Extracting just Month and Year separately from Pandas Datetime column

I have a Dataframe, df, with the following column:

df['ArrivalDate'] =
...
936   2012-12-31
938   2012-12-29
965   2012-12-31
966   2012-12-31
967   2012-12-31
968   2012-12-31
969   2012-12-31
970   2012-12-29
971   2012-12-31
972   2012-12-29
973   2012-12-29
...

The elements of the column are pandas.tslib.Timestamp.

I want to just include the year and month. I thought there would be simple way to do it, but I can't figure it out.

Here's what I've tried:

df['ArrivalDate'].resample('M', how = 'mean')

I got the following error:

Only valid with DatetimeIndex or PeriodIndex 

Then I tried:

df['ArrivalDate'].apply(lambda(x):x[:-2])

I got the following error:

'Timestamp' object has no attribute '__getitem__' 

Any suggestions?

Edit: I sort of figured it out.

df.index = df['ArrivalDate']

Then, I can resample another column using the index.

But I'd still like a method for reconfiguring the entire column. Any ideas?

If you want new columns showing year and month separately you can do this:

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

or...

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

Then you can combine them or work with them just as they are.

The df['date_column'] has to be in date time format.

df['month_year'] = df['date_column'].dt.to_period('M')

You could also use D for Day, 2M for 2 Months etc. for different sampling intervals, and in case one has time series data with time stamp, we can go for granular sampling intervals such as 45Min for 45 min, 15Min for 15 min sampling etc.

You can directly access the year and month attributes, or request a datetime.datetime:

In [15]: t = pandas.tslib.Timestamp.now()

In [16]: t
Out[16]: Timestamp('2014-08-05 14:49:39.643701', tz=None)

In [17]: t.to_pydatetime() #datetime method is deprecated
Out[17]: datetime.datetime(2014, 8, 5, 14, 49, 39, 643701)

In [18]: t.day
Out[18]: 5

In [19]: t.month
Out[19]: 8

In [20]: t.year
Out[20]: 2014

One way to combine year and month is to make an integer encoding them, such as: 201408 for August, 2014. Along a whole column, you could do this as:

df['YearMonth'] = df['ArrivalDate'].map(lambda x: 100*x.year + x.month)

or many variants thereof.

I'm not a big fan of doing this, though, since it makes date alignment and arithmetic painful later and especially painful for others who come upon your code or data without this same convention. A better way is to choose a day-of-month convention, such as final non-US-holiday weekday, or first day, etc., and leave the data in a date/time format with the chosen date convention.

The calendar module is useful for obtaining the number value of certain days such as the final weekday. Then you could do something like:

import calendar
import datetime
df['AdjustedDateToEndOfMonth'] = df['ArrivalDate'].map(
    lambda x: datetime.datetime(
        x.year,
        x.month,
        max(calendar.monthcalendar(x.year, x.month)[-1][:5])
    )
)

If you happen to be looking for a way to solve the simpler problem of just formatting the datetime column into some stringified representation, for that you can just make use of the strftime function from the datetime.datetime class, like this:

In [5]: df
Out[5]: 
            date_time
0 2014-10-17 22:00:03

In [6]: df.date_time
Out[6]: 
0   2014-10-17 22:00:03
Name: date_time, dtype: datetime64[ns]

In [7]: df.date_time.map(lambda x: x.strftime('%Y-%m-%d'))
Out[7]: 
0    2014-10-17
Name: date_time, dtype: object

If you want the month year unique pair, using apply is pretty sleek.

df['mnth_yr'] = df['date_column'].apply(lambda x: x.strftime('%B-%Y')) 

Outputs month-year in one column.

Don't forget to first change the format to date-time before, I generally forget.

df['date_column'] = pd.to_datetime(df['date_column'])

SINGLE LINE: Adding a column with 'year-month'-paires: ('pd.to_datetime' first changes the column dtype to date-time before the operation)

df['yyyy-mm'] = pd.to_datetime(df['ArrivalDate']).dt.strftime('%Y-%m')


Accordingly for an extra 'year' or 'month' column:

df['yyyy'] = pd.to_datetime(df['ArrivalDate']).dt.strftime('%Y')

df['mm'] = pd.to_datetime(df['ArrivalDate']).dt.strftime('%m')


Extracting the Year say from ['2018-03-04']

df['Year'] = pd.DatetimeIndex(df['date']).year  

The df['Year'] creates a new column. While if you want to extract the month just use .month

You can first convert your date strings with pandas.to_datetime, which gives you access to all of the numpy datetime and timedelta facilities. For example:

df['ArrivalDate'] = pandas.to_datetime(df['ArrivalDate'])
df['Month'] = df['ArrivalDate'].values.astype('datetime64[M]')

Thanks to jaknap32, I wanted to aggregate the results according to Year and Month, so this worked:

df_join['YearMonth'] = df_join['timestamp'].apply(lambda x:x.strftime('%Y%m'))

Output was neat:

0    201108
1    201108
2    201108

@KieranPC's solution is the correct approach for Pandas, but is not easily extendible for arbitrary attributes. For this, you can use getattr within a generator comprehension and combine using pd.concat:

# input data
list_of_dates = ['2012-12-31', '2012-12-29', '2012-12-30']
df = pd.DataFrame({'ArrivalDate': pd.to_datetime(list_of_dates)})

# define list of attributes required    
L = ['year', 'month', 'day', 'dayofweek', 'dayofyear', 'weekofyear', 'quarter']

# define generator expression of series, one for each attribute
date_gen = (getattr(df['ArrivalDate'].dt, i).rename(i) for i in L)

# concatenate results and join to original dataframe
df = df.join(pd.concat(date_gen, axis=1))

print(df)

  ArrivalDate  year  month  day  dayofweek  dayofyear  weekofyear  quarter
0  2012-12-31  2012     12   31          0        366           1        4
1  2012-12-29  2012     12   29          5        364          52        4
2  2012-12-30  2012     12   30          6        365          52        4

There is two steps to extract year for all the dataframe without using method apply.

Step1

convert the column to datetime :

df['ArrivalDate']=pd.to_datetime(df['ArrivalDate'], format='%Y-%m-%d')

Step2

extract the year or the month using DatetimeIndex() method

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

df['year_month']=df.datetime_column.apply(lambda x: str(x)[:7])

This worked fine for me, didn't think pandas would interpret the resultant string date as date, but when i did the plot, it knew very well my agenda and the string year_month where ordered properly... gotta love pandas!

> Then I tried:

df['ArrivalDate'].apply(lambda(x):x[:-2])

I think here the proper input should be string.

df['ArrivalDate'].astype(str).apply(lambda(x):x[:-2])