Converts list of dates to json list in python

user_17443466 0
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I have below code which make a list of date between start and end date with a frequency of 13 days. I'm not sure how I can make it a list with start and end date on it(as what is shown in my expected output)

Note: I should also added those remaining dates

Python Code

from datetime import datetime, timedelta
import pandas as pd

start_date = '2021-10-01T17:30:00Z'
end_date = '2021-10-30T21:00:00Z'

pd.date_range(start_date, end_date ,freq='13D')

Output:


DatetimeIndex(['2021-10-01 17:30:00+00:00', '2021-10-14 17:30:00+00:00','2021-10-27 17:30:00+00:00'],

Expected output:

[
{
"start_date": "2021-10-01 17:30:00+00:00",
"end_date":"2021-10-14 17:30:00+00:00"
},
{
"start_date": "2021-10-14 17:30:00+00:00",
"end_date":"2021-10-27 17:30:00+00:00"
},
{
"start_date": "2021-10-27 17:30:00+00:00",
"end_date":"2021-10-30 17:30:00+00:00"
}
]
pandas python-3.x list date python
4 Answers

You can use comprehensions to solve this problem (I'm assuming you might want to extend this to more than 3 dates in the future, otherwise you could just write out the expected output by hand and be done with it):

from datetime import datetime, timedelta
import pandas as pd

start_date = '2021-10-01T17:30:00Z'
end_date = '2021-10-30T21:00:00Z'
date_range = pd.date_range(start_date, end_date, freq='13D')

intervals = [{
    "start_date": str(date_range[i]),
    "end_date": str(date_range[i + 1])
} for i in range(len(date_range) - 1)]

if intervals and pd.Timestamp(end_date) != intervals[-1]["end_date"]:
    intervals.append({
        "start_date": intervals[-1]["end_date"],
        "end_date": end_date
    })
print(intervals)
user_177698150

Try this:

In [382]: x = pd.date_range(start_date, end_date ,freq='13D')
In [389]: l = []
In [393]: for ix, ele in enumerate(x):
     ...:     if ix + 1 < len(x):
     ...:         d = {"start_date": str(ele), "end_date": str(x[ix + 1])}
     ...:         l.append(d)
     ...: 

In [400]: if l[-1]['end_date'] < end_date:
     ...:     l.append({'start_date': l[-1]['end_date'], 'end_date':end_date})
     ...: 

In [401]: l
Out[401]: 
[{'start_date': '2021-10-01 17:30:00+00:00',
  'end_date': '2021-10-14 17:30:00+00:00'},
 {'start_date': '2021-10-14 17:30:00+00:00',
  'end_date': '2021-10-27 17:30:00+00:00'},
 {'start_date': '2021-10-27 17:30:00+00:00',
  'end_date': '2021-10-30T21:00:00Z'}]

Another solution List Comprehension:

In [403]: l = [{"start_date": str(ele), "end_date": str(x[ix + 1])} for ix, ele in enumerate(x) if ix + 1 < len(x)]

In [404]: if l[-1]['end_date'] < end_date:
     ...:     l.append({'start_date': l[-1]['end_date'], 'end_date':end_date})
     ...: 

In [405]: l
Out[405]: 
[{'start_date': '2021-10-01 17:30:00+00:00',
  'end_date': '2021-10-14 17:30:00+00:00'},
 {'start_date': '2021-10-14 17:30:00+00:00',
  'end_date': '2021-10-27 17:30:00+00:00'},
 {'start_date': '2021-10-27 17:30:00+00:00',
  'end_date': '2021-10-30T21:00:00Z'}]
user_58208140

This should get you basically what you want (assuming you want the final dates as strings):

import pandas as pd

start_date = '2021-10-01 17:30:00'
end_date = '2021-10-30 21:00:00'

dates = list(pd.date_range(start_date, end_date, freq='13D').astype(str))
if dates[-1] != end_date:
    dates.append(end_date)

result = [{'start_date': start, 'end_date': end} for start, end in zip(dates, dates[1:])]
user_51859360

try this:

import pandas as pd

start_date = '2021-10-01T17:30:00Z'
end_date = '2021-10-30T21:00:00Z'

s = pd.Series(pd.date_range(start_date, end_date ,freq='13D'), name='start_date')
e = s.shift(-1).rename('end_date')
df = pd.concat([s, e], axis=1).dropna(subset=['end_date']).astype('str')
df.to_dict('records')

>>>
[{'start_date': '2021-10-01 17:30:00+00:00',
  'end_date': '2021-10-14 17:30:00+00:00'},
 {'start_date': '2021-10-14 17:30:00+00:00',
  'end_date': '2021-10-27 17:30:00+00:00'}]
user_167556710
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