '6.4', '6.5', '6.6', '6.7', '6.8', '6.9', '6.10', '6.11', '6.12', '6.13', '6.14', '6.15', '6.16', '6.17', '6.18', '6.19', '6.20', '6.21', '6.22', '6.23', '6.24', '6.25', '6.26', '6.27', '6.28', '6.29', '6.30', '7.1', '7.2', '7.3', '7.4', '7.5', '7.6', '7.7', '7.8', '7.9', '7.10', '7.11', '7.12', '7.13', '7.14', '7.15', '7.16', '7.17', '7.18', '7.19', '7.20', '7.21', '7.22', '7.23', '7.24', '7.25', '7.26', '7.27', '7.28', '7.29', '7.30', '7.31', '8.1', '8.2', '8.3', '8.4', '8.5', '8.6', '8.7', '8.8', '8.9', '8.10', '8.11', '8.12', '8.13', '8.14', '8.15', '8.16', '8.17', '8.18', '8.19', '8.20', '8.21', '8.22', '8.23', '8.24', '8.25', '8.26', '8.27', '8.28', '8.29', '8.30', '8.31', '9.1', '9.2', '9.3', '9.4', '9.5', '9.6', '9.7', '9.8', '9.9', '9.10', '9.11', '9.12', '9.13', '9.14', '9.15', '9.16', '9.17', '9.18', '9.19', '9.20', '9.21', '9.22', '9.23', '9.24', '9.25', '9.26', '9.27', '9.28', '9.29', '9.30', '10.1', '10.2', '10.3', '10.4', '10.5', '10.6', '10.7', '10.8', '10.9', '10.10', '10.11', '10.12', '10.13', '10.14', '10.15', '10.16', '10.17', '10.18', '10.19', '10.20', '10.21', '10.22', '10.23', '10.24', '10.25', '10.26', '10.27', '10.28', '10.29', '10.30', '10.31', '11.1', '11.2', '11.3', '11.4', '11.5', '11.6', '11.7', '11.8', '11.9', '11.10', '11.11', '11.12', '11.13', '11.14', '11.15', '11.16', '11.17', '11.18', '11.19', '11.20', '11.21', '11.22', '11.23', '11.24', '11.25', '11.26', '11.27', '11.28', '11.29', '11.30', '12.1', '12.2', '12.3', '12.4', '12.5', '12.6', '12.7', '12.8', '12.9', '12.10', '12.11', '12.12', '12.13', '12.14', '12.15', '12.16', '12.17', '12.18', '12.19', '12.20', '12.21', '12.22', '12.23', '12.24', '12.25', '12.26', '12.27', '12.28', '12.29', '12.30', '12.31', '1.1', '1.2', '1.3', '1.4', '1.5', '1.6', '1.7', '1.8', '1.9', '1.10', '1.11', '1.12', '1.13', '1.14', '1.15', '1.16', '1.17', '1.18', '1.19', '1.20', '1.21', '1.22', '1.23', '1.24', '1.25', '1.26', '1.27', '1.28', '1.29', '1.30', '1.31', '2.1', '2.2', '2.3', '2.4', '2.5', '2.6', '2.7', '2.8', '2.9', '2.10', '2.11', '2.12', '2.13', '2.14', '2.15', '2.16', '2.17', '2.18', '2.19', '2.20', '2.21', '2.22', '2.23', '2.24', '2.25', '2.26', '2.27', '2.28', '3.1', '3.2', '3.3', '3.4', '3.5', '3.6', '3.7', '3.8', '3.9', '3.10', '3.11', '3.12', '3.13', '3.14', '3.15', '3.16', '3.17', '3.18', '3.19', '3.20', '3.21', '3.22', '3.23', '3.24', '3.25', '3.26', '3.27', '3.28', '3.29', '3.30', '3.31', '4.1', '4.2', '4.3', '4.4', '4.5', '4.6']
rb_count=[4472, 4979, 5553, 6188, 6926, 7423, 7693, 8191, 8723, 9297, 9849, 10434, 10810, 11157, 11581, 12023, 12480, 12868, 13238, 13441, 13614, 13895, 14119, 14305, 14571, 14877, 15079, 15253, 15374, 15477, 15575, 15663, 15777, 15847, 15968, 16049, 16120, 16203, 16253, 16310, 16337, 16367, 16394, 16433, 16518, 16543, 16569, 16611, 16632, 16662, 16696, 16759, 16833, 16877, 16912, 16949, 17000, 17031, 17078, 17118, 17164, 17202, 17223, 17268, 17306, 17347, 17404, 17454, 17529, 17601, 17645, 17689, 17759, 17816, 17881, 17937, 17982, 18034, 18130, 18212, 18317, 18409, 18522, 18631, 18769, 18895, 19090, 19329, 19602, 19822, 19998, 20209, 20413, 20767, 21179, 21581, 21991, 22252, 22583, 23008, 23645, 24235, 24916, 25425, 25844, 26476, 27270, 28200, 28984, 29782, 30656, 31249, 32244, 33474, 34809, 36366, 37925, 39255, 40212, 41455, 42804, 44286, 45889, 47464, 48817, 49746, 50444, 51425, 52602, 53961, 55193, 56214, 56854, 57761, 58848, 60033, 61066, 62046, 62790, 63283, 64000, 64897, 65763, 66638, 67488, 68088, 68516, 69151, 69743, 70405, 70994, 71585, 72037, 72321, 72833, 73337, 74026, 74688, 75334, 75774, 76039, 76571, 77121, 77650, 78182, 78782, 79260, 79571, 79902, 80116, 80592, 81169, 81806, 82285, 82583, 83115, 83689, 84335, 84874, 85451, 85851, 86135, 86635, 87145, 87762, 88374, 89054, 89491, 89769, 90269, 90818, 91526, 92167, 92787, 93219, 93533, 94015, 94634, 95248, 95995, 96720, 97218, 97617, 98262, 98995, 99804, 100577, 101453, 102068, 102548, 103413, 104036, 105082, 106221, 107557, 108503, 109280, 110616, 112164, 113808, 115518, 117261, 118702, 119652, 121336, 123544, 125932, 128348, 130941, 133117, 134635, 135846, 137786, 140288, 142818, 145502, 147568, 149002, 151018, 153456, 155964, 158411, 160917, 162942, 164462, 166618, 169431, 172416, 175207, 178242, 180630, 182305, 184732, 187718, 190935, 193757, 196746, 199248, 201048, 203717, 206984, 210723, 214553, 218430, 223766, 223771, 227375, 231223, 235751, 238999, 242052, 245212, 248531, 253432, 259438, 267004, 274883, 282662, 288751, 293637, 298168, 304040, 310627, 317772, 324785, 330544, 335465, 340780, 346309, 351976, 357021, 361733, 365723, 368485, 372332, 376300, 380427, 383958, 387303, 389975, 391763, 394087, 396716, 399289, 401661, 403938, 405562, 406775, 408345, 410231, 411921, 413219, 414582, 415945, 417746, 419050, 420497, 422035, 423336, 424568, 425600, 426375, 427457, 428376, 429453, 430509, 431722, 432720, 433417, 434304, 435547, 436717, 437862, 438916, 439981, 440580, 441706, 443018, 444297, 445606, 446923, 447912, 448606, 449739, 451272, 452771, 454232, 455747, 456865, 457686, 459176, 461101, 463015, 465040, 467112, 468896, 470233, 472325, 475168, 477774, 480532, 483305, 485775, 487346, 489986]
mg_date=['4.7', '4.8', '4.9', '4.10', '4.11', '4.12', '4.13', '4.14', '4.15', '4.16', '4.17', '4.18', '4.19', '4.20', '4.21', '4.22', '4.23', '4.24', '4.25', '4.26', '4.27', '4.28', '4.29', '4.30', '5.1', '5.2', '5.3', '5.4', '5.5', '5.6', '5.7', '5.8', '5.9', '5.10', '5.11', '5.12', '5.13', '5.14', '5.15', '5.16', '5.17', '5.18', '5.19', '5.20', '5.21', '5.22', '5.23', '5.24', '5.25', '5.26', '5.27', '5.28', '5.29', '5.30', '5.31', '6.1', '6.2', '6.3', '6.4', '6.5', '6.6', '6.7', '6.8', '6.9', '6.10', '6.11', '6.12', '6.13', '6.14', '6.15', '6.16', '6.17', '6.18', '6.19', '6.20', '6.21', '6.22', '6.23', '6.24', '6.25', '6.26', '6.27', '6.28', '6.29', '6.30', '7.1', '7.2', '7.3', '7.4', '7.5', '7.6', '7.7', '7.8', '7.9', '7.10', '7.11', '7.12', '7.13', '7.14', '7.15', '7.16', '7.17', '7.18', '7.19', '7.20', '7.21', '7.22', '7.23', '7.24', '7.25', '7.26', '7.27', '7.28', '7.29', '7.30', '7.31', '8.1', '8.2', '8.3', '8.4', '8.5', '8.6', '8.7', '8.8', '8.9', '8.10', '8.11', '8.12', '8.13', '8.14', '8.15', '8.16', '8.17', '8.18', '8.19', '8.20', '8.21', '8.22', '8.23', '8.24', '8.25', '8.26', '8.27', '8.28', '8.29', '8.30', '8.31', '9.1', '9.2', '9.3', '9.4', '9.5', '9.6', '9.7', '9.8', '9.9', '9.10', '9.11', '9.12', '9.13', '9.14', '9.15', '9.16', '9.17', '9.18', '9.19', '9.20', '9.21', '9.22', '9.23', '9.24', '9.25', '9.26', '9.27', '9.28', '9.29', '9.30', '10.1', '10.2', '10.3', '10.4', '10.5', '10.6', '10.7', '10.8', '10.9', '10.10', '10.11', '10.12', '10.13', '10.14', '10.15', '10.16', '10.17', '10.18', '10.19', '10.20', '10.21', '10.22', '10.23', '10.24', '10.25', '10.26', '10.27', '10.28', '10.29', '10.30', '10.31', '11.1', '11.2', '11.3', '11.4', '11.5', '11.6', '11.7', '11.8', '11.9', '11.10', '11.11', '11.12', '11.13', '11.14', '11.15', '11.16', '11.17', '11.18', '11.19', '11.20', '11.21', '11.22', '11.23', '11.24', '11.25', '11.26', '11.27', '11.28', '11.29', '11.30', '12.1', '12.2', '12.3', '12.4', '12.5', '12.6', '12.7', '12.8', '12.9', '12.10', '12.11', '12.12', '12.13', '12.14', '12.15', '12.16', '12.17', '12.18', '12.19', '12.20', '12.21', '12.22', '12.23', '12.24', '12.25', '12.26', '12.27', '12.28', '12.29', '12.30', '12.31', '1.1', '1.2', '1.3', '1.4', '1.5', '1.6', '1.7', '1.8', '1.9', '1.10', '1.11', '1.12', '1.13', '1.14', '1.15', '1.16', '1.17', '1.18', '1.19', '1.20', '1.21', '1.22', '1.23', '1.24', '1.25', '1.26', '1.27', '1.28', '1.29', '1.30', '1.31', '2.1', '2.2', '2.3', '2.4', '2.5', '2.6', '2.7', '2.8', '2.9', '2.10', '2.11', '2.12', '2.13', '2.14', '2.15', '2.16', '2.17', '2.18', '2.19', '2.20', '2.21', '2.22', '2.23', '2.24', '2.25', '2.26', '2.27', '2.28', '3.1', '3.2', '3.3', '3.4', '3.5', '3.6', '3.7', '3.8', '3.9', '3.10', '3.11', '3.12', '3.13', '3.14', '3.15', '3.16', '3.17', '3.18', '3.19', '3.20', '3.21', '3.22', '3.23', '3.24', '3.25', '3.26', '3.27', '3.28', '3.29', '3.30', '3.31', '4.1', '4.2', '4.3', '4.4', '4.5', '4.6']
mg_count=[394182, 425828, 463433, 498674, 530384, 559245, 586941, 610632, 641397, 674829, 710021, 738697, 762496, 789383, 825306, 844992, 877497, 916348, 955488, 985060, 1004942, 1029878, 1056646, 1092656, 1125305, 1156744, 1185167, 1209702, 1234592, 1256639, 1289028, 1318686, 1342723, 1365308, 1381665, 1406519, 1427587, 1453381, 1480975, 1503684, 1526134, 1532861, 1555133, 1573778, 1597130, 1627409, 1650677, 1672527, 1689727, 1711569, 1730595, 1750377, 1772643, 1797949, 1822117, 1842243, 1862879, 1887708, 1907840, 1928026, 1973883, 1995854, 2012001, 2030323, 2052816, 2072274, 2094368, 2123102, 2150245, 2166685, 2188198, 2215580, 2242914, 2275218, 2303692, 2342597, 2363825, 2396101, 2426500, 2474962, 2511784, 2571448, 2615417, 2649774, 2695685, 2744570, 2797737, 2855961, 2909123, 2948587, 2996027, 3055548, 3116430, 3174924, 3242086, 3311844, 3381274, 3436152, 3490706, 3565476, 3642907, 3719446, 3790373, 3854368, 3901026, 3979579, 4046552, 4118684, 4197515, 4273303, 4333464, 4384069, 4451396, 4515787, 4570103, 4655611, 4729242, 4778177, 4821556, 4873925, 4925454, 4988431, 5041384, 5108144, 5164221, 5212499, 5263777, 5321984, 5365527, 5427637, 5478009, 5539841, 5573475, 5614889, 5663371, 5710773, 5757944, 5809339, 5848860, 5886045, 5923582, 5967010, 6013451, 6059951, 6103820, 6141778, 6177207, 6214690, 6260881, 6302203, 6341126, 6400670, 6434850, 6466100, 6490632, 6521356, 6559509, 6594419, 6645500, 6684292, 6718969, 6753195, 6797153, 6838556, 6880182, 6932328, 6972440, 7011038, 7053783, 7106699, 7148986, 7196972, 7251328, 7294643, 7325115, 7365427, 7413600, 7457857, 7507524, 7564910, 7607545, 7644713, 7687269, 7730931, 7787879, 7842831, 7909037, 7957615, 8000852, 8049854, 8106263, 8165007, 8230184, 8306024, 8351444, 8393773, 8468223, 8528732, 8595023, 8675199, 8769097, 8839609, 8898410, 8976435, 9053778, 9136784, 9226558, 9336073, 9414641, 9487088, 9574004, 9702484, 9806960, 9937271, 10071095, 10191335, 10297867, 10433356, 10579938, 10725002, 10888324, 11078662, 11235666, 11381956, 11545530, 11699233, 11896039, 12094052, 12293770, 12463498, 12598889, 12786174, 12978833, 13150740, 13466984, 13612512, 13750608, 13920038, 13933653, 14131866, 14333241, 14568192, 14784825, 14995863, 15169648, 15383380, 15609610, 15845642, 16073829, 16308192, 16563650, 16752408, 16958845, 17159794, 17414880, 17654984, 17899267, 18086215, 18285335, 18494265, 18691742, 18937716, 19117212, 19217816, 19573847, 19781624, 19803605, 19990823, 20237964, 20462501, 20619032, 20918061, 21118056, 21365185, 21610422, 21869885, 22166149, 22492589, 22714728, 22935771, 23155164, 23386010, 23632112, 23870669, 24110448, 24315314, 24488739, 24632518, 24818518, 25023343, 25211649, 25397214, 25579523, 25713893, 25870814, 26030906, 26192314, 26359244, 26518148, 26663198, 26771042, 26916617, 27036145, 27162000, 27278671, 27412193, 27527512, 27616951, 27704111, 27806679, 27906565, 28010156, 28111899, 28203185, 28262656, 28320708, 28386112, 28455519, 28529053, 28606256, 28708395, 28769345, 28833194, 28903679, 28981169, 29059531, 29139466, 29203913, 29259111, 29317873, 29375702, 29458725, 29529369, 29599380, 29656182, 29698316, 29746358, 29802437, 29864187, 29929048, 29994880, 30046127, 30084925, 30141986, 30235739, 30301478, 30365645, 30430839, 30485146, 30525546, 30584133, 30642156, 30712693, 30780446, 30858924, 30924489, 30971471, 31038713, 31105552, 31171824, 31254320, 31319713, 31385250, 31425966, 31505372]line = Line(init_opts=opts.InitOpts(width="1000px", height="500px"))# 全局配置
line.set_global_opts(title_opts=opts.TitleOpts(title="2020年美国印度日本疫情数据折线图", pos_left="center"),legend_opts=opts.LegendOpts(pos_left="70%"),yaxis_opts=opts.AxisOpts(name="累计确诊数量"),xaxis_opts=opts.AxisOpts(name="日期")
)# 印度数据
line.add_xaxis(yd_date)
line.add_yaxis(series_name="印度数据",y_axis=yd_count,symbol_size=10,label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LineStyleOpts(width=3)
)# 日本数据
line.add_xaxis(rb_date)
line.add_yaxis(series_name="日本数据",y_axis=rb_count,symbol_size=10,label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LineStyleOpts(width=3)
)# 美国数据
line.add_xaxis(mg_date)
line.add_yaxis(series_name="美国数据",y_axis=mg_count,symbol_size=10,label_opts=opts.LabelOpts(is_show=False),linestyle_opts=opts.LineStyleOpts(width=3)
)line.render("美国印度日本疫情折线图.html")8.疫情地图示例:
import json
from pyecharts.charts import Map
import pyecharts.options as optswith open("疫情.json", "r", encoding="utf-8") as file:data = file.read()
data = json.loads(data)
# 获取所有省份数据
data = data["areaTree"][0]["children"]
# 专门存放所有省份确诊数据:{'台湾': 15880, '江苏': 1576, '云南': 982}
province_dic = {}
for province in data:province_dic[province["name"]] = province["total"]["confirm"]# 疫情地图需要的数据格式:[('台湾', 15880),('江苏',1576),('云南', 982)]
keys = province_dic.keys()
values = province_dic.values()
province_lst = list(zip(keys, values))my_map = (# 添加数据Map(init_opts=opts.InitOpts(width="1200px", height="600px")).add("疫情地图", province_lst, "china").set_global_opts(title_opts=opts.TitleOpts(title="疫情地图", pos_left="center"),legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,pieces=[{"min": 0, "max": 99, "label": "0-99人", "color": "#FFE89E"},{"min": 100, "max": 999, "label": "100-999人", "color": "#FFC700"},{"min": 1000, "max": 9999, "label": "1000-9999人", "color": "#DD5145"},{"min": 10000, "max": 99999, "label": "10000-99999人", "color": "#F82F2B"},{"min": 100000, "label": "100000以上", "color": "#960000"},]))
)my_map.render("map.html")
9.省份疫情地图:
import json
from pyecharts.charts import Map
import pyecharts.options as optswith open("疫情.json", "r", encoding="utf-8") as file:data = file.read()data = json.loads(data) # 将json格式转换为python格式
henan_data = data["areaTree"][0]["children"][3]
cities = {} # 存放所有市的数据 {'郑州市': 295,'商丘市':106}for item in henan_data["children"]:cities[item["name"] + "市"] = item["total"]["confirm"]
keys = cities.keys()
values = cities.values()
city_list = list(zip(keys, values))my_map = (Map().add("河南省疫情地图", city_list, "河南").set_global_opts(title_opts=opts.TitleOpts(title="河南疫情地图", pos_left="center"),legend_opts=opts.LegendOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(is_piecewise=True,pieces=[{"min": 0, "max": 9, "label": "0-9人", "color": "#FFE89E"},{"min": 10, "max": 99, "label": "10-99人", "color": "#FFC700"},{"min": 100, "max": 999, "label": "100-999人", "color": "#DD5145"},{"min": 1000, "max": 9999, "label": "1000-9999人", "color": "#F82F2B"},{"min": 10000, "label": "10000以上", "color": "#960000"},])
))
my_map.render("province.html")
)
