Nested condition in pandas caused ValueError: The truth value of a Series is ambiguous-CodePudding

The pandas dataframe is:

df = pd.DataFrame([['A', 1, 20], ['A', 2, 30], ['B', 1, 50], ['B', 3, 45], ['A', 4, 60], ['B', 5, 70]])
df.columns = ['Type', 'P', 'X']

df:

  Type  P   X
0    A  1  20

1    A  2  30

2    B  1  50

3    B  3  45

4    A  4  60

5    B  5  70

Expectation:

I want to apply nested conditions to calculate a value and append it as a new column.

This is what I did:

result = 0
if (df.Type == 'A'):
    if df.P % 2 == 0:
        result = df.X 10
    else:
        restult = df.X 20
else:
    if df.P % 2 == 0:
        result = df.X 30
    else:
        result = df.X 40
df['Result'] = result

The expected output is:

 Type  P   X   Result

0    A  1  20   40

1    A  2  30   40

2    B  1  50   90

3    B  3  45   85

4    A  4  60   70

5    B  5  70   110

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

I've dug other threads but they seem to be other issues.

Any suggestion is appreciated

CodePudding user response：

Use numpy's np.where(if condition, result if condition is met, alternative if condition is not met)

df['result']=np.where((df.Type == 'A')&(df.P % 2 == 0),df.X 10,df.X 30)



  Type  P   X  result
0    A  1  20      50
1    A  2  30      40
2    B  1  50      80
3    B  3  45      75
4    A  4  60      70
5    B  5  70     100

CodePudding user response：

You have to iterate over the rows to calculate the result

result = [] 
for i, row in df.iterrows(): 
    if (row.Type == 'A'): 
        if row.P % 2 == 0: 
           result.append(row['X'] 10) 
        else: 
           result.append(row['X'] 30) 
    else: 
        if row.P % 2 == 0: 
           result.append(row['X'] 30) 
        else: 
           result.append(row['X'] 10)

# and then add the column
df['Result'] = result