Extract Year from Date

How to extract the year from a Python datetime object?

It's in fact almost the same in Python.. :-)

import datetime
year = datetime.date.today().year

Of course, date doesn't have a time associated, so if you care about that too, you can do the same with a complete datetime object:

import datetime
year = datetime.datetime.today().year

(Obviously no different, but you can store datetime.datetime.today() in a variable before you grab the year, of course).

One key thing to note is that the time components can differ between 32-bit and 64-bit pythons in some python versions (2.5.x tree I think). So you will find things like hour/min/sec on some 64-bit platforms, while you get hour/minute/second on 32-bit.

Extract year from date

if all your dates are the same width, you can put the dates in a vector and use substring

Date
a <- c("01/01/2009", "01/01/2010" , "01/01/2011")
substring(a,7,10) #This takes string and only keeps the characters beginning in position 7 to position 10

output

[1] "2009" "2010" "2011"

python pandas extract year from datetime: df['year'] = df['date'].year is not working

If you're running a recent-ish version of pandas then you can use the datetime attribute dt to access the datetime components:

In [6]:

df['date'] = pd.to_datetime(df['date'])
df['year'], df['month'] = df['date'].dt.year, df['date'].dt.month
df
Out[6]:
        date  Count  year  month
0 2010-06-30    525  2010      6
1 2010-07-30    136  2010      7
2 2010-08-31    125  2010      8
3 2010-09-30     84  2010      9
4 2010-10-29   4469  2010     10

EDIT

It looks like you're running an older version of pandas in which case the following would work:

In [18]:

df['date'] = pd.to_datetime(df['date'])
df['year'], df['month'] = df['date'].apply(lambda x: x.year), df['date'].apply(lambda x: x.month)
df
Out[18]:
        date  Count  year  month
0 2010-06-30    525  2010      6
1 2010-07-30    136  2010      7
2 2010-08-31    125  2010      8
3 2010-09-30     84  2010      9
4 2010-10-29   4469  2010     10

Regarding why it didn't parse this into a datetime in read_csv you need to pass the ordinal position of your column ([0]) because when True it tries to parse columns [1,2,3] see the docs

In [20]:

t="""date   Count
6/30/2010   525
7/30/2010   136
8/31/2010   125
9/30/2010   84
10/29/2010  4469"""
df = pd.read_csv(io.StringIO(t), sep='\s+', parse_dates=[0])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 2 columns):
date     5 non-null datetime64[ns]
Count    5 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 120.0 bytes

So if you pass param parse_dates=[0] to read_csv there shouldn't be any need to call to_datetime on the 'date' column after loading.

Extract month and year from datetime in R

lubridate month and year will work.

as.data.frame(Order.Date) %>%
  mutate(Month = lubridate::month(Order.Date, label = FALSE),
         Year = lubridate::year(Order.Date))

  Order.Date Month Year
1 2011-10-20    10 2011
2 2011-12-25    12 2011
3 2012-04-15     4 2012
4 2012-08-23     8 2012
5 2013-09-25     9 2013

If you want month format as Jan, use month.abb and as January, use month.name

as.data.frame(Order.Date) %>%
  mutate(Month = month.abb[lubridate::month(Order.Date, label = TRUE)],
         Year = lubridate::year(Order.Date))

  Order.Date Month Year
1 2011-10-20   Oct 2011
2 2011-12-25   Dec 2011
3 2012-04-15   Apr 2012
4 2012-08-23   Aug 2012
5 2013-09-25   Sep 2013

as.data.frame(Order.Date) %>%
  mutate(Month = month.name[lubridate::month(Order.Date, label = TRUE)],
         Year = lubridate::year(Order.Date))

  Order.Date     Month Year
1 2011-10-20   October 2011
2 2011-12-25  December 2011
3 2012-04-15     April 2012
4 2012-08-23    August 2012
5 2013-09-25 September 2013

extract year from date and calculate datedifference

You didn't include the js code which is more important for this question. I'll include a couple functions that should help.

To find a year from a date, I use the following function

function getYearFromDate(date){  const yearFromDate = new Date();  yearFromDate.setTime(date.getTime());  yearFromDate.setFullYear(date.getFullYear() + 1);  return yearFromDate;}
console.log(getYearFromDate(new Date()));

df['year'] = pd.DatetimeIndex(df['ArrivalDate']).year
df['month'] = pd.DatetimeIndex(df['ArrivalDate']).month

df['year'] = df['ArrivalDate'].dt.year
df['month'] = df['ArrivalDate'].dt.month

year(@date)
year(getdate())
year('20120101')

update table
set column = year(date_column)
whre ....

 update t
   set column = year(t1.date_column)
     from table_source t1
     join table_target t on (join condition)
    where ....

df$Month_Yr <- format(as.Date(df$Date), "%Y-%m")

df
#>   ID       Date Month_Yr
#> 1  1 2004-02-06  2004-02
#> 2  2 2006-03-14  2006-03
#> 3  3 2007-07-16  2007-07

# your data sample
  df <- data.frame( ID=1:3,Date = c("2004-02-06" , "2006-03-14" , "2007-07-16") )

dates <- "2004-02-06"

format(as.Date(dates), "%Y-%m")
> "2004-02"

library(data.table)
setDT(df)[, Month_Yr := format(as.Date(Date), "%Y-%m") ]