Python–csv文件处理建议收藏

Supplier Name,Invoice Number,Part Number,Cost,Purchase Date
Supplier X,001-1001,2341,$500.00 ,1/20/14
Supplier X,001-1001,2341,$500.00 ,1/20/14
Supplier X,001-1001,5467,$750.00 ,1/20/14
Supplier X,001-1001,5467,$750.00 ,1/20/14
Supplier Y,50-9501,7009,$250.00 ,1/30/14
Supplier Y,50-9501,7009,$250.00 ,1/30/14
Supplier Y,50-9505,6650,$125.00 ,2002/3/14
Supplier Y,50-9505,6650,$125.00 ,2002/3/14
Supplier Z,920-4803,3321,$615.00 ,2002/3/14
Supplier Z,920-4804,3321,$615.00 ,2002/10/14
Supplier Z,920-4805,3321,"$6,015.00 ",2/17/14
Supplier Z,920-4806,3321,"$1,006,015.00 ",2/24/14

#coding=utf-8

import sys
import csv
import re

read_file = sys.argv[1]
write_file = sys.argv[2]

with open(read_file, "r") as readfile:
    with open(write_file, "w") as writefile:
        reader = csv.reader(readfile, delimiter=",")
        writer = csv.writer(writefile, delimiter=",")
        header = next(reader)
        writer.writerow(header)
        for rowlist in reader:
            #通过正则表达是进行行匹配
            if re.match(r"^001-*.", str(rowlist[1])):
                print (rowlist)
                writer.writerow(rowlist)

#coding=utf-8

''' 运用pandas包解析csv文件'''
import pandas
from pandas import Series,DataFrame
import sys

file_path = sys.argv[1]
write_path = sys.argv[2]
data_frame = pandas.read_csv(file_path)
#print (data_frame)

#注意str的使用
data_frame["Cost"] = data_frame["Cost"].str.replace(",", "").str.strip("$").astype(float)
#print (data_frame)

newa = data_frame.loc[data_frame["Cost"] > 600, :]
#print (newa)
newa.to_csv(write_path, index = False)