用Python制作酷炫的可视化大屏，特简单！

# 启动MySQL, 输入密码
mysql -u root -p

# 创建名为my_database的数据库
create database my_database;

# 显示MySQL中所有的数据库
show databases;

# 选择my_database数据库
use my_database;

# 显示my_database数据库中所有的表
show tables;

# 删除表
drop table info;
drop table `2021-12-26`;

# 显示表中的内容, 执行SQL查询语句
select * from info;
select * from `2021-12-26`;

import requests
import re
from bs4 import BeautifulSoup
import time
import random
import pandas as pd
from sqlalchemy import create_engine
import datetime as dt


def get_info():
    """获取大屏第一列信息数据"""
    headers = {
        'User-Agent': 'Mozilla/5.0 (MSIE 10.0; Windows NT 6.1; Trident/5.0)',
        'referer': 'https: // passport.csdn.net / login',
    }
    # 我的博客地址
    url = 'https://blog.csdn.net/river_star1/article/details/121463591'
    try:
        resp = requests.get(url, headers=headers)
        now = dt.datetime.now().strftime("%Y-%m-%d %X")
        soup = BeautifulSoup(resp.text, 'lxml')
        author_name = soup.find('div', class_='user-info d-flex flex-column profile-intro-name-box').find('a').get_text(strip=True)
        head_img = soup.find('div', class_='avatar-box d-flex justify-content-center flex-column').find('a').find('img')['src']
        row1_nums = soup.find_all('div', class_='data-info d-flex item-tiling')[0].find_all('span', class_='count')
        row2_nums = soup.find_all('div', class_='data-info d-flex item-tiling')[1].find_all('span', class_='count')
        level_mes = soup.find_all('div', class_='data-info d-flex item-tiling')[0].find_all('dl')[-1]['title'].split(',')[0]
        rank = soup.find('div', class_='data-info d-flex item-tiling').find_all('dl')[-1]['title']
        info = {
            'date': now,#时间
            'head_img': head_img,#头像
            'author_name': author_name,#用户名
            'article_num': str(row1_nums[0].get_text()),#文章数
            'fans_num': str(row2_nums[1].get_text()),#粉丝数
            'like_num': str(row2_nums[2].get_text()),#喜欢数
            'comment_num': str(row2_nums[3].get_text()),#评论数
            'level': level_mes,#等级
            'visit_num': str(row1_nums[3].get_text()),#访问数
            'score': str(row2_nums[0].get_text()),#积分
            'rank': str(row1_nums[2].get_text()),#排名
        }
        df_info = pd.DataFrame([info.values()], columns=info.keys())
        return df_info
    except Exception as e:
        print(e)
        return get_info()


def get_type(title):
    """设置文章类型(依据文章名称)"""
    the_type = '其他'
    article_types = ['项目', '数据可视化', '代码', '图表', 'Python', '可视化', '数据', '面试', '视频', '动态', '下载']
    for article_type in article_types:
        if article_type in title:
            the_type = article_type
            break
    return the_type


def get_blog():
    """获取大屏第二、三列信息数据"""
    headers = {
        'User-Agent': 'Mozilla/5.0 (MSIE 10.0; Windows NT 6.1; Trident/5.0)',
        'referer': 'https: // passport.csdn.net / login',
    }
    base_url = 'https://blog.csdn.net/river_star1/article/list/'
    resp = requests.get(base_url+"1", headers=headers,  timeout=3)
    max_page = int(re.findall(r'var listTotal = (\d+);', resp.text)[0])//40+1
    df = pd.DataFrame(columns=['url', 'title', 'date', 'read_num', 'comment_num', 'type'])
    count = 0
    for i in range(1, max_page+1):
        url = base_url + str(i)
        resp = requests.get(url, headers=headers)
        soup = BeautifulSoup(resp.text, 'lxml')
        articles = soup.find("div", class_='article-list').find_all('div', class_='article-item-box csdn-tracking-statistics')
        for article in articles[1:]:
            a_url = article.find('h4').find('a')['href']
            title = article.find('h4').find('a').get_text(strip=True)[2:]
            issuing_time = article.find('span', class_="date").get_text(strip=True)
            num_list = article.find_all('span', class_="read-num")
            read_num = num_list[0].get_text(strip=True)
            if len(num_list) > 1:
                comment_num = num_list[1].get_text(strip=True)
            else:
                comment_num = 0
            the_type = get_type(title)
            df.loc[count] = [a_url, title, issuing_time, int(read_num), int(comment_num), the_type]
            count += 1
        time.sleep(random.choice([1, 1.1, 1.3]))
    return df


if __name__ == '__main__':
    # 今天的时间
    today = dt.datetime.today().strftime("%Y-%m-%d")
    # 连接mysql数据库
    engine = create_engine('mysql+pymysql://root:123456@localhost/my_database?charset=utf8')

    # 获取大屏第一列信息数据, 并写入my_database数据库的info表中, 如若表已存在, 删除覆盖
    df_info = get_info()
    print(df_info)
    df_info.to_sql("info", con=engine, if_exists='replace', index=False)

    # 获取大屏第二、三列信息数据, 并写入my_database数据库的日期表中, 如若表已存在, 删除覆盖
    df_article = get_blog()
    print(df_article)
    df_article.to_sql(today, con=engine, if_exists='replace', index=True)

from spider_py import get_info, get_blog
from dash import dcc
import dash
from dash import html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output
import datetime as dt
from sqlalchemy import create_engine
from flask_caching import Cache
import numpy as np

# 今天的时间
today = dt.datetime.today().strftime("%Y-%m-%d")

# 连接数据库
engine = create_engine('mysql+pymysql://root:123456@localhost/my_database?charset=utf8')

# 导入css样式
external_css = [
    "https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css",
    "https://cdnjs.cloudflare.com/ajax/libs/skeleton/2.0.4/skeleton.min.css"
]

# 创建一个实例
app = dash.Dash(__name__, external_stylesheets=external_css)
server = app.server

# 可以选择使用缓存, 减少频繁的数据请求
# cache = Cache(app.server, config={
#     'CACHE_TYPE': 'filesystem',
#     'CACHE_DIR': 'cache-directory'
# })

# 读取info表的数据
info = pd.read_sql('info', con=engine)

# 图表颜色
color_scale = ['#2c0772', '#3d208e', '#8D7DFF', '#CDCCFF', '#C7FFFB', '#ff2c6d', '#564b43', '#161d33']

def indicator(text, id_value):
    """第一列的文字及数字信息显示"""
    return html.Div([
    html.P(text, className="twelve columns indicator_text"),
    html.P(id=id_value, className="indicator_value"),
], className="col indicator")


def get_news_table(data):
    """获取文章列表, 根据阅读排序"""
    df = data.copy()
    df.sort_values('read_num', inplace=True, ascending=False)
    titles = df['title'].tolist()
    urls = df['url'].tolist()

    return html.Table([html.Tbody([
        html.Tr([
            html.Td(
                html.A(titles[i], href=urls[i], target="_blank",))
        ], style={'height': '30px', 'fontSize': '16'})for i in range(min(len(df), 100))
    ])], style={"height": "90%", "width": "98%"})


# @cache.memoize(timeout=3590), 可选择设置缓存, 我没使用
def get_df():
    """获取当日最新的文章数据"""
    df = pd.read_sql(today, con=engine)
    df['date_day'] = df['date'].apply(lambda x: x.split(' ')[0]).astype('datetime64[ns]')
    df['date_month'] = df['date'].apply(lambda x: x[:7].split('-')[0] + "年" + x[:7].split('-')[-1] + "月")
    df['weekday'] = df['date_day'].dt.weekday
    df['year'] = df['date_day'].dt.year
    df['month'] = df['date_day'].dt.month
    df['week'] = df['date_day'].dt.isocalendar().week
    return df


# 导航栏的图片及标题
head = html.Div([
    html.Div(html.Img(src='./assets/img.jpg', height="100%"), style={"float": "left", "height": "90%", "margin-top": "5px", "border-radius": "50%", "overflow": "hidden"}),
    html.Span("{}博客的Dashboard".format(info['author_name'][0]), className='app-title'),
], className="row header")

# 第一列的文字及数字信息
columns = info.columns[3:]
col_name = ['文章数', '关注数', '喜欢数', '评论数', '等级', '访问数', '积分', '排名']
row1 = html.Div([
    indicator(col_name[i], col) for i, col in enumerate(columns)
], className='row')

# 第二列
row2 = html.Div([
    html.Div([
        html.P("每月文章写作情况"),
        dcc.Graph(id="bar", style={"height": "90%", "width": "98%"}, config=dict(displayModeBar=False),)
    ], className="col-4 chart_div",),
    html.Div([
        html.P("各类型文章占比情况"),
        dcc.Graph(id="pie", style={"height": "90%", "width": "98%"}, config=dict(displayModeBar=False),)
    ], className="col-4 chart_div"),
    html.Div([
        html.P("各类型文章阅读情况"),
        dcc.Graph(id="mix", style={"height": "90%", "width": "98%"}, config=dict(displayModeBar=False),)
    ], className="col-4 chart_div",)
], className='row')

# 年数统计, 我的是2019 2020 2021
years = get_df()['year'].unique()
select_list = ['每月文章', '类型占比', '类型阅读量', '每日情况']

# 两个可交互的下拉选项
dropDowm1 = html.Div([
    html.Div([
        dcc.Dropdown(id='dropdown1',
                 options=[{'label': '{}年'.format(year), 'value': year} for year in years],
                 value=years[1], style={'width': '40%'})
        ], className='col-6', style={'padding': '2px', 'margin': '0px 5px 0px'}),
    html.Div([
        dcc.Dropdown(id='dropdown2',
                 options=[{'label': select_list[i], 'value': item} for i, item in enumerate(['bar', 'pie', 'mix', 'heatmap'])],
                 value='heatmap', style={'width': '40%'})
        ], className='col-6', style={'padding': '2px', 'margin': '0px 5px 0px'})
], className='row')

# 第三列
row3 = html.Div([
    html.Div([
        html.P("每日写作情况"),
        dcc.Graph(id="heatmap", style={"height": "90%", "width": "98%"}, config=dict(displayModeBar=False),)
    ], className="col-6 chart_div",),
    html.Div([
        html.P("文章列表"),
        html.Div(get_news_table(get_df()), id='click-data'),
    ], className="col-6 chart_div", style={"overflowY": "scroll"})
], className='row')

# 总体情况
app.layout = html.Div([
    # 定时器
    dcc.Interval(id="stream", interval=1000*60, n_intervals=0),
    dcc.Interval(id="river", interval=1000*60*60, n_intervals=0),
    html.Div(id="load_info", style={"display": "none"},),
    html.Div(id="load_click_data", style={"display": "none"},),
    head,
    html.Div([
        row1,
        row2,
        dropDowm1,
        row3,
    ], style={'margin': '0% 30px'}),
])

# 回调函数, 60秒刷新info数据, 即第一列的数值实时刷新
@app.callback(Output('load_info', 'children'), [Input("stream", "n_intervals")])
def load_info(n):
    try:
        df = pd.read_sql('info', con=engine)
        return df.to_json()
    except:
        pass


# 回调函数, 60分钟刷新今日数据, 即第二、三列的数值实时刷新(爬取文章数据, 并写入数据库中)
@app.callback(Output('load_click_data', 'children'), [Input("river", "n_intervals")])
def cwarl_data(n):
    if n != 0:
        df_article = get_blog()
        df_article.to_sql(today, con=engine, if_exists='replace', index=True)


# 回调函数, 第一个柱状图
@app.callback(Output('bar', 'figure'), [Input("river", "n_intervals")])
def get_bar(n):
    df = get_df()
    df_date_month = pd.DataFrame(df['date_month'].value_counts(sort=False))
    df_date_month.sort_index(inplace=True)
    trace = go.Bar(
        x=df_date_month.index,
        y=df_date_month['date_month'],
        text=df_date_month['date_month'],
        textposition='auto',
        marker=dict(color='#33ffe6')
    )
    layout = go.Layout(
        margin=dict(l=40, r=40, t=10, b=50),
        yaxis=dict(gridcolor='#e2e2e2'),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
    )
    return go.Figure(data=[trace], layout=layout)


# 回调函数, 中间的饼图
@app.callback(Output('pie', 'figure'), [Input("river", "n_intervals")])
def get_pie(n):
    df = get_df()
    df_types = pd.DataFrame(df['type'].value_counts(sort=False))
    trace = go.Pie(
        labels=df_types.index,
        values=df_types['type'],
        marker=dict(colors=color_scale[:len(df_types.index)])
    )
    layout = go.Layout(
        margin=dict(l=50, r=50, t=50, b=50),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
    )
    return go.Figure(data=[trace], layout=layout)


# 回调函数, 左下角热力图
@app.callback(Output('heatmap', 'figure'),
              [Input("dropdown1", "value"), Input('river', 'n_intervals')])
def get_heatmap(value, n):
    df = get_df()
    grouped_by_year = df.groupby('year')
    data = grouped_by_year.get_group(value)
    cross = pd.crosstab(data['weekday'], data['week'])
    cross.sort_index(inplace=True)
    trace = go.Heatmap(
        x=['第{}周'.format(i) for i in cross.columns],
        y=["星期{}".format(i+1) if i != 6 else "星期日" for i in cross.index],
        z=cross.values,
        colorscale="Blues",
        reversescale=False,
        xgap=4,
        ygap=5,
        showscale=False
    )
    layout = go.Layout(
        margin=dict(l=50, r=40, t=30, b=50),
    )
    return go.Figure(data=[trace], layout=layout)


# 回调函数, 第二个柱状图(柱状图+折线图)
@app.callback(Output('mix', 'figure'), [Input("river", "n_intervals")])
def get_mix(n):
    df = get_df()
    df_type_visit_sum = pd.DataFrame(df['read_num'].groupby(df['type']).sum())
    df['read_num'] = df['read_num'].astype('float')
    df_type_visit_mean = pd.DataFrame(df['read_num'].groupby(df['type']).agg('mean').round(2))
    trace1 = go.Bar(
        x=df_type_visit_sum.index,
        y=df_type_visit_sum['read_num'],
        name='总阅读',
        marker=dict(color='#ffc97b'),
        yaxis='y',
    )
    trace2 = go.Scatter(
        x=df_type_visit_mean.index,
        y=df_type_visit_mean['read_num'],
        name='平均阅读',
        yaxis='y2',
        line=dict(color='#161D33')
    )
    layout = go.Layout(
        margin=dict(l=60, r=60, t=30, b=50),
        showlegend=False,
        yaxis=dict(
            side='left',
            title='阅读总数',
            gridcolor='#e2e2e2'
        ),
        yaxis2=dict(
            showgrid=False,  # 网格
            title='阅读平均',
            anchor='x',
            overlaying='y',
            side='right'
        ),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)',
    )
    return go.Figure(data=[trace1, trace2], layout=layout)


# 点击事件, 选择两个下拉选项, 点击对应区域的图表, 文章列表会刷新
@app.callback(Output('click-data', 'children'),
        [Input('pie', 'clickData'),
         Input('bar', 'clickData'),
         Input('mix', 'clickData'),
         Input('heatmap', 'clickData'),
         Input('dropdown1', 'value'),
         Input('dropdown2', 'value'),
         ])
def display_click_data(pie, bar, mix, heatmap, d_value, fig_type):
    try:
        df = get_df()
        if fig_type == 'pie':
            type_value = pie['points'][0]['label']
            # date_month_value = clickdata['points'][0]['x']
            data = df[df['type'] == type_value]
        elif fig_type == 'bar':
            date_month_value = bar['points'][0]['x']
            data = df[df['date_month'] == date_month_value]
        elif fig_type == 'mix':
            type_value = mix['points'][0]['x']
            data = df[df['type'] == type_value]
        else:
            z = heatmap['points'][0]['z']
            if z == 0:
                return None
            else:
                week = heatmap['points'][0]['x'][1:-1]
                weekday = heatmap['points'][0]['y'][-1]
                if weekday == '日':
                    weekday = 7
                year = d_value
                data = df[(df['weekday'] == int(weekday)-1) & (df['week'] == int(week)) & (df['year'] == year)]
        return get_news_table(data)
    except:
        return None


# 第一列的数值
def update_info(col):
    def get_data(json, n):
        df = pd.read_json(json)
        return df[col][0]
    return get_data


for col in columns:
    app.callback(Output(col, "children"),
                 [Input('load_info', 'children'), Input("stream", "n_intervals")]
     )(update_info(col))

if __name__ == '__main__':
    # debug模式, 端口7777
    app.run_server(debug=True, threaded=True, port=7777)
    # 正常模式, 网页右下角的调试按钮将不会出现
    # app.run_server(port=7777)

body{
    margin:0;
    padding: 0;
    background-color: #161D33;
    font-family: 'Open Sans', sans-serif;
    color: #506784;
    -webkit-user-select: none;  /* Chrome all / Safari all */
    -moz-user-select: none;     /* Firefox all */
    -ms-user-select: none;      /* IE 10+ */
    user-select: none;          /* Likely future */
}

.modal {
    display: block;  /*Hidden by default */
    position: fixed; /* Stay in place */
    z-index: 1000; /* Sit on top */
    left: 0;
    top: 0;
    width: 100%; /* Full width */
    height: 100%; /* Full height */
    overflow: auto; /* Enable scroll if needed */
    background-color: rgb(0,0,0); /* Fallback color */
    background-color: rgba(0,0,0,0.4); /* Black w/ opacity */
}

.modal-content {
    background-color: white;
    margin: 5% auto; /* 15% from the top and centered */
    padding: 20px;
    width: 30%; /* Could be more or less, depending on screen size */
    color:#506784;
}

._dash-undo-redo {
  display: none;
}

.app-title{
    color:white;
    font-size:3rem;
    letter-spacing:-.1rem;
    padding:10px;
    vertical-align:middle
}

.header{
    margin:0px;
    background-color:#161D33;
    height:70px;
    color:white;
    padding-right:2%;
    padding-left:2%
}

.indicator{
  border-radius: 5px;
  background-color: #f9f9f9;
  margin: 10px;
  padding: 15px;
  position: relative;
  box-shadow: 2px 2px 2px lightgrey;
}

.indicator_text{
    text-align: center;
    float: left;
    font-size: 17px;
    }

.indicator_value{
    text-align:center;
    color: #2a3f5f;
    font-size: 35px;
}

.add{
    height: 34px;
    background: #119DFF;
    border: 1px solid #119DFF;
    color: white;
}

.chart_div{
    background-color: #f9f9f9;
    border-radius: 5px;
    height: 390px;
    margin:5px;
    padding: 15px;
    position: relative;
    box-shadow: 2px 2px 2px lightgrey;
}

.col-4 {
    flex: 0 0 32.65%;
    max-width: 33%;
}

.col-6 {
    flex: 0 0 49.3%;
    max-width: 50%;
}

.chart_div p{
    color: #2a3f5f;
    font-size: 15px;
    text-align: center;
}

td{
    text-align: left;
    padding: 0px;
}

table{
    border: 1px;
    font-size:1.3rem;
    width:100%;
    font-family:Ubuntu;
}

.tabs_div{
    margin:0px;
    height:30px;
    font-size:13px;
    margin-top:1px
}

tr:nth-child(even) {
    background-color: #d6e4ea;
    -webkit-print-color-adjust: exact;
}