How to convert dataframe elements into conditions?

I have a dataframe and I want to convert one column into conditions.

For example, here is a sample dataframe:

df=pd.DataFrame({'a':['>28','27','<26'],'b':['1','2','3']})

df
     a  b
0  >28  1
1   27  2
2  <26  3

I want to generate a series of if statements to get ideal b value:

if a > 28:
    b=1
elif a=27:
    b=2
elif a < 26:
    b=3

How can I do that? In my dataframe, all of elements are stored as string.

I was trying to use the iloc() function to select, but it can't deal with range conditions (>)

If there an elegant way to do this? or do I have to manually type all the conditions?

If you modify slightly your column 'a', you can use numexpr:

from numexpr import evaluate

a = 10
expr = 'a' + df['a'].mask(df['a'].str.isdigit(), other='==' + df['a'])
mask = expr.apply(evaluate, global_dict=globals(), local_dict=locals())

b = pd.to_numeric(df.loc[mask, 'b'], errors='coerce').head(1).squeeze() if any(mask) else None

Output:

>>> b
3

>>> expr
0     a>28
1    a==27
2     a<26
Name: a, dtype: object

>>> mask
0    False
1    False
2     True
Name: a, dtype: bool

This iterates over the rows in the DataFrame and performs an if condition based on column a, you can replace the print statement at the bottom with your desired goal, which was unclear to me.
import pandas as pd

df = pd.DataFrame({'a': ['>28', '27', '<26'], 'b': ['1', '2', '3']})
#Iterate over rows in dataframe
for index, row in df.iterrows():
    a_value = row['a']

    #Seperating operators from integers for conditions
    if '<' or '>' in a_value:
        operator = a_value[0]
        num = a_value[1:]
    else:
        #adding equal sign for equal conditions
        operator = '='
        num = a_value

    #evaluating condition
    condition = f'a {operator} {num}'
    if eval('condition'):
        #Do something here (just printing b to show the condition is being met
        print(condition, row['b'])

For fun, you can try this :

In [1]: import pandas as pd # added ------v

In [2]: df = pd.DataFrame({'a': ['>28', '==27', '<26'], 'b': ['1', '2', '3']})

In [3]: print(df)
      a  b
0   >28  1
1  ==27  2
2   <26  3

In [4]: s = "\n".join([
   ...:     f"if a{a}:\n\tb = {b}" if idx == 0 else f"elif a{a}:\n\tb = {b}"
   ...:     for idx, (a, b) in enumerate(zip(df["a"], df["b"]))
   ...: ]) # s is a multi-line string

In [5]: print(s)
if a>28:
        b = 1
elif a==27:
        b = 2
elif a<26:
        b = 3

In [6]: a = 10

In [7]: exec(s)

In [8]: print(b)
3