下面的代码使我们从发送消息的时间段起每30天计算一次消息。
此代码为我们提供：（详细信息）
1.Amazon首先以特定阶段（这里是第一顺序）将邮件发送到我的邮件。
2。将纪元格式转换为时间日期并使用timedelta并获取30天间隔内发送的邮件数。
此代码的输出将如下所示：
Amazon first order:
1534476682000
Amazon total orders between 2018-08-01 and 2018-09-01: 20
Amazon total orders between 2018-09-01 and 2018-10-01: 11
Amazon total orders between 2018-10-01 and 2018-11-01: 15
Amazon total orders between 2018-11-01 and 2018-12-01: 7
Amazon total orders between 2018-12-01 and 2019-01-01: 19
Amazon total orders between 2019-01-01 and 2019-02-01: 23
Amazon total orders between 2019-02-01 and 2019-03-01: 12
代码：
#amazonfirstorder
from googleapiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools
from dateutil.relativedelta import relativedelta
from datetime import datetime
SCOPES = 'https://www.googleapis.com/auth/gmail.readonly'
def main():
store = file.Storage('token.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.flow_from_clientsecrets('credentials.json', SCOPES)
creds = tools.run_flow(flow, store)
service = build('gmail', 'v1', http=creds.authorize(Http()))
results = service.users().messages().list(userId='me', q='from:auto-confirm@amazon.in subject:(your amazon.in order of )',labelIds = ['INBOX']).execute()
messages = results.get('messages', [])
print('\nFilpkart first order:')
if not messages:
print (" ")
else:
print (" ")
msg = service.users().messages().get(userId='me', id=messages[-1]['id']).execute()
#print(msg['snippet'])
a=(msg['internalDate'])
ts = int(a)
ts /= 1000
year=int(datetime.utcfromtimestamp(ts).strftime('%Y'))
month=int(datetime.utcfromtimestamp(ts).strftime('%m'))
#print(year)
#print(month)
print(msg['internalDate'])
log_results = []
start_date = datetime(year,month,1)
#start_date = datetime(2016,1,1)
end_date = datetime.today()
increment = relativedelta(months=1)
target_date = start_date + increment
while target_date <= end_date:
timestamp_after = int(start_date.timestamp()) # timestamp of start day
timestamp_before = int(target_date.timestamp()) # timestamp of start day + 30 days
query = f'from:(auto-confirm@amazon.in) subject:(your amazon.in order of ) after:{timestamp_after} before:{timestamp_before}'
results = service.users().messages().list(userId='me', q=query, labelIds=['INBOX']).execute()
messages = results.get('messages', [])
orders = len(messages)
start_date_str = start_date.strftime('%Y-%m-%d')
target_date_str = target_date.strftime('%Y-%m-%d')
print(f"\nFlipkart total orders between {start_date.strftime('%Y-%m-%d')} and {target_date.strftime('%Y-%m-%d')}: {orders}")
log_results.append(dict(start=start_date_str, end=target_date_str, orders=orders))
# update interval
start_date += increment
target_date += increment
return log_results
if __name__ == '__main__':
log_results = main()
现在我有两个问题：
第一
如何将该代码的输出保存到csv文件中。
第二：
以上代码为我们提供了30天的邮件计数，我需要的是我需要按月将中午12点之前和按月中午12点之后收到的邮件计数保存在csv中。
我需要第二个问题的输出：
Amazon total orders between 2018-09-01 and 2018-10-01 before 12:00 PM : 11
Amazon total orders between 2018-10-01 and 2018-11-01 before 12:00 PM : 15
Amazon total orders between 2018-11-01 and 2018-12-01 before 12:00 PM : 7
Amazon total orders between 2018-12-01 and 2019-01-01 before 12:00 PM : 19
Amazon total orders between 2018-09-01 and 2018-10-01 after 12:00 PM : 3
Amazon total orders between 2018-10-01 and 2018-11-01 after 12:00 PM : 6
Amazon total orders between 2018-11-01 and 2018-12-01 after 12:00 PM : 88
Amazon total orders between 2018-12-01 and 2019-01-01 after 12:00 PM : 26
答案 0 :(得分：2)
您只需要按所需的时间间隔遍历日期即可。
下面的代码从特定时间段检索用户的消息，例如月份消息计数。
您将需要帮助使其自动化以每30天检索一次邮件计数。
例如，此代码获取2016年1月1日至2016年1月30日之间的消息。
因此，从2016年1月1日到2019年1月1日，您将需要以30天的定期间隔对其进行自动化。
from googleapiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools
import time
from dateutil.relativedelta import relativedelta
from datetime import datetime
SCOPES = 'https://www.googleapis.com/auth/gmail.readonly'
def main():
store = file.Storage('token.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.flow_from_clientsecrets('credentials.json', SCOPES)
creds = tools.run_flow(flow, store)
service = build('gmail', 'v1', http=creds.authorize(Http()))
end_date = datetime(2019, 1, 1)
interval = relativedelta(months=1)
current = datetime(2016, 1, 1) # init to the start date
while current < end_date + interval:
after = current.timestamp()
before = (current + interval).timestamp()
query = 'from:(auto-confirm@amazon.in) subject:(your amazon.in order of ) after:{} before:{}'.format(after, before)
results = service.users().messages().list(userId='me', q=query, labelIds = ['INBOX']).execute()
messages = results.get('messages', [])
print("\namazon total orders in {}: {}".format(current.strftime('%B %Y'), len(messages)))
current += interval
if __name__ == '__main__':
main()
答案 1 :(得分：2)
类似于已经提出的建议，但是在这种情况下，您将计算增量为恰好一个月而不是30天（请参见使用relativedelta而不是timedelta）：
from googleapiclient.discovery import build
from httplib2 import Http
from oauth2client import file, client, tools
from dateutil.relativedelta import relativedelta
from datetime import datetime
SCOPES = 'https://www.googleapis.com/auth/gmail.readonly'
def main():
store = file.Storage('token.json')
creds = store.get()
if not creds or creds.invalid:
flow = client.flow_from_clientsecrets('credentials.json', SCOPES)
creds = tools.run_flow(flow, store)
service = build('gmail', 'v1', http=creds.authorize(Http()))
log_results = []
start_date = datetime(2016, 1, 1)
end_date = datetime.today()
increment = relativedelta(months=1)
target_date = start_date + increment
while target_date <= end_date:
timestamp_after = int(start_date.timestamp()) # timestamp of start day
timestamp_before = int(target_date.timestamp()) # timestamp of start day + 30 days
query = f'from:(auto-confirm@amazon.in) subject:(your amazon.in order of ) after:{timestamp_after} before:{timestamp_before}'
results = service.users().messages().list(userId='me', q=query, labelIds=['INBOX']).execute()
messages = results.get('messages', [])
orders = len(messages)
start_date_str = start_date.strftime('%Y-%m-%d')
target_date_str = target_date.strftime('%Y-%m-%d')
print(f"\nAmazon total orders between {start_date.strftime('%Y-%m-%d')} and {target_date.strftime('%Y-%m-%d')}: {orders}")
log_results.append(dict(start=start_date_str, end=target_date_str, orders=orders))
# update interval
start_date += increment
target_date += increment
return log_results
if __name__ == '__main__':
log_results = main()
# Write to csv
import pandas as pd
df = pd.DataFrame(log_results)
df.to_csv('orders.csv')