import streamlit as st
def main():
st.title('웹 대시보드')
st.text('웹 대시보드 개발하기')
name = '홍길동' # 제 이름은 홍길동입니다.
print('제 이름은 {}입니다.'.format(name))
st.text('제 이름은 {}입니다.'.format(name))
st.header('이 영역은 헤더 영역')
st.subheader('서브헤더 영역')
st.success('작업이 성공했을때 사용')
st.warning('경고 문구 보여주고 싶을때 사용')
st.info('정보를 보여주고 싶을때 사용')
st.error('문제가 발생했을때 사용')
# 파이썬의 함수 사용을 보여주고 싶을때
st.help(sum)
st.help(len)
if __name__ == '__main__':
main()
import streamlit as st
import pandas as pd
# 이미지 처리를 위한 라이브러리
from PIL import Image
def main():
#1. 저장된 이미지파일을 화면에 표시하기
img = Image.open('data2/birds.jpg')
st.image(img)
st.image(img,use_column_width=True)
# 2. 인터넷상에있는 이미지 표시하기
# 이미지 찾아서 이미지 주소 복사
url =
'data:image/jpeg;base64,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'
st.image(url,use_column_width=True)
# 비디오
video_file = open('data2/secret_of_success.mp4','rb')
st.video(video_file)
# 오디오
audio_file = open('data2/song.mp3','rb')
st.audio(audio_file.read() , format = 'audio/mp3')
if __name__ == '__main__':
main()
from sqlalchemy import column
import streamlit as st
import pandas as pd
def main():
df= pd.read_csv('iris.csv')
# 버튼 만들기
#True가 됨
# if st.button('데이터 보기'):
# st.dataframe(df)
# 대문자 버튼을 만들고 버튼을 누르면 species 컬럼의 값들을 대문자로 변경
st.dataframe(df)
if st.button('대문자'):
df['species'] = df['species'].str.upper()
st.dataframe(df)
# 라디오 버튼: 여러개중 한개를 선택할때
my_order = ['오름차순 정렬','내림차순 정렬'] # 리스트를 먼저 만듦
status = st.radio('정렬방법 선택', my_order)
if status == my_order[0]: # petal_length 를 오름차순으로
st.dataframe(df.sort_values('petal_length')) # 정렬한것을 메모리에 저장해야 화면에 나오게 된다.
elif status == my_order[1]:
df.sort_values('petal_length', ascending=False, inplace= True)
st.dataframe(df)
#체크박스
if st.checkbox('헤드 5개 보기'):
st.dataframe(df.head())
else:
st.text('헤드를 숨겼습니다.')
# 셀렉트 박스: 여러개중 한개만 고른다.
langauge = ['Python','C','Java','Go','PHP']
my_choice = st.selectbox('좋아하는 언어 선택',langauge)
if my_choice == langauge[0]:
st.write('파이썬을 선택했습니다.')
elif my_choice == langauge[1]:
st.write('C를 선택했습니다.')
elif my_choice == langauge[2]:
st.write('자바를 선택했습니다.')
# 멀티셀렉트: 여러개중 여러개를 선택한다.
st.multiselect('좋아하는 언어 선택',langauge)
# 멀티셀렉트를 이용해서 특정 컬럼들만 가져오기
# 유저에게 iris의 컬럼들을 다 보여주고
# 유저가 선택한 컬럼들만 데이터프레임화면에 보여줄것
column_list = df.columns
choice_list = st.multiselect('컬럼을 고르세요',column_list)
st.dataframe(df[choice_list])
# 슬라이더: 숫자 조정에 사용
st.slider('나이',0,120, 30, 5)
# 익스펜더
with st.expander('Hello'):
st.text('안녕하세요')
st.dataframe(df)
if __name__ == '__main__':
main()
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