REALLOCATIONDETAILS

REALLOCATIONDETAILS sets out specific reallocation agreements.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

Not in Use - discontinued 10/06/2004

Source

As changes occur.

Column Schema

EFFECTIVEDATE

Calendar settlement date the agreement starts from

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is mandatory, and a primary key.

Example: 2026/01/20 00:05:00

REALLOCATIONID

Identification of the reallocation agreement

SQL Type: VARCHAR2(20) Polars Type: pl.Categorical

This column is mandatory, and a primary key.

VERSIONNO

Version number on the effective date, highest is the reallocation used on that date

SQL Type: NUMBER(3,0) Polars Type: pl.UInt16

This column is mandatory, and a primary key.

AUTHORISEDBY

User who authorised the record

SQL Type: VARCHAR2(10) Polars Type: str

This column is not mandatory.

AUTHORISEDDATE

Date the entry was authorised

Warning

Despite what the name suggests, this is a datetime, not a date.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/01/20 00:05:00

LASTCHANGED

Last date and time record changed

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2026/01/20 00:05:00

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("EFFECTIVEDATE", "AUTHORISEDDATE", "LASTCHANGED").str.strptime(
            pl.Datetime, dt_s_fmt
        ),
    )
    .cast(
        {
            "REALLOCATIONID": pl.Categorical,
            "VERSIONNO": int,  # or pl.UInt16
            "AUTHORISEDBY": str,
        }
    )
    # Deduplicate
    # We want one row per ['EFFECTIVEDATE', 'REALLOCATIONID', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["EFFECTIVEDATE", "REALLOCATIONID", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Availability

Visibility: Private

This table is not available in the public monthly MMSDM archive. Is is only available to registered market participants over FTP inside a VPN.

Deduplication and PDR Loader Configuration

The primary keys are:

• EFFECTIVEDATE

• REALLOCATIONID

• VERSIONNO

PDR Loader configuration does not mention this table, so it is unclear how to choose which row to keep when there are multiple rows for a given set of primary keys.

You should probably take the row with the maximum LASTCHANGED.

Indexes

• LASTCHANGED

Links

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: HISTORICAL TABLES

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