Skip to main content

Charts

Use this recipe to visualize data using Streamlit's built-in chart components: area charts, line charts, and bar charts. This example demonstrates loading data from a Unity Catalog table and creating various business insights through different chart visualizations.

Code snippet​

Load data from a table​

app.py
import streamlit as st
from databricks import sql
from databricks.sdk.core import Config
from databricks.sdk import WorkspaceClient
import pandas as pd

cfg = Config()
w = WorkspaceClient()

# List available SQL warehouses
warehouses = w.warehouses.list()
warehouse_paths = {wh.name: wh.odbc_params.path for wh in warehouses}

# Connect to SQL warehouse
@st.cache_resource
def get_connection(http_path):
    return sql.connect(
        server_hostname=cfg.host,
        http_path=http_path,
        credentials_provider=lambda: cfg.authenticate,
    )

# Read table
def read_table(table_name, conn):
    with conn.cursor() as cursor:
        cursor.execute(f"SELECT * FROM {table_name} LIMIT 1000")
        return cursor.fetchall_arrow().to_pandas()

# Get data
warehouse_name = "your_warehouse_name"
table_name = "samples.nyctaxi.trips"

http_path = warehouse_paths[warehouse_name]
conn = get_connection(http_path)
df = read_table(table_name, conn)

# Process datetime columns
df["tpep_pickup_datetime"] = pd.to_datetime(df["tpep_pickup_datetime"])
df["tpep_dropoff_datetime"] = pd.to_datetime(df["tpep_dropoff_datetime"])
df["pickup_hour"] = df["tpep_pickup_datetime"].dt.hour
df["trip_duration_minutes"] = (df["tpep_dropoff_datetime"] - df["tpep_pickup_datetime"]).dt.total_seconds() / 60

Demand analysis: Trips by hour​

app.py
import streamlit as st

# Count trips by hour to understand demand patterns
hourly_demand = df["pickup_hour"].value_counts().sort_index()
st.bar_chart(hourly_demand)

peak_hour = hourly_demand.idxmax()
st.info(f"Peak demand hour: {peak_hour}:00 with {hourly_demand.max()} trips")

Revenue analysis: Average fare by hour​

app.py
import streamlit as st

# Analyze when fares are highest
avg_fare_by_hour = df.groupby("pickup_hour")["fare_amount"].mean()
st.line_chart(avg_fare_by_hour)

best_hour = avg_fare_by_hour.idxmax()
st.success(f"Best earning hour: {best_hour}:00")

Location analysis: Top pickup zones​

app.py
import streamlit as st

# Identify high-demand pickup locations
top_pickups = df["pickup_zip"].value_counts().head(15)
st.bar_chart(top_pickups)

Cumulative revenue over time​

app.py
import streamlit as st

# Track total revenue accumulation
revenue_df = df.set_index("tpep_pickup_datetime")[["fare_amount"]].sort_index()
revenue_df["cumulative_revenue"] = revenue_df["fare_amount"].cumsum()
st.area_chart(revenue_df["cumulative_revenue"])

Resources​

Permissions​

Your app service principal needs the following permissions:

  • CAN USE on the SQL warehouse
  • SELECT on the Unity Catalog table

See Unity Catalog privileges and securable objects for more information.

Dependencies​

requirements.txt
streamlit
databricks-sdk
databricks-sql-connector
pandas