python可视化大屏-疫情监控图(3)条形图和面积图
作者:互联网
最终结果
数据准备
条形图
data_votes = pd.read_excel(r'地域划分副本.xlsx') # data_votes # 数据排序,倒过来画图好看点 data_votes = data_votes.sort_values(by='出现次数',ascending=True) # data_votes # 数据结构重组 data_votes_x = data_votes['职称'].tolist() data_votes_y = data_votes['出现次数'].tolist() # 画条形图 from pyecharts import options as opts from pyecharts.charts import Bar bar2 = ( Bar(init_opts=opts.InitOpts(width='400px',height='300px',theme=ThemeType.DARK)) .add_xaxis(data_votes_x) .add_yaxis("", data_votes_y ) .reversal_axis() # 旋转柱形图方向 .set_series_opts(label_opts=opts.LabelOpts(position="right")) # 设置数字标签位置 .set_global_opts(title_opts=opts.TitleOpts(title="《在一起》关键人物出现次数"), visualmap_opts=opts.VisualMapOpts( max_= max(data_votes_y), min_= min(data_votes_y), range_color = ['#ffe100','#e82727'], pos_right='5%', pos_top='49%', dimension = 0, # 柱形图需要加 ), ) ) bar2.render_notebook()
结果
面积图
import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.globals import ThemeType data = pd.read_excel(r'疫情华北华东西北新增趋势 - 副本.xlsx') x1 = data['日期'].agg(lambda x:str(x.day)).tolist() y_data1 = data['北京'] y_data2 = data['甘肃'] y_data1 c2 = ( Line(init_opts=opts.InitOpts(width='500px',height='400px',theme=ThemeType.DARK)) .add_xaxis(x1) .add_yaxis( series_name="北京", stack="新增量", y_axis=y_data1, areastyle_opts=opts.AreaStyleOpts(opacity=0.5), label_opts=opts.LabelOpts(is_show=False), color='#FF8C69', is_smooth=True, ) .add_yaxis( series_name="甘肃", stack="新增量", y_axis=y_data2, areastyle_opts=opts.AreaStyleOpts(opacity=10), label_opts=opts.LabelOpts(is_show=False), color='#CD5C5C', is_smooth=True ) .set_global_opts( title_opts=opts.TitleOpts( title="北京甘肃新增区域图"), tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"), yaxis_opts=opts.AxisOpts( type_="value", axistick_opts=opts.AxisTickOpts(is_show=True), splitline_opts=opts.SplitLineOpts(is_show=True), ), xaxis_opts=opts.AxisOpts(type_="category",boundary_gap=False), ) ) c2.render_notebook()
结果
标签:votes,python,True,add,条形图,大屏,import,data,opts 来源: https://www.cnblogs.com/qi-6666/p/15526836.html