diff --git a/01-CalculateSpecialBonus.py b/01-CalculateSpecialBonus.py
new file mode 100644
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+++ b/01-CalculateSpecialBonus.py
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+# Problem 1 :Calculate Special Bonus ( https://leetcode.com/problems/calculate-special-bonus/)
+
+import pandas as pd
+
+# solution 1: using a loop to iterate through the DataFrame
+def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
+    result = []
+    for i in range(len(employees)):
+        id = employees['employee_id'][i]
+        name = employees['name'][i]
+        salary = employees['salary'][i]
+        if (id%2==0) or (name[0]=='M'):
+            result.append([id, 0])
+        else: 
+            result.append([id, employees['salary'][i]])
+    df = pd.DataFrame(result, columns=(['employee_id', 'bonus'])).sort_values(by=['employee_id'])
+    return df
+
+# solution 2: using a loop to iterate through the DataFrame; just added the bonus column to the original DataFrame
+def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
+    employees['bonus'] = 0
+    for i in range(len(employees)):
+        id = employees['employee_id'][i]
+        name = employees['name'][i]
+        salary = employees['salary'][i]
+        if (id%2==0) or (name[0]=='M'):
+            employees['bonus'][i] = 0
+        else: 
+            employees['bonus'][i] = salary
+    return employees[['employee_id','bonus']].sort_values(by=['employee_id'])
+
+# solution 3: using a vectorized approach
+# Row-wise computation using DataFrame.apply() with a lambda function
+def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
+    employees['bonus'] = employees.apply(
+        lambda x: x['salary'] if x['employee_id']%2!=0 and not x['name'].startswith('M') else 0,
+        axis=1
+    )
+    return employees[['employee_id', 'bonus']].sort_values(by='employee_id')
+
+# solution 4: Fully vectorized approach using boolean indexing and Series.where()
+def calculate_special_bonus(employees: pd.DataFrame) -> pd.DataFrame:
+   employees['bonus'] = employees['salary'].where(
+       (~employees['name'].str.startswith('M')) & (employees['employee_id'] % 2 == 1), 0
+   )
+   return employees[['employee_id', 'bonus']].sort_values('employee_id')
+
+"""
+# Note:
+
+.str is used for vectorized string operations on a Series (e.g., employees['name'].str.startswith('M')).
+In apply with axis=1, x['name'] is a scalar string, so native startswith() works without .str.
+
+# The .where() method is used to replace values where the condition is False.
+It keeps the original values where the condition is True and replaces them with 0 where the condition is False.
+"""
diff --git a/02-FixNamesInATable.py b/02-FixNamesInATable.py
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+++ b/02-FixNamesInATable.py
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+# Problem 2 : Fix Names in a Table	(	https://leetcode.com/problems/fix-names-in-a-table/ )
+
+import pandas as pd
+
+# solution 1: manual implementation
+def fix_names(users: pd.DataFrame) -> pd.DataFrame:
+    users['name'] = users['name'].str[0].str.upper() + users['name'].str[1:].str.lower()
+    return users.sort_values(by=['user_id'])
+
+# solution 2: inbuilt method
+def fix_names(users: pd.DataFrame) -> pd.DataFrame:
+    users['name'] = users['name'].str.capitalize()
+    return users.sort_values(by=['user_id'])
+
+'''
+#Note: there is a .title(), but it is will capitalizes the first letter of each word.
+'''
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diff --git a/03-PatientsWithACondition.py b/03-PatientsWithACondition.py
new file mode 100644
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+++ b/03-PatientsWithACondition.py
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+# Problem 3 : Patients with a Condition ( https://leetcode.com/problems/patients-with-a-condition/)
+
+import pandas as pd
+
+def find_patients(patients: pd.DataFrame) -> pd.DataFrame:
+    result = []
+    for i in range(len(patients)):
+        p_id = patients['patient_id'][i]
+        p_name = patients['patient_name'][i]
+        cond = patients['conditions'][i]
+        for c in cond.split():
+            if c.startswith('DIAB1'):
+                result.append([p_id, p_name, cond])
+                break
+    return pd.DataFrame(result, columns=['patient_id','patient_name', 'conditions'])
+
+def find_patients(patients: pd.DataFrame) -> pd.DataFrame:
+    df = patients[patients['conditions'].str.startswith('DIAB1') | patients['conditions'].str.contains(' DIAB1')]
+    return df
+
+def find_patients(patients: pd.DataFrame) -> pd.DataFrame:
+    return patients[patients['conditions'].str.contains(r"(^|\s)DIAB1", regex=True)]
+
+"""
+# Note: isin matches the whole string and is not suitable for this case.
+r: raw string
+^: matches the start of the string
+\s: matches any whitespace character (space, tab, newline)
+DIAB1: the condition we are looking for
+The regex pattern r"(^|\s)DIAB1" ensures that "DIAB1" is either at the start of the string or preceded by a whitespace character.
+"""
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