import pointblank as pb
schema = pb.Schema(
user_id=pb.int_field(unique=True),
**pb.profile_fields(),
)
pb.preview(pb.generate_dataset(schema, n=100, seed=23))PolarsRows100Columns8 |
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profile_fields()
Create a dict of string field specifications representing a person profile.
Usage
profile_fields(
*,
set="standard",
split_name=True,
include=None,
exclude=None,
prefix=None,
)Returns a dictionary of StringField objects suitable for **-unpacking into a Schema(). Each field uses a preset that participates in the existing coherence system, so generated data will have coherent names, emails, addresses, and phone numbers within each row.
Parameters
set: Literal["minimal", "standard", "full"] = "standard"-
The base set of profile fields to include. Options are
"minimal"(name, email, phone; 3-4 columns depending onsplit_name=),"standard"(name, email, city, state, postcode, phone; 6-7 columns), and"full"(name, email, address, city, state, postcode, phone, company, job; 9-10 columns). Default is"standard". split_name: bool = True-
Whether to split the name into separate
first_nameandlast_namecolumns (True, the default) or use a single combinednamecolumn (False). include: list[str] | None = None-
List of additional preset names to add to the base set. For example,
include=["company"]adds a company column to the"standard"set. Presets already in the base set are silently ignored. exclude: list[str] | None = None-
List of preset names to remove from the (possibly augmented) set. For example,
exclude=["postcode"]removes the postcode column. Presets not in the set are silently ignored. prefix: str | None = None-
Optional string to prepend to every column name. For example,
prefix="customer_"produces keys like"customer_first_name","customer_email", etc. Each prefixed profile in a schema is a separate person, with its own name, location, and employer in each row. Other preset columns whose names start with the same prefix (e.g., a hand-written"customer_city"column) belong to that profile.
Returns
dict[str, StringField]-
A dictionary mapping column names to
StringFieldobjects, ordered logically (name fields first, then contact, address, phone, business).
Raises
ValueError-
If
set=is not one of"minimal","standard", or"full"; ifinclude=orexclude=contain unknown preset names; if a preset appears in bothinclude=andexclude=; or ifinclude=contains name presets incompatible with thesplit_name=setting.
Examples
The default call returns the "standard" set of profile columns. The ** operator unpacks the returned dictionary directly into Schema(), as if each string_field() call had been written by hand. All coherence rules apply automatically: emails are derived from names, and city/state/postcode/phone are internally consistent.
Use set= to control how many columns are generated. The "minimal" set includes only name, email, and phone, while "full" adds address, company, and job. Setting split_name=False collapses first_name and last_name into a single combined name column:
schema = pb.Schema(
**pb.profile_fields(set="minimal", split_name=False),
balance=pb.float_field(min_val=0, max_val=10000),
)
pb.preview(pb.generate_dataset(schema, n=50, seed=23))PolarsRows50Columns4 |
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The include= and exclude= parameters let you customize the column set without switching to a different base set. Here we start from the "full" set but drop the business columns:
schema = pb.Schema(
**pb.profile_fields(set="full", exclude=["company", "job"]),
)
pb.preview(pb.generate_dataset(schema, n=50, seed=23, country="DE"))PolarsRows50Columns8 |
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The prefix= parameter prepends a string to every column name, which helps when the profile columns sit alongside other columns in a schema:
schema = pb.Schema(
**pb.profile_fields(set="minimal", prefix="customer_"),
order_total=pb.float_field(min_val=5.0, max_val=500.0, precision=2),
)
pb.preview(pb.generate_dataset(schema, n=50, seed=23))PolarsRows50Columns5 |
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The emails are still derived from the names in each row. A schema can hold several prefixed profiles, and each one is a different person:
schema = pb.Schema(
**pb.profile_fields(set="minimal", prefix="sender_"),
**pb.profile_fields(set="minimal", prefix="recipient_"),
)
pb.preview(pb.generate_dataset(schema, n=50, seed=23))PolarsRows50Columns8 |
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Each sender’s email matches the sender’s name and each recipient’s email matches the recipient’s name, but the sender and the recipient in a row are two independent people.