python清洗疫情历史数据的过程详解

Calandra ·

更新时间:2024-11-10

· 472 次阅读

1. 数据获取

2. 使用python读取csv

3.使用pyhon进行数据清洗

4. 将清洗的数据自动导入MySql

在我2020年大三的一个实训的大作业中，我整了一个新冠肺炎疫情的数据采集和可视化分析系统，大致就是先找数据，然后将数据导入hive中，然后使用hive对数据进行清洗，然后将清洗后的数据使用hql导入MySql，之后就是用ssm开发后台数据接口，然后前端使用echarts和表格对数据进行可视化。具体可以查看：https://www.jb51.net/article/179889.htm。由于那时候主要要求使用hive处理数据，但那时的数据是来自于某位大佬的数据接口中获取的，最后用hive处理再导入数据库的确是大材小用。因此只是在数据的处理上不太妥，其他对数据的处理和数据的可视化做的还是不错的。
这次是有位小伙伴也想做一个疫情的数据采集和可视化系统，想借鉴我之前做的，并且让我指点。那么问题就来了：之前的数据是比较少的，直接从网上提供的免费接口就可以直接获取，而现在疫情已经过去了两年多，如果要整理出历史各省份、城市每一天的数据，那这个数据就相对庞大，再想找现成的符合功能的接口几乎是没有，因此我做了以下的工作获取数据和处理数据：

1. 数据获取

数据的来源是用了GitHub上这个我收藏了很久的项目：https://lab.isaaclin.cn/nCoV/
数据仓库链接：https://github.com/BlankerL/DXY-COVID-19-Data/releases

这个另外部署了一个数据仓库，每天0点，程序将准时执行，数据会被推送至Release中。
我们就可以从大佬的那个数据仓库直接下载现成爬虫爬取的数据，数据直接下载csv格式的DXYArea.csv就好了，方便用于做处理。
下载后打开，会发现这个92MB的的文件里面有近100W条数据。直接读取的话肯定会有点慢了。
因此这时候我就想到可以尝试使用python的pandas分块读取数据，这个工具对数据处理很方便，对数据的读取也贼快。

2. 使用python读取csv

读取csv选择使用pandas模块，使用原生读取很对很慢
注：py脚本文件和csv文件放在同一目录下

import pandas as pd
import numpy as np
# 读取的文件
filePath = "DXYArea.csv"
# 获取数据
def read_csv_feature(filePath):
    # 读取文件
    f = open(filePath, encoding='utf-8')
    reader = pd.read_csv(f, sep=',', iterator=True)
    loop = True
    chunkSize = 1000000
    chunks = []
    while loop:
        try:
            chunk = reader.get_chunk(chunkSize)
            chunks.append(chunk)
        except StopIteration:
            loop = False
    df = pd.concat(chunks, axis=0, ignore_index=True)
    f.close()
    return df 
data = read_csv_feature(filePath)
print('数据读取成功---------------')

csv数据读取成功之后，就全部存在data里面了，而这个data是一个数据集。
可以使用numpy模块工具对数据集进行筛选、导出转换成list,方便对数据进行操作

countryName = np.array(data["countryName"])
countryEnglishName = np.array(data["countryEnglishName"])
provinceName = np.array(data["provinceName"])
province_confirmedCount = np.array(data["province_confirmedCount"])
province_curedCount = np.array(data["province_curedCount"])
province_deadCount = np.array(data["province_deadCount"])
updateTime = np.array(data["updateTime"])
cityName = np.array(data["cityName"])
city_confirmedCount = np.array(data["city_confirmedCount"])
city_curedCount = np.array(data["city_curedCount"])
city_deadCount = np.array(data["city_deadCount"])

这样就把所有需要用到的数据筛选出来了。

3.使用pyhon进行数据清洗

这里的清洗我还是使用了笨方法，很直接暴力的把数据装进对应的list中：

# 全国历史数据
historyed = list()
# 全国最新数据
totaled = list()
# province最新数据
provinceed = list()
# area最新数据
areaed = list()
for i in range(len(data)):
    if(countryName[i] == "中国"):
        updatetimeList = str(updateTime[i]).split(' ')
        updatetime = updatetimeList[0]
        # 处理historyed
        historyed_temp = list()
        if(provinceName[i] == "中国"):
            # 处理totaled
            if(len(totaled) == 0):
                totaled.append(str(updateTime[i]))
                totaled.append(int(province_confirmedCount[i]))
                totaled.append(int(province_curedCount[i]))
                totaled.append(int(province_deadCount[i]))
            if((len(historyed) > 0) and (str(updatetime) != historyed[len(historyed) - 1][0])):
                historyed_temp.append(str(updatetime))
                historyed_temp.append(int(province_confirmedCount[i]))
                historyed_temp.append(int(province_curedCount[i]))
                historyed_temp.append(int(province_deadCount[i]))
            if(len(historyed) == 0):
                historyed_temp.append(str(updatetime))
                historyed_temp.append(int(province_confirmedCount[i]))
                historyed_temp.append(int(province_curedCount[i]))
                historyed_temp.append(int(province_deadCount[i]))
        if(len(historyed_temp) > 0):
            historyed.append(historyed_temp)
        # 处理areaed
        areaed_temp = list()
        if(provinceName[i] != "中国"):
            if(provinceName[i] != "内蒙古自治区" and provinceName[i] != "黑龙江省"):
                provinceName[i] = provinceName[i][0:2]
            else:
                provinceName[i] = provinceName[i][0:3]
            flag = 1
            for item in areaed:
                if(item[1] == str(cityName[i])):
                    flag = 0
            if(flag == 1):
                areaed_temp.append(str(provinceName[i]))
                areaed_temp.append(str(cityName[i]))
                areaed_temp.append(int(0 if np.isnan(city_confirmedCount[i]) else city_confirmedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_curedCount[i]) else city_curedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_deadCount[i]) else city_deadCount[i]))
                areaed.append(areaed_temp)
            flag = 1
            for item in areaed_tmp:
                if(item[0] == str(provinceName[i])):
                    flag = 0
            if(flag == 1):
                areaed_temp.append(str(provinceName[i]))
                areaed_temp.append(str(cityName[i]))
                areaed_temp.append(int(0 if np.isnan(city_confirmedCount[i]) else city_confirmedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_curedCount[i]) else city_curedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_deadCount[i]) else city_deadCount[i]))
                areaed_tmp.append(areaed_temp)
# 处理provinceed（需要根据areaed获取）
province_temp = list()
for temp in areaed_tmp:
    if(len(provinceed) == 0 and len(province_temp) == 0):
        province_temp.append(temp[0])
        province_temp.append(temp[2])
        province_temp.append(temp[3])
        province_temp.append(temp[4])
    else:
        if(temp[0] == province_temp[0]):
            province_temp[1] = province_temp[1] + temp[2]
            province_temp[1] = province_temp[2] + temp[3]
            province_temp[1] = province_temp[3] + temp[4]
        else:
            provinceed.append(province_temp)
            province_temp = list()
            province_temp.append(temp[0])
            province_temp.append(temp[2])
            province_temp.append(temp[3])
            province_temp.append(temp[4])
provinceed.append(province_temp)
print('数据清洗成功---------------')

这里没有什么说的，完全是体力活，将上面筛选出来的数据进行清洗，需要注意的是要仔细的观察读取出来的数据的数据格式，有些数据格式不是很标准，需要手动处理。

4. 将清洗的数据自动导入MySql

将数据导入Mysql这里还是使用python，使用了python的pymysql模块

import pymysql
"""
将数据导入数据库
"""
# 打开数据库连接
db=pymysql.connect(host="localhost",user="root",password="123456",database="yq")
# 使用 cursor() 方法创建一个游标对象 cursor
cursor = db.cursor()
#创建yq数据库
cursor.execute('CREATE DATABASE IF NOT EXISTS yq DEFAULT CHARSET utf8 COLLATE utf8_general_ci;')
print('创建yq数据库成功')
#创建相关表表
cursor.execute('drop table if exists areaed')
sql="""
CREATE TABLE IF NOT EXISTS `areaed`  (
  `provinceName` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `cityName` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `confirmedCount` int(11) NULL DEFAULT NULL,
  `deadCount` int(11) NULL DEFAULT NULL,
  `curedCount` int(11) NULL DEFAULT NULL,
  `currentCount` int(11) NULL DEFAULT NULL
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Compact;
"""
cursor.execute(sql)
cursor.execute('drop table if exists provinceed')
sql="""
CREATE TABLE `provinceed`  (
  `provinceName` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
  `confirmedNum` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `deathsNum` int(11) NULL DEFAULT NULL,
  `curesNum` int(11) NULL DEFAULT NULL,
  `currentNum` int(11) NULL DEFAULT NULL
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Compact;
"""
cursor.execute(sql)
cursor.execute('drop table if exists totaled')
sql="""
CREATE TABLE `totaled`  (
  `date` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `diagnosed` int(11) NULL DEFAULT NULL,
  `death` int(11) NULL DEFAULT NULL,
  `cured` int(11) NULL DEFAULT NULL,
  `current` int(11) NULL DEFAULT NULL
) ENGINE = MyISAM CHARACTER SET = latin1 COLLATE = latin1_swedish_ci ROW_FORMAT = Dynamic;
"""
cursor.execute(sql)
cursor.execute('drop table if exists historyed')
sql="""
CREATE TABLE `historyed`  (
  `date` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `confirmedNum` int(11) NULL DEFAULT NULL,
  `deathsNum` int(11) NULL DEFAULT NULL,
  `curesNum` int(11) NULL DEFAULT NULL,
  `currentNum` int(11) NULL DEFAULT NULL
) ENGINE = MyISAM CHARACTER SET = latin1 COLLATE = latin1_swedish_ci ROW_FORMAT = Dynamic;
"""
cursor.execute(sql)
print('创建相关表成功')
# 导入historyed
for item in historyed:
    sql='INSERT INTO historyed VALUES(%s,"%s","%s","%s","%s")'
    try:
        cursor.execute(sql,(str(item[0]),item[1],item[3],item[2],item[1]-item[2]-item[3]))
        db.commit()
    except Exception as ex:
        print("error:")
        print("出现如下异常%s"%ex)
        db.rollback()
        break
print("导入historyed成功-------------")
# 导入areaed
for item in areaed:
    sql='INSERT INTO areaed VALUES(%s,"%s","%s","%s","%s","%s")'
    try:
        cursor.execute(sql,(item[0],item[1],item[2],item[4],item[3],item[2]-item[3]-item[4]))
        db.commit()
    except Exception as ex:
        print("error:")
        print("出现如下异常%s"%ex)
        db.rollback()
        break
print("导入areaed成功-------------")
# 导入provinceed
for item in provinceed:
    sql='INSERT INTO provinceed VALUES(%s,"%s","%s","%s","%s")'
    try:
        cursor.execute(sql,(str(item[0]),item[1],item[3],item[2],item[1]-item[2]-item[3]))
        db.commit()
    except Exception as ex:
        print("error:")
        print("出现如下异常%s"%ex)
        db.rollback()
        break
print("导入provinceed成功-------------")
# 导入totaled
sql='INSERT INTO totaled VALUES(%s,"%s","%s","%s","%s")' 
try:
    cursor.execute(sql,(str(totaled[0]),totaled[1],totaled[3],totaled[2],totaled[1]-totaled[2]-totaled[3]))
    db.commit()
except Exception as ex:
    print("error:")
    print("出现如下异常%s"%ex)
    db.rollback()
print("导入totaled成功-------------")
cursor.close()#先关闭游标
db.close()#再关闭数据库连接

这里为了脚本的使用方便，首先进行了建库、然后建表、最后将清洗的数据导入MySql

完整代码

import pandas as pd
import numpy as np
import pymysql
"""
@ProjectName: cleanData
@FileName: cleanData.py
@Author: tao
@Date: 2022/05/03
"""
# 读取的文件
filePath = "DXYArea.csv"
# 全国历史数据
historyed = list()
# 全国最新数据
totaled = list()
# province最新数据
provinceed = list()
# area最新数据
areaed = list()
# 获取数据
def read_csv_feature(filePath):
    # 读取文件
    f = open(filePath, encoding='utf-8')
    reader = pd.read_csv(f, sep=',', iterator=True)
    loop = True
    chunkSize = 1000000
    chunks = []
    while loop:
        try:
            chunk = reader.get_chunk(chunkSize)
            chunks.append(chunk)
        except StopIteration:
            loop = False
    df = pd.concat(chunks, axis=0, ignore_index=True)
    f.close()
    return df 
data = read_csv_feature(filePath)
print('数据读取成功---------------')
areaed_tmp = list()
countryName = np.array(data["countryName"])
countryEnglishName = np.array(data["countryEnglishName"])
provinceName = np.array(data["provinceName"])
province_confirmedCount = np.array(data["province_confirmedCount"])
province_curedCount = np.array(data["province_curedCount"])
province_deadCount = np.array(data["province_deadCount"])
updateTime = np.array(data["updateTime"])
cityName = np.array(data["cityName"])
city_confirmedCount = np.array(data["city_confirmedCount"])
city_curedCount = np.array(data["city_curedCount"])
city_deadCount = np.array(data["city_deadCount"])
for i in range(len(data)):
    if(countryName[i] == "中国"):
        updatetimeList = str(updateTime[i]).split(' ')
        updatetime = updatetimeList[0]
        # 处理historyed
        historyed_temp = list()
        if(provinceName[i] == "中国"):
            # 处理totaled
            if(len(totaled) == 0):
                totaled.append(str(updateTime[i]))
                totaled.append(int(province_confirmedCount[i]))
                totaled.append(int(province_curedCount[i]))
                totaled.append(int(province_deadCount[i]))
            if((len(historyed) > 0) and (str(updatetime) != historyed[len(historyed) - 1][0])):
                historyed_temp.append(str(updatetime))
                historyed_temp.append(int(province_confirmedCount[i]))
                historyed_temp.append(int(province_curedCount[i]))
                historyed_temp.append(int(province_deadCount[i]))
            if(len(historyed) == 0):
                historyed_temp.append(str(updatetime))
                historyed_temp.append(int(province_confirmedCount[i]))
                historyed_temp.append(int(province_curedCount[i]))
                historyed_temp.append(int(province_deadCount[i]))
        if(len(historyed_temp) > 0):
            historyed.append(historyed_temp)
        # 处理areaed
        areaed_temp = list()
        if(provinceName[i] != "中国"):
            if(provinceName[i] != "内蒙古自治区" and provinceName[i] != "黑龙江省"):
                provinceName[i] = provinceName[i][0:2]
            else:
                provinceName[i] = provinceName[i][0:3]
            flag = 1
            for item in areaed:
                if(item[1] == str(cityName[i])):
                    flag = 0
            if(flag == 1):
                areaed_temp.append(str(provinceName[i]))
                areaed_temp.append(str(cityName[i]))
                areaed_temp.append(int(0 if np.isnan(city_confirmedCount[i]) else city_confirmedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_curedCount[i]) else city_curedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_deadCount[i]) else city_deadCount[i]))
                areaed.append(areaed_temp)
            flag = 1
            for item in areaed_tmp:
                if(item[0] == str(provinceName[i])):
                    flag = 0
            if(flag == 1):
                areaed_temp.append(str(provinceName[i]))
                areaed_temp.append(str(cityName[i]))
                areaed_temp.append(int(0 if np.isnan(city_confirmedCount[i]) else city_confirmedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_curedCount[i]) else city_curedCount[i]))
                areaed_temp.append(int(0 if np.isnan(city_deadCount[i]) else city_deadCount[i]))
                areaed_tmp.append(areaed_temp)
# 处理provinceed（需要根据areaed获取）
province_temp = list()
for temp in areaed_tmp:
    if(len(provinceed) == 0 and len(province_temp) == 0):
        province_temp.append(temp[0])
        province_temp.append(temp[2])
        province_temp.append(temp[3])
        province_temp.append(temp[4])
    else:
        if(temp[0] == province_temp[0]):
            province_temp[1] = province_temp[1] + temp[2]
            province_temp[1] = province_temp[2] + temp[3]
            province_temp[1] = province_temp[3] + temp[4]
        else:
            provinceed.append(province_temp)
            province_temp = list()
            province_temp.append(temp[0])
            province_temp.append(temp[2])
            province_temp.append(temp[3])
            province_temp.append(temp[4])
provinceed.append(province_temp)
print('数据清洗成功---------------')
# print(historyed)
# print(areaed)
print(totaled)
# print(provinceed)
"""
print(len(provinceed))
for item in provinceed:
    print(item[1]-item[2]-item[3])
"""
"""
将数据导入数据库
"""
# 打开数据库连接
db=pymysql.connect(host="localhost",user="root",password="123456",database="yq")
# 使用 cursor() 方法创建一个游标对象 cursor
cursor = db.cursor()
#创建yq数据库
cursor.execute('CREATE DATABASE IF NOT EXISTS yq DEFAULT CHARSET utf8 COLLATE utf8_general_ci;')
print('创建yq数据库成功')
#创建相关表表
cursor.execute('drop table if exists areaed')
sql="""
CREATE TABLE IF NOT EXISTS `areaed`  (
  `provinceName` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `cityName` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `confirmedCount` int(11) NULL DEFAULT NULL,
  `deadCount` int(11) NULL DEFAULT NULL,
  `curedCount` int(11) NULL DEFAULT NULL,
  `currentCount` int(11) NULL DEFAULT NULL
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Compact;
"""
cursor.execute(sql)
cursor.execute('drop table if exists provinceed')
sql="""
CREATE TABLE `provinceed`  (
  `provinceName` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
  `confirmedNum` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `deathsNum` int(11) NULL DEFAULT NULL,
  `curesNum` int(11) NULL DEFAULT NULL,
  `currentNum` int(11) NULL DEFAULT NULL
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Compact;
"""
cursor.execute(sql)
cursor.execute('drop table if exists totaled')
sql="""
CREATE TABLE `totaled`  (
  `date` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `diagnosed` int(11) NULL DEFAULT NULL,
  `death` int(11) NULL DEFAULT NULL,
  `cured` int(11) NULL DEFAULT NULL,
  `current` int(11) NULL DEFAULT NULL
) ENGINE = MyISAM CHARACTER SET = latin1 COLLATE = latin1_swedish_ci ROW_FORMAT = Dynamic;
"""
cursor.execute(sql)
cursor.execute('drop table if exists historyed')
sql="""
CREATE TABLE `historyed`  (
  `date` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NULL DEFAULT NULL,
  `confirmedNum` int(11) NULL DEFAULT NULL,
  `deathsNum` int(11) NULL DEFAULT NULL,
  `curesNum` int(11) NULL DEFAULT NULL,
  `currentNum` int(11) NULL DEFAULT NULL
) ENGINE = MyISAM CHARACTER SET = latin1 COLLATE = latin1_swedish_ci ROW_FORMAT = Dynamic;
"""
cursor.execute(sql)
print('创建相关表成功')
# 导入historyed
for item in historyed:
    sql='INSERT INTO historyed VALUES(%s,"%s","%s","%s","%s")'
    try:
        cursor.execute(sql,(str(item[0]),item[1],item[3],item[2],item[1]-item[2]-item[3]))
        db.commit()
    except Exception as ex:
        print("error:")
        print("出现如下异常%s"%ex)
        db.rollback()
        break
print("导入historyed成功-------------")
# 导入areaed
for item in areaed:
    sql='INSERT INTO areaed VALUES(%s,"%s","%s","%s","%s","%s")'
    try:
        cursor.execute(sql,(item[0],item[1],item[2],item[4],item[3],item[2]-item[3]-item[4]))
        db.commit()
    except Exception as ex:
        print("error:")
        print("出现如下异常%s"%ex)
        db.rollback()
        break
print("导入areaed成功-------------")
# 导入provinceed
for item in provinceed:
    sql='INSERT INTO provinceed VALUES(%s,"%s","%s","%s","%s")'
    try:
        cursor.execute(sql,(str(item[0]),item[1],item[3],item[2],item[1]-item[2]-item[3]))
        db.commit()
    except Exception as ex:
        print("error:")
        print("出现如下异常%s"%ex)
        db.rollback()
        break
print("导入provinceed成功-------------")
# 导入totaled
sql='INSERT INTO totaled VALUES(%s,"%s","%s","%s","%s")' 
try:
    cursor.execute(sql,(str(totaled[0]),totaled[1],totaled[3],totaled[2],totaled[1]-totaled[2]-totaled[3]))
    db.commit()
except Exception as ex:
    print("error:")
    print("出现如下异常%s"%ex)
    db.rollback()
print("导入totaled成功-------------")
cursor.close()#先关闭游标
db.close()#再关闭数据库连接

脚本运行效果