ValueError (while creating a function in python): The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() [duplicate]

Question

def function(df):
    if (df['Total'] > 0) & (df['Total'] <= 50000):
         df['X'] = (df['Total']*(2/150)) * 0.2
    elif (df['Total'] > 50000):
        df['X'] = ((50000*(2/150))*0.2) + ((df['Total']-50000)*(2/150)*0.2)

I am trying to run the above code but getting a value error saying

File "<ipython-input-31-f77514d81c6f>", line 1, in <module>
    platinumplus(cust_spends)

File "<ipython-input-30-da2fd213761f>", line 2, in platinumplus
    if (df['Total'] > 0) & (df['Total'] <= 50000):

File "E:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", 
    line 1573, in __nonzero__ .format(self.__class__.__name__))

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

Answer 1

I think the issue is in your if statements. (df['Total'] > 0) & (df['Total'] <= 50000) will return a boolean series rather than a single True or False so python doesn't know how to handle this. If you want the case where all of the values in the series are True you can use:

((df['Total'] > 0) & (df['Total'] <= 50000)).all()

Answer 2

I believe you need numpy.where for set new values of column by boolean mask:

df = pd.DataFrame({'Total':[10, 10000, 40000, 100]})

def function(df):
    mask = (df['Total'] > 0) & (df['Total'] <= 50000)
    v1 = (df['Total']*(2/150)) * 0.2
    v2 = ((50000*(2/150))*0.2) + ((df['Total']-50000)*(2/150)*0.2)

    df['X'] = np.where(mask, v1, v2)
    return df

df1 = df.pipe(function)
print (df1)
   Total           X
0     10    0.026667
1  10000   26.666667
2  40000  106.666667
3    100    0.266667

If there is multiple conditions use numpy.select:

df = pd.DataFrame({'Total':[10, 10000, 40000, 100, -2]})


def function(df):
    mask1 = (df['Total'] > 0) & (df['Total'] <= 50000)
    mask2 = df['Total'] > 50000
    v1 = (df['Total']*(2/150)) * 0.2
    v2 = ((50000*(2/150))*0.2) + ((df['Total']-50000)*(2/150)*0.2)

    df['X'] = np.select([mask1, mask2], [v1, v2], default=np.nan)
    return df

df1 = df.pipe(function)
print (df1)
   Total           X
0     10    0.026667
1  10000   26.666667
2  40000  106.666667
3    100    0.266667
4     -2         NaN