全国高校分布可视化.zip

# -*- coding: utf-8 -*- # Date: 2021/6/8 1:31 # Author: libiao # wechat: 一条IT # Software: PyCharm import numpy as np import pandas as pd from pyecharts.charts import Pie, Bar, Map, Line, Grid, Page from pyecharts import options as opts from pyecharts.globals import SymbolType, WarningType WarningType.ShowWarning = False df = pd.read_excel('./data/全国高校数据.xlsx') pd.set_option('display.max_columns', None) # 显示完整的列 pd.set_option('display.max_rows', None) # 显示完整的行 pd.set_option('display.expand_frame_repr', False) # 设置不折叠数据 pd.set_option('display.max_colwidth', 100) # 字段选择 # df.drop('address', axis=1, inplace=True) print(df.head()) print(df.info()) # 985/211/双非高校 num_985 = df[df['f985']==1].groupby('province_name')['f985'].sum().reset_index() num_211 = df[(df['f211']==1)&(df['f985']==2)].groupby('province_name')['f211'].sum().reset_index() num_shuangfei = df[(df['f985']==2)&(df['f211']==2)]['province_name'].value_counts().reset_index().rename({ 'index': 'province_name', 'province_name': 'num' }, axis=1) # 合并数据 df_merge = pd.merge(num_shuangfei, num_985, on='province_name', how='left') df_merge = pd.merge(df_merge, num_211, on='province_name', how='left') # 填充数据 df_merge.fillna(0, inplace=True) # 计算合计 df_merge['all_num'] = df_merge['num'] + df_merge['f985'] + df_merge['f211'] df_merge = df_merge.sort_values('all_num') print(df_merge.head()) school_num = df.province_name.value_counts().sort_values() school_num[:5] # 产生数据 x_data = school_num.index.tolist() y_data = school_num.values.tolist() bar0 = Bar(init_opts=opts.InitOpts(width='1350px', height='1000px')) bar0.add_xaxis(xaxis_data=x_data) bar0.add_yaxis('', y_data, color='#F29027') bar0.set_global_opts(title_opts=opts.TitleOpts('各省市地区高校数量分布'), legend_opts=opts.LegendOpts(pos_left='20%')) bar0.set_series_opts(label_opts=opts.LabelOpts(position='right')) bar0.reversal_axis() bar0.render() # 地图 map1 = Map(init_opts=opts.InitOpts(width='1350px', height='750px')) map1.add("", [list(z) for z in zip(school_num.index.tolist(), school_num.values.tolist())], maptype='china' ) map1.set_global_opts(title_opts=opts.TitleOpts(title='各省市地区高校数量分布'), visualmap_opts=opts.VisualMapOpts(max_=174), ) map1.render() # 产生数据 x_data = df_merge['province_name'].values.tolist() y_data1 = df_merge['f985'].values.tolist() y_data2 = df_merge['f211'].values.tolist() y_data3 = df_merge['num'].values.tolist() bar1 = Bar(init_opts=opts.InitOpts(width='1350px', height='750px')) bar1.add_xaxis(x_data) bar1.add_yaxis('985', y_data1, stack='stack1') bar1.add_yaxis('211', y_data2, stack='stack1') bar1.add_yaxis('双非', y_data3, stack='stack1') bar1.set_global_opts(opts.TitleOpts(title='各个省的高校层次分布'), xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate='60')) ) bar1.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) bar1.render() type_num = df.type_name.value_counts() type_num[:5] # 数据对 data_pair = [list(z) for z in zip(type_num.index.tolist(), type_num.values.tolist())] # 绘制饼图 pie1 = Pie(init_opts=opts.InitOpts(width='1350px', height='750px')) pie1.add('', data_pair, radius=['35%', '60%']) pie1.set_global_opts(title_opts=opts.TitleOpts(title='全国高校类型分布'), legend_opts=opts.LegendOpts(is_show=False, orient='vertical', pos_top='15%', pos_left='2%')) pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%")) # pie1.set_colors(['#EF9050', '#3B7BA9', '#6FB27C', '#FFAF34', '#D8BFD8', '#00BFFF', '#7FFFAA']) pie1.render() # 生成page page1 = Page() page1.add(bar0, map1, bar1, pie1) page1.render('./全国高校分析.html')