Python

The Python package reads the same .tdc config and, for the same seed, produces the same output as the TypeScript, Java, C# and Rust implementations. That cross-language guarantee is one of TDC's core promises.

Getting it

On PyPI — version 0.3.2

pip install tdcv2

That gives you both the library and the tdcv2 command, with a starter set of data packs inside the wheel. See Installation for the whole picture.

Using it

from tdcv2 import TDC

data = TDC(config_file="users.tdc")
print(data)

for row in data:
    print(row["Gender"])

data.write_file("users.csv")

The whole output is str(data), the rows are what you iterate over, one row is data[3], and len(data) is how many there are — the object behaves like the Python you already write.

Beside those, the package answers to the names every implementation shares — to_string, to_array, iterate, get_at, to_columns, write_file, seed_info, preflight — so an example written in another language reads here unchanged. See the same names everywhere. Python's own to_list, rows, uses_http, diagnostics, count and engine are unaffected and not deprecated.

One value, without a config

The package also exports tdc, which draws a single value from the same data packs a config reads — no file, no <env>, one call:

from tdcv2 import tdc

tdc.person.lastName()                             # Jones
tdc.country.usa.docs.ssn()                        # 699209702, with its real check digits
tdc.person.lastName.many(5)                       # five of them
tdc.seed("demo").locale("ru").person.lastName()   # pinned and in Russian

The segments stay camelCase here, unlike the method names above. They are addresses the packs already carry, not identifiers this package chose, and person.lastName has to read the same way in a config, in the reference and in the other four implementations. One value at a time is the whole surface.