Load national-level data

Simulated data is provided as an example for each data type and format in the following sections.

CSV

To load the simulated CSV data using CsvDataLoader:

1. Map the column names to the variable types. The required variable types are time, controls, kpi, revenue_per_kpi, media and media_spend. For the definition of each variable, see Collect and organize your data.

   coord_to_columns = load.CoordToColumns(
       time='time',
       controls=['GQV', 'Discount', 'Competitor_Sales'],
       kpi='conversions',
       revenue_per_kpi='revenue_per_conversion',
       media=[
           'Channel0_impression',
           'Channel1_impression',
           'Channel2_impression',
           'Channel3_impression',
           'Channel4_impression',
           'Channel5_impression',
       ],
       media_spend=[
           'Channel0_spend',
           'Channel1_spend',
           'Channel2_spend',
           'Channel3_spend',
           'Channel4_spend',
           'Channel5_spend',
       ],
   )
   

2. Map the media variables and the media spends to the designated channel names that you want to display in the two-page output. In the following example, Channel0_impression and Channel0_spend are connected to the same channel, Channel0.

   correct_media_to_channel = {
       'Channel0_impression': 'Channel0',
       'Channel1_impression': 'Channel1',
       'Channel2_impression': 'Channel2',
       'Channel3_impression': 'Channel3',
       'Channel4_impression': 'Channel4',
       'Channel5_impression': 'Channel5',
   }
   correct_media_spend_to_channel = {
       'Channel0_spend': 'Channel0',
       'Channel1_spend': 'Channel1',
       'Channel2_spend': 'Channel2',
       'Channel3_spend': 'Channel3',
       'Channel4_spend': 'Channel4',
       'Channel5_spend': 'Channel5',
   }
   

3. Load the data using CsvDataLoader:

   loader = load.CsvDataLoader(
       csv_path=f'/{PATH}/{FILENAME}.csv',
       kpi_type='non_revenue',
       coord_to_columns=coord_to_columns,
       media_to_channel=correct_media_to_channel,
       media_spend_to_channel=correct_media_spend_to_channel,
   )
   data = loader.load()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.
   • PATH is the path to the data file location.
   • FILENAME is the name of your data file.

Xarray Dataset

To load the simulated Xarray Dataset using XrDatasetDataLoader:

1. Load the data using pickle:

   import pickle
   with open(f'/{PATH}/{FILENAME}.pkl', 'r') as fh:
     XrDataset=pickle.load(fh)
   

   Where:

   • PATH is the path to the data file location.
   • FILENAME is the name of your data file.

2. Pass the dataset to XrDatasetDataLoader. Use the name_mapping argument to map the coordinates and arrays. Provide mapping if the names in the input dataset are different from the required names. The required coordinate names are time, control_variable, and media_channel. The required data variables names are kpi, revenue_per_kpi, controls, media, and media_spend.

   loader = load.XrDatasetDataLoader(
       XrDataset,
       kpi_type='non_revenue',
       name_mapping={'channel': 'media_channel',
                     'control': 'control_variable',
                     'conversions': 'kpi',
                     'revenue_per_conversion': 'revenue_per_kpi',
                     'control_value': 'controls',
                     'spend': 'media_spend'},
   )
   
   data = loader.load()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.

Numpy ndarray

To load numpy ndarrays directly, use NDArrayInputDataBuilder:

1. Create the data into separate numpy ndarrays.

   import numpy as np
   
   kpi_nd = np.array([[1, 2, 3]])
   controls_nd = np.array([[[1, 2], [3, 4], [5, 6]]])
   revenue_per_kpi_nd = np.array([[1, 2, 3]])
   media_nd = np.array([[[1, 2], [3, 4], [5, 6]]])
   media_spend_nd = np.array([[[1, 2], [3, 4], [5, 6]]])
   

2. Use a NDArrayInputDataBuilder to set times, as well as give channel or dimension names as required in a Meridian input data. For the definition of each variable, see Collect and organize your data.

   from meridian.data import nd_array_input_data_builder as data_builder
   
   builder = (
       data_builder.NDArrayInputDataBuilder(kpi_type='non_revenue')
   )
   builder.time_coords = ['2024-01-02', '2024-01-03', '2024-01-01']
   builder.media_time_coords = ['2024-01-02', '2024-01-03', '2024-01-01']
   builder = (
     builder
       .with_kpi(kpi_nd)
       .with_revenue_per_kpi(revenue_per_kpi_nd)
       .with_controls(
         controls_nd,
         control_names=["control0", "control1"])
       .with_media(
         m_nd=media_nd,
         ms_nd=media_spend_nd,
         media_channels=["channel0", "channel1"]
       )
   )
   
   data = builder.build()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.

Pandas DataFrames or other data formats

To load the simulated other data format (such as excel) using DataFrameInputDataBuilder:

1. Read the data (such as an excel spreadsheet) into one or more Pandas DataFrame(s).

   import pandas as pd
   
   df = pd.read_excel(
       'https://github.com/google/meridian/raw/main/meridian/data/simulated_data/xlsx/national_media.xlsx',
       engine='openpyxl',
   )
   

2. Use a DataFrameInputDataBuilder to map column names to the variable types required in a Meridian input data. For the definition of each variable, see Collect and organize your data.

   from meridian.data import data_frame_input_data_builder as data_builder
   
   builder = (
       data_builder.DataFrameInputDataBuilder(kpi_type='non_revenue')
           .with_kpi(df, kpi_col="conversions")
           .with_revenue_per_kpi(df, revenue_per_kpi_col="revenue_per_conversion")
           .with_controls(df, control_cols=["GQV", "Discount", "Competitor_Sales"])
   )
   channels = ["Channel0", "Channel1", "Channel2", "Channel3", "Channel4", "Channel5"]
   builder = builder.with_media(
       df,
       media_cols=[f"{channel}_impression" for channel in channels],
       media_spend_cols=[f"{channel}_spend" for channel in channels],
       media_channels=channels,
   )
   
   data = builder.build()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.

Next, you can create your model.