Python + Pandas Analysis Notebook
Part of the Data Analyst Roadmap
In short
A clean, well-explained notebook is like a lab record that recruiters can actually run. It shows you can code, think and explain in one place.
A polished, well-commented Jupyter notebook showcasing your Python and Pandas fluency on a dataset distinct from your other portfolio pieces, to show range.
Think of it like this
The chemistry lab record
In the college lab, your record book has aim, apparatus, procedure, observations and result, in that order. Anyone can repeat your experiment from it. A good analysis notebook follows the same pattern, with code and charts instead of beakers.
Watch it happen
A notebook that reads like a lab record
1/5A markdown cell at the top: the question, the dataset and what you expect to find.
The big ideas
What you need to know
Markdown is half the notebook
Explain what each cell does and why. A notebook with only code is hard for anyone else to follow.
Restart and run all
Before sharing, restart the kernel and run every cell top to bottom. If it breaks, fix the order.
Learn groupby well
groupby with named aggregations answers most business questions in one readable block.
Share it properly
Put it on GitHub or Kaggle with a short README, so it renders and anyone can view it.
See it in code
Walk through the code
1import pandas as pd2 3df = pd.read_csv("orders.csv", parse_dates=["order_date"])4df["month"] = df["order_date"].dt.to_period("M")5 6summary = (df.groupby(["month", "category"])7 .agg(orders=("order_id", "nunique"),8 revenue=("amount", "sum"))9 .reset_index())10 11top = summary.sort_values("revenue", ascending=False).head(10)12top1/3Parse dates while loading, then add a month column to group by.
The core of many analysis notebooks: load, add a month column, group and aggregate with clear names.
Save yourself the pain
Common mistakes
Oops
Cells that only work if run in a weird order you remember but nobody else does.
Do this instead
Restart and run all before every share. Keep cells in the order they should run.
Oops
Leaving dozens of unexplained print(df.head()) cells everywhere.
Do this instead
Keep the checks that matter and delete the rest. Add a markdown line where a check revealed something.
Your mission
First milestone: a clean skeleton
Quick check
What does .agg(orders=("order_id", "nunique")) do inside a groupby?
Go deeper
- 1Pick a dataset and question different from your other portfolio projects.
- 2Use markdown cells throughout so the notebook reads clearly without running the code.
- 3Push it to GitHub with a clear README explaining the question and key findings.
More projects at this stop
End-to-End SQL + BI Dashboard Project
Exploratory Data Analysis (EDA) on a Public Dataset
A/B Test Analysis Case Study
Business Metrics Deep-Dive Report
Data Cleaning Case Study (Messy Dataset)
Portfolio Website or GitHub Showcase
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