To create a bayesian Network for the given dataset in Python

! pip install pybbn


import pandas as pd # for data manipulation
import networkx as nx # for drawing graphs
import matplotlib.pyplot as plt # for drawing graphs

from pybbn.graph.dag import Bbn
from pybbn.graph.edge import Edge, EdgeType
from pybbn.graph.jointree import EvidenceBuilder
from pybbn.graph.node import BbnNode
from pybbn.graph.variable import Variable
from pybbn.pptc.inferencecontroller import InferenceController
#Set Pandas options to display more columns
pd.options.display.max_columns=50

df=pd.read_csv('weatherAUS.csv', encoding='utf-8')


df=df[pd.isnull(df['RainTomorrow'])==False]


df=df.fillna(df.mean())


df['WindGustSpeedCat']=df['WindGustSpeed'].apply(lambda x: '0.<=40'   if x<=40 else
                                                            '1.40-50' if 40<x<=50 else '2.>50')
df['Humidity9amCat']=df['Humidity9am'].apply(lambda x: '1.>60' if x>60 else '0.<=60')
df['Humidity3pmCat']=df['Humidity3pm'].apply(lambda x: '1.>60' if x>60 else '0.<=60')


print(df)


def probs(data, child, parent1=None, parent2=None):
    if parent1==None:
       
        prob=pd.crosstab(data[child], 'Empty', margins=False, normalize='columns').sort_index().to_numpy().reshape(-1).tolist()
    elif parent1!=None:
           
            if parent2==None:
                
                prob=pd.crosstab(data[parent1],data[child], margins=False, normalize='index').sort_index().to_numpy().reshape(-1).tolist()
            else:
               
                prob=pd.crosstab([data[parent1],data[parent2]],data[child], margins=False, normalize='index').sort_index().to_numpy().reshape(-1).tolist()
    else: print("Error in Probability Frequency Calculations")
    return prob

H9am = BbnNode(Variable(0, 'H9am', ['<=60', '>60']), probs(df, child='Humidity9amCat'))
H3pm = BbnNode(Variable(1, 'H3pm', ['<=60', '>60']), probs(df, child='Humidity3pmCat', parent1='Humidity9amCat'))
W = BbnNode(Variable(2, 'W', ['<=40', '40-50', '>50']), probs(df, child='WindGustSpeedCat'))
RT = BbnNode(Variable(3, 'RT', ['No', 'Yes']), probs(df, child='RainTomorrow', parent1='Humidity3pmCat', parent2='WindGustSpeedCat'))


bbn = Bbn() \
    .add_node(H9am) \
    .add_node(H3pm) \
    .add_node(W) \
    .add_node(RT) \
    .add_edge(Edge(H9am, H3pm, EdgeType.DIRECTED)) \
    .add_edge(Edge(H3pm, RT, EdgeType.DIRECTED)) \
    .add_edge(Edge(W, RT, EdgeType.DIRECTED))


join_tree = InferenceController.apply(bbn)

pos = {0: (-1, 2), 1: (-1, 0.5), 2: (1, 0.5), 3: (0, -1)}


options = {
    "font_size": 16,
    "node_size": 4000,
    "node_color": "white",
    "edgecolors": "black",
    "edge_color": "red",
    "linewidths": 5,
    "width": 5,}

n, d = bbn.to_nx_graph()
nx.draw(n, with_labels=True, labels=d, pos=pos, **options)


ax = plt.gca()
ax.margins(0.10)
plt.axis("off")
plt.show()