系列文章

• 手把手教你：基于Django的新闻文本分类可视化系统（文本分类由bert实现）
• 手把手教你：基于python的文本分类（sklearn-决策树和随机森林实现）
• 手把手教你：基于TensorFlow的语音识别系统

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一、项目简介

本文主要介绍的基于Python和Django框架构建的英文数据分析与可视化系统。

该系统使用的是英语四六级考试相关文章、题目、答案，并对各种文本进行分析，包括且不限于以下关键技术：

实现了对英文文本数据的处理，包括词频统计、情感分析等，并将分析结果以图表形式进行展示。通过这个系统，用户可以便捷地进行英文数据的分析和可视化。

完整代码在最下方，想要先看源码的同学可以移步本文最下方进行下载。

博主也参考过文本分类相关模型的文章，但大多是理论大于方法。很多同学肯定对原理不需要过多了解，只需要搭建出一个可视化系统即可。

也正是因为我发现网上大多的帖子只是针对原理进行介绍，功能实现的相对很少。

如果您有以上想法，那就找对地方了！

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不多废话，直接进入正题！

二、系统功能

1、数据导入

支持多种格式的英文文本数据导入，如.txt, .csv, .json等。

2、数据清洗

提供数据清洗功能，包括去除停用词、标点符号、数字等。

3、词频统计

统计文本中单词的频率，并以词云图、条形图等形式展示。

4、情感分析

基于NLP技术对文本进行情感分析，判断文本的积极、消极或中性情绪。

5、可视化

采用多种图表进行数据可视化，如折线图、饼图、散点图等。

6、用户交互

提供友好的用户界面，方便用户进行操作和交互。

三.界面简介

系统完成后界面如下：

1、首页

• 首页：提供了三个模块：Materials、Business Intelligence、User Management
• 分别为：1、CET的题目信息查询。2、数据分析与可视化。3、用户注册与登录
  

2、题库界面

• 页面二：可以查看CET的2016年-2020年的题目和答案，用于自行练习。

• 页面三：答题界面
  

3、数据分析可视化界面

• 页面四：题目及答案的数据分析及词频统计界面
  

• 单词长度统计

• 高频单词统计
  

4、用户管理界面

• 用户注册

• 用户登录

四、界面架构

整个系统架构以及功能模块不复杂，可以参考下图：

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五、代码功能介绍

1、依赖环境

本项目使用的是pycharm的python编译环境，如不清楚如何使用的同学可以参考csdn上其他博主的基础教程，这里就不进行赘述。

后端：Python, Django, NLTK（自然语言处理工具包）

前端：HTML, CSS, JavaScript, Bootstrap, jQuery

数据库：MySQL

可视化库：Matplotlib, Pyecharts, WordCloud

2、创建数据库

• 创建mysql数据库，并写入相关数据

import mysql.connector
import pandas as pdclass DbCreate:def __init__(self):self.my_db = mysql.connector.connect(host="localhost",user="root",passwd="123456",auth_plugin='mysql_native_password')def create_database(self, database_name):"""创建databasedatabase_name:数据库名"""# 实例化db_cursor = self.my_db.cursor()try:sql = "CREATE DATABASE " + database_name + ";"db_cursor.execute(sql)# 提交self.my_db.commit()except Exception as e:print("create_database 失败，错误信息：", str(e))else:print("create_database 成功......")def drop_database(self, database_name):"""删除databasedatabase_name:数据库名"""# 实例化db_cursor = self.my_db.cursor()try:sql = "DROP DATABASE " + database_name + ";"db_cursor.execute(sql)# 提交self.my_db.commit()except Exception as e:print("drop_database 失败，错误信息：", str(e))else:print("drop_database 成功......")class DbConnect:def __init__(self, dbname):self.my_db = mysql.connector.connect(host="localhost",user="root",passwd="123456",auth_plugin='mysql_native_password',database=dbname)def insert_user(self, email, name, password):"""创建用户"""my_cursor = self.my_db.cursor()try:sql = "INSERT INTO user (email,name,password) VALUES (%s, %s, %s);"val = (email, name, password)# 执行my_cursor.execute(sql, val)# 提交self.my_db.commit()except Exception as e:print("insert_user 失败，错误信息：", str(e))else:print('insert_user 成功......')def create_tb_user(self):"""创建user表"""my_cursor = self.my_db.cursor()try:# 用户表建表语句sql = "CREATE TABLE user (id INT AUTO_INCREMENT PRIMARY KEY, email VARCHAR(255), name VARCHAR(255)" \", password VARCHAR(255));"my_cursor.execute(sql)# 提交self.my_db.commit()except Exception as e:print("create_tb_user 失败，错误信息：", str(e))else:print('create_tb_user 成功......')# 创建管理员print("创建管理员......")self.insert_user("email@163.com", "test", "123456")def create_tb_cet(self):"""创建题库表"""my_cursor = self.my_db.cursor()try:# 用户表建表语句sql = "CREATE TABLE exam (id INT AUTO_INCREMENT PRIMARY KEY, 编号 VARCHAR(255),级别 VARCHAR(255)" \", 年份 VARCHAR(255),月份 VARCHAR(255),套数 VARCHAR(255),题型 VARCHAR(255),内容 TEXT," \"选项 TEXT,答案 TEXT ,翻译 TEXT );"my_cursor.execute(sql)# 提交self.my_db.commit()except Exception as e:print("create_tb_cet 失败，错误信息：", str(e))else:print('create_tb_cet 成功......')def insert_exam(self, nbr, level, year, month, t_nbr, tx, text, choose, answer, translation):"""执行单个插入题库信息"""my_cursor = self.my_db.cursor()try:sql = "INSERT INTO exam (编号,级别,年份,月份,套数,题型,内容,选项,答案,翻译) VALUES " \"(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s);"val = (nbr, level, year, month, t_nbr, tx, text, choose, answer, translation)# 执行my_cursor.execute(sql, val)# 提交self.my_db.commit()except Exception as e:print("insert_user 失败，错误信息：", str(e))def insert_exam_all(self):"""读取Excel信息，插入所有题库"""data_exam = pd.read_excel("data/data_exam.xlsx", engine='openpyxl')print("读取题库信息成功，开始更新题库信息至数据库......")for i in range(len(data_exam)):try:self.insert_exam(str(data_exam['编号'][i]),str(data_exam['级别'][i]),str(data_exam['年份'][i]),str(data_exam['月份'][i]),str(data_exam['套数'][i]),str(data_exam['题型'][i]),str(data_exam['内容'][i]),str(data_exam['选项'][i]),str(data_exam['答案'][i]),str(data_exam['翻译'][i]))except Exception as e:print("insert_exam_all 执行异常，错误信息如下：", e)else:print("第", i + 1, "条题库信息插入成功......")print("完成题库信息导入.......")def create_tb_text(self):"""创建文章表"""my_cursor = self.my_db.cursor()try:# 用户表建表语句sql = "CREATE TABLE text (id INT AUTO_INCREMENT PRIMARY KEY,  级别 VARCHAR(255)" \", 年份 VARCHAR(255),月份 VARCHAR(255),套数 VARCHAR(255),题型 VARCHAR(255),内容 TEXT)"my_cursor.execute(sql)# 提交self.my_db.commit()except Exception as e:print("create_tb_text 失败，错误信息：", str(e))else:print('create_tb_text 成功......')def insert_text(self, level, year, month, t_nbr, tx, text):"""执行单个插入文章信息"""my_cursor = self.my_db.cursor()try:sql = "INSERT INTO text (级别,年份,月份,套数,题型,内容) VALUES " \"(%s, %s, %s, %s, %s, %s);"val = (level, year, month, t_nbr, tx, text)# 执行my_cursor.execute(sql, val)# 提交self.my_db.commit()except Exception as e:print("insert_text 失败，错误信息：", str(e))def insert_text_all(self):"""读取Excel信息，插入所有文章"""data_exam = pd.read_excel("data/data_text.xlsx", engine='openpyxl')print("读取题库信息成功，开始更新题库信息至数据库......")for i in range(len(data_exam)):try:self.insert_text(str(data_exam['级别'][i]),str(data_exam['年份'][i]),str(data_exam['月份'][i]),str(data_exam['套数'][i]),str(data_exam['题型'][i]),str(data_exam['内容'][i]))except Exception as e:print("insert_text_all 执行异常，错误信息如下：", e)else:print("第", i + 1, "条题库信息插入成功......")print("完成题库信息导入.......")if __name__ == '__main__':# 建库名db_name = 'exam'# # 建库# my_db_create = DbCreate()# # my_db_create.drop_database('english')# my_db_create.drop_database(db_name)# my_db_create.create_database(db_name)# mysql创建连接my_db_connect = DbConnect(db_name)# # 创建user表# my_db_connect.create_tb_user()# # 创建题库表# my_db_connect.create_tb_cet()# # 插入题库信息# my_db_connect.insert_exam_all()# 创建文章表my_db_connect.create_tb_text()# 插入题库信息my_db_connect.insert_text_all()

3、数据分析模块

from collections import Counter
import mysql.connector
import pandas as pd
from pyecharts.charts import Bar
from pyecharts import options as opts
from pyecharts.charts import Pieclass DbConnect:def __init__(self):self.my_db = mysql.connector.connect(host="localhost",user="root",passwd="123456",auth_plugin='mysql_native_password',database='exam')def select_text_len(self):"""获取文章内容、年度、AB"""text_result = {}try:my_cursor = self.my_db.cursor()print("连接数据库成功，开始读取数据......")sql = "SELECT 内容,年份 FROM text WHERE 题型 = 'A' AND 级别=4"my_cursor.execute(sql)result = my_cursor.fetchall()text_4_a = []text_year = []# 获取4级文章和年度for i in range(len(result)):text_4_a.append(result[i][0])text_year.append(result[i][1])sql = "SELECT 内容 FROM text WHERE 题型 = 'A' AND 级别=6"my_cursor.execute(sql)result = my_cursor.fetchall()text_6_a = []# 获取6级文章for i in range(len(result)):text_6_a.append(result[i][0])sql = "SELECT 内容 FROM text WHERE 题型 = 'B' AND 级别=4"my_cursor.execute(sql)result = my_cursor.fetchall()text_4_b = []# 获取B卷4级文章for i in range(len(result)):text_4_b.append(result[i][0])sql = "SELECT 内容 FROM text WHERE 题型 = 'B' AND 级别=6"my_cursor.execute(sql)result = my_cursor.fetchall()text_6_b = []# 获取B卷6级文章for i in range(len(result)):text_6_b.append(result[i][0])text_result = {'year': text_year,'4_a': text_4_a,'4_b': text_4_b,'6_a': text_6_a,'6_b': text_6_b}except Exception as e:msg = "查询失败，错误信息：", str(e)print(msg)return text_resultdef deal_length(my_l):"""处理文章长度"""result = []for text in my_l:result.append(len(text.split(" ")))return resultdef show_length():"""展示A、B文章长度"""my_dbc = DbConnect()result = my_dbc.select_text_len()year = result['year']text_4a = result['4_a']text_4b = result['4_b']text_6a = result['6_a']text_6b = result['6_b']# 处理文章长度text_4a_l = deal_length(text_4a)text_4b_l = deal_length(text_4b)text_6a_l = deal_length(text_6a)text_6b_l = deal_length(text_6b)# 生成4级网页print("开始生成网页......")bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px", page_title="img_view",))# 展示cet4的文章长度bar.add_xaxis(year)bar.add_yaxis("CET4 A Length", text_4a_l)bar.add_yaxis("CET4 B Length", text_4b_l)bar.set_global_opts(title_opts=opts.TitleOpts(title="CET4", subtitle="Statistical"))bar.render("./templates/bi_show_cet4l.html")print("生成成功，请见目录：/templates/bi_show_cet4l.html ......")# 生成6级网页bar = Bar(init_opts=opts.InitOpts(width="800px", height="400px", page_title="img_view",))# 展示cet6的文章长度bar.add_xaxis(year)bar.add_yaxis("CET6 A Length", text_6a_l)bar.add_yaxis("CET6 B Length", text_6b_l)bar.set_global_opts(title_opts=opts.TitleOpts(title="CET6", subtitle="Statistical"))bar.render("./templates/bi_show_cet6l.html")print("生成成功，请见目录：/templates/bi_show_cet6l.html ......")def read_stop_txt(rootdir):"""读取停用词表"""lines = []with open(rootdir, 'r', encoding="utf-8") as file_to_read:while True:line = file_to_read.readline()if not line:breakline = line.strip('\n').strip(' ')lines.append(line)return linesdef words_clean(data):"""数据清洗"""lines = []for word in data:word = word.replace("\n", '').replace("\\u", '').replace("\\", '').replace("_", '') \.replace("—", '').replace("-", '').replace("?", '').replace("[", '').replace("]", '') \.replace(".", '').replace("“", '').replace(",", '').replace("$", '').replace(";", '') \.replace("(", '').replace(")", '').replace("\"", '').replace(":", '').replace("”", '')# if word.isalpha():#     lines.append(word)# else:#     print(word)lines.append(word)return linesdef show_words_length():"""展示A、B文章单词长度占比"""my_dbc = DbConnect()result = my_dbc.select_text_len()list_4_a = result['4_a']list_4_b = result['4_b']list_6_a = result['6_a']list_6_b = result['6_b']# 合并文章,处理分词text_4_a = words_clean(' '.join(list_4_a).split(" "))text_4_b = words_clean(' '.join(list_4_b).split(" "))text_6_a = words_clean(' '.join(list_6_a).split(" "))text_6_b = words_clean(' '.join(list_6_b).split(" "))len_4a = [len(word) for word in text_4_a if word != '']len_4b = [len(word) for word in text_4_b if word != '']len_6a = [len(word) for word in text_6_a if word != '']len_6b = [len(word) for word in text_6_b if word != '']# 获取单词长度c_4a = Counter(len_4a).most_common(12)c_4b = Counter(len_4b).most_common(12)c_6a = Counter(len_6a).most_common(12)c_6b = Counter(len_6b).most_common(12)# 设置主标题与副标题，标题设置居中，设置宽度w = "700px"h = "600px"center = [400, 300]pos_left = 200# 生成4级网页pie = Pie(init_opts=opts.InitOpts(width=w, height=h, page_title="img_view",))# 加入数据，上方的colums选项取消显示# 文章Apie.add("words length", c_4a, center=center)pie.set_global_opts(title_opts=opts.TitleOpts(title="CET4-A", subtitle="words length statistical"), legend_opts=opts.LegendOpts(pos_left=pos_left))pie.render("./templates/bi_show_cet4pa.html")print("生成成功，请见目录：/templates/bi_show_cet4pa.html ......")# 文章Bpie = Pie(init_opts=opts.InitOpts(width=w, height=h, page_title="img_view",))# 加入数据，上方的colums选项取消显示pie.add("words length", c_4b, center=center)pie.set_global_opts(title_opts=opts.TitleOpts(title="CET4-B", subtitle="words length statistical"), legend_opts=opts.LegendOpts(pos_left=pos_left))pie.render("./templates/bi_show_cet4pb.html")print("生成成功，请见目录：/templates/bi_show_cet4pb.html ......")# 生成6级网页# 设置主标题与副标题，标题设置居中，设置宽度# 文章Apie = Pie(init_opts=opts.InitOpts(width=w, height=h, page_title="img_view",))# 加入数据，上方的colums选项取消显示pie.add("words length", c_6a, center=center)pie.set_global_opts(title_opts=opts.TitleOpts(title="CET6-A", subtitle="words length statistical"),legend_opts=opts.LegendOpts(pos_left=pos_left))pie.render("./templates/bi_show_cet6pa.html")print("生成成功，请见目录：/templates/bi_show_cet6pa.html ......")# 文章Bpie = Pie(init_opts=opts.InitOpts(width=w, height=h, page_title="img_view",))# 加入数据，上方的colums选项取消显示pie.add("words length", c_6b, center=center)pie.set_global_opts(title_opts=opts.TitleOpts(title="CET6-B", subtitle="words length statistical"),legend_opts=opts.LegendOpts(pos_left=pos_left))pie.render("./templates/bi_show_cet6pb.html")print("生成成功，请见目录：/templates/bi_show_cet6pb.html ......")def show_words(top=20):"""展示A、B文章Top关键词"""my_dbc = DbConnect()result = my_dbc.select_text_len()list_4 = result['4_a'] + result['4_b']list_6 = result['6_a'] + result['6_b']# 合并文章,处理分词text_4 = words_clean(' '.join(list_4).split(" "))text_6 = words_clean(' '.join(list_6).split(" "))# 去停用词stop_words = read_stop_txt('./data/stopwords.txt')stop_4 = [word for word in text_4 if word not in stop_words and word != '']stop_6 = [word for word in text_6 if word not in stop_words and word != '']# 获取关键词c_4 = Counter(stop_4).most_common(top)c_4.reverse()show_x = list(dict(c_4).keys())show_y = list(dict(c_4).values())# 生成4级网页print("开始生成网页......")bar = Bar(init_opts=opts.InitOpts(width="800px", height="700px", page_title="img_view",))# 展示cet4的高频词bar.add_xaxis(show_x)bar.add_yaxis("Frequency of high frequency words", show_y, itemstyle_opts=opts.ItemStyleOpts(color='#8f4b2e'))bar.reversal_axis()bar.set_global_opts(title_opts=opts.TitleOpts(title="CET4", subtitle="Statistical"))bar.set_series_opts(label_opts=opts.LabelOpts(position="right"))bar.render("./templates/bi_show_cet4f.html")print("生成成功，请见目录：/templates/bi_show_cet4f.html ......")# 生成6级网页c_6 = Counter(stop_6).most_common(top)c_6.reverse()show_x = list(dict(c_6).keys())show_y = list(dict(c_6).values())bar = Bar(init_opts=opts.InitOpts(width="800px", height="700px", page_title="img_view",))# 展示cet6的高频词bar.add_xaxis(show_x)bar.add_yaxis("Frequency of high frequency words", show_y, itemstyle_opts=opts.ItemStyleOpts(color='#2a5caa'))bar.reversal_axis()bar.set_global_opts(title_opts=opts.TitleOpts(title="CET6", subtitle="Statistical"))bar.set_series_opts(label_opts=opts.LabelOpts(position="right"))bar.render("./templates/bi_show_cet6f.html")print("生成成功，请见目录：/templates/bi_show_cet6f.html ......")if __name__ == '__main__':show_words_length()show_words()show_length()

4、用户管理模块

• 用户注册

def do_signup(request):email = request.POST['email']name = request.POST['name']password1 = request.POST['password1']password2 = request.POST['password2']if email == '' or name == '' or password1 == '' or password2 == '':context = {"text": "Input error, please complete all forms above!"}elif password1 != password2:context = {"text": "Operation error. The passwords you entered twice are inconsistent!"}else:import app.db_model as db_modeldb_c = db_model.DbConnect()msg = db_c.insert_user(email, name, password1)context = {"text": msg}return render(request, 'user_signup_result.html', context)

• 用户登录

def user_login(request):"""访问登录界面"""rep = render(request, 'user_login.html', {})return repdef get_ans(request):"""登录界面跳转"""import app.db_model as db_modeltry:# if user_name is None or password is None:user_name = request.POST["user_name"]password = request.POST["password"]except KeyError:# 无登录信息try:# 是否已登录，已登录直接返回主页user_name = json.loads(request.COOKIES.get('user_name'))password = json.loads(request.COOKIES.get('password'))# user_name = request.COOKIES.get('user_name')# password = request.COOKIES.get('password')print(user_name, password)except TypeError:context = {"state": "1","text": "Login status has expired, please login again!"}return render(request, 'user_error.html', context)# print("获取的用户名和密码：", user_name, ';', password)if password is "" or user_name is "":context = {"state": "1","text": "The entered content is empty, please re-enter the login information!"}return render(request, 'user_error.html', context)else:db_c = db_model.DbConnect()login_msg = db_c.user_login(user_name, password)if login_msg['state'] == 0:context = {"state": login_msg['state'],"text": "The user name does not exist, please enter the correct user name!"}return render(request, 'user_error.html', context)if login_msg['state'] == -1:context = {"state": login_msg['state'],"text": login_msg['msg']}return render(request, 'user_error.html', context)elif login_msg['state'] == 2:context = {"state": login_msg['state'],"text": "Wrong password, please enter the correct password!"}return render(request, 'user_error.html', context)else:"""登录成功，设置cookie"""rep = render(request, 'user_home.html', login_msg)'''字符转换'''user_name = json.dumps(user_name)password = json.dumps(password)''' max_age 设置过期时间，单位是秒 '''rep.set_cookie('user_name', str(user_name), max_age=1200)rep.set_cookie('password', str(password), max_age=1200)return rep

5、Django展示界面构建

由于展示界面代码较多，这里就不一一进行展示，感兴趣的同学可以在文章下方找到完整代码下载地址。

• 这里就附上加载较为关键的前端代码。
• 展示数据分析结果页面：

{% extends 'base_user.html' %}
{% block content %}
{% load static %}
<div class="container"><!--<img id="home_img" class="img-thumbnail" src="{% static 'img/home_img.jpg' %}">--><div id="carouselExampleIndicators" class="carousel slide" data-ride="carousel"><ol class="carousel-indicators"><li data-target="#carouselExampleIndicators" data-slide-to="0" class="active"></li><li data-target="#carouselExampleIndicators" data-slide-to="1"></li><li data-target="#carouselExampleIndicators" data-slide-to="2"></li></ol><div class="carousel-inner" id="home_carousel"><div class="carousel-item active"><img src="{% static 'img/bi_img1.jpg' %}" class="d-block w-100" alt="home_img1"></div><div class="carousel-item"><img src="{% static 'img/bi_img2.jpg' %}" class="d-block w-100" alt="home_img2"></div><div class="carousel-item"><img src="{% static 'img/bi_img3.jpg' %}" class="d-block w-100" alt="home_img3"></div></div><button class="carousel-control-prev" type="button" data-target="#carouselExampleIndicators" data-slide="prev"><span class="carousel-control-prev-icon" aria-hidden="true"></span><span class="sr-only">Previous</span></button><button class="carousel-control-next" type="button" data-target="#carouselExampleIndicators" data-slide="next"><span class="carousel-control-next-icon" aria-hidden="true"></span><span class="sr-only">Next</span></button></div>
</div><div class="container"><div class="jumbotron"><div class="row"><div class="col-md-12"><div class="card text-left"><h4 class="card-header">Articles Length Analysis</h4><div class="card-body"><div class="tab-content"><p class="card-title">Length analysis of reading comprehension articles over the years</p><hr class="my-4"><div id="img_l_cet4"><iframe src="l_show_cet4" width="100%" height="450" scrolling="no" frameborder="0"οnlοad="changeFrameHeight()"></iframe></div><hr class="my-4"><div id="img_l_cet6"><iframe src="l_show_cet6" width="100%" height="450" scrolling="no" frameborder="0"οnlοad="changeFrameHeight()"></iframe></div></div></div></div></div></div><br><div class="row"><div class="col-md-12"><div class="card text-left"><h4 class="card-header">CET4: Word Length Analysis</h4><div class="card-body"><div class="tab-content"><p class="card-title">Analysis of word length in reading comprehension articles</p><hr class="my-4"><div id="img_p_cet4a"><iframe src="p_show_cet4a" width="100%" height="650" scrolling="no"frameborder="0"οnlοad="changeFrameHeight()"></iframe></div><hr class="my-4"><div id="img_p_cet4b"><iframe src="p_show_cet4b" width="100%" height="650" scrolling="no"frameborder="0"οnlοad="changeFrameHeight()"></iframe></div></div></div></div></div></div><br><div class="row"><div class="col-md-12"><div class="card text-left"><h4 class="card-header">CET6: Word Length Analysis</h4><div class="card-body"><div class="tab-content"><p class="card-title">Analysis of word length in reading comprehension articles</p><hr class="my-4"><div id="img_p_cet6a"><iframe src="p_show_cet6a" width="100%" height="650" scrolling="no"frameborder="0"οnlοad="changeFrameHeight()"></iframe></div><hr class="my-4"><div id="img_p_cet6b"><iframe src="p_show_cet6b" width="100%" height="650" scrolling="no"frameborder="0"οnlοad="changeFrameHeight()"></iframe></div></div></div></div></div></div><br><div class="row"><div class="col-md-12"><div class="card text-left"><h4 class="card-header">High frequency words</h4><div class="card-body"><div class="tab-content"><p class="card-title">High frequency words and their frequency in reading comprehension Aand reading comprehension B</p><hr class="my-4"><div id="img_f_cet4"><iframe src="f_show_cet4" width="100%" height="750" scrolling="no" frameborder="0"οnlοad="changeFrameHeight()"></iframe></div><hr class="my-4"><div id="img_f_cet6"><iframe src="f_show_cet6" width="100%" height="750" scrolling="no" frameborder="0"οnlοad="changeFrameHeight()"></iframe></div></div></div></div></div></div></div>
</div>
{% endblock %}

• 展示词频统计：

<!DOCTYPE html>
<html>
<head><meta charset="UTF-8"><title>img_view</title><script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script></head>
<body><div id="52278b250e324ef4b6962156dc3c3ce8" class="chart-container" style="width:800px; height:700px;"></div><script>var chart_52278b250e324ef4b6962156dc3c3ce8 = echarts.init(document.getElementById('52278b250e324ef4b6962156dc3c3ce8'), 'white', {renderer: 'canvas'});var option_52278b250e324ef4b6962156dc3c3ce8 = {"animation": true,"animationThreshold": 2000,"animationDuration": 1000,"animationEasing": "cubicOut","animationDelay": 0,"animationDurationUpdate": 300,"animationEasingUpdate": "cubicOut","animationDelayUpdate": 0,"color": ["#c23531","#2f4554","#61a0a8","#d48265","#749f83","#ca8622","#bda29a","#6e7074","#546570","#c4ccd3","#f05b72","#ef5b9c","#f47920","#905a3d","#fab27b","#2a5caa","#444693","#726930","#b2d235","#6d8346","#ac6767","#1d953f","#6950a1","#918597"],"series": [{"type": "bar","name": "Frequency of high frequency words","legendHoverLink": true,"data": [9,9,9,10,10,10,11,11,12,12,12,14,18,18,19,19,20,21,26,28],"showBackground": false,"barMinHeight": 0,"barCategoryGap": "20%","barGap": "30%","large": false,"largeThreshold": 400,"seriesLayoutBy": "column","datasetIndex": 0,"clip": true,"zlevel": 0,"z": 2,"label": {"show": true,"position": "right","margin": 8},"itemStyle": {"color": "#2a5caa"},"rippleEffect": {"show": true,"brushType": "stroke","scale": 2.5,"period": 4}}],"legend": [{"data": ["Frequency of high frequency words"],"selected": {"Frequency of high frequency words": true},"show": true,"padding": 5,"itemGap": 10,"itemWidth": 25,"itemHeight": 14}],"tooltip": {"show": true,"trigger": "item","triggerOn": "mousemove|click","axisPointer": {"type": "line"},"showContent": true,"alwaysShowContent": false,"showDelay": 0,"hideDelay": 100,"textStyle": {"fontSize": 14},"borderWidth": 0,"padding": 5},"xAxis": [{"show": true,"scale": false,"nameLocation": "end","nameGap": 15,"gridIndex": 0,"inverse": false,"offset": 0,"splitNumber": 5,"minInterval": 0,"splitLine": {"show": false,"lineStyle": {"show": true,"width": 1,"opacity": 1,"curveness": 0,"type": "solid"}}}],"yAxis": [{"show": true,"scale": false,"nameLocation": "end","nameGap": 15,"gridIndex": 0,"inverse": false,"offset": 0,"splitNumber": 5,"minInterval": 0,"splitLine": {"show": false,"lineStyle": {"show": true,"width": 1,"opacity": 1,"curveness": 0,"type": "solid"}},"data": ["So","learning","research","photos","screen","make","children","percent","social","devices","year","found","online","data","people","years","parents","technology","wellbeing","time"]}],"title": [{"text": "CET6","subtext": "Statistical","padding": 5,"itemGap": 10}]
};chart_52278b250e324ef4b6962156dc3c3ce8.setOption(option_52278b250e324ef4b6962156dc3c3ce8);</script>
</body>
</html>

六、代码下载地址

由于项目代码量和数据集较大，感兴趣的同学可以直接下载代码，使用过程中如遇到任何问题可以在评论区进行评论，我都会一一解答。

代码下载：

• 代码分享：基于python+Django的英文数据分析与可视化系统