1. Demand
Recently, our data visualization teacher asked us to visualize the housing price data in Guangzhou's historical housing prices, and then sent us Guangzhou's historical housing prices.xls, and then looked at the data. Borrowing python to help me modify the data into what I copied with one click.
2. Install the xlrd module
pip install xlrd
Usually pip comes with it, we can import xlrd in the development tool .
Here's how to achieve cutting each month of the year
import xlrd
path = r'E:\数据分析\07广州历史房价.xls'
#sheetName是你这个excel文件中的表，如Sheet1（注意大小写问题）
sheetName = 'Sheet1'
data = xlrd.open_workbook(path)
table = data.sheet_by_name(sheetName)
# 行数
rowAmount = table.nrows
# 列数
colAmount = table.ncols
# 显示第n列中所有格中的内容
datas=[]
for rowIndex in range(1,rowAmount):
datas.append(table.cell_value(rowIndex, 1))
datas.reverse()
index1=0
index2=12
time=2009
while index2<len(datas):
print(str(time)+"年")
time=time+1
# print(str(index1)+" "+str(index2))
print(datas[index1:index2])
index1=index2
index2=index2+12
print(str(time)+"年")
print(datas[index1:index2-2])The data obtained:
3. Use echart to express in html
Find the style to be represented in the link below: (remember to add echart.js)
https://echarts.apache.org/examples/zh/index.html
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>广州历史房价</title>
<script src="echarts.js"></script>
</head>
<script>
window.onload = function(){
// 在<head>中写浮现窗口
var a = echarts.init(document.getElementById("main"));
var b =option = {
title: {
text: '广州历史房价',
},
tooltip: {
trigger: 'axis'
},
legend: {
data: ['2009年', '2010年', '2011年', '2012年', '2013年','2014年', '2015年', '2016年', '2017年', '2018年']
},
grid: {
left: '3%',
right: '4%',
bottom: '3%',
containLabel: true
},
toolbox: {
feature: {
saveAsImage: {}
}
},
xAxis: {
type: 'category',
boundaryGap: false,
data: ['一月', '二月', '三月', '四月', '五月', '六月', '七月','八月', '九月', '十月', '十一月','十二月']
},
yAxis: {
type: 'value'
},
series: [
{
name: '2009年',
type: 'line',
stack: 'Total',
data: [6991.0, 6963.0, 7305.0, 8051.0, 8191.0, 8168.0, 8431.0, 8620.0, 8927.0, 9113.0, 9318.0, 9718.0]
},
{
name: '2010年',
type: 'line',
stack: 'Total',
data: [9873.0, 10000.0, 10000.0, 10351.0, 10610.0, 10787.0, 10622.0, 10878.0, 11505.0, 12062.0, 12413.0, 12944.0]
},
{
name: '2011年',
type: 'line',
stack: 'Total',
data: [13535.0, 14114.0, 14680.0, 14998.0, 14977.0, 14938.0, 14855.0, 14654.0, 14547.0, 14521.0, 14677.0, 14762.0]
},
{
name: '2012年',
type: 'line',
stack: 'Total',
data: [14993.0, 15194.0, 15215.0, 15203.0, 15148.0, 15152.0, 15246.0, 15467.0, 15754.0, 15886.0, 16207.0, 16555.0]
},
{
name: '2013年',
type: 'line',
stack: 'Total',
data: [17003.0, 17423.0, 17665.0, 17651.0, 17304.0, 17515.0, 17759.0, 18293.0, 19011.0, 19445.0, 19589.0, 19208.0]
},
{
name: '2014年',
type: 'line',
stack: 'Total',
data: [18893.0, 18977.0, 19460.0, 19040.0, 18757.0, 18440.0, 17764.0, 17450.0, 17312.0, 17338.0, 18081.0, 18564.0]
},
{
name: '2015年',
type: 'line',
stack: 'Total',
data: [18792.0, 18851.0, 19024.0, 19417.0, 19562.0, 19902.0, 20014.0, 19997.0, 19988.0, 19921.0, 19996.0, 20016.0]
},
{
name: '2016年',
type: 'line',
stack: 'Total',
data: [20623.0, 20643.0, 20811.0, 21133.0, 21107.0, 21144.0, 21264.0, 21553.0, 21720.0, 22242.0, 22590.0, 22926.0]
},
{
name: '2017年',
type: 'line',
stack: 'Total',
data: [23744.0, 24427.0, 25131.0, 25369.0, 26061.0, 27329.0, 28196.0, 28508.0, 28814.0, 28254.0, 28009.0, 28578.0]
},
{
name: '2018年',
type: 'line',
stack: 'Total',
data: [28602.0, 29683.0, 30413.0, 31044.0, 31472.0, 32021.0, 32670.0, 33289.0, 33455.0, 33197.0]
},
]
};
a.setOption(b);
}
</script>
<body> <!-- 在<body>处完善窗口尺寸 -->
<div id="main" style="width: 1100px;height: 800px;"></div>
</body>
</html>
4. Effects
