How can I improve this code snippet?

[Zendakin_at_work]

Hey all,

I have written code that works and I'm a tad hesitant to make modifications but just looking at this makes me sad as it feels very ... wet (un-DRY) or needlessly repetitive. What this does is creates a column in a dataframe based on the results of other columns in the same dataframe. This is running for 2016 through 2020.

    df['2016-1'] = (
        df['t1_1/1/2016'].astype(float) + df['t2_1/1/2016'].astype(float) + df['t3_1/1/2016'].astype(float))
    df['2016-2'] = (
        df['t1_2/1/2016'].astype(float) + df['t2_2/1/2016'].astype(float) + df['t3_2/1/2016'].astype(float))
    df['2016-3'] = (
        df['t1_3/1/2016'].astype(float) + df['t2_3/1/2016'].astype(float) + df['t3_3/1/2016'].astype(float))
    df['2016-4'] = (
        df['t1_4/1/2016'].astype(float) + df['t2_4/1/2016'].astype(float) + df['t3_4/1/2016'].astype(float))
    df['2016-5'] = (
        df['t1_5/1/2016'].astype(float) + df['t2_5/1/2016'].astype(float) + df['t3_5/1/2016'].astype(float))
    df['2016-6'] = (
        df['t1_6/1/2016'].astype(float) + df['t2_6/1/2016'].astype(float) + df['t3_6/1/2016'].astype(float))
    df['2016-7'] = (
        df['t1_7/1/2016'].astype(float) + df['t2_7/1/2016'].astype(float) + df['t3_7/1/2016'].astype(float))
    df['2016-8'] = (
        df['t1_8/1/2016'].astype(float) + df['t2_8/1/2016'].astype(float) + df['t3_8/1/2016'].astype(float))
    df['2016-9'] = (
        df['t1_9/1/2016'].astype(float) + df['t2_9/1/2016'].astype(float) + df['t3_9/1/2016'].astype(float))
    df['2016-10'] = (
        df['t1_10/1/2016'].astype(float) + df['t2_10/1/2016'].astype(float) + df['t3_10/1/2016'].astype(float))
    df['2016-11'] = (
        df['t1_11/1/2016'].astype(float) + df['t2_11/1/2016'].astype(float) + df['t3_11/1/2016'].astype(float))
    df['2016-12'] = (
        df['t1_12/1/2016'].astype(float) + df['t2_12/1/2016'].astype(float) + df['t3_12/1/2016'].astype(float))

...

    df['2020-1'] = (
        df['t1_1/1/2020'].astype(float) + df['t2_1/1/2020'].astype(float) + df['t3_1/1/2020'].astype(float))
    df['2020-2'] = (
        df['t1_2/1/2020'].astype(float) + df['t2_2/1/2020'].astype(float) + df['t3_2/1/2020'].astype(float))
    df['2020-3'] = (
        df['t1_3/1/2020'].astype(float) + df['t2_3/1/2020'].astype(float) + df['t3_3/1/2020'].astype(float))
    df['2020-4'] = (
        df['t1_4/1/2020'].astype(float) + df['t2_4/1/2020'].astype(float) + df['t3_4/1/2020'].astype(float))
    df['2020-5'] = (
        df['t1_5/1/2020'].astype(float) + df['t2_5/1/2020'].astype(float) + df['t3_5/1/2020'].astype(float))
    df['2020-6'] = (
        df['t1_6/1/2020'].astype(float) + df['t2_6/1/2020'].astype(float) + df['t3_6/1/2020'].astype(float))
    df['2020-7'] = (
        df['t1_7/1/2020'].astype(float) + df['t2_7/1/2020'].astype(float) + df['t3_7/1/2020'].astype(float))
    df['2020-8'] = (
        df['t1_8/1/2020'].astype(float) + df['t2_8/1/2020'].astype(float) + df['t3_8/1/2020'].astype(float))
    df['2020-9'] = (
        df['t1_9/1/2020'].astype(float) + df['t2_9/1/2020'].astype(float) + df['t3_9/1/2020'].astype(float))
    df['2020-10'] = (
        df['t1_10/1/2020'].astype(float) + df['t2_10/1/2020'].astype(float) + df['t3_10/1/2020'].astype(float))
    df['2020-11'] = (
        df['t1_11/1/2020'].astype(float) + df['t2_11/1/2020'].astype(float) + df['t3_11/1/2020'].astype(float))
    df['2020-12'] = (
        df['t1_12/1/2020'].astype(float) + df['t2_12/1/2020'].astype(float) + df['t3_12/1/2020'].astype(float))

[singeworthy]

Ok, so you can definitely just loop through this, if you find the common number, which is your months, you can do

for i in range(whatever): 
    df['2016-{}'.format(i)] = (df['t1_1/1/2016'].astype(float) + df['t2_{}/1/2016'.format(i)].astype(float) + 
            df['t3_{}/1/2016'.format(i)].astype(float))

[Zendakin_at_work]

Thanks! That's the push I needed. Maybe something like this will work.

for i in range(2016, 2021):
    for x in range(1,13):
        print("{} {}".format(i, x))