A library for facilitating data analysis over Enade microdata

pip install enade-py==0.1.0



A library for facilitating data analysis over Enade microdata.


enade-py comprises a set of functions for helping researchers and Educational Data Mining (EDM) enthusiasts through the data mining process using Enade microdata.

The Enade microdata datasets are provided by Inep and consist of informations from brazilian undergraduate students and their performance on Enade (a national exam taken at the end of the course).

Enade datasets contain attributes related to:

  • the exam itself;
  • the student;
  • the institution;
  • performance on the exam;
  • socioeconomic questions;
  • and more.

Getting Started

Installing with pip

To install the latest release of enade-py, just run

pip install enade-py

Manual Installation

In order to set up the necessary environment:

  1. clone the repository on your machine
    git clone https://github.com/mchoji/enade-py.git
  2. create an environment enade-py with the help of conda,
    conda env create -f environment.yaml
  3. activate the new environment with
    conda activate enade-py
  4. install enade-py with:
    python setup.py install # or `develop`

Optional and needed only once after git clone:

  1. install several pre-commit git hooks with:

    pre-commit install

    and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

  2. install nbstripout git hooks to remove the output cells of committed notebooks with:

    nbstripout --install --attributes notebooks/.gitattributes

    This is useful to avoid large diffs due to plots in your notebooks. A simple nbstripout --uninstall will revert these changes.

Then take a look into the scripts and notebooks folders.

Dependency Management & Reproducibility

  1. Abstract (unpinned) dependencies are kept in environment.yaml (for development) and in setup.cfg (for pip)
  2. Concrete dependencies are kept in environment.lock.yaml for the exact reproduction of the development environment with:
    conda env export -n enade-py -f environment.lock.yaml
    For multi-OS development, consider using --no-builds during the export.
  3. Update your current environment with respect to a new environment.lock.yaml using:
    conda env update -f environment.lock.yaml --prune
  4. Data files are tracked with dvc. To pull the latest version, run the following command after git clone or git pull:
    dvc pull

Project Organization

├── AUTHORS.rst             <- List of developers and maintainers.
├── CHANGELOG.rst           <- Changelog to keep track of new features and fixes.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── preprocessed        <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yaml        <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-mc-initial-data-exploration`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of this project.
├── setup.py                <- Use `python setup.py develop` to install for development or
|                              or create a distribution with `python setup.py bdist_wheel`.
├── src
│   └── enadepy             <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `py.test`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.


Versions defined according to SemVer. For the versions available, see the tags on this repository.



enade-py is licensed under the MIT License. See LICENSE for more information.


This project has been set up using PyScaffold 3.2.3 and the dsproject extension 0.4. For details and usage information on PyScaffold see https://pyscaffold.org/.