输入输出：gr.Image(图像), gr.Textbox(文本框), gr.DataFrame(数据框), gr.Dropdown(下拉选项), ...
if __name__ == '__main__': df = pd.read_csv('file_path') st.title('Datetime Filter') filtered_df = df_filter('Move sliders to filter dataframe',df) column_1, column_2 = st.beta_columns(2) with column_1: st.title('Data Frame') st.write(filtered_df) with column_2: st....
df = pd.read_csv('file_path') st.title('Datetime Filter') filtered_df = df_filter('Move sliders to filter dataframe',df) column_1, column_2 = st.beta_columns(2) with column_1: st.title('Data Frame') st.write(filtered_df) with column_2: st.title('Chart') st.line_chart(filte...
title('Datetime Filter') filtered_df = df_filter('Move sliders to filter dataframe',df) column_1, column_2 = st.beta_columns(2) with column_1: st.title('Data Frame') st.write(filtered_df) with column_2: st.title('Chart') st.line_chart(filtered_df['value']) st.markdown(...
# Some numberinthe range0-23hour_to_filter=st.slider('hour',0,23,17)filtered_data=data[data[DATE_COLUMN].dt.hour==hour_to_filter]st.subheader('Map of all pickups at %s:00'%hour_to_filter)st.map(filtered_data)dataframe=pd.DataFrame(np.random.randn(10,20),columns=('col %d'%ifor...
我们还可以通过显示Dataframe来展示过滤后数据集的所有数据，也就是显示详情数据。#a dividing linest.divider()#showing the dataframest.dataframe(df_selection,hide_index=True,column_config={ # we can also config the formatting of a given column "ID": st.column_config.NumberColumn( #show th...
#filter the dataframe using ord_level and role_title df_filter = df_sub[(df_sub["Assigned Role"].str.contains( role_title ,case=False,na=False)) & (df_sub["Career Level To"].isin(org_level))] 构建导航栏 Streamlit虽然允许我们控制元素布局,但是它的控制项还是比较简单,比如菜单栏必须要放...
(七)解析Streamlit的数据元素:探索st.dataframe、st.data_editor、st.column_config、st.table、st.metric和st.json的神奇之处
=24, range=(0,24))[0] # 将时间按照2小时绘制频率图st.subheader('TimehisChart')st.bar_chart(hist_values)# Slider组件hour_to_filter= st.sidebar.slider('hour',0,23,17) # 此时并未关联某个模块st.subheader('MapwithSlider')st.map(data[data[DATE_COLUMN].dt.hour == hour_to_filter])...
#filter the dataframe using ord_level and role_title df_filter = df_sub[(df_sub["Assigned Role"].str.contains( role_title ,case=False,na=False)) & (df_sub["Career Level To"].isin(org_level))] 1. 2. 3. 4. 5. 6. 7. ...