"""An API for building differentially private queries from basic operations.

The QueryBuilder class allows users to construct differentially private queries
using SQL-like commands. These queries can then be used with a
:class:`~tmlt.analytics.session.Session` to obtain results or construct views on
which further queries can be run.

The QueryBuilder class can apply transformations, such as joins or filters, as
well as compute aggregations like counts, sums, and standard deviations. See
:class:`QueryBuilder` for a full list of supported transformations and
aggregations.

After each transformation, the QueryBuilder is modified and returned with that
transformation applied. To re-use the transformations in a :class:`QueryBuilder`
as the base for multiple queries, create a view using
:func:`~tmlt.analytics.session.Session.create_view` and write queries on that
view.

At any point, a QueryBuilder instance can have an aggregation like
:meth:`~tmlt.analytics.query_builder.QueryBuilder.count` applied to it,
potentially after a
:meth:`~tmlt.analytics.query_builder.QueryBuilder.groupby`, yielding a
:class:`~tmlt.analytics.query_expr.QueryExpr` object. This QueryExpr can then be
passed to :func:`~tmlt.analytics.session.Session.evaluate` to obtain
differentially private results to the query.
"""

# SPDX-License-Identifier: Apache-2.0
# Copyright Tumult Labs 2024

import datetime
import warnings
from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union

from pyspark.sql import DataFrame

from tmlt.analytics._schema import ColumnDescriptor, ColumnType, Schema
from tmlt.analytics.binning_spec import BinningSpec, BinT
from tmlt.analytics.constraints import Constraint
from tmlt.analytics.keyset import KeySet
from tmlt.analytics.query_expr import (
    AverageMechanism,
    CountDistinctMechanism,
    CountMechanism,
    DropInfinity,
    DropNullAndNan,
    EnforceConstraint,
    Filter,
    FlatMap,
    GetGroups,
    GroupByBoundedAverage,
    GroupByBoundedSTDEV,
    GroupByBoundedSum,
    GroupByBoundedVariance,
    GroupByCount,
    GroupByCountDistinct,
    GroupByQuantile,
    JoinPrivate,
    JoinPublic,
    Map,
    PrivateSource,
    QueryExpr,
    Rename,
    ReplaceInfinity,
    ReplaceNullAndNan,
    Select,
    StdevMechanism,
    SumMechanism,
    VarianceMechanism,
)
from tmlt.analytics.truncation_strategy import TruncationStrategy

# Override exported names to include ColumnType and ColumnDescriptor.
__all__ = [
    "Row",
    "QueryBuilder",
    "GroupedQueryBuilder",
    "ColumnDescriptor",
    "ColumnType",
]

Row = Dict[str, Any]
"""Type alias for a dictionary with string keys."""


class QueryBuilder:
    """High-level interface for specifying DP queries.

    Each instance corresponds to applying a transformation.
    The full graph of QueryBuilder objects can be traversed from root to a node.

    ..
        >>> from tmlt.analytics.privacy_budget import PureDPBudget
        >>> from tmlt.analytics.protected_change import AddOneRow
        >>> import tmlt.analytics.session
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession
        >>> spark = SparkSession.builder.getOrCreate()
        >>> my_private_data = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
        ...     )
        ... )

    Example:
        >>> budget = PureDPBudget(float("inf"))
        >>> sess = tmlt.analytics.session.Session.from_dataframe(
        ...     privacy_budget=budget,
        ...     source_id="my_private_data",
        ...     dataframe=my_private_data,
        ...     protected_change=AddOneRow(),
        ... )
        >>> my_private_data.toPandas()
           A  B  X
        0  0  1  0
        1  1  0  1
        2  1  2  1
        >>> sess.private_sources
        ['my_private_data']
        >>> sess.get_schema("my_private_data").column_types
        {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
        >>> # Building a query
        >>> query = QueryBuilder("my_private_data").count()
        >>> # Answering the query with infinite privacy budget
        >>> answer = sess.evaluate(
        ...     query,
        ...     PureDPBudget(float("inf"))
        ... )
        >>> answer.toPandas()
           count
        0      3
    """

    def __init__(self, source_id: str):
        """Constructor.

        Args:
            source_id: The source id used in the query_expr.
        """
        self._source_id: str = source_id
        self._query_expr: QueryExpr = PrivateSource(source_id)

    @property
    def query_expr(self):
        """Returns the query_expr being built."""
        return self._query_expr

    def join_public(
        self,
        public_table: Union[DataFrame, str],
        join_columns: Optional[Sequence[str]] = None,
    ) -> "QueryBuilder":
        """Updates the current query to join with a dataframe or public source.

        This operation is an inner join.

        This operation performs a natural join between two tables. This means
        that the resulting table will contain all columns unique to each input
        table, along with one copy of each common column. In most cases, the
        columns have the same names they did in the input tables.

        By default, the input tables are joined on all common columns (i.e.,
        columns whose names and data types match). However if ``join_columns``
        is given, the tables will be joined only on the given columns, and the
        remaining common columns will be disambiguated in the resulting table by
        the addition of a ``_left`` or ``_right`` suffix to their names. If
        given, ``join_columns`` must contain a non-empty subset of the tables'
        common columns. For example, two tables with columns ``A,B,C`` and ``A,B,D``
        would by default be joined on columns ``A`` and ``B``, resulting in a
        table with columns ``A,B,C,D``; if ``join_columns=["B"]`` were given
        when performing this join, the resulting table would have columns
        ``A_left,A_right,B,C,D``. The order of columns in the resulting table is
        not guaranteed.

        .. note::
            Columns must share both names and data types for them to be used in
            joining. If this condition is not met, one of the data sources must be
            transformed to be eligible for joining (e.g., by using :func:`rename`
            or :func:`map`).

        Every row within a join group (i.e., every row that shares values in the join
        columns) from the private table will be joined with every row from that same
        group in the public table. For example, if a group has :math:`X` rows in the
        private table and :math:`Y` rows in the public table, then the output table
        will contain :math:`X*Y` rows for this group.

        .. note::
            Tables with a :class:`~tmlt.analytics.protected_change.ProtectedChange` of
            :class:`~tmlt.analytics.protected_change.AddRowsWithID` must include the
            *privacy ID* column in the join columns.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> public_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 0], ["0", 1], ["1", 1], ["1", 2]], columns=["A", "C"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> public_data.toPandas()
               A  C
            0  0  0
            1  0  1
            2  1  1
            3  1  2
            >>> # Create a query joining with public_data as a dataframe:
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .join_public(public_data)
            ...     .groupby(KeySet.from_dict({"C": [0, 1, 2]}))
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("C").toPandas()
               C  count
            0  0      1
            1  1      3
            2  2      2
            >>> # Alternatively, the dataframe can be added to the Session as a public
            >>> # source, and its source ID can be used to perform the join:
            >>> sess.add_public_dataframe(
            ...     source_id="my_public_data", dataframe=public_data
            ... )
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .join_public("my_public_data")
            ...     .groupby(KeySet.from_dict({"C": [0, 1, 2]}))
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("C").toPandas()
               C  count
            0  0      1
            1  1      3
            2  2      2

        Args:
            public_table: A dataframe or source ID for a public source to natural join
                          with private data.
            join_columns: The columns to join on. If ``join_columns`` is not specified,
                          the tables will be joined on all common columns.
        """
        self._query_expr = JoinPublic(
            child=self._query_expr,
            public_table=public_table,
            join_columns=list(join_columns) if join_columns is not None else None,
        )
        return self

    def join_private(
        self,
        right_operand: Union["QueryBuilder", str],
        truncation_strategy_left: Optional[TruncationStrategy.Type] = None,
        truncation_strategy_right: Optional[TruncationStrategy.Type] = None,
        join_columns: Optional[Sequence[str]] = None,
    ) -> "QueryBuilder":
        # pylint: disable=protected-access
        """Updates the current query to join with another :class:`QueryBuilder`.

        The current query can also join with a named private table (represented
        as a string).

        This operation is an inner join.

        This operation is a natural join, with the same behavior and requirements as
        :func:`join_public`.

        For operations on tables with a
        :class:`~tmlt.analytics.protected_change.ProtectedChange` that protects
        adding or removing rows (e.g.
        :class:`~tmlt.analytics.protected_change.AddMaxRows`), there is a key
        difference: before the join is performed, each table is *truncated*
        based on the corresponding
        :class:`~tmlt.analytics.truncation_strategy.TruncationStrategy`.

        In contrast, operations on tables with a
        :class:`~tmlt.analytics.protected_change.AddRowsWithID`
        :class:`~tmlt.analytics.protected_change.ProtectedChange` do not require a
        :class:`~tmlt.analytics.truncation_strategy.TruncationStrategy`, as no
        truncation is necessary while performing the join.

        .. note::
            Tables with a :class:`~tmlt.analytics.protected_change.ProtectedChange` of
            :class:`~tmlt.analytics.protected_change.AddRowsWithID` must include the
            *privacy ID* column in the join columns.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> from tmlt.analytics.query_builder import TruncationStrategy
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> sess.create_view(
            ...     QueryBuilder("my_private_data")
            ...     .select(["A", "X"])
            ...     .rename({"X": "C"})
            ...     .query_expr,
            ...     source_id="my_private_view",
            ...     cache=False
            ... )
            >>> # A query where only one row with each join key is kept on the left
            >>> # table, but two are kept on the right table.
            >>> query_drop_excess = (
            ...     QueryBuilder("my_private_data")
            ...     .join_private(
            ...         QueryBuilder("my_private_view"),
            ...         truncation_strategy_left=TruncationStrategy.DropExcess(1),
            ...         truncation_strategy_right=TruncationStrategy.DropExcess(2),
            ...     )
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query_drop_excess,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               count
            0      3
            >>> # A query where all rows that share a join key with another row in
            >>> # their table are dropped, in both the left and right tables.
            >>> query_drop_non_unique = (
            ...     QueryBuilder("my_private_data")
            ...     .join_private(
            ...         QueryBuilder("my_private_view"),
            ...         truncation_strategy_left=TruncationStrategy.DropNonUnique(),
            ...         truncation_strategy_right=TruncationStrategy.DropNonUnique(),
            ...     )
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query_drop_non_unique,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               count
            0      1

        Args:
            right_operand: QueryBuilder object representing the table to be joined with.
                            When calling ``query_a.join_private(query_b, ...)``, we
                            refer to ``query_a`` as the *left table* and ``query_b`` as
                            the *right table*.
                            ``query_a.join_private("table")`` is shorthand for
                            ``query_a.join_private(QueryBuilder("table"))``.
            truncation_strategy_left: Strategy for truncation of the left table.
            truncation_strategy_right: Strategy for truncation of the right table.
            join_columns: The columns to join on. If ``join_columns`` is not specified,
                          the tables will be joined on all common columns.
        """
        if isinstance(right_operand, str):
            right_operand = QueryBuilder(right_operand)
        self._query_expr = JoinPrivate(
            self._query_expr,
            right_operand._query_expr,
            truncation_strategy_left,
            truncation_strategy_right,
            list(join_columns) if join_columns is not None else None,
        )
        return self

    def replace_null_and_nan(
        self,
        replace_with: Optional[
            Mapping[str, Union[int, float, str, datetime.date, datetime.datetime]]
        ] = None,
    ) -> "QueryBuilder":
        """Updates the current query to replace null and NaN values in some columns.

        .. note::
            Null values *cannot* be replaced in the ID column of a table initialized
            with a :class:`~tmlt.analytics.protected_change.AddRowsWithID`
            :class:`~tmlt.analytics.protected_change.ProtectedChange`, nor on a column
            generated by a :meth:`~tmlt.analytics.query_builder.QueryBuilder.flat_map`
            with the grouping parameter set to True.

        .. warning::
            If null values are replaced in a column, then Analytics will raise
            an error if you use a KeySet with a null value for that column.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [[None, 0, 0.0], ["1", None, 1.1], ["2", 2, None]],
            ...         columns=["A", "B", "X"],
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
                  A    B    X
            0  None  0.0  0.0
            1     1  NaN  1.1
            2     2  2.0  NaN
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'DECIMAL', 'X': 'DECIMAL'}
            >>> # Building a query with a replace_null_and_nan transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .replace_null_and_nan(
            ...         replace_with={
            ...             "A": "new_value",
            ...             "B": 1234,
            ...             "X": 56.78,
            ...         },
            ...     )
            ...     .groupby(KeySet.from_dict({"A": ["new_value", "1", "2"]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
                       A  count
            0          1      1
            1          2      1
            2  new_value      1

        Args:
            replace_with: A dictionary mapping column names to values used to
                replace null and NaN values.
                If None (or empty), all columns will have null and NaN
                values replaced with Analytics defaults; see
                :class:`tmlt.analytics.query_expr.AnalyticsDefault`.
        """
        if replace_with is None:
            replace_with = {}
        # this assert is for mypy
        assert replace_with is not None
        self._query_expr = ReplaceNullAndNan(
            child=self.query_expr, replace_with=replace_with
        )
        return self

    def replace_infinity(
        self, replace_with: Optional[Dict[str, Tuple[float, float]]] = None
    ) -> "QueryBuilder":
        """Updates the current query to replace +inf and -inf values in some columns.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [
            ...             ["a1", 0, 0.0],
            ...             ["a1", None, float("-inf")],
            ...             ["a2", 2, float("inf")]
            ...         ],
            ...         columns=["A", "B", "X"],
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
                A    B    X
            0  a1  0.0  0.0
            1  a1  NaN -inf
            2  a2  2.0  inf
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'DECIMAL', 'X': 'DECIMAL'}
            >>> # Building a query with a replace_infinity transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .replace_infinity(
            ...         replace_with={
            ...             "X": (-100, 100),
            ...         },
            ...     )
            ...     .groupby(KeySet.from_dict({"A": ["a1", "a2"]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
                A  count
            0  a1      2
            1  a2      1

        Args:
            replace_with: A dictionary mapping column names to values used to
                replace -inf and +inf.
                If None (or empty), all columns will have infinite
                values replaced with Analytics defaults; see
                :class:`tmlt.analytics.query_expr.AnalyticsDefault`.
        """
        if replace_with is None:
            replace_with = {}
        # this assert is for mypy
        assert replace_with is not None
        self._query_expr = ReplaceInfinity(
            child=self.query_expr, replace_with=replace_with
        )
        return self

    def drop_null_and_nan(self, columns: Optional[List[str]]) -> "QueryBuilder":
        """Updates the current query to drop rows containing null or NaN values.

        .. note::
            Null values *cannot* be dropped in the ID column of a table initialized
            with a :class:`~tmlt.analytics.protected_change.AddRowsWithID`
            :class:`~tmlt.analytics.protected_change.ProtectedChange`, nor on a column
            generated by a :meth:`~tmlt.analytics.query_builder.QueryBuilder.flat_map`
            with the grouping parameter set to True.

        .. warning::
            If null and NaN values are dropped from a column, then Analytics will
            raise an error if you use a :class:`~tmlt.analytics.keyset.KeySet`
            that contains a null value for that column.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> from pyspark.sql.types import (
            ...     DoubleType,
            ...     LongType,
            ...     StringType,
            ...     StructField,
            ...     StructType,
            ... )
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     [["a1", 2, 0.0], ["a1", None, 1.1], ["a2", 2, None]],
            ...     schema=StructType([
            ...         StructField("A", StringType(), nullable=True),
            ...         StructField("B", LongType(), nullable=True),
            ...         StructField("X", DoubleType(), nullable=True),
            ...     ])
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
                A    B    X
            0  a1  2.0  0.0
            1  a1  NaN  1.1
            2  a2  2.0  NaN
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'DECIMAL'}
            >>> # Count query on the original data
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["a1", "a2"], "B": [None, 2]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A", "B").toPandas()
                A    B  count
            0  a1  NaN      1
            1  a1  2.0      1
            2  a2  NaN      0
            3  a2  2.0      1
            >>> # Building a query with a transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .drop_null_and_nan(columns=["B"])
            ...     .groupby(KeySet.from_dict({"A": ["a1", "a2"]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
                A  count
            0  a1      1
            1  a2      1

        Args:
            columns: A list of columns in which to look for null and NaN values.
                If ``None`` or an empty list, then *all* columns will be considered,
                meaning that if *any* column has a null/NaN value then the row it
                is in will be dropped.
        """
        if columns is None:
            columns = []
        # this assert is for mypy
        assert columns is not None
        self._query_expr = DropNullAndNan(child=self.query_expr, columns=columns)
        return self

    def drop_infinity(self, columns: Optional[List[str]]) -> "QueryBuilder":
        """Updates the current query to drop rows containing infinite values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> from pyspark.sql.types import (
            ...     DoubleType,
            ...     LongType,
            ...     StringType,
            ...     StructField,
            ...     StructType,
            ... )
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     [["a1", 2, 0.0], ["a1", 1, 1.1], ["a2", 2, float("inf")]],
            ...     schema=StructType([
            ...         StructField("A", StringType(), nullable=True),
            ...         StructField("B", LongType(), nullable=True),
            ...         StructField("X", DoubleType(), nullable=True),
            ...     ])
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.sort("A", "B", "X").toPandas()
                A  B    X
            0  a1  1  1.1
            1  a1  2  0.0
            2  a2  2  inf
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'DECIMAL'}
            >>> # Count query on the original data
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["a1", "a2"]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
                A  count
            0  a1      2
            1  a2      1

            >>> # Building a query with a drop_infinity transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .drop_infinity(columns=["X"])
            ...     .groupby(KeySet.from_dict({"A": ["a1", "a2"]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
                A  count
            0  a1      2
            1  a2      0

        Args:
            columns: A list of columns in which to look for positive and negative
                infinities. If ``None`` or an empty list, then *all* columns will
                be considered, meaning that if *any* column has an infinite value
                then the row it is in will be dropped.
        """
        if columns is None:
            columns = []
        # this assert is for mypy
        assert columns is not None
        self._query_expr = DropInfinity(child=self.query_expr, columns=columns)
        return self

    def rename(self, column_mapper: Dict[str, str]) -> "QueryBuilder":
        """Updates the current query to rename the columns.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a query with a rename transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .rename({"X": "C"})
            ...     .groupby(KeySet.from_dict({"C": [0, 1]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("C").toPandas()
               C  count
            0  0      1
            1  1      2

        Args:
            column_mapper: A mapping of columns to new column names.
                Columns not specified in the mapper will remain the same.
        """
        self._query_expr = Rename(child=self._query_expr, column_mapper=column_mapper)
        return self

    def filter(self, condition: str) -> "QueryBuilder":
        """Updates the current query to filter for rows matching a condition.

        The ``condition`` parameter accepts the same syntax as in PySpark's
        :meth:`~pyspark.sql.DataFrame.filter` method: valid expressions are
        those that can be used in a `WHERE clause
        <https://spark.apache.org/docs/latest/sql-ref-syntax-qry-select-where.html>`__
        in Spark SQL. Examples of valid conditions include:

        * ``age < 42``
        * ``age BETWEEN 17 AND 42``
        * ``age < 42 OR (age < 60 AND gender IS NULL)``
        * ``LENGTH(name) > 17``
        * ``favorite_color IN ('blue', 'red')``
        * ``date = '2022-03-14'``
        * ``time < '2022-01-01T12:45:00'``

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a query with a filter transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .filter("A == '0'")
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               count
            0      1

        Args:
            condition: A string of SQL expressions specifying the filter to apply to the
                data. For example, the string "A > B" matches rows where column A is
                greater than column B.
        """
        self._query_expr = Filter(child=self._query_expr, condition=condition)
        return self

    def select(self, columns: Sequence[str]) -> "QueryBuilder":
        """Updates the current query to select certain columns.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> # Typeguard doesn't like implicit doctest imports
            >>> from tmlt.analytics.query_builder import QueryBuilder
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Create a new view using a select query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .select(["A", "B"])
            ... )
            >>> sess.create_view(query, "selected_data", cache=True)
            >>> # Inspect the schema of the resulting view
            >>> sess.get_schema("selected_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER'}

        Args:
            columns: The columns to select.
        """
        self._query_expr = Select(
            child=self._query_expr,
            columns=list(columns) if columns is not None else None,
        )
        return self

    def map(
        self,
        f: Callable[[Row], Row],
        new_column_types: Mapping[str, Union[ColumnDescriptor, ColumnType]],
        augment: bool = False,
    ) -> "QueryBuilder":
        """Updates the current query to apply a mapping function to each row.

        If you provide only a ColumnType for the new column types, Analytics
        assumes that all new columns created may contain null values (and that
        DECIMAL columns may contain NaN or infinite values).

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a query with a map transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .map(
            ...         lambda row: {"new": row["B"]*2},
            ...         new_column_types={"new": 'INTEGER'},
            ...         augment=True
            ...     )
            ...     .groupby(KeySet.from_dict({"new": [0, 1, 2, 3, 4]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("new").toPandas()
               new  count
            0    0      1
            1    1      0
            2    2      1
            3    3      0
            4    4      1

        Args:
            f: The function to be applied to each row.
                The function's input is a dictionary matching each column name to
                its value for that row.
                This function should return a dictionary, which should always
                have the same keys regardless of input, and the values in that
                dictionary should match the column type specified in
                ``new_column_types``. The function should not have any side effects
                (in particular, f cannot raise exceptions).
            new_column_types: Mapping from column names to types, for new columns
                produced by f. Using
                :class:`~tmlt.analytics.query_builder.ColumnDescriptor`
                is preferred.
            augment: If True, add new columns to the existing dataframe (so new
                     schema = old schema + schema_new_columns).
                     If False, make the new dataframe with schema = schema_new_columns
        """
        self._query_expr = Map(
            child=self._query_expr,
            f=f,
            schema_new_columns=Schema(
                dict(new_column_types),
                grouping_column=None,
                default_allow_nan=True,
                default_allow_null=True,
                default_allow_inf=True,
            ),
            augment=augment,
        )
        return self

    def flat_map(
        self,
        f: Callable[[Row], List[Row]],
        new_column_types: Mapping[str, Union[str, ColumnDescriptor, ColumnType]],
        augment: bool = False,
        grouping: bool = False,
        max_rows: Optional[int] = None,
        max_num_rows: Optional[int] = None,
    ) -> "QueryBuilder":
        """Updates the current query to apply a flat map.

        If you provide only a ColumnType for the new column types, Analytics
        assumes that all new columns created may contain null values (and that
        DECIMAL columns may contain NaN or infinite values).

        Operations on tables with a
        :class:`~tmlt.analytics.protected_change.AddRowsWithID`
        :class:`~tmlt.analytics.protected_change.ProtectedChange` do not require a
        ``max_rows`` argument, since it is not necessary to impose a limit on
        the number of new rows.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> from tmlt.analytics.keyset import KeySet
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1], ["1", 3, 1]],
            ...         columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            3  1  3  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a query with a flat map transformation
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .flat_map(
            ...         lambda row: [{"i_B": i} for i in range(int(row["B"])+1)],
            ...         new_column_types={"i_B": ColumnDescriptor(
            ...             ColumnType.INTEGER,
            ...             allow_null=False,
            ...         )},
            ...         augment=True,
            ...         grouping=False,
            ...         max_rows=3,
            ...     )
            ...     .groupby(KeySet.from_dict({"B": [0, 1, 2, 3]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("B").toPandas()
               B  count
            0  0      1
            1  1      2
            2  2      3
            3  3      3

        Args:
            f: The function to be applied to each row.
                The function's input is a dictionary matching a column name to
                its value for that row.
                This function should return a list of dictionaries.
                Those dictionaries should always
                have the same keys regardless of input, and the values in those
                dictionaries should match the column type specified in
                ``new_column_types``. The function should not have any side effects
                (in particular, ``f`` must not raise exceptions), and must be
                deterministic (running it multiple times on a fixed input should
                always return the same output).
            new_column_types: Mapping from column names to types, for new columns
                produced by ``f``. Using
                :class:`~tmlt.analytics.query_builder.ColumnDescriptor`
                is preferred.
            augment: If True, add new columns to the existing dataframe (so new
                     schema = old schema + schema_new_columns).
                     If False, make the new dataframe with schema = schema_new_columns
            grouping: Whether this produces a new column that we want to groupby.
                If True, this requires that any groupby aggregations following this
                query include the new column as a groupby column. Only one new column
                is supported, and the new column must have distinct values for each
                input row.
            max_rows: The enforced limit on the number of rows from each ``f(row)``.
                If ``f`` produces more rows than this, only the first ``max_rows``
                rows will be in the output.
            max_num_rows: Deprecated synonym for ``max_rows``.
        """
        grouping_column: Optional[str]
        if grouping:
            if len(new_column_types) != 1:
                raise ValueError(
                    "new_column_types contains "
                    f"{len(new_column_types)} "
                    "columns, grouping flat map can only result in 1 new column"
                )
            (grouping_column,) = new_column_types
        else:
            grouping_column = None
        if max_num_rows is not None:
            if max_rows is not None:
                raise ValueError(
                    "You must use either max_rows or max_num_rows, not both"
                )
            warnings.warn(
                "max_num_rows is deprecated and will be removed in a future release",
                DeprecationWarning,
            )
            max_rows = max_num_rows
        self._query_expr = FlatMap(
            child=self._query_expr,
            f=f,
            schema_new_columns=Schema(
                dict(new_column_types),
                grouping_column=grouping_column,
                default_allow_null=True,
                default_allow_nan=True,
                default_allow_inf=True,
            ),
            augment=augment,
            max_rows=max_rows,
        )
        return self

    def bin_column(
        self, column: str, spec: BinningSpec, name: Optional[str] = None
    ) -> "QueryBuilder":
        """Create a new column by assigning the values in a given column to bins.

        ..
            >>> from tmlt.analytics.query_builder import QueryBuilder
            >>> from tmlt.analytics.session import Session
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> from tmlt.analytics.keyset import KeySet
            >>> from pyspark.sql import SparkSession
            >>> import pandas as pd
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         {
            ...             "age": [11, 17, 30, 18, 59, 48, 76, 91, 48, 53],
            ...             "income": [0, 6, 54, 14, 126, 163, 151, 18, 97, 85],
            ...         }
            ...     )
            ... )

        Example:
            >>> from tmlt.analytics.binning_spec import BinningSpec
            >>> sess = Session.from_dataframe(
            ...     PureDPBudget(float("inf")),
            ...     source_id="private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               age  income
            0   11       0
            1   17       6
            2   30      54
            3   18      14
            4   59     126
            5   48     163
            6   76     151
            7   91      18
            8   48      97
            9   53      85
            >>> age_binspec = BinningSpec(
            ...     [0, 18, 65, 100], include_both_endpoints=False
            ... )
            >>> income_tax_rate_binspec = BinningSpec(
            ...     [0, 10, 40, 86, 165], names=[10, 12, 22, 24]
            ... )
            >>> keys = KeySet.from_dict(
            ...     {
            ...         "age_binned": age_binspec.bins(),
            ...         "marginal_tax_rate": income_tax_rate_binspec.bins()
            ...     }
            ... )
            >>> query = (
            ...     QueryBuilder("private_data")
            ...     .bin_column("age", age_binspec)
            ...     .bin_column(
            ...         "income", income_tax_rate_binspec, name="marginal_tax_rate"
            ...     )
            ...     .groupby(keys).count()
            ... )
            >>> answer = sess.evaluate(query, PureDPBudget(float("inf")))
            >>> answer.sort("age_binned", "marginal_tax_rate").toPandas()
               age_binned  marginal_tax_rate  count
            0     (0, 18]                 10      2
            1     (0, 18]                 12      1
            2     (0, 18]                 22      0
            3     (0, 18]                 24      0
            4    (18, 65]                 10      0
            5    (18, 65]                 12      0
            6    (18, 65]                 22      2
            7    (18, 65]                 24      3
            8   (65, 100]                 10      0
            9   (65, 100]                 12      1
            10  (65, 100]                 22      0
            11  (65, 100]                 24      1

        Args:
            column: Name of the column used to assign bins.
            spec: A :class:`~tmlt.analytics.binning_spec.BinningSpec` that defines the
                binning operation to be performed.
            name: The name of the column that will be created. If None (the default),
                the input column name with ``_binned`` appended to it.
        """
        if name is None:
            name = f"{column}_binned"
        binning_fn = lambda row: {name: spec(row[column])}
        return self.map(
            binning_fn, new_column_types={name: spec.column_descriptor}, augment=True
        )

    def histogram(
        self,
        column: str,
        bin_edges: Union[Sequence[BinT], BinningSpec],
        name: Optional[str] = None,
    ) -> "QueryExpr":
        """Returns a count query containing the frequency of values in specified column.

        ..
            >>> from tmlt.analytics.query_builder import QueryBuilder
            >>> from tmlt.analytics.session import Session
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> from tmlt.analytics.keyset import KeySet
            >>> from pyspark.sql import SparkSession
            >>> import pandas as pd
            >>> spark = SparkSession.builder.getOrCreate()

        Example:
            >>> from tmlt.analytics.binning_spec import BinningSpec
            >>> private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         {
            ...          "income_thousands": [83, 85, 86, 73, 82, 95,
            ...                               74, 92, 71, 86, 97]
            ...         }
            ...     )
            ... )
            >>> session = Session.from_dataframe(
            ...     privacy_budget=PureDPBudget(epsilon=float('inf')),
            ...     source_id="private_data",
            ...     dataframe=private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> income_binspec = BinningSpec(
            ...     bin_edges=[i for i in range(70,110,10)],
            ...     include_both_endpoints=False
            ... )
            >>> binned_income_count_query = (
            ...     QueryBuilder("private_data")
            ...     .histogram("income_thousands", income_binspec, "income_binned")
            ... )
            >>> binned_income_counts = session.evaluate(
            ...     binned_income_count_query,
            ...     privacy_budget=PureDPBudget(epsilon=10),
            ... )
            >>> print(binned_income_counts.sort("income_binned").toPandas())
              income_binned  count
            0      (70, 80]      3
            1      (80, 90]      5
            2     (90, 100]      3

        Args:
            column: Name of the column used to assign bins.
            bin_edges: The bin edges for the histogram; provided as either a
                :class:`~tmlt.analytics.binning_spec.BinningSpec` or as a list of
                :data:`supported data types
                <tmlt.analytics.session.SUPPORTED_SPARK_TYPES>`.
                Values outside the range of the provided bins, ``None`` types,
                and NaN values are all mapped to ``None`` (``null`` in Spark).

            name: The name of the column that will be created. If None (the default),
                the input column name with ``_binned`` appended to it.
        """
        if not isinstance(bin_edges, BinningSpec):
            spec = BinningSpec(bin_edges)
        else:
            spec = bin_edges
        if not name:
            name = column + "_binned"

        keys = KeySet.from_dict({name: spec.bins()})
        return self.bin_column(column, spec, name).groupby(keys).count()

    def enforce(self, constraint: Constraint) -> "QueryBuilder":
        """Enforce a :mod:`~tmlt.analytics.constraints.Constraint` on the current table.

        This method can be used to enforce constraints on the current table. See
        the :mod:`~tmlt.analytics.constraints` module for information about the
        available constraints and what they are used for.

        ..
            >>> from tmlt.analytics.query_builder import QueryBuilder
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.session import Session
            >>> from tmlt.analytics.protected_change import AddRowsWithID
            >>> from tmlt.analytics.constraints import MaxRowsPerID
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]],
            ...         columns=["id", "B", "X"]
            ...     )
            ... )

        Example:
            >>> my_private_data.toPandas()
              id  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess = (
            ...     Session.Builder()
            ...     .with_privacy_budget(PureDPBudget(float("inf")))
            ...     .with_id_space("a")
            ...     .with_private_dataframe(
            ...         "my_private_data",
            ...         my_private_data,
            ...         protected_change=AddRowsWithID("id", "a"),
            ...     )
            ...     .build()
            ... )
            >>> # No ID contributes more than 2 rows, so no rows are dropped when
            >>> # enforcing the constraint
            >>> query = QueryBuilder("my_private_data").enforce(MaxRowsPerID(2)).count()
            >>> sess.evaluate(query, sess.remaining_privacy_budget).toPandas()
               count
            0      3
            >>> # ID 1 contributes more than one row, so one of the rows with ID 1 will
            >>> # be dropped when enforcing the constraint
            >>> query = QueryBuilder("my_private_data").enforce(MaxRowsPerID(1)).count()
            >>> sess.evaluate(query, sess.remaining_privacy_budget).toPandas()
               count
            0      2

        Args:
            constraint: The constraint to enforce.
        """
        self._query_expr = EnforceConstraint(self._query_expr, constraint, options={})
        return self

    def get_groups(self, columns: Optional[List[str]] = None) -> "QueryExpr":
        """Returns a query that gets combinations of values in the listed columns.

        .. note::
            Because this uses differential privacy, it won't include all of the values
            in the input dataset columns, and may even return no results at all on
            datasets that have few values for each set of group keys.

        ..
            >>> from tmlt.analytics.privacy_budget import ApproxDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()

        Example:
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0] for _ in range(10000)]
            ...         + [["1", 2, 1] for _ in range(10000)],
            ...         columns=["A", "B", "X"],
            ...     )
            ... )
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=ApproxDPBudget(1, 1e-5),
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> # Building a get_groups query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .get_groups()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     sess.remaining_privacy_budget
            ... )
            >>> answer.toPandas()
               A  B  X
            0  0  1  0
            1  1  2  1

        Args:
            columns: Name of the column used to assign bins. If empty or none
                are provided, all of the columns in the table will be used, excluding
                any column marked as a *privacy ID* in a table with a
                :class:`~tmlt.analytics.protected_change.ProtectedChange` of
                :class:`~tmlt.analytics.protected_change.AddRowsWithID`.
        """
        query_expr = GetGroups(child=self._query_expr, columns=columns)
        return query_expr

    def groupby(self, keys: KeySet) -> "GroupedQueryBuilder":
        """Groups the query by the given set of keys, returning a GroupedQueryBuilder.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Examples:
            >>> from tmlt.analytics.keyset import KeySet
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}

        Answering a query with the exact groupby domain:
            >>> groupby_keys = KeySet.from_dict({"A": ["0", "1"]})
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(groupby_keys)
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  count
            0  0      1
            1  1      2

        Answering a query with an omitted domain value:
            >>> groupby_keys = KeySet.from_dict({"A": ["0"]})
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(groupby_keys)
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               A  count
            0  0      1

        Answering a query with an added domain value:
            >>> groupby_keys = KeySet.from_dict({"A": ["0", "1", "2"]})
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(groupby_keys)
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  count
            0  0      1
            1  1      2
            2  2      0

        Answering a query with a multi-column domain:
            >>> groupby_keys = KeySet.from_dict(
            ...    {"A": ["0", "1"], "B": [0, 1, 2]}
            ... )
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(groupby_keys)
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A", "B").toPandas()
               A  B  count
            0  0  0      0
            1  0  1      1
            2  0  2      0
            3  1  0      1
            4  1  1      0
            5  1  2      1

        Answering a query with a multi-column domain and structural zeros:
            >>> # Suppose it is known that A and B cannot be equal. This set of
            >>> # groupby keys prevents those impossible values from being computed.
            >>> keys_df = pd.DataFrame({
            ...     "A": ["0", "0", "1", "1"],
            ...     "B": [1, 2, 0, 2],
            ... })
            >>> groupby_keys = KeySet.from_dataframe(spark.createDataFrame(keys_df))
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(groupby_keys)
            ...     .count()
            ... )
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A", "B").toPandas()
               A  B  count
            0  0  1      1
            1  0  2      0
            2  1  0      1
            3  1  2      1

        Args:
            keys: A KeySet giving the set of groupby keys to be used when
                performing an aggregation.
        """
        return GroupedQueryBuilder(
            source_id=self._source_id, query_expr=self._query_expr, groupby_keys=keys
        )

    def count(
        self,
        name: Optional[str] = None,
        mechanism: CountMechanism = CountMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a count query that is ready to be evaluated.

        .. note::
            Differentially private counts may return values that are not
            possible for a non-DP query - including negative values. You can enforce
            non-negativity once the query returns its results; see the example below.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a count query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               count
            0      3
            >>> # Ensuring all results are non-negative
            >>> import pyspark.sql.functions as sf
            >>> answer = answer.withColumn(
            ...     "count", sf.when(sf.col("count") < 0, 0).otherwise(
            ...             sf.col("count")
            ...     )
            ... )
            >>> answer.toPandas()
               count
            0      3

        Args:
            name: Name for the resulting aggregation column. Defaults to "count".
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        return self.groupby(KeySet.from_dict({})).count(name=name, mechanism=mechanism)

    def count_distinct(
        self,
        columns: Optional[List[str]] = None,
        name: Optional[str] = None,
        mechanism: CountDistinctMechanism = CountDistinctMechanism.DEFAULT,
        cols: Optional[List[str]] = None,
    ) -> QueryExpr:
        """Returns a count_distinct query that is ready to be evaluated.

        .. note::
            Differentially private counts may returns values that are not
            possible for a non-DP query - including negative values. You can enforce
            non-negativity once the query returns its results; see the example below.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["0", 1, 0], ["1", 0, 1], ["1", 2, 1]],
            ...         columns=["A", "B", "X"],
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  0  1  0
            2  1  0  1
            3  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a count_distinct query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .count_distinct()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               count_distinct
            0               3
            >>> # Ensuring all results are non-negative
            >>> import pyspark.sql.functions as sf
            >>> answer = answer.withColumn(
            ...     "count_distinct", sf.when(
            ...         sf.col("count_distinct") < 0, 0
            ...     ).otherwise(
            ...         sf.col("count_distinct")
            ...     )
            ... )
            >>> answer.toPandas()
               count_distinct
            0               3

        Args:
            columns: Columns in which to count distinct values. If none are provided,
                the query will count every distinct row.
            name: Name for the resulting aggregation column. Defaults to
                "count_distinct" if no columns are provided, or
                "count_distinct(A, B, C)" if the provided columns are A, B, and C.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
            cols: Deprecated; use ``columns`` instead.
        """
        return self.groupby(KeySet.from_dict({})).count_distinct(
            columns=columns, name=name, mechanism=mechanism, cols=cols
        )

    def quantile(
        self,
        column: str,
        quantile: float,
        low: float,
        high: float,
        name: Optional[str] = None,
    ) -> QueryExpr:
        """Returns a quantile query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER = '1.331107'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a quantile query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .quantile(column="B", quantile=0.6, low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas() # doctest: +ELLIPSIS
               B_quantile(0.6)
            0         1.331107

        Args:
            column: The column to compute the quantile over.
            quantile: A number between 0 and 1 specifying the quantile to compute.
                For example, 0.5 would compute the median.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low`` is
                less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_quantile({quantile})"``.
        """
        return self.groupby(KeySet.from_dict({})).quantile(
            column=column, quantile=quantile, low=low, high=high, name=name
        )

    def min(
        self, column: str, low: float, high: float, name: Optional[str] = None
    ) -> QueryExpr:
        """Returns a quantile query requesting a minimum value, ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER = '0.213415'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # 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() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
                  min_B
            0  0.213415

        Args:
            column: The column to compute the quantile over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low`` is
                less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_min"``.
        """
        return self.groupby(KeySet.from_dict({})).min(
            column=column, low=low, high=high, name=name
        )

    def max(
        self, column: str, low: float, high: float, name: Optional[str] = None
    ) -> QueryExpr:
        """Returns a quantile query requesting a maximum value, ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER='2.331871'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a quantile query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .max(column="B", low=0, high=5, name="max_B")
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
                  max_B
            0  2.331871

        Args:
            column: The column to compute the quantile over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_max"``.
        """
        return self.groupby(KeySet.from_dict({})).max(
            column=column, low=low, high=high, name=name
        )

    def median(
        self, column: str, low: float, high: float, name: Optional[str] = None
    ) -> QueryExpr:
        """Returns a quantile query requesting a median value, ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER='1.221197'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a quantile query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .median(column="B", low=0, high=5, name="median_B")
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
               median_B
            0  1.221197


        Args:
            column: The column to compute the quantile over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_median"``.
        """
        return self.groupby(KeySet.from_dict({})).median(
            column=column, low=low, high=high, name=name
        )

    def sum(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: SumMechanism = SumMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a sum query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a sum query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .sum(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               B_sum
            0      3

        Args:
            column: The column to compute the sum over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_sum"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        return self.groupby(KeySet.from_dict({})).sum(
            column=column, low=low, high=high, name=name, mechanism=mechanism
        )

    def average(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: AverageMechanism = AverageMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns an average query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building an average query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .average(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               B_average
            0        1.0

        Args:
            column: The column to compute the average over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_average"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        return self.groupby(KeySet.from_dict({})).average(
            column=column, low=low, high=high, name=name, mechanism=mechanism
        )

    def variance(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: VarianceMechanism = VarianceMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a variance query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a variance query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .variance(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               B_variance
            0         1.0

        Args:
            column: The column to compute the variance over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_variance"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        return self.groupby(KeySet.from_dict({})).variance(
            column=column, low=low, high=high, name=name, mechanism=mechanism
        )

    def stdev(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: StdevMechanism = StdevMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a standard deviation query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~drop_null_and_nan` query will be performed first. If the
            column being measured contains infinite values, a
            :meth:`~drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a standard deviation query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .stdev(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.toPandas()
               B_stdev
            0      1.0

        Args:
            column: The column to compute the stdev over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_stdev"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        return self.groupby(KeySet.from_dict({})).stdev(
            column=column, low=low, high=high, name=name, mechanism=mechanism
        )


class GroupedQueryBuilder:
    """A QueryBuilder that is grouped by a set of columns and can be aggregated."""

    def __init__(self, source_id, query_expr, groupby_keys):
        """Constructor.

        Do not construct directly; use :func:`~QueryBuilder.groupby`.
        """
        # pylint: disable=pointless-string-statement
        """
        Args:
            source_id: The source id used in the query_expr.
            query_expr: A query expression describing transformations before the
                application of a GroupedQueryBuilder.
            groupby_keys: A KeySet giving the possible combinations of values for
                the groupby columns.
        """
        self._source_id: str = source_id
        self._query_expr: QueryExpr = query_expr
        self._groupby_keys: KeySet = groupby_keys

    def count(
        self,
        name: Optional[str] = None,
        mechanism: CountMechanism = CountMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a count query that is ready to be evaluated.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby count query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .count()
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  count
            0  0      1
            1  1      2

        Args:
            name: Name for the resulting aggregation column. Defaults to "count".
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        if name is None:
            name = "count"
        query_expr = GroupByCount(
            child=self._query_expr,
            groupby_keys=self._groupby_keys,
            output_column=name,
            mechanism=mechanism,
        )
        return query_expr

    def count_distinct(
        self,
        columns: Optional[List[str]] = None,
        name: Optional[str] = None,
        mechanism: CountDistinctMechanism = CountDistinctMechanism.DEFAULT,
        cols: Optional[List[str]] = None,
    ) -> QueryExpr:
        """Returns a count_distinct query that is ready to be evaluated.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["0", 1, 0], ["1", 0, 1], ["1", 2, 1]],
            ...         columns=["A", "B", "X"],
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  0  1  0
            2  1  0  1
            3  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby count_distinct query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .count_distinct(["B", "X"])
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  count_distinct(B, X)
            0  0                     1
            1  1                     2

        Args:
            columns: Columns in which to count distinct values. If none are provided,
                the query will count every distinct row.
            name: Name for the resulting aggregation column. Defaults to
                "count_distinct" if no columns are provided, or
                "count_distinct(A, B, C)" if the provided columns are A, B, and C.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
            cols: Deprecated; use ``columns`` instead.
        """
        if cols is not None:
            warnings.warn(
                "The `cols` argument is deprecated; use `columns` instead",
                DeprecationWarning,
            )
            if columns is not None:
                raise ValueError(
                    "cannot provide both `cols` and `columns` arguments to"
                    " count_distinct"
                )
            columns = cols
        columns_to_count: Optional[List[str]] = None
        if columns is not None and len(columns) > 0:
            columns_to_count = list(columns)
        if not name:
            if columns_to_count:
                name = f"count_distinct({', '.join(columns_to_count)})"
            else:
                name = "count_distinct"
        query_expr = GroupByCountDistinct(
            child=self._query_expr,
            columns_to_count=columns_to_count,
            groupby_keys=self._groupby_keys,
            output_column=name,
            mechanism=mechanism,
        )
        return query_expr

    def quantile(
        self,
        column: str,
        quantile: float,
        low: float,
        high: float,
        name: Optional[str] = None,
    ) -> QueryExpr:
        """Returns a quantile query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER = '1.331107'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby quantile query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .quantile(column="B", quantile=0.6, low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas() # doctest: +ELLIPSIS
               A  B_quantile(0.6)
            0  0         1.331107
            1  1         1.331107

        Args:
            column: The column to compute the quantile over.
            quantile: A number between 0 and 1 specifying the quantile to compute.
                For example, 0.5 would compute the median.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_quantile({quantile})"``.
        """
        if name is None:
            name = f"{column}_quantile({quantile})"
        query_expr = GroupByQuantile(
            child=self._query_expr,
            groupby_keys=self._groupby_keys,
            measure_column=column,
            quantile=quantile,
            low=low,
            high=high,
            output_column=name,
        )
        return query_expr

    def min(
        self, column: str, low: float, high: float, name: Optional[str] = None
    ) -> QueryExpr:
        """Returns a quantile query requesting a minimum value, ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER = '0.213415'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # 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() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
               A     min_B
            0  0  0.213415
            1  1  0.213415

        ..
            >>> # Reset the ellipsis marker
            >>> doctest.ELLIPSIS_MARKER = '...'

        Args:
            column: The column to compute the quantile over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_min"``.
        """
        if not name:
            name = f"{column}_min"
        return self.quantile(column=column, quantile=0, low=low, high=high, name=name)

    def max(
        self, column: str, low: float, high: float, name: Optional[str] = None
    ) -> QueryExpr:
        """Returns a quantile query requesting a maximum value, ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER='2.331871'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a quantile query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .max(column="B", low=0, high=5, name="max_B")
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
               A     max_B
            0  0  2.331871
            1  1  2.331871

        ..
            >>> # Reset the ellipsis marker
            >>> doctest.ELLIPSIS_MARKER = '...'

        Args:
            column: The column to compute the quantile over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_max"``.
        """
        if not name:
            name = f"{column}_max"
        return self.quantile(column=column, quantile=1, low=low, high=high, name=name)

    def median(
        self, column: str, low: float, high: float, name: Optional[str] = None
    ) -> QueryExpr:
        """Returns a quantile query requesting a median value, ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )
            >>> import doctest
            >>> doctest.ELLIPSIS_MARKER='1.221197'

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a quantile query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .median(column="B", low=0, high=5, name="median_B")
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
               A  median_B
            0  0  1.221197
            1  1  1.221197


        Args:
            column: The column to compute the quantile over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_median"``.
        """
        if not name:
            name = f"{column}_median"
        return self.quantile(column=column, quantile=0.5, low=low, high=high, name=name)

    def sum(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: SumMechanism = SumMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a sum query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby sum query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .sum(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  B_sum
            0  0      1
            1  1      2

        Args:
            column: The column to compute the sum over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_sum"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        if name is None:
            name = f"{column}_sum"
        query_expr = GroupByBoundedSum(
            child=self._query_expr,
            groupby_keys=self._groupby_keys,
            measure_column=column,
            low=low,
            high=high,
            output_column=name,
            mechanism=mechanism,
        )
        return query_expr

    def average(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: AverageMechanism = AverageMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns an average query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby average query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .average(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  B_average
            0  0        1.0
            1  1        1.0

        Args:
            column: The column to compute the average over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_average"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        if name is None:
            name = f"{column}_average"
        query_expr = GroupByBoundedAverage(
            child=self._query_expr,
            groupby_keys=self._groupby_keys,
            measure_column=column,
            low=low,
            high=high,
            output_column=name,
            mechanism=mechanism,
        )
        return query_expr

    def variance(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: VarianceMechanism = VarianceMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a variance query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> import numpy as np
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby variance query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .variance(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  B_variance
            0  0         NaN
            1  1         1.0

        Args:
            column: The column to compute the variance over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_variance"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        if name is None:
            name = f"{column}_variance"
        query_expr = GroupByBoundedVariance(
            child=self._query_expr,
            groupby_keys=self._groupby_keys,
            measure_column=column,
            low=low,
            high=high,
            output_column=name,
            mechanism=mechanism,
        )
        return query_expr

    def stdev(
        self,
        column: str,
        low: float,
        high: float,
        name: Optional[str] = None,
        mechanism: StdevMechanism = StdevMechanism.DEFAULT,
    ) -> QueryExpr:
        """Returns a standard deviation query that is ready to be evaluated.

        .. note::
            If the column being measured contains NaN or null values, a
            :meth:`~QueryBuilder.drop_null_and_nan` query will be performed
            first. If the column being measured contains infinite values, a
            :meth:`~QueryBuilder.drop_infinity` query will be performed first.

        .. note::
            Regarding the clamping bounds:

            #. The values for ``low`` and ``high`` are a choice the caller must make.
            #. All data will be clamped to lie within this range.
            #. The narrower the range, the less noise. Larger bounds mean more data \
                is kept, but more noise needs to be added to the result.
            #. The clamping bounds are assumed to be public information. Avoid using \
                the private data to set these values.

        ..
            >>> from tmlt.analytics.privacy_budget import PureDPBudget
            >>> from tmlt.analytics.protected_change import AddOneRow
            >>> import tmlt.analytics.session
            >>> import pandas as pd
            >>> from pyspark.sql import SparkSession
            >>> spark = SparkSession.builder.getOrCreate()
            >>> my_private_data = spark.createDataFrame(
            ...     pd.DataFrame(
            ...         [["0", 1, 0], ["1", 0, 1], ["1", 2, 1]], columns=["A", "B", "X"]
            ...     )
            ... )

        Example:
            >>> budget = PureDPBudget(float("inf"))
            >>> sess = tmlt.analytics.session.Session.from_dataframe(
            ...     privacy_budget=budget,
            ...     source_id="my_private_data",
            ...     dataframe=my_private_data,
            ...     protected_change=AddOneRow(),
            ... )
            >>> my_private_data.toPandas()
               A  B  X
            0  0  1  0
            1  1  0  1
            2  1  2  1
            >>> sess.private_sources
            ['my_private_data']
            >>> sess.get_schema("my_private_data").column_types
            {'A': 'VARCHAR', 'B': 'INTEGER', 'X': 'INTEGER'}
            >>> # Building a groupby standard deviation query
            >>> query = (
            ...     QueryBuilder("my_private_data")
            ...     .groupby(KeySet.from_dict({"A": ["0", "1"]}))
            ...     .stdev(column="B",low=0, high=2)
            ... )
            >>> # Answering the query with infinite privacy budget
            >>> answer = sess.evaluate(
            ...     query,
            ...     PureDPBudget(float("inf"))
            ... )
            >>> answer.sort("A").toPandas()
               A  B_stdev
            0  0      NaN
            1  1      1.0

        Args:
            column: The column to compute the stdev over.
            low: The lower bound for clamping.
            high: The upper bound for clamping. Must be such that ``low``
                is less than ``high``.
            name: The name to give the resulting aggregation column. Defaults to
                ``f"{column}_stdev"``.
            mechanism: Choice of noise mechanism. By default, the framework
                automatically selects an appropriate mechanism.
        """
        if name is None:
            name = f"{column}_stdev"
        query_expr = GroupByBoundedSTDEV(
            child=self._query_expr,
            groupby_keys=self._groupby_keys,
            measure_column=column,
            low=low,
            high=high,
            output_column=name,
            mechanism=mechanism,
        )
        return query_expr
