pyechart+星巴克门店分布源码

# 1、星巴克全球分布图 import pandas as pd from pyecharts.charts import * import pyecharts.options as opts df = pd.read_csv('./directory.csv') a = list(df.Country.value_counts().to_dict().items()) print(a) map = Map() map.add('', list(df.Country.value_counts().to_dict().items()),maptype='world',is_roam=False, is_map_symbol_show=False,label_opts=opts.LabelOpts(is_show=False)) map.set_global_opts(title_opts=opts.TitleOpts(title='星巴克全球分布图',pos_left='center'), visualmap_opts=opts.VisualMapOpts(max_=14000)) map.render('bbb.html') map.set_global_opts(title_opts=opts.TitleOpts(title='星巴克全球分布图',pos_left='center'), visualmap_opts=opts.VisualMapOpts(range_text=['门店数量'], is_piecewise=True, #分段显示 pieces=[{'min':1000},{'min':500,'max':1000}, {'min':100,'max':500},{'max': 100}])) map.render('ccc.html') # 各国/地区星巴克门店数量（可点击下钻到城市） # 空值填充 df_t = df.fillna(value=dict(county_name='NA', city_name='NA')) df_t = df_t.groupby(['Country', 'City'])['Brand'].count().reset_index() data = [] country = [] # 数据处理成Pyecharts需要的格式 for idx, row in df_t.iterrows(): if row['Country'] in country: data[-1]['children'].append(dict(name=row['City'], value=row['Brand'])) else: data.append(dict(name=row['Country'], children=[dict(name=row['City'], value=row['Brand'])])) country.append(row['Country']) tree = TreeMap() tree.add('星巴克门店',data,leaf_depth=1, # 叶子节点深度 国家和城市两层，深度为1 label_opts=opts.LabelOpts(position="inside",formatter='{b}:{c}门店'), # 标签设置 levels=[ # 针对每一层的样式设置 opts.TreeMapLevelsOpts( treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color="#555",border_width=4,gap_width=4)), opts.TreeMapLevelsOpts( color_saturation=[0.3, 0.6], # 颜色饱和度范围 treemap_itemstyle_opts=opts.TreeMapItemStyleOpts( border_color_saturation=0.7,gap_width=2,border_width=2))]) tree.set_global_opts(title_opts=opts.TitleOpts(title="各国/地区星巴克门店数量(可点击下钻到城市)"), legend_opts=opts.LegendOpts(is_show=False)) tree.render('hhh.html') # 2、门店数量在前15的城市 b = list(df.City.value_counts().to_dict().items())[0:15] data = dict(b) print(list(data.keys())) bar = Bar() bar.add_xaxis(list(data.keys())) bar.add_yaxis('', list(data.values()), label_opts=opts.LabelOpts(position='right')) bar.set_global_opts(title_opts=opts.TitleOpts(title='门店数量在前15的城市'), xaxis_opts=opts.AxisOpts(position='top'), yaxis_opts=opts.AxisOpts(is_inverse=True), visualmap_opts=opts.VisualMapOpts(is_show=False, dimension=0, max_=300, range_color=['#FFE7D3','#7A0616'])) bar.reversal_axis() bar.render('ddd.html') # 3、门店所有权占比 c = list(df['Ownership Type'].value_counts().to_dict().items()) d = df.groupby('Ownership Type').Brand.agg('count').sort_values(ascending=False) print(c) p = Pie() p.add('', c, rosetype='area',label_opts=opts.LabelOpts(formatter='{b}:{d}%'), radius=['10%', '45%']) p.set_global_opts(title_opts=opts.TitleOpts(title='门店所有权占比')) p.render('eee.html') # 4、星巴克在中国的分布 # 4.1根据经纬度绘制热力地图 df_china = df[df['Country'] == 'CN'] a = df_china.groupby(['Longitude', 'Latitude']).Brand.value_counts() print(list(a.to_dict().items())) jwd, data = [], [] for i ,j in list(a.to_dict().items()): jwd.append((str(i[0])+'-'+str(i[1]), i[0], i[1])) data.append((str(i[0])+'-'+str(i[1]), j)) geo = Geo() for i in jwd: geo.add_coordinate(i[0], i[1], i[2]) geo.add_schema(maptype='china', is_roam=False) geo.add('', data, type_='heatmap', is_large=True, blur_size=10, point_size=2,) geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(is_show=False, max_=1)) geo.render('fff.html') # 4.2国内星巴克门店最多的20个城市 e = list(df[df['Country'] == 'CN']['City'].value_counts().to_dict().items())[0:20] print(e) bar2 = Bar() bar2.add_xaxis(list(dict(e).keys())) bar2.add_yaxis('', list(dict(e).values()), label_opts=opts.LabelOpts(position='right')) bar2.set_global_opts(yaxis_opts=opts.AxisOpts(is_inverse=True), xaxis_opts=opts.AxisOpts(position='top'), visualmap_opts=opts.VisualMapOpts(is_show=False, dimension=0, max_=300)) bar2.reversal_axis() bar2.render('ggg.html')