Core API Reference

DataFrameAssertion

The main assertion class for DataFrame validation.

from ml_assert import Assertion

# Create an assertion instance
assertion = Assertion(df)

# Chain assertions
assertion.satisfies(schema).no_nulls().validate()

Methods

satisfies(schema)

Validates the DataFrame against a schema definition.

Parameters: - schema: A schema object created using the schema() builder

Returns: - self for method chaining

no_nulls(columns=None)

Checks for null values in specified columns.

Parameters: - columns: Optional list of column names to check. If None, checks all columns.

Returns: - self for method chaining

validate()

Executes all chained assertions.

Raises: - AssertionError if any assertion fails

schema

A builder for creating DataFrame validation schemas.

from ml_assert import schema

# Create a schema
s = schema()
s.col("user_id").is_unique()
s.col("age").in_range(18, 70)

Methods

col(column_name)

Starts a column validation chain.

Parameters: - column_name: Name of the column to validate

Returns: - A column validator object

Column Validator Methods

is_unique()

Checks if column values are unique.

in_range(min_val, max_val)

Checks if column values are within a range.

Parameters: - min_val: Minimum allowed value - max_val: Maximum allowed value

is_type(dtype)

Checks if column has the specified data type.

Parameters: - dtype: Expected data type (e.g., "int64", "float64", "object")