在兩個 DataFrame 列上運行 Python function

Question

我遇到了一個問題，我認為它應該很簡單。 問題是我有一個 function，我想將其應用於我的 dataframe 的兩列。但我收到一個錯誤：

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

向您展示我正在嘗試做的事情：

# 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

然后，我嘗試將它應用於兩列（稱為 Actuals_March 和 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

希望您能夠幫助我。 提前致謝

Answer 1

將df替換為x以按列匹配標量值：

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

矢量化替代方案：

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)