Selecting value from Pandas without going through .values[0]

Question

An example dataset I'm working with

df = pd.DataFrame({"competitorname": ["3 Musketeers", "Almond Joy"], "winpercent": [67.602936, 50.347546] }, index = [1, 2])

I am trying to see whether 3 Musketeers or Almond Joy has a higher winpercent. The code I wrote is:

more_popular = '3 Musketeers' if df.loc[df["competitorname"] == '3 Musketeers', 'winpercent'].values[0] > df.loc[df["competitorname"] == 'Almond Joy', 'winpercent'].values[0] else 'Almond Joy'

My question is

Can I select the values I am interested in without python returning a Series? Is there a way to just do

df[df["competitorname"] == 'Almond Joy', 'winpercent']

and then it would return a simple

50.347546

?

I know this doesn't make my code significantly shorter but I feel like I am missing something about getting values from pandas that would help me avoid constantly adding

.values[0]

Answer 1

The underlying issue is that there could be multiple matches, so we will always need to extract the match(es) at some point in the pipeline:

• Use Series.idxmax on the boolean mask

  Since False is 0 and True is 1, using Series.idxmax on the boolean mask will give you the index of the first True:

  df.loc[df['competitorname'].eq('Almond Joy').idxmax(), 'winpercent']
  # 50.347546
  

  This assumes there is at least 1 True match, otherwise it will return the first False.

• Or use Series.item on the result

  This is basically just an alias for Series.values[0]:

  df.loc[df['competitorname'].eq('Almond Joy'), 'winpercent'].item()
  # 50.347546
  

  This assumes there is exactly 1 True match, otherwise it will throw a ValueError.