behave-tables

Polished API for Behave Data Tables — convert to dicts, Pydantic models, CSV, JSON & JSON Lines. Zero deps · Type-safe · Python 3.11+ · 100% test coverage

Why?

Behave's Table requires manual iteration:

# Without behave-tables — repetitive, error-prone
for row in context.table.rows:
    name = row["name"]
    age = row["age"]
    # No dicts, no models, no CSV, no JSON...

Every project reimplements the same helpers. behave-tables fixes this with a single wrap() call.

Install

pip install behave-tables

# Optional: pydantic for as_models() with validation & type coercion
pip install behave-tables[pydantic]

Quick start

from behave_tables import TableWrapper, wrap

@then("the users should be")
def step_impl(context):
    table = wrap(context.table)

    # Convert to dicts
    users = table.as_dicts()
    # [{"name": "Alice", "age": "30"}, {"name": "Bob", "age": "25"}]

    # Convert to models (Pydantic v2 or dataclass)
    users = table.as_models(User)

    # Extract a single column
    names = table.column("name")  # ["Alice", "Bob"]

    # Find the first matching row
    alice = table.find_row(name="Alice")  # {"name": "Alice", "age": "30"}

    # Find all matching rows
    thirty_somethings = table.find_all_rows(age="30")

    # Validate columns (strict mode detects extras too)
    table.validate_columns("name", "age", strict=True)

    # Export
    csv_str = table.to_csv(delimiter=";")
    json_str = table.to_json(sort_keys=True)

    # Transpose rows and columns
    transposed = table.transpose()

    # Transform
    selected = table.select("name", "age")
    cleaned = table.drop("internal_id")
    renamed = table.rename_columns({"name": "full_name"})
    sorted_wt = table.sort("age", reverse=True)
    deduped = table.distinct()

    # Query
    cities = table.unique("city")
    count = table.count(age="30")
    first = table.first()
    last = table.last()

    # Export & import (round-trip)
    jsonl = table.to_jsonl()
    restored = TableWrapper.from_csv(table.to_csv())
    restored = TableWrapper.from_json(table.to_json())

    # Iterate, index, and measure
    for row in table:
        print(row["name"])

    table[0]     # {"name": "Alice", "age": "30"}
    table[0:2]   # [{"name": "Alice", "age": "30"}, {"name": "Bob", "age": "25"}]
    len(table)   # 2

    # Compare and check membership
    assert table == wrap(other_table)
    assert {"name": "Alice", "age": "30"} in table

API

wrap(table)

Wrap a behave.model.Table (or any table-like object) with TableWrapper.

TableWrapper

ColumnMismatchError

Raised by validate_columns() when expected columns are missing or unexpected columns are found. Subclass of ValueError.

from behave_tables import wrap, ColumnMismatchError

table = wrap(context.table)
try:
    table.validate_columns("name", "email")
except ColumnMismatchError as e:
    print(e.missing)  # ["email"]

# Strict mode: also detects unexpected columns
try:
    table.validate_columns("name", strict=True)
except ColumnMismatchError as e:
    print(e.missing)  # []
    print(e.extra)   # ["age", "email"]

Examples

With dataclasses

from dataclasses import dataclass

@dataclass
class User:
    name: str
    age: int

@then("the users should be")
def step_impl(context):
    users = wrap(context.table).as_models(User)
    assert users[0].name == "Alice"

With Pydantic v2

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

@then("the users should be")
def step_impl(context):
    users = wrap(context.table).as_models(User)
    # Pydantic validates and coerces types automatically
    assert users[0].age == 30  # int, not str

Validate columns

@then("the user list should be")
def step_impl(context):
    table = wrap(context.table)
    table.validate_columns("name", "email", "role")
    # Proceed with confidence that columns exist

# Strict mode: ensure no extra columns
@then("the user list has exactly these columns")
def step_impl(context):
    table = wrap(context.table)
    table.validate_columns("name", "email", strict=True)

Transpose

@then("the data should be")
def step_impl(context):
    table = wrap(context.table)
    transposed = table.transpose()
    # Original headers become the first column ("_column")
    # Original row indices become the new headers ("0", "1", ...)

Find rows

@then("users aged 30 should exist")
def step_impl(context):
    table = wrap(context.table)
    users = table.find_all_rows(age="30")
    assert len(users) == 2

    # Find first match
    alice = table.find_row(name="Alice")

Compare and check membership

@then("the table matches expected data")
def step_impl(context):
    table = wrap(context.table)
    other = wrap(other_table)
    assert table == other

    # Check if a row exists
    assert {"name": "Alice", "age": "30"} in table

Export & import

@then("the report is generated")
def step_impl(context):
    table = wrap(context.table)
    csv_output = table.to_csv(delimiter=";")
    json_output = table.to_json(indent=4, sort_keys=True)
    jsonl_output = table.to_jsonl()  # one JSON object per line

# Round-trip: export then re-import
restored = TableWrapper.from_csv(csv_output)
restored = TableWrapper.from_json(json_output)

Transform

@then("the user names are extracted")
def step_impl(context):
    table = wrap(context.table)
    names_only = table.select("name")
    without_id = table.drop("internal_id")
    renamed = table.rename_columns({"name": "full_name"})
    sorted_by_age = table.sort("age", reverse=True)
    deduped = table.distinct()

Query

@then("the statistics are computed")
def step_impl(context):
    table = wrap(context.table)
    cities = table.unique("city")
    count = table.count(age="30")
    first_user = table.first()
    last_user = table.last()

Design principles

• Zero dependencies — no required packages. as_models() works with stdlib dataclasses out of the box. Install pydantic optionally for validation and type coercion.
• Immutable returns — all public methods return copies of internal data. Modifying results never affects the wrapper state.
• Type-safe — ships with py.typed (PEP 561) for full type checker support (mypy, pyright).
• Defensive by default — to_csv() handles missing values and extra keys gracefully. is_pydantic_model() won't crash on non-class input.
• Protocol-based — TableLike protocol accepts any object with headings and rows, not just behave.model.Table.

Development

make dev            # install with dev dependencies
make test           # run tests
make test-cov       # run tests with coverage
make lint           # check code style
make lint-fix       # auto-fix lint issues
make format         # format code with ruff
make format-check   # verify code is formatted

See CONTRIBUTING.md for details.

License

MIT