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Cómo cargar datos a nivel de la ubicación geográfica con tratamientos de medios orgánicos y no relacionados con los medios
Organiza tus páginas con colecciones
Guarda y categoriza el contenido según tus preferencias.
Se proporcionan datos simulados como ejemplo para cada tipo y formato de datos en las siguientes secciones:
CSV
Para cargar los datos de CSV simulados con CsvDataLoader, haz lo siguiente:
Asocia los nombres de las columnas con los tipos de variables. Los tipos de variables requeridos son time, geo, controls, population, kpi, revenue_per_kpi, media y media_spend. Para los canales de medios que no tienen un costo directo, debes asignar su exposición a organic_media. Para los tratamientos que no son de medios, debes asignar los nombres de las columnas correspondientes a non_media_treatments.
Para obtener la definición de cada variable, consulta Cómo recopilar y organizar tus datos.
coord_to_columns = load.CoordToColumns(
time='time',
geo='geo',
controls=['GQV', 'Competitor_Sales'],
population='population',
kpi='conversions',
revenue_per_kpi='revenue_per_conversion',
media=[
'Channel0_impression',
'Channel1_impression',
'Channel2_impression',
'Channel3_impression',
'Channel4_impression',
],
media_spend=[
'Channel0_spend',
'Channel1_spend',
'Channel2_spend',
'Channel3_spend',
'Channel4_spend',
],
organic_media=['Organic_channel0_impression'],
non_media_treatments=['Promo'],
)
Asocia las variables de medios y las inversiones en medios con los nombres de los canales designados que deseas mostrar en el resultado de dos páginas. En el siguiente ejemplo, Channel0_impression y Channel0_spend están conectados al mismo canal, Channel0.
correct_media_to_channel = {
'Channel0_impression': 'Channel0',
'Channel1_impression': 'Channel1',
'Channel2_impression': 'Channel2',
'Channel3_impression': 'Channel3',
'Channel4_impression': 'Channel4',
}
correct_media_spend_to_channel = {
'Channel0_spend': 'Channel0',
'Channel1_spend': 'Channel1',
'Channel2_spend': 'Channel2',
'Channel3_spend': 'Channel3',
'Channel4_spend': 'Channel4',
}
Carga los datos con 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()
Donde:
kpi_type es 'revenue' o 'non_revenue'.PATH es la ruta de acceso a la ubicación del archivo de datos.FILENAME es el nombre de tu archivo de datos.
Conjunto de datos Xarray
Para cargar el conjunto de datos Xarray simulado con XrDatasetDataLoader, haz lo siguiente:
Carga los datos con pickle:
import pickle
with open(f'/{PATH}/{FILENAME}.pkl', 'r') as fh:
XrDataset=pickle.load(fh)
Donde:
PATH es la ruta de acceso a la ubicación del archivo de datos.FILENAME es el nombre de tu archivo de datos.
Pasa el conjunto de datos a XrDatasetDataLoader. Usa el argumento name_mapping para asociar las coordenadas y los arrays. Procede a la asociación si los nombres incluidos en el conjunto de datos de entrada no coinciden con los nombres requeridos. Los nombres de las coordenadas son geo, time, control_variable, media_channel,
organic_media_channel y non_media_channel. Los nombres de las variables de datos requeridos son kpi, revenue_per_kpi, controls, population, media, media_spend, organic_media y non_media_treatments.
loader = load.XrDatasetDataLoader(
XrDataset,
kpi_type='non_revenue',
name_mapping={'channel': 'media_channel',
'control': 'control_variable',
'organic_channel': 'organic_media_channel',
'non_media_treatment': 'non_media_channel',
'conversions': 'kpi',
'revenue_per_conversion': 'revenue_per_kpi',
'control_value': 'controls',
'spend': 'media_spend',
'non_media_treatment_value': 'non_media_treatments'},
)
data = loader.load()
Donde:
kpi_type es 'revenue' o 'non_revenue'.
Ndarray de NumPy
Para cargar ndarrays de numPy directamente, usa NDArrayInputDataBuilder:
Crea los datos en ndarrays de numPy separados.
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]])
media_nd = np.array([
[[1, 5], [2, 6], [3, 4]],
[[7, 8], [9, 10], [11, 12]],
[[13, 14], [15, 16], [17, 18]],
])
media_spend_nd = np.array([
[[1, 5], [2, 6], [3, 4]],
[[7, 8], [9, 10], [11, 12]],
[[13, 14], [15, 16], [17, 18]],
])
organic_media_nd = np.array([
[[1, 5], [2, 6], [3, 4]],
[[7, 8], [9, 10], [11, 12]],
[[13, 14], [15, 16], [17, 18]],
])
non_media_treatments_nd = np.array([
[[1, 5], [2, 6], [3, 4]],
[[7, 8], [9, 10], [11, 12]],
[[13, 14], [15, 16], [17, 18]],
])
Usa un NDArrayInputDataBuilder para establecer la hora y las ubicaciones geográficas, así como para asignar nombres de canales o dimensiones según sea necesario en los datos de entrada de Meridian.
Para conocer la definición de cada variable, consulta Cómo recopilar y organizar tus datos.
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_media(
m_nd=media_nd,
ms_nd=media_spend_nd,
media_channels=["channel0", "channel1"]
)
.with_organic_media(
organic_media_nd,
organic_media_channels=["organic_channel0", "organic_channel1"]
).with_non_media_treatments(
non_media_treatments_nd,
non_media_channel_names=["non_media_channel0", "non_media_channel1"]
)
)
data = builder.build()
Donde:
kpi_type es 'revenue' o 'non_revenue'.
Para cargar otro formato de datos simulados (como excel) con DataFrameInputDataBuilder, haz lo siguiente:
Lee los datos (como una hoja de cálculo de excel) en uno o más DataFrame de Pandas.
import pandas as pd
df = pd.read_excel(
'https://github.com/google/meridian/raw/main/meridian/data/simulated_data/xlsx/geo_all_channels.xlsx',
engine='openpyxl',
)
Usa DataFrameInputDataBuilder para asociar los nombres de las columnas con los tipos de variables requeridos en los datos de entrada de Meridian.
Para conocer la definición de cada variable, consulta Cómo recopilar y organizar tus datos.
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", "Competitor_Sales"])
)
channels = ["Channel0", "Channel1", "Channel2", "Channel3", "Channel4"]
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,
)
builder = (
builder
.with_organic_media(
df,
organic_media_cols = ["Organic_channel0_impression"],
organic_media_channels = ["Organic_channel0"],
)
.with_non_media_treatments(
df,
non_media_treatment_cols=['Promo']
)
)
data = builder.build()
Donde:
kpi_type es 'revenue' o 'non_revenue'.
A continuación, puedes crear tu modelo.
Salvo que se indique lo contrario, el contenido de esta página está sujeto a la licencia Atribución 4.0 de Creative Commons, y los ejemplos de código están sujetos a la licencia Apache 2.0. Para obtener más información, consulta las políticas del sitio de Google Developers. Java es una marca registrada de Oracle o sus afiliados.
Última actualización: 2025-08-16 (UTC)
[null,null,["Última actualización: 2025-08-16 (UTC)"],[[["\u003cp\u003eSimulated data examples are provided for CSV, Xarray Dataset, and other formats like Excel, showcasing how to load data with different loaders.\u003c/p\u003e\n"],["\u003cp\u003eWhen loading CSV data, you need to map column names to variable types like \u003ccode\u003etime\u003c/code\u003e, \u003ccode\u003egeo\u003c/code\u003e, \u003ccode\u003econtrols\u003c/code\u003e, \u003ccode\u003epopulation\u003c/code\u003e, \u003ccode\u003ekpi\u003c/code\u003e, \u003ccode\u003erevenue_per_kpi\u003c/code\u003e, \u003ccode\u003emedia\u003c/code\u003e, and \u003ccode\u003emedia_spend\u003c/code\u003e using \u003ccode\u003eCsvDataLoader\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eLoading Xarray Dataset data requires using \u003ccode\u003eXrDatasetDataLoader\u003c/code\u003e and mapping coordinates and arrays through the \u003ccode\u003ename_mapping\u003c/code\u003e argument, accommodating variations in input dataset names.\u003c/p\u003e\n"],["\u003cp\u003eFor other data formats, like Excel, \u003ccode\u003eDataFrameDataLoader\u003c/code\u003e is used after mapping column names to the required variable types, and reading the data into a DataFrame.\u003c/p\u003e\n"],["\u003cp\u003eEach data loader, such as \u003ccode\u003eCsvDataLoader\u003c/code\u003e, \u003ccode\u003eXrDatasetDataLoader\u003c/code\u003e, and \u003ccode\u003eDataFrameDataLoader\u003c/code\u003e, requires setting the \u003ccode\u003ekpi_type\u003c/code\u003e as either \u003ccode\u003e'revenue'\u003c/code\u003e or \u003ccode\u003e'non_revenue'\u003c/code\u003e.\u003c/p\u003e\n"]]],["Three data formats—CSV, Xarray Dataset, and others (like Excel)—are detailed. For CSV and other formats, users must map column names to variable types like `time`, `geo`, `controls`, `population`, `kpi`, etc., and map media variables to channel names. They load data using `CsvDataLoader` or `DataFrameDataLoader`, specifying `kpi_type`, paths, and mappings. For Xarray, users load the data using pickle and pass the data to `XrDatasetDataLoader`, providing mappings for coordinates and arrays, then load it. Each loader has a load function to load the data.\n"],null,["# Load geo-level data with organic media and non-media treatments\n\nSimulated data is provided as an example for each data type and format in the\nfollowing sections.\n\nCSV\n---\n\nTo load the [simulated\nCSV](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/csv/geo_all_channels.csv)\ndata using `CsvDataLoader`:\n\n1. Map the column names to the variable types. The required variable types are\n `time`, `geo`, `controls`, `population`, `kpi`, `revenue_per_kpi`, `media`,\n and `media_spend`. For media channels that have no direct cost, you must\n assign their media exposure to `organic_media`. For non-media treatments,\n you must assign the corresponding columns names to `non_media_treatments`.\n For the definition of each variable, see [Collect and organize your\n data](/meridian/docs/user-guide/collect-data).\n\n coord_to_columns = load.CoordToColumns(\n time='time',\n geo='geo',\n controls=['GQV', 'Competitor_Sales'],\n population='population',\n kpi='conversions',\n revenue_per_kpi='revenue_per_conversion',\n media=[\n 'Channel0_impression',\n 'Channel1_impression',\n 'Channel2_impression',\n 'Channel3_impression',\n 'Channel4_impression',\n ],\n media_spend=[\n 'Channel0_spend',\n 'Channel1_spend',\n 'Channel2_spend',\n 'Channel3_spend',\n 'Channel4_spend',\n ],\n organic_media=['Organic_channel0_impression'],\n non_media_treatments=['Promo'],\n )\n\n2. Map the media variables and the media spends to the designated channel names\n that you want to display in the two-page output. In the following example,\n `Channel0_impression` and `Channel0_spend` are connected to the same\n channel, `Channel0`.\n\n correct_media_to_channel = {\n 'Channel0_impression': 'Channel0',\n 'Channel1_impression': 'Channel1',\n 'Channel2_impression': 'Channel2',\n 'Channel3_impression': 'Channel3',\n 'Channel4_impression': 'Channel4',\n }\n correct_media_spend_to_channel = {\n 'Channel0_spend': 'Channel0',\n 'Channel1_spend': 'Channel1',\n 'Channel2_spend': 'Channel2',\n 'Channel3_spend': 'Channel3',\n 'Channel4_spend': 'Channel4',\n }\n\n3. Load the data using `CsvDataLoader`:\n\n loader = load.CsvDataLoader(\n csv_path=f'/{PATH}/{FILENAME}.csv',\n kpi_type='non_revenue',\n coord_to_columns=coord_to_columns,\n media_to_channel=correct_media_to_channel,\n media_spend_to_channel=correct_media_spend_to_channel,\n )\n data = loader.load()\n\n Where:\n - `kpi_type` is either `'revenue'` or `'non_revenue'`.\n - `PATH` is the path to the data file location.\n - `FILENAME` is the name of your data file.\n\nXarray Dataset\n--------------\n\nTo load the [simulated Xarray\nDataset](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/pkl/geo_all_channels.pkl)\nusing `XrDatasetDataLoader`:\n\n1. Load the data using `pickle`:\n\n import pickle\n with open(f'/{PATH}/{FILENAME}.pkl', 'r') as fh:\n XrDataset=pickle.load(fh)\n\n Where:\n - `PATH` is the path to the data file location.\n - `FILENAME` is the name of your data file.\n2. Pass the dataset to `XrDatasetDataLoader`. Use the `name_mapping` argument\n to map the coordinates and arrays. Provide mapping if the names in the input\n dataset are different from the required names. The required coordinate names\n are `geo`, `time`, `control_variable`, `media_channel`,\n `organic_media_channel`, and `non_media_channel`. The required data\n variables names are `kpi`, `revenue_per_kpi`, `controls`, `population`,\n `media`, `media_spend`, `organic_media`, and `non_media_treatments`.\n\n loader = load.XrDatasetDataLoader(\n XrDataset,\n kpi_type='non_revenue',\n name_mapping={'channel': 'media_channel',\n 'control': 'control_variable',\n 'organic_channel': 'organic_media_channel',\n 'non_media_treatment': 'non_media_channel',\n 'conversions': 'kpi',\n 'revenue_per_conversion': 'revenue_per_kpi',\n 'control_value': 'controls',\n 'spend': 'media_spend',\n 'non_media_treatment_value': 'non_media_treatments'},\n )\n\n data = loader.load()\n\n Where:\n - `kpi_type` is either `'revenue'` or `'non_revenue'`.\n\nNumpy ndarray\n-------------\n\nTo load numpy ndarrays directly, use `NDArrayInputDataBuilder`:\n\n1. Create the data into separate numpy ndarrays.\n\n import numpy as np\n\n kpi_nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n controls_nd = np.array([\n [[1, 5], [2, 6], [3, 4]],\n [[7, 8], [9, 10], [11, 12]],\n [[13, 14], [15, 16], [17, 18]],\n ])\n population_nd = np.array([1, 2, 3])\n revenue_per_kpi_nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n media_nd = np.array([\n [[1, 5], [2, 6], [3, 4]],\n [[7, 8], [9, 10], [11, 12]],\n [[13, 14], [15, 16], [17, 18]],\n ])\n media_spend_nd = np.array([\n [[1, 5], [2, 6], [3, 4]],\n [[7, 8], [9, 10], [11, 12]],\n [[13, 14], [15, 16], [17, 18]],\n ])\n organic_media_nd = np.array([\n [[1, 5], [2, 6], [3, 4]],\n [[7, 8], [9, 10], [11, 12]],\n [[13, 14], [15, 16], [17, 18]],\n ])\n non_media_treatments_nd = np.array([\n [[1, 5], [2, 6], [3, 4]],\n [[7, 8], [9, 10], [11, 12]],\n [[13, 14], [15, 16], [17, 18]],\n ])\n\n2. Use a\n [`NDArrayInputDataBuilder`](https://github.com/google/meridian/blob/4624447e0aace5c24d42b58dd1cfd8fe0dc00971/meridian/data/nd_array_input_data_builder.py#L25)\n to set time and geos, as well as give channel or dimension\n names as required in a Meridian input data.\n For the definition of each variable, see\n [Collect and organize your data](/meridian/docs/user-guide/collect-data).\n\n from meridian.data import nd_array_input_data_builder as data_builder\n\n builder = (\n data_builder.NDArrayInputDataBuilder(kpi_type='non_revenue')\n )\n builder.time_coords = ['2024-01-02', '2024-01-03', '2024-01-01']\n builder.media_time_coords = ['2024-01-02', '2024-01-03', '2024-01-01']\n builder.geos = ['B', 'A', 'C']\n builder = (\n builder\n .with_kpi(kpi_nd)\n .with_revenue_per_kpi(revenue_per_kpi_nd)\n .with_population(population_nd)\n .with_controls(\n controls_nd,\n control_names=[\"control0\", \"control1\"])\n .with_media(\n m_nd=media_nd,\n ms_nd=media_spend_nd,\n media_channels=[\"channel0\", \"channel1\"]\n )\n .with_organic_media(\n organic_media_nd,\n organic_media_channels=[\"organic_channel0\", \"organic_channel1\"]\n ).with_non_media_treatments(\n non_media_treatments_nd,\n non_media_channel_names=[\"non_media_channel0\", \"non_media_channel1\"]\n )\n )\n\n data = builder.build()\n\n Where:\n - `kpi_type` is either `'revenue'` or `'non_revenue'`.\n\nPandas DataFrame or other data formats\n--------------------------------------\n\nTo load the [simulated other data\nformat](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/xlsx/geo_all_channels.xlsx)\n(such as `excel`) using `DataFrameInputDataBuilder`:\n\n1. Read the data (such as an `excel` spreadsheet) into one or more Pandas `DataFrame`(s).\n\n import pandas as pd\n\n df = pd.read_excel(\n 'https://github.com/google/meridian/raw/main/meridian/data/simulated_data/xlsx/geo_all_channels.xlsx',\n engine='openpyxl',\n )\n\n2. Use a\n [`DataFrameInputDataBuilder`](https://github.com/google/meridian/blob/4624447e0aace5c24d42b58dd1cfd8fe0dc00971/meridian/data/data_frame_input_data_builder.py#L25)\n to map column names to the variable types required in a Meridian input data.\n For the definition of each variable, see\n [Collect and organize your data](/meridian/docs/user-guide/collect-data).\n\n from meridian.data import data_frame_input_data_builder as data_builder\n\n builder = data_builder.DataFrameInputDataBuilder(\n kpi_type='non_revenue',\n default_kpi_column=\"conversions\",\n default_revenue_per_kpi_column=\"revenue_per_conversion\",\n )\n builder = (\n builder\n .with_kpi(df)\n .with_revenue_per_kpi(df)\n .with_population(df)\n .with_controls(df, control_cols=[\"GQV\", \"Competitor_Sales\"])\n )\n channels = [\"Channel0\", \"Channel1\", \"Channel2\", \"Channel3\", \"Channel4\"]\n builder = builder.with_media(\n df,\n media_cols=[f\"{channel}_impression\" for channel in channels],\n media_spend_cols=[f\"{channel}_spend\" for channel in channels],\n media_channels=channels,\n )\n builder = (\n builder\n .with_organic_media(\n df,\n organic_media_cols = [\"Organic_channel0_impression\"],\n organic_media_channels = [\"Organic_channel0\"],\n )\n .with_non_media_treatments(\n df,\n non_media_treatment_cols=['Promo']\n )\n )\n\n data = builder.build()\n\n Where:\n - `kpi_type` is either `'revenue'` or `'non_revenue'`.\n\nNext, you can [create your model](/meridian/docs/user-guide/modeling-overview)."]]
