meridian.data.load.CsvDataLoader

Reads data from a CSV file.

Inherits From: InputDataLoader

meridian.data.load.CsvDataLoader(
    csv_path: str,
    coord_to_columns: meridian.data.load.CoordToColumns,
    kpi_type: str,
    media_to_channel: (Mapping[str, str] | None) = None,
    media_spend_to_channel: (Mapping[str, str] | None) = None,
    reach_to_channel: (Mapping[str, str] | None) = None,
    frequency_to_channel: (Mapping[str, str] | None) = None,
    rf_spend_to_channel: (Mapping[str, str] | None) = None,
    organic_reach_to_channel: (Mapping[str, str] | None) = None,
    organic_frequency_to_channel: (Mapping[str, str] | None) = None
)

This class reads input data from a CSV file. The coord_to_columns attribute stores a mapping from target InputData coordinates and array names to the CSV column names, if they are different. The fields are:

  • geo, time, kpi, revenue_per_kpi, population (single column)
  • controls (multiple columns, optional)
  • (1) media, media_spend (multiple columns)
  • (2) reach, frequency, rf_spend (multiple columns)
  • non_media_treatments (multiple columns, optional)
  • organic_media (multiple columns, optional)
  • organic_reach, organic_frequency (multiple columns, optional)

The DataFrame must include either (1) or (2), but doesn't need to include both.

Internally, this class reads the CSV file into a Pandas DataFrame and then loads the data using DataFrameDataLoader.

csv_path The path to the CSV file to read from. One of the following conditions is required:

  • There are no gaps in the data.
  • For up to max_lag initial periods there is only media data and empty cells in all the data columns different from media, reach, frequency, organic_media, organic_reach and organic_frequency (kpi, revenue_per_kpi, media_spend, rf_spend, controls, population and non_media_treatments).
coord_to_columns A CoordToColumns object whose fields are the desired coordinates of the InputData and the values are the current names of columns (or lists of columns) in the CSV file. Example: 
coord_to_columns = CoordToColumns(
    geo='dmas',
    time='dates',
    kpi='revenue',
    revenue_per_kpi='revenue_per_conversions',
    media=['impressions_tv', impressions_yt', 'impressions_search'],
    spend=['spend_tv', 'spend_yt', 'spend_search'],
    reach=['reach_fb'],
    frequency=['frequency_fb'],
    rf_spend=['rf_spend_fb'],
    controls=['control_income'],
    population='population',
    non_media_treatments=['price', 'discount'],
    organic_media=['organic_impressions_blog'],
    organic_reach=['organic_reach_newsletter'],
    organic_frequency=['organic_frequency_newsletter'],
)

kpi_type A string denoting whether the KPI is of a 'revenue' or 'non-revenue' type. When the kpi_type is 'non-revenue' and there exists a revenue_per_kpi, ROI calibration is used and the analysis is run on revenue. When the revenue_per_kpi doesn't exist for the same kpi_type, custom ROI calibration is used and the analysis is run on KPI.
media_to_channel A dictionary whose keys are the actual column names for media data in the CSV file and values are the desired channel names, the same as for the media_spend data. Example:

media_to_channel = {
    'media_tv': 'tv', 'media_yt': 'yt', 'media_fb': 'fb'
}

media_spend_to_channel A dictionary whose keys are the actual column names for media_spend data in the CSV file and values are the desired channel names, the same as for the media data. Example:

`media_spend_to_channel = {
    'spend_tv': 'tv', 'spend_yt': 'yt', 'spend_fb': 'fb'
}

reach_to_channel A dictionary whose keys are the actual column names for reach data in the dataframe and values are the desired channel names, the same as for the rf_spend data. Example:

reach_to_channel = {
    'reach_tv': 'tv', 'reach_yt': 'yt', 'reach_fb': 'fb'
}

frequency_to_channel A dictionary whose keys are the actual column names for frequency data in the dataframe and values are the desired channel names, the same as for the rf_spend data. Example:

frequency_to_channel = {
    'frequency_tv': 'tv', 'frequency_yt': 'yt', 'frequency_fb': 'fb'
}

rf_spend_to_channel A dictionary whose keys are the actual column names for rf_spend data in the dataframe and values are the desired channel names, the same as for the reach and frequency data. Example:

rf_spend_to_channel = {
    'rf_spend_tv': 'tv', 'rf_spend_yt': 'yt', 'rf_spend_fb': 'fb'
}

organic_reach_to_channel A dictionary whose keys are the actual column names for organic_reach data in the dataframe and values are the desired channel names, the same as for the organic_frequency. Example:

organic_reach_to_channel = {
    'organic_reach_newsletter': 'newsletter',
}

organic_frequency_to_channel A dictionary whose keys are the actual column names for organic_frequency data in the dataframe and values are the desired channel names, the same as for the organic_reach data. Example:

organic_frequency_to_channel = {
    'organic_frequency_newsletter': 'newsletter',
}

Methods

load

View source

load() -> meridian.data.input_data.InputData

Reads data from a CSV file and returns an InputData object.