1 views collected around this technical thread.
This tutorial provides 17 practical Python plotting examples—from basic line and bar charts to 3D visualizations and real‑time updates—complete with ready‑to‑run code snippets using Matplotlib, Seaborn, and Plotly, helping newcomers quickly master data visualization techniques.
This article demonstrates how to replace common Excel operations such as VLOOKUP, data pivot tables, and charting with Python's pandas and plotly libraries, providing code examples, explanations of data import/export settings, and performance considerations for large‑scale data analysis.
This article demonstrates how to use Python, pandas, and Plotly to download, clean, and visualize World Happiness Report data from 2015‑2019, revealing the strongest correlations between happiness scores and factors such as GDP per capita, healthy life expectancy, and social support.
This article provides a detailed tutorial on using Plotly (with Cufflinks) for creating interactive, high‑quality visualizations in Python, covering installation, basic charts, advanced plots, themes, and integration with Jupyter and Plotly Chart Studio to enhance data analysis workflows.
Through an exploration of sensitivity analysis and 3D visualization techniques—using tools like Plotly—this article demonstrates how to identify the most influential input parameters in mathematical models, illustrate complex relationships with interactive graphics, and support model validation, optimization, and decision‑making.
This article demonstrates how to install the prometheus_api_client library, fetch time‑series data from Prometheus with Python, process it using pandas, and create interactive visualizations with Plotly, providing a complete workflow from data collection to insight generation.
This guide demonstrates how to install common Python plotting libraries and provides ready-to-use functions for creating line, scatter, bar, stacked bar, pie, histogram, heatmap, boxplot, interactive Plotly scatter, and pandas DataFrame visualizations with example code snippets.
This article presents a series of Python code examples that demonstrate how to generate statistical reports, pivot tables, various charts, heatmaps, PDF reports, and an interactive dashboard for sales data analysis using libraries such as pandas, matplotlib, seaborn, reportlab, and plotly/dash.
This content introduces Mayavi, Matplotlib, and Plotly as Python libraries for 3D data visualization, providing code examples for creating surface plots, scatter plots, and interactive charts, with a focus on handling large-scale scientific datasets.
This article introduces five unconventional Plotly visualizations—including animated bar charts, sunburst charts, parallel categories, parallel coordinates, and gauge indicators—demonstrating how to create dynamic, interactive graphics in Python to make data stories more engaging and informative.
This tutorial explains how to install Python Dash, create a basic layout with a title, dropdown, and graph, add interactive callbacks and custom sliders, and finally run and deploy a weather data dashboard using Plotly for visualization.
This article introduces ten popular Python visualization and GUI libraries—Matplotlib, Seaborn, Plotly, Bokeh, Altair, Plotnine, VisPy, Pygame, Kivy, and PyQt/PySide—providing concise code examples and brief explanations of their typical use cases and strengths.
This article introduces ten Python visualization libraries—ranging from the classic Matplotlib to newer tools like Plotly and Leather—detailing their features, typical use cases, developer backgrounds, and how they complement each other for creating static, interactive, and geographic visualizations across various fields.
This article reviews popular Python visualization libraries—Matplotlib, Seaborn, Plotly, Altair, Bokeh, and Folium—by evaluating their interactivity, syntax flexibility, and data‑type support, and provides practical code examples and pros‑cons to help beginners choose the most suitable tool for their data‑visualization needs.
This article explains the origins and applications of Sankey diagrams, demonstrates how to create them with Plotly in Python across various scenarios such as website navigation, energy conversion, cost breakdown, financial flows, data migration, and confusion matrix visualization, and provides complete code examples.
This article demonstrates how to replace typical Excel operations such as data import, VLOOKUP, pivot tables, and charting with Python's pandas and Plotly libraries, providing code examples and step‑by‑step explanations for each task.
This tutorial demonstrates how to perform A/B testing on two marketing campaigns using Python, covering data loading, cleaning, merging, and visual analysis of key metrics such as impressions, spend, clicks, searches, content views, add‑to‑cart actions, and purchases to identify the most effective strategy.
This article introduces Plotly, a powerful Python library for creating interactive line, scatter, and bar charts, explains how to install and use it in offline mode within Jupyter notebooks, and provides complete code examples for each chart type along with visual results.
This tutorial introduces five non‑traditional Plotly visualization techniques—animation, sunburst, parallel categories, parallel coordinates, and gauge/indicator charts—showing how to create dynamic, interactive, and aesthetically appealing graphics that enhance data storytelling and communication.
This article introduces the powerful open‑source Plotly library for Python, showing how a single line of code can create interactive charts such as bar, box, scatter, time‑series, and advanced visualizations, while also covering installation, theme customization, and integration with Jupyter Notebook and Plotly Chart Studio.