I am new to python and was trying to sort the dates in a list. Below is the code that I wrote and getting
following error on the below line
#### date_object = datetime_object.date() ## list' object has no attribute 'date'
from datetime import datetime,date
lst_dates = ['01 Apr 2017', '01 Apr 2018', '01 Aug 2017', '01 Aug 2018', '01 Dec 2017', '01 Dec 2018', '01 Feb 2017', '01 Feb 2018', '01 Jan 2017', '01 Jan 2018']
datetime_object = sorted(lst_dates, key=lambda x: datetime.strptime(x, '%d %b %Y'))
date_object = datetime_object.date()
print(date_object)
Please assist in helping me understand what the issues is. Thanks
Python don't have the list.date() function, with below code list of dates can be sorted.
from datetime import datetime
lst_dates = ['01 Apr 2017', '01 Apr 2018', '01 Aug 2017', '01 Aug 2018', '01 Dec 2017', '01 Dec 2018', '01 Feb 2017', '01 Feb 2018', '01 Jan 2017', '01 Jan 2018']
lst_dates.sort(key=lambda date: datetime.strptime(date, "%d %b %Y"))
print(lst_dates)
The problem with you code is on line #3 when you are writing
datetime_object = sorted(lst_dates, key=lambda x: datetime.strptime(x, '%d %b %Y'))
sorted function in Python returns a new python list object. If you want to check then run
type(datetime_object)
So in order to achieve what you want here you need to iterate over that list. Your final code would be something like this
from datetime import datetime,date
lst_dates = ['01 Apr 2017', '01 Apr 2018', '01 Aug 2017', '01 Aug 2018', '01 Dec 2017', '01 Dec 2018', '01 Feb 2017', '01 Feb 2018', '01 Jan 2017', '01 Jan 2018']
datetime_obj_list = sorted(lst_dates, key=lambda x: datetime.strptime(x, '%d %b %Y'))
for datetime_object in datetime_obj_list:
datetime_object = datetime.strptime(datetime_object, "%d %b %Y")
print(datetime_object.date())
UPDATE:
Here's a working sample of the code https://ideone.com/YRDQR7
the problem is on 4th line
it should be date_object = datetime.date()
This works just fine:
from datetime import datetime,date
lst_dates = ['01 Apr 2017', '01 Apr 2018', '01 Aug 2017', '01 Aug 2018', '01 Dec 2017', '01 Dec 2018', '01 Feb 2017', '01 Feb 2018', '01 Jan 2017', '01 Jan 2018']
datetime_object = sorted(lst_dates, key=lambda x: datetime.strptime(x, '%d %b %Y'))
#date_object = datetime_object.date() # <<-- remove this line
print(datetime_object)
testing:
>>> from datetime import datetime,date
>>> lst_dates = ['01 Apr 2017', '01 Apr 2018', '01 Aug 2017', '01 Aug 2018', '01 Dec 2017', '01 Dec 2018', '01 Feb 2017', '01 Feb 2018', '01 Jan 2017', '01 Jan 2018']
>>> datetime_object = sorted(lst_dates, key=lambda x: datetime.strptime(x, '%d %b %Y'))
>>> print(datetime_object)
['01 Jan 2017', '01 Feb 2017', '01 Apr 2017', '01 Aug 2017', '01 Dec 2017', '01 Jan 2018', '01 Feb 2018', '01 Apr 2018', '01 Aug 2018', '01 Dec 2018']
>>>
Related
In Pandas (Juypter) I have a column with dates in string format:
koncerti.Date.values[:20]
array(['15 September 2010', '16 September 2010', '18 September 2010',
'20 September 2010', '21 September 2010', '23 September 2010',
'24 September 2010', '26 September 2010', '28 September 2010',
'30 September 2010', '1 October 2010', '3 October 2010',
'5 October 2010', '6 October 2010', '8 October 2010',
'10 October 2010', '12 October 2010', '13 October 2010',
'15 October 2010', '17 October 2010'], dtype=object)
I try to convert them to date format with the following statement:
koncerti.Date = pd.to_datetime(koncerti.Date, format='%d %B %Y')
Unfortunatelly, it produces the following error: ValueError: unconverted data remains: [31]
What does it mean this error?
Solution: koncerti.Date = pd.to_datetime(koncerti.Date, format='%d %B %Y', exact=False)
Addditional parameter was needed: exact=False
I have the following dataset
id
date
7510
15 Jun 2020
7510
16 Jun 2020
7512
15 Jun 2020
7512
07 Jul 2020
7520
15 Jun 2020
7520
16 Aug 2020
I need to convert this to a dictionary which is quite straight forward, but need each unique id as a key and all corresponding values as values to the unique key.
for example;
dictionary = {7510: ["15 Jun 2020", "16 Jun 2020"], 7512: ["15 Jun 2020", "07 Jul 2020"],
7520: ["15 Jun 2020", "16 Aug 2020"] }
Try this:
df.groupby('id')['date'].agg(list).to_dict()
Output:
{7510: ['15 Jun 2020', '16 Jun 2020'],
7512: ['15 Jun 2020', '07 Jul 2020'],
7520: ['15 Jun 2020', '16 Aug 2020']}
I'm trying to sort the values with current year.
Current year values should show first.
mdlist = [{0:'31 Jan 2022', 1:'', 2:'10 Feb 2022'},
{0:'10 Feb 2021', 1:'20, Feb 2021', 2:''},
{0:'10 Feb 2022', 1:'10 Feb 2022', 2:'10 Feb 2022'}]
mdlist = sorted(mdlist, key=lambda d:d[0])
but it is not working as expected
expected output:
mdlist = [{0:'31 Jan 2022', 1:'', 2:'10 Feb 2022'},
{0:'10 Feb 2022', 1:'10 Feb 2022', 2:'10 Feb 2022'},
{0:'10 Feb 2021', 1:'20 Feb 2021', 2:''}]
Maybe you could leverage the fact that these are datetimes by using the datetime module and sort it by the years in descending order and the month-days in ascending order:
from datetime import datetime
def sorting_key(dct):
ymd = datetime.strptime(dct[0], "%d %b %Y")
return -ymd.year, ymd.month, ymd.day
mdlist.sort(key=sorting_key)
Output:
[{0: '31 Jan 2022', 1: '', 2: '10 Feb 2022'},
{0: '10 Feb 2022', 1: '10 Feb 2022', 2: '10 Feb 2022'},
{0: '10 Feb 2021', 1: '20 Feb 2021', 2: ''}]
Use a key function that returns 0 if the year is 2022, 1 otherwise. This will sort all the 2022 dates first.
firstyear = '2022'
mdlist = sorted(mdlist, key=lambda d: 0 if d:d[0].split()[-1] == firstyear else 1)
Given the following list of strings
from datetime import datetime
import numpy as np
strings = ['Nov 1 2021', 'Oct 25 2021', 'Oct 18 2021', 'Oct 11 2021', 'Oct 4 2021', 'Sep 27 2021',
'Sep 20 2021', 'Aug 24 2021', 'Aug 16 2021', 'Aug 9 2021', 'Aug 2 2021', 'Jul 26 2021',
'Jun 28 2021', 'Jun 21 2021', 'Jun 14 2021', 'Jun 7 2021', 'May 24 2021', 'May 10 2021',
'May 3 2021', 'Apr 26 2021', 'Apr 12 2021', 'Apr 12 2021', 'Apr 5 2021', 'Mar 22 2021',
'Feb 22 2021', 'Feb 13 2021', 'Feb 8 2021', 'Feb 1 2021', 'Nov 2 2020', 'Sep 28 2020',
'Aug 31 2020', 'Aug 20 2020', 'Aug 10 2020', 'Jun 29 2020', 'Jun 22 2020', 'Jun 15 2020',
'Mar 2 2020', 'Feb 10 2020', 'Feb 3 2020', 'Jan 27 2020', 'Jan 20 2020', 'Jan 13 2020',
'Jan 6 2020', 'Aug 26 2019', 'Aug 5 2019', 'Jul 29 2019', 'Jul 22 2019', 'Jul 15 2019']
What's the most efficient way to return a list of those dates that fall within the last 365 days?
Here's my failed attempt:
# Converts strings to datetime format and appends to new list, 'dates.'
dates = []
for item in strings:
convert_string = datetime.strptime(item, "%b %d %Y").date()
dates.append(convert_string)
# Given each item in 'dates' list returns corresponding
# list showing elapsed time between each item and today (Nov 11th 2021).
elapsed_time = []
def dateDelta(i):
today = datetime.fromisoformat(datetime.today().strftime('%Y-%m-%d')).date()
date = i
delta = (today - date).days
elapsed_time.append(delta)
for i in dates:
dateDelta(i)
# Concatenates 'dates' list and 'elapsed_times_list' in attempt to somehow connect the two.
date_and_elapsed_time = []
date_and_elapsed_time.append(dates)
date_and_elapsed_time.append(elapsed_time)
# Takes 'elapsed_time list' appends only the dates that fall within the last 365 days.
relevant_elapsed_time_list = []
for i in elapsed_time:
if i <= 365:
relevant_elapsed_time_list.append(i)
# Finds indices of 'relevant_elapsed_time_list' within last 365 days.
# After trawling StackOverflow posts, I import numpy in an effort to help with indexing.
# My thinking is I can use the indices of the relevant elapsed times from the
# 'elapsed_time_list' and return the corresponding date from the 'dates' list.
relevant_elapsed_time_list_indices = []
for item in relevant_elapsed_time_list:
indexes = []
for index, sub_lst in enumerate(date_and_elapsed_time):
try:
indexes.append((index, sub_lst.index(item)))
except ValueError:
pass
relevant_elapsed_time_list_indices.append(indexes)
relevant_elapsed_time_list_indices = np.array([[x[0][0], x[0][1]] for x in relevant_elapsed_time_list_indices])
At this point, I'm as yet unable to convert the relevant_elapsed_time_list_indices list to the corresponding indices for the first sub-list in date_and_elapsed_time. The point of this would be to then isolate those indices (i.e. dates).
What's the most efficient way to solve this problem?
You can convert the strings to datetime objects using .strptime, then use a conditional list comprehension that uses timedelta to pick ones that fall within the last 365 days:
from datetime import datetime, timedelta
last_365_days = [s for s in strings if datetime.strptime(s, "%b %d %Y") + timedelta(days=365) >= datetime.today()]
Alternatively you can compute the cutoff date in advance:
cutoff = datetime.today() - timedelta(days=365)
last_365_days = [s for s in strings if datetime.strptime(s, "%b %d %Y") >= cutoff]
The value of last_365_days should then be (for today):
['Nov 1 2021', 'Oct 25 2021', 'Oct 18 2021', 'Oct 11 2021', 'Oct 4 2021',
'Sep 27 2021', 'Sep 20 2021', 'Aug 24 2021', 'Aug 16 2021', 'Aug 9 2021',
'Aug 2 2021', 'Jul 26 2021', 'Jun 28 2021', 'Jun 21 2021', 'Jun 14 2021',
'Jun 7 2021', 'May 24 2021', 'May 10 2021', 'May 3 2021', 'Apr 26 2021',
'Apr 12 2021', 'Apr 12 2021', 'Apr 5 2021', 'Mar 22 2021', 'Feb 22 2021',
'Feb 13 2021', 'Feb 8 2021', 'Feb 1 2021']
I have a Python list of dates and I'm using min and max to find the most recent and the oldest (first, is that the best method?), but also I need to format the dates into something where I can figure out the current time and subtract the oldest date in the list so I can say something like "In the last 27 minutes..." where I can state how many days, hours, or minutes have past since the oldest. Here is my list (the dates change obviously depending on what I'm pulling) so you can see the current format. How do I get the info I need?
[u'Sun Oct 06 18:00:55 +0000 2013', u'Sun Oct 06 17:57:41 +0000 2013', u'Sun Oct 06 17:55:44 +0000 2013', u'Sun Oct 06 17:54:10 +0000 2013', u'Sun Oct 06 17:35:58 +0000 2013', u'Sun Oct 06 17:35:58 +0000 2013', u'Sun Oct 06 17:35:25 +0000 2013', u'Sun Oct 06 17:34:39 +0000 2013', u'Sun Oct 06 17:34:39 +0000 2013', u'Sun Oct 06 17:34:39 +0000 2013', u'Sun Oct 06 17:30:35 +0000 2013', u'Sun Oct 06 17:25:28 +0000 2013', u'Sun Oct 06 17:24:04 +0000 2013', u'Sun Oct 06 17:24:04 +0000 2013', u'Sun Oct 06 17:22:08 +0000 2013', u'Sun Oct 06 17:22:08 +0000 2013', u'Sun Oct 06 17:21:00 +0000 2013', u'Sun Oct 06 17:18:49 +0000 2013', u'Sun Oct 06 17:18:49 +0000 2013', u'Sun Oct 06 17:15:29 +0000 2013', u'Sun Oct 06 17:15:29 +0000 2013', u'Sun Oct 06 17:13:35 +0000 2013', u'Sun Oct 06 17:12:18 +0000 2013', u'Sun Oct 06 17:12:00 +0000 2013', u'Sun Oct 06 17:07:34 +0000 2013', u'Sun Oct 06 17:03:59 +0000 2013']
You won't get the oldest and newest date/time entries from your list with the entries by using min and max - "Fri" will come before "Mon", for example. So you'll want to put things into a data structure that represents date/time stamps properly.
Fortunately, Python's datetime module comes with a method to convert lots of date/time stamp strings into a proper representation - datetime.datetime.strptime. See the guide for how to use it.
Once that's done you can use min and max and then timedelta to compute the difference.
from datetime import datetime
# Start with the initial list
A = [u'Sun Oct 06 18:00:55 +0000 2013', u'Sun Oct 06 17:57:41 +0000 2013', u'Sun Oct 06 17:55:44 +0000 2013', u'Sun Oct 06 17:54:10 +0000 2013', u'Sun Oct 06 17:35:58 +0000 2013', u'Sun Oct 06 17:35:58 +0000 2013', u'Sun Oct 06 17:35:25 +0000 2013', u'Sun Oct 06 17:34:39 +0000 2013', u'Sun Oct 06 17:34:39 +0000 2013', u'Sun Oct 06 17:34:39 +0000 2013', u'Sun Oct 06 17:30:35 +0000 2013', u'Sun Oct 06 17:25:28 +0000 2013', u'Sun Oct 06 17:24:04 +0000 2013', u'Sun Oct 06 17:24:04 +0000 2013', u'Sun Oct 06 17:22:08 +0000 2013', u'Sun Oct 06 17:22:08 +0000 2013', u'Sun Oct 06 17:21:00 +0000 2013', u'Sun Oct 06 17:18:49 +0000 2013', u'Sun Oct 06 17:18:49 +0000 2013', u'Sun Oct 06 17:15:29 +0000 2013', u'Sun Oct 06 17:15:29 +0000 2013', u'Sun Oct 06 17:13:35 +0000 2013', u'Sun Oct 06 17:12:18 +0000 2013', u'Sun Oct 06 17:12:00 +0000 2013', u'Sun Oct 06 17:07:34 +0000 2013', u'Sun Oct 06 17:03:59 +0000 2013']
# This is the format string the date/time stamps are using
# On Python 3.3 on Windows you can use this format
# s_format = "%a %b %d %H:%M:%S %z %Y"
# However, Python 2.7 on Windows doesn't work with that. If all of your date/time stamps use the same timezone you can do:
s_format = "%a %b %d %H:%M:%S +0000 %Y"
# Convert the text list into datetime objects
A = [datetime.strptime(d, s_format) for d in A]
# Get the extremes
oldest = min(A)
newest = max(A)
# If you substract oldest from newest you get a timedelta object, which can give you the total number of seconds between them. You can use this to calculate days, hours, and minutes.
delta = int((newest - oldest).total_seconds())
delta_days, rem = divmod(delta, 86400)
delta_hours, rem = divmod(rem, 3600)
delta_minutes, delta_seconds = divmod(rem, 60)
your question can be divided into three pieces:
A)
how to read string formated dates
B)
how to sort list of dates in python
C)
how to calculate the difference between two dates