fix: Properly check for decimal precision and scale

[Copilot]

Motivation

Schema.validate() silently accepts Decimal columns with mismatched precision or scale, allowing data with wrong decimal format to pass validation.

Changes

• Modified Decimal.validate_dtype() to validate both precision and scale, not just the decimal type
  • Scale must always match the schema definition
  • Precision must match when explicitly specified (accepts any precision when precision=None)
• Added parametrized tests for precision/scale validation and casting behavior
• Updated existing test expectations to reflect corrected validation behavior

Before:

class T(dy.Schema):
    value = dy.Decimal(precision=38, scale=10)

# Silently accepts scale=3
df = T.validate(
    pl.DataFrame({"value": [Decimal("38.1")]})
    .with_columns(pl.col("value").cast(pl.Decimal(38, 3)))
)
# Result: Decimal[38,3] ❌

After:

# Without cast: raises SchemaError for mismatched scale
df = T.validate(...)  # SchemaError: got Decimal[38,3], expected Decimal[38,10]

# With cast: properly converts to schema's precision/scale
df = T.validate(..., cast=True)  # Result: Decimal[38,10] ✅

Original prompt

---

This section details on the original issue you should resolve

<issue_title>Decimals validation / cast ignores scale</issue_title>
<issue_description>Schema.validate() silently ignores the Decimal scale declared in the schema:

import polars as pl
import dataframely as dy
from decimal import Decimal

class T(dy.Schema):
    value = dy.Decimal(precision=38, scale=10)

df = T.validate(
    pl.DataFrame(
        {
            T.value.name: [Decimal("38.1")],
        }
    )
    .with_columns(
        T.value.col.cast(pl.Decimal(precision=38, scale=3))
    )
)
print(df)

This results in:

shape: (1, 1)
┌───────────────┐
│ value         │
│ ---           │
│ decimal[38,3] │
╞═══════════════╡
│ 38.100        │
└───────────────┘

Casting also ignores the scale defined in the schema, as the resulting column keeps the inputs scale:

class T(dy.Schema):
    value = dy.Decimal(precision=38, scale=10)

df = T.validate(
    pl.DataFrame(
        {
            T.value.name: [Decimal("38.1")],
        }
    ),
    cast=True
)
print(df)

Same result:

shape: (1, 1)
┌───────────────┐
│ value         │
│ ---           │
│ decimal[38,1] │
╞═══════════════╡
│ 38.1          │
└───────────────┘

Running with dataframely version 2.1.0</issue_description>

<agent_instructions>Fix this issue by adapting the dtype check for the Decimal column: it should pay attention to the precision and scale.</agent_instructions>

Comments on the Issue (you are @copilot in this section)

• Fixes Decimals validation / cast ignores scale #240

---

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 100.00%. Comparing base (8cc54d9) to head (6dbfd08).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff            @@
##              main      #241   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
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  Files           53        53           
  Lines         3066      3066           
=========================================
  Hits          3066      3066           

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