Descriptor

Descriptor analysis focuses on understanding your data through descriptive statistics. It provides insights into top/bottom performers, most/least contributing entities, overall performance and more.

Input Combinations

There are several different input combinations that you can leverage to bring out the different types of insights we offer for the Descriptor analysis.

Sr No	Combination
1	Metric and Dimension
2	Date and Metric
3	Date, Metric and Dimension

All the above mentioned input combinations are to be set using set_column_meta method.

Topics

This section gives description about the topics that are associated with Descriptor Analysis. You can choose the topics of your choice to generate insights according to your requirements.

Topic	Description
overall_primary_performance	Analyzes the overall performance of the metric. Input - Date and Metric
best_performing	Provides insights into the best-performing value/entity of the dimension for the given metric. Input - Date, Metric and Dimension
best_performing_2d	Offers insights regarding the top-performing value or entity within one dimension concerning a specific metric, while also analyzing performance across another dimension. Input - Date, Metric and 2 Dimensions Requires two dimensions in input.
under_performing	Provides insights into the under-performing value/entity of the dimension for the given metric. Input - Date, Metric and Dimension
under_performing_2d	Offers insights regarding the under-performing value or entity within one dimension concerning a specific metric, while also analyzing performance across another dimension Input - Date, Metric and 2 Dimensions Requires two dimensions in input.
most_contribution	Provides insights into the most contributing value/entity of the dimension for the given metric. Input - Date, Metric and Dimension
most_contributing_2d	Offers insights regarding the most contributing value or entity within one dimension concerning a specific metric, while also analyzing performance across another dimension. Input - Date, Metric and 2 Dimensions Requires two dimensions in input.
least_contribution	Provides insights into the least contributing value/entity of the dimension for the given metric. Input - Date, Metric and Dimension
least_contributing_2d	Offers insights regarding the least contributing value or entity within one dimension concerning a specific metric, while also analyzing performance across another dimension Input - Date, Metric and 2 Dimensions Requires two dimensions in input.
top_ranked	Provides insights into the top ranked value/entity of the dimension for the given metric. Input - Date, Metric and Dimension
top_ranked_2d	Offers insights regarding the top ranked value or entity within one dimension concerning a specific metric, while also analyzing performance across another dimension Input - Date, Metric and 2 Dimensions Requires two dimensions in input.
descriptor_other	Gives other miscellaneous descriptive insights. Input - Metric and Dimension / Date and Metric / Date, Metric and Dimension

Examples

Generate Insights by Analysis

Generate Insights on Monthly Sales for each Region.

# Set column meta according to the input combination.
phrzr.set_column_meta(
    date_column={"name": "Date", "period": "month"},
    metric_column="Sales",
    dimension_column="Region" 
)

insights = phrzr.get_insights('descriptor')

print(insights)

Generate Insights by Topics

Let's analyze and identify the top-performing and lowest-performing Regions based on monthly Sales.

# Set column meta according to the topic input combination.
phrzr.set_column_meta(
    date_column={"name": "Date", "period": "month"},
    metric_column="Sales",
    dimension_column="Region" 
)

insights = phrzr.get_insights(["best_performing","under_performing"])

print(insights)

Let's dive deeper to pinpoint the specific State within the lowest-performing Region that is underperforming.

# Set column meta according to the topic input combination.
phrzr.set_column_meta(
    date_column={"name": "Date", "period": "month"},
    metric_column="Sales",
    dimension_column=["Region","State"]
)

insights = phrzr.get_insights(["under_performing_2d"])

print(insights)