用Python pyecharts v1.x 绘制图形【转】
关于pyecharts
pyecharts是一个用于生成echart（百度开源的数据可视化javascript库）图表的类库。
pyecharts 分为 v0.5.x 和 v1.x 两个大版本，版本不兼容，本篇所有的案例基于v1.6.2。
柱状图
# 柱状图 import random import pyecharts.options as opts from pyecharts.charts import Bar x_vals = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子'] bar = ( Bar() .add_xaxis(x_vals) .add_yaxis('商家A', [random.randint(10, 100) for _ in range(6)]) .add_yaxis('商家B', [random.randint(10, 100) for _ in range(6)]) .add_yaxis('商家C', [random.randint(10, 100) for _ in range(6)]) .add_yaxis('商家D', [random.randint(10, 100) for _ in range(6)]) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, font_size=14), markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(y=40, name="达标线=40")])) .set_global_opts(title_opts=opts.TitleOpts(title='柱状图示例-销量', subtitle='四个商家'), xaxis_opts=opts.AxisOpts(name='商品'), yaxis_opts=opts.AxisOpts(name='单位:件')) ) bar.render('柱状图.html')
堆叠柱状图
# 柱状堆叠图 import pyecharts.options as opts from pyecharts.charts import Bar goods = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子'] bar = ( Bar() .add_xaxis(goods) .add_yaxis('商家A', [random.randint(10, 100) for _ in range(6)], stack='stack1') .add_yaxis('商家B', [random.randint(10, 100) for _ in range(6)], stack='stack1') .add_yaxis('商家C', [random.randint(10, 100) for _ in range(6)], stack='stack1') .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts(title_opts=opts.TitleOpts(title='柱状堆叠图示例-商品销量'), xaxis_opts=opts.AxisOpts(name='品类'), yaxis_opts=opts.AxisOpts(name='销量（单位:件）')) ) bar.render('柱状堆叠图.html')
条形图
# 条形图 x_vals1 = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子'] x_vals2 = ['POLO', '篮球鞋', '羽绒服', '皮鞋', '领带', '睡衣'] x_vals3 = ['羽毛球服', '羽毛球鞋', '护腕', '护膝', '护踝', '毛巾'] y_vals = [random.randint(10, 100) for _ in range(18)] bar = Bar().add_xaxis(x_vals1 + x_vals2 + x_vals3) bar.add_yaxis('商家A', y_vals, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='average'), opts.MarkPointItem(type_='max'), opts.MarkPointItem(type_='min')], symbol_size=80) ) bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='right')) bar.set_global_opts(title_opts=opts.TitleOpts(title='条形图示例-商品销量', subtitle='条目较多条形图比较好看点')) bar.reversal_axis() #翻转XY轴，将柱状图转换为条形图 bar.render('条形图.html')
直方图
# 直方图 # 直方图 import random import pyecharts.options as opts from pyecharts.charts import Bar x_vals = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子'] xlen = len(x_vals) # 设置成两种颜色 y_vals = [] for idx, item in enumerate(x_vals): if idx % 2 == 0: y_vals.append( opts.BarItem( name = item, value = random.randint(10, 100), itemstyle_opts = opts.ItemStyleOpts(color="#749f83"), ) ) else: y_vals.append( opts.BarItem( name = item, value = random.randint(10, 100), itemstyle_opts = opts.ItemStyleOpts(color="#d48265"), ) ) bar_histogram = ( Bar() .add_xaxis(x_vals) .add_yaxis('商家A', y_vals, category_gap=0) # .add_yaxis('商家A', [random.randint(10, 100) for _ in range(6)], category_gap=0) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, font_size=14)) .set_global_opts(title_opts=opts.TitleOpts(title='直方图示例-选择赠品', subtitle=''), xaxis_opts=opts.AxisOpts(name='赠品类型'), yaxis_opts=opts.AxisOpts(name='选择相应赠品的人数')) ) bar_histogram.render('直方图.html')
帕累托图（复合图）
# 帕累托图--# 左边纵坐标表示频数,右边纵坐标表示频率.分析线表示累积频率 import random from pyecharts import options as opts from pyecharts.charts import Bar, Line import pandas as pd # 随机颜色, from faker def rand_color() -> str: return random.choice( [ "#c23531", "#2f4554", "#61a0a8", "#d48265", "#749f83", "#ca8622", "#bda29a", "#6e7074", "#546570", "#c4ccd3", "#f05b72", "#444693", "#726930", "#b2d235", "#6d8346", "#ac6767", "#1d953f", "#6950a1", ] ) df_origin = pd.DataFrame({'categories':['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子'],'sales': [random.randint(10, 100) for _ in range(6)]}) print(df_origin) # 按销量降序排列 df_sorted = df_origin.sort_values(by='sales' , ascending=False) print(df_sorted) # 折线图x轴 x_line_categories = [*range(7)] # 折线图y轴--向下累积频率 cum_percent = df_sorted['sales'].cumsum() / df_sorted['sales'].sum() * 100 cum_percent = cum_percent.append(pd.Series([0])) # 添加起始频率0 cum_percent = cum_percent.sort_values(ascending=True) print(df_sorted.categories.values.tolist()) print(cum_percent.values.tolist()) def pareto_bar() -> Bar: line = ( Line() .add_xaxis(x_line_categories) .add_yaxis("累计百分比", cum_percent.values.tolist(), xaxis_index=1, yaxis_index=1, # 使用次y坐标轴，即bar中的extend_axis label_opts=opts.LabelOpts(is_show=False), is_smooth=True, ) ) bar = ( Bar() .add_xaxis(df_sorted.categories.values.tolist()) .add_yaxis('销售额', df_sorted.sales.values.tolist(), category_gap=0) # .add_yaxis('总额百分比', cum_percent.values.tolist()) .extend_axis(xaxis=opts.AxisOpts(is_show=False, position='top')) .extend_axis(yaxis=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_inside=True), # 刻度尺朝内 axislabel_opts=opts.LabelOpts(formatter='{value}%'), position='right') ) .set_series_opts(label_opts=opts.LabelOpts(is_show=True, font_size=14)) .set_global_opts(title_opts=opts.TitleOpts(title='帕累托图示例-销售额', subtitle=''), xaxis_opts=opts.AxisOpts(name='商品类型', type_='category'), yaxis_opts=opts.AxisOpts( axislabel_opts=opts.LabelOpts(formatter="{value} 件") ) ) ) bar.overlap(line) return bar pareto_bar().render('帕累托图.html')
饼图
# 饼图 from pyecharts import options as opts from pyecharts.charts import Page, Pie pie = ( Pie() .add('鼠标选中分区后的tip', [list(z) for z in zip(['20{}年第{}季'.format(year,season) for year in [19, 20] # count 2 for season in range(1,5)] # count 2 ,[random.randint(2, 10) for _ in range(8)])]) # count 8 .set_series_opts(label_opts=opts.LabelOpts(formatter='{b}: {c}万套')) .set_global_opts(title_opts=opts.TitleOpts(title='饼图实例-近两年季度销售'), legend_opts=opts.LegendOpts(is_show=False)) ) pie.render('饼图.html')
圆环图
from pyecharts.charts import Pie pie = ( Pie() .add( '鼠标选中分区后的tip', [list(z) for z in zip(['20{}年第{}季'.format(year,season) for year in [19, 20] # count 2 for season in range(1,5)] # count 2 ,[random.randint(2, 10) for _ in range(8)])], radius=['50%', '75%'], #设置内径外径 label_opts=opts.LabelOpts(is_show=True) ) .set_global_opts(title_opts=opts.TitleOpts(title='圆环图示例-近两年季度销售'), legend_opts=opts.LegendOpts(is_show=False)) ) pie.render('圆环图.html')
玫瑰图
# 玫瑰图 from pyecharts.charts import Pie pie = ( Pie() .add( '鼠标选中分区后的tip', [list(z) for z in zip(['20{}年第{}季'.format(year,season) for year in [19, 20] # count 2 for season in range(1,5)] # count 2 ,[random.randint(0, 10) for _ in range(8)])], radius=['10%', '75%'], #设置内径外径 # rosetype='radius' 圆心角展现数据百分比，半径展现数据大小 # rosetype='area' 圆心角相同，为通过半径展现数据大小 rosetype='radius', label_opts=opts.LabelOpts(is_show=True) ) .set_global_opts(title_opts=opts.TitleOpts(title='玫瑰图示例-近两年季度销售'), legend_opts=opts.LegendOpts(is_show=False)) ) pie.render('玫瑰图.html')
折线图
# 折线图 import random import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.commons.utils import JsCode line = ( Line() .add_xaxis(['{}月第{}周周赛'.format(y,z) for y in range(1, 3) # 1、2月 for z in range(1, 5)]) # 1-4周 .add_yaxis('A题', [random.randint(10, 20) for _ in range(8)], is_smooth=True, # 平滑 markpoint_opts=opts.MarkPointOpts( # 使用coord这个属性设置自定义标记点数值，我这儿随便写 data=[opts.MarkPointItem(name='自定义标记点',coord=[2,18],value='标注值')] ) ) .add_yaxis('B题', [random.randint(5, 20) for _ in range(8)]) .add_yaxis('C题', [random.randint(5, 20) for _ in range(8)]) .set_series_opts(label_opts=opts.LabelOpts( formatter=JsCode( # 通过定义JavaScript回调函数自定义标签 "function(params){" "return params.value[1].toString() + '%';}" # 外层单引号内存双引号亲测不行！ ) )) .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)), # 设置x轴标签旋转角度 yaxis_opts=opts.AxisOpts(name='AC率', min_=3), title_opts=opts.TitleOpts(title='折线示例_ACM题目分析')) ) line.render('折线图.html')
折线面积图
# 折线面积图 import random import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.commons.utils import JsCode line = ( Line() .add_xaxis(['{}月第{}周周赛'.format(y,z) for y in range(1, 3) # 1、2月 for z in range(1, 5)]) # 1-4周 .add_yaxis('蔡队', [random.randint(10, 20) for _ in range(8)], is_symbol_show=False, areastyle_opts=opts.AreaStyleOpts(opacity=0.5), markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='average', name='均值'), opts.MarkPointItem(type_='max', name='最大值'), opts.MarkPointItem(type_='min', name='最小值')], symbol_size=50) ) .add_yaxis('旺神', [random.randint(6, 20) for _ in range(8)], is_smooth=True, is_symbol_show=False, areastyle_opts=opts.AreaStyleOpts(opacity=0.5) ) .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)), # 设置x轴标签旋转角度 yaxis_opts=opts.AxisOpts(name='完成积分', min_=5), title_opts=opts.TitleOpts(title='折线面积图示例_周赛分析')) ) line.render('折线面积图.html')
散点图
# 散点图 from pyecharts.charts import Scatter from pyecharts import options as opts from pyecharts.commons.utils import JsCode import pandas as pd def scatter_simple() -> Scatter: # 数据源 df = pd.DataFrame({'AC':[21,22,23,24,28,30,34,35,40,44,45], # 刷题数 'ACB':[140,120,380,120,200,190,160,300,300,400,500], '姓名':['小军','NIL','假冒NOI','小白','弱刚','晓雷','窜天','云云','依图','蔡队','旺神',]}) # inplace=True：不创建新的对象，直接对原始对象进行修改 # 升序 df.sort_values('AC', inplace=True, ascending=True) c = ( Scatter() .add_xaxis(df.AC.values.tolist()) .add_yaxis( '刷题_能力_姓名', df[['ACB','姓名']].values.tolist(), label_opts=opts.LabelOpts( formatter=JsCode( 'function(params){return params.value[2];}' #通过定义JavaScript回调函数自定义标签 ) ) ) .set_global_opts( title_opts=opts.TitleOpts(title='散点图示例--ACM集训队队员能力'), xaxis_opts=opts.AxisOpts(name='AC(刷题数)', type_='value', min_=20), #x轴从20开始，原点不为0 yaxis_opts=opts.AxisOpts(name='ACB(能力值)', min_=100), # y轴起始点的值 legend_opts=opts.LegendOpts(is_show=True) ) ) return c scatter_simple().render('散点图.html')
雷达图
# 雷达图 import random from pyecharts import options as opts from pyecharts.charts import Page, Radar def radar_simple() -> Radar: c = ( Radar() .add_schema( # 各项的max_值可以不同 schema=[ opts.RadarIndicatorItem(name='计算几何学', max_=100), opts.RadarIndicatorItem(name='动态规划', max_=100), opts.RadarIndicatorItem(name='图论', max_=100), opts.RadarIndicatorItem(name='搜索', max_=100), opts.RadarIndicatorItem(name='模拟', max_=100), opts.RadarIndicatorItem(name='数论', max_=100), ] ) .add('旺神', [[random.randint(10, 101) for _ in range(6)]], color='red', areastyle_opts = opts.AreaStyleOpts( #设置填充的属性 opacity = 0.5, color='red' ),) .add('蔡队', [[random.randint(10, 101) for _ in range(6)]], color='blue', areastyle_opts = opts.AreaStyleOpts( opacity = 0.5,#透明度 color='blue' ),) .set_series_opts(label_opts=opts.LabelOpts(is_show=True)) .set_global_opts(title_opts=opts.TitleOpts(title='雷达图示例-ACM集训队队员能力')) ) return c radar_simple().render('雷达图.html')
箱线图
# 箱线图--描述离散程度 from pyecharts import options as opts from pyecharts.charts import Boxplot def boxpolt_base() -> Boxplot: v_sophomore = [ [1.1, 2.2, 2.6, 3.2, 3.7, 4.2, 4.7, 4.7, 5.5, 6.3, 8.0], [2.5, 2.5, 2.8, 3.2, 3.7, 4.2, 4.7, 4.7, 5.5, 6.3, 7.0] ] v_junior = [ [3.6, 3.7, 4.7, 4.9, 5.1, 5.2, 5.3, 5.4, 5.7, 5.8, 5.8], [3.6, 3.7, 4.7, 4.9, 5.1, 5.2, 5.3, 5.4, 5.7, 5.8, 5.8] ] # 最小值，下四分位数，中位数、上四分位数、最大值 # [min, Q1, median (or Q2), Q3, max] c = ( Boxplot() .add_xaxis(['寒假作业','暑假作业']) .add_yaxis('大二队员', Boxplot.prepare_data(v_sophomore)) .add_yaxis('大三队员', Boxplot.prepare_data(v_junior)) .set_series_opts(label_opts=opts.LabelOpts(is_show=True)) .set_global_opts(title_opts=opts.TitleOpts(title='ACM集训队祖传练习完成时长离散度'), xaxis_opts=opts.AxisOpts(name='单位：小时'), legend_opts=opts.LegendOpts(is_show=True)) .reversal_axis() #翻转XY轴 ) return c boxpolt_base().render('箱线图.html')
词云图
# 词云图 from pyecharts import options as opts from pyecharts.charts import WordCloud from pyecharts.globals import SymbolType words = [ ('背包问题', 10000), ('大整数', 6181), ('Karatsuba乘法算法', 4386), ('穷举搜索', 4055), ('傅里叶变换', 2467), ('状态树遍历', 2244), ('剪枝', 1868), ('Gale-shapley', 1484), ('最大匹配与匈牙利算法', 1112), ('线索模型', 865), ('关键路径算法', 847), ('最小二乘法曲线拟合', 582), ('二分逼近法', 555), ('牛顿迭代法', 550), ('Bresenham算法', 462), ('粒子群优化', 366), ('Dijkstra', 360), ('A*算法', 282), ('负极大极搜索算法', 273), ('估值函数', 265) ] def wordcloud_base() -> WordCloud: c = ( WordCloud() .add("", words, word_size_range=[20, 100], shape=SymbolType.ROUND_RECT) .set_global_opts(title_opts=opts.TitleOpts(title='WordCloud示例-OJ搜索关键字')) ) return c wordcloud_base().render('词云图.html')
转自
(3条消息)用Python pyecharts v1.x 绘制图形（一）:柱状图、柱状堆叠图、条形图、直方图、帕累托图、饼图、圆环图、玫瑰图_Python_shineych的专栏-CSDN博客: https://blog.csdn.net/shineych/article/details/104204330
(3条消息)用Python pyecharts v1.x 绘制图形（二）:折线图、折线面积图、散点图、雷达图、箱线图、词云图_Python_shineych的专栏-CSDN博客: https://blog.csdn.net/shineych/article/details/104225239
