Frame the Problem - EDA with Python, Pandas and Jupyter

Description

Nothing downstream is trustworthy if the question is wrong. This phase turns a business need into a chosen analysis type and a concrete set of answerable questions, checks that the analyst is bringing the right mindset, and selects and vets candidate datasets from the provided datalake catalog before any data is touched.

Activities in this Phase

Order	Name	Actions
Frame the Problem
#1	Choose the analysis type
#2	Formulate the driving questions	Read the 'FPM-2 Formulate the driving questions' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. Write down the specific descriptive question(s) first ("what is happening / what is causing this"), then the predictive/prescriptive question(s) that follow from them ("what do we want to happen / what actions get us there"). Keep the questions in front of you for the rest of the playbook — every later chart and model exists to answer one of them.
#3	Assess analyst readiness	Read the 'FPM-3 Assess analyst readiness' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. Before opening any data, check you're bringing genuine interest/curiosity, skepticism, methodical practice, pattern-spotting, and enough domain familiarity to know what "normal" looks like for this data. Also plan to alternate between the statistician, programmer, and domain-expert "hats" as the work proceeds.
#4	Select candidate datasets from the datalake catalog	Read the 'FPM-4 Select candidate datasets from the datalake catalog' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. State what the "ideal" dataset for your questions would look like, then browse the provided datalake catalog (a Phainestai-style dataset catalog) for the closest available match(es), accepting a smaller/less feature-rich substitute if the ideal isn't present. Record which catalog entries you selected and why. This step replaces raw web/DB/file acquisition with selection from a pre-built catalog, since this playbook assumes the datalake already exists.
#5	Assess dataset quality against meaningfulness traits	Read the 'FPM-5 Assess dataset quality against meaningfulness traits' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. For each selected catalog entry, check it against the traits of meaningful data: sufficient volume, historical depth, consistency over time, multivariate coverage, atomic-level (not pre-aggregated) records, clean values, clear coding, dimensional structure, rich segmentation, and known pedigree/lineage. Drop or flag entries that fail badly on a trait that matters for your questions.
#6	Write the Framing Brief	Read the 'FPM-6 Write the Framing Brief' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Frame the Problem'). Follow its guidance when assisting me. Consolidate the chosen analysis type, the driving questions, and the vetted dataset selection into a single short brief. This is the contract the rest of the playbook works against — Phase 2 starts from it, and Phase 2's final conclusions get checked back against it.