Python通过pyecharts对爬虫房地产数据进行数据可视化分析（一）
一、背景
对Python通过代理使用多线程爬取安居客二手房数据（二）中爬取的房地产数据进行数据分析与可视化展示
我们爬取到的房产数据，主要是武汉二手房的房源信息，主要包括了待售房源的户型、面积、朝向、楼层、建筑年份、小区名称、小区所在的城区-镇-街道、房子被打的标签、总价、单价等信息。
库：numpy、pandas、pyecharts、jieba
图形：Bar（柱状图）、Pie（饼图）、Histogram（直方图） 、Scatter（散点图）、Map（地图）和WordCloud（词云图）
分析思路：
- 按房屋面积区间分布的房屋单价情况：柱状图
- 按房子户型的房屋单价情况：柱状图
- 小区房价Top10：柱状图（横向）
- 待售卖的二手房中，不同建筑年份的房子数量占比情况：饼图
- 不同单价和总价的房子在不同价格区间的分布数量情况：直方图
6.分析 房子面积跟房子单价之间是什么关系：散点图 - 不同区的二手房房价情况：地图
- 分析购房者最关注的房屋关键词有哪些：词云
二、代码实战
"""
读取excel数据，分析数据并生成图表
"""
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Bar, Pie, Scatter, WordCloud, Map, Page
import numpy as np
import jieba
import jieba.analyse
from pyecharts.commons.utils import JsCode
# 面积分区
def cal_square_district(row):
if row['面积'] <= 60:
return '[0,60]'
if row['面积'] > 60 and row['面积'] <= 90:
return '[60,90]'
if row['面积'] > 90 and row['面积'] <= 120:
return '[90,120]'
if row['面积'] > 120 and row['面积'] <= 150:
return '[120,150]'
if row['面积'] > 150 and row['面积'] <= 200:
return '[150,200]'
if row['面积'] > 200 and row['面积'] <= 300:
return '[200, 300]'
if row['面积'] > 300:
return '[300,-]'
return '[未知]'
# 几室量化
def order_layout_ascending(row):
if row['室'] == '1室':
return 0
if row['室'] == '2室':
return 1
if row['室'] == '3室':
return 2
if row['室'] == '4室':
return 3
if row['室'] == '5室':
return 4
if row['室'] == '6室':
return 5
# 颜色配置
layout_color_function = """
function (params) {
if (params.value > 17000 && params.value < 18000) {
return 'red';
} else if (params.value > 18000 && params.value < 20000) {
return 'blue';
}else if (params.value > 20000 && params.value < 25000){
return 'green'
}else if (params.value > 25000 && params.value < 35000){
return 'purple'
}else if (params.value > 35000 && params.value < 40000){
return 'black'
}
return 'brown';
}
"""
# 按室均价
def unit_price_analysis_by_layout(df, isembed):
# 增加一列[面积区间]
df['面积区间'] = df.apply(cal_square_district, args=(), axis=1)
# 获取要分析的数据行和列
analysis_df = df.loc[:, ['室', '均价']]
analysis_df.loc[:, '室'] = analysis_df.loc[:, '室'].astype('str')
# 对面积区间列group by，然后按分组计算总价和均价的平均值
group = analysis_df.groupby('室', as_index=False)
group_df = group.mean()
group_df.loc[:, '均价'] = group_df.loc[:, '均价'].astype('int')
# 给室这个字段排个序
group_df['order'] = group_df.apply(order_layout_ascending, axis=1)
group_df.sort_values('order', ascending=True, inplace=True)
bar = (
Bar()
.add_xaxis(group_df['室'].tolist())
.add_yaxis("单价均价", group_df["均价"].tolist(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(layout_color_function)))
.set_global_opts(title_opts=opts.TitleOpts(title="武汉二手房按户型的房屋单价"),
legend_opts=opts.LegendOpts(is_show=False))
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return bar.render_embed()
else:
return bar
def order_square_ascending(row):
if row['面积区间'] == '[0,60]':
return 0
if row['面积区间'] == '[60,90]':
return 1
if row['面积区间'] == '[90,120]':
return 2
if row['面积区间'] == '[120,150]':
return 3
if row['面积区间'] == '[150,200]':
return 4
if row['面积区间'] == '[200,300]':
return 5
if row['面积区间'] == '[300,-]':
return 6
square_color_function = """
function (params) {
if (params.value > 17000 && params.value < 18000) {
return 'red';
} else if (params.value > 18000 && params.value < 20000) {
return 'blue';
}else if (params.value > 20000 && params.value < 25000){
return 'green'
}else if (params.value > 25000 && params.value < 35000){
return 'purple'
}else if (params.value > 35000 && params.value < 40000){
return 'black'
}
return 'brown';
}
"""
# 按面积区间均价分布
def unit_price_analysis_by_square(df, isembed):
# 增加一列[面积区间]
df['面积区间'] = df.apply(cal_square_district, args=(), axis=1)
# 获取要分析的数据行和列
analysis_df = df.loc[:, ['面积区间', '均价']]
analysis_df.loc[:, '面积区间'] = analysis_df.loc[:, '面积区间'].astype('str')
# 对面积区间列group by，然后按分组计算总价和均价的平均值
group = analysis_df.groupby('面积区间', as_index=False)
group_df = group.mean()
group_df.loc[:, '均价'] = group_df.loc[:, '均价'].astype('int')
# 把面积区间按从小到大排个序
group_df['order'] = group_df.apply(order_square_ascending, axis=1)
group_df.sort_values('order', ascending=True, inplace=True)
bar = (
Bar()
.add_xaxis(group_df['面积区间'].tolist())
.add_yaxis("单价均价", group_df["均价"].tolist(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(square_color_function)))
.set_global_opts(
title_opts=opts.TitleOpts(title="武汉二手房按面积区间的房屋单价"),
legend_opts=opts.LegendOpts(is_show=False))
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return bar.render_embed()
else:
return bar
top10_color_function = """
function (params) {
if (params.value > 27000 && params.value < 27500) {
return 'red';
} else if (params.value > 27500 && params.value < 27800) {
return 'blue';
}else if (params.value > 27800 && params.value < 28000){
return 'green'
}else if (params.value > 28000 && params.value < 29000){
return 'purple'
}else if (params.value > 29000 && params.value < 30000){
return 'brown'
}else if (params.value > 30000 && params.value < 35200){
return 'gray'
}else if (params.value > 35200 && params.value < 37000){
return 'orange'
}else if (params.value > 37000 && params.value < 40000){
return 'pink'
}else if (params.value > 40000 && params.value < 45000){
return 'navy'
}
return 'gold';
}
"""
# 小区均价top10
def unit_price_analysis_by_estate(df, isembed):
# 获取要分析的数据列
analysis_df = df.loc[:, ['小区名称', '均价']]
analysis_df.loc[:, '小区名称'] = analysis_df.loc[:, '小区名称'].astype('str')
# 对小区名称分组，然后按照分组计算单价均价
group = analysis_df.groupby('小区名称', as_index=False)
group_df = group.mean()
group_df.loc[:, '均价'] = group_df.loc[:, '均价'].astype('int')
# 按照均价列降序排序
group_df.sort_values('均价', ascending=False, inplace=True)
# 取Top10
top10_df = group_df.head(10)
# print(top10_df)
# 为了横向柱状图展示，再从低到高排序一下
top10_df.sort_values('均价', ascending=True, inplace=True)
bar = (
Bar(init_opts=opts.InitOpts(width="1500px"))
.add_xaxis(top10_df['小区名称'].tolist())
.add_yaxis("房价单价", top10_df['均价'].tolist(),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode(top10_color_function)))
.reversal_axis()
.set_series_opts(label_opts=opts.LabelOpts(position="right"))
.set_global_opts(title_opts=opts.TitleOpts(title="武汉各小区二手房房价TOP10"),
xaxis_opts=opts.AxisOpts(axislabel_opts={'interval': '0'}),
legend_opts=opts.LegendOpts(is_show=False))
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return bar.render_embed()
else:
return bar
# 按区均价分布
def unit_price_analysis_by_district(df):
# 获取要分析的数据列
analysis_df = df.loc[:, ['区', '均价']]
analysis_df.loc[:, '区'] = analysis_df.loc[:, '区'].astype('str')
# 对小区名称分组，然后按照分组计算单价均价
group = analysis_df.groupby('区', as_index=False)
group_df = group.mean()
group_df.loc[:, '均价'] = group_df.loc[:, '均价'].astype('int')
# 按照均价列降序排序
group_df.sort_values('均价', ascending=True, inplace=True)
bar = (
Bar(init_opts=opts.InitOpts(width="1500px"))
.add_xaxis(group_df['区'].tolist())
.add_yaxis("房价单价", group_df['均价'].tolist())
.reversal_axis()
.set_series_opts(label_opts=opts.LabelOpts(position="right"))
.set_global_opts(title_opts=opts.TitleOpts(title="武汉各区域二手房房价排行榜"),
xaxis_opts=opts.AxisOpts(axislabel_opts={'interval': '0'}))
)
return bar.render_embed()
def add_sale_estate_col(row):
return 0
# 不同建筑年份的待售数量
def sale_estate_analysis_by_year(df, isembed):
# 增加一列待售房屋数，初始值均为0
df.loc[:, '待售房屋数'] = df.apply(add_sale_estate_col, axis=1)
# 获取要用作数据分析的两列：建筑年份和待售房屋数
analysis_df = df.loc[:, ['建筑年份', '待售房屋数']]
# 因为建筑年份列有空值，先预处理一下
analysis_df.dropna(inplace=True)
# 按照建筑年份进行分组
group = analysis_df.groupby('建筑年份', as_index=False)
# 对每个分组进行统计计数
group_df = group.count()
group_df.loc[:, '待售房屋数'] = group_df.loc[:, '待售房屋数'].astype('int')
pie = Pie(init_opts=opts.InitOpts(width='800px', height='600px', bg_color='white'))
pie.add("pie", [list(z) for z in zip(group_df['建筑年份'].tolist(), group_df['待售房屋数'].tolist())]
, radius=['40%', '60%']
, center=['50%', '50%']
, label_opts=opts.LabelOpts(
position="outside",
formatter="{b}:{c}:{d}%", )
).set_global_opts(
title_opts=opts.TitleOpts(title='武汉二手房不同建筑年份的待售数量', pos_left='300', pos_top='20',
title_textstyle_opts=opts.TextStyleOpts(color='black', font_size=16)),
legend_opts=opts.LegendOpts(is_show=False))
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return pie.render_embed()
else:
return pie
# 均价价格分布
def unit_price_analysis_by_histogram(df, isembed):
hist, bin_edges = np.histogram(df['均价'], bins=100)
bar = (
Bar()
.add_xaxis([str(x) for x in bin_edges[:-1]])
.add_yaxis('价格分布', [float(x) for x in hist], category_gap=0)
.set_global_opts(
title_opts=opts.TitleOpts(title='武汉二手房房价-单价分布-直方图', pos_left='center'),
legend_opts=opts.LegendOpts(is_show=False)
)
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return bar.render_embed()
else:
return bar
# 总价价格分布
def total_price_analysis_by_histogram(df, isembed):
hist, bin_edges = np.histogram(df['总价'], bins=100)
bar = (
Bar()
.add_xaxis([str(x) for x in bin_edges[:-1]])
.add_yaxis('价格分布', [float(x) for x in hist], category_gap=0)
.set_global_opts(
title_opts=opts.TitleOpts(title='武汉二手房房价-总价分布-直方图', pos_left='center'),
legend_opts=opts.LegendOpts(is_show=False)
)
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return bar.render_embed()
else:
return bar
# 面积——单价关系
def unit_price_analysis_by_scatter(df, isembed):
df.sort_values('面积', ascending=True, inplace=True)
square = df['面积'].to_list()
unit_price = df['均价'].to_list()
scatter = (
Scatter()
.add_xaxis(xaxis_data=square)
.add_yaxis(
series_name='',
y_axis=unit_price,
symbol_size=4,
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(type_='value'),
yaxis_opts=opts.AxisOpts(type_='value'),
title_opts=opts.TitleOpts(title='武汉二手房面积-单价关系图', pos_left='center')
)
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return scatter.render_embed()
else:
return scatter
# 房屋标题标签热度词
def hot_word_analysis_by_wordcloud(df, isembed):
txt = ''
for index, row in df.iterrows():
txt = txt + str(row['待售房屋']) + ';' + str(row['标签']) + '\n'
word_weights = jieba.analyse.extract_tags(txt, topK=100, withWeight=True)
word_cloud = (
WordCloud()
.add(series_name='高频词语', data_pair=word_weights, word_size_range=[10, 100])
.set_global_opts(
title_opts=opts.TitleOpts(
title='武汉二手房销售热度词',
title_textstyle_opts=opts.TextStyleOpts(font_size=23),
pos_left='center'
)
)
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return word_cloud.render_embed()
else:
# png_name = 'hot_word_analysis_by_wordcloud.png'
# make_snapshot(snapshot, word_cloud.render(), f"crawler/anjuke/static/{png_name}")
# return png_name
return word_cloud
# 规范区名
def transform_name(row):
district_name = row['区'].strip()
if district_name == '江汉' or district_name == '江岸' or district_name == '硚口' or district_name == '汉阳' or district_name == '武昌' or district_name == '东西湖' or district_name == '洪山':
district_name = district_name + '区'
return district_name
# 按区均价分布地图
def unit_price_analysis_by_map(df, isembed):
data = []
# 获取要分析的数据列
analysis_df = df.loc[:, ['区', '均价']]
# 按区列分组
group_df = analysis_df.groupby('区', as_index=False)
# 根据分组对均价列求平均值
group_df = group_df.mean('均价')
# print(group_df)
# 将区的名字做一下转换，为下面的地图匹配做准备
group_df['区'] = group_df.apply(transform_name, axis=1)
group_df.loc[:, '均价'] = group_df.loc[:, '均价'].astype('int')
# 将数据转换成map需要的数据格式
for index, row in group_df.iterrows():
district_array = [row['区'], row['均价']]
data.append(district_array)
map = (
Map()
.add('武汉各区域二手房房价', data, '武汉')
.set_global_opts(
title_opts=opts.TitleOpts(title='武汉各区域二手房房价地图', pos_left='center'),
visualmap_opts=opts.VisualMapOpts(max_=26000),
legend_opts=opts.LegendOpts(is_show=False)
)
)
# 判断是否单独显示，还是和其他图表一起显示
if isembed:
return map.render_embed()
else:
# png_name = 'unit_price_analysis_by_map.png'
# make_snapshot(snapshot, map.render(), f"crawler/anjuke/static/{png_name}")
# return png_name
return map
# 主函数
if __name__ == '__main__':
# 读取csv
fpath = 'data/wuhanSecondHouse.csv'
df = pd.read_csv(fpath, header=[0], encoding='gbk')
df.drop_duplicates(keep='first', inplace=True)
# 可视化
# 获取按面积区间的单价分析-柱状图
unit_price_analysis_by_square = unit_price_analysis_by_square(df, False)
# 获取按室区分的单价分析-柱状图
unit_price_analysis_by_layout = unit_price_analysis_by_layout(df, False)
# 获取苏州各小区二手房房价TOP10横向-柱状图
unit_price_analysis_by_estate = unit_price_analysis_by_estate(df, False)
# 获取不同建筑年份的待售房屋数-饼图
sale_estate_analysis_by_year = sale_estate_analysis_by_year(df, False)
# 苏州二手房房价-单价分布-直方图
unit_price_analysis_by_histogram = unit_price_analysis_by_histogram(df, False)
# 苏州二手房房价-总价分布-直方图
total_price_analysis_by_histogram = total_price_analysis_by_histogram(df, False)
# 苏州二手房面积-单价关系图
unit_price_analysis_by_scatter = unit_price_analysis_by_scatter(df, False)
# 苏州二手房销售热度词-词云
# hot_word_analysis_by_wordcloud_png_name = dbc.hot_word_analysis_by_wordcloud(df,False)
hot_word_analysis_by_wordcloud = hot_word_analysis_by_wordcloud(df, False)
# 苏州各区域二手房房价分布-地图
# unit_price_analysis_by_map_png_name = dbc.unit_price_analysis_by_map(df,False)
unit_price_analysis_by_map = unit_price_analysis_by_map(df, False)
# web展示所有图
page = Page(layout=Page.DraggablePageLayout) # 可拖动布局
page.add(
unit_price_analysis_by_square,
unit_price_analysis_by_layout,
unit_price_analysis_by_estate,
sale_estate_analysis_by_year,
unit_price_analysis_by_histogram,
total_price_analysis_by_histogram,
unit_price_analysis_by_scatter,
hot_word_analysis_by_wordcloud,
unit_price_analysis_by_map
)
page.render("武汉二手房数据分析.html")
三、可视化展示效果
执行上述代码后会生成一个网页文件：武汉二手房数据分析.html，如下图所示：
完整代码：
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts-wordcloud.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/maps/hu2_bei3_wu3_han4.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/jquery.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/jquery-ui.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/ResizeSensor.js"></script>
<link rel="stylesheet" href="https://assets.pyecharts.org/assets/v5/jquery-ui.css">
</head>
<body >
<style>.box { } </style>
<button onclick="downloadCfg()">Save Config</button>
<div class="box">
<div id="1b112349eb8148e0b585ffd506a12607" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_1b112349eb8148e0b585ffd506a12607 = echarts.init(
document.getElementById('1b112349eb8148e0b585ffd506a12607'), 'white', {renderer: 'canvas'});
var option_1b112349eb8148e0b585ffd506a12607 = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "bar",
"name": "\u5355\u4ef7\u5747\u4ef7",
"legendHoverLink": true,
"data": [
17466.0,
18258.0,
18405.0,
17497.0,
31886.0,
42885.0,
40402.0
],
"realtimeSort": false,
"showBackground": false,
"stackStrategy": "samesign",
"cursor": "pointer",
"barMinHeight": 0,
"barCategoryGap": "20%",
"barGap": "30%",
"large": false,
"largeThreshold": 400,
"seriesLayoutBy": "column",
"datasetIndex": 0,
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downLink.download = fileName
let result = []
for(let i=0; i<charts_id.length; i++) {
chart = $('#'+charts_id[i])
result.push({
cid: charts_id[i],
width: chart.css("width"),
height: chart.css("height"),
top: chart.offset().top + "px",
left: chart.offset().left + "px"
})
}
let blob = new Blob([JSON.stringify(result)])
downLink.href = URL.createObjectURL(blob)
document.body.appendChild(downLink)
downLink.click()
document.body.removeChild(downLink)
}
</script>
</body>
</html>
选择一个浏览器打开如图所示：
可自由拖拽调整布局
四、分析结论
- 房子的均价主要集中在17646元，总价146-172万元附近，其中总价107万元左右的房子也很多，面积区间【120-150】的均价比【90-120】的还要低些，3室的单价和2室的也没差多少，说明购房者的需求主要是3室及面积区间【60-120】的房子；
- 从图中可以看出，最贵的小区是西北湖一号御玺湾，其次是都会轩、泛海国际居住区松海园、世纪江尚；
- 待售数量最多的建筑年份是2019年，至今没超过5年，占比21.29%，之前年份的明显少了很多，应该是跟满二的政策有关系；
- 二手房的单价跟房子面积并不是呈线性相关的关系，也即不是面积越大，单价越高，房子单价的高点出现在150-200平方这个区间，然后随着面积逐渐增大单价呈逐渐下降趋势，因此是一个曲线相关的关系；
- 从地图上可以很直观的看到，江岸区的平均房价是最高的，其次是武昌区和江汉区，东西湖区均价是最低的；
- 从词云图我们可以看到，交通、朝向是购房者第一位关注的房子信息，其次是是否满五（二）唯一、是否新房、是否精装修等。
注意：由于爬取数据不完整，所以不能保证最后的分析结论准确，只是简单的提供分析思路和方法。