Simulated data is provided as an example for each data type and format in the following sections. ## CSV To load the [simulated CSV data](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/csv/geo_media_rf.csv) 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](https://developers.google.com/meridian/docs/pre-modeling/collect-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, currency_code='USD', ) data = loader.load() Where: - `kpi_type` is either `'revenue'` or `'non_revenue'`. - `currency_code` (optional) is a 3-letter ISO 4217 currency code (such as `'USD'`, `'EUR'`, `'GBP'`) specifying the monetary unit of spend and revenue. When set, reporting visualizers automatically format monetary values with the corresponding currency symbol. - `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](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/pkl/geo_media_rf.pkl) 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 variable 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`](https://github.com/google/meridian/blob/4624447e0aace5c24d42b58dd1cfd8fe0dc00971/meridian/data/nd_array_input_data_builder.py#L25) to set time and geos, as well as give channel or dimension names as required in Meridian input data. For the definition of each variable, see [Collect and organize your data](https://developers.google.com/meridian/docs/pre-modeling/collect-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](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/xlsx/geo_media_rf.xlsx) (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`](https://github.com/google/meridian/blob/4624447e0aace5c24d42b58dd1cfd8fe0dc00971/meridian/data/data_frame_input_data_builder.py#L25) to map column names to the variable types required in Meridian input data. For the definition of each variable, see [Collect and organize your data](https://developers.google.com/meridian/docs/pre-modeling/collect-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.currency_code = 'USD' 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'`. - `currency_code` is the optional ISO 4217 currency code (e.g. `'USD'`). Next, you can [create your model](https://developers.google.com/meridian/docs/user-guide/modeling-overview).