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p = pd.read_csv("Occupancy_Estimation.csv", delimiter=",")
light=p[["Room_Occupancy_Count", "S1_Light","S2_Light","S3_Light","S4_Light","Time"]]
sound=p[["Room_Occupancy_Count", "S1_Sound","S2_Sound","S3_Sound","S4_Sound","Time"]]


p["Datetime"] = p["Date"] + ' '+ p["Time"]

p["Timestamp"]=pd.to_datetime(p["Time"], format = "%H:%M:%S").dt.time
p["Hour"]=pd.to_datetime(p["Time"], format = "%H:%M:%S").dt.hour


p["Datetime"] = pd.to_datetime(p["Datetime"], format = "%Y/%m/%d %H:%M:%S")
print(p["Datetime"])


def transformTime(x):
    if (x > 4) and (x <= 8):
        return 'Early Morning (Later than 4AM and sooner than or equal to 8 AM)'
    elif (x > 8) and (x <= 12 ):
        return 'Morning (Later than 8 AM and sooner than or equal to 12 AM)'
    elif (x > 12) and (x <= 16):
        return'Noon (Later than 12PM and sooner than or equal to 16 PM)'
    elif (x > 16) and (x <= 20) :
        return 'Eve (Later than 16PM and sooner than or equal to 20 PM)'
    elif (x > 20) and (x <= 24):
        return'Night (Later than 20 PM and sooner than or equal to 12AM)'
    elif (x <= 4):
        return'Late Night (Sooner than or equal to 4 AM)'
    

p['TimeOfTheDay'] = p["Hour"].apply(transformTime)