Rejoignez la nouvelle communauté
Discord pour discuter en temps réel, obtenir l'aide de vos pairs et communiquer directement avec l'équipe Meridian.
Charger des données au niveau géographique avec la couverture et la fréquence
Restez organisé à l'aide des collections
Enregistrez et classez les contenus selon vos préférences.
Des données simulées sont fournies à titre d'exemple pour chaque type et format de données dans les sections suivantes.
CSV
Pour charger les données CSV simulées à l'aide de CsvDataLoader :
Mappez les noms de colonnes avec les types de variables. Les types de variables requis sont time, geo, controls, population, kpi et revenue_per_kpi. Pour les canaux média qui ne disposent pas de données de couverture et de fréquence, vous devez attribuer l'exposition média et les dépenses média aux catégories media et media_spend respectivement. À l'inverse, pour les canaux média qui disposent de données de couverture et de fréquence, vous devez mapper leur couverture, leur fréquence et les dépenses média sur les catégories de reach, frequency et rf_spend de façon proportionnelle. Pour connaître la définition de chaque variable, consultez Collecter et organiser vos données.
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'],
)
Mappez l'exposition média, la couverture, la fréquence et les dépenses média sur les noms de canaux désignés que vous souhaitez afficher dans la sortie sur deux pages. Dans l'exemple suivant, Channel0_impression et Channel0_spend sont connectés au même canal : Channel0. De plus, Channel4_reach, Channel4_frequency et Channel4_spend sont connectés au même canal : 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',
}
Chargez les données à l'aide de 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()
Où :
kpi_type correspond à 'revenue' ou 'non_revenue'.PATH est le chemin d'accès au fichier de données.FILENAME est le nom de votre fichier de données.
Ensemble de données Xarray
Pour charger l'ensemble de données Xarray simulées sérialisé à l'aide de XrDatasetDataLoader :
Chargez les données à l'aide de pickle :
import pickle
with open(f'/{PATH}/{FILENAME}.pkl', 'r') as fh:
dataset=pickle.load(fh)
Où :
PATH est le chemin d'accès au fichier de données.FILENAME est le nom de votre fichier de données.
Transmettez l'ensemble de données à XrDatasetDataLoader. Utilisez l'argument name_mapping pour mapper les coordonnées et les tableaux. Fournissez un mappage si les noms de l'ensemble de données d'entrée sont différents des noms requis. Les noms de coordonnées requis sont geo, time, control_variable, media_channel et rf_channel, où rf_channel désigne les canaux disposant de données de couverture et de fréquence. Les noms des variables de données requises sont kpi, revenue_per_kpi, controls, population, media, media_spend, reach, frequency et 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()
Où :
kpi_type correspond à 'revenue' ou 'non_revenue'.
ndarray Numpy
Pour charger directement des tableaux ndarray Numpy, utilisez NDArrayInputDataBuilder :
Ajoutez les données dans des tableaux ndarray Numpy distincts.
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]],
])
Utilisez un module NDArrayInputDataBuilder pour définir l'heure et les données géographiques, ainsi que pour attribuer les noms requis aux canaux ou aux dimensions dans les données d'entrée de Meridian.
Pour connaître la définition de chaque variable, consultez Collecter et organiser vos données.
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()
Où :
kpi_type correspond à 'revenue' ou 'non_revenue'.
Pour charger un autre format de données simulées (par exemple, excel) à l'aide de DataFrameInputDataBuilder :
Lisez les données (par exemple, une feuille de calcul excel) dans un ou plusieurs DataFrame pandas.
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',
)
Utilisez un module DataFrameInputDataBuilder pour mapper les noms de colonnes aux types de variables requis dans les données d'entrée de Meridian.
Pour connaître la définition de chaque variable, consultez Collecter et organiser vos données.
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()
Où :
kpi_type correspond à 'revenue' ou 'non_revenue'.
Vous pouvez ensuite créer votre modèle.
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2025/08/04 (UTC).
[null,null,["Dernière mise à jour le 2025/08/04 (UTC)."],[[["\u003cp\u003eSimulated data examples are provided for CSV, Xarray Dataset, and other data formats like Excel, with each format having its own loading method.\u003c/p\u003e\n"],["\u003cp\u003eLoading CSV data requires mapping column names to variable types such as \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, and \u003ccode\u003erevenue_per_kpi\u003c/code\u003e, and also differentiating between media channels with and without reach and frequency data.\u003c/p\u003e\n"],["\u003cp\u003eLoading Xarray Dataset involves using \u003ccode\u003epickle\u003c/code\u003e to read the data, then mapping coordinate and array names within the dataset to required names like \u003ccode\u003egeo\u003c/code\u003e, \u003ccode\u003etime\u003c/code\u003e, \u003ccode\u003econtrol_variable\u003c/code\u003e, \u003ccode\u003emedia_channel\u003c/code\u003e, and \u003ccode\u003erf_channel\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eLoading data from other formats, like Excel, requires mapping column names to variable types, just like CSV, and then loading the data into a \u003ccode\u003eDataFrameDataLoader\u003c/code\u003e after reading it into a dataframe.\u003c/p\u003e\n"],["\u003cp\u003eIn all three data formats, media exposure, reach, frequency, and media spend must be mapped to their corresponding channel names for output display.\u003c/p\u003e\n"]]],["The document details how to load simulated data in CSV, Xarray Dataset, and other formats using specific data loaders. For CSV and other formats, users must map column names to variable types like `time`, `geo`, and `kpi`, differentiating between media channels with and without reach/frequency data. They also need to map media, spend, reach, and frequency to channel names. For Xarray Dataset, users load data via pickle and map dataset coordinates and variables using `name_mapping`. Then load the data.\n"],null,["# Load geo-level data with reach and frequency\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\n[simulated CSV data](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/csv/geo_media_rf.csv)\nusing `CsvDataLoader`:\n\n1. Map the column names to the variable types. The required variable types are\n `time`, `geo`, `controls`, `population`, `kpi`, and `revenue_per_kpi`. For\n media channels that don't have reach and frequency data, you must assign\n their media exposure and media spend to the categories of `media` and\n `media_spend`, respectively. Conversely, for media channels that do possess\n reach and frequency data, you must map their reach, frequency, and media\n spend to the categories of `reach`, `frequency`, and `rf_spend`\n correspondingly. For the definition of each variable, see [Collect and\n organize your data](/meridian/docs/user-guide/collect-data).\n\n coord_to_columns = load.CoordToColumns(\n time='time',\n geo='geo',\n controls=['GQV', 'Discount', '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 ],\n media_spend=[\n 'Channel0_spend',\n 'Channel1_spend',\n 'Channel2_spend',\n 'Channel3_spend',\n ],\n reach =['Channel4_reach', 'Channel5_reach'],\n frequency=['Channel4_frequency', 'Channel5_frequency'],\n rf_spend=['Channel4_spend', 'Channel5_spend'],\n )\n\n2. Map the media exposure, reach, frequency, and the media spends to the\n designated channel names that you want to display in the two-page output. In\n the following example, `Channel0_impression` and `Channel0_spend` are\n connected to the same channel, `Channel0`. Additionally, `Channel4_reach`,\n `Channel4_frequency`, and `Channel4_spend` are connected to the same\n channel, `Channel4`.\n\n correct_media_to_channel = {\n 'Channel0_impression': 'Channel0',\n 'Channel1_impression': 'Channel1',\n 'Channel2_impression': 'Channel2',\n 'Channel3_impression': 'Channel3',\n }\n correct_media_spend_to_channel = {\n 'Channel0_spend': 'Channel0',\n 'Channel1_spend': 'Channel1',\n 'Channel2_spend': 'Channel2',\n 'Channel3_spend': 'Channel3',\n }\n\n correct_reach_to_channel = {\n 'Channel4_reach': 'Channel4',\n 'Channel5_reach': 'Channel5',\n }\n correct_frequency_to_channel = {\n 'Channel4_frequency': 'Channel4',\n 'Channel5_frequency': 'Channel5',\n }\n correct_rf_spend_to_channel = {\n 'Channel4_spend': 'Channel4',\n 'Channel5_spend': 'Channel5',\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 reach_to_channel=correct_reach_to_channel,\n frequency_to_channel=correct_frequency_to_channel,\n rf_spend_to_channel=correct_rf_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 pickled\n[simulated Xarray Dataset](https://github.com/google/meridian/tree/main/meridian/data/simulated_data/pkl/geo_media_rf.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 dataset=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\n names are `geo`, `time`, `control_variable`, `media_channel`, and\n `rf_channel`, where `rf_channel` designates the channels having reach and\n frequency data. The required data variables names are `kpi`,\n `revenue_per_kpi`, `controls`, `population`, `media`, `media_spend`,\n `reach`, `frequency`, and `rf_spend`.\n\n loader = load.XrDatasetDataLoader(\n dataset,\n kpi_type='non_revenue',\n name_mapping={\n 'channel': 'media_channel',\n 'control': 'control_variable',\n 'conversions': 'kpi',\n 'revenue_per_conversion': 'revenue_per_kpi',\n 'control_value': 'controls',\n 'spend': 'media_spend',\n 'reach': 'reach',\n 'frequency': 'frequency',\n 'rf_spend': 'rf_spend',\n },\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 reach_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 frequency_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 rf_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\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_reach(\n r_nd=reach_nd,\n f_nd=frequency_nd,\n rfs_nd=rf_spend_nd,\n rf_channels=[\"channel0\", \"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_media_rf.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_media_rf.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\", \"Discount\", \"Competitor_Sales\"])\n .with_reach(\n df,\n reach_cols = ['Channel4_reach', 'Channel5_reach'],\n frequency_cols = ['Channel4_frequency', 'Channel5_frequency'],\n rf_spend_cols = ['Channel4_spend', 'Channel5_spend'],\n rf_channels = ['Channel4', 'Channel5'],\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)."]]
