Python - Insert column based on condition check from dictionary

I am trying to insert a column with values 'True' and 'False' based on a validation using a separate column. The issue I'm having is that the condition is dependent on another column, acting as the dictionary (which uses regex) key.

E.g.

Table I have:

Dictionary I have:

Result I want:

Any help would be appreciated!

I have tried playing around with numpy.where() but haven't had much luck.

Just One Line

df=pd.DataFrame({'Type':'TypeA','TypeB','TypeC','TypeD','TypeD'],
                 'Value':['a1111','1b111','11c11','111d1','1111e']})
re_dict={'A':r'\w\d\d\d\d','B':r'\d\w\d\d\d','C':r'\d\d\w\d\d','D':r'\d\d\d\w\d'}

df['result']=df.apply(lambda row:re.match(re_dict[row['Type'][-1]],row['Value'])!=None,axis=1)


	Type	Value	result
0	TypeA	a1111	True
1	TypeB	1b111	True
2	TypeC	11c11	True
3	TypeD	111d1	True
4	TypeD	1111e	False

You can use np.vectorize to create a lambda function that takes Value and pattern, and return True or False based on the output of re.match.

import re

# Create the dataframe
df = pd.DataFrame({
    "Type": ["TypeA", "TypeB", "TypeC", "TypeD", "TypeD"], 
    "Value": ["a1111", "1b111", "11c11", "111d1", "1111e"]
})

# Create the dictionary dataframe
df_dict = pd.DataFrame({
    "Column A": ["A", "B", "C", "D"], 
    "Column B": [r"\w\d\d\d\d", r"\d\w\d\d\d", r"\d\d\w\d\d", r"\d\d\d\w\d"]
})
# Add "Type" to the beginning of each value to match the "Type" column in the main dataframe
df_dict["Column A"] = "Type" + df_dict["Column A"]

# Merge the two dataframes to get corresponding regex pattern for "Type"
df = df.merge(df_dict, left_on="Type", right_on="Column A")

match_func = np.vectorize(lambda x, pattern: True if re.match(pattern, x) else False)  # Create a vectorized function to match the regex pattern
df["Result"] = match_func(df["Value"], df["Column B"])  # Add the result to the dataframe
df = df.drop(columns=["Column A", "Column B"])  # Drop the columns that are no longer needed
df

    Type  Value  Result
0  TypeA  a1111    True
1  TypeB  1b111    True
2  TypeC  11c11    True
3  TypeD  111d1    True
4  TypeD  1111e   False

With Series.map and re.match functions:

d = {'A':'\w\d\d\d\d','B':'\d\w\d\d\d','C':'\d\d\w\d\d','D':'\d\d\d\w\d'}
df['Result'] = df.Type.str[-1].map(d)
df['Result'] = df.apply(lambda x: bool(re.match(x.Result, x.Value)), axis=1)

   Type  Value  Result
0  TypeA  a1111    True
1  TypeB  1b111    True
2  TypeC  11c11    True
3  TypeD  111d1    True
4  TypeD  1111e   False