Streamlit 入门教程：构建一个Dashboard

 import pandas as pd
 import numpy as np
 import plotly.express as px
 import streamlit as st
 
 def dateAge(x):
     #function for generating future bucket grouping
     a = (x - np.datetime64("today","D")) / np.timedelta64(1,"D")
     if a <= 0:
         y = "0 or Less"
     elif a >0 and a <= 5:
         y = "5 days"
     elif a > 5 and a <= 14:
         y = "6 to 14 days"
     elif a > 14 and a <= 30:
         y = "15 to 30 days"
     else:
         y = "over 30 days"
     return y
 
 #built as a function for cache use with StreamLit later
 def getData():
     x = pd.read_csv(r'C:\Users\aryan.sinanan\Desktop\Python\raw_data\demand.csv')
    return x
 
 #assign data to df variable
 df = getData()
 
 #Set Org level max and role title or partial name you are looking for
 org_level = ["6","7","8"]
 role_title = "Data"
 
 #Datatype convert to date from dd-MMM-yy
 df["Resource Start Date"] = pd.to_datetime(df["Resource Start Date"],format="%d-%b-%y")
 df["Resource End Date"] = pd.to_datetime(df["Resource End Date"],format="%d-%b-%y")
 
 #Define Future Bucket
 df["Date Bucket"] = df["Resource Start Date"].apply(dateAge)
 
 #clean up Location names
 df.loc[df["Role Work Location"] == "melbourne", "Role Work Location"] = "Melbourne"
 df.loc[df["Role Work Location"] == "canberra", "Role Work Location"] = "Canberra"
 
 #rename columns 
 df.rename(columns={
     "Project Has Security/ Nationality Restriction":"Clearance Required",
     "Resource Start Date":"Start Date",
     "Resource End Date":"End Date",
     "Role ID":"ID",
     "Role Title":"Title",
     "Role Description":"Description",
     "Role Talent Segment":"Talent Segment",
     "Role Career Level From":"Career Level From",
     "Role Career Level To":"Career Level To",
     "Role Work Location":"Work Location",
     "Role Location Type":"Location Type",
     "Role Fulfillment Entity L3":"Fulfillment Entity L3"
     }, inplace = True)
 
 #drop the unncessary columns
 df_sub = df.loc[:,("ID","Clearance Required","Start Date","End Date","Date Bucket","Title","Description","Talent Segment","Assigned Role","Career Level To","Work Location","Location Type","Role Primary Contact","Role Primary Contact\n(Email ID)")]
 
 #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))]

 #title
 st.markdown("# Roles Dashbaord")
 
 #defining side bar
 st.sidebar.header("Filters:")
 
 #placing filters in the sidebar using unique values.
 location = st.sidebar.multiselect(
     "Select Location:",
     options=df_filter["Work Location"].unique(),
     default=df_filter["Work Location"].unique()
 )
 
 #placing filters in the sidebar using unique values.
 work_type = st.sidebar.multiselect(
     "Select Work Type:",
     options=df_filter["Location Type"].unique(),
     default=df_filter["Location Type"].unique()
 )

 #taking the filtered dataframe created in a previous step and applying a query
 df_selection = df_filter.query(
     "`Work Location`== @location & `Location Type` == @work_type"
 )
 
 #defining our metrics
 total_roles = df_selection["ID"].value_counts().sum()
 bucket_0 = df_selection[df_selection["Date Bucket"]=="0 or Less"].value_counts().sum()
 bucket_5 = df_selection[df_selection["Date Bucket"]=="5 days"].value_counts().sum()
 bucket_14 = df_selection[df_selection["Date Bucket"]=="6 to 14 days"].value_counts().sum()
 bucket_30 = df_selection[df_selection["Date Bucket"]=="15 to 30 days"].value_counts().sum()
 bucket_31 = df_selection[df_selection["Date Bucket"]=="over 30 days"].value_counts().sum()
 
 #placing our metrics within columns in the dashboard
 col1,col2,col3,col4,col5,col6=st.columns(6)
 col1.metric(label="No. Roles",value=total_roles)
 col2.metric(label="Already Started",value=bucket_0)
 col3.metric(label="In 5 Days",value=bucket_5)
 col4.metric(label="In 14 Days",value=bucket_14)
 col5.metric(label="In 30 Days",value=bucket_30)
 col6.metric(label="Over 30 Days",value=bucket_31)

 #a dividing line
 st.divider()
 
 #dataframe for chart
 df_1 = df_selection.groupby(["Work Location","Location Type"])["Work Location"].value_counts().reset_index()
 
 #defining the chart 
 fig_count_location_type = px.bar(df_1,x="Work Location",y="count",color="Location Type",title="Role by Location and Type - Plotly")
 
 #displaying the chart on the dashboard
 st.plotly_chart(fig_count_location_type, use_container_width=True)

 #a dividing line
 st.divider()
 
 #showing the dataframe
 st.dataframe(df_selection,hide_index=True,column_config={
     # we can also config the formatting of a given column
     "ID": st.column_config.NumberColumn(
         #show the ID as a number no formatting
         format="%d"
     ),
     #set the formatting of the dateColumn to dd-MMM-yyyy
     "Start Date": st.column_config.DateColumn(
         format="DD-MMM-YYYY"
     ),
     #set the formatting of the dateColumn to dd-MMM-yyyy    
     "End Date": st.column_config.DateColumn(
         format="DD-MMM-YYYY"
     ),
     #reduce the column size to medium
     "Title": st.column_config.TextColumn(
         width="medium"
     ),
     #reduce the column size to medium
     "Description": st.column_config.TextColumn(
         width="medium"
     )
 })