Baidu paddlepaddle installation and visualization of actual combat epidemic

Baidu paddlepaddle installation and visualization of actual combat epidemic

1. paddle installation can refer to the official documentation

Address link: https://www.paddlepaddle.org.cn/install/quick
choose the right way to install

2. Data Visualization epidemic

Thinking: data obtained by the crawler and visualized by pyecharts

Lilac Garden crawling public statistics

Internet the whole process:

Normal user
opens a browser -> transmission request to a target site -> the received response data -> to render the page.

Crawler
analog Browser -> transmission request to a target site -> the received response data -> extract useful data -> saved to the local / database.

Reptile process:

1. The transmission request (Requests module)

2. The fetch response data (the server returns)

3. parse and extract data (re regular)

4. Save the data

We use:
Request module:

HTTP requests are simple-to-use library, the official website address python implementation: http: //cn.python-requests.org/zh_CN/latest/

re module:

python re module is used for string matching module, which provides many of the features are implemented based on regular expressions,

import json
import re
import requests
import datetime

today = datetime.date.today().strftime('%Y%m%d')   #20200315

def crawl_dxy_data():
    """
    爬取丁香园实时统计数据，保存到data目录下，以当前日期作为文件名，存JSON文件
    """
    response = requests.get('https://ncov.dxy.cn/ncovh5/view/pneumonia') #request.get()用于请求目标网站
    print(response.status_code)                                          # 打印状态码


    try:
        url_text = response.content.decode()                             #更推荐使用response.content.deocde()的方式获取响应的html页面
        #print(url_text)
        url_content = re.search(r'window.getAreaStat = (.*?)}]}catch',   #re.search():扫描字符串以查找正则表达式模式产生匹配项的第一个位置 ，然后返回相应的match对象。
                                url_text, re.S)                          #在字符串a中，包含换行符\n，在这种情况下：如果不使用re.S参数，则只在每一行内进行匹配，如果一行没有，就换下一行重新开始;
                                                                         #而使用re.S参数以后，正则表达式会将这个字符串作为一个整体，在整体中进行匹配。
        texts = url_content.group()                                      #获取匹配正则表达式的整体结果
        content = texts.replace('window.getAreaStat = ', '').replace('}catch', '') #去除多余的字符
        json_data = json.loads(content)                                         
        with open('data/' + today + '.json', 'w', encoding='UTF-8') as f:
            json.dump(json_data, f, ensure_ascii=False)
    except:
        print('<Response [%s]>' % response.status_code)


def crawl_statistics_data():
    """
    获取各个省份历史统计数据，保存到data目录下，存JSON文件
    """
    with open('data/'+ today + '.json', 'r', encoding='UTF-8') as file:
        json_array = json.loads(file.read())

    statistics_data = {}
    for province in json_array:
        response = requests.get(province['statisticsData'])
        try:
            statistics_data[province['provinceShortName']] = json.loads(response.content.decode())['data']
        except:
            print('<Response [%s]> for url: [%s]' % (response.status_code, province['statisticsData']))

    with open("data/statistics_data.json", "w", encoding='UTF-8') as f:
        json.dump(statistics_data, f, ensure_ascii=False)


if __name__ == '__main__':
    crawl_dxy_data()
    crawl_statistics_data()

Run the above code, we need to create the code in the same directory data folder
after a successful run in data folders can get two files
are:
statistics_data.json
json and a date today

Installation of third-party libraries pyecharts

Echarts of Baidu is a open source data visualization tools, with a good interactive, sophisticated graphic design, it has been recognized by many developers.
The Python is an expressive language, it is suitable for data processing. When the data analysis encounter data visualization, pyecharts born. pyecharts api can refer to: https://pyecharts.org/#/zh-cn/chart_api

Use options configuration items in pyecharts, everything all Options.

The main series is divided into components and configuration global configuration components.

(1) CI Series set_series_opts (), can be configured renditions, text style, tab style, line style, and so the point;

(2) global configuration item set_global_opts (), the title can be configured, animation, axes, legends and the like;

pyecharts very easy to use! ! !
We install pip

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyecharts

If the speed is too slow and break a problem downloading the download fails, you can try other domestic sources Appears when Successfully installed pyecharts-1.7.1 simplejson-3.17.0 we have installed a success

Data Visualization map

National map

import json
import datetime
from pyecharts.charts import Map
from pyecharts import options as opts

# 读原始数据文件
today = datetime.date.today().strftime('%Y%m%d')   #20200315
datafile = 'data/'+ today + '.json'
with open(datafile, 'r', encoding='UTF-8') as file:
    json_array = json.loads(file.read())

# 分析全国实时确诊数据：'confirmedCount'字段
china_data = []
for province in json_array:
    china_data.append((province['provinceShortName'], province['confirmedCount']))
china_data = sorted(china_data, key=lambda x: x[1], reverse=True)                 #reverse=True,表示降序，反之升序

print(china_data)
# 全国疫情地图
# 自定义的每一段的范围，以及每一段的特别的样式。
pieces = [
    {'min': 10000, 'color': '#540d0d'},
    {'max': 9999, 'min': 1000, 'color': '#9c1414'},
    {'max': 999, 'min': 500, 'color': '#d92727'},
    {'max': 499, 'min': 100, 'color': '#ed3232'},
    {'max': 99, 'min': 10, 'color': '#f27777'},
    {'max': 9, 'min': 1, 'color': '#f7adad'},
    {'max': 0, 'color': '#f7e4e4'},
]
labels = [data[0] for data in china_data]
counts = [data[1] for data in china_data]

m = Map()
m.add("累计确诊", [list(z) for z in zip(labels, counts)], 'china')

#系列配置项,可配置图元样式、文字样式、标签样式、点线样式等
m.set_series_opts(label_opts=opts.LabelOpts(font_size=12),
                  is_show=False)
#全局配置项,可配置标题、动画、坐标轴、图例等
m.set_global_opts(title_opts=opts.TitleOpts(title='全国实时确诊数据',
                                            subtitle='数据来源：丁香园'),
                  legend_opts=opts.LegendOpts(is_show=False),
                  visualmap_opts=opts.VisualMapOpts(pieces=pieces,
                                                    is_piecewise=True,   #是否为分段型
                                                    is_show=True))       #是否显示视觉映射配置
#render（）会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件，也可以传入路径参数，如 m.render("mycharts.html")
m.render(path='./data/数据.html')

这里代码都有完善的注释，就不拆解来讲了

### 数据可视化湖北省地图

```python
import json
import datetime
from pyecharts.charts import Map
from pyecharts import options as opts

# 读原始数据文件
today = datetime.date.today().strftime('%Y%m%d')   #20200315
datafile = 'data/'+ today + '.json'
with open(datafile, 'r', encoding='UTF-8') as file:
    json_array = json.loads(file.read())

# 分析湖北省实时确诊数据
# 读入规范化的城市名称，用于规范化丁香园数据中的城市简称
with open('/home/aistudio/data/data24815/pycharts_city.txt', 'r', encoding='UTF-8') as f:
    defined_cities = [line.strip() for line in f.readlines()]


def format_city_name(name, defined_cities):
    for defined_city in defined_cities:
        if len((set(defined_city) & set(name))) == len(name):
            name = defined_city
            if name.endswith('市') or name.endswith('区') or name.endswith('县') or name.endswith('自治州'):
                return name
            return name + '市'
    return None


province_name = '湖北'
for province in json_array:
    if province['provinceName'] == province_name or province['provinceShortName'] == province_name:
        json_array_province = province['cities']
        hubei_data = [(format_city_name(city['cityName'], defined_cities), city['confirmedCount']) for city in
                      json_array_province]
        hubei_data = sorted(hubei_data, key=lambda x: x[1], reverse=True)

        print(hubei_data)

labels = [data[0] for data in hubei_data]
counts = [data[1] for data in hubei_data]
pieces = [
    {'min': 10000, 'color': '#540d0d'},
    {'max': 9999, 'min': 1000, 'color': '#9c1414'},
    {'max': 999, 'min': 500, 'color': '#d92727'},
    {'max': 499, 'min': 100, 'color': '#ed3232'},
    {'max': 99, 'min': 10, 'color': '#f27777'},
    {'max': 9, 'min': 1, 'color': '#f7adad'},
    {'max': 0, 'color': '#f7e4e4'},
]

m = Map()
m.add("累计确诊", [list(z) for z in zip(labels, counts)], '湖北')
m.set_series_opts(label_opts=opts.LabelOpts(font_size=12),
                  is_show=False)
m.set_global_opts(title_opts=opts.TitleOpts(title='湖北省实时确诊数据',
                                            subtitle='数据来源：丁香园'),
                  legend_opts=opts.LegendOpts(is_show=False),
                  visualmap_opts=opts.VisualMapOpts(pieces=pieces,
                                                    is_piecewise=True,
                                                    is_show=True))
m.render(path='./data/湖北省数据.html')

growth trend

on
from pyecharts.charts import Line
from pyecharts import options as opts

# 读原始数据文件
datafile = 'data/statistics_data.json'
with open(datafile, 'r', encoding='UTF-8') as file:
    json_dict = json.loads(file.read())

# 分析各省份2月1日至今的新增确诊数据：'confirmedIncr'
statistics__data = {}
for province in json_dict:
    statistics__data[province] = []
    for da in json_dict[province]:
        if da['dateId'] >= 20200201:
            statistics__data[province].append(da['confirmedIncr'])

# 获取日期列表
dateId = [str(da['dateId'])[4:6] + '-' + str(da['dateId'])[6:8] for da in json_dict['湖北'] if
          da['dateId'] >= 20200201]

# 全国新增趋势
all_statis = np.array([0] * len(dateId))
for province in statistics__data:
    all_statis = all_statis + np.array(statistics__data[province])

all_statis = all_statis.tolist()
# 湖北新增趋势
hubei_statis = statistics__data['湖北']
# 湖北以外的新增趋势
other_statis = [all_statis[i] - hubei_statis[i] for i in range(len(dateId))]

line = Line()
line.add_xaxis(dateId)
line.add_yaxis("全国新增确诊病例",   #图例
                all_statis,       #数据
                is_smooth=True,   #是否平滑曲线
               linestyle_opts=opts.LineStyleOpts(width=4, color='#B44038'),#线样式配置项
               itemstyle_opts=opts.ItemStyleOpts(color='#B44038',          #图元样式配置项
                                                 border_color="#B44038",   #颜色
                                                 border_width=10))         #图元的大小
line.add_yaxis("湖北新增确诊病例", hubei_statis, is_smooth=True,
               linestyle_opts=opts.LineStyleOpts(width=2, color='#4E87ED'),
               label_opts=opts.LabelOpts(position='bottom'),              #标签在折线的底部
               itemstyle_opts=opts.ItemStyleOpts(color='#4E87ED',
                                                 border_color="#4E87ED",
                                                 border_width=3))
line.add_yaxis("其他省份新增病例", other_statis, is_smooth=True,
               linestyle_opts=opts.LineStyleOpts(width=2, color='#F1A846'),
               label_opts=opts.LabelOpts(position='bottom'),              #标签在折线的底部
               itemstyle_opts=opts.ItemStyleOpts(color='#F1A846',
                                                 border_color="#F1A846",
                                                 border_width=3))
line.set_global_opts(title_opts=opts.TitleOpts(title="新增确诊病例", subtitle='数据来源：丁香园'),
                     yaxis_opts=opts.AxisOpts(max_=16000, min_=1, type_="log",    #坐标轴配置项
                                              splitline_opts=opts.SplitLineOpts(is_show=True),#分割线配置项
                                              axisline_opts=opts.AxisLineOpts(is_show=True)))#坐标轴刻度线配置项
line.render(path='./data/趋势图.html')

You need to complete their own pie chart

import json
import datetime
from pyecharts.charts import Map, Pie
from pyecharts import options as opts

# 读原始数据文件
today = datetime.date.today().strftime('%Y%m%d')   #20200315
datafile = 'data/'+ today + '.json'
with open(datafile, 'r', encoding='UTF-8') as file:
    json_array = json.loads(file.read())

# 分析全国实时确诊数据：'confirmedCount'字段
china_data = []
for province in json_array:
    china_data.append((province['provinceShortName'], province['confirmedCount']))
china_data = sorted(china_data, key=lambda x: x[1], reverse=True)                 #reverse=True,表示降序，反之升序

print(china_data)
labels = [data[0] for data in china_data]
counts = [data[1] for data in china_data]

m = Pie(init_opts=opts.InitOpts(height='900px'))
m.add("累计确诊", [list(z) for z in zip(labels, counts)], center=["50%", "60%"], radius=['0%', '40%']) #,)center=[100,50]

m.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}", font_size=12),
                  is_show=False)

#全局配置项,可配置标题、动画、坐标轴、图例等
m.set_global_opts(title_opts=opts.TitleOpts(title='全国实时确诊数据',
                                            subtitle='数据来源：丁香园'),
                  legend_opts=opts.LegendOpts(is_show=False, type_="scroll", pos_left="80%", orient="vertical"),
                  )

#render（）会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件，也可以传入路径参数，如 m.render("mycharts.html")
m.render(path='./data/饼状图.html')

over！！！