I have a date column in csv file say Date having dates in this format 04/21/2013 and I have one more column Next_Day. In Next_Day column I want to populate the date which comes immediately after the date mentioned in date column. For eg. if date column has 04/21/2013 as date then I want 04/22/2013 in Next_Day column.
We can use +1 in excel but I don't know how to perform this in Python.
Please help me in resolving this.
Using datetime.timedelta
>>> import datetime
>>> s = '04/21/2013'
>>> d = datetime.datetime.strptime(s, '%m/%d/%Y') + datetime.timedelta(days=1)
>>> print(d.strftime('%m/%d/%Y'))
04/22/2013
Same answer as a one-liner. Prints 1 day 1 hour & 30 minutes ago from now:
python -c 'import datetime;print(datetime.datetime.now() - datetime.timedelta(days=1,hours=1,minutes=30))'
2022-02-08 13:11:06.304608
Related
I have a df with dates in a column converted to a datetime. the current format is YYYYDDMM. I need this converted to YYYYMMDD. I tried the below code but it does not change the format and still gives me YYYYDDMM. the end goal is to subtract 1 business day from the effective date but the format needs to be in YYYYMMDD to do this otherwise it subtracts 1 day from the M and not D. can someone help?
filtered_df['Effective Date'] = pd.to_datetime(filtered_df['Effective Date'])
# Effective Date = 20220408 (4th Aug 2022 for clarity)
filtered_df['Effective Date new'] = filtered_df['Effective Date'].dt.strftime("%Y%m%d")
# Effective Date new = 20220408
desired output -- > Effective Date new = 20220804
By default, .to_datetime will interpret the input YYYYDDMM as YYYYMMDD, and therefore print the same thing with %Y%m%d as the format. You can fix this and make it properly parse days in the month greater than 12 by adding the dayfirst keyword argument.
filtered_df['Effective Date'] = pd.to_datetime(filtered_df['Effective Date'], dayfirst=True)
I like to use the datetime library for this purpose. You can use strptime to convert a string into the datetime object and strftime to convert your datetime object to the new string.
from datetime import datetime
def change_date(row):
row["Effective Date new"] = datetime.strptime(row["Effective Date"], "%Y%d%m").strftime("%Y%m%d")
return row
df2 = df.apply(change_date, axis=1)
The output df2 will have Effective Date new as your new column.
I know I should import datetime to have actual date. But the rest is black magic for me right now.
ex.
dates = ['2019-010-11', '2013-05-16', '2011-06-16', '2000-04-22']
actual_date = datetime.datetime.now()
How can I subtract this and as a result have new list with days that passed by from dates to actual_date?
If I'm understanding correctly, you need to find the current date, and then find the number of days between the current date and the dates in your list?
If so, you could try this:
from datetime import datetime, date
dates = ['2019-10-11', '2013-05-16', '2011-06-16', '2000-04-22']
actual_date = date.today()
days = []
for date in dates:
date_object = datetime.strptime(date, '%Y-%m-%d').date()
days_difference = (actual_date - date_object).days
days.append(days_difference)
print(days)
What I am doing here is:
Converting the individual date strings to a "date" object
Subtracting the this date from the actual date. This gets you the time as well, so to strip that out we add .days.
Save the outcome to a list, although of course you could do whatever you wanted with the output.
My code is the following:
date = datetime.datetime.now()- datetime.datetime.now()
print date
h, m , s = str(date).split(':')
When I print h the result is:
-1 day, 23
How do I get only the hour (the 23) from the substract using datetime?
Thanks.
If you subtract the current date from a past date, you would get a negative timedelta value.
You can get the seconds with td.seconds and corresponding hour value via just dividing by 3600.
from datetime import datetime
import time
date1 = datetime.now()
time.sleep(3)
date2 = datetime.now()
# timedelta object
td = date2 - date1
print(td.days, td.seconds // 3600, td.seconds)
# 0 0 3
You're not too far off but you should just ask your question as opposed to a question with a "real scenario" later as those are often two very different questions. That way you get an answer to your actual question.
All that said, rather than going through a lot of hoop-jumping with splitting the datetime object, assigning it to a variable which you then later use look for what you need in, it's better to just know what DateTime can do since that can be such a common part of your coding. You would also do well to look at timedelta (which is part of datetime) and if you use pandas, timestamp.
from datetime import datetime
date = datetime.now()
print(date)
print(date.hour)
I can get you the hour of datetime.datetime.now()
You could try indexing a list of a string of datetime.datetime.now():
print(list(str(datetime.datetime.now()))[11] + list(str(datetime.datetime.now()))[12])
Output (in my case when tested):
09
Hope I am of help!
I have a panda dataframe (stock prices) with an index in a date format. It is daily but only for working days.
I basically try to compute some price performance YTD and from a year ago.
To get the first date of the actual year in my dataframe I used the following method:
today = str(datetime.date.today())
curr_year = int(today[:4])
curr_month = int(today[5:7])
first_date_year = (df[str(curr_year)].first_valid_index())
Now I try to get the closest date a year ago (exactly one year from the last_valid_index()). I could extract the month and the year but then it wouldn't be as precise. Any suggestion ?
Thanks
Since you didn't provide any data, I am assuming that you have a list of dates (string types) like the following:
dates = ['11/01/2016', '12/01/2016', '02/01/2017', '03/01/2017']
You then need to transform that into datetime format, I would suggest using pandas:
pd_dates = pd.to_datetime(dates)
Then you have to define today and one year ago. I would suggest using datetime for that:
today = datetime.today()
date_1yr_ago = datetime(today.year-1, today.month, today.day)
Lastly, you slice the date list for dates larger than the date_1yr_ago value and get the first value of that slice:
pd_dates[pd_dates > date_1yr_ago][0]
This will return the first date that is larger than the 1 year ago date.
output:
Timestamp('2017-02-01 00:00:00')
You can convert that datetime value to string with the following code:
datetime.strftime(pd_dates[pd_dates > date_1yr_ago][0], '%Y/%m/%d')
output:
'2017/02/01'
I have a Dataframe traindf having Date column with the date in the format "YYYY-MM-DD". I am trying to convert the date in day in the year and append to the year. For ex. "2010-02-05" to "2010036". I got the below code working but want to check if there are any efficient way to get it.
dtstrip = [int('%d%03d' % (datetime.datetime.strptime(dt, fmt).timetuple().tm_year, datetime.datetime.strptime(dt, fmt).timetuple().tm_yday)) for dt in traindf['Date']]
traindf['Date'] = dtstrip
You want something like this?
today = datetime.datetime.now()
print(today.strftime('%Y%j'))
For today the output is:
2017260