# -*- coding: utf-8 -*-
# modified from https://github.com/CamDavidsonPilon/lifelines/

import pandas as pd
from pkg_resources import resource_filename

__all__ = [
    'load_cdnow',
    'load_transaction_data',
]


def load_dataset(filename, **kwargs):
    '''
    Load a dataset from lifetimes.datasets

    Parameters:
    filename : for example "larynx.csv"
    usecols : list of columns in file to use

    Returns : Pandas dataframe
    '''
    return pd.read_csv(resource_filename('lifetimes', 'datasets/' + filename), **kwargs)


def load_cdnow(**kwargs):
    return load_dataset('cdnow_customers.csv', **kwargs)


def load_transaction_data(**kwargs):
    return load_dataset('example_transactions.csv', **kwargs)

# -*- coding: utf-8 -*-
# modified from https://github.com/CamDavidsonPilon/lifelines/

import pandas as pd
from pkg_resources import resource_filename

__all__ = [
    'load_cdnow',
    'load_transaction_data',
]


def load_dataset(filename, **kwargs):
    '''
    Load a dataset from lifetimes.datasets

    Parameters:
    filename : for example "larynx.csv"
    usecols : list of columns in file to use

    Returns : Pandas dataframe
    '''
    return pd.read_csv(resource_filename('lifetimes', 'datasets/' + filename), **kwargs)


def load_cdnow(**kwargs):
    return load_dataset('cdnow_customers.csv', **kwargs)


def load_transaction_data(**kwargs):
    """
    Returns a Pandas dataframe of transactional data. Looks like:

                      date  id
    0  2014-03-08 00:00:00   0
    1  2014-05-21 00:00:00   1
    2  2014-03-14 00:00:00   2
    3  2014-04-09 00:00:00   2
    4  2014-05-21 00:00:00   2

    The data was artificially created using Lifetimes data generation routines. Data was generated 
    between 2014-01-01 to 2014-12-31.

    """
    return load_dataset('example_transactions.csv', **kwargs)

Add a doc string to make using this dataset easier
Add a doc string to make using this dataset easier
