Source code for woodwork.demo.retail

import pandas as pd

import woodwork as ww
from woodwork.logical_types import (

[docs]def load_retail(id='demo_retail_data', nrows=None, init_woodwork=True): """Load a demo retail dataset into a DataFrame, optionally initializing Woodwork's typing information. Args: id (str, optional): The name to assign to the DataFrame, if returning a DataFrame with Woodwork typing information initialized. If not returning a DataFrame with Woodwork initialized, this will be ignored. Defaults to ``demo_retail_data``. nrows (int, optional): The number of rows to return in the dataset. If None, will return all possible rows. Defaults to None. init_woodwork (bool): If True, will return a pandas DataFrame with Woodwork typing information initialized. If False, will return a DataFrame without Woodwork initialized. Defaults to False. Returns: pd.DataFrame: A DataFrame containing the demo data with Woodwork typing initialized. If `init_woodwork` is False, will return an uninitialized DataFrame. """ csv_s3_gz = "" + ww.__version__ csv_s3 = "" + ww.__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"]) # Add unique column for index df.insert(0, 'order_product_id', range(len(df))) if init_woodwork: logical_types = { 'order_product_id': Categorical, 'order_id': Categorical, 'product_id': Categorical, 'description': NaturalLanguage, 'quantity': Integer, 'order_date': Datetime, 'unit_price': Double, 'customer_name': Categorical, 'country': Categorical, 'total': Double, 'cancelled': Boolean, } df.ww.init(name=id, index='order_product_id', time_index='order_date', logical_types=logical_types) return df