Pyechars练习分析二手房数据

淩亂°似流年 2023-10-08 15:50 8阅读 0赞

###### 直接上代码 ######

import pandas as pd
    from pyecharts.charts import Map
    from pyecharts.charts import Bar
    from pyecharts.charts import Line
    from pyecharts.charts import Grid
    from pyecharts.charts import Pie
    from pyecharts.charts import Scatter
    from pyecharts import options as opts
    
    if __name__ == '__main__':
        """ 分析二手房数据，使用pyecharts进行可视化 """
        # 1.读取数据
        dataDF = pd.read_csv('二手房数据.csv', encoding='GB18030')
        print(dataDF.head().to_string())
    
        # 2.查看表格数据，一共有23677条数据。
        print(dataDF.describe())
    
        # 3.查看是否缺失
        print(dataDF.isnull().sum())
    
        # 4.将看到电梯数据缺失8257行，将缺失数据填充为“未知”：
        dataDF['电梯'].fillna('未知', inplace=True)
        print(dataDF.isnull().sum())
    
        # 5.统计各城区二手房数量
        g = dataDF.groupby('市区')
        df_region = g['小区'].count()
        region = df_region.index.tolist()
        list_region = []
        for row in region:
            list_region.append(row + '区')
        count = df_region.values.tolist()
        print(df_region)
    
        # 实列化Map对象
        map = (
            Map()
                .add(series_name='', data_pair=[list(z) for z in zip(list_region, count)], maptype='北京')
                .set_global_opts(
                title_opts=opts.TitleOpts(title='北京市二手房各区分布'),
                visualmap_opts=opts.VisualMapOpts(is_show=True, min_=0, max_=3000)
            )
        )
        map.render('北京各城区二手房数量地图分布.html')
    
        # 6.可视化展示-北京各城区二手房数量-平均价格柱状图
        mean = g.mean()
        price = round(mean['价格(万元)'], 2)
        mean_price = price.values.tolist()
        print(mean_price)
    
        # 实例化一个柱形图对象
        bar = (Bar().add_xaxis(xaxis_data=region))
        bar.add_yaxis(series_name='数量', y_axis=count)
        bar.extend_axis(yaxis=opts.AxisOpts(name='价格(万元)', max_=900, min_=200, interval=100))
        bar.set_global_opts(title_opts=opts.TitleOpts(title='各城区二手房数量-平均价格柱状图'),
                            yaxis_opts=opts.AxisOpts(name='数量', max_=3000, min_=0),
                            tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
                            xaxis_opts=opts.AxisOpts(axispointer_opts=opts.AxisPointerOpts(is_show=True, type_='shadow')), )
        # 实例化一个折线图
        line = (Line().add_xaxis(xaxis_data=region))
        line.add_yaxis(series_name='价格', y_axis=mean_price, yaxis_index=1)
        # 重叠overlap()
        # bar.overlap(line).render('北京各城区二手房数量-平均价格柱状图.html')
        # 组合图
        bar.overlap(line)
        grid = Grid()
        grid.add(bar, grid_opts=opts.GridOpts(), is_control_axis_index=True)
        grid.render('北京各城区二手房数量-平均价格柱状图.html')
    
        # 7.可视化展示-二手房价格最高的TOP15
        top_price = dataDF.sort_values(by='价格(万元)', ascending=False)[:15]
        housing = top_price['小区'].values.tolist()
        price_top15 = top_price['价格(万元)'].values.tolist()
    
        # 实列化一个柱形图对象
        bar1 = (Bar().add_xaxis(xaxis_data=housing))
        bar1.add_yaxis(series_name='数量', y_axis=price_top15, category_gap='50%')
        bar1.set_global_opts(title_opts=opts.TitleOpts(title='二手房价格最高的TOP15'),
                             yaxis_opts=opts.AxisOpts(name='价格(万元)', min_=0, max_=6000, interval=1000),
                             xaxis_opts=opts.AxisOpts(name='小区',
                                                      axispointer_opts=opts.AxisPointerOpts(is_show=True, type_='shadow')),
                             tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'), )
        bar1.extend_axis(yaxis=opts.AxisOpts(name='价格(万元)', max_=900, min_=200, interval=100))
        bar1.render('北京二手房价格最高的TOP15.html')
    
        # 8.装修情况-有无电梯（玫瑰图）
        situation = dataDF.groupby('装修情况').count()
        renovate = situation.index.tolist()
        renovate_count = situation['小区'].values.tolist()
        lift = dataDF.groupby('电梯').count()
        lift_index = lift.index.tolist()
        lift_count = lift['小区'].values.tolist()
    
        # 实列化一个柱形图对象
        bar2 = (Bar().add_xaxis(xaxis_data=renovate))
        bar2.add_yaxis(series_name='', y_axis=renovate_count, category_gap='50%',
                       label_opts=opts.LabelOpts(position='right'))
        bar2.set_global_opts(title_opts=opts.TitleOpts(title='装修情况-有无电梯（玫瑰图）'),
                             tooltip_opts=opts.TooltipOpts(trigger='axis', axis_pointer_type='cross'),
                             xaxis_opts=opts.AxisOpts(name='数量'),
                             yaxis_opts=opts.AxisOpts(name='装修情况',
                                                      axispointer_opts=opts.AxisPointerOpts(is_show=True, type_='shadow')))
        bar2.reversal_axis()
    
        # 实列化一个饼图对象
        pie = Pie()
        pie.add(series_name='', data_pair=[list(z) for z in zip(lift_index, lift_count)],
                radius=['30%', '65%'],
                center=['50%', '50%'],
                rosetype='raduis',
                )
        pie.set_series_opts(label_opts=opts.LabelOpts(is_show=True, formatter='{b}: {c}({d}%)'))
        # 重叠overlap()
        # bar2.overlap(pie)
        # 组合图
        grid = Grid()
        grid.add(bar2, grid_opts=opts.GridOpts())
        grid.add(pie, grid_opts=opts.GridOpts(pos_left="60%", pos_right="75%"))
        grid.render('装修情况-有无电梯（玫瑰图）.html')
    
        # 9.二手房总价与面积（散点图）
        total_price = dataDF['价格(万元)'].values.tolist()
        total_area = dataDF['面积(㎡)'].values.tolist()
    
        # 实列化一个散点图对象
        scatter = Scatter()
        scatter.add_xaxis(xaxis_data=total_area)
        scatter.add_yaxis(series_name='', y_axis=total_price)
        scatter.set_global_opts(title_opts=opts.TitleOpts(title='二手房总价与面积（散点图）'),
                                xaxis_opts=opts.AxisOpts(
                                    name='面积(㎡)', type_='value'
                                ),
                                yaxis_opts=opts.AxisOpts(name='价格(万元)'))
        scatter.render('二手房总价与面积（散点图）.html')

###### 效果展示 ######

> ![这里是引用][cabc97e2f60544b98a642e38a141fb55.png]

![这里是引用][6ddc7067cf9a4beba0780df62d359202.png]

![在这里插入图片描述][b7627ca1e54e4365ac2fd0031a0ed35a.png]  
![在这里插入图片描述][9ba62787c59240e3b96f14edfee1b8a1.png]  
![在这里插入图片描述][a6fb0f72e99c4fbf81e15f9da246df61.png]

[cabc97e2f60544b98a642e38a141fb55.png]: https://img-blog.csdnimg.cn/cabc97e2f60544b98a642e38a141fb55.png
[6ddc7067cf9a4beba0780df62d359202.png]: https://img-blog.csdnimg.cn/6ddc7067cf9a4beba0780df62d359202.png
[b7627ca1e54e4365ac2fd0031a0ed35a.png]: https://img-blog.csdnimg.cn/b7627ca1e54e4365ac2fd0031a0ed35a.png
[9ba62787c59240e3b96f14edfee1b8a1.png]: https://img-blog.csdnimg.cn/9ba62787c59240e3b96f14edfee1b8a1.png
[a6fb0f72e99c4fbf81e15f9da246df61.png]: https://img-blog.csdnimg.cn/a6fb0f72e99c4fbf81e15f9da246df61.png