This article provides guidance on reshaping and pivoting data within Assette Data Blocks using Python’s pandas library. Pivoting is a common data transformation technique that converts rows into columns, enabling more efficient data analysis and presentation in financial reporting, portfolio comparison, and other Assette-powered deliverables.
Simply put, pivoting data transforms data from a “long” format (many rows, few columns) to a “wide” format (fewer rows, more columns). In Assette Data Blocks, this transformation is typically accomplished using Python’s pandas library, specifically the df.pivot() or df.pivot_table() methods. Understanding when and how to apply pivoting operations is essential for developers creating Data Blocks that power comparative analyses, cross-tabular reports, and multi-dimensional visualizations.
Understanding Pivot Methods in Python #
The pandas library provides two primary methods for pivoting data, each suited to different use cases. It is important to understand the distinction between these methods to select the appropriate approach for your Data Block implementation.
df.pivot() #
The pivot() method is used for basic reshaping operations where the data contains unique combinations of index and column values. This method does not perform any aggregation and will raise an error if duplicate index-column pairs exist in the source data.
df.pivot(index=None, columns=None, values=None)
Use pivot() when your source data already contains unique index-column combinations and no aggregation is required.
df.pivot_table() #
The pivot_table() method is a more powerful and flexible approach that can handle duplicate values for pivoted index-column pairs by applying aggregation functions. This method is a generalization of pivot() and is the recommended approach for most Assette Data Block implementations.
df.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', sort=True)
Key features of pivot_table() include the ability to specify aggregation functions using the aggfunc keyword argument (the default aggregation function is numpy.mean), support for multiple columns in the index and column parameters which automatically generates a hierarchical index structure, and the ability to handle missing values with the fill_value parameter.
Approaches to Pivoting Data in Assette #
There are two primary approaches to pivoting data within the Assette platform, each with its own advantages depending on your specific requirements and data architecture.
Approach 1: Pivoting Within a Python Data Block
The recommended approach is to perform the pivot operation within a Python Data Block. This method provides flexibility and allows for complex transformations while keeping the pivot logic within the Assette platform.
When using this approach, the Data Block reads data from a source Data Block using the read() function, converts the result to a pandas DataFrame, applies the pivot operation, and assigns the pivoted result to the response object.
This approach may require an intermediate Data Block depending on the structure of your source data. The intermediate Data Block can handle data preparation, filtering, or initial transformations before the final pivot operation.
Approach 2: Pivoting at the Data Source
An alternative approach involves pivoting the data at the source level, such as within a SQL view, prior to sending the data to Assette. This approach can be beneficial when the pivot logic is stable and does not require frequent changes, when performance optimization is critical and the database can perform the pivot operation more efficiently, or when the pivoted structure is required by multiple consumers beyond Assette.
Required Dependencies #
When implementing a pivot operation in a Python Data Block, the following dependencies must be configured:
The Calculation Environment Data Block must be included as a dependency. This Configuration Data Block provides the Python execution environment, including access to the pandas library and other essential modules.
A source Data Block might also be needed as a dependency, depending on the scenario. This is the Data Block that provides the raw data to be pivoted. The specific source Data Block depends on your implementation and data requirements. For example, the DEMO_HoldingsforFundOverlap Data Block could serve as a source for portfolio holdings data.
Implementation Example #
The following example demonstrates a pivot operation that transforms portfolio holdings data to compare market values across multiple investment strategies.
# Read portfolio data from the source Data Block
portfolio_data = pd.DataFrame(read("DEMO_HoldingsforFundOverlap", {
"AsofDate": AsofDate,
"CurrencyCode": CurrencyCode
})["data"])
# Create a DataFrame from the portfolio data
df = pd.DataFrame(portfolio_data)
# Create a pivot table with issue names as rows, strategies as columns, and market values as the data
pivot = df.pivot_table(
index="ISSUEDISPLAYNAME",
columns="ATTRIBUTETYPEVALUE",
values="MARKETVALUE",
fill_value=0
)
# Filter the pivot table to include only the selected strategies
selected_portfolios = [Strategy1, Strategy2, Strategy3, Strategy4]
pivot_filtered = pivot[selected_portfolios]
# Assign the pivoted data to the response
response["data"] = pivot_filtered
In this example, the index parameter specifies that each unique value in the ISSUEDISPLAYNAME column becomes a row in the pivoted table. The columns parameter specifies that unique values in the ATTRIBUTETYPEVALUE column become the column headers. The values parameter identifies the MARKETVALUE column as the source of the cell values. The fill_value parameter of 0 ensures that any missing combinations are filled with zero rather than NaN values.
The final step filters the pivot table to include only the strategies specified by the parameter variables (Strategy1 through Strategy4), enabling dynamic selection of comparison portfolios.
Troubleshooting #
Common issues encountered when implementing pivot operations in Data Blocks include the following scenarios.
If you receive a “ValueError: Index contains duplicate entries, cannot reshape” error when using pivot(), your source data contains duplicate index-column pairs. Switch to using pivot_table() with an appropriate aggregation function to resolve this issue.
If the pivot table contains unexpected NaN values, verify that the source data contains all expected combinations of index and column values, or use the fill_value parameter to specify a default value for missing combinations.
If the pivot operation is slow or consumes excessive memory, consider filtering the source data before pivoting, using more efficient aggregation functions, or performing the pivot operation at the database level.
Additional Resources #
- Introduction to the Data Block Editor – Overview of the Data Block Editor and its features
- Creating & Editing Data Blocks – Guide to creating and modifying Data Blocks
- Understanding the ‘read’ Keyword in Assette Data Blocks – Documentation on the read function for accessing dependent Data Blocks
- Python Data Calculation Modules Data Block – Information on available Python libraries in the calculation environment
- Calculation Environment Data Block – Documentation on the CalculationEnv dependency
- pandas.DataFrame.pivot_table — pandas documentation – Official pandas documentation for pivot_table
- pandas.DataFrame.pivot — pandas documentation – Official pandas documentation for pivot
- Reshaping and pivot tables — pandas documentation – Comprehensive guide to data reshaping in pandas