CODEWITHSUNDEEP
pythonpandasfunctionif-statement
stvlam22
stvlam22

Reputation: 147

how to apply if conditional using def with multi parameter

I am new to def function , I am trying to get the logic in def function with multiple if condition. I want x,y,z to be flexible parameter so I can change parameter value in x,y,z. but i can't get the desired output. anyone help ?

df =

    date      comp  mark    value   score   test1
0   2022-01-01  a      1       10     100   
1   2022-01-02  b      2       20     200   
2   2022-01-03  c      3       30     300   
3   2022-01-04  d      4       40     400   
4   2022-01-05  e      5       50     500

Desired ouput =

        date    comp    mark    value   score   test1
0   2022-01-01  a          1    10       100    200
1   2022-01-02  b          2    20       200    400
2   2022-01-03  c          3    30       300    600
3   2022-01-04  d          4    40       400    4000
4   2022-01-05  e          5    50       500    5000

I can get the result use:

    def frml(df):
        if (df['mark'] > 3) and (df['value'] > 30):
            return df['score'] * 10
        else:
            return df['score'] * 2

df['test1'] = df.apply(frml,axis=1)

but i can't get the result use this: isn't the logic is the same?

 x = df['mark']
 y = df['value']
 z = df['score']

def frml(df):
    if (x > 3) and (y > 30):
        return z * 10
    else:
        return z * 2

df['test1'] = df.apply(frml,axis=1)

Upvotes: 0

Views: 79

Answers (1)

Panda Kim
Panda Kim

Reputation: 13212

you can use mask instead apply

cond1 = (df['mark'] > 3) & (df['value'] > 30)
df['score'].mul(2).mask(cond1, df['score'].mul(10))

output:

0     200
1     400
2     600
3    4000
4    5000
Name: score, dtype: int64

make output to test1 column

df.assign(test1=df['score'].mul(2).mask(cond1, df['score'].mul(10)))

result:

    date        comp    mark    value   score   test1
0   2022-01-01  a       1       10      100     200
1   2022-01-02  b       2       20      200     400
2   2022-01-03  c       3       30      300     600
3   2022-01-04  d       4       40      400     4000
4   2022-01-05  e       5       50      500     5000



It's possible to explain why your 2nd function doesn't work, but it's complicated.

Also, making your output don't need apply def func.

So tell you another way.


use mask or np.where or np.select instead apply def func

Upvotes: 1

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