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Introduction
When you combine your strength, making interactive web -based data -based data dashboards is much easier than ever before StreamlitFor, for, for,. PandasAnd From the plot. These three libraries work together without interruption to convert static datases to responsible, vulnerable applications – all of this without needing background in web development.
However, there is an important architecture difference to understand before our start. Unlike libraries such as metaphotleb or marine bourgeois that operate directly into the Gapter notebook, Streamlit standstone develops web applications that need to be run from the command line. You will write your code text -based IDE in VS code, save as a .PY fileAnd drive it using it Streamlit Run Filename.py. This change in script -based development from the notebook environment opens new possibilities for sharing and deploying your data applications.
In this Hand on Tutorial, you will learn how to make a full sales dashboard Two clear steps. We will only start with basic functionality using streamlines and pandas, then increase the dashboard with interactive concepts using the plot.
Setup
Install the desired packages:
pip install streamlit pandas plotlyCreate a new folder for your project and open it in the VS code (or your preferred text editor).
Step 1: Streamlit + Pandas Dashboard
Let’s just start making a functional dashboard using streamlets and pandas. This shows how the Streamllate Interactive Web Interface creates and how Pandas handles data filtering.
Create a file that says Step1_dashboard_basic.py:
import streamlit as st
import pandas as pd
import numpy as np
# Page config
st.set_page_config(page_title="Basic Sales Dashboard", layout="wide")
# Generate sample data
np.random.seed(42)
df = pd.DataFrame({
'Date': pd.date_range('2024-01-01', periods=100),
'Sales': np.random.randint(500, 2000, size=100),
'Region': np.random.choice(('North', 'South', 'East', 'West'), size=100),
'Product': np.random.choice(('Product A', 'Product B', 'Product C'), size=100)
})
# Sidebar filters
st.sidebar.title('Filters')
regions = st.sidebar.multiselect('Select Region', df('Region').unique(), default=df('Region').unique())
products = st.sidebar.multiselect('Select Product', df('Product').unique(), default=df('Product').unique())
# Filter data
filtered_df = df((df('Region').isin(regions)) & (df('Product').isin(products)))
# Display metrics
col1, col2, col3 = st.columns(3)
col1.metric("Total Sales", f"${filtered_df('Sales').sum():,}")
col2.metric("Average Sales", f"${filtered_df('Sales').mean():.0f}")
col3.metric("Records", len(filtered_df))
# Display filtered data
st.subheader("Filtered Data")
st.dataframe(filtered_df)
Let’s break the key streamlit methods used here:
- st.set_page_config () Browser tabs form title and layout
- St. Side Bar Creating left navigation panels for filters
- St.multisElect () Produces dropdown menu for user selection
- St. Columns () Formally form layout parts
- St. Metric () Displays large numbers with labels
- St.dataframe () Interactive Data Table offers
When the selection changes, these methods automatically handle the user’s conversation and trigger the app’s updates.
Run it from your terminal (or integrated terminal of VS code):
streamlit run step1_dashboard_basic.py
Your browser will open Showing an interactive dashboard.
Try changing filters in the Sidebar – see how the matrix and data tables automatically update! This demonstrates the nature of the streamllate’s reaction in pandas’s data, combined with manipulation capabilities.
Step 2: Add to the plot for interactive concepts
Now let’s increase your dashboard by adding an interactive chart of the plotley. This shows that these three libraries work without interruption. Let’s create a new file and call it step2_dashboard_plotly.py:
import streamlit as st
import pandas as pd
import plotly.express as px
import numpy as np
# Page config
st.set_page_config(page_title="Sales Dashboard with Plotly", layout="wide")
# Generate data
np.random.seed(42)
df = pd.DataFrame({
'Date': pd.date_range('2024-01-01', periods=100),
'Sales': np.random.randint(500, 2000, size=100),
'Region': np.random.choice(('North', 'South', 'East', 'West'), size=100),
'Product': np.random.choice(('Product A', 'Product B', 'Product C'), size=100)
})
# Sidebar filters
st.sidebar.title('Filters')
regions = st.sidebar.multiselect('Select Region', df('Region').unique(), default=df('Region').unique())
products = st.sidebar.multiselect('Select Product', df('Product').unique(), default=df('Product').unique())
# Filter data
filtered_df = df((df('Region').isin(regions)) & (df('Product').isin(products)))
# Metrics
col1, col2, col3 = st.columns(3)
col1.metric("Total Sales", f"${filtered_df('Sales').sum():,}")
col2.metric("Average Sales", f"${filtered_df('Sales').mean():.0f}")
col3.metric("Records", len(filtered_df))
# Charts
col1, col2 = st.columns(2)
with col1:
fig_line = px.line(filtered_df, x='Date', y='Sales', color="Region", title="Sales Over Time")
st.plotly_chart(fig_line, use_container_width=True)
with col2:
region_sales = filtered_df.groupby('Region')('Sales').sum().reset_index()
fig_bar = px.bar(region_sales, x='Region', y='Sales', title="Total Sales by Region")
st.plotly_chart(fig_bar, use_container_width=True)
# Data table
st.subheader("Filtered Data")
st.dataframe(filtered_df)
Run it from your terminal (or integrated terminal of VS code):
streamlit run step2_dashboard_plotly.py
Now you have a complete interactive dashboard!
The plot charts are fully interactive – you can rotate the data points, zoom in specific time periods, and even click on legends of legends to display/hide the data series.
How three libraries work together
This combination is powerful because every library works better than that.
Pandas Management of all data operations:
- Making and loading datases
- Filtering data based on user selection
- To collect the DATA data of concepts
- Handle data changes
Streamlit Web interface provides:
- Creates interactive widgets (multi -selected, sliders etc.)
- Automatically re -develop the entire app when users interact with the widget
- Handles the reaction programming model
- Manages the setting with columns and containers
From the plot Full, interactive concepts produces:
- Charts that consumers can rotate, zoom and discover
- Professional visible graphs with minimum code
- Automatic integration with streamlit’s reaction
Key development workflow
The development process follows a straightforward style. Start writing your code in a VS code or any text editor, save it as a .PY file. Next, run the application from your terminal Streamlit Run Filename.pyWhich opens your dashboard in the browser . When you edit and preserve your code, harmony automatically detect changes and offer to restart the application. Once you are satisfied with your dashboard, you can deploy it using Streamlit Community Cloud to share with others.
Next steps
Try these hikes:
Add real data:
# Replace sample data with CSV upload
uploaded_file = st.sidebar.file_uploader("Upload CSV", type="csv")
if uploaded_file:
df = pd.read_csv(uploaded_file)
Keep in mind that real datases will require specific pre -processing measures from your data structure. You will need to adjust the column names, handle the lost values ​​and edit the filter options to meet your original data fields. The sample code provides a template, but there will be unique requirements for cleaning and manufacturing each dataset.
More Types of Chart:
# Pie chart for product distribution
fig_pie = px.pie(filtered_df, values="Sales", names="Product", title="Sales by Product")
st.plotly_chart(fig_pie)
You can take advantage of the whole Gallery of Platley’s graphing capabilities.
Deploying your dashboard
Once your dashboard is working locally, sharing it with others is straightened through the Streamlit Community Cloud. First, push your code to a public gut hub repository, ensuring that adding this Requirements. txt File on your list of your dependents (streamlit, pandas, platli). Then see https://streamlit.io/cloudSign in with your Gut Hub Account, and select your reservoir. Streamllate will automatically build and deploy your app, providing a public URL that anyone can access. Free Tier supports multiple apps and handles appropriate traffic loads, which is excellent to share dashboards with colleagues or showcase your work in a portfolio.
Conclusion
Streamlines, pandas, and plots convert data analysis into interactive web applications from static reports. With just two compliments and handful of methods, you have created a complete dashboard that competitions expensive business intelligence tools.
This tutorial shows a significant change in how data scientists can share their work. Instead of sending static charts or operating a Gapter notebook, you can now create web applications that anyone can use via browser. The notebook -based analysis opens new opportunities for professionals to transfer data into script -based applications to make their insights more capable and efficient.
When you keep building with these tools, consider how interactive dashboards can change traditional reporting to your organization. You have learned the same principles here to handle real datases, complex calculations, and sophisticated concepts. Whether you are creating executive dashboards, research data tools, or client -facing applications, this three library collection provides a solid foundation for professional data applications.
Born in India and brought up in Japan, Vinod Data brings a global context of science and machine learning education. It eliminates the difference between emerging AI technologies and practical implementation for working professionals. Winode is focused on complex titles such as agent AI, performance correction, and AI engineering -learn learning accessories. He focuses on implementing the implementation of practical machine learning and direct sessions and personal guidance and guidance of data professionals.