随笔：Python批量合并csv文件的数据

随笔：Python批量合并csv文件的数据

os板块不是很会用，哈哈

import glob
import os
import pandas
import csv

#需要合并的文件路径
inputfile = str(os.path.dirname(r'D:	estcloudAI	est_data1	est_dataclassifydata'))+r'classifydata*.csv'
#合并后生成的文件保存的位置
outputfile = str(os.path.dirname(r'D:	estcloudAI	est_data1	est_data\classifydata
esult'))+r'
esult
esult.csv'

csv_list = glob.glob(inputfile)
filepath = csv_list[0]
df = pandas.read_csv(filepath)
df.to_csv(outputfile,index=False,encoding='utf-8')

for i in range(1,len(csv_list)):
    filepath = csv_list[i]
    d = pandas.read_csv(filepath)
    d.to_csv(outputfile,index=False,header=False,mode='a+',encoding='utf-8')

print('***文件生成完成***')

#打印生成文件的行数
csv_reader = csv.reader(open(outputfile,encoding='utf-8'))
l = len(list(csv_reader))
print(l)

番外：
如果需要控制行数的话需要加入循环但是这样会比较慢，慢的原因是每次循环都会计算一次行数，当行数比较大的时候就会非常慢，我是放弃了

import glob
import os
import pandas
import csv


inputfile = str(os.path.dirname(r'D:	estcloudAI	est_data1	est_dataclassifydata'))+r'classifydata*.csv'
outputfile = str(os.path.dirname(r'D:	estcloudAI	est_data1	est_data\classifydata
esult'))+r'
esult
esult.csv'
csv_list = glob.glob(inputfile)
filepath = csv_list[0]
df = pandas.read_csv(filepath)
df.to_csv(outputfile,index=False,encoding='utf-8')

for i in range(1,len(csv_list)):
    filepath = csv_list[i]
    d = pandas.read_csv(filepath)
    d.to_csv(outputfile,index=False,header=False,mode='a+',encoding='utf-8')
    csv_reader = csv.reader(open(outputfile, encoding='utf-8'))
    l = len(list(csv_reader))
    if l>=26000000:
        break

print('***文件生成完成***')


csv_reader = csv.reader(open(outputfile,encoding='utf-8'))
l = len(list(csv_reader))
print(l)