python分析中国大学分布情况

# coding = utf-8 """ @author: zhou @time:2019/9/3 20:29 @File: analyse.py """ import pandas as pd from pyecharts.charts import Bar, Pie, Geo, Liquid from pyecharts import options as opts from pyecharts.commons.utils import JsCode from pyecharts.globals import ChartType, SymbolType df = pd.read_csv("college_data.csv") df_new = df.drop_duplicates(subset=['name']) # 有重复的数据，需要删除 df_site = df_new[df_new['site'] != '——'] df_site = df_site[df_site['site'] != '------'] # 高校总数量分析 site_counts = df_site['site'].value_counts() dict_site = {'name': site_counts.index, 'counts': site_counts.values} data = pd.DataFrame(dict_site) # 各城市高校数量 bar = Bar() bar.add_xaxis(data['name'].values.tolist()) bar.add_yaxis("", data['counts'].values.tolist()) bar.set_global_opts( xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)), title_opts=opts.TitleOpts(title="各城市高校数量", subtitle="仅仅统计数量"), datazoom_opts=opts.DataZoomOpts(), ) bar.set_series_opts(itemstyle_opts={ "normal": { "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: 'rgba(0, 244, 255, 1)' }, { offset: 1, color: 'rgba(0, 77, 167, 1)' }], false)"""), "barBorderRadius": [30, 30, 30, 30], "shadowColor": 'rgb(0, 160, 221)', }}) bar.render_notebook() # 高校数量前十名 pie = Pie() pie.add("", [list(z) for z in zip(data['name'].values.tolist()[:10], data['counts'].values.tolist()[:10])], radius=["30%", "75%"], center=["40%", "50%"], rosetype="radius") pie.set_global_opts( title_opts=opts.TitleOpts(title="高校数量前十名"), legend_opts=opts.LegendOpts( type_="scroll", pos_left="80%", orient="vertical" ), ) pie.render_notebook() # 高校数量后十名 pie = Pie() pie.add("", [list(z) for z in zip(data['name'].values.tolist()[-10:], data['counts'].values.tolist()[-10:])], radius=["30%", "75%"], center=["40%", "50%"], rosetype="radius") pie.set_global_opts( title_opts=opts.TitleOpts(title="高校数量后十名"), legend_opts=opts.LegendOpts( type_="scroll", pos_left="80%", orient="vertical" ), ) pie.render_notebook() # 高校分布热力图 geo = Geo() geo.add_schema(maptype="china") geo.add("高校分布热力图", [list(z) for z in zip(data['name'].values.tolist(), data['counts'].values.tolist())], type_=ChartType.HEATMAP) geo.set_global_opts( visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="全国高校热力图"), ) geo.render_notebook() # 各地区高校数量段位图 geo = Geo() geo.add_schema(maptype="china") geo.add("各地区高校数量段位图", [list(z) for z in zip(data['name'].values.tolist(), data['counts'].values.tolist())], type_=ChartType.EFFECT_SCATTER) geo.set_global_opts( visualmap_opts=opts.VisualMapOpts(is_piecewise=True, max_=150), title_opts=opts.TitleOpts(title="各地区高校数量段位图"), ) geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) geo.render_notebook() # 高校质量分析 df_title = df_new[df_new['title'] != '——'] df_985 = df_title[df_title['title'] == '211985'] site_counts_985 = df_985['site'].value_counts() dict_site_985 = {'name': site_counts_985.index, 'counts': site_counts_985.values} data_985 = pd.DataFrame(dict_site_985) # 985高校排行 pie = Pie() pie.add("", [list(z) for z in zip(data_985['name'].values.tolist(), data_985['counts'].values.tolist())], radius=["30%", "75%"], center=["40%", "50%"], rosetype="radius") pie.set_global_opts( title_opts=opts.TitleOpts(title="985高校排行"), legend_opts=opts.LegendOpts( type_="scroll", pos_left="80%", orient="vertical" ), ) pie.render_notebook() df_211 = df_title[df_title['title'] == '211'] site_counts_211 = df_211['site'].value_counts() dict_site_211 = {'name': site_counts_211.index, 'counts': site_counts_211.values} data_211 = pd.DataFrame(dict_site_211) # 211高校排行 pie = Pie() pie.add("", [list(z) for z in zip(data_211['name'].values.tolist(), data_211['counts'].values.tolist())], radius=["30%", "75%"], center=["40%", "50%"], rosetype="radius") pie.set_global_opts( title_opts=opts.TitleOpts(title="211高校排行"), legend_opts=opts.LegendOpts( type_="scroll", pos_left="80%", orient="vertical" ), ) pie.render_notebook() # 211 985 综合 df_985_211 = pd.concat([df_211, df_985], ignore_index=True) site_counts_985_211 = df_985_211['site'].value_counts() dict_site_985_211 = {'name': site_counts_985_211.index, 'counts': site_counts_985_211.values} data_985_211 = pd.DataFrame(dict_site_985_211) sum_counts = data_985_211['counts'].sum() data_985_211['rate'] = data_985_211['counts'].apply(lambda x : x/sum_counts) # 各城市高质量高校数量 bar = Bar() bar.add_xaxis(data_985_211['name'].values.tolist()) bar.add_yaxis("", data_985_211['counts'].values.tolist()) bar.set_global_opts( xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)), title_opts=opts.TitleOpts(title="各城市高质量高校数量", subtitle="统计高质量高校数量"), datazoom_opts=opts.DataZoomOpts(), ) bar.set_series_opts(itemstyle_opts={ "normal": { "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: 'rgba(0, 244, 255, 1)' }, { offset: 1, color: 'rgba(0, 77, 167, 1)' }], false)"""), "barBorderRadius": [30, 30, 30, 30], "shadowColor": 'rgb(0, 160, 221)', }}) bar.render_notebook() # 高质量高校分布热力图 geo = Geo() geo.add_schema(maptype="china") geo.add("高质量高校分布热力图", [list(z) for z in zip(data_985_211['name'].values.tolist(), data_985_211['counts'].values.tolist())], type_=ChartType.HEATMAP) geo.set_global_opts( visualmap_opts=opts.VisualMapOpts(max_=50), title_opts=opts.TitleOpts(title="高质量高校分布热力图"), ) geo.render_notebook() # 北京高质量高校占比 liquid = Liquid() liquid.add('', [data_985_211['rate'].values[0]]) liquid.set_global_opts(title_opts=opts.TitleOpts(title="北京高质量高校占比")) liquid.render_notebook() # 北京上海江苏高质量高校占比 liquid = Liquid() liquid.add('', [sum(data_985_211['rate'].values[:3])]) liquid.set_global_opts(title_opts=opts.TitleOpts(title="北京上海江苏高质量高校占比")) liquid.render_notebook() # 占比前十城市高质量高校占比 liquid = Liquid() liquid.add('', [sum(data_985_211['rate'].values[:11])]) liquid.set_global_opts(title_opts=opts.TitleOpts(title="占比前十城市高质量高校占比")) liquid.render_notebook() # 高校类型分析 df_type = df_new[df_new['type'] != '——'] df_type = df_type[df_type['type'] != '------'] df_type_counts = df_type['type'].value_counts() dict_type_counts = {'name': df_type_counts.index, 'counts': df_type_counts.values} data_type_counts = pd.DataFrame(dict_type_counts) pie = Pie() pie.add("", [list(z) for z in zip(data_type_counts['name'].values.tolist(), data_type_counts['counts'].values.tolist())], radius=["30%", "75%"], center=["40%", "50%"], rosetype="radius") pie.set_global_opts( title_opts=opts.TitleOpts(title="高校类型排行"), legend_opts=opts.LegendOpts( type_="scroll", pos_left="80%", orient="vert