Python data analysis notebooks

Jupyter notebooks working through the everyday mechanics of analysis in pandas: preparing messy data, filtering it, combining it, and turning it into something a business can act on.

Written in 2020 alongside my MSc Data Analytics. This is learning and exploration work, kept public for the fundamentals it shows rather than as production code.

Notebooks

Notebook	What it covers
Data Preparation	Cleaning, type handling and reshaping a raw extract into an analysable frame
Filtering	Selecting, slicing and conditional filtering patterns in pandas
Combining data	Merging every CSV in a directory into a single frame
Sales data analysis	Exploratory analysis of sales performance, with aggregation and visualisation
Marketing campaign analysis	Campaign response analysis over the bundled marketing dataset
Customer segmentation	Grouping customers by behaviour to support targeting decisions

Stack

Python · pandas · NumPy · matplotlib · scikit-learn · Jupyter

Running it

pip install -r requirements.txt
jupyter notebook

Data

One notebook is self-contained; the rest read inputs that were never committed. Rather than pretend otherwise, here is exactly what each needs, and the schema to match if you substitute your own.

Notebook	Input	Status
Marketing campaign analysis	Marketing Campaign data.csv	included — runs as-is
Customer Segmentation	Online Retail.csv	not included. Needs CustomerID, InvoiceDate, InvoiceNo, Quantity, UnitPrice — the standard schema of the UCI Online Retail dataset, which is what the RFM analysis is built for
Sales data analysis · Combining data	Sales_Data/ — twelve monthly CSVs	not included. Each needs Order ID, Product, Quantity Ordered, Price Each, Order Date, Purchase Address
Filtering	Summer.csv	not included. Needs at least product_id and title_orig
Data Preparation	EnquiryTotal.csv	not included — fabricated travel-enquiry data, not a public dataset

The two travel-enquiry datasets were fabricated for coursework and are not redistributable public sets. The others follow well-known public schemas, so any file matching the columns above will drive the notebook.

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For current work, see rag-eval-harness and medallion-duckdb.