.. _salesparty2:

Joining data - [Party 2]
===================================

In this tutorial, we will demonstrate how the second party in a collaboration would upload their data to the engine analysis to be performed by :ref:`Party 1<salesparty1>`.

Step 1: Start the Roseman Labs Engine
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In this step, we import the necessary libraries, including crandas and pandas.

.. code:: python

    import crandas as cd
    import pandas as pd
    from pathlib import Path


Step 2: Input data
~~~~~~~~~~~~~~~~~~

In this step, we define some parameters to limit the amount of data and set a table name as a reference for the other party (same as for Party 1). We also specify the relevant columns that we will use in our analysis.

.. code:: python

    #Set this to limit the amount of data
    rows_per_dataset = 1000
    # Use this name as a reference for the other party 
    nutrition_table_name = 'nutrition'

.. code:: python

    # Select relevant columns
    relevant_columns = ['ean_code',
                        'SPP_DESCRIPTION',
                        'ENERGY_VALUE_IN_KJ',
                        'ENERGY_VALUE_IN_KCAL',
                        'SODIUM_IN_MG',
                        'SATURATED_FATTY_ACIDS_IN_G',
                        'TOTAL_PROTEIN_IN_G',
                        'MONO_AND_DISACCHARIDES_IN_G',
                        'DIETARY_FIBER_IN_G',
                        'STANDARD_PORTION_SIZE',
                        'NUMBER_OF_PORTIONS_PER_PACKAGE']

Read the local file and upload it:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Now, we read the local CSV file containing the nutrition data using pandas and limit the number of rows based on the rows_per_dataset we set earlier. We then convert the ``ean_code`` column to a string data type.

.. code:: python

    file_path = '../../data/sales_nutrition_data/nutrition_data.csv'
    
    # Read the local csv using pandas
    nutrition_table_data = pd.read_csv(file_path, nrows=rows_per_dataset)
    
    # Upload nutrition data to the engine
    nutrition_table = cd.upload_pandas_dataframe(nutrition_table_data[relevant_columns]) 
        
    # show metadata for the table (column titles and field types, i=integer, s=string)
    print("Table meta-data:\n", repr(nutrition_table))

.. parsed-literal::

    Reading data...
    Uploading data...
    Table meta-data:
    Handle: 94EDD8E55C616AEB5C734AE212064DA5DFB4D59018BD8DCC8DC44C3F71BF610F
    Size: 1000 rows x 11 columns
    CIndex([Col("ean_code", "s", 14), Col("SPP_DESCRIPTION", "s", 14), Col("ENERGY_VALUE_IN_KJ", "i", 1), Col("ENERGY_VALUE_IN_KCAL", "i", 1), Col("SODIUM_IN_MG", "i", 1), Col("SATURATED_FATTY_ACIDS_IN_G", "i", 1), Col("TOTAL_PROTEIN_IN_G", "i", 1), Col("MONO_AND_DISACCHARIDES_IN_G", "i", 1), Col("DIETARY_FIBER_IN_G", "i", 1), Col("STANDARD_PORTION_SIZE", "i", 1), Col("NUMBER_OF_PORTIONS_PER_PACKAGE", "i", 1)])

.. important::
    
    When executing this script, the table handle will be different. Copy it and paste it in the right place in the :ref:`Party 1<salesparty1>` script.

Now our data is in the database, ready to be used by another party by simply retrieving it by name.