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' 
    , 
    default_kpi_column 
    = 
    "conversions" 
    , 
    default_revenue_per_kpi_column 
    = 
    "revenue_per_conversion" 
    , 
    ) 
    builder 
    = 
    ( 
    builder 
    . 
    with_kpi 
    ( 
    df 
    ) 
    . 
    with_revenue_per_kpi 
    ( 
    df 
    ) 
    . 
    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 .