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Using the time functions

I have a small question. I have an array that saves dates in the following format.
'01/02/20|07/02/20'
It is saved as a string, which uses the start date on the left side of the "|" and end date on the other side.
It is only the end date that matters here, but is there a function or algorithm I can use to automatically calculate the difference in days and months between now.datetime and the end date (right-hand side of "|")?
Thanks, everyone

datetime.strptime is the main routine for parsing strings into datetimes. It can handle all sorts of formats, with the format determined by a format string you give it:
In [34]: from datetime import datetime
In [35]: end_date = datetime.strptime(s.split('|')[1], '%d/%m/%y')
In [36]: diff = datetime.now() - end_date
In [37]: diff
Out[37]: datetime.timedelta(days=81, seconds=81712, microseconds=14069)
In [38]: diff.days
Out[38]: 81

You might be looking for something like this.
Python datetime module is the way to go for a problem like this
import datetime
dates = '01/02/20|07/02/20'
enddate = dates.split('|')[-1]
# Use %y for 2 digits year else %Y for 4 digit year
enddate = datetime.datetime.strptime(enddate, "%d/%m/%y")
today = datetime.date.today()
print(abs(enddate.date() - today).days)
Output:
81

How to play around with JSON date format?

I have a JSON date data set and trying to calculate the time difference between two different JSON DateTime.
For example :
'2015-01-28T21:41:38.508275' - '2015-01-28T21:41:34.921589'
Please look at the python code below:
#let's say 'time' is my data frame and JSON formatted time values are under the 'due_date' column
time_spent = time.iloc[2]['due_date'] - time.iloc[10]['due_date']
This doesn't work. I also tried to cast each operand to int, but it also didn't help. What are the different ways to perform this calculation?

I use parser from dateutil.
Something like that:
from dateutil.parser import parse
first_date_obj = parse("2015-01-28T21:41:38.508275")
second_date_obj = parse("2015-02-28T21:41:38.508275")
print(second_date_obj - first_date_obj)
You can also access the year, month, day of the date object like that:
print(first_date_obj.year)
print(first_date_obj.month)
print(first_date_obj.day)
# and so on

from datetime import datetime
date_format = '%Y-%m-%dT%H:%M:%S.%f'
d2 = time.iloc[2]['due_date']
d1 = time.iloc[10]['due_date']
time_spent = datetime.strptime(d2, date_format) - datetime.strptime(d1, date_format)
print(time_spent.days) # 0
print(time_spent.microseconds) # 586686
print(time_spent.seconds) # 3
print(time_spent.total_seconds()) # 3.586686

The easiest thing to do is to use the pandas datetime capability (since you are already using iloc I assume you are using pandas). You can convert the entire dataframe column labeled due_date to be a pandas datetime datatype using
import pandas as pd
time['due_date'] = pd.to_datetime(time['due_date']
then calculate the time difference you want using
time_spent = time.iloc[2]['due_date'] - time.iloc[10]['due_date']
time_spent will be a pandas timedelta object that you can then manipulate as necessary.
See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html and https://pandas.pydata.org/pandas-docs/stable/user_guide/timedeltas.html.

Comparison between datetime and datetime64[ns] in pandas

I'm writing a program that checks an excel file and if today's date is in the excel file's date column, I parse it
I'm using:
cur_date = datetime.today()
for today's date. I'm checking if today is in the column with:
bool_val = cur_date in df['date'] #evaluates to false
I do know for a fact that today's date is in the file in question. The dtype of the series is datetime64[ns]
Also, I am only checking the date itself and not the timestamp afterwards, if that matters. I'm doing this to make the timestamp 00:00:00:
cur_date = datetime.strptime(cur_date.strftime('%Y_%m_%d'), '%Y_%m_%d')
And the type of that object after printing is datetime as well

For anyone who also stumbled across this when comparing a dataframe date to a variable date, and this did not exactly answer your question; you can use the code below.
Instead of:
self.df["date"] = pd.to_datetime(self.df["date"])
You can import datetime and then add .dt.date to the end like:
self.df["date"] = pd.to_datetime(self.df["date"]).dt.date

You can use
pd.Timestamp('today')
or
pd.to_datetime('today')
But both of those give the date and time for 'now'.
Try this instead:
pd.Timestamp('today').floor('D')
or
pd.to_datetime('today').floor('D')
You could have also passed the datetime object to pandas.to_datetime but I like the other option mroe.
pd.to_datetime(datetime.datetime.today()).floor('D')
Pandas also has a Timedelta object
pd.Timestamp('now').floor('D') + pd.Timedelta(-3, unit='D')
Or you can use the offsets module
pd.Timestamp('now').floor('D') + pd.offsets.Day(-3)
To check for membership, try one of these
cur_date in df['date'].tolist()
Or
df['date'].eq(cur_date).any()

When converting datetime64 type using pd.Timestamp() it is important to note that you should compare it to another timestamp type. (not a datetime.date type)
Convert a date to numpy.datetime64
date = '2022-11-20 00:00:00'
date64 = np.datetime64(date)
Seven days ago - timestamp type
sevenDaysAgoTs = (pd.to_datetime('today')-timedelta(days=7))
convert date64 to Timestamp and see if it was in the last 7 days
print(pd.Timestamp(pd.to_datetime(date64)) >= sevenDaysAgoTs)

Check date is within one year python

I have a date string formatted like this: "2017-05-31T06:44:13Z".
I need to check whether this date is within a one year span from today's date.
Which is the best method to do it: convert it into a timestamp and check, or convert into a date format?

Convert the timestamp to a datetime object so it can be compared with other datetime objects using <, >, =.
from datetime import datetime
from dateutil.relativedelta import relativedelta
# NOTE this format basically ignores the timezone. This may or may not be what you want
date_to_check = datetime.strptime('2017-05-31T06:44:13Z', '%Y-%m-%dT%H:%M:%SZ')
today = datetime.today()
one_year_from_now = today + relativedelta(years=1)
if today <= date_to_check <= one_year_from_now:
# do whatever

Use the datetime package together with timedelta:
import datetime
then = datetime.datetime.strptime("2017-05-31T06:44:13Z".replace('T',' ')[:-1],'%Y-%m-%d %H:%M:%S')
now = datetime.datetime.now()
d = datetime.timedelta(days = 365)
and simply check if now-d > then.

Extract Date from excel and append it in a list using python

I have an column in excel which has dates in the format ''17-12-2015 19:35". How can I extract the first 2 digits as integers and append it to a list? In this case I need to extract 17 and append it to a list. Can it be done using pandas also?
Code thus far:
import pandas as pd
Location = r'F:\Analytics Materials\files\paymenttransactions.csv'
df = pd.read_csv(Location)
time = df['Creation Date'].tolist()
print (time)

You could extract the day of each timestamp like
from datetime import datetime
import pandas as pd
location = r'F:\Analytics Materials\files\paymenttransactions.csv'
df = pd.read_csv(location)
timestamps = df['Creation Date'].tolist()
dates = [datetime.strptime(timestamp, '%d-%m-%Y %H:%M') for timestamp in timestamps]
days = [date.strftime('%d') for date in dates]
print(days)
The '%d-%m-%Y %H:%M'and '%d' bits are format specififers, that describe how your timestamp is formatted. See e.g. here for a complete list of directives.
datetime.strptime parses a string into a datetimeobject using such a specifier. dateswill thus hold a list of datetime instances instead of strings.
datetime.strftime does the opposite: It turns a datetime object into string, again using a format specifier. %d simply instructs strftime to only output the day of a date.