MLA 009 Charting and Visualization Tools for Data Science

Python charting libraries - Matplotlib, Seaborn, and Bokeh - explaining, their strengths from quick EDA to interactive, HTML-exported visualizations, and clarifies where D3.js fits as a JavaScript alternative for end-user applications. It also evaluates major software solutions like Tableau, Power BI, QlikView, and Excel, detailing how modern BI tools now integrate drag-and-drop analytics with embedded machine learning, potentially allowing business users to automate entire workflows without coding. Links • Notes and resources at ocdevel.com/mlg/mla-9 Try a walking desk • stay healthy & sharp while you learn & code Core Phases in Data Science Visualization Exploratory Data Analysis (EDA): • • EDA occupies an early stage in the Business Intelligence (BI) pipeline, positioned just before or sometimes merged with the data cleaning (“munging”) phase. • The outputs of EDA (e.g., correlation matrices, histograms) often serve as inputs to subsequent machine learning steps. • Python Visualization Libraries 1. Matplotlib • The foundational plotting library in Python, supporting static, basic chart types. • Requires substantial boilerplate code for custom visualizations. • Serves as the core engine for many higher-level visualization tools. • Common EDA tasks (like plotting via .corr() • , .hist() • , and .scatter() • methods on pandas DataFrames) depend on Matplotlib under the hood. 2. Pandas Plotting • Pandas integrates tightly with Matplotlib and exposes simple, one-line commands for common plots (e.g., df.corr() • , df.hist() • ). • Designed to make quick EDA accessible without requiring detailed knowledge of Matplotlib’s verbose syntax. 3. Seaborn • A high-level wrapper around Matplotlib, analogous to how Keras wraps TensorFlow. • Sets sensible defaults for chart styles, fonts, colors, and sizes, improving aesthetics with minimal effort. • Importing Seaborn can globally enhance the appearance of all Matplotlib plots, even without direct usage of Seaborn’s plotting functions. 4. Bokeh • A powerful library for creating interactive, web-ready plots from Python. • Enables user interactions such as hovering, zooming, and panning within rendered plots. • Exports visualizations as standalone HTML files or can operate as a server-linked app for live data exploration. • Supports advanced features like cross-filtering, allowing dynamic slicing and dicing of data across multiple axes or columns. • More suited for creating reusable, interactive dashboards rather than quick, one-off EDA visuals. 5. D3.js • Unlike previous libraries, D3.js is a JavaScript framework for creating complex, highly customized data visualizations for web and mobile apps. • Used predominantly on the client-side to build interactive front-end graphics for end users, not as an EDA tool for analysts. • Common in production-grade web apps, but not typically part of a Python-based data science workflow. Dedicated Visualization and BI Software Tableau • Leading commercial drag-and-drop BI tool for data visualization and dashboarding. • Connects to diverse data sources (CSV, Excel, databases), auto-detects column types, and suggests default chart types. • Users can interactively build visualizations, cross-filter data, and switch chart types without coding. Power BI • Microsoft’s BI suite, similar to Tableau, supporting end-to-end data analysis and visualization. • Integrates data preparation, visualization, and increasingly, built-in machine learning workflows. • Focused on empowering business users or analysts to run the BI pipeline without programming. QlikView • Another major BI offering is QlikView, emphasizing interactive dashboards and data exploration. Excel • Still widely used for basic EDA and visualizations directly on spreadsheets. • Offers limited but accessible charting tools for histograms, scatter plots, and simple summary statistics. • Data often originates from Excel/CSV files before being ingested for further analysis in Python/pandas. Trends & Insights Workflow Integration: • Modern BI tools are converging, adding both classic EDA capabilities and basic machine learning modeling, often through a code-free interface. Automation Risks and Opportunities: • As drag-and-drop BI tools increase in capabilities (including model training and selection), some data science coding work traditionally required for BI pipelines may become accessible to non-programmers. Distinctions in Use: • • Python libraries (Matplotlib, Seaborn, Bokeh) excel in automating and scripting EDA, report generation, and static analysis as part of data pipelines. • BI software (Tableau, Power BI, QlikView) shines for interactive exploration and democratized analytics, integrated from ingestion to reporting. • D3.js stands out for tailored, production-level, end-user app visualizations, rarely leveraged by data scientists for EDA. • Key Takeaways For quick, code-based EDA: • Use Pandas’ built-in plotters (wrapping Matplotlib). For pre-styled, pretty plots: • Use Seaborn (with or without direct API calls). For interactive, shareable dashboards: • Use Bokeh for Python or BI tools for no-code operation. For enterprise, end-user-facing dashboards: • Choose BI software like Tableau or build custom apps using D3.js for total control.