This article introduces Streamlit, a Python library for building data dashboards, as a solution for Python programmers to create graphical front-ends without needing to delve into CSS, HTML, or JavaScript. The author, a seasoned data engineer, explains how Streamlit and similar tools enable the creation of attractive dashboards, marking a shift from traditional tools like Tableau or Quicksight. This piece serves as the first in a series focusing on Streamlit, with future articles planned on Gradio and Taipy. The author aims to replicate similar layouts and functionalities across dashboards using consistent data.
ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Shiny for Python lets you build interactive web applications with ease. It utilizes reactive programming for efficient and dynamic visualizations.
Intro to Streamlit
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
NiceGUI is a Python-based UI framework that works smoothly with web browsers or as a desktop app
Easy-to-use interface with many features, including buttons, switches, sliders, input fields, charts, tables, and visuals
Integrates with data visualization libraries like Matplotlib and Plotly
Customizable with styles and colors
Open-source and backed by a smaller community
Deployment on cloud platforms like FastAPI, Vue, and Quasar
Limited by the popularity of other frameworks like Streamlit, but offers unique features and capabilities
Easy-to-use interface with many features, including buttons, switches, sliders, input fields, charts, tables, and visuals
Integrates with data visualization libraries like Matplotlib and Plotly
Customizable with styles and colors
Open-source and backed by a smaller community
Deployment on cloud platforms like FastAPI, Vue, and Quasar
Limited by the popularity of other frameworks like Streamlit, but offers unique features and capabilities
Prompting GPT-4 for multi-visual interactive dashboard creation
Build and Deploy a LangChain-Powered Chat App with Docker and Streamlit
