Mean Summary¶

The Mean Summary metric calculates arithmetic mean values for specified variables, with optional segmentation.

Configuration Fields¶

Required Fields¶

name (string or list): Metric identifier(s)
dataset (string or list): Dataset identifier(s) to analyze
type: Must be "mean"
variable (string or list): variable_column name(s) for mean calculation

Optional Fields¶

segment (string or list): Segment identifier(s) for grouping analysis

Output Columns¶

The Mean Summary produces these output columns:

Standard identification columns (name, dataset, segment)
mean: The calculated arithmetic mean value

Data Requirements¶

Data must contain the specified variable_column(s) with numeric values
Missing/null values are excluded from calculation
If segments are specified, data must contain the segment column(s)

Fan-out Examples¶

Basic Configuration¶

- name: average_loan_amount
  type: mean
  dataset: loan_data
  variable: loan_amount

Multiple Variables¶

- name: [avg_income, avg_score, avg_debt]
  type: mean
  dataset: customer_data
  variable: [annual_income, credit_score, total_debt]

This expands to:

avg_income calculating mean of annual_income
avg_score calculating mean of credit_score
avg_debt calculating mean of total_debt

Segmented Analysis¶

- name: regional_income_avg
  type: mean
  dataset: customer_data
  variable: annual_income
  segment: [north, south, east, west]

This creates separate mean calculations for each region.

Multiple Datasets and Variables¶

- name: [q1_revenue, q2_revenue]
  type: mean
  dataset: [q1_sales, q2_sales]
  variable: [revenue, revenue]

Complex Multi-dimensional Fan-out¶

- name: [income_north, income_south, score_north, score_south]
  type: mean
  dataset: customer_data
  variable: [annual_income, annual_income, credit_score, credit_score]
  segment: [north, south, north, south]

Usage Notes¶

Numeric Data: Variable must contain numeric data types
Missing Values: Automatically excluded from mean calculation
Zero Values: Included in calculation unless explicitly filtered in data
Segmentation: Each segment produces separate mean calculation
Large Datasets: Efficient calculation even with millions of records

Fan-out Expansion Rules¶

When using lists in configuration:

name, dataset, variable must have matching lengths when specified as lists
segment can be a single value (applied to all) or list matching other field lengths
Each combination creates a separate metric calculation
All metrics of this type will have the same output column structure

Statistical Notes¶

Arithmetic Mean: Simple average of all values
Outlier Sensitivity: Mean can be affected by extreme values; consider median for robust central tendency