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import pandas as pd
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import numpy as np
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import streamlit as st
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import matplotlib.pyplot as plt
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import seaborn as sns
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import plotly.express as px
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def run():
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st.title("Exploratory Data Analysis FIFA 2022")
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st.write('Halaman ini berisi hasil eksplorasi terkait data analisis')
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st.write('---')
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link_gambar = 'data:image/jpeg;base64,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'
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st.image(link_gambar,
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caption = 'source: google.com')
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st.write('## Dataframe')
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data = pd.read_csv('dataset.csv')
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st.dataframe(data.head())
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st.write('''
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Federasi Sepak Bola Internasional (bahasa Prancis: Fédération Internationale de Football Association, disingkat FIFA, pengucapan bahasa Prancis: [\fi.fa\]) adalah badan pengendali internasional sepak bola. FIFA bermarkas di Zurich dan memiliki 211 anggota assoasi.
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FIFA didirikan di Paris pada 21 Mei 1904 dan merayakan hari jadinya yang ke-100 pada 2004. Pada April 2004, FIFA mengumumkan bahwa mereka memperkirakan akan meraup keuntungan sebesar $144 juta dari $1,64 miliar dalam pendapatan antara tahun 2003 dan 2006.
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Peta pembagian konfederasi anggota FIFA.
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FIFA juga mempromosikan sepak bola, mengatur transfer pemain antar tim, memberikan gelar Pemain Terbaik Dunia FIFA, dan menerbitkan daftar Peringkat Dunia FIFA setiap bulannya.
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''')
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st.write('## visualization')
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st.write('### Attack Work Rate Bar Chart')
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fig = plt.figure(figsize=(10,8))
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sns.countplot(x='AttackingWorkRate', data=data, palette='deep')
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st.pyplot(fig)
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st.write('**Insight:**')
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st.write('-Point 1:')
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st.write('-Point 2:')
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st.write('### Skill Distribution')
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opsi=st.selectbox('Choose Skill: ', ('PaceTotal', 'ShootingTotal', 'PassingTotal', 'DribblingTotal', 'DefendingTotal'))
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fig = plt.figure(figsize=(8,5))
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sns.histplot(data[opsi], bins=20, kde=True)
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st.pyplot(fig)
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st.write('### Relation Between Overall vs Value EUR')
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fig = px.scatter(data, x='ValueEUR', y='Overall')
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st.plotly_chart(fig)
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if __name__ == '__main__':
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run() |