基于python和flask的农产品数据可视化，主要功能数据爬取，数据分析代码

以下是一个简单的基于Python和Flask的农产品数据可视化的示例代码，其中包括数据爬取和数据分析的基本功能。

1. 数据爬取部分示例代码：

import requests
from bs4 import BeautifulSoup

# 爬取天气数据
def get_weather():
    url = 'https://www.weather.com.cn/weather/101010100.shtml'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')
    temp = soup.select(".tem i")
    weather = soup.select(".wea")
    return {'temp': temp[0].text, 'weather': weather[0].text}

# 爬取农产品价格数据
def get_price():
    url = 'http://nc.mofcom.gov.cn/channel/gxdj/jghq/jg_list.shtml'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')
    table = soup.find_all('table')[0]
    rows = table.find_all('tr')
    prices = []
    for row in rows[1:]:
        cols = row.find_all('td')
        prices.append({'name': cols[0].text, 'price': cols[1].text})
    return prices

# 爬取土壤质量数据
def get_soil():
    url = 'http://www.tureal.com.cn/special/20171107/3147.html'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')
    data = soup.select(".table-data")[0].text
    return data

2. 数据分析部分示例代码：

import pandas as pd
import matplotlib.pyplot as plt

# 分析天气数据
def analyze_weather():
    data = pd.read_csv('weather.csv')
    data['temp'] = data['temp'].apply(lambda x: int(x.replace('℃', '')))
    temp_mean = data['temp'].mean()
    temp_std = data['temp'].std()
    plt.hist(data['temp'], bins=20)
    plt.title('Temperature Distribution')
    plt.xlabel('Temperature')
    plt.ylabel('Frequency')
    plt.figtext(0.7, 0.8, 'Mean: %.2f\nStandard Deviation: %.2f' % (temp_mean, temp_std))
    plt.show()

# 分析农产品价格数据
def analyze_price():
    data = pd.read_csv('price.csv')
    price_mean = data['price'].mean()
    price_std = data['price'].std()
    plt.plot(data['price'])
    plt.title('Price Trend')
    plt.xlabel('Time')
    plt.ylabel('Price')
    plt.figtext(0.7, 0.8, 'Mean: %.2f\nStandard Deviation: %.2f' % (price_mean, price_std))
    plt.show()

# 分析土壤质量数据
def analyze_soil():
    data = pd.read_csv('soil.csv')
    # 进行数据清洗和处理
    # ...

3. Flask应用部分示例代码：

from flask import Flask, render_template, jsonify
import json

app = Flask(__name__)

# 页面路由
@app.route('/')
def index():
    return render_template('index.html')

# API接口路由
@app.route('/weather')
def weather():
    data = get_weather()
    return jsonify(data)

@app.route('/price')
def price():
    data = get_price()
    return jsonify(data)

@app.route('/soil')
def soil():
    data = get_soil()
    return jsonify(data)

if __name__ == '__main__':
    app.run(debug=True)

以上是一个简单的示例代码，供您参考。实际应用中需要根据具体需求进行更加详细的实现。