flask将echars和mysql连接_Flask+MySQL+Echarts: 实现数据可视化

不念不忘少年蓝@ 2022-10-28 03:48 204阅读 0赞

一、软件及对应版本Python 3.6.1 |Anaconda custom

Flask 0.12.2

Echarts 4.0

二、项目目录![48daf30f2b261ad7ab0837998adc003d.png][]

三、代码展示from flask import Flask,render\_template, url\_for import pymysql import pandas as pd app = Flask(\_\_name\_\_) app.jinja\_env.filters\['zip'\] = zip def conn\_db(): conn = pymysql.connect(host="your\_host", user="your\_username", password="your\_password", db="your\_database", charset="utf8") return conn def get\_data(conn=conn\_db(), n=10): \# Get the top10 products sql = "select ProductMName, date\_format(FeedbackDate, '%Y-%m') Month, count(FeedBack) Count from Feedbacks group by ProductMName, Month order by Count desc, Month asc" df = pd.read\_sql(sql, conn) conn.close() product\_count = \[\] for index, value in enumerate(df\["ProductMName"\].unique()): product\_count.append(\[value, index+1\]) product\_count = dict(product\_count) df\["Rank"\] = df\["ProductMName"\].map(lambda x: product\_count\[x\]) df = df\[df\["Rank"\]<=n\]\[\["ProductMName", "Month", "Count"\]\] df.sort\_values(by="Month", inplace=True) df\_TotleCount = df.groupby(\["ProductMName"\])\["Count"\].sum() df\_TotleCount.sort\_values(ascending=False, inplace=True) df\_pivot = pd.pivot\_table(df, index="ProductMName", columns="Month", values="Count", fill\_value=0) return df\_TotleCount, df\_pivot @app.route('/') def display\_FeedbackCountByProject(): df\_TotleCount, df\_pivot = get\_data(n=10) TotleCount = df\_TotleCount.values ProductMName = df\_pivot.index.tolist() Month = df\_pivot.columns.tolist() Count = df\_pivot.values.tolist() return render\_template('FeedbackCountByProject.html', ProductMName=ProductMName, Month=Month, Count=Count, TotleCount=TotleCount) if \_\_name\_\_ == "\_\_main\_\_": app.run(debug = True)FeedbackCountByProject.html

四、图表展示![48daf30f2b261ad7ab0837998adc003d.png][]

![48daf30f2b261ad7ab0837998adc003d.png][]

[48daf30f2b261ad7ab0837998adc003d.png]: /images/20221024/2d130723d2f24d578e44eac1f17b7805.png