TypeError: only size-1 arrays can be converted to Python scalars during quadrature

Question

I'm trying to code the Heston Model in Python. In my final stages, I have to integrate a bunch of integrands. The problem is arising in these lines.

integrands_list_1 and integrands_list_2 are lists of lambda functions of variable phi, I have to integrate these functions to get my final result. When I pass a df of a single row I get the desired result however the problem arises when I try to pass a df with multiple rows.

In this version of code, I created a list of functions and used a loop to iterate through these functions and then integrate them, however, I get the TypeError as stated in the heading.

The error occurs in the P1 line where I get the error as mentioned earlier.

for integrand1, integrand2 in zip(integrands_list_1, integrands_list_2):
        P1 = 0.5 + (1/np.pi) * quad(integrand1, 0, 100)[0]
        P2 = 0.5 + (1/np.pi) * quad(integrand2, 0, 100)[0]
        results.append((P1, P2))

An example df is as follows:

[QUOTE_DATE]	[UNDERLYING_LAST]	[EXPIRE_DATE]	[STRIKE]	[DTE]	[C_LAST]	[DTB3]
2022-03-01	4305.46	2022-03-09	4000	8.0	379.71	0.0032
2022-03-01	4305.46	2022-03-09	4050	8.0	325.41	0.0032
2022-03-01	4305.46	2022-03-09	4140	8.0	171.22	0.0032
2022-03-01	4305.46	2022-03-09	4150	8.0	168.82	0.0032

The Full Error Message:

TypeError                                 Traceback (most recent call last)
Cell In[24], line 1
----> 1 df_8['Heston Initial'] = Heston_Model.heston_price(df_8, v_0, theta, sigma, rho, kappa)

 File ~/Desktop/Python/BS option pricing/Heston_Model.py:103, in heston_price(df, v_0, theta, sigma, rho, kappa)
    101 print(type(integrand1))
    102 print(integrand1)
--> 103 P1 = 0.5 + (1/np.pi) * quad(integrand1, 0, 100)[0]
    104 P2 = 0.5 + (1/np.pi) * quad(integrand2, 0, 100)[0]
    105 results.append((P1, P2))
TypeError: only size-1 arrays can be converted to Python scalars

Heston Price and Integrand Functions:

def heston_price(df, v_0, theta, sigma, rho, kappa):
    
    St = df['[UNDERLYING_LAST]']
    K = df['[STRIKE]']
    r = df['[DTB3]']
    t = df['[DTE]'] / 252  # Assuming [DTE] is in days

    ### Looping into List
    integrands_list_1 = []
    integrands_list_2 = []

    results = []
    
    for index, row in df.iterrows():
        
        phi_values = np.linspace(1, 100, 1000)
        
        integrands = heston_integrand(phi_values, df, v_0, theta, sigma, rho, kappa)
        integrands_list_1.append(integrands[0])
        integrands_list_2.append(integrands[1])

    for integrand1, integrand2 in zip(integrands_list_1, integrands_list_2):
        print(type(integrand1))
        print(integrand1)
        P1 = 0.5 + (1/np.pi) * quad(integrand1, 0, 100)[0]
        P2 = 0.5 + (1/np.pi) * quad(integrand2, 0, 100)[0]
        results.append((P1, P2))

    
    C = St * P1 - K * np.exp(-r*t) * P2
    
    return C

def heston_integrand(phi,df, v_0, theta, sigma, rho, kappa):
    St = df['[UNDERLYING_LAST]']
    K = df['[STRIKE]']
    r = df['[DTB3]']
    t = df['[DTE]'] / 252
    
    integrand1 = lambda phi: np.real(np.exp(-1j * phi * np.log(K)) * 
                        heston_char(St, v_0, t, r, kappa * theta, 0.5, kappa - rho * sigma, rho, sigma, phi) / (1j * phi))
    
    integrand2 = lambda phi: np.real(np.exp(-1j * phi * np.log(K)) * 
                        heston_char(St, v_0, t, r, kappa * theta, -0.5, kappa, rho, sigma, phi) / (1j * phi))

    return integrand1, integrand2