一、爬虫部分
爬虫说明：
1、本爬虫是以面向对象的方式进行代码架构的
2、本爬虫爬取的数据存入到MongoDB数据库中
3、爬虫代码中有详细注释
代码展示
import re
import time
from pymongo import MongoClient
import requests
from lxml import html
from urllib import parse
class CatMovie():
def __init__(self):
self.start_url = 'https://maoyan.com/films?showType=3&offset=0'
self.url_temp = 'https://maoyan.com/films?showType=3&offset={}'
self.detail_url = 'https://maoyan.com/films/{}'
# 构造响应头
self.headers = {
"Cookie": "__mta=143397386.1607405956154.1608533524873.1608533569928.76; _lxsdk_cuid=174f6b873b49b-005ed8da7476a-3d634f03-144000-174f6b873b5c8; uuid_n_v=v1; __utma=17099173.1780976830.1607406113.1607406113.1607406113.1; __utmz=17099173.1607406113.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); recentCis=52%3D73%3D1; _lxsdk=92DE8D903FA311EB97145540D12763BA74A99EC69EF74E288E03A6373ED78378; _lx_utm=utm_source%3Dmeituanweb; Hm_lvt_703e94591e87be68cc8da0da7cbd0be2=1608533269,1608533300,1608533544,1608533623; Hm_lpvt_703e94591e87be68cc8da0da7cbd0be2=1608533623; _lxsdk_s=176840dd994-603-17f-1df%7C%7C34; __mta=143397386.1607405956154.1608533569928.1608533622616.77; uuid=70554980435911EB91B6516866DCC34951FCE88748C84B79A0630808E1889048; _csrf=be6825573a1247a5dcf2ed5a6100bacaacccd21643c45ee79a3a7a28c1bb32e9; lt=3dN05zd6hwM_WEa3scYBnu5qcEoAAAAARgwAAMUreBNzDKR9eCuGuYWtOPWt5ULO65alj1dffuIQJisgN0lrWp0kJkyABp6Ly8cJ2A; lt.sig=y5Xz3WT9ooI2TpIM7pzKU9CROfo",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36"
}
# 猫眼电影的自定义字体映射
self.font_dict = {
'unif4ef': '6', 'unif848': '3', 'unif88a': '7', 'unie7a1': '9',
'unie343':'1','unie137':'8','unif489':'0','unie5e2':'4','unif19b':'2','unie8cd':'5'}
self.client = MongoClient()
self.collection = self.client['test']['cat_movie']
# 构造请求详情页url
def get_url_list(self):
total_num = 2000
page = total_num//30+1
return [self.url_temp.format(i*30) for i in range(0,page+1)]
# 解析请求并解析url地址
def parse_url(self,url):
rest = requests.get(url,headers=self.headers)
return rest.content.decode()
# 解析并获取列表页数据
def get_content_list(self,html_str):
movie_ids = re.findall(r'href="/films/(.*?)" target',html_str)
item_list = []
for i in movie_ids[::3]:
item = {
}
detail_url = self.detail_url.format(i)
# 获取到每一个详情数据的唯一标志在通过urljoin构造详情页url
item['detail_url'] = parse.urljoin(self.start_url,detail_url)
item = self.parse_detail(item['detail_url'],item)
print(item)
item_list.append(item)
return item_list
# 解析并获取详情页数据
def parse_detail(self,url,item):
time.sleep(0.1)
rest = requests.get(url,headers=self.headers)
# 先替换掉页面中加密字体的&#x 通过用**包裹方便后续锁定
html_str = re.sub(r'&#x(\w+)?;',r'*uni\1*',rest.content.decode())
html_str = html.etree.HTML(html_str)
# 获取信息多采用三目运算符的方式 防止因获取的内容不存在而报异常
# 通过三目运算符进行多重判断可以增加程序的稳定性
movie_name = html_str.xpath('//div[@class="movie-brief-container"]/h1/text()')
item['movie_name'] = movie_name[0] if len(movie_name)>0 else None
movie_type = html_str.xpath('//div[@class="movie-brief-container"]/ul/li[1]/a/text()')
movie_type = movie_type if len(movie_type)>0 else None
if movie_type is not None:
item['movie_type'] = '·'.join([i.strip() for i in movie_type])
else:
item['movie_type'] = '类型未知'
area_time = html_str.xpath('//div[@class="movie-brief-container"]/ul/li[2]/text()')
area_time = area_time[0] if len(area_time)>0 else None
if area_time is not None:
area_time = area_time.split('/')
item['movie_area'] = area_time[0].strip() if len(area_time)>0 else '上映国家未知'
item['movie_duration'] = area_time[1].strip() if len(area_time)>1 else '电影时长未知'
else:
item['movie_area'] = '上映国家未知'
item['movie_duration'] = '电影时长未知'
movie_publish = html_str.xpath('//div[@class="movie-brief-container"]/ul/li[3]/text()')
movie_publish = movie_publish[0] if len(movie_publish)>0 else None
if movie_publish is not None:
item['movie_publish'] = re.findall(r'(\d+-\d+-\d+)',movie_publish)
item['movie_publish'] = item['movie_publish'][0] if len(item['movie_publish'])>0 else movie_publish
else:
item['movie_publish'] = '上映时间未知'
movie_score = html_str.xpath('//div[@class="movie-index-content score normal-score"]/span/span/text()')
movie_score = movie_score[0] if len(movie_score)>0 else None
if movie_score is not None:
item['movie_score'] = re.sub(r'(\*[a-z0-9]+?\*)',lambda x:self.font_dict[x.group(1).strip('*')],movie_score)
else:
item['movie_score'] = '电影评分未知'
movie_comments = html_str.xpath('//span[@class="score-num"]/span/text()')
movie_comments = movie_comments[0] if len(movie_comments)>0 else None
if movie_comments is not None:
item['movie_comments'] = re.sub(r'(\*[a-z0-9]+?\*)',lambda x:self.font_dict[x.group(1).strip('*')],movie_comments)
else:
item['movie_comments'] = '评论人数未知'
movie_booking = html_str.xpath('//div[@class="movie-index-content box"]/span[1]/text()')
movie_booking = movie_booking[0] if len(movie_booking)>0 else None
if movie_booking is not None:
unit = html_str.xpath('//div[@class="movie-index-content box"]/span[2]/text()')
unit = unit[0] if len(unit) > 0 else ''
item['movie_booking'] = re.sub(r'(\*[a-z0-9]+?\*)', lambda x: self.font_dict[x.group(1).strip('*')],movie_booking) + unit
else:
item['movie_booking'] = '电影票房未知'
movie_director = html_str.xpath('//div[@class="celebrity-container"]//div[1]//div[@class="info"]//a/text()')
movie_director = movie_director[0] if len(movie_director)>0 else None
if movie_director is not None:
item['movie_director'] = movie_director.strip()
else:
item['movie_director'] = '导演未知'
return item
# 保存数据
def save(self,content_list):
for i in content_list:
self.collection.insert(i)
# 程序主方法
def run(self):
url_list = self.get_url_list()
for i in url_list:
time.sleep(0.5)
html_str = self.parse_url(i)
item_list = self.get_content_list(html_str)
# self.save(item_list)
if __name__ == '__main__':
movie = CatMovie()
movie.run()
二、数据分析和数据可视化部分
数据分析和数据可视化说明：
1、本博客通过Flask框架来进行数据分析和数据可视化
2、项目的架构图为
代码展示
- 数据分析代码展示（analysis.py）
import re
from pymongo import MongoClient
import pandas as pd
import numpy as np
import pymysql
# 不同年份上映的电影数量
def movie_date_publish_count(df):
grouped = df.groupby('movie_publish_year')['movie_type'].count().reset_index()
data = grouped.to_dict(orient='records')
# 将数据转换成数组嵌套数组的格式
data = [[str(i['movie_publish_year']),i['movie_type']] for i in data]
return data
# 不同地区的电影上映数量最多的前十个地区
def movie_country_publish_top10(df):
# 原数据中可能每个电影上映在多个地区 且不同地区之间使用逗号分隔 因此将movie_area列数据以逗号进行分隔变成列表
series_country = df['movie_area'].str.split(',').tolist()
# 利用set函数的特性 当数据出现重复时 只保留一个数据
list_country = set([j for i in series_country for j in i])
# 创建0矩阵统计不同地区电影上映的数量
zero_list = pd.DataFrame(np.zeros((len(series_country),len(list_country))),columns=list_country)
for i in range(len(zero_list)):
zero_list.loc[i][series_country[i]] = 1
# 使用聚合函数对不同地区的电影进行统计
country_movie_counts = zero_list.sum().astype(np.int)
country_movie_counts = country_movie_counts.reset_index()
country_movie_counts.columns = ['movie_area','count']
# 对数据进行排序并取出数量最多的前十个地区
country_movie_counts = country_movie_counts.sort_values(by='count',ascending=False)[:10]
data = [[i['movie_area'],i['count']] for i in country_movie_counts.to_dict(orient='records')]
return data
# 统计票房前十的电影
def movie_booking_top10(df):
# 按照票房数量进行排序并取出前十的数据
df = df.sort_values(by = 'movie_booking',ascending=False)
movie_name_to_booking = df[['movie_name','movie_booking']][:10]
data = [[i['movie_name'],i['movie_booking']] for i in movie_name_to_booking.to_dict(orient='records')]
print(data)
return data
# 统计评论前十的电影
def movie_comment_top10(df):
# 按照评论数量进行排序并取出前十的数据
df = df.sort_values(by = 'movie_comments',ascending=False)
movie_name_to_booking = df[['movie_name','movie_comments']][:10]
data = [[i['movie_name'],i['movie_comments']] for i in movie_name_to_booking.to_dict(orient='records')]
print(data)
return data
# 统计不同评分区间的电影数量
def movie_score_count(df):
# 根据不同区间划分电影评分数据 区间分别为 <7.0 7.0-8.0 >8.0 三个区间
grouped1 = df[df['movie_score']<7.0]['movie_score']
grouped2 = df[(df['movie_score']>=7.0) & (df['movie_score']<=8.0)]['movie_score']
grouped3 = df[df['movie_score']>8]['movie_score']
movie_score_to_count = [{
'movie_score':'<7.0','count':len(grouped1)},{
'movie_score':'7.0-8.0','count':len(grouped2)},{
'movie_score':'>8.0','count':len(grouped3)}]
data = [[i['movie_score'],i['count']] for i in movie_score_to_count]
return data
# 统计不同类型的电影数量最多的前十个类型
def movie_type_count(df):
# 原数据中可能每个电影有多个电影类型 且不同电影类型之间使用点号分隔 因此将movie_type列数据以点号进行分隔变成列表
series_movie_type = df['movie_type'].str.split('·').tolist()
movie_type_list = [j for i in series_movie_type for j in i]
# 利用set函数的特性 当数据出现重复时 只保留一个数据
movie_type = set(movie_type_list)
# 创建0矩阵统计不同电影类型的数量
zero_list = pd.DataFrame(np.zeros((len(df),len(movie_type))),columns=movie_type)
for i in range(len(df)):
zero_list.loc[i][series_movie_type[i]] = 1
# 使用聚合函数对不同类型的电影进行统计
movie_type_counts = zero_list.sum().astype(np.int)
movie_type_counts = movie_type_counts.reset_index()
movie_type_counts.columns = ['movie_type','count']
# 对数据进行排序并取出数量最多的前十个类型
movie_type_counts = movie_type_counts.sort_values(by='count',ascending=False)[:10]
data = [[i['movie_type'],i['count']] for i in movie_type_counts.to_dict(orient='records')]
return data
if __name__ == '__main__':
# 初始化MondoDB数据库
client = MongoClient()
collection = client['test']['cat_movie']
# 获取数据
movies = collection.find({
},{
'_id':0})
df = pd.DataFrame(movies)
# 打印基础信息
print(df.info())
print(df.head())
# 保留有用字段
df = df[['movie_name','movie_type','movie_area','movie_duration','movie_publish','movie_score','movie_comments','movie_booking']]
# 过滤数据
# 过滤movie_type列数据
df = df[df['movie_type'].str.contains('类型未知')==False]
# 过滤movie_area列数据
df = df[df['movie_area'].str.contains('上映国家未知')==False]
# 过滤movie_duration列数据
df = df[df['movie_duration'].str.contains('电影时长未知') == False]
# 过滤movie_publish列数据
df = df[df['movie_publish'].str.contains('上映时间未知') == False]
# 过滤movie_score列数据
df = df[df['movie_score'].str.contains('电影评分未知') == False]
# 过滤movie_comments列数据
df = df[df['movie_comments'].str.contains('评论人数未知') == False]
# 过滤movie_booking列数据
df = df[df['movie_booking'].str.contains('暂无') == False]
# 处理数据转换数据类型
# 去掉movie_duration列数据中的分钟并将数据转换成int数据类型
df['movie_duration'] = df['movie_duration'].apply(lambda x:int(re.findall(r'(\d+)分钟',x)[0]))
# 将movie_score列数据转换成float类型
df['movie_score'] = df['movie_score'].apply(lambda x:float(x))
# 将movie_comments列数据统一单位
df['movie_comments'] = df['movie_comments'].apply(lambda x:int(float(re.findall(r'(.*)万',x)[0])*10000) if len(re.findall(r'万',x))>0 else int(x))
# 将movie_booking列数据统一单位
df['movie_booking'] = df['movie_booking'].apply(lambda x: float(re.findall(r'(.*)亿', x)[0]) if len(re.findall('亿', x)) > 0 else round(float(x.split('万')[0])/10000,2))
# 将movie_publish转换成pandas时间类型并将数据转换成具体的年添加到pd中
df['movie_publish'] = df['movie_publish'].apply(lambda x:re.findall(r'(.*)中国大陆上映',x)[0] if len(re.findall(r'中国大陆上映',x))>0 else x)
df['movie_publish'] = pd.to_datetime(df['movie_publish'])
date = pd.DatetimeIndex(df['movie_publish'])
df['movie_publish_year'] = date.year
# 不同年份上映的电影数量
# data = movie_date_publish_count(df)
# 不同地区的电影上映数量最多的前十个地区
# data = movie_country_publish_top10(df)
# 统计票房前十的电影
# data = movie_booking_top10(df)
# 统计评论前十的电影
# data = movie_comment_top10(df)
# 统计不同评分区间的电影数量
# data = movie_score_count(df)
# 统计不同类型的电影数量最多的前十个类型
data = movie_type_count(df)
# 创建数据库连接
conn = pymysql.connect(host='localhost',user='root',password='123456',port=3306,database='cateye',charset='utf8')
# 获取游标对象
with conn.cursor() as cursor:
# 不同年份上映的电影数量
# sql = 'insert into db_year_movie_count(year,count) values(%s,%s)'
# 不同地区的电影上映数量最多的前十个地区
# sql = 'insert into db_area_movie_count(area,count) values(%s,%s)'
# 统计票房前十的电影
# sql = 'insert into db_booking_movie_count(name,booking) values(%s,%s)'
# 统计评论前十的电影
# sql = 'insert into db_comment_movie_count(name,comment) values(%s,%s)'
# 统计不同评分区间的电影数量
# sql = 'insert into db_score_movie_count(score,count) values(%s,%s)'
# 统计不同类型的电影数量最多的前十个类型
sql = 'insert into db_type_movie_count(type,count) values(%s,%s)'
try:
result = cursor.executemany(sql,data)
if result:
print('数据库插入数据成功')
conn.commit()
except pymysql.MySQLError as error:
print(error)
conn.rollback()
finally:
conn.close()
- 数据库模型文件展示（models.py）
from . import db
# 年份与上映电影数量模型
class YearMovieCount(db.Model):
__tablename__ = 'db_year_movie_count'
id = db.Column(db.Integer,primary_key=True,autoincrement=True)
year = db.Column(db.String(64),nullable=False)
count = db.Column(db.Integer,nullable=False)
# 地区与上映电影数量前十模型
class AreaMovieCount(db.Model):
__tablename__ = 'db_area_movie_count'
id = db.Column(db.Integer,primary_key=True,autoincrement=True)
area = db.Column(db.String(64),nullable=False)
count = db.Column(db.Integer,nullable=False)
# 票房前十电影模型
class BookingMovieCount(db.Model):
__tablename__ = 'db_booking_movie_count'
id = db.Column(db.Integer,primary_key=True,autoincrement=True)
name = db.Column(db.String(64), nullable=False)
booking = db.Column(db.Float, nullable=False)
# 评论前十电影模型
class CommentMovieCount(db.Model):
__tablename__ = 'db_comment_movie_count'
id = db.Column(db.Integer,primary_key=True,autoincrement=True)
name = db.Column(db.String(64),nullable=False)
comment = db.Column(db.BigInteger,nullable=False)
# 票房前十电影模型
class ScoreMovieCount(db.Model):
__tablename__ = 'db_score_movie_count'
id = db.Column(db.Integer,primary_key=True,autoincrement=True)
score = db.Column(db.String(64),nullable=False)
count = db.Column(db.Integer,nullable=False)
# 类型与上映电影数量前十模型
class TypeMovieCount(db.Model):
__tablename__ = 'db_type_movie_count'
id = db.Column(db.Integer,primary_key=True,autoincrement=True)
type = db.Column(db.String(64),nullable=False)
count = db.Column(db.Integer,nullable=False)
- 配置文件代码展示（config.py）
class Config(object):
SECRET_KEY = 'ma5211314'
SQLALCHEMY_DATABASE_URI = 'mysql://root:123456@localhost:3306/cateye'
SQLALCHEMY_TRACK_MODIFICATIONS = True
class DevelopmentConfig(Config):
DEBUG = True
class ProjectConfig(Config):
pass
# 采用映射方式方便后续调用配置类
config_map = {
'develop':DevelopmentConfig,
'project':ProjectConfig
}
- 主工程目录代码展示（api_1_0/_init_.py）
from flask import Flask
import pymysql
from flask_sqlalchemy import SQLAlchemy
# python3的pymysql取代了mysqldb库 为了防止出现 ImportError: No module named ‘MySQLdb’的错误
pymysql.install_as_MySQLdb()
from config import config_map
db = SQLAlchemy()
# 采用工厂模式创建app实例
def create_app(mode = 'develop'):
app = Flask(__name__)
config = config_map[mode]
# 加载配置类
app.config.from_object(config)
db.init_app(app)
# 导入蓝图
from . import views
app.register_blueprint(views.blue,url_prefix='/show')
return app
- 主程序文件代码展示（manager.py）
from api_1_0 import create_app,db
from flask_script import Manager
from flask_migrate import Migrate,MigrateCommand
from flask import render_template
app = create_app()
manager = Manager(app)
Migrate(app,db)
manager.add_command('db',MigrateCommand)
# 首页视图函数
@app.route('/')
def index():
return render_template('index.html')
if __name__ == '__main__':
manager.run()
- 视图文件代码展示（api_1_0/views/_init_.py，show.py）
_init_.py
from flask import Blueprint
# 为了在主程序运行时能够加载到模型类
from api_1_0 import model
blue = Blueprint('show',__name__)
# 导入定义的视图函数
from . import show
show.py
from api_1_0.views import blue
from api_1_0.model import db,AreaMovieCount,BookingMovieCount,CommentMovieCount,ScoreMovieCount,TypeMovieCount,YearMovieCount
from flask import render_template
'''
将数据转换成列表的形式或者是列表嵌套字典的形式
locals()方法能够以字典的形式返回函数内所有声明的变量
'''
# 年份与上映电影数量折线图
@blue.route('/drawLine')
def drawLine():
year_count = YearMovieCount.query.all()
year = [i.year for i in year_count]
count = [i.count for i in year_count]
return render_template('drawLine.html',**locals())
# 票房前十电影柱状图和评论前十电影模型柱状图
@blue.route('/drawBar')
def drawBar():
movie_booking = BookingMovieCount.query.all()
booking_name = [i.name for i in movie_booking]
new_booking_name = []
for i in booking_name:
i = list(i)
if len(i)>6:
i.insert(len(i)//2,'\n')
new_booking_name.append(''.join(i))
booking = [i.booking for i in movie_booking]
movie_comment = CommentMovieCount.query.all()
comment_name = [i.name for i in movie_comment]
new_comment_name = []
for i in comment_name:
i = list(i)
if len(i) > 6:
i.insert(len(i) // 2, '\n')
new_comment_name.append(''.join(i))
comment = [i.comment//10000 for i in movie_comment]
return render_template('drawBar.html',**locals())
# 地区与上映电影数量前十模型占比图和类型与上映电影数量前十模型占比图
@blue.route('/drawPie')
def drawPie():
area_movie_count = AreaMovieCount.query.all()
area_count = [{
'name':i.area,'value':i.count} for i in area_movie_count]
type_movie_count = TypeMovieCount.query.all()
type_count = [{
'name':i.type,'value':i.count} for i in type_movie_count]
return render_template('drawPie.html',**locals())
# 不同评分区间电影数量占比图
@blue.route('/drawSignalPie')
def drawSignalPie():
score_movie_count = ScoreMovieCount.query.all()
score_count = [{
'name':i.score,'value':i.count} for i in score_movie_count]
return render_template('drawSignalPie.html',**locals())
- 主页展示（index.html）
主页简单创建了四个超链接指向对应的图表
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>首页说明</title>
<style>
.container{
width: 100%;
height: 600px;
padding: 40px;
line-height: 60px;
}
ul{
margin: auto;
width: 60%;
}
</style>
</head>
<body>
<div class="container">
<ul>
<li><a href="http://127.0.0.1:5000/show/drawLine" target="_blank"><h3>年份与上映电影数量折线图</h3></a></li>
<li><a href="http://127.0.0.1:5000/show/drawPie" target="_blank"><h3>地区与上映电影数量前十模型占比图和类型与上映电影数量前十模型占比图</h3></a></li>
<li><a href="http://127.0.0.1:5000/show/drawBar" target="_blank"><h3>票房前十电影柱状图和评论前十电影模型柱状图</h3></a></li>
<li><a href="http://127.0.0.1:5000/show/drawSignalPie" target="_blank"><h3>不同评分区间电影数量占比图</h3></a></li>
</ul>
</div>
</body>
</html>
- 模板文件代码展示（drawBar.html，drawLine.html，drawSignalPie.html，drawPie.html）
drawBar.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>地区与上映电影数量前十模型占比图和类型与上映电影数量前十模型占比图</title>
<script src="../static/js/echarts.min.js"></script>
<script src="../static/theme/vintage.js"></script>
<style>
.cart-group{
display: flex;
width: 100%;
height: 600px;
justify-content: space-between;
}
</style>
</head>
<body>
<div class="cart-group">
<div class="cart1" style="width: 700px;height: 400px"></div>
<div class="cart2" style="width: 700px;height: 400px"></div>
</div>
<script>
var myCharts1 = echarts.init(document.querySelector('.cart1'),'vintage')
var myCharts2 = echarts.init(document.querySelector('.cart2'),'vintage')
var booking_name = {
{
new_booking_name|tojson }}
var booking = {
{
booking|tojson }}
var comment_name = {
{
new_comment_name|tojson }}
var comment = {
{
comment|tojson }}
function getOptions(category,data,title_text,desc,unit){
var option = {
title:{
text:title_text,
textStyle:{
fontFamily:'楷体',
fontSize:21
}
},
legend:{
name:[desc]
},
xAxis:{
type:'category',
data:category,
axisLabel:{
interval:0,
rotate:30,
margin:16
}
},
yAxis:{
type:'value',
scale:true
},
tooltip:{
trigger:'item',
triggerOn:'mousemove',
formatter:function(arg){
return '电影名称：'+arg.name+"<br>"+arg.seriesName+"："+arg.value+unit
}
},
series:[
{
name:desc,
type:'bar',
data:data,
label:{
show:true,
position:'top',
rotate:30,
distance:10
}
}
]
}
return option
}
var option1 = getOptions(booking_name,booking,'票房前十电影柱状图','电影票房','亿')
var option2 = getOptions(comment_name,comment,'评论前十电影柱状图','电影评论','万')
myCharts1.setOption(option1)
myCharts2.setOption(option2)
</script>
</body>
</html>
结论：
战狼2无论是票房还是评论数都稳居第一，电影票房一般和电影评论成正比，电影票房的高低在一定程度上反映电影评论的多少。
drawLine.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>年份与上映电影数量折线图</title>
<script src="../static/js/echarts.min.js"></script>
<script src="../static/theme/vintage.js"></script>
</head>
<body>
<div class="cart" style="width: 1000px;height: 600px;margin: auto"></div>
<script>
var myCharts = echarts.init(document.querySelector('.cart'),'vintage')
var year = {
{
year|tojson }}
var count = {
{
count|tojson }}
var option = {
title:{
text:'年份与上映电影数量折线图',
textStyle:{
fontFamily:'楷体',
fontSize:21
}
},
legend:{
name:['电影年份数量变化']
},
xAxis: {
type: 'category',
data: year,
axisLabel: {
interval: 0,
rotate: 40,
margin: 10
}
},
yAxis:{
type:'value',
scale:true
},
tooltip:{
trigger:'axis',
triggerOn:'mousemove',
formatter:function(arg)
{
return '上映年份：'+arg[0].name+"<br>"+'电影数量：'+arg[0].value
}
},
series:[
{
name:'电影年份数量变化',
type:'line',
data:count,
label:{
show:true,
rotate: 40
},
smooth:true,
markLine:{
data:[
{
name:'平均值',
type:'average',
label: {
formatter:function(arg)
{
return arg.name+'：\n'+Math.ceil(arg.value)
}
}
}
]
},
markPoint:{
data:[
{
name:'最大值',
type:'max',
label:{
show: true,
formatter:function (arg)
{
return arg.name
}
},
symbolSize:[40,40],//容器大小
symbolOffset:[0,-20]
},
{
name:'最小值',
type:'min',
label:{
show: true,
formatter:function (arg)
{
return arg.name
}
},
symbolSize:[40,40],//容器大小
symbolOffset:[0,-20]
}
]
}
}
]
}
myCharts.setOption(option)
</script>
</body>
</html>
结论：
年份与电影上映数量成正比，电影上映数量随时间的增长而增加，而2020年由于疫情原因，使得电影上映数量同比上一年较低。
drawPie.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>工作经验与薪资关系图&学历与薪资关系图</title>
<script src="../static/js/echarts.min.js"></script>
<script src="../static/theme/dark.js"></script>
<script src="../static/theme/my_theme.js"></script>
<script src="../static/theme/vintage.js"></script>
<style>
.chart-group{
width: 100%;
height: 1000px;
padding: 50px;
display: flex;
justify-content: space-evenly;
margin: auto;
}
.chart1{
width: 600px;
height: 400px;
}
.chart2{
width: 600px;
height: 400px;
}
</style>
</head>
<body>
<div class="chart-group">
<div class="chart1"></div>
<div class="chart2"></div>
</div>
<script>
// 初始化echarts
var myCharts1 = echarts.init(document.querySelector('.chart1'),'vintage')
var myCharts2 = echarts.init(document.querySelector('.chart2'),'vintage')
// 获取视图函数传入数据并转换成json格式
var wel_count = {
{
wel_count|tojson }}
var edu_count = {
{
edu_count|tojson }}
var value = []
function getOption(data,desc){
var option = {
title:{
text : desc,
textStyle:{
fontFamily:'楷体',
fontSize:21
},
},
tooltip:{
trigger:'item',
triggerOn:'mousemove',
formatter:function(rst){
if(desc=="工作经验的人数分布图"){
return '福利类型：'+rst.name+'<br>'+'企业数量：'+rst.value+'个'+'<br>'+'占比：'+rst.percent+"%"
}else{
return '学历：'+rst.name+'<br>'+'所需岗位：'+rst.value+'人'+'<br>'+'占比：'+rst.percent+"%"
}
}
},
legend:{
name:['人数占比'],
orient: 'vertical',
bottom:10,
right:10,
},
series:[
{
name:'人数占比',
type:'pie',
data:data,
label:{
show:true
},
roseType:'radius',
selectedMode:'multiple',
selectedOffset: 10
}
]
}
return option
}
var option1 = getOption(wel_count,'福利类型数量前十占比')
var option2 = getOption(edu_count,'学历人数占比图')
myCharts1.setOption(option1)
myCharts2.setOption(option2)
</script>
</body>
</html>
结论：
中国大陆电影上映数量占40%左右，中国电影上映数量占全国电影数量的一半左右；电影类型主要以剧情、喜剧、动作为主，占总电影类型的一半左右。
drawSignalPie.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>不同评分区间电影数量占比图</title>
<script src="../static/js/echarts.min.js"></script>
<script src="../static/theme/vintage.js"></script>
</head>
<body>
<div class="cart" style="width: 1000px;height: 600px;margin: auto"></div>
<script>
var myCharts = echarts.init(document.querySelector('.cart'),'vintage')
data = {
{
score_count|tojson }}
var option = {
title:{
text:'不同评分区间电影数量占比图',
textStyle:{
fontFamily:'楷体',
fontSize:21
}
},
tooltip:{
trigger:'item',
triggerOn:'mousemove',
formatter:function (arg)
{
return '评分：'+arg.name+'<br>'+'数量：'+arg.value+'<br>'+'占比：'+arg.percent+'%'
}
},
series:[
{
name:'电影评分',
type:'pie',
data:data,
label:{
show:true,
formatter:function (arg)
{
return arg.seriesName+':'+arg.name
}
},
roseType:'radius',
selectedMode:'multiple',
selectedOffset:20
}
]
}
myCharts.setOption(option)
</script>
</body>
</html>
结论：
电影评分主要在8.0分以上，占总上映电影的六成以上，上映的电影水平普遍很高
以下是项目源码，希望能够帮助你们，如有疑问，下方评论
flask项目代码链接