Table of contents
Initialize the Flask framework and set up routing
Analysis of the histogram of the number of houses in each administrative district
Distribution of second-hand housing sources in the region
Top 10 second-hand houses with the highest unit prices
echarts renders a histogram of the number of houses
1. Actual combat scenarios
Flask + Pandas + echarts uses pie charts, etc. to analyze and visualize second-hand housing data
2. Knowledge points
python basic syntax
Python file read and write
pandas data processing
flask web framework
echarts chart
bootstrap
jinja template
3. Rookie actual combat
Initialize the Flask framework and set up routing
'''
Description: 分页读取并显示 csv 文件数据
'''
from math import ceil
import csv
import json
from flask import Flask, render_template, request, redirect, jsonify
from spiders.file_manager import FileManager
# 初始化框架
web = Flask(__name__)
@web.route('/')
def index():
# 首页
return redirect('/list')
@web.route('/table/<int:page_num>')
def table(page_num):
# 分页读取并显示 csv 文件数据
file_manager = FileManager()
file = file_manager.get_data_file_path("tao365_detail.csv")
# 若不指定limits默认为 5
limits = request.args.get('limits', 5, type=int)
def show_csv(reader, page=page_num, limits=limits):
# 内部函数,根据limits和所在页数生成数据
df = []
for row in reader:
if page_num * limits >= (reader.line_num - 1) > (page_num - 1) * limits:
df.append(row)
return df
with open(file, 'r+', encoding='utf-8') as f:
# 计算页面数
row_length = len(f.readlines()) - 1
pages = int(ceil(row_length / limits))
with open(file, 'r+', encoding='utf-8') as f:
# 计算数据
reader = csv.reader(f)
next(reader)
df = show_csv(reader, page_num, limits)
# 加载模版和模版数据
return render_template('table.html', df=df, pages=pages, page_num=page_num, limits=limits)
@web.route('/table_detail')
def table_detail():
# 二手房详情
row = request.args.get('row').split(',')
return render_template('table_detail.html', row=row)
@web.route('/list')
def list_house():
# 二手房列表
return render_template('list.html')
@web.route('/chart1')
def chart1():
# 柱状图
return render_template('chart1.html')
@web.route('/chart2')
def chart2():
# 横向柱状图
return render_template('chart2.html')
@web.route('/chartBie')
def chartBie():
# 饼图
return render_template('chartBie.html')
@web.route('/chart_data')
def chart_data():
# 获取图标数据
# 图标类型
type = request.args.get("type")
# 二手房数据
file_name = 'chart00' + type+'.json'
file_manager = FileManager()
file = file_manager.get_data_file_path(file_name)
# file = os.path.join(config.DATA_DIR,'chart00'+ type+'.json' )
with open(file, encoding='utf8') as fp:
data_list = json.load(fp)
return jsonify(data_list)
# 启动 flask web 框架
web.run(debug=True)
Analysis of the histogram of the number of houses in each administrative district
def chart002(self):
# 柱状图 - 展示各行政区房屋数量
# 读取清洗后的数据文件
result_df = self.read_clean_result_file()
# 从字典对象导入数据
df = pd.DataFrame(result_df)
# 将数据根据地址分组
g = df.groupby('地址')
# 统计分组后的数量
df_region = g.count()['小区']
# 获取分组后的地址数据
address = df_region.index.tolist()
# 获取分组后的数量
count = df_region.values.tolist()
# 定义表格顶部title
title = "各行政区房屋数量"
# 定义表格顶部说明
text = self.top_text
# 定义图表title
chart_title = '各行政区房屋数量'
# 图表横坐标
chart_x = '所在地区'
# 图表纵坐标
chart_y = '房源数量/套'
# 图表单位
unit = '套'
# 图表类型
types = 'bar'
# 组装图表json数据
data_json = {
'data1': address,
'data2': count,
'title': title,
'text': text,
'chartTitle': chart_title,
'chartX': chart_x,
'chartY': chart_y,
'unit': unit,
'type': types
}
# 将json数据写入文件
with open('../data/chart002.json', 'w', encoding='utf-8') as write_f: # 打开本地文件
json.dump(data_json, write_f, indent=4, ensure_ascii=False) # python对象转换成json对象
Distribution of second-hand housing sources in the region
def chart005(self):
# 柱状图 - 南京各区域二手房房源朝向
# 读取清洗后的数据文件
result_df = self.read_clean_result_file()
# 从字典对象导入数据
df = pd.DataFrame(result_df)
# 将数据根据房屋朝向分组
g = df.groupby('房屋朝向')
# 统计分组后的数量
df_region = g.count()['小区']
# 获取分组后的房屋朝向分组数据
address = df_region.index.tolist()
# 获取分组后的房屋朝向数量
count = df_region.values.tolist()
# 定义表格顶部title
title = "区域二手房房源朝向分布情况"
# 定义表格顶部说明
text = self.top_text
# 定义图表说明
chart_title = '各区域二手房房源朝向分布情况'
# 定义图表横坐标
chart_x = ''
# 定义图表纵坐标
chart_y = '建筑面积(㎡)'
# 定义单位
unit = ''
# 定义图表类型
types = 'bar'
# 组装图表json数据
data_json = {
'data1': address,
'data2': count,
'title': title,
'text': text,
'chartTitle': chart_title,
'chartX': chart_x,
'chartY': chart_y,
'unit': unit,
'type': types
}
# 将json数据写入文件
with open('../data/chart005.json', 'w', encoding='utf-8') as write_f: # 打开本地文件
json.dump(data_json, write_f, indent=4, ensure_ascii=False) # python对象转换成json对象
Top 10 second-hand houses with the highest unit prices
def chart008(self):
# 横向柱状图 - 各面积区间房屋数量占比图
# 读取清洗后的数据文件
result_df = self.read_clean_result_file()
# 从字典对象导入数据
df = pd.DataFrame(result_df)
# 取出每平方价格列前10的数据
top_price = df.sort_values(by="每平方价格", ascending=False)[:10]
# 取出每平方价格列前10的小区数据
area0 = top_price['小区'].values.tolist()
# 取出每平方价格列前10的价格数据
count = top_price['每平方价格'].values.tolist()
arr = []
color_arr = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22",
"#17becf"
]
for k, i in enumerate(count):
arr.append({'value': i, 'itemStyle': {'color': color_arr[k]}})
# 定义表格顶部title
title = "二手房单价最高Top10"
# 定义表格顶部说明
text = self.top_text
# 图表title
chart_title = '二手房单价最高Top10'
# 组装图表json数据
data_json = {
'data1': area0,
'data2': arr,
'title': title,
'text': text,
'chartTitle': chart_title,
}
# 将json数据写入文件
with open('../data/chart008.json', 'w', encoding='utf-8') as write_f: # 打开本地文件
json.dump(data_json, write_f, indent=4, ensure_ascii=False) # python对象转换成json对象
echarts renders a histogram of the number of houses
function drawCharts(data) {
var optionMap = {
title: {
text: data.chartTitle,
left: 'center',
},
tooltip: {
trigger: 'axis',
axisPointer: {
type: 'shadow'
},
},
toolbox: {
feature: {
saveAsImage: {
title: ''
}
}
},
grid: {
left: '3%',
right: '4%',
bottom: '3%',
containLabel: true
},
xAxis: {
type: 'value',
boundaryGap: [0, 0.01]
},
yAxis: {
type: 'category',
data: data.data1
},
series: [{
type: 'bar',
data: data.data2
}, ]
};
//初始化echarts实例
var myChart = echarts.init(document.getElementById('dom1'));
//使用制定的配置项和数据显示图表
myChart.setOption(optionMap);
}
operation result
run screenshot
* Serving Flask app 'app_tao06'
* Debug mode: on
* Running on http://127.0.0.1:5000
Open http://127.0.0.1:5000 in the browser
data example
The orientation distribution of houses in each region
Statistics on the number of houses in each area
Statistics on the proportion of second-hand housing units
Top 10 second-hand houses with the highest unit price
Source link
Rookie actual combat, keep learning!