一、 json模塊
JSON（JavaScript Object Notation）是一種輕量級(jí)的數(shù)據(jù)交換格式，易于閱讀和編寫(xiě)，同時(shí)也易于機(jī)器解析和生成，并有效地提升網(wǎng)絡(luò)傳輸效率。
- json.loads()：將json格式的str轉(zhuǎn)化成python的數(shù)據(jù)格式；
- json.loads()：將python的數(shù)據(jù)格式（字典或列表）轉(zhuǎn)化成json格式；
# 如何將json數(shù)據(jù)解析成我們所熟悉的Python數(shù)據(jù)類型？
import json
# 將json格式的str轉(zhuǎn)化成python的數(shù)據(jù)格式：字典
dic = json.loads('{"name":"Tom","age":23}')
res = json.loads('["name","age","gender"]')
print(f'利用loads將json字符串轉(zhuǎn)化成Python數(shù)據(jù)類型{dic}',type(dic))
print(f'利用loads將json字符串轉(zhuǎn)化成Python數(shù)據(jù)類型{res}',type(res))
dics = {"name":"Tom","age":23}
result = json.dumps(dics)
print(type(result))
result
二、通過(guò)Python實(shí)現(xiàn)疫情地圖可視化
需求：爬取疫情的數(shù)據(jù)、如何處理json數(shù)據(jù)以及根據(jù)疫情數(shù)據(jù)如何利用pyecharts繪制疫情地圖。
1.數(shù)據(jù)的獲取(基于request模塊)
import requests
import json
# 國(guó)內(nèi)疫情數(shù)據(jù)
China_url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5'
headers = {
# 瀏覽器偽裝
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Safari/537.36',
'referer': 'https://news.qq.com/',
}
# 發(fā)起get請(qǐng)求，獲取響應(yīng)數(shù)據(jù)
response = requests.get(China_url,headers=headers).json()
data = json.loads(response['data'])
# 保存數(shù)據(jù)
with open('./2021-02-03國(guó)內(nèi)疫情.json','w',encoding='utf-8') as f:
# 不采用ASCII編碼
f.write(json.dumps(data,ensure_ascii=False,indent=2))
爬取的數(shù)據(jù)保存格式為json，開(kāi)頭的部分?jǐn)?shù)據(jù)如下：
2.將json格式的數(shù)據(jù)保存到Excel
無(wú)論是json數(shù)據(jù)存儲(chǔ)的，還是Python的基本數(shù)據(jù)類型存儲(chǔ)的，對(duì)于數(shù)據(jù)分析都不是很友好，所以我們可以將其數(shù)據(jù)存儲(chǔ)類型轉(zhuǎn)化為pandas的DataFrame類型，因?yàn)镈ataFrame和Excel可以更好的相互轉(zhuǎn)換。
生成的數(shù)據(jù)模式如下：
將以上的數(shù)據(jù)進(jìn)行處理，獲得Excel表一樣規(guī)范的數(shù)據(jù)格式。
import pandas as pd
chinaTotalData = pd.DataFrame(china_citylist)
# 將整體數(shù)據(jù)chinaTotalData中的today和total數(shù)據(jù)添加到DataFrame中
# 處理total字典里面的各個(gè)數(shù)據(jù)項(xiàng)
# ======================================================================
confirmlist = []
suspectlist = []
deadlist = []
heallist = []
deadRatelist = []
healRatelist = []
# print(chinaTotalData['total'].values.tolist()[0])
for value in chinaTotalData['total'].values.tolist():
confirmlist.append(value['confirm'])
suspectlist.append(value['suspect'])
deadlist.append(value['dead'])
heallist.append(value['heal'])
deadRatelist.append(value['deadRate'])
healRatelist.append(value['healRate'])
chinaTotalData['confirm'] = confirmlist
chinaTotalData['suspect'] = suspectlist
chinaTotalData['dead'] = deadlist
chinaTotalData['heal'] = heallist
chinaTotalData['deadRate'] = deadRatelist
chinaTotalData['healRate'] = healRatelist
# ===================================================================
# 創(chuàng)建全國(guó)today數(shù)據(jù)
today_confirmlist = []
today_confirmCutslist = []
for value in chinaTotalData['today'].values.tolist():
today_confirmlist.append(value['confirm'])
today_confirmCutslist.append(value['confirmCuts'])
chinaTotalData['today_confirm'] = today_confirmlist
chinaTotalData['today_confirmCuts'] = today_confirmCutslist
# ==================================================================
# 刪除total、today兩列
chinaTotalData.drop(['total','today'],axis=1,inplace=True)
chinaTotalData.head()
# 將其保存到Excel中
chinaTotalData.to_excel('2021-02-03國(guó)內(nèi)疫情.xlsx',index=False)
處理好的數(shù)據(jù)結(jié)構(gòu)如下表：
3.應(yīng)用pyecharts進(jìn)行數(shù)據(jù)可視化
pyecharts是一款將python與echarts結(jié)合的強(qiáng)大的數(shù)據(jù)可視化工具。繪制出來(lái)的圖比Python的Matplotlib簡(jiǎn)單美觀。使用之前需要在Python環(huán)境中按照pycharts。在終端中輸入命令：pip install pyecharts
利用pyecharts繪制疫情地圖
根據(jù)上面的疫情數(shù)據(jù)，我們可以利用其畫(huà)出全國(guó)的疫情地圖
在繪制前，我們需要安裝echarts的地圖包（可根據(jù)不同的地圖需求進(jìn)行安裝）
pip install echarts-countries-pypkg
pip install echarts-china-provinces-pypkg
pip install echarts-china-cities-pypkg
pip install echarts-china-misc-pypkg
pip install echarts-china-countries-pypkg
pip install echarts-united-kingdom-pypkg
# 導(dǎo)入對(duì)應(yīng)的繪圖工具包
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Map
df = pd.read_excel('./2021-02-03國(guó)內(nèi)疫情.xlsx')
# 1.根據(jù)繪制國(guó)內(nèi)總疫情圖（確診）
data = df.groupby(by='province',as_index=False).sum()
data_list = list(zip(data['province'].values.tolist(),data['confirm'].values.tolist()))
# 數(shù)據(jù)格式[(黑龍江,200),(吉林,300),...]
def map_china() -> Map:
c = (
Map()
.add(series_name="確診病例",data_pair=data_list,maptype='china')
.set_global_opts(
title_opts = opts.TitleOpts(title='疫情地圖'),
visualmap_opts=opts.VisualMapOpts(is_piecewise=True,
pieces = [{"max":9, "min":0, "label":"0-9","color":"#FFE4E1"},
{"max":99, "min":10, "label":"10-99","color":"#FF7F50"},
{"max":499, "min":100, "label":"100-4999","color":"#F08080"},
{"max":999, "min":500, "label":"500-999","color":"#CD5C5C"},
{"max":9999, "min":1000, "label":"1000-9999","color":"#990000"},
{"max":99999, "min":10000, "label":"10000-99999","color":"#660000"},]
)
)
)
return c
d_map = map_china()
d_map.render("mapEchrts.html")
最終的運(yùn)行效果如下：
注：以上的運(yùn)行環(huán)境是Python3.7版本，IDE是基于瀏覽器端的Jupter Notebook。
以上就是Python實(shí)現(xiàn)疫情地圖可視化的詳細(xì)內(nèi)容，更多關(guān)于python 疫情地圖可視化的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！
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