EDA with Python, Pandas and Jupyter

Released v30.8 Research Public

admin Updated 1 month, 2 weeks ago

Description

Turn a provided datalake catalog (akin to Phainestai) into a decision-ready EDA: frame the problem as questions, explore data to identify target(s) Y and build feature tables with information gain on Y, design visual encodings using a systematic analytical-pattern method, then build the Jupyter storybook with Plotly. The Plotly implementation phase is original synthesis.

Quick Stats

Workflows

FPM Frame the Problem
Produce a written set of driving questions, a chosen analysis type, and a vetted list of candidate datasets from the …
ODA Organize and Clean Data
Turn the selected raw datasets into one clean, combined, correctly-shaped working DataFrame — the shared substrate every later exploration step …
EFS Explore and Engineer Features
Profile the cleaned data, choose candidate target variable(s) Y, and construct a feature table plus first-pass relationship checks (crosstabs, correlations) …
ACE Assess Information Gain and Conclude
Rigorously test whether the feature table actually carries information about Y — via significance-tested correlation, a validated predictive experiment, and …
ECR Encode and Reduce Clutter
For each conclusion, classify its analytical pattern and choose the scale, reference lines, pre-attentive encodings and layout that make the …
CTE Choose Chart per Analysis Type
Apply a chart-selection rule set for each of the six recurring analysis types so every finding gets the display type …
SNK Storyboard the Notebook
Sequence the chosen displays into a coherent question-evidence-conclusion narrative and record, per planned chart, exactly what it must show — …
IVS Implement Plotly Visuals
Turn every storyboard entry into a working Plotly figure with the exact scale/reference-line/layout decisions applied, assemble the finished Jupyter notebook, …

Metadata

Category:: Research
Author:: admin
Created:: 1 month, 2 weeks ago
Source:: Owned

Order	Abbrev.	Name	Description	Activities
1	FPM	Frame the Problem	Produce a written set of driving questions, a chosen analysis …	6
2	ODA	Organize and Clean Data	Turn the selected raw datasets into one clean, combined, correctly-shaped …	5
3	EFS	Explore and Engineer Features	Profile the cleaned data, choose candidate target variable(s) Y, and …	5
4	ACE	Assess Information Gain and Conclude	Rigorously test whether the feature table actually carries information about …	5
5	ECR	Encode and Reduce Clutter	For each conclusion, classify its analytical pattern and choose the …	7
6	CTE	Choose Chart per Analysis Type	Apply a chart-selection rule set for each of the six …	6
7	SNK	Storyboard the Notebook	Sequence the chosen displays into a coherent question-evidence-conclusion narrative and …	3
8	IVS	Implement Plotly Visuals	Turn every storyboard entry into a working Plotly figure with …	6

Lifecycle Phases

Frame the Problem Order: 1

6 activities

Nothing downstream is trustworthy if the question is wrong. This phase turns a business need into a chosen analysis type …

Explore Data and Find Signal Order: 2

15 activities

Go from raw selected datasets to validated conclusions: organize and clean the data, engineer a feature table aimed at one …

Design the Visual Story Order: 3

16 activities

A correct conclusion still fails if it's shown badly. Apply a systematic analytical-pattern method to pick, per finding, the right …

Build the Story in Plotly Order: 4

6 activities

Turn the storyboard into a real, shareable Jupyter notebook: implement every planned chart in Plotly with the exact scale/reference-line/layout decisions …

Agents

EDA Practitioner

The person or AI system running this playbook end to end — analyst, programmer, and domain expert by turns. Frames …

Version history

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