Python实现Excel文件的合并(以新冠疫情数据为例)

import requests
import json
import openpyxl
import datetime
import datetime as dt
import time
import pandas as pd
import csv
from openpyxl import load_workbook
from sqlalchemy import create_engine
import math
import os
import glob

csv_list=glob.glob(r'D:\Python\03DataAcquisition\COVID-19\2020\*.csv')
print("所有数据文件总共有%s" %len(csv_list))
for i in csv_list:
    fr=open(i,"rb").read() #除了第一个数据文件外，其他不读取表头
    with open('../output/covid19temp0314.csv','ab') as f:
        f.write(fr)
    f.close()
print('数据合成完毕！')

## 02 使用函数进行数据合并
import os
import pandas as pd 
# 定义函数(具有递归功能)
def mergeFile(parent,path="",pathdeep=0,filelist=[],csvdatadf=pd.DataFrame(),csvdata=pd.DataFrame()):
    fileAbsPath=os.path.join(parent,path)
    if os.path.isdir(fileAbsPath)==True:
        if(pathdeep!=0 and ('.ipynb_checkpoints' not in str(fileAbsPath))): # =0代表没有下一层目录
            print('--'+path)
        for filename2 in os.listdir(fileAbsPath):
            mergeFile(fileAbsPath,filename2,pathdeep=pathdeep+1)
    else:
        if(pathdeep==2 and path.endswith(".csv") and os.path.getsize(parent+'/'+path)>0):
            filelist.append(parent+'/'+path)
    return filelist

# D:\Python\03DataAcquisition\COVID-19
path=input("请输入数据文件所在目录:")
filelist=mergeFile(path)

filelist

csvdata=pd.DataFrame()
csvdatadf=pd.DataFrame()

for m in filelist:
    csvdata=pd.read_csv(m,encoding='utf-8-sig')
    csvdatadf=csvdatadf.append(csvdata)
# 由于2023年的数据还没有，所以不合并

csvdatadf.to_csv("covid190314.csv",index=None,encoding='utf-8-sig')

csvdatadf=pd.read_csv("covid190314.csv",encoding='utf-8-sig')
csvdatadf.info()

beforedf=pd.read_csv(r'D:\Python\03DataAcquisition\COVID-19\before20201111.csv',encoding='utf-8-sig')

beforedf.info()

tempalldf=beforedf.append(csvdatadf)
tempalldf.head()

beforedf.loc[beforedf['Country/Region']=='Taiwan']
beforedf.loc[beforedf['Country/Region'].str.contains('Taiwan')]

beforedf.loc[beforedf['Country/Region'].str.contains('Taiwan'),'Province/State']='Taiwan'
beforedf.loc[beforedf['Province/State']=='Taiwan','Country/Region']='China'
beforedf.loc[beforedf['Province/State']=='Taiwan']

beforedf.loc[beforedf['Country/Region'].str.contains('Hong Kong'),'Province/State']='Hong Kong'
beforedf.loc[beforedf['Province/State']=='Hong Kong','Country/Region']='China'
afterdf.loc[afterdf['Country_Region'].str.contains('Hong Kong'),'Province_State']='Hong Kong'
afterdf.loc[afterdf['Province_State']=='Hong Kong','Country_Region']='China'

beforedf.loc[beforedf['Country/Region'].str.contains('Macau'),'Province/State']='Macau'
beforedf.loc[beforedf['Province/State']=='Macau','Country/Region']='China'
afterdf.loc[afterdf['Country_Region'].str.contains('Macau'),'Province_State']='Macau'
afterdf.loc[afterdf['Province_State']=='Macau','Country_Region']='China'

beforedf.to_csv("beforedf0314.csv",index=None,encoding='utf-8-sig')
afterdf.to_csv("afterdf0314.csv",index=None,encoding='utf-8-sig')