QueryBuilder.min

from tmlt.analytics import QueryBuilder

QueryBuilder.min(column, low, high, name=None)

Returns a quantile query requesting a minimum value, ready to be evaluated.

Note

If the column being measured contains NaN or null values, a drop_null_and_nan() query will be performed first. If the column being measured contains infinite values, a drop_infinity() query will be performed first.

Example

>>> my_private_data.toPandas()
   A  B  X
0  0  1  0
1  1  0  1
2  1  2  1
>>> budget = PureDPBudget(float("inf"))
>>> sess = Session.from_dataframe(
...     privacy_budget=budget,
...     source_id="my_private_data",
...     dataframe=my_private_data,
...     protected_change=AddOneRow(),
... )
>>> # Building a quantile query
>>> query = (
...     QueryBuilder("my_private_data")
...     .min(column="B", low=0, high=5, name="min_B")
... )
>>> # Answering the query with infinite privacy budget
>>> answer = sess.evaluate(
...     query,
...     PureDPBudget(float("inf"))
... )
>>> answer.toPandas() 
      min_B
0  0.213415

Parameters:

• column (str) – The column to compute the quantile over.

• low (float) – The lower bound for clamping.

• high (float) – The upper bound for clamping. Must be such that low is less than high.

• name (Optional[str]) – The name to give the resulting aggregation column. Defaults to f"{column}_min".

Return type:

Query

from tmlt.analytics import GroupedQueryBuilder

GroupedQueryBuilder.min(column, low, high, name=None)

Returns a Query with a quantile query requesting a minimum value.

Note

If the column being measured contains NaN or null values, a drop_null_and_nan() query will be performed first. If the column being measured contains infinite values, a drop_infinity() query will be performed first.

Example

>>> my_private_data.toPandas()
   A  B  X
0  0  1  0
1  1  0  1
2  1  2  1
>>> budget = PureDPBudget(float("inf"))
>>> sess = Session.from_dataframe(
...     privacy_budget=budget,
...     source_id="my_private_data",
...     dataframe=my_private_data,
...     protected_change=AddOneRow(),
... )
>>> # Building a quantile query
>>> query = (
...     QueryBuilder("my_private_data")
...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
...     .min(column="B", low=0, high=5, name="min_B")
... )
>>> # Answering the query with infinite privacy budget
>>> answer = sess.evaluate(
...     query,
...     PureDPBudget(float("inf"))
... )
>>> answer.sort("A").toPandas() 
   A     min_B
0  0  0.213415
1  1  0.213415

Parameters:

• column (str) – The column to compute the quantile over.

• low (float) – The lower bound for clamping.

• high (float) – The upper bound for clamping. Must be such that low is less than high.

• name (Optional[str]) – The name to give the resulting aggregation column. Defaults to f"{column}_min".

Return type:

Query