I have the data with an array containing dates (YYYY-MM-DD) starting from 2005-12-01 till 2012-30-12. The dates are irregular and some of the dates are missing in between. I want to take the reference date as 2005-11-30 and calculate the integer number of all the dates in the array.
How can I convert my date array into an integer number from the reference date in Python?
If I understood your question correctly you have a list of dates which you want to find and write the difference of each between a fixed date.
You can use list comprehension;
from datetime import date
start_date = date(2005, 11, 30)
# assuming your list is named my_date_list
differences = [d - start_date for d in my_date_list]
If your list members are not date type, but formatted strings. You can convert them to dates on the run.
from datetime import datetime
date_format = "%Y-%m-%d"
differences = [datetime.strptime(d, date_format) - start_date for d in my_date_list]
Related
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.
I want to add a blank column of date of format "%Y-%m-%d" to a dataframe. I tried datetime.datetime.strptime('0000-00-00',"%Y-%m-%d")
But I get an error ValueError: time data '0000-00-00' does not match format '%Y-%m-%d'
How can I create a column of blank date of format "%Y-%m-%d"?
In R following works.
df$date =""
class(df$date) = "Date"
How can I achieve this in Python?
Thank you.
I don't think that's possible with datetime module. The oldest you can go to is answered here:
What is the oldest time that can be represented in Python?
datetime.MINYEAR
The smallest year number allowed in a date or datetime object. MINYEAR is 1.
datetime.MAXYEAR
The largest year number allowed in a date or datetime object. MAXYEAR is 9999.
source: datetime documentation
initial_date = request.GET.get('data') or datetime.min # datetime.min is 1
end_date = request.GET.get('data_f') or datetime.max # datetime.max is 9999
I have a data set consisting of time stamps looking as following:
['2019-12-18T12:06:55.697975Z"', '2019-12-18T12:06:55.707017Z"',...]
I need a for loop which will go through all timestamps and convert each to a time structure. But what I created, does not work - I am not sure about data types and also about indexing when using strings.
My attempt is following:
from time import struct_time
for idx in enumerate(tm_str): #tm_str is a string:['2019-12-18T12:06:55.697975Z"', ...]
a=idx*30+2 #a should be first digit in year - third position in string -
b=idx*30+6 # b should last dgit of year, 30 is period to next year
tm_year=tm_str[a:b]
Month, day, hour etc. should be done is similar way.
Have you tried the datetime library / datetime objects?
https://docs.python.org/3/library/datetime.html
This will create datetime objects that you can use to do a lot of handy calculations.
from datetime import datetime
# your original list:
time_stamp_list = ['2019-12-18T12:06:55.697975Z"', '2019-12-18T12:06:55.707017Z"']
# empty list of datetime objects:
datetime_list = []
# iterate over each time_stamp in the original list:
for time_stamp in time_stamp_list:
# convert to datetime object using datetime.strptime():
datetime_object = datetime.strptime(time_stamp, '%Y-%m-%dT%H:%M:%S.%fZ"')
# append datetime object to datetime object list:
datetime_list.append(datetime_object)
I've been looking through every thread that I can find, and the only one that is relevant to this type of formatting issue is here, but it's for java...
How parse 2013-03-13T20:59:31+0000 date string to Date
I've got a column with values like 201604 and 201605 that I need to turn into date values like 2016-04-01 and 2016-05-01. To accomplish this, I've done what is below.
#Create Number to build full date
df['DAY_NBR'] = '01'
#Convert Max and Min date to string to do date transformation
df['MAXDT'] = df['MAXDT'].astype(str)
df['MINDT'] = df['MINDT'].astype(str)
#Add the day number to the max date month and year
df['MAXDT'] = df['MAXDT'] + df['DAY_NBR']
#Add the day number to the min date month and year
df['MINDT'] = df['MINDT'] + df['DAY_NBR']
#Convert Max and Min date to integer values
df['MAXDT'] = df['MAXDT'].astype(int)
df['MINDT'] = df['MINDT'].astype(int)
#Convert Max date to datetime
df['MAXDT'] = pd.to_datetime(df['MAXDT'], format='%Y%m%d')
#Convert Min date to datetime
df['MINDT'] = pd.to_datetime(df['MINDT'], format='%Y%m%d')
To be honest, I can work with this output, but it's a little messy because the unique values for the two columns are...
MAXDT Values
['2016-07-01T00:00:00.000000000' '2017-09-01T00:00:00.000000000'
'2018-06-01T00:00:00.000000000' '2017-07-01T00:00:00.000000000'
'2017-03-01T00:00:00.000000000' '2018-12-01T00:00:00.000000000'
'2017-12-01T00:00:00.000000000' '2019-01-01T00:00:00.000000000'
'2018-09-01T00:00:00.000000000' '2018-10-01T00:00:00.000000000'
'2016-04-01T00:00:00.000000000' '2018-03-01T00:00:00.000000000'
'2017-05-01T00:00:00.000000000' '2018-08-01T00:00:00.000000000'
'2017-02-01T00:00:00.000000000' '2016-12-01T00:00:00.000000000'
'2018-01-01T00:00:00.000000000' '2018-02-01T00:00:00.000000000'
'2017-06-01T00:00:00.000000000' '2018-11-01T00:00:00.000000000'
'2018-05-01T00:00:00.000000000' '2019-11-01T00:00:00.000000000'
'2016-06-01T00:00:00.000000000' '2017-10-01T00:00:00.000000000'
'2016-08-01T00:00:00.000000000' '2018-04-01T00:00:00.000000000'
'2016-03-01T00:00:00.000000000' '2016-10-01T00:00:00.000000000'
'2016-11-01T00:00:00.000000000' '2019-12-01T00:00:00.000000000'
'2016-09-01T00:00:00.000000000' '2017-08-01T00:00:00.000000000'
'2016-05-01T00:00:00.000000000' '2017-01-01T00:00:00.000000000'
'2017-11-01T00:00:00.000000000' '2018-07-01T00:00:00.000000000'
'2017-04-01T00:00:00.000000000' '2016-01-01T00:00:00.000000000'
'2016-02-01T00:00:00.000000000' '2019-02-01T00:00:00.000000000'
'2019-07-01T00:00:00.000000000' '2019-10-01T00:00:00.000000000'
'2019-09-01T00:00:00.000000000' '2019-03-01T00:00:00.000000000'
'2019-05-01T00:00:00.000000000' '2019-04-01T00:00:00.000000000'
'2019-08-01T00:00:00.000000000' '2019-06-01T00:00:00.000000000'
'2020-02-01T00:00:00.000000000' '2020-01-01T00:00:00.000000000']
MINDT Values
['2016-04-01T00:00:00.000000000' '2017-07-01T00:00:00.000000000'
'2016-02-01T00:00:00.000000000' '2017-01-01T00:00:00.000000000'
'2017-02-01T00:00:00.000000000' '2018-12-01T00:00:00.000000000'
'2017-08-01T00:00:00.000000000' '2018-04-01T00:00:00.000000000'
'2017-10-01T00:00:00.000000000' '2019-01-01T00:00:00.000000000'
'2018-05-01T00:00:00.000000000' '2018-09-01T00:00:00.000000000'
'2018-10-01T00:00:00.000000000' '2016-01-01T00:00:00.000000000'
'2016-03-01T00:00:00.000000000' '2017-11-01T00:00:00.000000000'
'2017-05-01T00:00:00.000000000' '2018-07-01T00:00:00.000000000'
'2018-06-01T00:00:00.000000000' '2017-12-01T00:00:00.000000000'
'2016-10-01T00:00:00.000000000' '2018-02-01T00:00:00.000000000'
'2017-06-01T00:00:00.000000000' '2018-08-01T00:00:00.000000000'
'2018-03-01T00:00:00.000000000' '2018-11-01T00:00:00.000000000'
'2016-08-01T00:00:00.000000000' '2016-06-01T00:00:00.000000000'
'2018-01-01T00:00:00.000000000' '2016-07-01T00:00:00.000000000'
'2016-11-01T00:00:00.000000000' '2016-09-01T00:00:00.000000000'
'2017-04-01T00:00:00.000000000' '2016-05-01T00:00:00.000000000'
'2017-09-01T00:00:00.000000000' '2016-12-01T00:00:00.000000000'
'2017-03-01T00:00:00.000000000']
I'm trying to build a loop that runs through these dates, and it works, but I don't want to have an index with all of these irrelevant zeros and a T in it. How can I convert these empty timestamp values to just the date that is in yyyy-mm-dd format?
Thank you!
Unfortunately, I believe Pandas always stores datetime objects as datetime64[ns], meaning the precision has to be like that. Even if you attempt to save as datetime64[D], it will be cast to datetime64[ns].
It's possible to just store these datetime objects as strings instead, but the simplest solution is likely to just strip the extra zeroes when you're looping through them (i.e, using df['MAXDT'].to_numpy().astype('datetime64[D]') and looping through the formatted numpy array), or just reformatting using datetime.
I am creating a module in python, in which I am receiving the date in integer format like 20120213, which signifies the 13th of Feb, 2012. Now, I want to convert this integer formatted date into a python date object.
Also, if there is any means by which I can subtract/add the number of days in such integer formatted date to receive the date value in same format? like subtracting 30 days from 20120213 and receive answer as 20120114?
This question is already answered, but for the benefit of others looking at this question I'd like to add the following suggestion: Instead of doing the slicing yourself as suggested in the accepted answer, you might also use strptime() which is (IMHO) easier to read and perhaps the preferred way to do this conversion.
import datetime
s = "20120213"
s_datetime = datetime.datetime.strptime(s, '%Y%m%d')
I would suggest the following simple approach for conversion:
from datetime import datetime, timedelta
s = "20120213"
# you could also import date instead of datetime and use that.
date = datetime(year=int(s[0:4]), month=int(s[4:6]), day=int(s[6:8]))
For adding/subtracting an arbitary amount of days (seconds work too btw.), you could do the following:
date += timedelta(days=10)
date -= timedelta(days=5)
And convert back using:
s = date.strftime("%Y%m%d")
To convert the integer to a string safely, use:
s = "{0:-08d}".format(i)
This ensures that your string is eight charecters long and left-padded with zeroes, even if the year is smaller than 1000 (negative years could become funny though).
Further reference: datetime objects, timedelta objects
Here is what I believe answers the question (Python 3, with type hints):
from datetime import date
def int2date(argdate: int) -> date:
"""
If you have date as an integer, use this method to obtain a datetime.date object.
Parameters
----------
argdate : int
Date as a regular integer value (example: 20160618)
Returns
-------
dateandtime.date
A date object which corresponds to the given value `argdate`.
"""
year = int(argdate / 10000)
month = int((argdate % 10000) / 100)
day = int(argdate % 100)
return date(year, month, day)
print(int2date(20160618))
The code above produces the expected 2016-06-18.
import datetime
timestamp = datetime.datetime.fromtimestamp(1500000000)
print(timestamp.strftime('%Y-%m-%d %H:%M:%S'))
This will give the output:
2017-07-14 08:10:00