Package to easily import datasets from the UC Irvine Machine Learning Repository into scripts and notebooks.
Current Version: 0.0.7

In a Jupyter notebook, install with the command

!pip3 install -U ucimlrepo 

Restart the kernel and import the module ucimlrepo.

from ucimlrepo import fetch_ucirepo, list_available_datasets

# check which datasets can be imported
list_available_datasets()

# import dataset
heart_disease = fetch_ucirepo(id=45)
# alternatively: fetch_ucirepo(name='Heart Disease')

# access data
X = heart_disease.data.features
y = heart_disease.data.targets
# train model e.g. sklearn.linear_model.LinearRegression().fit(X, y)

# access metadata
print(heart_disease.metadata.uci_id)
print(heart_disease.metadata.num_instances)
print(heart_disease.metadata.additional_info.summary)

# access variable info in tabular format
print(heart_disease.variables)

Loads a dataset from the UCI ML Repository, including the dataframes and metadata information.

Provide either a dataset ID or name as keyword (named) arguments. Cannot accept both.

• id: Dataset ID for UCI ML Repository
• name: Dataset name, or substring of name

• dataset
  • data: Contains dataset matrices as pandas dataframes
    • ids: Dataframe of ID columns
    • features: Dataframe of feature columns
    • targets: Dataframe of target columns
    • original: Dataframe consisting of all IDs, features, and targets
    • headers: List of all variable names/headers
  • metadata: Contains metadata information about the dataset
    • See Metadata section below for details
  • variables: Contains variable details presented in a tabular/dataframe format
    • name: Variable name
    • role: Whether the variable is an ID, feature, or target
    • type: Data type e.g. categorical, integer, continuous
    • demographic: Indicates whether the variable represents demographic data
    • description: Short description of variable
    • units: variable units for non-categorical data
    • missing_values: Whether there are missing values in the variable's column

Prints a list of datasets that can be imported via fetch_ucirepo

• filter: Optional keyword argument to filter available datasets based on a category
  • Valid filters: aim-ahead
• search: Optional keyword argument to search datasets whose name contains the search query

none

• uci_id: Unique dataset identifier for UCI repository
• name
• abstract: Short description of dataset
• area: Subject area e.g. life science, business
• task: Associated machine learning tasks e.g. classification, regression
• characteristics: Dataset types e.g. multivariate, sequential
• num_instances: Number of rows or samples
• num_features: Number of feature columns
• feature_types: Data types of features
• target_col: Name of target column(s)
• index_col: Name of index column(s)
• has_missing_values: Whether the dataset contains missing values
• missing_values_symbol: Indicates what symbol represents the missing entries (if the dataset has missing values)
• year_of_dataset_creation
• dataset_doi: DOI registered for dataset that links to UCI repo dataset page
• creators: List of dataset creator names
• intro_paper: Information about dataset's published introductory paper
• repository_url: Link to dataset webpage on the UCI repository
• data_url: Link to raw data file
• additional_info: Descriptive free text about dataset
  • summary: General summary
  • purpose: For what purpose was the dataset created?
  • funding: Who funded the creation of the dataset?
  • instances_represent: What do the instances in this dataset represent?
  • recommended_data_splits: Are there recommended data splits?
  • sensitive_data: Does the dataset contain data that might be considered sensitive in any way?
  • preprocessing_description: Was there any data preprocessing performed?
  • variable_info: Additional free text description for variables
  • citation: Citation Requests/Acknowledgements
• external_url: URL to external dataset page. This field will only exist for linked datasets i.e. not hosted by UCI

• UCI Machine Learning Repository home page
• PyPi repository for this package
• Submit an issue