rmasemone

Print the full Python source of six RMA practicals in Jupyter output cells. The functions print code; they do not execute the practicals. No scientific packages or CSV files are needed to print the code.

import rmasemone

rmasemone.prac1()
rmasemone.prac2()
rmasemone.prac3()
rmasemone.prac4()
rmasemone.prac5()
rmasemone.prac6()

rmasemone.show(6)
code = rmasemone.get_code(6)

Practical contents

1. Retail transaction exploration and cleaning
2. Exploratory data analysis and boxplots
3. Descriptive statistics and distribution analysis
4. Retail feature engineering
5. Multiple linear regression
6. Binary logistic regression

The source is extracted from the six supplied code-only notebooks. The practical text contains no comments.

Running the printed code

Copy the printed code to a new code cell. Install pandas, numpy, matplotlib, seaborn, scipy, statsmodels, scikit-learn and patsy. Supply the original CSV datasets alongside the notebook: Retail_Transaction_Dataset.csv, Retail_Data.csv, Performance Index.csv, Performance Index new.csv and BANK LOAN.csv. Actual results depend on these files. Runtime checks of the source used schema-matched sample data, not the original datasets.

Local installation

From this project folder:

python -m pip install .
python -m unittest discover -s tests

See PUBLISH_GUIDE.md for publishing steps.