Polished API for Behave Data Tables — as_dicts, as_models, transpose, to_csv, to_json
Project description
behave-tables
Polished API for Behave Data Tables.
Why?
Behave's Table requires manual iteration: for row in table.rows: name = row["name"]. No conversion to dicts, models, CSV, or JSON. Every project reimplements helpers.
Install
pip install behave-tables
# Optional: pydantic for as_models() with validation
pip install behave-tables[pydantic]
Quick start
from behave_tables import wrap
# In a step definition
@then("the users should be")
def step_impl(context):
table = wrap(context.table)
# As dicts
users = table.as_dicts()
# [{"name": "Alice", "age": "30"}, {"name": "Bob", "age": "25"}]
# As models (Pydantic or dataclass)
users = table.as_models(User)
# Column extraction
names = table.column("name") # ["Alice", "Bob"]
# Find first matching row
alice = table.find_row(name="Alice") # {"name": "Alice", "age": "30"}
# Export
csv_str = table.to_csv()
json_str = table.to_json()
# Transpose
transposed = table.transpose()
# Iterate as dicts
for row in table:
print(row["name"])
# Index
table[0] # {"name": "Alice", "age": "30"}
len(table) # 2
API
| Method | Returns | Description |
|---|---|---|
as_dicts() |
list[dict[str, str]] |
All rows as dicts |
as_models(model) |
list[Model] |
Rows as Pydantic or dataclass instances |
column(name) |
list[str] |
Values for a single column |
find_row(**filters) |
dict[str, str] | None |
First row matching all filters |
validate_columns(*expected) |
None |
Raise ColumnMismatchError if columns missing |
transpose() |
TableWrapper |
New wrapper with rows and columns swapped |
to_csv() |
str |
CSV string |
to_json() |
str |
JSON string |
headers |
list[str] |
Column names |
__iter__ |
Iterator[dict] |
Iterate rows as dicts |
__getitem__(i) |
dict[str, str] |
Row at index as dict |
__len__ |
int |
Number of rows |
Zero dependencies
behave-tables has no required dependencies. as_models() works with stdlib dataclasses out of the box. Install pydantic optionally for validation.
License
MIT
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