Streamlit 기본 문법(2) : 데이터프레임 시각화

데이터프레임 시각화

1. 필요한 라이브러리 import

# -*- coding:utf-8 -*-
import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import plotly.graph_objects as go
from plotly.subplots import make_subplots

 

2. 함수 작성 : 데이터프레임 불러오기, 요약 metrix 보여주기

def main():
	## 데이터 프레임
    st.title('데이터 프레임')
    df = sns.load_dataset('iris')
    st.dataframe(df.head(50)) #옵션이 다양함, documents 참고
    st.write(df.head(50))

    ## metric(전체 요약 현황판; 보통 대시보드 상단에 배치)
    tips = sns.load_dataset('tips')
    tip_max = tips['tip'].max()
    tip_min = tips['tip'].min()
    st.metric(label = "Max Tip", value = tip_max, delta= tip_max - tip_min)
    st.table(tips.describe())

 

3. 시각화 라이브러리 활용하기 : matplotlib, seaborn, plotly

    ## visualization with matplotlib
    # 남여 데이터 구분
    m_tips = tips.loc[tips['sex'] == 'Male', :]
    f_tips = tips.loc[tips['sex'] == 'Female', :]
    st.dataframe(f_tips)

    fig, ax = plt.subplots(ncols=2, figsize=(10, 6), sharex=True, sharey=True)
    ax[0].scatter(x = m_tips['total_bill'], y = m_tips['tip'])
    ax[0].set_title('Male')
    ax[1].scatter(x = f_tips['total_bill'], y = f_tips['tip'])
    ax[1].set_title('Female')
    fig.supxlabel('Total Bill($)')
    fig.supylabel('Tip($)')
    st.pyplot(fig)
	
    #구분선 긋기
	st.write('*' * 50)

    ## visualization with seaborn
    # 남여 데이터 구분
    m_tips = tips.loc[tips['sex'] == 'Male', :]
    f_tips = tips.loc[tips['sex'] == 'Female', :]
    fig1, ax = plt.subplots(ncols=2, figsize=(10, 6), sharex=True, sharey=True)
    sns.scatterplot(data= m_tips, x = 'total_bill', y = 'tip', hue= 'smoker', ax= ax[0]) #이것만 sns 코드
    ax[0].set_title('Male')
    ax[0].set_xlabel('Total_Bill')

    fig1.supxlabel('Total Bill($)')
    fig1.supylabel('Tip($)')
    st.pyplot(fig1)

    st.write('*' * 50)

    ## visualization with plotly
    fig = make_subplots(rows = 1,
                        cols = 2,
                        subplot_titles=('Male', 'Female'),
                        shared_yaxes=True,
                        shared_xaxes=True,
                        x_title='Total Bill($)'
                        )
    fig.add_trace(go.Scatter(x = m_tips['total_bill'], y = m_tips['tip'], mode='markers'), row=1, col=1)
    fig.add_trace(go.Scatter(x = f_tips['total_bill'], y = f_tips['tip'], mode='markers'), row=1, col=2)
    fig.update_yaxes(title_text="Tip($)", row=1, col=1)
    fig.update_xaxes(range=[0, 60])
    fig.update_layout(showlegend=False)
    
    # Display visualization
    st.plotly_chart(fig, use_container_width=True)

if __name__ == "__main__":      #실행관련 문법
    main()

matplotlib

seaborn

plotly