import numpy as np
import pandas as pd
import streamlit as st


import warnings
warnings.simplefilter(action='ignore', category=UserWarning)
warnings.simplefilter(action='ignore', category=FutureWarning)


import numpy as np
import pandas as pd
import streamlit as st

import warnings
warnings.simplefilter(action='ignore', category=UserWarning)
warnings.simplefilter(action='ignore', category=FutureWarning)

# Load datasets
df1 = pd.read_csv('./data/ausprivauto0405.csv')
df2 = pd.read_csv('./data/swmotorcycle.csv')

st.title("Summary Statistics")

# ---------------------- DATASET 1 ------------------------
st.header("Dataset 1: ausprivauto0405")

if st.checkbox("Show raw data (Dataset 1)"):
    st.subheader("Raw Data")
    st.write(df1)

if st.checkbox("Show summary statistics (Dataset 1)"):
    st.subheader('Categorical Variables')
    obj_cols = df1.select_dtypes(include='object')
    st.write(obj_cols.describe().T)

    st.subheader('Numerical Variables')
    num_cols = df1.select_dtypes(exclude='object')
    st.write(num_cols.describe().T)

st.markdown("""
## ausprivauto0405 
is a data frame of 9 columns and 67,856 rows:

-Exposure: The number of policy years.

-VehValue: The vehicle value in thousand of AUD.

-VehAge: The vehicle age group.

-VehBody: The vehicle body group.

-Gender: The gender of the policyholder.

-DrivAge: The age of the policyholder.

-ClaimOcc: Indicates occurence of a claim.

-ClaimNb: The number of claims.

-ClaimAmount: The sum of claim payments. 
""")


# ---------------------- DATASET 2 ------------------------
st.header("Dataset 2: swmotorcycle")

if st.checkbox("Show raw data (Dataset 2)"):
    st.subheader("Raw Data")
    st.write(df2)

if st.checkbox("Show summary statistics (Dataset 2)"):
    st.subheader('Categorical Variables')
    obj_cols2 = df2.select_dtypes(include='object')
    st.write(obj_cols2.describe().T)

    st.subheader('Numerical Variables')
    num_cols2 = df2.select_dtypes(exclude='object')
    st.write(num_cols2.describe().T)


st.markdown("""
## swmotorcycle 
is a data frame of 9 columns and 64,548 rows:

-OwnerAge: The owner age.

-Gender: The gender.

-Area: The type of area.

-RiskClass: The motorcycle class, a classification by the so called EV ratio, defined as (Engine
power in kW x 100) / (Vehicle weight in kg + 75), rounded to the nearest lower integer. The
75 kg represent the average driver weight. The EV ratios are divided into seven classes.

-VehAge: The Vehicle age, between 0 and 99.

-BonusClass: The bonusclass, taking values from 1 to 7. A new driver starts with bonus class 1;
for each claim-free year the bonus class is increased by 1. After the first claim the bonus is
decreased by 2; the driver can not return to class 7 with less than 6 consecutive claim free
years.

-Exposure: The number of policy years.

-ClaimNb: The number of claims.

-ClaimAmount: The sum of claim payments.
""")