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import pandas as pd

dt = {'Outlook': {'Overcast': 'Yes', 'Rain': {'Wind': {'Strong': 'No', 'Weak': 'Yes'}}, 'Sunny': {'Temperature': {'Cool': 'Yes', 'Hot': 'No', 'Mild': 'No'}}}}
dt = {'Odour': {'a': 'e', 'c': 'p', 'n': {'spore_print_color': {'b': 'No', 'Weak': 'Yes'}}, 'Sunny': {'Temperature': {'Cool': 'Yes', 'Hot': 'No', 'Mild': 'No'}}}}

df = pd.DataFrame(data=[['Sunny', 'Mild', 'Normal', 'Strong', 'Yes']],columns=['Outlook', 'Temperature', 'Humidity', 'Wind', 'Decision'])
print(df)
def fun(d, t):
    """
    d -- decision tree dictionary
    t -- testing examples in form of pandas dataframe
    """
    res = []
    for _, e in t.iterrows():
        res.append(predict(d, e))
    return res

def predict(d, e):
    """
    d -- decision tree dictionary
    e -- a testing example in form of pandas series
    """
    current_node = list(d.keys())[0]
    current_branch = d[current_node][e[current_node]]
    # if leaf node value is string then its a decision
    if isinstance(current_branch, str):
        return current_branch
    # else use that node as new searching subtree
    else:
        return predict(current_branch, e)

print(fun(dt, df))