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Create new pandas column with apply based on conditions of multiple other columns

April 4, 2022

I have a df with time stamps in a basketball game (this is a sample. my actual df is much larger)

    year    period  p_sec_rem
0   2015    1   556
1   2013    1   455
2   2001    2   67
3   2008    3   177
4   2017    1   172

period is the period of the game and p_sec_remaining is the seconds remaining in the period. I would like to calculate how many seconds have elapsed since the beginning of the game (time) and have some logic that does this. It is a bit complicated, but explaining it is besides the point as I am sure it is correct:

df['time'] = None

def secondsPlayed(df):
  if df.year >= 2006:
    if df.period == 1:
      df['time'] = 600 - df.p_sec_rem
    elif df.period > 1 & df.period < 5:
      df['time'] = ((df.period * 10) * 60) - df.p_sec_rem
    elif df.period == 5:
      df['time'] = (((4 * 10) * 60) + 300) - df.p_sec_rem
    elif df.period == 6:
      df['time'] = (((4 * 10) * 60) + 600) - df.p_sec_rem
    elif df.period == 7:
      df['time'] = (((4 * 10) * 60) + 900) - df.p_sec_rem
  elif df.year <= 2005:
    if df.period == 1:
      df['time'] = 1200 - df.p_sec_rem
    elif df.period == 2:
      df['time'] = 2400 - df.p_sec_rem
    elif df.period == 3:
      df['time'] = (((2 * 20) * 60) + 300) - df.p_sec_rem
    elif df.period == 4:
      df['time'] = (((2 * 20) * 60) + 600) - df.p_sec_rem
    elif df.period == 5:
      df['time'] = (((2 * 20) * 60) + 900) - df.p_sec_rem
    elif df.period == 6:
      df['time'] = (((2 * 20) * 60) + 1200) - df.p_sec_rem
    elif df.period == 7:
      df['time'] = (((2 * 20) * 60) + 1500) - df.p_sec_rem

I would like to apply this logic to each row of my df, so I figured using *.apply() would do the trick, but alas:

df.apply(secondsPlayed,axis=1)

0    None
1    None
2    None
3    None
4    None

All None are returned, when my desired output is:

I came across this question but I think it is slightly different from my example since I need to pass an entire df to the function as I utilize multiple different columns for the conditions that determine the output df.time value. I suspect I am close, but have been playing around with this for a while to no avail.

>Solution :

You just need to return the df in your function:

def secondsPlayed(df):
    if df.year >= 2006:
        if df.period == 1:
            df['time'] = 600 - df.p_sec_rem
        elif df.period > 1 & df.period < 5:
            df['time'] = ((df.period * 10) * 60) - df.p_sec_rem
        elif df.period == 5:
            df['time'] = (((4 * 10) * 60) + 300) - df.p_sec_rem
        elif df.period == 6:
            df['time'] = (((4 * 10) * 60) + 600) - df.p_sec_rem
        elif df.period == 7:
            df['time'] = (((4 * 10) * 60) + 900) - df.p_sec_rem
    elif df.year <= 2005:
        if df.period == 1:
            df['time'] = 1200 - df.p_sec_rem
        elif df.period == 2:
            df['time'] = 2400 - df.p_sec_rem
        elif df.period == 3:
            df['time'] = (((2 * 20) * 60) + 300) - df.p_sec_rem
        elif df.period == 4:
            df['time'] = (((2 * 20) * 60) + 600) - df.p_sec_rem
        elif df.period == 5:
            df['time'] = (((2 * 20) * 60) + 900) - df.p_sec_rem
        elif df.period == 6:
            df['time'] = (((2 * 20) * 60) + 1200) - df.p_sec_rem
        elif df.period == 7:
            df['time'] = (((2 * 20) * 60) + 1500) - df.p_sec_rem
    return df

output:

    year    period  p_sec_rem   time
0   2015         1      556     44
1   2013         1      455     145
2   2001         2       67     2333
3   2008         3      177     1623
4   2017         1      172     428