End-to-End Python Analysis on a Real Dataset
Part of the Data Analyst Roadmap
In short
This is where everything joins up: load, clean, merge, group, chart and explain. One complete notebook like this says more about you than ten certificates.
The practice project for this phase — take a public dataset from raw file to cleaned data to visualized findings, entirely in a single well-documented notebook.
Think of it like this
Cooking a full thali
A thali isn't one dish. You shop, wash, chop, cook each item, taste, and serve it neatly on one plate. Skip the washing and the whole meal suffers. An end-to-end analysis is the same: every step matters, and the plate you serve is the story.
Watch it happen
The build plan
1/5Write 2 or 3 business questions first, like 'Which categories grew in the festive months?'
The big ideas
What you need to know
Questions before code
Without questions you'll just make random charts. Write them in the first markdown cell.
Show your cleaning
Row counts before and after each step prove you handled the data honestly.
Few charts, well labelled
Four sharp charts with finding-style titles beat twenty default ones.
Publish it
Put the notebook and a short README on GitHub or Kaggle so recruiters can actually see it.
See it in code
Walk through the code
1orders = clean(pd.read_csv("orders.csv", parse_dates=["order_date"]))2orders["month"] = orders["order_date"].dt.to_period("M")3 4monthly = (5 orders.groupby(["month", "category"])["amount"]6 .sum()7 .unstack("category")8)9growth = monthly.pct_change().iloc[-1].sort_values(ascending=False)10 11ax = monthly.plot(figsize=(9, 4), title="Monthly revenue by category")12ax.set_ylabel("Revenue (₹)")13print("Fastest growing last month:", growth.index[0], f"{growth.iloc[0]:.0%}")1/4Load with real dates, reuse your clean function, then add a month column.
The core of a sales analysis: monthly revenue per category, the latest month-on-month growth, and one clear chart.
Save yourself the pain
Common mistakes
Oops
Picking a toy dataset like Titanic that every portfolio already has.
Do this instead
Choose something closer to business: retail sales, food delivery, app reviews, public transport usage.
Oops
Ending the notebook with a chart and no conclusion.
Do this instead
Write what you found, how sure you are, and what someone should do next.
Your mission
Milestone one: questions and clean data
Quick check
monthly.pct_change() on revenue of 200 then 250 gives what for the second month?
Go deeper
- 1Choose a public dataset (Kaggle, government open data) you're genuinely curious about.
- 2Work through load, clean, explore, visualize, and conclude, in that order.
- 3Write markdown explanations alongside your code so the notebook reads like a report.
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