那么 今天的这个案例是怎么做出来的？我们一步步为大家讲解。
1.构造数据
1）导入相关库
import random import pandas as pd import math
2）构造数据
cats0 = ["鹿", "飞机", "火车", "船", "汽车"] color0 = ["#D6A5DD", "#BCEE68", "#EBBDBF", "#76CBE8", "#A020F0"] year0 = list(range(1977, 2021)) year = [] for i in year0: for j in range(len(cats0)): year.append(i) print(year[:20]) ------------------------------------------------------------- cats = cats0 * len(year0) print("cats的值为：", cats[:20]) color = color0 * len(year0) print("color的值为：", color[:20]) ------------------------------------------------------------- value = [] for i in year: value.append(math.pow(random.randint(0,50), 2)) print(value[:20]) ------------------------------------------------------------- df = pd.DataFrame() df["year"] = year df["value"] = value df["cats"] = cats df["color"] = color df["csum"] = df.groupby(["cats"])["value"].cumsum() df.head(10)
结果如下：
关于数据构造这一块儿的代码，应该很容易，这里就不一一赘述了，但是有一点我需要说明：关于颜色的调整。这里我提供一个网址给大家，如何配色好看取决于你们自己。
颜色选择：http://tools.jb51.net/static/colorpicker/
这里插入一句：我这里pyecharts的官网老是打不开，不知道是怎么回事。因此关于图中一些参数的说明，我就不一一介绍了。
那么你应该怎么学呢？其实很容易。打开官网，用到了什么参数，就把这个参数粘贴到官网中，进行查询。由于pyecharts的官网是中文，相信大家不会有什么难度。
pyecharts的官网：http://pyecharts.org/#/zh-cn/intro
2.直接使用官网代码绘制【时间轮播图】
from pyecharts import options as opts from pyecharts.charts import Bar, Timeline tl = Timeline() for i in range(1977,2021): df_sub = df[df["year"]==i].sort_values(by="csum") cats_list = list(df_sub["cats"]) csum_list = list(df_sub["csum"]) color_list = list(df_sub["color"]) y = [] bar = ( Bar(init_opts=opts.InitOpts(width='720px', height='320px')) .add_xaxis(xaxis_data=cats_list) .add_yaxis(series_name='', yaxis_data=csum_list, label_opts = opts.LabelOpts(position="right",font_weight="bold"),category_gap=30) .reversal_axis() .set_global_opts( title_opts = opts.TitleOpts("看看谁跑得快（时间：{} 年）".format(i),pos_left=350,padding=[30,20]), xaxis_opts = opts.AxisOpts(max_=50000) ) ) tl.add(bar, "{} 年".format(i)) tl.add_schema(play_interval=300, is_loop_play=False) tl.render("a.html")
结果如下：
观察上图可以发现：效果看起来还不错，有一点不太好的就是颜色太单调。
3.不同的柱形条，显示不同的颜色
from pyecharts import options as opts from pyecharts.charts import Bar, Timeline tl = Timeline() for i in range(1977,2021): df_sub = df[df["year"]==i].sort_values(by="csum") cats_list = list(df_sub["cats"]) csum_list = list(df_sub["csum"]) color_list = list(df_sub["color"]) y = [] for j in range(5): y.append( opts.BarItem( name = cats_list[j], value = csum_list[j], itemstyle_opts = opts.ItemStyleOpts(color=color_list[j]) ) ) bar = ( Bar(init_opts=opts.InitOpts(width='720px', height='320px')) .add_xaxis(xaxis_data=cats_list) .add_yaxis(series_name='', yaxis_data=y, label_opts = opts.LabelOpts(position="right",font_weight="bold"),category_gap=30) .reversal_axis() .set_global_opts( title_opts = opts.TitleOpts("看看谁跑得快（时间：{} 年）".format(i),pos_left=350,padding=[30,20]), xaxis_opts = opts.AxisOpts(max_=50000) ) ) tl.add(bar, "{} 年".format(i)) tl.add_schema(play_interval=300, is_loop_play=False) tl.render("b.html")
结果如下：
观察上图可以发现：此时我们已经为不同的柱形条，加上了不同的颜色，但是如果能为每个柱形条，加上“图形标志”就完美了。
4.给不同柱形条加上特殊标志
from pyecharts import options as opts from pyecharts.charts import Bar, Timeline symbols = { '鹿':'path://M807.911811 243.502362c-4.837795 10.48189-12.900787 33.058268-18.544882 50.796851-5.644094 17.738583-14.513386 42.733858-20.15748 56.440944-9.675591 24.188976-9.675591 24.188976-24.188977-5.644094-7.256693-16.932283-16.932283-28.220472-20.963779-25.801575-12.094488 7.256693-6.450394 50.79685 9.67559 77.404725 18.544882 30.63937 22.576378 108.044094 6.450394 129.814173-8.869291 11.288189-16.125984 12.094488-48.377953 4.837795-38.702362-8.869291-38.702362-8.869291-37.896063-66.116535 0-42.733858 4.837795-64.503937 15.319685-82.24252 49.990551-78.211024 52.409449-104.818898 6.450394-55.634646-30.63937 31.445669-46.765354 33.864567-31.445669 4.837796 4.837795-9.675591 8.869291-36.283465 8.869291-59.666142-0.806299-38.702362-1.612598-41.12126-12.094488-27.414173-6.450394 8.062992-13.707087 30.63937-16.932283 49.990551-5.644094 37.896063-11.288189 42.733858-27.414174 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11.288189-2.418898 30.63937 34.670866 36.283465 50.79685 8.062992 62.891339 1.612598 31.445669-16.932284-24.995276-14.513386-26.607874-18.544882-26.607874-58.859842 0-42.733858 0-42.733858 25.801575-37.896063 14.513386 2.418898 70.148031 11.288189 123.36378 19.351181 96.755906 14.513386 96.755906 14.513386 106.431496 45.959055 4.837795 17.738583 9.675591 41.927559 9.67559 54.022047 0.806299 20.15748 13.707087 37.089764 21.770079 28.220473 2.418898-2.418898 0-33.864567-4.837795-70.148032-8.869291-62.891339-8.062992-67.729134 8.062992-84.661417 19.351181-20.96378 32.251969-60.472441 32.251968-102.4 0-63.697638 24.188976-72.566929 70.954331-24.188976 33.864567 34.670866 84.661417 54.022047 108.044095 41.12126 15.319685-8.062992 20.15748-23.382677 6.450393-23.382678-4.031496 0-8.062992-4.031496-8.062992-8.062992 0-4.837795 11.288189-8.062992 24.188977-8.062992 13.707087 0 24.188976-1.612598 24.188976-4.031496 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27.414173-12.900787 34.670866-1.612598z', '飞机':'path://M181.881905 610.06019l206.116571 12.580572-196.510476 387.413333h119.466667l365.421714-380.440381h229.961143c60.757333 0 152.478476-47.981714 152.478476-107.178666 0-59.14819-91.721143-107.178667-152.478476-107.178667h-229.863619L310.905905 34.816h-119.466667l196.510476 381.025524-223.085714 15.555047c-5.36381 0-10.48381 0.633905-15.652571 1.170286l-64.365715-69.241905H13.994667l54.223238 159.98781 0.877714 2.584381-55.100952 153.112381H84.845714l66.316191-71.387429 30.72 2.438095z', '火车':'path://M477.491 204.8a68.267 68.267 0 0 1 68.267 68.267v68.266A68.267 68.267 0 0 1 477.49 409.6h-102.4a68.267 68.267 0 0 1-68.266-68.267v-68.266A68.267 68.267 0 0 1 375.09 204.8h102.4z m-102.4 136.533h102.4v-68.266h-102.4v68.266zM785.067 819.2H0v-68.267h750.353c116.736 2.902 274.671-42.325 473.77-135.714a68.267 68.267 0 0 0 26.864-101.069c-31.813-45.226-58.71-80.076-80.657-104.55H682.223a68.267 68.267 0 0 1-68.267-68.267v-68.266a68.267 68.267 0 0 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25.2zM136.5 534l-63.7 12.7L60 496.2l76.5-12.7V534z m101.9-12.6l-76.5 12.7V471l76.5-12.7v63.1z m165.8-25.3V382.5c0-26.2-12.8-50.5-12.8-50.5l153-12.7c6.7 15 12.8 50.5 12.8 50.5L570 470.9l-165.8 25.2z m204-37.8c-8.4-82-25.9-141.7-25.9-141.7l49.6-3c36.2 46.5 65.5 132 65.5 132l-89.2 12.7z', '汽车':'path://M740.288 548.256c-54 0-97.76 44.336-97.76 99.04s43.776 99.04 97.76 99.04 97.76-44.336 97.76-99.04-43.76-99.04-97.76-99.04z m0 154.064c-30 0-54.304-24.64-54.304-55.024s24.32-55.024 54.304-55.024c30 0 54.304 24.64 54.304 55.024 0.016 30.4-24.304 55.024-54.304 55.024z m-358.464-22H620.8v-44.016H381.824v44.016z m456.224-220.096S620.8 249.552 229.744 372.192c0 0-13.088 3.808-31.392 12.208l-33.776-34.224v52.48c-14.864 9.392-30.16 21.136-43.456 35.552 0 0-86.896-6.288-86.896 88.048v66.032s-12.416 91.184 108.624 88.048V636.32h22.368c-0.4 3.616-0.64 7.28-0.64 11.008 0 54.704 43.776 99.04 97.76 99.04s97.76-44.336 97.76-99.04-43.776-99.04-97.76-99.04c-50.32 0-91.712 38.528-97.12 88.032-88.496-2.016-86.928-31.568-86.928-31.568v-75.76c0-60.288 57.216-49.664 57.216-49.664 39.472-38.4 120.4-75.76 120.4-75.76 355.248-99.824 582.128 100.704 582.128 100.704 61.904 1.36 108.624 60.56 108.624 60.56v53.76c-2.816 25.136-86.896 17.744-86.896 17.744v44.016h65.168s62.08 25.152 65.184-44.016v-66.032c0.016-0.032-6.192-72.352-152.064-110.08zM262.336 592.272c30 0 54.304 24.64 54.304 55.024s-24.32 55.024-54.304 55.024c-30 0-54.304-24.64-54.304-55.024s24.32-55.024 54.304-55.024z' } tl = Timeline() for i in range(1977,2021): df_sub = df[df["year"]==i].sort_values(by="csum") cats_list = list(df_sub["cats"]) csum_list = list(df_sub["csum"]) color_list = list(df_sub["color"]) y = [] for j in range(5): y.append( opts.BarItem( name = cats_list[j], value = csum_list[j], itemstyle_opts = opts.ItemStyleOpts(color=color_list[j]) ) ) bar = ( Bar(init_opts=opts.InitOpts(width='720px', height='320px')) .add_xaxis(xaxis_data=cats_list) .add_yaxis(series_name='', yaxis_data=y, label_opts = opts.LabelOpts(position="right",font_weight="bold"),category_gap=30, markpoint_opts = opts.MarkPointOpts( data = [ opts.MarkPointItem(name="",coord=[csum_list[0],cats_list[0]],symbol=symbols[cats_list[0]], itemstyle_opts=opts.ItemStyleOpts(color=color_list[0],border_color="grey")), opts.MarkPointItem(name="",coord=[csum_list[1],cats_list[1]],symbol=symbols[cats_list[1]], itemstyle_opts=opts.ItemStyleOpts(color=color_list[1],border_color="grey")), opts.MarkPointItem(name="",coord=[csum_list[2],cats_list[2]],symbol=symbols[cats_list[2]], itemstyle_opts=opts.ItemStyleOpts(color=color_list[2],border_color="grey")), opts.MarkPointItem(name="",coord=[csum_list[3],cats_list[3]],symbol=symbols[cats_list[3]], itemstyle_opts=opts.ItemStyleOpts(color=color_list[3],border_color="grey")), opts.MarkPointItem(name="",coord=[csum_list[4],cats_list[4]],symbol=symbols[cats_list[4]], itemstyle_opts=opts.ItemStyleOpts(color=color_list[4],border_color="grey")), ] ) ) .reversal_axis() .set_global_opts( title_opts = opts.TitleOpts("看看谁跑得快（时间：{} 年）".format(i),pos_left=350,padding=[30,20]), xaxis_opts = opts.AxisOpts(max_=50000) ) ) tl.add(bar, "{} 年".format(i)) tl.add_schema(play_interval=300, is_loop_play=True) tl.render("c.html")
结果如下：
5.如何获取图形的矢量化地址
观察上述代码，可以发现有类似于下图这样的代码，这个是什么呢？其实就是这些标志的矢量化代码，那么我们如何获取这些小图形的矢量化代码呢？这里先提供给大家一个网站。
获取图象的矢量化地址：http://suo.im/6dXHdH
那么如何获取图形的矢量化地址呢？我们详细讲解一下他的步骤。
① 打开上述网址
② 搜索你要展示的图形
③ 选择一个喜欢的图形，下载其svg格式图形
④ 用“记事本”打开这个图片文件
⑤ 复制它的path路径
