Advanced / GroupBy Layer

Perform advanced grouping operations with multiple aggregations, similar to pandas groupby() with agg() or dplyr's group_by() with summarize().

Example scenarios:

Sales Analysis

Operation	Purpose
by=[region, product_type]	Group by region and product
agg=Sum(sales_amount)	Total sales in each group
agg=Count(transaction_id)	Number of transactions
agg=Mean(unit_price)	Average price point

Customer Behavior

Operation	Purpose
by=[customer_segment, month]	Group by segment and time
agg=NUnique(customer_id)	Unique customers
agg=Mean(purchase_value)	Average purchase value
agg=Max(transaction_amount)	Largest transactions

Quality Control

Operation	Purpose
by=[batch_id, production_line]	Group by batch and line
agg=Mean(measurement)	Average measurements
agg=Std(measurement)	Measurement variation
agg=Count(defect_id)	Number of defects

Common applications:

Sales analysis by region/product
Customer behavior segmentation
Performance metrics by category
Time-based aggregations
Quality metrics by batch
Resource utilization analysis

Table

By

[column, ...]

Columns to group by. Creates distinct groups based on unique combinations of values in these columns. Common grouping columns:

Categorical: region, product_type, customer_segment
Temporal: year, month, day
Hierarchical: department, team, employee

Aggregations

[, ...]

Defines aggregation operations to apply to grouped data. Multiple aggregations can be combined to create comprehensive group summaries.

Select

[column, ...]

Columns to aggregate. Requirements vary by aggregation type:

Numeric required: Mean, Sum, Std, Var, Min, Max, Median
Any type allowed: Count, NUnique, First, Last, None Multiple columns can be selected for the same aggregation.

Agg

enum

Min

Available aggregation functions for grouped data analysis. Different functions suit different analytical needs and data types.

Min ~

Minimum value in each group. Used for:

Lowest price points
Best performance times
Temperature minimums

Max ~

Maximum value in each group. Used for:

Peak sales amounts
Highest temperatures
Maximum load points

Mean ~

Arithmetic mean of each group. Used for:

Average transaction value
Typical response times
Mean daily temperatures

Median ~

Middle value in each group. Used for:

Typical household income
Central tendency analysis
Robust average measures

NUnique ~

Count of unique values per group. Used for:

Distinct customer count
Product variety analysis
Unique error codes

Sum ~

Sum of values in each group. Used for:

Total revenue by region
Cumulative quantities
Total expenses

Std ~

Standard deviation of group values. Used for:

Price variation analysis
Quality control measures
Performance consistency

Var ~

Variance of group values. Used for:

Risk assessment
Spread analysis
Variability measures

First ~

First row in each group. Used for:

Initial readings
Starting values
First occurrences

Last ~

Last row in each group. Used for:

Final status
Most recent values
Ending conditions

Count ~

Row count per group. Used for:

Transaction frequency
Event counts
Occurrence analysis

None ~

No aggregation, maintains original values. Used for:

Keeping original data
Preparation for further processing
Custom aggregations