Build a shareable sales dashboard with Python, pandas, Plotly, and Streamlit. The finished app loads and validates a CSV, filters records by date, region, and category, recalculates KPIs, renders interactive charts, shows filtered rows, and offers a CSV download. You can run it locally with streamlit run app.py and deploy the same project from GitHub.
What Streamlit is—and when to use it
Streamlit is an open-source Python framework for turning data and model code into browser-based applications without requiring HTML, CSS, JavaScript, React, or Flask. Its execution model is simple: when a user changes a widget, Streamlit normally reruns the script from top to bottom. That makes small analytical apps quick to build, but it also means data loading, state, caching, and expensive queries need deliberate design. See the Streamlit documentation.
Streamlit is a strong fit for exploratory data apps, internal dashboards, machine-learning demos, portfolio projects, lightweight reporting tools, and prototypes. A conventional front end, BI platform, Flask/FastAPI service, or more specialized dashboard framework is usually a better choice when you need highly customized consumer UX, complex client-side interactions, a public multi-tenant SaaS, sophisticated background jobs, or a full API.
What you will build
The example is a sales dashboard backed by data/sales.csv. The sample data is assumed to contain these columns:
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order_dateregioncategoryproductsalesprofitquantity
The interface will contain sidebar filters, sales and profit KPIs, a time-series chart, category and regional comparisons, a filtered data table, and a download button. Adapt the names and calculations to your own data model; never assume that one row is one order unless the source defines it that way.
Set up the project
Recommended structure
streamlit-dashboard/
├── app.py
├── data/
│ └── sales.csv
├── requirements.txt
├── README.md
└── .gitignoreStart with one file so the execution flow is easy to understand. When the app grows, move loading, metric, and chart functions into a src/ package and use Streamlit’s pages/ convention for multiple views.
Create an environment and install packages
python -m venv .venv
# macOS/Linux
source .venv/bin/activate
# Windows PowerShell
.venvScriptsActivate.ps1
pip install streamlit pandas plotlyPut the packages your app actually imports in requirements.txt:
streamlit
pandas
plotlyAfter testing, pin versions for reproducible deployment, for example streamlit==<tested-version>; do not copy untested version numbers into production.
Load and validate the data
Use a path based on the script location rather than a path from your laptop. Parse dates and numeric fields explicitly, check the schema, and stop with a useful message when the file is absent or malformed.
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from pathlib import Path
import pandas as pd
import streamlit as st
DATA_PATH = Path(__file__).parent / "data" / "sales.csv"
REQUIRED_COLUMNS = {
"order_date", "region", "category", "product",
"sales", "profit", "quantity",
}
@st.cache_data
def load_data(path: str) -> pd.DataFrame:
df = pd.read_csv(path)
missing = REQUIRED_COLUMNS - set(df.columns)
if missing:
raise ValueError(
"Dataset is missing required columns: "
+ ", ".join(sorted(missing))
)
df["order_date"] = pd.to_datetime(df["order_date"], errors="coerce")
for column in ["sales", "profit", "quantity"]:
df[column] = pd.to_numeric(df[column], errors="coerce")
return df.dropna(
subset=["order_date", "region", "category", "sales", "profit", "quantity"]
)
try:
df = load_data(str(DATA_PATH))
except FileNotFoundError:
st.error(f"Could not find the data file: {DATA_PATH}")
st.stop()
except ValueError as error:
st.error(str(error))
st.stop()st.cache_data is intended for serializable results such as DataFrames. It avoids reading and transforming the same data on every widget rerun. Use st.cache_resource instead for shared resources such as database connections or machine-learning models. The distinction and cache trade-offs are documented in Streamlit’s caching guide.
Create the page and sidebar filters
import streamlit as st
st.set_page_config(
page_title="Sales Dashboard",
page_icon="📊",
layout="wide",
)
st.title("Sales Dashboard")
st.caption("Explore sales performance by date, region, and category.")
st.sidebar.header("Filters")
regions = sorted(df["region"].dropna().unique())
categories = sorted(df["category"].dropna().unique())
selected_regions = st.sidebar.multiselect(
"Region", regions, default=regions
)
selected_categories = st.sidebar.multiselect(
"Category", categories, default=categories
)
min_date = df["order_date"].min().date()
max_date = df["order_date"].max().date()
selected_dates = st.sidebar.date_input(
"Order date",
value=(min_date, max_date),
min_value=min_date,
max_value=max_date,
)
filtered_df = df[
df["region"].isin(selected_regions)
& df["category"].isin(selected_categories)
].copy()
if len(selected_dates) == 2:
start_date, end_date = selected_dates
filtered_df = filtered_df[
filtered_df["order_date"].dt.date.between(start_date, end_date)
]
if filtered_df.empty:
st.warning("No records match the selected filters.")
st.stop()Apply every filter before calculating metrics or charts so the dashboard describes the visible selection. A cleared multiselect returns an empty list, and a date input can temporarily return one date rather than a two-date tuple; the checks above handle both cases.
Calculate and display KPIs
Use labels that identify the measure and format values for the relevant currency and geography. If the dataset has an order_id, count unique orders rather than rows.
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total_sales = filtered_df["sales"].sum()
total_profit = filtered_df["profit"].sum()
total_quantity = filtered_df["quantity"].sum()
profit_margin = total_profit / total_sales if total_sales else 0
col1, col2, col3, col4 = st.columns(4)
col1.metric("Sales", f"${total_sales:,.0f}")
col2.metric("Profit", f"${total_profit:,.0f}")
col3.metric("Quantity", f"{total_quantity:,.0f}")
col4.metric("Profit margin", f"{profit_margin:.1%}")The zero check prevents division errors. Also distinguish rows, line items, orders, and customers: len(filtered_df) is only an order count when each row represents exactly one order.
Add interactive charts
Sales over time
import plotly.express as px
sales_by_date = (
filtered_df.groupby("order_date", as_index=False)["sales"].sum()
)
sales_chart = px.line(
sales_by_date,
x="order_date",
y="sales",
title="Sales over time",
markers=True,
)
st.plotly_chart(sales_chart, use_container_width=True)Category and region comparisons
left, right = st.columns(2)
with left:
sales_by_category = (
filtered_df.groupby("category", as_index=False)["sales"]
.sum().sort_values("sales", ascending=False)
)
chart = px.bar(
sales_by_category, x="category", y="sales",
title="Sales by category", text_auto=".2s"
)
st.plotly_chart(chart, use_container_width=True)
with right:
profit_by_region = (
filtered_df.groupby("region", as_index=False)["profit"]
.sum().sort_values("profit", ascending=False)
)
chart = px.bar(
profit_by_region, x="region", y="profit",
title="Profit by region", text_auto=".2s"
)
st.plotly_chart(chart, use_container_width=True)| Question | Useful chart |
|---|---|
| How does a measure change over time? | Line chart |
| Which groups rank highest? | Bar chart |
| How are two numeric variables related? | Scatter plot |
| What is the distribution? | Histogram or box plot |
| What are the exact records? | Dataframe or table |
Use clear axis labels and units. Avoid pie charts with many categories, 3D charts for ordinary business data, and unexplained abbreviations.
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Show and download the filtered data
st.subheader("Filtered records")
st.dataframe(
filtered_df.sort_values("order_date", ascending=False),
use_container_width=True,
hide_index=True,
)
csv = filtered_df.to_csv(index=False).encode("utf-8")
st.download_button(
"Download filtered CSV",
data=csv,
file_name="filtered_sales.csv",
mime="text/csv",
)The download reflects the current filters, not necessarily the original file. Treat that as a data-export capability: do not expose confidential columns or records without appropriate access controls.
Understand reruns, caching, and state
Streamlit reruns the script when a widget changes or source code is updated. Keep loading and deterministic transformations cacheable, and make filtering and chart generation safe to execute repeatedly. st.cache_data returns cached data results; st.cache_resource is for shared resources and can expose the same object across sessions, so avoid mutating cached resources.
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Use st.session_state for per-user values that must survive reruns, such as a selected record, a multi-step workflow, or a page-specific toggle. It is not durable storage and should not replace a database. Caching can also produce stale data or memory pressure; choose an expiration or refresh strategy when freshness matters.
Run the app locally
- Place
app.pyand thedata/directory at the project root. - Activate the virtual environment.
- Install the packages listed in
requirements.txt. - Run
streamlit run app.py. - Open the local URL printed in the terminal if a browser does not open automatically.
A local absolute path such as /Users/name/Desktop/sales.csv may work only on one computer. The Path(__file__).parent pattern keeps the app portable.
Deploy to Streamlit Community Cloud
- Commit
app.py,requirements.txt, and any permitted sample data to a GitHub repository. - Confirm that every path is relative to the repository and that the entry-point file runs locally.
- Sign in to Streamlit Community Cloud with GitHub.
- Choose the repository, branch, and app file, then deploy.
- Read the deployment logs if the build or app fails.
Streamlit describes Community Cloud as a free service for creating, deploying, managing, and sharing apps, and says it connects to public and private GitHub repositories; most apps launch within a few minutes. Free hosting does not make every workload appropriate for the service: confidential or regulated data, guaranteed uptime, private networking, enterprise identity, and specialized infrastructure may require another deployment model. See the Community Cloud overview and deployment guide.
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Keep credentials out of Git
Never put passwords, API keys, or database credentials in Python, screenshots, query parameters, or a committed secrets file. For local development, create .streamlit/secrets.toml and add it to .gitignore:
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host = "example-host"
username = "example-user"
password = "example-password"import streamlit as st
password = st.secrets["database"]["password"]Enter deployment secrets through the app settings rather than committing the file. Follow Community Cloud secrets management and Streamlit’s general secrets guidance. If a credential has been pushed, revoke and replace it; deleting the latest copy does not erase its exposure from repository history.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use an API or database when a CSV is no longer enough
A local CSV is excellent for a tutorial, small static data, and reproducible portfolio demos. An API suits frequently changing external data. A database is more appropriate for larger datasets, controlled updates, multiple users, and centralized permissions.
For database-backed apps, use parameterized queries, limit data at the source, put credentials in secrets, cache connections with st.cache_resource where appropriate, and define how users refresh data. Streamlit documents connections to CSVs, APIs, and databases in Connecting to data. Its local filesystem should not be treated as permanent storage on Community Cloud.
Troubleshoot common failures
The app works locally but deployment fails
- Check that every imported package appears in
requirements.txt. - Check filename capitalization and replace absolute paths.
- Confirm the data file is committed and the selected entry point is correct.
- Add missing secrets through deployment settings.
- Read the build and runtime logs for the first error, not just the final message.
The app is slow
- Cache file reads and deterministic transformations with
st.cache_data. - Cache database connections with
st.cache_resource. - Filter in the database before downloading rows.
- Aggregate before plotting and limit very large tables.
- Do not mutate shared cached resources.
Filters show nothing
Display a warning and stop instead of rendering blank charts. Suggest broadening the date range or selecting more categories. Check for inconsistent whitespace and capitalization in categorical fields.
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Date filtering is wrong
Convert strings with pd.to_datetime before comparison, decide whether the end date is inclusive, handle invalid dates, and guard against a one-value date input before unpacking two dates.
Metrics are misleading
Verify the dataset grain and denominator. For example, if there is an order_id, use filtered_df["order_id"].nunique() for orders rather than counting line-item rows.
Choose the right tool and host
| Need | Likely choice |
|---|---|
| Python-first interactive analysis or prototype | Streamlit |
| Sequential exploration and narrative code | Notebook |
| Governed drag-and-drop reporting for many business authors | BI platform |
| Custom web UI or API service | Flask, FastAPI, or a separate front end |
| Highly component-driven Plotly dashboard | Dash may be preferable |
For hosting, Community Cloud is the simplest route for a public demo or student project. Streamlit in Snowflake is aimed at organizations already using Snowflake; its costs depend on app runtime and query-warehouse usage, as described in Snowflake’s billing documentation. Hugging Face Spaces can suit machine-learning demos and offers hardware tiers through its pricing page. Other hosting options involve compute, TLS, authentication, monitoring, backups, networking, and CI/CD decisions; see Streamlit’s deployment overview.
Frequently Asked Questions
Can I build a Streamlit dashboard without JavaScript?
Yes. Streamlit lets you create the interface and interactions primarily in Python, although its predefined layout and widget model are less customizable than a conventional front end.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhy does my Streamlit code run again after every filter change?
That is Streamlit’s normal rerun model. Cache expensive data or resource creation, and use session state for per-user values that must persist between reruns.
Should I use a CSV or a database?
Use a CSV for small, static, tutorial, or portfolio data. Move to an API or database when data changes frequently, grows large, needs controlled updates, or must support multiple users and permissions.
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