Load geo-level data with reach and frequency

    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 , geo , controls , population , kpi , and revenue_per_kpi . For media channels that don't have reach and frequency data, you must assign their media exposure and media spend to the categories of media and media_spend , respectively. Conversely, for media channels that do possess reach and frequency data, you must map their reach, frequency, and media spend to the categories of reach , frequency , and rf_spend correspondingly. For the definition of each variable, see Collect and organize your data .

        coord_to_columns 
 = 
 load 
 . 
 CoordToColumns 
 ( 
 time 
 = 
 'time' 
 , 
 geo 
 = 
 'geo' 
 , 
 controls 
 = 
 [ 
 'GQV' 
 , 
 'Discount' 
 , 
 'Competitor_Sales' 
 ], 
 population 
 = 
 'population' 
 , 
 kpi 
 = 
 'conversions' 
 , 
 revenue_per_kpi 
 = 
 'revenue_per_conversion' 
 , 
 media 
 = 
 [ 
 'Channel0_impression' 
 , 
 'Channel1_impression' 
 , 
 'Channel2_impression' 
 , 
 'Channel3_impression' 
 , 
 ], 
 media_spend 
 = 
 [ 
 'Channel0_spend' 
 , 
 'Channel1_spend' 
 , 
 'Channel2_spend' 
 , 
 'Channel3_spend' 
 , 
 ], 
 reach 
 = 
 [ 
 'Channel4_reach' 
 , 
 'Channel5_reach' 
 ], 
 frequency 
 = 
 [ 
 'Channel4_frequency' 
 , 
 'Channel5_frequency' 
 ], 
 rf_spend 
 = 
 [ 
 'Channel4_spend' 
 , 
 'Channel5_spend' 
 ], 
 )

    2. Map the media exposure, reach, frequency, 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 . Additionally, Channel4_reach , Channel4_frequency , and Channel4_spend are connected to the same channel, Channel4 .

        correct_media_to_channel 
 = 
 { 
 'Channel0_impression' 
 : 
 'Channel0' 
 , 
 'Channel1_impression' 
 : 
 'Channel1' 
 , 
 'Channel2_impression' 
 : 
 'Channel2' 
 , 
 'Channel3_impression' 
 : 
 'Channel3' 
 , 
 } 
 correct_media_spend_to_channel 
 = 
 { 
 'Channel0_spend' 
 : 
 'Channel0' 
 , 
 'Channel1_spend' 
 : 
 'Channel1' 
 , 
 'Channel2_spend' 
 : 
 'Channel2' 
 , 
 'Channel3_spend' 
 : 
 'Channel3' 
 , 
 } 
 correct_reach_to_channel 
 = 
 { 
 'Channel4_reach' 
 : 
 'Channel4' 
 , 
 'Channel5_reach' 
 : 
 'Channel5' 
 , 
 } 
 correct_frequency_to_channel 
 = 
 { 
 'Channel4_frequency' 
 : 
 'Channel4' 
 , 
 'Channel5_frequency' 
 : 
 'Channel5' 
 , 
 } 
 correct_rf_spend_to_channel 
 = 
 { 
 '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 
 , 
 reach_to_channel 
 = 
 correct_reach_to_channel 
 , 
 frequency_to_channel 
 = 
 correct_frequency_to_channel 
 , 
 rf_spend_to_channel 
 = 
 correct_rf_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 pickled simulated Xarray Dataset using XrDatasetDataLoader :

    1. Load the data using pickle :

        import 
  
 pickle 
 with 
 open 
 ( 
 f 
 '/ 
 { 
 PATH 
 } 
 / 
 { 
 FILENAME 
 } 
 .pkl' 
 , 
 'r' 
 ) 
 as 
 fh 
 : 
 dataset 
 = 
 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 geo , time , control_variable , media_channel , and rf_channel , where rf_channel designates the channels having reach and frequency data. The required data variables names are kpi , revenue_per_kpi , controls , population , media , media_spend , reach , frequency , and rf_spend .

        loader 
 = 
 load 
 . 
 XrDatasetDataLoader 
 ( 
 dataset 
 , 
 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' 
 , 
 'reach' 
 : 
 'reach' 
 , 
 'frequency' 
 : 
 'frequency' 
 , 
 'rf_spend' 
 : 
 'rf_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 
 ], 
 [ 
 4 
 , 
 5 
 , 
 6 
 ], 
 [ 
 7 
 , 
 8 
 , 
 9 
 ]]) 
 controls_nd 
 = 
 np 
 . 
 array 
 ([ 
 [[ 
 1 
 , 
 5 
 ], 
 [ 
 2 
 , 
 6 
 ], 
 [ 
 3 
 , 
 4 
 ]], 
 [[ 
 7 
 , 
 8 
 ], 
 [ 
 9 
 , 
 10 
 ], 
 [ 
 11 
 , 
 12 
 ]], 
 [[ 
 13 
 , 
 14 
 ], 
 [ 
 15 
 , 
 16 
 ], 
 [ 
 17 
 , 
 18 
 ]], 
 ]) 
 population_nd 
 = 
 np 
 . 
 array 
 ([ 
 1 
 , 
 2 
 , 
 3 
 ]) 
 revenue_per_kpi_nd 
 = 
 np 
 . 
 array 
 ([[ 
 1 
 , 
 2 
 , 
 3 
 ], 
 [ 
 4 
 , 
 5 
 , 
 6 
 ], 
 [ 
 7 
 , 
 8 
 , 
 9 
 ]]) 
 reach_nd 
 = 
 np 
 . 
 array 
 ([ 
 [[ 
 1 
 , 
 5 
 ], 
 [ 
 2 
 , 
 6 
 ], 
 [ 
 3 
 , 
 4 
 ]], 
 [[ 
 7 
 , 
 8 
 ], 
 [ 
 9 
 , 
 10 
 ], 
 [ 
 11 
 , 
 12 
 ]], 
 [[ 
 13 
 , 
 14 
 ], 
 [ 
 15 
 , 
 16 
 ], 
 [ 
 17 
 , 
 18 
 ]], 
 ]) 
 frequency_nd 
 = 
 np 
 . 
 array 
 ([ 
 [[ 
 1 
 , 
 5 
 ], 
 [ 
 2 
 , 
 6 
 ], 
 [ 
 3 
 , 
 4 
 ]], 
 [[ 
 7 
 , 
 8 
 ], 
 [ 
 9 
 , 
 10 
 ], 
 [ 
 11 
 , 
 12 
 ]], 
 [[ 
 13 
 , 
 14 
 ], 
 [ 
 15 
 , 
 16 
 ], 
 [ 
 17 
 , 
 18 
 ]], 
 ]) 
 rf_spend_nd 
 = 
 np 
 . 
 array 
 ([ 
 [[ 
 1 
 , 
 5 
 ], 
 [ 
 2 
 , 
 6 
 ], 
 [ 
 3 
 , 
 4 
 ]], 
 [[ 
 7 
 , 
 8 
 ], 
 [ 
 9 
 , 
 10 
 ], 
 [ 
 11 
 , 
 12 
 ]], 
 [[ 
 13 
 , 
 14 
 ], 
 [ 
 15 
 , 
 16 
 ], 
 [ 
 17 
 , 
 18 
 ]], 
 ])

    2. Use a NDArrayInputDataBuilder to set time and geos, 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 
 . 
 geos 
 = 
 [ 
 'B' 
 , 
 'A' 
 , 
 'C' 
 ] 
 builder 
 = 
 ( 
 builder 
 . 
 with_kpi 
 ( 
 kpi_nd 
 ) 
 . 
 with_revenue_per_kpi 
 ( 
 revenue_per_kpi_nd 
 ) 
 . 
 with_population 
 ( 
 population_nd 
 ) 
 . 
 with_controls 
 ( 
 controls_nd 
 , 
 control_names 
 = 
 [ 
 "control0" 
 , 
 "control1" 
 ]) 
 . 
 with_reach 
 ( 
 r_nd 
 = 
 reach_nd 
 , 
 f_nd 
 = 
 frequency_nd 
 , 
 rfs_nd 
 = 
 rf_spend_nd 
 , 
 rf_channels 
 = 
 [ 
 "channel0" 
 , 
 "channel1" 
 ] 
 ) 
 ) 
 data 
 = 
 builder 
 . 
 build 
 ()

      Where:

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

    Pandas DataFrame 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/geo_media_rf.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_population 
 ( 
 df 
 ) 
 . 
 with_controls 
 ( 
 df 
 , 
 control_cols 
 = 
 [ 
 "GQV" 
 , 
 "Discount" 
 , 
 "Competitor_Sales" 
 ]) 
 . 
 with_reach 
 ( 
 df 
 , 
 reach_cols 
 = 
 [ 
 'Channel4_reach' 
 , 
 'Channel5_reach' 
 ], 
 frequency_cols 
 = 
 [ 
 'Channel4_frequency' 
 , 
 'Channel5_frequency' 
 ], 
 rf_spend_cols 
 = 
 [ 
 'Channel4_spend' 
 , 
 'Channel5_spend' 
 ], 
 rf_channels 
 = 
 [ 
 'Channel4' 
 , 
 'Channel5' 
 ], 
 ) 
 ) 
 data 
 = 
 builder 
 . 
 build 
 ()

      Where:

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

    Next, you can create your model .

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