| 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) |
|
|
| |
| df1 = pd.read_csv('./data/ausprivauto0405.csv') |
| df2 = pd.read_csv('./data/swmotorcycle.csv') |
|
|
| st.title("Summary Statistics") |
|
|
| |
| 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. |
| """) |
|
|
|
|
| |
| 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. |
| """) |
|
|
|
|
