Polars Genson

A Polars plugin for working with JSON schemas. Infer schemas from JSON data and convert between JSON Schema and Polars schema formats.

Installation

pip install polars-genson[polars]

On older CPUs, use Polars' compatibility runtime:

pip install polars-genson[rtcompat]

polars-genson requires Polars 2 or later.

Features

Schema Inference

• JSON Schema Inference: Generate JSON schemas from JSON strings in Polars columns
• Polars Schema Inference: Directly infer Polars data types and schemas from JSON data
• Multiple JSON Objects: Handle columns with varying JSON schemas across rows
• Complex Types: Support for nested objects, arrays, and mixed types
• Map Inference: Recognise objects whose keys are data (IDs, languages, dates) and type them as pl.Map columns, instead of structs with a null-padded field per key
• Flexible Input: Support for both single JSON objects and arrays of objects

Schema Conversion

• Polars → JSON Schema: Convert existing DataFrame schemas to JSON Schema format
• JSON Schema → Polars: Convert JSON schemas to equivalent Polars schemas
• Round-trip Support: Full bidirectional conversion with validation
• Schema Manipulation: Validate, transform, and standardize schemas

Usage

The plugin adds a genson namespace to Polars DataFrames for schema inference and conversion.

import polars as pl
import polars_genson
import json

# Create a DataFrame with JSON strings
df = pl.DataFrame({
    "json_data": [
        '{"name": "Alice", "age": 30, "scores": [95, 87]}',
        '{"name": "Bob", "age": 25, "city": "NYC", "active": true}',
        '{"name": "Charlie", "age": 35, "metadata": {"role": "admin"}}'
    ]
})

print("Input DataFrame:")
print(df)

shape: (3, 1)
┌─────────────────────────────────┐
│ json_data                       │
│ ---                             │
│ str                             │
╞═════════════════════════════════╡
│ {"name": "Alice", "age": 30, "… │
│ {"name": "Bob", "age": 25, "ci… │
│ {"name": "Charlie", "age": 35,… │
└─────────────────────────────────┘

JSON Schema Inference

# Infer JSON schema from the JSON column
schema = df.genson.infer_json_schema("json_data")

print("Inferred JSON schema:")
print(json.dumps(schema, indent=2))

{
  "$schema": "http://json-schema.org/schema#",
  "properties": {
    "name": {
      "type": "string"
    },
    "age": {
      "type": "integer"
    },
    "scores": {
      "items": {
        "type": "integer"
      },
      "type": "array"
    }
    "city": {
      "type": "string"
    },
    "active": {
      "type": "boolean"
    },
    "metadata": {
      "properties": {
        "role": {
          "type": "string"
        }
      },
      "required": [
        "role"
      ],
      "type": "object"
    },
  },
  "required": [
    "age",
    "name"
  ],
  "type": "object"
}

Polars Schema Inference

Directly infer Polars data types and schemas:

# Infer Polars schema from the JSON column
polars_schema = df.genson.infer_polars_schema("json_data")

print("Inferred Polars schema:")
print(polars_schema)

Schema({
    'name': String,
    'age': Int64,
    'scores': List(Int64),
    'city': String,
    'active': Boolean,
    'metadata': Struct({'role': String}),
})

The Polars schema inference automatically handles:

• ✅ Complex nested structures with proper Struct types
• ✅ Typed arrays like List(Int64), List(String)
• ✅ Mixed data types (integers, floats, booleans, strings)
• ✅ Optional fields present in some but not all objects
• ✅ Deep nesting with multiple levels of structure

Map vs Record Inference Control

For objects with varying keys, you can control whether they're inferred as Maps (dynamic key-value pairs) or Records (fixed fields) using the map_threshold and map_max_required_keys parameters:

# Data with different key patterns
df = pl.DataFrame({
    "json_data": [
        '{"user": {"id": 1, "name": "Alice"}, "attributes": {"source": "web", "campaign": "summer"}}',
        '{"user": {"id": 2, "name": "Bob"}, "attributes": {"source": "mobile"}}'
    ]
})

# Default: both user and attributes become Records
schema_default = df.genson.infer_json_schema("json_data")

# Lower thresholds: distinguish structured Records from dynamic Maps
schema_controlled = df.genson.infer_json_schema("json_data", 
    map_threshold=2,           # Objects with ≥2 keys can be Maps
    map_max_required_keys=1    # Maps can have ≤1 required key
)

In the controlled example:

• user has 2 required keys (id, name) > 1 → Record (structured)
• attributes has 1 required key (source) ≤ 1 → Map (dynamic)

This gives you fine-grained control over how objects with different key stability patterns are classified.

Schema Unification

For objects with heterogeneous but compatible record structures, polars-genson can unify them into a single map schema instead of creating separate fixed fields. This is useful for dynamic data where keys represent similar entities with slightly different structures.

Unifying Compatible Record Types

import polars as pl

# Example: Letter frequency data with vowel/consonant variants
df = pl.DataFrame({
    "json_data": [
        '{"letter": {"a": {"alphabet": 0, "vowel": 0, "frequency": 0.0817}, "b": {"alphabet": 1, "consonant": 0, "frequency": 0.0150}, "c": {"alphabet": 2, "consonant": 1, "frequency": 0.0278}}}'
    ]
})

# Without unification: creates fixed record with separate a, b, c fields
schema_default = df.genson.infer_json_schema("json_data", avro=True, map_threshold=3)

# With unification: creates map with unified record values
schema_unified = df.genson.infer_json_schema("json_data", avro=True, map_threshold=3, unify_maps=True)

Without unification, you get separate fields:

{
  "letter": {
    "type": "record",
    "fields": [
      {"name": "a", "type": {...}},
      {"name": "b", "type": {...}},
      {"name": "c", "type": {...}}
    ]
  }
}

With unification (unify_maps=True), compatible records are merged:

{
  "letter": {
    "type": "map",
    "values": {
      "type": "record",
      "fields": [
        {"name": "alphabet", "type": "int"},      // shared field (always present)
        {"name": "frequency", "type": "float"},   // shared field (always present)  
        {"name": "vowel", "type": ["null", "int"]},     // optional (vowels only)
        {"name": "consonant", "type": ["null", "int"]}  // optional (consonants only)
      ]
    }
  }
}

Normalization with Unified Schema

# Normalise with unified schema - each key gets the same record structure
normalized = df.genson.normalise_json("json_data", map_threshold=3, unify_maps=True).to_dicts()

print(normalized[0])

Output:

{
  'letter': {
    'a': {'alphabet': 0, 'frequency': 0.0817, 'vowel': 0, 'consonant': None},
    'b': {'alphabet': 1, 'frequency': 0.015, 'vowel': None, 'consonant': 0},
    'c': {'alphabet': 2, 'frequency': 0.0278, 'vowel': None, 'consonant': 1}
  }
}

Parquet I/O

For working with JSON data stored in Parquet files, polars-genson provides direct I/O functions that handle reading from and writing to Parquet columns without needing to load data into DataFrames first.

Schema Inference from Parquet

from polars_genson import infer_from_parquet

# Infer schema from a Parquet column
schema = infer_from_parquet(
    "data.parquet",
    column="claims",
    map_threshold=0,
    unify_maps=True,
)

# Or write schema to a file
infer_from_parquet(
    "data.parquet",
    column="claims",
    output_path="schema.json",
    avro=True
)

Normalization with Parquet

from polars_genson import normalise_from_parquet

# Normalize JSON in a Parquet column and write back to Parquet
normalise_from_parquet(
    input_path="input.parquet",
    column="claims",
    output_path="normalized.parquet",
    map_threshold=0,
    unify_maps=True
)

# In-place normalization (overwrites source file)
normalise_from_parquet(
    input_path="data.parquet",
    column="claims",
    output_path="data.parquet"
)

Both functions accept the same schema inference and normalization options as the DataFrame methods, making it easy to work with Parquet files directly.

Root Wrapping (wrap_root)

By default, inferred schemas treat each JSON object as the root.
Sometimes you may want to wrap the schema in an extra record layer — for example, to make Avro schemas compatible with systems that require a named top-level record.

You can control this behavior with the wrap_root option:

• wrap_root="true" → Wraps using the column name as the record name
• wrap_root="<string>" → Wraps using the given string as the record name
• wrap_root=None (default) → No wrapping (root is just "document" for Avro)

Example: Avro schema with wrap_root

df = pl.DataFrame({
    "json_data": [
        '{"value": "A"}',
        '{"value": "B"}'
    ]
})

schema = df.genson.infer_json_schema("json_data", avro=True, wrap_root="payload")

print(json.dumps(schema, indent=2))

{
  "type": "record",
  "name": "document",
  "namespace": "genson",
  "fields": [
    {
      "name": "payload",
      "type": {
        "type": "record",
        "name": "payload",
        "namespace": "genson.document_types",
        "fields": [
          {
            "name": "value",
            "type": "string"
          }
        ]
      }
    }
  ]
}

This is especially useful when:

• Exporting Avro to systems that require a named top-level record
• Keeping schema names consistent with your column names or domain models

Normalisation

In addition to schema inference, polars-genson can normalise JSON columns so that every row conforms to a single, consistent Avro schema.

This is especially useful for semi-structured data where fields may be missing, empty arrays/maps may need to collapse to null, or numeric/boolean values may sometimes be encoded as strings.

Features

• Converts empty arrays/maps to null (default)
• Preserves empties with empty_as_null=False
• Ensures missing fields are inserted with null
• Supports per-field coercion of numeric/boolean strings via coerce_strings=True
• Supports top-level schema evolution with wrap_root

Example: Maps

A JSON object whose keys are data, like {"en":"Hello","fr":"Bonjour"}, would become a struct with one field per distinct key, padded with nulls in every row that lacks it. polars-genson infers such objects as maps and normalises them to a pl.Map, where each row holds only its own keys:

import polars as pl
import polars_genson

df = pl.DataFrame({
    "json_data": [
        '{"id": 123, "tags": [], "labels": {}, "active": true}',
        '{"id": 456, "tags": ["x","y"], "labels": {"fr":"Bonjour"}, "active": false}',
        '{"id": 789, "labels": {"en": "Hi", "es": "Hola"}}'
    ]
})

print(df.genson.normalise_json("json_data", map_threshold=0))

Output:

shape: (3, 4)
┌─────┬────────────┬────────────────────────────┬────────┐
│ id  ┆ tags       ┆ labels                     ┆ active │
│ --- ┆ ---        ┆ ---                        ┆ ---    │
│ i64 ┆ list[str]  ┆ map[str, str]              ┆ bool   │
╞═════╪════════════╪════════════════════════════╪════════╡
│ 123 ┆ null       ┆ null                       ┆ true   │
│ 456 ┆ ["x", "y"] ┆ {"fr": "Bonjour"}          ┆ false  │
│ 789 ┆ null       ┆ {"en": "Hi", "es": "Hola"} ┆ null   │
└─────┴────────────┴────────────────────────────┴────────┘

In the example above, normalise_json reshaped jagged JSON into a consistent, schema-aligned form:

• Row 1

  • tags was present but empty ([]) → normalised to null (this prevents row elimination when exploding the column)
  • labels was present but empty ({}) → normalised to null
  • active stayed true

• Row 2

  • tags had two values (["x","y"]) → preserved as a list of strings
  • labels had one entry ({"fr":"Bonjour"}) → a map with one key
  • active stayed false

• Row 3

  • tags was missing entirely → injected as null
  • labels had two entries ({"en":"Hi","es":"Hola"}) → a map with two keys
  • active was missing → injected as null

To get maps as lists of {key, value} structs instead (the form Arrow stores a map in), pass map_encoding="kv". The same option gives that form in infer_polars_schema, avro_to_polars_schema and typed Parquet output. See Map types in the docs.

Example: Empty Arrays

df = pl.DataFrame({"json_data": ['{"labels": []}', '{"labels": {"en": "Hello"}}']})

out = df.genson.normalise_json("json_data")
print(out)

Output:

shape: (2, 1)
┌─────────────────────────────┐
│ normalised                  │
│ ---                         │
│ str                         │
╞═════════════════════════════╡
│ {"labels": null}            │
│ {"labels": {"en": "Hello"}} │
└─────────────────────────────┘

Example: Preserving Empty Arrays

out = df.genson.normalise_json("json_data", empty_as_null=False)
print(out)

Output:

┌─────────────────────────────┐
│ normalised                  │
╞═════════════════════════════╡
│ {"labels": []}              │
│ {"labels": {"en": "Hello"}} │
└─────────────────────────────┘

Example: String Coercion

df = pl.DataFrame({
    "json_data": [
        '{"id": "42", "active": "true"}',
        '{"id": 7, "active": false}'
    ]
})

# Default: no coercion
print(df.genson.normalise_json("json_data").to_list())
# ['{"id": null, "active": null}', '{"id": 7, "active": false}']

# With coercion
print(df.genson.normalise_json("json_data", coerce_strings=True).to_list())
# ['{"id": 42, "active": true}', '{"id": 7, "active": false}']

Schema-Aware Decoding

The decode parameter can be either a boolean or a schema.

• decode=True → Infer a schema automatically, then decode JSON into native Polars types.
• decode=False → Leave values as normalised JSON strings.
• decode=pl.Schema | pl.Struct → Use your own schema for decoding (skip re-inference).

import polars as pl
import polars_genson

df = pl.DataFrame({
    "json_data": [
        '{"id": 1, "active": true}',
        '{"id": 2, "active": false}'
    ]
})

# Explicit schema
schema = pl.Struct({
    "id": pl.Int64,
    "active": pl.Boolean,
})

# Use schema directly for decoding
decoded = df.genson.normalise_json("json_data", decode=schema)
print(decoded)

Output:

shape: (2, 2)
┌─────┬────────┐
│ id  ┆ active │
│ --- ┆ ---    │
│ i64 ┆ bool   │
╞═════╪════════╡
│ 1   ┆ true   │
│ 2   ┆ false  │
└─────┴────────┘

Note: Normalisation always aligns rows to a consistent schema internally. Passing your own schema skips the extra inference step, which can improve performance, but if your schema doesn’t match what’s in the data, you'll hit a decoding error (polars.exceptions.ComputeError from .str.json_decode). That may in fact be desirable to halt on though.

For the best of both worlds, you can run with decode=True once, capture the resulting .schema, and then reuse it in future calls.

Advanced Usage

Per-Row Schema Processing

• Only available with JSON schema currently (per-row/unmerged Polars schemas TODO)

# Get individual schemas and process them
df = pl.DataFrame({
    "ABCs": [
        '{"a": 1, "b": 2}',
        '{"a": 1, "c": true}',
    ]
})

# Analyze schema variations
individual_schemas = df.genson.infer_json_schema("ABCs", merge_schemas=False)

The result is a list of one schema per row. With merge_schemas=True you would get all 3 keys (a, b, c) in a single schema.

[{'$schema': 'http://json-schema.org/schema#',
  'properties': {'a': {'type': 'integer'}, 'b': {'type': 'integer'}},
  'required': ['a', 'b'],
  'type': 'object'},
 {'$schema': 'http://json-schema.org/schema#',
  'properties': {'a': {'type': 'integer'}, 'c': {'type': 'boolean'}},
  'required': ['a', 'c'],
  'type': 'object'}]

JSON Schema Options

# Use the expression directly for more control
result = df.select(
    polars_genson.infer_json_schema(
        pl.col("json_data"),
        merge_schemas=False,  # Get individual schemas instead of merged
    ).alias("individual_schemas")
)

# Or use with different options
schema = df.genson.infer_json_schema(
    "json_data",
    ignore_outer_array=False,  # Treat top-level arrays as arrays
    ndjson=True,               # Handle newline-delimited JSON
    schema_uri="https://json-schema.org/draft/2020-12/schema",  # Specify a schema URI
    merge_schemas=True         # Merge all schemas (default)
)

Polars Schema Options

# Infer Polars schema with options
polars_schema = df.genson.infer_polars_schema(
    "json_data",
    ignore_outer_array=True,  # Treat top-level arrays as streams of objects
    ndjson=False,            # Not newline-delimited JSON
    debug=False              # Disable debug output
)

# Note: merge_schemas=False not yet supported for Polars schemas

Method Reference

The genson namespace provides three main methods:

infer_json_schema(column, **kwargs) -> dict | list[dict]

Infers a JSON Schema (or Avro, if requested) from a string column.

Parameters:

• column: Name of the column containing JSON strings

• ignore_outer_array: Treat top-level arrays as streams of objects (default: True)

• ndjson: Treat input as newline-delimited JSON (default: False)

• schema_uri: Schema URI to embed in the output (default: "http://json-schema.org/schema#"). Ignored by some consumers when avro=True.

• merge_schemas: Merge schemas from all rows (default: True). If False, returns one schema per row as a list.

• debug: Print debug information (default: False)

• profile: Print profiling information on the duration of each step (default: False)

• map_threshold: Detect maps when object has more than N keys (default: 20)

• map_max_required_keys: Maximum required keys for Map inference (default: None). Objects with more required keys will be forced to Record type. If None, no gating based on required key count.

• force_field_types: Dict of per-field overrides, values must be "map" or "record". Example: {"labels": "map", "claims": "record"}

• avro: Output Avro schema instead of JSON Schema (default: False)

• wrap_root: Control root wrapping.

  • True → wrap using the column name
  • str → wrap using the given name
  • None → no wrapping (default)

Returns:

• dict when merge_schemas=True
• list[dict] when merge_schemas=False

infer_polars_schema(column, **kwargs) -> pl.Schema

Infers a native Polars schema from a string column.

Parameters:

• column: Name of the column containing JSON strings

• ignore_outer_array: Treat top-level arrays as streams of objects (default: True)

• ndjson: Treat input as newline-delimited JSON (default: False)

• merge_schemas: Merge schemas from all rows (default: True). (Currently the only supported mode.)

• debug: Print debug information (default: False)

• profile: Print profiling information on the duration of each step (default: False)

• map_threshold: Detect maps when object has more than N keys (default: 20)

• map_max_required_keys: Maximum required keys for Map inference (default: None). Objects with more required keys will be forced to Record type. If None, no gating based on required key count.

• force_field_types: Dict of per-field overrides, values must be "map" or "record"

• avro: Infer and convert through an Avro schema, so the dtypes match what normalise_json produces (default: True). With avro=False the conversion goes through JSON Schema, which reports unions and nullable fields as String

• map_encoding: The dtype for maps: "mapping" gives pl.Map (default), "kv" a list of {key, value} structs

• wrap_root: Control root wrapping.

  • True → wrap using the column name
  • str → wrap using the given name
  • None → no wrapping (default)

Returns:

• pl.Schema

Note: merge_schemas=False is not supported for Polars schema inference.

normalise_json(column, **kwargs) -> pl.DataFrame | pl.Series

Normalises each JSON string in the column against a single, inferred Avro schema. Ensures every row matches the same structure and datatypes.

Parameters:

• column: Name of the column containing JSON strings

• decode: If True, decode to native Polars types (default: True)

• unnest: If decode=True, expand the decoded struct into separate columns (default: True)

• ignore_outer_array: Treat top-level arrays as streams of objects (default: True)

• ndjson: Treat input as newline-delimited JSON (default: False)

• empty_as_null: Convert empty arrays/maps to null (default: True)

• coerce_strings: Coerce numeric/boolean strings (e.g. "42", "true") into numbers/booleans where the schema expects them (default: False)

• map_encoding: Encoding for Avro maps: "mapping" (default, decoded as pl.Map), "kv" (decoded as lists of {key, value} structs), or "entries" (JSON only)

• map_threshold: Detect maps when object has more than N keys (default: 20)

• map_max_required_keys: Maximum required keys for Map inference (default: None). Objects with more required keys will be forced to Record type. If None, no gating based on required key count.

• force_field_types: Dict of per-field overrides ("map"/"record")

• wrap_root: Control root wrapping.

  • True → wrap using the column name
  • str → wrap using the given name
  • None → no wrapping (default)

Returns:

• If decode=True:

  • unnest=True → pl.DataFrame with one column per schema field
  • unnest=False → pl.DataFrame with a single struct column

• If decode=False → pl.Series of normalised JSON strings

Example:

df = pl.DataFrame({"json_data": ['{"labels": []}', '{"labels": {"en": "Hello"}}']})
out = df.genson.normalise_json("json_data")
print(out.to_list())
# ['{"labels": null}', '{"labels": {"en": "Hello"}}']

Schema Comparison Helper: schema_to_dict

For when you need to compare Polars schemas structurally — for example, to verify that a round-tripped or inferred schema is equivalent to another, polars-genson provides a small utility function, schema_to_dict, to make life easier.

from polars_genson import schema_to_dict
import polars as pl

schema1 = pl.Schema({"id": pl.Int64, "data": pl.Struct({"x": pl.Int32, "y": pl.Utf8})})
schema2 = pl.Schema({"data": pl.Struct({"y": pl.Utf8, "x": pl.Int32}), "id": pl.Int64})

assert schema_to_dict(schema1) == schema_to_dict(schema2)

Unlike direct schema equality (schema1 == schema2), this approach:

• Recursively normalises nested Struct, List, and Array types
• Ignores field order when comparing
• Produces a pure-Python nested dict, suitable for JSON serialization or snapshot tests

This helper is used internally in polars-genson’s test suite (see tests/schema_roundtrip_test.py) to verify equivalence of inferred, converted, and round-tripped schemas.

Examples

Working with Complex JSON

# Complex nested JSON with arrays of objects
df = pl.DataFrame({
    "complex_json": [
        '{"user": {"profile": {"name": "Alice", "preferences": {"theme": "dark"}}}, "posts": [{"title": "Hello", "likes": 5}]}',
        '{"user": {"profile": {"name": "Bob", "preferences": {"theme": "light"}}}, "posts": [{"title": "World", "likes": 3}, {"title": "Test", "likes": 1}]}'
    ]
})

schema = df.genson.infer_polars_schema("complex_json")
print(schema)

Schema({
    'user': Struct({
        'profile': Struct({
            'name': String, 
            'preferences': Struct({'theme': String})
        })
    }),
    'posts': List(Struct({'likes': Int64, 'title': String})),
})

Using Inferred Schema

# You can use the inferred schema for validation or DataFrame operations
inferred_schema = df.genson.infer_polars_schema("json_data")

# Use with other Polars operations
print(f"Schema has {len(inferred_schema)} fields:")
for name, dtype in inferred_schema.items():
    print(f"  {name}: {dtype}")

Contributing

This crate is part of the polars-genson project. See the main repository for the contribution and development docs.

License

MIT License

• Contains vendored and slightly adapted copy of the Apache 2.0 licensed fork of genson-rs crate