如何使用python过滤CSV文件中两个日期之间的行并重定向到另一个文件?

对于熊猫来说,这很简单:

import pandas as pd

# read file
df = pd.read_csv('file.csv')

# convert to datetime
df['LAST USE'] = pd.to_datetime(df['LAST USE'])

# calculate mask
mask = df['LAST USE'].between('2018-08-01', '2018-08-28')

# output masked dataframes
df[~mask].to_csv('out1.csv', index=False)
df[mask].to_csv('out2.csv', index=False)

也可以组合布尔数组来构造 mask

m1 = df['LAST USE'] >= (pd.to_datetime('now') - pd.DateOffset(days=30))
m2 = df['LAST USE'] <= pd.to_datetime('now')
mask = m1 & m2

dict阅读器文档: https://docs.python.org/3/library/csv.html#csv.DictReader

strtime文档: https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior

对于每一行,我们将日期/时间字符串转换为实际的日期/时间对象,然后python可以将其与日期范围进行比较。如果值在范围内,我们将把整行写入一个单独的CSV文件。

import datetime, csv

#define all the fieldnames in the input CSV file (for use in creating / appending to output CSV file)
fieldnames = ['NUMBER','MAIL','COMMENT','COUNT','LAST USE','PERCENTAGE','TEXTN']

#open input CSV file as readonly
with open("input.csv", "r") as fin:
    #create a CSV dictionary reader object
    csv_dreader = csv.DictReader(fin)
    #iterate over all rows in CSV dict reader
    for row in csv_dreader:
        #check for invalid Date values
        if 'N/A' not in row['LAST USE']:
            #convert date string to a date object
            datetime_val = datetime.datetime.strptime(row['LAST USE'], '%Y-%m-%d')
            #check if date falls within requested range
            if datetime_val > datetime.datetime(2018, 8, 1) and datetime_val < datetime.datetime(2018, 8, 28):                
                #if it does, open output CSV file for appending
                with open("output.csv", "a") as fout:
                    #create a csv writer object using the fieldnames defined above
                    csv_writer = csv.DictWriter(fout, fieldnames=fieldnames)
                    #write the current row (from the input CSV) to the output CSV file
                    csv_writer.writerow(row)