NOTICE

The upcoming release of Featuretools 1.0.0 contains several breaking changes. Users are encouraged to test this version prior to release by installing from GitHub:

pip install https://github.com/alteryx/featuretools/archive/woodwork-integration.zip

For details on migrating to the new version, refer to Transitioning to Featuretools Version 1.0. Please report any issues in the Featuretools GitHub repo or by messaging in Alteryx Open Source Slack.

Source code for featuretools.demo.retail

import pandas as pd
from woodwork.logical_types import NaturalLanguage

import featuretools as ft


def load_retail(id='demo_retail_data', nrows=None, return_single_table=False):
    '''Returns the retail entityset example.
    The original dataset can be found `here <https://archive.ics.uci.edu/ml/datasets/online+retail>`_.

    We have also made some modifications to the data. We
    changed the column names, converted the ``customer_id``
    to a unique fake ``customer_name``, dropped duplicates,
    added columns for ``total`` and ``cancelled`` and
    converted amounts from GBP to USD. You can download the modified CSV `in gz compressed (7 MB)
    <"https://api.featurelabs.com/datasets/online-retail-logs-2018-08-28.csv.gz">`_
    or `uncompressed (43 MB)
    <"https://api.featurelabs.com/datasets/online-retail-logs-2018-08-28.csv">`_ formats.

    Args:
        id (str):  Id to assign to EntitySet.
        nrows (int):  Number of rows to load of the underlying CSV.
            If None, load all.
        return_single_table (bool): If True, return a CSV rather than an EntitySet. Default is False.

    Examples:

        .. ipython::
            :verbatim:

            In [1]: import featuretools as ft

            In [2]: es = ft.demo.load_retail()

            In [3]: es
            Out[3]:
            Entityset: demo_retail_data
              DataFrames:
                orders (shape = [22190, 3])
                products (shape = [3684, 3])
                customers (shape = [4372, 2])
                order_products (shape = [401704, 7])

        Load in subset of data

        .. ipython::
            :verbatim:

            In [4]: es = ft.demo.load_retail(nrows=1000)

            In [5]: es
            Out[5]:
            Entityset: demo_retail_data
              DataFrames:
                orders (shape = [67, 5])
                products (shape = [606, 3])
                customers (shape = [50, 2])
                order_products (shape = [1000, 7])

'''
    es = ft.EntitySet(id)
    csv_s3_gz = "https://api.featurelabs.com/datasets/online-retail-logs-2018-08-28.csv.gz?library=featuretools&version=" + ft.__version__
    csv_s3 = "https://api.featurelabs.com/datasets/online-retail-logs-2018-08-28.csv?library=featuretools&version=" + ft.__version__
    # Try to read in gz compressed file
    try:
        df = pd.read_csv(csv_s3_gz,
                         nrows=nrows,
                         parse_dates=["order_date"])
    # Fall back to uncompressed
    except Exception:
        df = pd.read_csv(csv_s3,
                         nrows=nrows,
                         parse_dates=["order_date"])
    if return_single_table:
        return df

    es.add_dataframe(dataframe_name="order_products",
                     dataframe=df,
                     index="order_product_id",
                     make_index=True,
                     time_index="order_date",
                     logical_types={'description': NaturalLanguage})

    es.normalize_dataframe(new_dataframe_name="products",
                           base_dataframe_name="order_products",
                           index="product_id",
                           additional_columns=["description"])

    es.normalize_dataframe(new_dataframe_name="orders",
                           base_dataframe_name="order_products",
                           index="order_id",
                           additional_columns=["customer_name", "country", "cancelled"])

    es.normalize_dataframe(new_dataframe_name="customers",
                           base_dataframe_name="orders",
                           index="customer_name")
    es.add_last_time_indexes()

    return es