Data analysis turns vague stakeholder questions into answerable questions, evaluates whether available data is suitable, and produces pragmatic findings for decisions. It combines inspection, transformation, statistical summaries, visualization, and communication rather than treating implementation as the whole task. section 1.0 section 1.1
A practical analysis workflow includes:
- Frame the human question and define the data needed to answer it. section 1.1
- Load, join, clean, transform, and inspect datasets before drawing conclusions. section 1.1 section 5.4.3
- Explore patterns with averages, medians, distributions, grouped summaries, charts, and dashboards. section 1.1 section 5.4.3
- Pivot the analytical question when the available data cannot support the original one. section 1.1
- Separate logical stages, document assumptions, communicate results plainly, and iterate toward a useful answer. section 1.4 section 5.4.3
Comparing cinema admissions across COVID periods
Monthly admissions are converted into dates, assigned to ordered pre-COVID, during-COVID, and post-lockdown categories, then checked for missing values. Line charts, grouped mean and median summaries, and histograms reveal changes across periods. Related genre and distributor data support revenue, release, and market-share comparisons. section 5.4.3
Analysis as a proof of concept
When stakeholders need to know whether data can support a vehicle-market application, exploratory analysis can establish similarity rules and expose obstacles before a full product is built. section 10.0
COVID_START_DATE = datetime.datetime(2020, 3, 1) #1
LOCKDOWN_END_DATE = datetime.datetime(2021, 7, 1)
admissions["date"] = (
"1 " + #2
admissions["Month"] +
" " +
admissions["Year"].astype(str)
)
admissions["date"] = pd.to_datetime(admissions["date"], format="%d %B %Y")
admissions.head()
Inspect the executable date transformation used to turn separate month and year fields into an analyzable date column. section 5.4.3
