apply different function on each row

Question

I have a dataframe with 2 columns field and value(number of rows maximum 10). I need to perform some checks depending on the field(ie need to apply different function on each row) and store its result in status column. Below is sample:

data = {
    'field': ['a', 'b'],
    'value': [5, 20],
}

df = pd.DataFrame(data)
print('Initial DF')
print(f"{df=}")

def _check_field_a(value):
    _min = 1
    _max = 10

    if _min <= value <= _max:
        return True
    return False

def _check_field_b(value):
    
    values = [10, 20, 30, 40]

    if value in values:
        return True
    return False

func = [_check_field_a, _check_field_b]

df['status'] = df.apply(lambda row: func[row.name](row['value']), axis=1)

print('After check DF')
print(f'{df=}')

output

Initial DF
df=  field  value
0     a      5
1     b     20

After check DF
df=  field  value  status
0     a      5    True
1     b     20    True

The above code is working, just wondering is there any other better way to achieve the same?

Edit-1
Getting inspired from all below answers, I have modified the code but currently not working.

data = {
    'field': ['a', 'b', 'c'],
    'value': [5, 20, 80],
}

df = pd.DataFrame(data)

print('Initial DF')
print(f"{df=}")

conditions = {
    'a': {'values': (1, 10), 'check_type': 'between'},
    'b': {'values': [10, 20, 30, 40], 'check_type': 'isin'},
    'c': {'values': (50, 100), 'check_type': 'between'},
}

df['status'] = False

for key, condition in conditions.items():
    if condition['check_type'] == 'between':
        df['status'] = df.loc[df['field'] == key].between(condition['values'])
    elif condition['check_type'] == 'isin': 
        df['status'] = df.loc[df['field'] == key].isin(condition['values'])

print('After check DF')
print(f'{df=}')

Edit-2
Using np.select, This is working and so can add as many fields easily

data = {
    'field': ['a', 'b', 'c'],
    'value': [5, 20, 80],
}

df = pd.DataFrame(data)

print('Initial DF')
print(f"{df=}")

condlist = [df['field'] == 'a', df['field'] == 'b', df['field'] == 'c']
choicelist = [df['value'].between(1,10), df['value'].isin([10,20,30,40]), df['value'].between(50,100)]

df['status'] = np.select(condlist, choicelist, False)

print('After check DF')
print(f'{df=}')

Asked By: winter

||

Source

Answer 1

you can also use a dictionary 

import pandas as pd

data = {
    'field': ['a', 'b'],
    'value': [5, 20],
}

df = pd.DataFrame(data)
print('Initial DF')
print(df)

def check_field_a(value):
    return 1 <= value <= 10

def check_field_b(value):
    return value in [10, 20, 30, 40]

# Map fields to their respective functions
check_functions = {'a': check_field_a, 'b': check_field_b}


df['status'] = df.apply(lambda row: check_functions[row['field']](row['value']), axis=1)

print('nAfter check DF')
print(df)

Answered By: Ugochukwu Obinna

Answer 2

For your two specific functions, you can vectorize them:

df['output'] = np.where(
    df['field'] == 'a',
    df['value'].between(1,10), # change min,max here if needed
    df['value'].isin([10,20,30,40])
)

Answered By: Quang Hoang

Answer 3

You can use np.where() with multiple conditions.

a_min = 1
a_max = 10
b_vals = [10,20,30,40]

df['status'] = np.where(
                        ((df['field']=='a') & (df['value'].between(a_min, a_max))) |
                        ((df['field']=='b') & (df['value'].isin(b_vals))),
                        True,
                        False
                       )

Answered By: amance

apply different function on each row

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