"""Utility to load datasets."""
import pandas as pd
import pickle as pkl
import numpy as np
import os


def load_food_search_trends(groups=False):
    """
    Data from: http://foodb.ca/
    https://www.google.com/intl/es419/search/about/
    """
    filename = os.path.join(os.path.dirname(__file__), "food_search_trends/food.csv")
    data = pd.read_csv(filename, index_col=0)
    if groups:
        filename = os.path.join(os.path.dirname(__file__), "food_search_trends/groups_of_food.pkl")
        with open(filename, "rb") as f:
            groups_dict = pkl.load(f)
        connections = np.zeros((len(groups_dict), data.shape[1]))
        connections = pd.DataFrame(connections, index=list(groups_dict.keys()), columns=data.columns)

        for g, v in groups_dict.items():
            connections.loc[g, v] = 1

        return data, connections, groups_dict
    else:
        return data


def load_finance(condensed=True, sectors=False):
    """
    Data from:  https://quantquote.com/historical-stock-data
    https://datahub.io/core/s-and-p-500-companies
    """
    if condensed:
        filename = os.path.join(os.path.dirname(__file__), "finance/finance.csv")
        data = pd.read_csv(filename, index_col=0)
    else:
        filename = os.path.join(os.path.dirname(__file__), "finance/not_collapsed.csv")
        data = pd.read_csv(filename, index_col=0)

    if sectors:
        filename = os.path.join(os.path.dirname(__file__), "finance/industries_sector.csv")
        sectors = pd.read_csv(filename, index_col=0)

732bf8194a7952ea96af3a97632fcb5d43058b80
<mask token>

<mask token>


def load_finance(condensed=True, sectors=False):
    """
    Data from:  https://quantquote.com/historical-stock-data
    https://datahub.io/core/s-and-p-500-companies
    """
    if condensed:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/finance.csv')
        data = pd.read_csv(filename, index_col=0)
    else:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/not_collapsed.csv')
        data = pd.read_csv(filename, index_col=0)
    if sectors:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/industries_sector.csv')
        sectors = pd.read_csv(filename, index_col=0)

<mask token>


def load_food_search_trends(groups=False):
    """
    Data from: http://foodb.ca/
    https://www.google.com/intl/es419/search/about/
    """
    filename = os.path.join(os.path.dirname(__file__),
        'food_search_trends/food.csv')
    data = pd.read_csv(filename, index_col=0)
    if groups:
        filename = os.path.join(os.path.dirname(__file__),
            'food_search_trends/groups_of_food.pkl')
        with open(filename, 'rb') as f:
            groups_dict = pkl.load(f)
        connections = np.zeros((len(groups_dict), data.shape[1]))
        connections = pd.DataFrame(connections, index=list(groups_dict.keys
            ()), columns=data.columns)
        for g, v in groups_dict.items():
            connections.loc[g, v] = 1
        return data, connections, groups_dict
    else:
        return data


def load_finance(condensed=True, sectors=False):
    """
    Data from:  https://quantquote.com/historical-stock-data
    https://datahub.io/core/s-and-p-500-companies
    """
    if condensed:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/finance.csv')
        data = pd.read_csv(filename, index_col=0)
    else:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/not_collapsed.csv')
        data = pd.read_csv(filename, index_col=0)
    if sectors:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/industries_sector.csv')
        sectors = pd.read_csv(filename, index_col=0)

<mask token>
import pandas as pd
import pickle as pkl
import numpy as np
import os


def load_food_search_trends(groups=False):
    """
    Data from: http://foodb.ca/
    https://www.google.com/intl/es419/search/about/
    """
    filename = os.path.join(os.path.dirname(__file__),
        'food_search_trends/food.csv')
    data = pd.read_csv(filename, index_col=0)
    if groups:
        filename = os.path.join(os.path.dirname(__file__),
            'food_search_trends/groups_of_food.pkl')
        with open(filename, 'rb') as f:
            groups_dict = pkl.load(f)
        connections = np.zeros((len(groups_dict), data.shape[1]))
        connections = pd.DataFrame(connections, index=list(groups_dict.keys
            ()), columns=data.columns)
        for g, v in groups_dict.items():
            connections.loc[g, v] = 1
        return data, connections, groups_dict
    else:
        return data


def load_finance(condensed=True, sectors=False):
    """
    Data from:  https://quantquote.com/historical-stock-data
    https://datahub.io/core/s-and-p-500-companies
    """
    if condensed:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/finance.csv')
        data = pd.read_csv(filename, index_col=0)
    else:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/not_collapsed.csv')
        data = pd.read_csv(filename, index_col=0)
    if sectors:
        filename = os.path.join(os.path.dirname(__file__),
            'finance/industries_sector.csv')
        sectors = pd.read_csv(filename, index_col=0)

"""Utility to load datasets."""
import pandas as pd
import pickle as pkl
import numpy as np
import os


def load_food_search_trends(groups=False):
    """
    Data from: http://foodb.ca/
    https://www.google.com/intl/es419/search/about/
    """
    filename = os.path.join(os.path.dirname(__file__), "food_search_trends/food.csv")
    data = pd.read_csv(filename, index_col=0)
    if groups:
        filename = os.path.join(os.path.dirname(__file__), "food_search_trends/groups_of_food.pkl")
        with open(filename, "rb") as f:
            groups_dict = pkl.load(f)
        connections = np.zeros((len(groups_dict), data.shape[1]))
        connections = pd.DataFrame(connections, index=list(groups_dict.keys()), columns=data.columns)

        for g, v in groups_dict.items():
            connections.loc[g, v] = 1

        return data, connections, groups_dict
    else:
        return data


def load_finance(condensed=True, sectors=False):
    """
    Data from:  https://quantquote.com/historical-stock-data
    https://datahub.io/core/s-and-p-500-companies
    """
    if condensed:
        filename = os.path.join(os.path.dirname(__file__), "finance/finance.csv")
        data = pd.read_csv(filename, index_col=0)
    else:
        filename = os.path.join(os.path.dirname(__file__), "finance/not_collapsed.csv")
        data = pd.read_csv(filename, index_col=0)

    if sectors:
        filename = os.path.join(os.path.dirname(__file__), "finance/industries_sector.csv")
        sectors = pd.read_csv(filename, index_col=0)
