In Python I want to parse a date range from a string to get a date_from and date

Working in Django I have a date range submitted on a form like this:

<input type="text"  id="formGroupExampleInput" placeholder="Example input" name="daterange" value="02/01/2022 - 02/15/2022" />

I am passing this to a view and want to split out the from and to dates so that I have a 'date_from' and 'date_to' that I can work with.

This is my view:

from datetime import datetime
from django.shortcuts import render, get_object_or_404


from vans.models import Van


def booking(request):
    """This view returns the booking form page"""

    return render(request, 'booking/booking.html')


def add_to_cart(request, item_id):
    """Adds the van booking to the cart"""
    
    van = get_object_or_404(Van, pk=item_id)
    # This will return the date range as a string
    date_range_str = request.POST.get('daterange')
    # In order to work with it I need it as two dates, date_from and date_to


    return render(request, 'booking/cart.html')

CodePudding user response：

since you have a fixed format to receive the daterange what you can do is this in your views.py from datetime import datetime as dt

def add_to_cart(request, item_id):
    """Adds the van booking to the cart"""
    
    van = get_object_or_404(Van, pk=item_id)
    # This will return the date range as a string
    date_range_str = request.POST.get('daterange')
    

    date_range_lst = [data.strip() for data in date_range_str.split('-')]
    date_from = date_range_lst[0]
    date_to = date_range_lst[1]

    # if you want to convert it to datetime object you can change it using:
    date_from = dt.strftime(date_range_lst[0], '%m/%d/%Y')
    date_to = dt.strftime(date_range_lst[1], '%m/%d/%Y')

    return render(request, 'booking/cart.html')

CodePudding user response：

well you're in luck, pandas has a convent and aptly named function called pd.date_range which takes a start and end date argument, as well as a frequency.

more here in the offical docs

pd.date_range('01 Jan 2022', '05 Jan 2022', freq='D')

DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',
               '2022-01-05'],
              dtype='datetime64[ns]', freq='D')

each object is of a pandas._libs.tslibs.timestamps.Timestamp type so you can apply any number of operations such as calculations or another.

What I would recommend is just creating a calendar table in your database that you can join to get your frequencies but I guess that's subjective.