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This video demonstrates how to use the Streamlit Dashboard Generator template.
from google_sheet_integration import load_google_sheet_data
import streamlit as st
# Use st.cache_data for caching to avoid deprecation warning
import pandas as pd
import numpy as np
import pydeck as pdk
import plotly.express as px
def load_data(session_state):
if 'df' not in session_state:
session_state.df = pd.DataFrame(columns=['Date', 'Value'])
return session_state.df
def add_data(session_state, date, value):
new_data = {'Date': date, 'Value': value}
session_state.df = pd.concat([session_state.df, pd.DataFrame([new_data])], ignore_index=True)
def main():
st.title("Interactive Dashboard")
st.markdown("### Dashboard dataset with date time series i.e 1st column contain date time + bonus to use interactive map add data_longitude and data_latitude columns in your csv files")
session_state = st.session_stateFrequently Asked Questions
How can businesses benefit from using the Streamlit Dashboard Generator?
The Streamlit Dashboard Generator offers numerous benefits for businesses. It allows for quick and easy creation of interactive dashboards from various data sources, including manual entry, Google Sheets, and CSV files. This versatility enables businesses to visualize and analyze their data in real-time, facilitating data-driven decision-making. The template's ability to generate different chart types (line, bar, and area charts) and display geographical data on maps makes it particularly useful for businesses dealing with time-series data or location-based information.
Can the Streamlit Dashboard Generator be customized for specific industry needs?
Absolutely! The Streamlit Dashboard Generator is highly customizable and can be adapted to various industry-specific needs. For example, a retail business could use it to track sales data over time and across different locations, while a weather monitoring service could utilize it to visualize temperature and humidity trends. The template's flexibility allows for easy modification of data input methods, chart types, and map visualizations to suit particular industry requirements.
What are some potential applications of the Streamlit Dashboard Generator in the financial sector?
In the financial sector, the Streamlit Dashboard Generator can be incredibly useful for various applications. It can be used to create dashboards for monitoring stock prices, visualizing market trends, tracking investment portfolio performance, or analyzing economic indicators over time. The ability to integrate data from Google Sheets and CSV files makes it easy for financial analysts to update and visualize data from multiple sources, while the interactive map feature could be used to display geographical financial data, such as regional sales or investment distributions.
How can I add custom styling to the charts generated by the Streamlit Dashboard Generator?
To add custom styling to the charts in the Streamlit Dashboard Generator, you can use Plotly Express instead of Streamlit's built-in chart functions. Here's an example of how you can modify the code to create a custom-styled line chart:
```python import plotly.express as px
# Assuming 'df' is your DataFrame fig = px.line(df, x='Date', y='Value', title='Custom Styled Line Chart') fig.update_layout( font_family="Arial", font_color="blue", title_font_family="Times New Roman", title_font_color="red", legend_title_font_color="green" ) st.plotly_chart(fig) ```
This code creates a line chart with custom font families and colors for different elements of the chart.
How can I extend the Streamlit Dashboard Generator to include more advanced data processing capabilities?
The Streamlit Dashboard Generator can be extended to include more advanced data processing capabilities by incorporating additional Python libraries and functions. For example, you could add data cleaning and transformation features using pandas. Here's a simple example of how you might add a moving average calculation to the template:
```python import pandas as pd
def calculate_moving_average(df, window=7): df['Moving Average'] = df['Value'].rolling(window=window).mean() return df
# In the main function, after loading the data: if not df.empty: df = calculate_moving_average(df) st.line_chart(df.set_index('Date')[['Value', 'Moving Average']]) ```
This code adds a 7-day moving average to the data and plots it alongside the original values. You can further extend the Streamlit Dashboard Generator with more complex data processing functions as needed for your specific use case.
Created: | Last Updated:
Introduction to the Streamlit Dashboard Generator Template
This template is designed to help you create interactive dashboards using Streamlit, a powerful tool for building data applications. The template includes features for manual data entry, loading datasets from Google Sheets, uploading CSV files, and visualizing data with different chart types and an interactive map. It's perfect for non-technical builders who want to visualize and interact with their data without delving into the complexities of coding.
Getting Started with the Template
To begin using this template, simply click on "Start with this Template" on the Lazy platform. This will pre-populate the code in the Lazy Builder interface, so you won't need to copy, paste, or delete any code.
Initial Setup
Before you can use the template, you'll need to ensure that you have the necessary Google Sheet URLs that the template will use to load data. These URLs should be direct links to CSV exports of your Google Sheets. If you need to obtain these URLs, follow these steps:
1. Open your Google Sheet. 2. Click on 'File' in the top menu. 3. Select 'Publish to the web'. 4. Choose the sheet you want to publish and select 'Comma-separated values (.csv)' as the format. 5. Click 'Publish', then copy the provided URL.
Test: Deploying the App
Once you have your Google Sheet URLs ready, click the "Test" button on the Lazy platform. This will begin the deployment of your app and launch the Lazy CLI. The Lazy platform handles all the deployment details, so you don't need to worry about installing libraries or setting up your environment.
Entering Input
If the template requires any user input, you will be prompted to provide it through the Lazy CLI after pressing the "Test" button. Follow the prompts to enter any necessary information.
Using the App
After deployment, you will be provided with a dedicated server link to access your interactive dashboard. The dashboard allows you to:
- Manually enter data and visualize it with line, bar, or area charts. - Load datasets from Google Sheets and visualize them. - Upload CSV files and create separate charts for each file. - View an interactive map if your dataset includes 'data_longitude' and 'data_latitude' columns.
Navigate through the dashboard's interface to explore and interact with your data. You can add new data points, select different chart types, and switch between datasets to see various visualizations.
Integrating the App
If you wish to integrate the dashboard into another service or frontend, you can use the server link provided by Lazy. For example, you might want to embed the dashboard into a website or share it with others. Simply use the link in the appropriate place, such as an iframe on a webpage or a link in an email.
Remember, the Lazy platform has taken care of the deployment, so you can focus on using and sharing your interactive dashboard.
If you encounter any issues or need further assistance, refer to the documentation provided in the code or reach out to Lazy's customer support for help.
Template Benefits
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Rapid Data Visualization: This template allows businesses to quickly create interactive dashboards from various data sources, including manual entry, Google Sheets, and CSV files. This rapid visualization capability enables faster decision-making and trend analysis.
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Flexible Data Integration: The ability to load data from multiple sources (manual entry, Google Sheets, and CSV uploads) provides businesses with flexibility in how they manage and input their data, accommodating different workflows and data storage preferences.
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Customizable Visualizations: Users can choose from multiple chart types (line, bar, area) for each dataset, allowing for tailored visual representations that best suit the data and business needs. This customization enhances data interpretation and presentation.
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Geospatial Analysis: The integrated map view feature enables businesses to visualize geographical data, which is particularly valuable for organizations dealing with location-based information, such as retail chains, logistics companies, or market analysis firms.
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User-Friendly Interface: The streamlined, interactive interface makes it easy for non-technical users to create and modify dashboards, democratizing data analysis across the organization and reducing dependence on specialized data teams.
Technologies
Streamlit Templates for Dashboards, Apps, Design and UI
