import numpy as np
import pandas as pd

df = pd.DataFrame({'Type': np.random.choice(['A', 'B', 'C', 'D'], 100),
                   'Var1': np.random-randn(100),
                   'Var2': np.linpace(30, 200, 100)})

# 1. Assignment depending on other columns
df['Var3'] = df.Var2 / df.Var1  # "Normal" assignment
df.assign(Var3 = df.Var2 / df.Var1)  # Assign statement

# 2. Assignment based on a conditional function:
df['CondVar'] = df.apply(lambda x: x['Var1'] * np.sin(x['Var2'] if x['Type'] == 'A' else 2, axis='columns')  # Via apply
df.assign(CondVar = lambda x: x['Var1'] * np.sin(x['Var2'] if x['Type'] == 'A' else 2, axis='columns')  # assign statement