Plotting Markowitz Efficient Frontier with Python

Question

I'm trying to Plott the Markowitz Efficient Frontier with Python but I'm not getting the desired bullet shape that is usually found and I don't know what I might have done wrong, any help is much appreciated !

(I'm using Average Equal daily Weighted Returns as input data)

the link from which I'm trying to plott the Markowitz Efficient Frontier: https://towardsdatascience.com/python-markowitz-optimization-b5e1623060f5

enter image description here

import pandas as pd
import numpy as np


df = pd.read_excel (r'C:\Users\brade\Desktop\Python\49_Industry_Portfolios_Daily.xlsx', skiprows=9, nrows= 25187, index_col=0, parse_dates=True)


df.info()
print (df)


np.random.seed(42)
num_ports = 6000

all_weights = np.zeros((num_ports, len(df.columns)))
ret_arr = np.zeros(num_ports)
vol_arr = np.zeros(num_ports)
sharpe_arr = np.zeros(num_ports)

for x in range(num_ports):
    
    # Weights
    weights = np.array(np.random.random(49))
    weights = weights/np.sum(weights)
    
    # Save weights
    all_weights[x,:] = weights
    
    # Expected return
    ret_arr[x] = np.sum((df.mean() * weights * 252))
    
    # Expected volatility
    vol_arr[x] = np.sqrt(np.dot(weights.T, np.dot(df.cov()*252, weights)))
    
    # Sharpe Ratio
    sharpe_arr[x] = ret_arr[x]/vol_arr[x]

#Maximum SHarpe Ratio

    print('Max sharpe ratio in the array: {}'.format(sharpe_arr.max()))
    print("Its location in the array: {}".format(sharpe_arr.argmax()))
#Max sharpe ratio in the array: -8.525157669319496
#Its location in the array: 5185

#get the allocation in this max
    print(all_weights[5185,:])

    max_sr_ret = ret_arr[sharpe_arr.argmax()]
    max_sr_vol = vol_arr[sharpe_arr.argmax()]

#Max sharpe ratio in the array: -8.525157669319496
#Its location in the array: 5185
#[0.01117167 0.03558722 0.00194414 0.03362501 0.01413863 0.03196054
# 0.02553783 0.0291854  0.01097694 0.00207069 0.0011559  0.00010691
# 0.0016922  0.03941764 0.03785089 0.01865089 0.0390962  0.01874136
# 0.01186381 0.00486033 0.02981664 0.03378699 0.03989109 0.00922282
# 0.03618472 0.0004863  0.0125648  0.03059016 0.01388772 0.01990928
# 0.01206905 0.01063152 0.01711765 0.01018931 0.01223478 0.00130421
# 0.02504012 0.03362276 0.02425936 0.02570623 0.03723166 0.02867486
# 0.01846097 0.03365429 0.02681322 0.03384932 0.00382821 0.02903587
# 0.0203019 ]


from matplotlib import pyplot as plt

plt.figure(figsize=(12,8))
plt.scatter(vol_arr, ret_arr, c=sharpe_arr, cmap='viridis')
plt.colorbar(label='Sharpe Ratio')
plt.xlabel('Volatility')
plt.ylabel('Return')
plt.scatter(max_sr_vol, max_sr_ret,c='red', s=50) # red dot
plt.show()

########################################################################

def get_ret_vol_sr(weights):
    weights = np.array(weights)
    ret = np.sum(df.mean() * weights) * 252
    vol = np.sqrt(np.dot(weights.T, np.dot(df.cov()*252, weights)))
    sr = ret/vol
    return np.array([ret, vol, sr])

def neg_sharpe(weights):
# the number 2 is the sharpe ratio index from the get_ret_vol_sr
    return get_ret_vol_sr(weights)[2] * -1

def check_sum(weights):
    #return 0 if sum of the weights is 1
    return np.sum(weights)-1

cons = ({'type':'eq','fun':check_sum})
bounds = ((0,1), (0,1), (0,1), (0,1))
init_guess = [0.25,0.25,0.25,0.25]

opt_results = minimize(neg_sharpe, init_guess, method='SLSQP' ,bounds=bounds, constraints=cons)
print(opt_results)