lale/datasets/multitable/fetch_datasets.py
def fetch_go_sales_dataset(datatype="pandas"):

    """
    Fetches the Go_Sales dataset from IBM's Watson's ML samples.
    It contains information about daily sales, methods, retailers
    and products of a company in form of 5 CSV files.
    This method downloads and stores these 5 CSV files under the
    'lale/lale/datasets/multitable/go_sales_data' directory. It creates
    this directory by itself if it does not exists.

    Dataset URL: https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/go_sales/

    Parameters
    ----------
    datatype : string, optional, default 'pandas'

      If 'pandas',
      Returns a list of singleton dictionaries (each element of the list is one
      table from the dataset) after reading the downloaded CSV files. The key of
      each dictionary is the name of the table and the value contains a pandas
      dataframe consisting of the data.

      If 'spark',
      Returns a list of singleton dictionaries (each element of the list is one
      table from the dataset) after reading the downloaded CSV files. The key of
      each dictionary is the name of the table and the value contains a spark
      dataframe consisting of the data.

      Else,
      Throws an error as it does not support any other return type.

    Returns
    -------
    go_sales_list : list of singleton dictionary of pandas / spark dataframes
    """

    download_data_dir = os.path.join(os.path.dirname(__file__), "go_sales_data")
    base_url = "https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/go_sales/"
    filenames = [
        "go_1k.csv",
        "go_daily_sales.csv",
        "go_methods.csv",
        "go_products.csv",
        "go_retailers.csv",
    ]
    go_sales_list = []
    for file in filenames:
        data_file_name = os.path.join(download_data_dir, file)
        if not os.path.exists(data_file_name):
            if not os.path.exists(download_data_dir):
                os.makedirs(download_data_dir)
            urllib.request.urlretrieve(base_url + file, data_file_name)
            logger.info(" Created: {}".format(data_file_name))
        table_name = file.split(".")[0]
        data_frame = get_data_from_csv(datatype, data_file_name)
        go_sales_list.append(add_table_name(data_frame, table_name))
    logger.info(" Fetched the Go_Sales dataset. Process completed.")
    return go_sales_list

    Fetches the Go_Sales dataset from IBM's Watson's ML samples.
    It contains information about daily sales, methods, retailers
    and products of a company in form of 5 CSV files.
    This method downloads and stores these 5 CSV files under the
    'lale/lale/datasets/multitable/go_sales_data' directory. It creates
    this directory by itself if it does not exists.

    Dataset URL: https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/go_sales/

    Parameters
    ----------
    datatype : string, optional, default 'pandas'

      If 'pandas',
      Returns a list of singleton dictionaries (each element of the list is one
      table from the dataset) after reading the downloaded CSV files. The key of
      each dictionary is the name of the table and the value contains a pandas
      dataframe consisting of the data.

      If 'spark',
      Returns a list of singleton dictionaries (each element of the list is one
      table from the dataset) after reading the downloaded CSV files. The key of
      each dictionary is the name of the table and the value contains a spark
      dataframe consisting of the data.

      Else,
      Throws an error as it does not support any other return type.

    Returns
    -------
    go_sales_list : list of singleton dictionary of pandas / spark dataframes
    
Fetches the Go_Sales dataset from IBM's Watson's ML samples.
It contains information about daily sales, methods, retailers
and products of a company in form of 5 CSV files.
This method downloads and stores these 5 CSV files under the
'lale/lale/datasets/multitable/go_sales_data' directory. It creates
this directory by itself if it does not exists.



Parameters

datatype : string, optional, default 'pandas'

If 'pandas',
Returns a list of singleton dictionaries (each element of the list is one
table from the dataset) after reading the downloaded CSV files. The key of
each dictionary is the name of the table and the value contains a pandas
dataframe consisting of the data.

If 'spark',
Returns a list of singleton dictionaries (each element of the list is one
table from the dataset) after reading the downloaded CSV files. The key of
each dictionary is the name of the table and the value contains a spark
dataframe consisting of the data.

Else,
Throws an error as it does not support any other return type.

Returns

go_sales_list : list of singleton dictionary of pandas / spark dataframes
Fetches the Go_Sales dataset from IBM ' s Watson ' s ML samples . It contains information about daily sales methods retailers and products of a company in form of 5 CSV files . This method downloads and stores these 5 CSV files under the ' lale / lale / datasets / multitable / go_sales_data ' directory . It creates this directory by itself if it does not exists . Parameters datatype : string optional default ' pandas ' If ' pandas ' Returns a list of singleton dictionaries ( each element of the list is one table from the dataset ) after reading the downloaded CSV files . The key of each dictionary is the name of the table and the value contains a pandas dataframe consisting of the data . If ' spark ' Returns a list of singleton dictionaries ( each element of the list is one table from the dataset ) after reading the downloaded CSV files . The key of each dictionary is the name of the table and the value contains a spark dataframe consisting of the data . Else Throws an error as it does not support any other return type . Returns go_sales_list : list of singleton dictionary of pandas / spark dataframes
def fetch_go_sales_dataset(datatype="pandas"):
    download_data_dir = os.path.join(os.path.dirname(__file__), "go_sales_data")
    base_url = "https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/go_sales/"
    filenames = [
        "go_1k.csv",
        "go_daily_sales.csv",
        "go_methods.csv",
        "go_products.csv",
        "go_retailers.csv",
    ]
    go_sales_list = []
    for file in filenames:
        data_file_name = os.path.join(download_data_dir, file)
        if not os.path.exists(data_file_name):
            if not os.path.exists(download_data_dir):
                os.makedirs(download_data_dir)
            urllib.request.urlretrieve(base_url + file, data_file_name)
            logger.info(" Created: {}".format(data_file_name))
        table_name = file.split(".")[0]
        data_frame = get_data_from_csv(datatype, data_file_name)
        go_sales_list.append(add_table_name(data_frame, table_name))
    logger.info(" Fetched the Go_Sales dataset. Process completed.")
    return go_sales_list
def fetch_go_sales_dataset ( datatype = "pandas" ) : download_data_dir = os . path . join ( os . path . dirname ( __file__ ) , "go_sales_data" ) base_url = "https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/go_sales/" filenames = [ "go_1k.csv" , "go_daily_sales.csv" , "go_methods.csv" , "go_products.csv" , "go_retailers.csv" , ] go_sales_list = [ ] for file in filenames : data_file_name = os . path . join ( download_data_dir , file ) if not os . path . exists ( data_file_name ) : if not os . path . exists ( download_data_dir ) : os . makedirs ( download_data_dir ) urllib . request . urlretrieve ( base_url + file , data_file_name ) logger . info ( " Created: {}" . format ( data_file_name ) ) table_name = file . split ( "." ) [ 0 ] data_frame = get_data_from_csv ( datatype , data_file_name ) go_sales_list . append ( add_table_name ( data_frame , table_name ) ) logger . info ( " Fetched the Go_Sales dataset. Process completed." ) return go_sales_list
Fetches the Go_Sales dataset from IBM's Watson's ML samples.
Fetches the Go_Sales dataset from IBM ' s Watson ' s ML samples .
"""
    Fetches the Go_Sales dataset from IBM's Watson's ML samples.
    It contains information about daily sales, methods, retailers
    and products of a company in form of 5 CSV files.
    This method downloads and stores these 5 CSV files under the
    'lale/lale/datasets/multitable/go_sales_data' directory. It creates
    this directory by itself if it does not exists.

    Dataset URL: https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/go_sales/

    Parameters
    ----------
    datatype : string, optional, default 'pandas'

      If 'pandas',
      Returns a list of singleton dictionaries (each element of the list is one
      table from the dataset) after reading the downloaded CSV files. The key of
      each dictionary is the name of the table and the value contains a pandas
      dataframe consisting of the data.

      If 'spark',
      Returns a list of singleton dictionaries (each element of the list is one
      table from the dataset) after reading the downloaded CSV files. The key of
      each dictionary is the name of the table and the value contains a spark
      dataframe consisting of the data.

      Else,
      Throws an error as it does not support any other return type.

    Returns
    -------
    go_sales_list : list of singleton dictionary of pandas / spark dataframes
    """
datatype