How Can I Effectively Pivot Data on Multiple Columns Using PostgreSQL's tablefunc?

Use Tablefunc for multi-column pivot data

When processing data that contains multiple attributes and measures, it may be necessary to convert it from long to wide format for efficient analysis. PostgreSQL's tablefunc functionality provides a convenient solution for such conversions. However, it's important to understand its limitations when working with multiple pivot columns.

In a reply to a previous query, a user asked for guidance on using tablefunc for pivoting but encountered challenges when working with multiple pivot columns. Since tablefunc expects consistent extra columns for each row name, the original query results in incomplete data.

Problem Solved

To resolve this issue, be sure to adhere to the order specified by tablefunc:

1. Row Name: This column must always come first.
2. Extra columns (optional): Any additional columns should come after the row name column if needed.
3. Category and Value (last two columns): The Pivot Category and Value columns must be in this order as the last two columns.

Implementation

In the given example, the desired output requires pivoting on two columns (entity and status). To do this, the query was modified as follows:

<code class="language-sql">SELECT *
FROM crosstab(
   'SELECT entity, timeof, status, ct
    FROM t4
    ORDER BY 1'
 , 'VALUES (1), (0)'
   ) AS ct (
      "Attribute" character
    , "Section" timestamp
    , "status_1" int
    , "status_0" int
      );</code>

By using entity as the row name and swapping the order of timeof and entity, the query successfully pivots on multiple columns.

Variations with different settings

For the setup mentioned in the response, where the data is sorted by localt and entity , the modified query is as follows:

<code class="language-sql">SELECT localt, entity
     , msrmnt01, msrmnt02, msrmnt03, msrmnt04, msrmnt05  -- , more?
FROM crosstab(
  'SELECT dense_rank() OVER (ORDER BY localt, entity)::int AS row_name
        , localt, entity -- additional columns
        , msrmnt, val
   FROM test
-- WHERE  ???   -- instead of LIMIT at the end
   ORDER BY localt, entity, msrmnt
-- LIMIT ???'   -- instead of LIMIT at the end
, 'SELECT generate_series(1,5)'  -- more?
   ) AS ct (row_name int, localt timestamp, entity int
          , msrmnt01 float8, msrmnt02 float8, msrmnt03 float8, msrmnt04 float8, msrmnt05 float8 -- , more?
            )
LIMIT 1000  -- ?!</code>

This query uses dense_rank() to generate proxy row names and includes an optional WHERE clause to filter the data before processing. Additionally, the LIMIT condition was removed from the subquery to improve performance by processing only necessary rows.

Conclusion

By understanding the limitations and following the order specified by tablefunc, you can effectively pivot on multiple columns, even for large data sets. Remember to optimize queries by using appropriate WHERE clauses or LIMIT conditions to avoid unnecessary processing.

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