Run Python function over two DataFrame columns

Question

I am stuck with an issue, and I think it should be straightforward. The problem is that I have a function, that I would like to apply to two columns of my dataframe. But I receive an error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

To show you what I am trying to do:

# Calculate the accuracy 
def mape(actual,pred):
  if actual == 0:
    if pred == 0:
      return 0
    else:
      return 100
  else:
    return np.mean(np.abs((actual - pred) / actual)) * 100

Then, I try to apply it on two columns (called Actuals_March & Forecast_March).

# This line runs into the ValueError above. 
# I removed all NaN values before running this. 
df['MAPE_Mar'] = df.apply(lambda x: mape(df.Actuals_March , df.Forecast_March), axis=1)

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

#This is an snapshot of my data: 
df.Actuals_March       df.Forecast_March
          0.0     0.0
          0.0     0.0
          0.0     0.0
          4.0     0.0
          0.0     0.0
          5.0     0.0
         20.0     0.0
          0.0     0.0
          2.0     0.0
         13.0     0.0

Hope you can help me. Thanks in advance

jezrael · Accepted Answer · 2021-10-08 14:21:41Z

1

Repalce df to x for match values of scalars by columns:

df['MAPE_Mar'] = df.apply(lambda x: mape(x.Actuals_March , x.Forecast_March), axis=1)

Vectorized alternative:

m1 = df['Actuals_March'] == 0
m2 = df['Forecast_March'] == 0
s = (np.abs(df['Actuals_March'] - df['Forecast_March']) / df['Actuals_March']) * 100

df['MAPE_Mar1'] = np.select([m1 & m2, ~m1 & m2], [0, 100], s)

edited Oct 8, 2021 at 14:21

answered Oct 8, 2021 at 14:14

jezrael

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1 Comment

R overflow Over a year ago

I just wanted to comment that I exactly wrote that line. I replaced 'df' for 'x' and it works. Thanks a lot, was struggling with this one for a while.

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