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Convert panda date list to python list of date strings


Convert panda date list to python list of date strings

By : hairul
Date : November 23 2020, 03:01 PM
With these it helps Use list comprehension
code :
In [4553]: [x.strftime('%m/%d/%Y') for x in panda_datelist]
Out[4553]: ['10/20/2017', '10/23/2017', '10/24/2017']
In [4554]: map(lambda x: x.strftime('%m/%d/%Y'), panda_datelist)
Out[4554]: ['10/20/2017', '10/23/2017', '10/24/2017']
In [4559]: pd.bdate_range(pd.datetime.today(), periods=3).strftime('%m/%d/%Y')
Out[4559]:
array([u'10/20/2017', u'10/23/2017', u'10/24/2017'],
      dtype='<U10')
In [4555]: panda_datelist
Out[4555]:
[Timestamp('2017-10-20 00:00:00', freq='B'),
 Timestamp('2017-10-23 00:00:00', freq='B'),
 Timestamp('2017-10-24 00:00:00', freq='B')]


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Convert list/vector of strings with date format into posix date class with R

Convert list/vector of strings with date format into posix date class with R


By : Abhilasha Abhinav
Date : March 29 2020, 07:55 AM
around this issue I have a list of strings that is in our date format and I want to convert it into a list of posix dates that I can manipulate with R, how can I do that? , strptime is vectorized:
code :
a <- c("2009.01.01 12:20:10", "2009.01.01 04:12:14")
> out <- strptime(a, "%Y.%m.%d %H:%M:%S")
> str(out)
 POSIXlt[1:2], format: "2009-01-01 12:20:10" "2009-01-01 04:12:14"
convert a list of unix timestamps to a list of date and time strings for some other time zone in python

convert a list of unix timestamps to a list of date and time strings for some other time zone in python


By : K.Madhuri
Date : March 29 2020, 07:55 AM
will help you You can convert a UNIX timestamp into a datetime object and then format it using its strftime method. However, this will give you a timezone-unaware datetime. In order to make it timezone-aware, you'll need to get the timezone from pytz and use the localize method:
code :
from datetime import datetime
from pytz import timezone

# ...

def format_time(time, tz):
  localized = tz.localize(datetime.fromtimestamp(time))
  return localized.strftime('%d/%m/%Y %H:%M:%S')

us_pacific = timezone('US/Pacific')
dates = map(lambda t: format_time(t, us_pacific), posix_times)
Convert range of two date time's into a list of dates and count of seconds per that date python

Convert range of two date time's into a list of dates and count of seconds per that date python


By : Nu Nu Khaing
Date : March 29 2020, 07:55 AM
it fixes the issue I'm interested in creating a function that gets two following datetime objects (start date and end date) as input and returns a list of tuples. I would like a tuple for each date between start and end date, and the number of seconds in this date. For example: , Try the following:
code :
from datetime import timedelta

def date_range(start, end):                               
    current = start
    while True:
        next_stop = (current + timedelta(days=1)).replace(
            hour=0, minute=0, second=0)
        if next_stop > end:
            next_stop = end
        yield current.date(), (next_stop - current).total_seconds()
        if next_stop == end:
            break
        current = next_stop
Convert integers inside a list into strings and then a date in python 3.x

Convert integers inside a list into strings and then a date in python 3.x


By : one.bogdan
Date : March 29 2020, 07:55 AM
This might help you To split your list into size 3 chunks you use a range with a step of 3
code :
for i in range(0, len(l), 3):
    print(l[i:i+3])
'/'.join([str(x) for x in l[i:i+3]])
def make_times(l):
    results = []
    for i in range(0, len(l), 3):
        results.append('/'.join([str(x) for x in l[i:i+3]]))
    return results
Convert Python list of duplicate date entries/values to Pandas Dataframe sorted by date

Convert Python list of duplicate date entries/values to Pandas Dataframe sorted by date


By : bitwizard
Date : March 29 2020, 07:55 AM
it should still fix some issue Maybe not the most simple or efficient answer, but this works. Basically I'm creating two DataFrame objects, getting rid of all of the nan's and then merging them on the 'Date' column.
code :
import pandas as pd

list_ex = [{'Date': '12/31/2018', 'A': 'N/A'},
           {'Date': '09/30/2018', 'A': '$5.75'},
           {'Date': '06/30/2018', 'A': '$5.07'},
           {'Date': '03/31/2018', 'A': '$3.27'},
           {'Date': '12/31/2018', 'B': 'N/A'},
           {'Date': '09/30/2018', 'B': '$56,576.00'},
           {'Date': '06/30/2018', 'B': '$52,886.00'},
           {'Date': '03/31/2018', 'B': '$51,042.00'}]

df1 = pd.DataFrame(data=list_ex, columns=['Date', 'A']).dropna()
df2 = pd.DataFrame(data=list_ex, columns=['Date', 'B']).dropna()

df3 = pd.merge(df1, df2, on='Date')

print(df3)
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