CODEWITHSUNDEEP
pandastimedelta
Cagdas Kanar
Cagdas Kanar

Reputation: 763

converting timedeltas to integer values in pandas

I've read many topics here and tried many different things but it did not work somehow. Basically, I have a field called order_date which was "object" initially. I've converted it to datetime64[ns] by applying this function :

customer_data['order_date'] = pd.to_datetime(customer_data['order_date'])

Now, I'd like to calculate the difference between two timedeltas and get an integer value like this :

customer_data['recency']= (customer_data.order_date.max() - customer_data['order_date'])

But when I do this, I want my new column "recency" to be an INTEGER value rather than a timedelta64[ns] . Any idea how to do this?

Many thanks in advance.

Upvotes: 1

Views: 79

Answers (1)

jezrael
jezrael

Reputation: 862511

I think you can use dt.total_seconds with casting to int by astype:

customer_data['recency'] = customer_data['recency'].dt.total_seconds().astype(int)

Sample:

rng = pd.date_range('2017-04-03', periods=10)
customer_data = pd.DataFrame({'order_date': rng, 'a': range(10)})  
#print (customer_data)

customer_data['recency']= (customer_data.order_date.max() - customer_data['order_date'])
customer_data['recency'] = customer_data['recency'].dt.total_seconds().astype(int)
print (customer_data)
   a order_date  recency
0  0 2017-04-03   777600
1  1 2017-04-04   691200
2  2 2017-04-05   604800
3  3 2017-04-06   518400
4  4 2017-04-07   432000
5  5 2017-04-08   345600
6  6 2017-04-09   259200
7  7 2017-04-10   172800
8  8 2017-04-11    86400
9  9 2017-04-12        0

Another solution with dt.days:

customer_data['recency'] = customer_data['recency'].dt.days.astype(int)
print (customer_data)
   a order_date  recency
0  0 2017-04-03        9
1  1 2017-04-04        8
2  2 2017-04-05        7
3  3 2017-04-06        6
4  4 2017-04-07        5
5  5 2017-04-08        4
6  6 2017-04-09        3
7  7 2017-04-10        2
8  8 2017-04-11        1
9  9 2017-04-12        0

Upvotes: 1

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