dah33

{# Modelled after Python's functools.partial #}
{% macro make_partial(func) %}
    {% set partial = dict(func=func, args=varargs, keywords=kwargs) %}
    {% do return(partial) %}
{% endmacro %}

{% macro is_partial(partial) %}
    {% do return(partial is mapping 
              and partial.func
              and partial.args

dah33

{# Save these macros in your dbt project /macros folder: #}

{% macro enclose(fn, env) %}
    {% set closure = namespace(fn=fn, env=env) %}
    {% do return(closure) %}
{% endmacro %}

{% macro call1(closure, x1) %}
    {% do return(closure.fn(x1, closure.env)) %}
{% endmacro %}

dah33

with t1 as (
  select 
    column1 as x,
    column2 as y
  from your_table
),
t as (
  select x, y
  from t1
  where x is not null and y is not null

dah33

#
# PREDICTING LONG TERM CUSTOMER VALUE WITH BTYD PACKAGE
# Pareto/NBD (negative binomial distribution) modeling of 
# repeat-buying behavior in a noncontractual setting
#
# Matthew Baggott, [email protected]
#
# Accompanying slides at:
# http://www.slideshare.net/mattbagg/baggott-predict-customerinrpart1#
#