MLA 009 Charting and Visualization Tools for Data Science
Machine Learning Guide
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Machine Learning - Charting and EDA Tools
Most of the time, as has been my experience, you are working with excel files. You can pull in these excel files into pandas and then call things like d f dot cor or d f dot hist to generate some of these plots. Or you can open up the excel file itself, your data source, and there's a excel has a bunch of charting utilities built right into excel itself.
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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
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Another major BI offering is QlikView, emphasizing interactive dashboards and data exploration.
- 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.
- 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.
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