二手房数据的分析与挖掘（四）-- 描述性分析3D

实验环境

Windows7
Anaconda3
pyecharts
pandas
numpy

实验内容

在Jupyter notebook中利用numpy、pandas、pyecharts进行数据的3D可视化，通过数据分析完成一份基于Python的郑州市二手房数据分析报告。

实验步骤

一、数据来源

数据下载：https://download.csdn.net/download/qq_35809147/11176627

二、代码解析

import numpy as np
import pandas as pd
from pyecharts import online
online()

data = pd.read_excel(u'C://Users/liulei/Desktop/正常值数据.xlsx')
data.head()

data.info()

data.loc[:,['UnitPrice', 'TotalPrice', 'Conarea']].describe()

from sklearn.preprocessing import LabelEncoder
class_le = LabelEncoder()

data['IsLift_num'] = class_le.fit_transform(data['IsLift'])
data.IsLift_num[:5]

data['PropertyNature_num']= class_le.fit_transform(data.PropertyNature.values)
data['PropertyNature_num'][:5]

lift_proerty_price = []
for i in range(data.__len__()):
    one_info = []
    one_info.append(data.loc[i]['PropertyNature_num'])
    one_info.append(data.loc[i]['IsLift_num'])
    one_info.append(data.loc[i]['UnitPrice'])
    lift_proerty_price.append(one_info)


from pyecharts import Bar3D

bar3d = Bar3D("PropertyNature/IsLift/UnitPrice", "PropertyNature、IsLift、UnitPrice三者之间的关系",width=1200, height=600)
x_axis = data.PropertyNature.unique().tolist()
y_axis = data.IsLift.unique().tolist()
range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
               '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
bar3d.add("", x_axis, y_axis, [[d[0], d[1], d[2]] for d in lift_proerty_price],
          is_visualmap=True, visual_range=[0, 60000],
          visual_range_color=range_color, grid3d_width=200,
          grid3d_depth=80, is_grid3d_rotate=True, grid3d_shading='lambert')
bar3d

conarea_unit_total_price = []
for i in range(data.__len__()):
    one_info = []
    one_info.append(data.loc[i]['Conarea'])
    one_info.append(data.loc[i]['UnitPrice']/10000)
    one_info.append(data.loc[i]['TotalPrice'])
    conarea_unit_total_price.append(one_info)


from pyecharts import Line3D
range_color = [
    '#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
    '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
line3d = Line3D("Conarea/UnitPrice/TotalPrice", "Conarea、UnitPrice、TotalPrice三者之间的关系",width=1200, height=600)
line3d.add("", conarea_unit_total_price, is_visualmap=True, visual_range_color=range_color,
           visual_range=[0, 1000], grid3d_rotate_sensitivity=5, is_grid3d_rotate=True, grid3d_rotate_speed=20)
line3d

from pyecharts import Scatter3D

range_color = [
    '#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
    '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
scatter3D = Scatter3D("Conarea/UnitPrice/TotalPrice", "Conarea、UnitPrice、TotalPrice三者之间的关系", width=1200, height=600)
scatter3D.add("", conarea_unit_total_price, is_visualmap=True, visual_range=[0, 1000], visual_range_color=range_color)
scatter3D

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