eda.py

import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from PIL import Image

def run():
    st.title('FIFA 2022 Player Rating Prediction')

    st.subheader('EDA untuk Analisa Dataset FIFA 2022')

    input_image = Image.open('bola.jpg')
    st.image(input_image, caption='FIFA')

    st.write('Developed by Lis')

    st.write('# Halo')
    st.write('## Halo')
    st.write('**Halo**') # bold
    st.write('*Halo*') # italic

    st.markdown('---')

    # show dataframe
    url = "https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv"
    data = pd.read_csv(url)
    st.dataframe(data)

    # membuat bar plot
    st.write('#### Plot AttackingWorkRate')
    fig = plt.figure(figsize=(15,5))
    sns.countplot(x='AttackingWorkRate', data=data)
    st.pyplot(fig)

    # membuat histogram
    st.write('#### Histogram of R  ating')
    fig2 = plt.figure(figsize=(15,5))
    sns.histplot(data['Overall'], bins = 30, kde = True)
    st.pyplot(fig2)

    # membuat histogram berdasarkan input user
    st. write('#### Histogram Berdasarkan Input User')
    option = st.selectbox('Pilih column: ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
    fig3 = plt.figure(figsize=(15,5))
    sns.histplot(data[option], bins = 30, kde = True)
    st.pyplot(fig3)

    # membuat plotly plot
    # membandingkan rating pemain bola dengan price-nya
    st. write('#### Plotly plot - ValueEUR vs Overall')
    fig4 = px.scatter(data, x='ValueEUR', y='Overall', hover_data=['Name','Age'])
    st.plotly_chart(fig4)

if __name__ == '__main__':
    run()