DataType

• Numeric: FLOAT, INTEGER - enable statistical analysis
• Categorical: BOOLEAN, CATEGORY - enable distribution analysis
• Textual: STRING - enable text-based monitoring
• Temporal: TIMESTAMP - enable time-based analysis
• Vector: VECTOR - enable embedding-based monitoring

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Examples

from fiddler import Column, DataType

# Define columns with appropriate data types
columns = [
    Column(name='age', data_type=DataType.INTEGER),
    Column(name='income', data_type=DataType.FLOAT),
    Column(name='is_member', data_type=DataType.BOOLEAN),
    Column(name='category', data_type=DataType.CATEGORY),
    Column(name='description', data_type=DataType.STRING),
    Column(name='created_at', data_type=DataType.TIMESTAMP),
    Column(name='embedding', data_type=DataType.VECTOR)
]

# Create model schema
schema = fdl.ModelSchema(columns=columns)

# Numeric types enable statistical monitoring
if column.data_type.is_numeric():

    # Statistical drift detection available
    # Range validation enabled
    # Distribution analysis supported
    pass

    # Categorical types enable distribution monitoring
    if column.data_type.is_bool_or_cat():

        # Category distribution tracking
        # New category detection
        # Frequency analysis
        pass

        # Vector types enable embedding monitoring
        if column.data_type.is_vector():

            # Embedding drift detection
            # Clustering analysis
            # Dimensionality monitoring
            pass

​

FLOAT = "float"

• Decimal precision values
• Statistical distribution analysis
• Range and outlier detection
• Correlation analysis support

• Mean, median, standard deviation tracking
• Distribution drift detection (KS test, PSI)
• Range violation alerts
• Outlier detection and analysis

• Prices, costs, revenues
• Probabilities and confidence scores
• Measurements and sensor readings
• Performance metrics and ratios
• Model prediction scores

​

INTEGER = "int"

• Whole number values only
• Discrete distribution analysis
• Count-based statistics
• Range validation

• Count distribution tracking
• Range violation detection
• Discrete value frequency analysis
• Statistical drift detection

• Counts and quantities
• Age, years, days
• IDs and identifiers (when numeric)
• Ranking positions
• Categorical codes (when numeric)

​

BOOLEAN = "bool"

• Exactly two values (True/False, 1/0, Yes/No)
• Binary distribution analysis
• Proportion-based metrics
• Simple categorical handling

• True/False ratio tracking
• Binary distribution drift
• Proportion change detection
• Flag frequency analysis

• Feature flags and indicators
• Binary classifications
• Yes/No survey responses
• Membership status
• Activation states

​

STRING = "str"

• Variable length text
• Text-based analysis
• String pattern detection
• Encoding-aware processing

• Length distribution tracking
• Pattern and format analysis
• Text embedding integration
• String uniqueness analysis

• Names and descriptions
• Comments and reviews
• URLs and paths
• Free-form text inputs
• JSON or XML strings

• Can be converted to embeddings for semantic monitoring
• Supports text enrichment features
• May require text preprocessing

​

CATEGORY = "category"

• Limited set of possible values
• Categorical distribution tracking
• Category frequency analysis
• New category detection

• Category distribution drift
• New/missing category alerts
• Frequency change detection
• Category proportion analysis

• Product categories
• Geographic regions
• Status codes
• Demographic categories
• Classification labels

• Use for data with < 1000 unique values
• Consider STRING type for high-cardinality categories
• Define expected categories during schema creation

​

TIMESTAMP = "timestamp"

• Date/time information
• Temporal ordering
• Time-based aggregations
• Timezone awareness

• Temporal pattern analysis
• Time gap detection
• Seasonal trend monitoring
• Data freshness tracking

• Event timestamps
• Creation/modification dates
• Transaction times
• Log timestamps
• Scheduled events

• Unix timestamps
• ISO 8601 strings
• Pandas datetime objects
• Various date formats (with parsing)

​

VECTOR = "vector"

• Fixed-dimension numerical arrays
• Embedding-based analysis
• Vector similarity metrics
• Clustering support

• Embedding drift detection
• Cluster analysis and visualization
• Vector similarity tracking
• Dimensionality validation

• Text embeddings (Word2Vec, BERT, etc.)
• Image embeddings (CNN features)
• User/item embeddings
• Feature vectors from neural networks
• Recommendation system embeddings

• Requires consistent vector dimensions
• Benefits from custom feature definitions
• Supports clustering and UMAP visualization

​

is_numeric()

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Returns

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is_bool_or_cat()

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Returns

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is_vector()

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Returns