/pandas/io/sql.py

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"""
Collection of query wrappers / abstractions to both facilitate data
retrieval and to reduce dependency on DB-specific API.
"""

from __future__ import annotations

from contextlib import contextmanager
from datetime import (
    date,
    datetime,
    time,
)
from functools import partial
import re
from typing import (
    Any,
    Iterator,
    Sequence,
    cast,
    overload,
)
import warnings

import numpy as np

import pandas._libs.lib as lib
from pandas._typing import DtypeArg
from pandas.compat._optional import import_optional_dependency
from pandas.errors import AbstractMethodError

from pandas.core.dtypes.common import (
    is_datetime64tz_dtype,
    is_dict_like,
    is_list_like,
)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.missing import isna

from pandas import get_option
from pandas.core.api import (
    DataFrame,
    Series,
)
from pandas.core.base import PandasObject
from pandas.core.tools.datetimes import to_datetime
from pandas.util.version import Version


class DatabaseError(OSError):
    pass


# -----------------------------------------------------------------------------
# -- Helper functions


def _gt14() -> bool:
    """
    Check if sqlalchemy.__version__ is at least 1.4.0, when several
    deprecations were made.
    """
    import sqlalchemy

    return Version(sqlalchemy.__version__) >= Version("1.4.0")


def _convert_params(sql, params):
    """Convert SQL and params args to DBAPI2.0 compliant format."""
    args = [sql]
    if params is not None:
        if hasattr(params, "keys"):  # test if params is a mapping
            args += [params]
        else:
            args += [list(params)]
    return args


def _process_parse_dates_argument(parse_dates):
    """Process parse_dates argument for read_sql functions"""
    # handle non-list entries for parse_dates gracefully
    if parse_dates is True or parse_dates is None or parse_dates is False:
        parse_dates = []

    elif not hasattr(parse_dates, "__iter__"):
        parse_dates = [parse_dates]
    return parse_dates


def _handle_date_column(
    col, utc: bool | None = None, format: str | dict[str, Any] | None = None
):
    if isinstance(format, dict):
        # GH35185 Allow custom error values in parse_dates argument of
        # read_sql like functions.
        # Format can take on custom to_datetime argument values such as
        # {"errors": "coerce"} or {"dayfirst": True}
        error = format.pop("errors", None) or "ignore"
        return to_datetime(col, errors=error, **format)
    else:
        # Allow passing of formatting string for integers
        # GH17855
        if format is None and (
            issubclass(col.dtype.type, np.floating)
            or issubclass(col.dtype.type, np.integer)
        ):
            format = "s"
        if format in ["D", "d", "h", "m", "s", "ms", "us", "ns"]:
            return to_datetime(col, errors="coerce", unit=format, utc=utc)
        elif is_datetime64tz_dtype(col.dtype):
            # coerce to UTC timezone
            # GH11216
            return to_datetime(col, utc=True)
        else:
            return to_datetime(col, errors="coerce", format=format, utc=utc)


def _parse_date_columns(data_frame, parse_dates):
    """
    Force non-datetime columns to be read as such.
    Supports both string formatted and integer timestamp columns.
    """
    parse_dates = _process_parse_dates_argument(parse_dates)

    # we want to coerce datetime64_tz dtypes for now to UTC
    # we could in theory do a 'nice' conversion from a FixedOffset tz
    # GH11216
    for col_name, df_col in data_frame.items():
        if is_datetime64tz_dtype(df_col.dtype) or col_name in parse_dates:
            try:
                fmt = parse_dates[col_name]
            except TypeError:
                fmt = None
            data_frame[col_name] = _handle_date_column(df_col, format=fmt)

    return data_frame


def _wrap_result(
    data,
    columns,
    index_col=None,
    coerce_float: bool = True,
    parse_dates=None,
    dtype: DtypeArg | None = None,
):
    """Wrap result set of query in a DataFrame."""
    frame = DataFrame.from_records(data, columns=columns, coerce_float=coerce_float)

    if dtype:
        frame = frame.astype(dtype)

    frame = _parse_date_columns(frame, parse_dates)

    if index_col is not None:
        frame.set_index(index_col, inplace=True)

    return frame


def execute(sql, con, params=None):
    """
    Execute the given SQL query using the provided connection object.

    Parameters
    ----------
    sql : string
        SQL query to be executed.
    con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
        Using SQLAlchemy makes it possible to use any DB supported by the
        library.
        If a DBAPI2 object, only sqlite3 is supported.
    params : list or tuple, optional, default: None
        List of parameters to pass to execute method.

    Returns
    -------
    Results Iterable
    """
    pandas_sql = pandasSQL_builder(con)
    args = _convert_params(sql, params)
    return pandas_sql.execute(*args)


# -----------------------------------------------------------------------------
# -- Read and write to DataFrames


@overload
def read_sql_table(
    table_name,
    con,
    schema=None,
    index_col=None,
    coerce_float=True,
    parse_dates=None,
    columns=None,
    chunksize: None = None,
) -> DataFrame:
    ...


@overload
def read_sql_table(
    table_name,
    con,
    schema=None,
    index_col=None,
    coerce_float=True,
    parse_dates=None,
    columns=None,
    chunksize: int = 1,
) -> Iterator[DataFrame]:
    ...


def read_sql_table(
    table_name: str,
    con,
    schema: str | None = None,
    index_col: str | Sequence[str] | None = None,
    coerce_float: bool = True,
    parse_dates=None,
    columns=None,
    chunksize: int | None = None,
) -> DataFrame | Iterator[DataFrame]:
    """
    Read SQL database table into a DataFrame.

    Given a table name and a SQLAlchemy connectable, returns a DataFrame.
    This function does not support DBAPI connections.

    Parameters
    ----------
    table_name : str
        Name of SQL table in database.
    con : SQLAlchemy connectable or str
        A database URI could be provided as str.
        SQLite DBAPI connection mode not supported.
    schema : str, default None
        Name of SQL schema in database to query (if database flavor
        supports this). Uses default schema if None (default).
    index_col : str or list of str, optional, default: None
        Column(s) to set as index(MultiIndex).
    coerce_float : bool, default True
        Attempts to convert values of non-string, non-numeric objects (like
        decimal.Decimal) to floating point. Can result in loss of Precision.
    parse_dates : list or dict, default None
        - List of column names to parse as dates.
        - Dict of ``{column_name: format string}`` where format string is
          strftime compatible in case of parsing string times or is one of
          (D, s, ns, ms, us) in case of parsing integer timestamps.
        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
          to the keyword arguments of :func:`pandas.to_datetime`
          Especially useful with databases without native Datetime support,
          such as SQLite.
    columns : list, default None
        List of column names to select from SQL table.
    chunksize : int, default None
        If specified, returns an iterator where `chunksize` is the number of
        rows to include in each chunk.

    Returns
    -------
    DataFrame or Iterator[DataFrame]
        A SQL table is returned as two-dimensional data structure with labeled
        axes.

    See Also
    --------
    read_sql_query : Read SQL query into a DataFrame.
    read_sql : Read SQL query or database table into a DataFrame.

    Notes
    -----
    Any datetime values with time zone information will be converted to UTC.

    Examples
    --------
    >>> pd.read_sql_table('table_name', 'postgres:///db_name')  # doctest:+SKIP
    """
    pandas_sql = pandasSQL_builder(con, schema=schema)
    if not pandas_sql.has_table(table_name):
        raise ValueError(f"Table {table_name} not found")

    table = pandas_sql.read_table(
        table_name,
        index_col=index_col,
        coerce_float=coerce_float,
        parse_dates=parse_dates,
        columns=columns,
        chunksize=chunksize,
    )

    if table is not None:
        return table
    else:
        raise ValueError(f"Table {table_name} not found", con)


@overload
def read_sql_query(
    sql,
    con,
    index_col=None,
    coerce_float=True,
    params=None,
    parse_dates=None,
    chunksize: None = None,
    dtype: DtypeArg | None = None,
) -> DataFrame:
    ...


@overload
def read_sql_query(
    sql,
    con,
    index_col=None,
    coerce_float=True,
    params=None,
    parse_dates=None,
    chunksize: int = 1,
    dtype: DtypeArg | None = None,
) -> Iterator[DataFrame]:
    ...


def read_sql_query(
    sql,
    con,
    index_col=None,
    coerce_float: bool = True,
    params=None,
    parse_dates=None,
    chunksize: int | None = None,
    dtype: DtypeArg | None = None,
) -> DataFrame | Iterator[DataFrame]:
    """
    Read SQL query into a DataFrame.

    Returns a DataFrame corresponding to the result set of the query
    string. Optionally provide an `index_col` parameter to use one of the
    columns as the index, otherwise default integer index will be used.

    Parameters
    ----------
    sql : str SQL query or SQLAlchemy Selectable (select or text object)
        SQL query to be executed.
    con : SQLAlchemy connectable, str, or sqlite3 connection
        Using SQLAlchemy makes it possible to use any DB supported by that
        library. If a DBAPI2 object, only sqlite3 is supported.
    index_col : str or list of str, optional, default: None
        Column(s) to set as index(MultiIndex).
    coerce_float : bool, default True
        Attempts to convert values of non-string, non-numeric objects (like
        decimal.Decimal) to floating point. Useful for SQL result sets.
    params : list, tuple or dict, optional, default: None
        List of parameters to pass to execute method.  The syntax used
        to pass parameters is database driver dependent. Check your
        database driver documentation for which of the five syntax styles,
        described in PEP 249's paramstyle, is supported.
        Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
    parse_dates : list or dict, default: None
        - List of column names to parse as dates.
        - Dict of ``{column_name: format string}`` where format string is
          strftime compatible in case of parsing string times, or is one of
          (D, s, ns, ms, us) in case of parsing integer timestamps.
        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
          to the keyword arguments of :func:`pandas.to_datetime`
          Especially useful with databases without native Datetime support,
          such as SQLite.
    chunksize : int, default None
        If specified, return an iterator where `chunksize` is the number of
        rows to include in each chunk.
    dtype : Type name or dict of columns
        Data type for data or columns. E.g. np.float64 or
        {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}

        .. versionadded:: 1.3.0

    Returns
    -------
    DataFrame or Iterator[DataFrame]

    See Also
    --------
    read_sql_table : Read SQL database table into a DataFrame.
    read_sql : Read SQL query or database table into a DataFrame.

    Notes
    -----
    Any datetime values with time zone information parsed via the `parse_dates`
    parameter will be converted to UTC.
    """
    pandas_sql = pandasSQL_builder(con)
    return pandas_sql.read_query(
        sql,
        index_col=index_col,
        params=params,
        coerce_float=coerce_float,
        parse_dates=parse_dates,
        chunksize=chunksize,
        dtype=dtype,
    )


@overload
def read_sql(
    sql,
    con,
    index_col=None,
    coerce_float=True,
    params=None,
    parse_dates=None,
    columns=None,
    chunksize: None = None,
) -> DataFrame:
    ...


@overload
def read_sql(
    sql,
    con,
    index_col=None,
    coerce_float=True,
    params=None,
    parse_dates=None,
    columns=None,
    chunksize: int = 1,
) -> Iterator[DataFrame]:
    ...


def read_sql(
    sql,
    con,
    index_col: str | Sequence[str] | None = None,
    coerce_float: bool = True,
    params=None,
    parse_dates=None,
    columns=None,
    chunksize: int | None = None,
) -> DataFrame | Iterator[DataFrame]:
    """
    Read SQL query or database table into a DataFrame.

    This function is a convenience wrapper around ``read_sql_table`` and
    ``read_sql_query`` (for backward compatibility). It will delegate
    to the specific function depending on the provided input. A SQL query
    will be routed to ``read_sql_query``, while a database table name will
    be routed to ``read_sql_table``. Note that the delegated function might
    have more specific notes about their functionality not listed here.

    Parameters
    ----------
    sql : str or SQLAlchemy Selectable (select or text object)
        SQL query to be executed or a table name.
    con : SQLAlchemy connectable, str, or sqlite3 connection
        Using SQLAlchemy makes it possible to use any DB supported by that
        library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible
        for engine disposal and connection closure for the SQLAlchemy connectable; str
        connections are closed automatically. See
        `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_.
    index_col : str or list of str, optional, default: None
        Column(s) to set as index(MultiIndex).
    coerce_float : bool, default True
        Attempts to convert values of non-string, non-numeric objects (like
        decimal.Decimal) to floating point, useful for SQL result sets.
    params : list, tuple or dict, optional, default: None
        List of parameters to pass to execute method.  The syntax used
        to pass parameters is database driver dependent. Check your
        database driver documentation for which of the five syntax styles,
        described in PEP 249's paramstyle, is supported.
        Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
    parse_dates : list or dict, default: None
        - List of column names to parse as dates.
        - Dict of ``{column_name: format string}`` where format string is
          strftime compatible in case of parsing string times, or is one of
          (D, s, ns, ms, us) in case of parsing integer timestamps.
        - Dict of ``{column_name: arg dict}``, where the arg dict corresponds
          to the keyword arguments of :func:`pandas.to_datetime`
          Especially useful with databases without native Datetime support,
          such as SQLite.
    columns : list, default: None
        List of column names to select from SQL table (only used when reading
        a table).
    chunksize : int, default None
        If specified, return an iterator where `chunksize` is the
        number of rows to include in each chunk.

    Returns
    -------
    DataFrame or Iterator[DataFrame]

    See Also
    --------
    read_sql_table : Read SQL database table into a DataFrame.
    read_sql_query : Read SQL query into a DataFrame.

    Examples
    --------
    Read data from SQL via either a SQL query or a SQL tablename.
    When using a SQLite database only SQL queries are accepted,
    providing only the SQL tablename will result in an error.

    >>> from sqlite3 import connect
    >>> conn = connect(':memory:')
    >>> df = pd.DataFrame(data=[[0, '10/11/12'], [1, '12/11/10']],
    ...                   columns=['int_column', 'date_column'])
    >>> df.to_sql('test_data', conn)

    >>> pd.read_sql('SELECT int_column, date_column FROM test_data', conn)
       int_column date_column
    0           0    10/11/12
    1           1    12/11/10

    >>> pd.read_sql('test_data', 'postgres:///db_name')  # doctest:+SKIP

    Apply date parsing to columns through the ``parse_dates`` argument

    >>> pd.read_sql('SELECT int_column, date_column FROM test_data',
    ...             conn,
    ...             parse_dates=["date_column"])
       int_column date_column
    0           0  2012-10-11
    1           1  2010-12-11

    The ``parse_dates`` argument calls ``pd.to_datetime`` on the provided columns.
    Custom argument values for applying ``pd.to_datetime`` on a column are specified
    via a dictionary format:
    1. Ignore errors while parsing the values of "date_column"

    >>> pd.read_sql('SELECT int_column, date_column FROM test_data',
    ...             conn,
    ...             parse_dates={"date_column": {"errors": "ignore"}})
       int_column date_column
    0           0  2012-10-11
    1           1  2010-12-11

    2. Apply a dayfirst date parsing order on the values of "date_column"

    >>> pd.read_sql('SELECT int_column, date_column FROM test_data',
    ...             conn,
    ...             parse_dates={"date_column": {"dayfirst": True}})
       int_column date_column
    0           0  2012-11-10
    1           1  2010-11-12

    3. Apply custom formatting when date parsing the values of "date_column"

    >>> pd.read_sql('SELECT int_column, date_column FROM test_data',
    ...             conn,
    ...             parse_dates={"date_column": {"format": "%d/%m/%y"}})
       int_column date_column
    0           0  2012-11-10
    1           1  2010-11-12
    """
    pandas_sql = pandasSQL_builder(con)

    if isinstance(pandas_sql, SQLiteDatabase):
        return pandas_sql.read_query(
            sql,
            index_col=index_col,
            params=params,
            coerce_float=coerce_float,
            parse_dates=parse_dates,
            chunksize=chunksize,
        )

    try:
        _is_table_name = pandas_sql.has_table(sql)
    except Exception:
        # using generic exception to catch errors from sql drivers (GH24988)
        _is_table_name = False

    if _is_table_name:
        pandas_sql.meta.reflect(bind=pandas_sql.connectable, only=[sql])
        return pandas_sql.read_table(
            sql,
            index_col=index_col,
            coerce_float=coerce_float,
            parse_dates=parse_dates,
            columns=columns,
            chunksize=chunksize,
        )
    else:
        return pandas_sql.read_query(
            sql,
            index_col=index_col,
            params=params,
            coerce_float=coerce_float,
            parse_dates=parse_dates,
            chunksize=chunksize,
        )


def to_sql(
    frame,
    name: str,
    con,
    schema: str | None = None,
    if_exists: str = "fail",
    index: bool = True,
    index_label=None,
    chunksize: int | None = None,
    dtype: DtypeArg | None = None,
    method: str | None = None,
    engine: str = "auto",
    **engine_kwargs,
) -> None:
    """
    Write records stored in a DataFrame to a SQL database.

    Parameters
    ----------
    frame : DataFrame, Series
    name : str
        Name of SQL table.
    con : SQLAlchemy connectable(engine/connection) or database string URI
        or sqlite3 DBAPI2 connection
        Using SQLAlchemy makes it possible to use any DB supported by that
        library.
        If a DBAPI2 object, only sqlite3 is supported.
    schema : str, optional
        Name of SQL schema in database to write to (if database flavor
        supports this). If None, use default schema (default).
    if_exists : {'fail', 'replace', 'append'}, default 'fail'
        - fail: If table exists, do nothing.
        - replace: If table exists, drop it, recreate it, and insert data.
        - append: If table exists, insert data. Create if does not exist.
    index : bool, default True
        Write DataFrame index as a column.
    index_label : str or sequence, optional
        Column label for index column(s). If None is given (default) and
        `index` is True, then the index names are used.
        A sequence should be given if the DataFrame uses MultiIndex.
    chunksize : int, optional
        Specify the number of rows in each batch to be written at a time.
        By default, all rows will be written at once.
    dtype : dict or scalar, optional
        Specifying the datatype for columns. If a dictionary is used, the
        keys should be the column names and the values should be the
        SQLAlchemy types or strings for the sqlite3 fallback mode. If a
        scalar is provided, it will be applied to all columns.
    method : {None, 'multi', callable}, optional
        Controls the SQL insertion clause used:

        - None : Uses standard SQL ``INSERT`` clause (one per row).
        - 'multi': Pass multiple values in a single ``INSERT`` clause.
        - callable with signature ``(pd_table, conn, keys, data_iter)``.

        Details and a sample callable implementation can be found in the
        section :ref:`insert method <io.sql.method>`.
    engine : {'auto', 'sqlalchemy'}, default 'auto'
        SQL engine library to use. If 'auto', then the option
        ``io.sql.engine`` is used. The default ``io.sql.engine``
        behavior is 'sqlalchemy'

        .. versionadded:: 1.3.0

    **engine_kwargs
        Any additional kwargs are passed to the engine.
    """
    if if_exists not in ("fail", "replace", "append"):
        raise ValueError(f"'{if_exists}' is not valid for if_exists")

    pandas_sql = pandasSQL_builder(con, schema=schema)

    if isinstance(frame, Series):
        frame = frame.to_frame()
    elif not isinstance(frame, DataFrame):
        raise NotImplementedError(
            "'frame' argument should be either a Series or a DataFrame"
        )

    pandas_sql.to_sql(
        frame,
        name,
        if_exists=if_exists,
        index=index,
        index_label=index_label,
        schema=schema,
        chunksize=chunksize,
        dtype=dtype,
        method=method,
        engine=engine,
        **engine_kwargs,
    )


def has_table(table_name: str, con, schema: str | None = None):
    """
    Check if DataBase has named table.

    Parameters
    ----------
    table_name: string
        Name of SQL table.
    con: SQLAlchemy connectable(engine/connection) or sqlite3 DBAPI2 connection
        Using SQLAlchemy makes it possible to use any DB supported by that
        library.
        If a DBAPI2 object, only sqlite3 is supported.
    schema : string, default None
        Name of SQL schema in database to write to (if database flavor supports
        this). If None, use default schema (default).

    Returns
    -------
    boolean
    """
    pandas_sql = pandasSQL_builder(con, schema=schema)
    return pandas_sql.has_table(table_name)


table_exists = has_table


def pandasSQL_builder(con, schema: str | None = None):
    """
    Convenience function to return the correct PandasSQL subclass based on the
    provided parameters.
    """
    import sqlite3

    if isinstance(con, sqlite3.Connection) or con is None:
        return SQLiteDatabase(con)

    sqlalchemy = import_optional_dependency("sqlalchemy")

    if isinstance(con, str):
        con = sqlalchemy.create_engine(con)

    if isinstance(con, sqlalchemy.engine.Connectable):
        return SQLDatabase(con, schema=schema)

    raise ValueError(
        "pandas only support SQLAlchemy connectable(engine/connection) or"
        "database string URI or sqlite3 DBAPI2 connection"
    )


class SQLTable(PandasObject):
    """
    For mapping Pandas tables to SQL tables.
    Uses fact that table is reflected by SQLAlchemy to
    do better type conversions.
    Also holds various flags needed to avoid having to
    pass them between functions all the time.
    """

    # TODO: support for multiIndex

    def __init__(
        self,
        name: str,
        pandas_sql_engine,
        frame=None,
        index=True,
        if_exists="fail",
        prefix="pandas",
        index_label=None,
        schema=None,
        keys=None,
        dtype: DtypeArg | None = None,
    ):
        self.name = name
        self.pd_sql = pandas_sql_engine
        self.prefix = prefix
        self.frame = frame
        self.index = self._index_name(index, index_label)
        self.schema = schema
        self.if_exists = if_exists
        self.keys = keys
        self.dtype = dtype

        if frame is not None:
            # We want to initialize based on a dataframe
            self.table = self._create_table_setup()
        else:
            # no data provided, read-only mode
            self.table = self.pd_sql.get_table(self.name, self.schema)

        if self.table is None:
            raise ValueError(f"Could not init table '{name}'")

    def exists(self):
        return self.pd_sql.has_table(self.name, self.schema)

    def sql_schema(self):
        from sqlalchemy.schema import CreateTable

        return str(CreateTable(self.table).compile(self.pd_sql.connectable))

    def _execute_create(self):
        # Inserting table into database, add to MetaData object
        if _gt14():
            self.table = self.table.to_metadata(self.pd_sql.meta)
        else:
            self.table = self.table.tometadata(self.pd_sql.meta)
        self.table.create(bind=self.pd_sql.connectable)

    def create(self):
        if self.exists():
            if self.if_exists == "fail":
                raise ValueError(f"Table '{self.name}' already exists.")
            elif self.if_exists == "replace":
                self.pd_sql.drop_table(self.name, self.schema)
                self._execute_create()
            elif self.if_exists == "append":
                pass
            else:
                raise ValueError(f"'{self.if_exists}' is not valid for if_exists")
        else:
            self._execute_create()

    def _execute_insert(self, conn, keys: list[str], data_iter):
        """
        Execute SQL statement inserting data

        Parameters
        ----------
        conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
        keys : list of str
           Column names
        data_iter : generator of list
           Each item contains a list of values to be inserted
        """
        data = [dict(zip(keys, row)) for row in data_iter]
        conn.execute(self.table.insert(), data)

    def _execute_insert_multi(self, conn, keys: list[str], data_iter):
        """
        Alternative to _execute_insert for DBs support multivalue INSERT.

        Note: multi-value insert is usually faster for analytics DBs
        and tables containing a few columns
        but performance degrades quickly with increase of columns.
        """

        from sqlalchemy import insert

        data = [dict(zip(keys, row)) for row in data_iter]
        stmt = insert(self.table).values(data)
        conn.execute(stmt)

    def insert_data(self):
        if self.index is not None:
            temp = self.frame.copy()
            temp.index.names = self.index
            try:
                temp.reset_index(inplace=True)
            except ValueError as err:
                raise ValueError(f"duplicate name in index/columns: {err}") from err
        else:
            temp = self.frame

        column_names = list(map(str, temp.columns))
        ncols = len(column_names)
        data_list = [None] * ncols

        for i, (_, ser) in enumerate(temp.items()):
            vals = ser._values
            if vals.dtype.kind == "M":
                d = vals.to_pydatetime()
            elif vals.dtype.kind == "m":
                # store as integers, see GH#6921, GH#7076
                d = vals.view("i8").astype(object)
            else:
                d = vals.astype(object)

            assert isinstance(d, np.ndarray), type(d)

            if ser._can_hold_na:
                # Note: this will miss timedeltas since they are converted to int
                mask = isna(d)
                d[mask] = None

            # error: No overload variant of "__setitem__" of "list" matches
            # argument types "int", "ndarray"
            data_list[i] = d  # type: ignore[call-overload]

        return column_names, data_list

    def insert(self, chunksize: int | None = None, method: str | None = None):

        # set insert method
        if method is None:
            exec_insert = self._execute_insert
        elif method == "multi":
            exec_insert = self._execute_insert_multi
        elif callable(method):
            exec_insert = partial(method, self)
        else:
            raise ValueError(f"Invalid parameter `method`: {method}")

        keys, data_list = self.insert_data()

        nrows = len(self.frame)

        if nrows == 0:
            return

        if chunksize is None:
            chunksize = nrows
        elif chunksize == 0:
            raise ValueError("chunksize argument should be non-zero")

        chunks = (nrows // chunksize) + 1

        with self.pd_sql.run_transaction() as conn:
            for i in range(chunks):
                start_i = i * chunksize
                end_i = min((i + 1) * chunksize, nrows)
                if start_i >= end_i:
                    break

                chunk_iter = zip(*(arr[start_i:end_i] for arr in data_list))
                exec_insert(conn, keys, chunk_iter)

    def _query_iterator(
        self,
        result,
        chunksize: str | None,
        columns,
        coerce_float: bool = True,
        parse_dates=None,
    ):
        """Return generator through chunked result set."""
        has_read_data = False
        while True:
            data = result.fetchmany(chunksize)
            if not data:
                if not has_read_data:
                    yield DataFrame.from_records(
                        [], columns=columns, coerce_float=coerce_float
                    )
                break
            else:
                has_read_data = True
                self.frame = DataFrame.from_records(
                    data, columns=columns, coerce_float=coerce_float
                )

                self._harmonize_columns(parse_dates=parse_dates)

                if self.index is not None:
                    self.frame.set_index(self.index, inplace=True)

                yield self.frame

    def read(self, coerce_float=True, parse_dates=None, columns=None, chunksize=None):
        from sqlalchemy import select

        if columns is not None and len(columns) > 0:
            cols = [self.table.c[n] for n in columns]
            if self.index is not None:
                for idx in self.index[::-1]:
                    cols.insert(0, self.table.c[idx])
            sql_select = select(*cols) if _gt14() else select(cols)
        else:
            sql_select = select(self.table) if _gt14() else self.table.select()

        result = self.pd_sql.execute(sql_select)
        column_names = result.keys()

        if chunksize is not None:
            return self._query_iterator(
                result,
                chunksize,
                column_names,
                coerce_float=coerce_float,
                parse_dates=parse_dates,
            )
        else:
            data = result.fetchall()
            self.frame = DataFrame.from_records(
                data, columns=column_names, coerce_float=coerce_float
            )

            self._harmonize_columns(parse_dates=parse_dates)

            if self.index is not None:
                self.frame.set_index(self.index, inplace=True)

            return self.frame

    def _index_name(self, index, index_label):
        # for writing: index=True to include index in sql table
        if index is True:
            nlevels = self.frame.index.nlevels
            # if index_label is specified, set this as index name(s)
            if index_label is not None:
                if not isinstance(index_label, list):
                    index_label = [index_label]
                if len(index_label) != nlevels:
                    raise ValueError(
                        "Length of 'index_label' should match number of "
                        f"levels, which is {nlevels}"
                    )
                else:
                    return index_label
            # return the used column labels for the index columns
            if (
                nlevels == 1
                and "index" not in self.frame.columns
                and self.frame.index.name is None
            ):
                return ["index"]
            else:
                return [
                    l if l is not None else f"level_{i}"
                    for i, l in enumerate(self.frame.index.names)
                ]

        # for reading: index=(list of) string to specify column to set as index
        elif isinstance(index, str):
            return [index]
        elif isinstance(index, list):
            return index
        else:
            return None

    def _get_column_names_and_types(self, dtype_mapper):
        column_names_and_types = []
        if self.index is not None:
            for i, idx_label in enumerate(self.index):
                idx_type = dtype_mapper(self.frame.index._get_level_values(i))
                column_names_and_types.append((str(idx_label), idx_type, True))

        column_names_and_types += [
            (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
            for i in range(len(self.frame.columns))
        ]

        return column_names_and_types

    def _create_table_setup(self):
        from sqlalchemy import (
            Column,
            PrimaryKeyConstraint,
            Table,
        )
        from sqlalchemy.schema import MetaData

        column_names_and_types = self._get_column_names_and_types(self._sqlalchemy_type)

        columns = [
            Column(name, typ, index=is_index)
            for name, typ, is_index in column_names_and_types
        ]

        if self.keys is not None:
            if not is_list_like(self.keys):
                keys = [self.keys]
            else:
                keys = self.keys
            pkc = PrimaryKeyConstraint(*keys, name=self.name + "_pk")
            columns.append(pkc)

        schema = self.schema or self.pd_sql.meta.schema

        # At this point, attach to new metadata, only attach to self.meta
        # once table is created.
        meta = MetaData()
        return Table(self.name, meta, *columns, schema=schema)

    def _harmonize_columns(self, parse_dates=None):
        """
        Make the DataFrame's column types align with the SQL table
        column types.
        Need to work around limited NA value support. Floats are always
        fine, ints must always be floats if there are Null values.
        Booleans are hard because converting bool column with None replaces
        all Nones with false. Therefore only convert bool if there are no
        NA values.
        Datetimes should already be converted to np.datetime64 if supported,
        but here we also force conversion if required.
        """
        parse_dates = _process_parse_dates_argument(parse_dates)

        for sql_col in self.table.columns:
            col_name = sql_col.name
            try:
                df_col = self.frame[col_name]

                # Handle date parsing upfront; don't try to convert columns
                # twice
                if col_name in parse_dates:
                    try:
                        fmt = parse_dates[col_name]
                    except TypeError:
                        fmt = None
                    self.frame[col_name] = _handle_date_column(df_col, format=fmt)
                    continue

                # the type the dataframe column should have
                col_type = self._get_dtype(sql_col.type)

                if (
                    col_type is datetime
                    or col_type is date
                    or col_type is DatetimeTZDtype
                ):
                    # Convert tz-aware Datetime SQL columns to UTC
                    utc = col_type is DatetimeTZDtype
                    self.frame[col_name] = _handle_date_column(df_col, utc=utc)
                elif col_type is float:
                    # floats support NA, can always convert!
                    self.frame[col_name] = df_col.astype(col_type, copy=False)

                elif len(df_col) == df_col.count():
                    # No NA values, can convert ints and bools
                    if col_type is np.dtype("int64") or col_type is bool:
                        self.frame[col_name] = df_col.astype(col_type, copy=False)
            except KeyError:
                pass  # this column not in results

    def _sqlalchemy_type(self, col):

        dtype: DtypeArg = self.dtype or {}
        if is_dict_like(dtype):
            dtype = cast(dict, dtype)
            if col.name in dtype:
                return dtype[col.name]

        # Infer type of column, while ignoring missing values.
        # Needed for inserting typed data containing NULLs, GH 8778.
        col_type = lib.infer_dtype(col, skipna=True)

        from sqlalchemy.types import (
            TIMESTAMP,
            BigInteger,
            Boolean,
            Date,
            DateTime,
            Float,
            Integer,
            SmallInteger,
            Text,
            Time,
        )

        if col_type == "datetime64" or col_type == "datetime":
            # GH 9086: TIMESTAMP is the suggested type if the column contains
            # timezone information
            try:
                if col.dt.tz is not None:
                    return TIMESTAMP(timezone=True)
            except AttributeError:
                # The column is actually a DatetimeIndex
                # GH 26761 or an Index with date-like data e.g. 9999-01-01
                if getattr(col, "tz", None) is not None:
                    return TIMESTAMP(timezone=True)
            return DateTime
        if col_type == "timedelta64":
            warnings.warn(
                "the 'timedelta' type is not supported, and will be "
                "written as integer values (ns frequency) to the database.",
                UserWarning,
                stacklevel=8,
            )
            return BigInteger
        elif col_type == "floating":
            if col.dtype == "float32":
                return Float(precision=23)
            else:
                return Float(precision=53)
        elif col_type == "integer":
            # GH35076 Map pandas integer to optimal SQLAlchemy integer type
            if col.dtype.name.lower() in ("int8", "uint8", "int16"):
                return SmallInteger
            elif col.dtype.name.lower() in ("uint16", "int32"):
                return Integer
            elif col.dtype.name.lower() == "uint64":
                raise ValueError("Unsigned 64 bit integer datatype is not supported")
            else:
                return BigInteger
        elif col_type == "boolean":
            return Boolean
        elif col_type == "date":
            return Date
        elif col_type == "time":
            return Time
        elif col_type == "complex":
            raise ValueError("Complex datatypes not supported")

        return Text

    def _get_dtype(self, sqltype):
        from sqlalchemy.types import (
            TIMESTAMP,
            Boolean,
            Date,
            DateTime,
            Float,
            Integer,
        )

        if isinstance(sqltype, Float):
            return float
        elif isinstance(sqltype, Integer):
            # TODO: Refine integer size.
            return np.dtype("int64")
        elif isinstance(sqltype, TIMESTAMP):
            # we have a timezone capable type
            if not sqltype.timezone:
                return datetime
            return DatetimeTZDtype
        elif isinstance(sqltype, DateTime):
            # Caution: np.datetime64 is also a subclass of np.number.
            return datetime
        elif isinstance(sqltype, Date):
            return date
        elif isinstance(sqltype, Boolean):
            return bool
        return object


class PandasSQL(PandasObject):
    """
    Subclasses Should define read_sql and to_sql.
    """

    def read_sql(self, *args, **kwargs):
        raise ValueError(
            "PandasSQL must be created with an SQLAlchemy "
            "connectable or sqlite connection"
        )

    def to_sql(
        self,
        frame,
        name,
        if_exists="fail",
        index=True,
        index_label=None,
        schema=None,
        chunksize=None,
        dtype: DtypeArg | None = None,
        method=None,
    ):
        raise ValueError(
            "PandasSQL must be created with an SQLAlchemy "
            "connectable or sqlite connection"
        )


class BaseEngine:
    def insert_records(
        self,
        table: SQLTable,
        con,
        frame,
        name,
        index=True,
        schema=None,
        chunksize=None,
        method=None,
        **engine_kwargs,
    ):
        """
        Inserts data into already-prepared table
        """
        raise AbstractMethodError(self)


class SQLAlchemyEngine(BaseEngine):
    def __init__(self):
        import_optional_dependency(
            "sqlalchemy", extra="sqlalchemy is required for SQL support."
        )

    def insert_records(
        self,
        table: SQLTable,
        con,
        frame,
        name,
        index=True,
        schema=None,
        chunksize=None,
        method=None,
        **engine_kwargs,
    ):
        from sqlalchemy import exc

        try:
            table.insert(chunksize=chunksize, method=method)
        except exc.SQLAlchemyError as err:
            # GH34431
            # https://stackoverflow.com/a/67358288/6067848
            msg = r"""(\(1054, "Unknown column 'inf(e0)?' in 'field list'"\))(?#
            )|inf can not be used with MySQL"""
            err_text = str(err.orig)
            if re.search(msg, err_text):
                raise ValueError("inf cannot be used with MySQL") from err
            else:
                raise err


def get_engine(engine: str) -> BaseEngine:
    """return our implementation"""
    if engine == "auto":
        engine = get_option("io.sql.engine")

    if engine == "auto":
        # try engines in this order
        engine_classes = [SQLAlchemyEngine]

        error_msgs = ""
        for engine_class in engine_classes:
            try:
                return engine_class()
            except ImportError as err:
                error_msgs += "\n - " + str(err)

        raise ImportError(
            "Unable to find a usable engine; "
            "tried using: 'sqlalchemy'.\n"
            "A suitable version of "
            "sqlalchemy is required for sql I/O "
            "support.\n"
            "Trying to import the above resulted in these errors:"
            f"{error_msgs}"
        )

    elif engine == "sqlalchemy":
        return SQLAlchemyEngine()

    raise ValueError("engine must be one of 'auto', 'sqlalchemy'")


class SQLDatabase(PandasSQL):
    """
    This class enables conversion between DataFrame and SQL databases
    using SQLAlchemy to handle DataBase abstraction.

    Parameters
    ----------
    engine : SQLAlchemy connectable
        Connectable to connect with the database. Using SQLAlchemy makes it
        possible to use any DB supported by that library.
    schema : string, default None
        Name of SQL schema in database to write to (if database flavor
        supports this). If None, use default schema (default).

    """

    def __init__(self, engine, schema: str | None = None):
        from sqlalchemy.schema import MetaData

        self.connectable = engine
        self.meta = MetaData(schema=schema)
        self.meta.reflect(bind=engine)

    @contextmanager
    def run_transaction(self):
        from sqlalchemy.engine import Engine

        if isinstance(self.connectable, Engine):
            with self.connectable.connect() as conn:
                with conn.begin():
                    yield conn
        else:
            yield self.connectable

    def execute(self, *args, **kwargs):
        """Simple passthrough to SQLAlchemy connectable"""
        return self.connectable.execution_options().execute(*args, **kwargs)

    def read_table(
        self,
        table_name: str,
        index_col: str | Sequence[str] | None = None,
        coerce_float: bool = True,
        parse_dates=None,
        columns=None,
        schema: str | None = None,
        chunksize: int | None = None,
    ):
        """
        Read SQL database table into a DataFrame.

        Parameters
        ----------
        table_name : str
            Name of SQL table in database.
        index_col : string, optional, default: None
            Column to set as index.
        coerce_float : bool, default True
            Attempts to convert values of non-string, non-numeric objects
            (like decimal.Decimal) to floating point. This can result in
            loss of precision.
        parse_dates : list or dict, default: None
            - List of column names to parse as dates.
            - Dict of ``{column_name: format string}`` where format string is
              strftime compatible in case of parsing string times, or is one of
              (D, s, ns, ms, us) in case of parsing integer timestamps.
            - Dict of ``{column_name: arg}``, where the arg corresponds
              to the keyword arguments of :func:`pandas.to_datetime`.
              Especially useful with databases without native Datetime support,
              such as SQLite.
        columns : list, default: None
            List of column names to select from SQL table.
        schema : string, default None
            Name of SQL schema in database to query (if database flavor
            supports this).  If specified, this overwrites the default
            schema of the SQL database object.
        chunksize : int, default None
            If specified, return an iterator where `chunksize` is the number
            of rows to include in each chunk.

        Returns
        -------
        DataFrame

        See Also
        --------
        pandas.read_sql_table
        SQLDatabase.read_query

        """
        table = SQLTable(table_name, self, index=index_col, schema=schema)
        return table.read(
            coerce_float=coerce_float,
            parse_dates=parse_dates,
            columns=columns,
            chunksize=chunksize,
        )

    @staticmethod
    def _query_iterator(
        result,
        chunksize: int,
        columns,
        index_col=None,
        coerce_float=True,
        parse_dates=None,
        dtype: DtypeArg | None = None,
    ):
        """Return generator through chunked result set"""
        has_read_data = False
        while True:
            data = result.fetchmany(chunksize)
            if not data:
                if not has_read_data:
                    yield _wrap_result(
                        [],
                        columns,
                        index_col=index_col,
                        coerce_float=coerce_float,
                        parse_dates=parse_dates,
                    )
                break
            else:
                has_read_data = True
                yield _wrap_result(
                    data,
                    columns,
                    index_col=index_col,
                    coerce_float=coerce_float,
                    parse_dates=parse_dates,
                    dtype=dtype,
                )

    def read_query(
        self,
        sql: str,
        index_col: str | None = None,
        coerce_float: bool = True,
        parse_dates=None,
        params=None,
        chunksize: int | None = None,
        dtype: DtypeArg | None = None,
    ):
        """
        Read SQL query into a DataFrame.

        Parameters
        ----------
        sql : str
            SQL query to be executed.
        index_col : string, optional, default: None
            Column name to use as index for the returned DataFrame object.
        coerce_float : bool, default True
            Attempt to convert values of non-string, non-numeric objects (like
            decimal.Decimal) to floating point, useful for SQL result sets.
        params : list, tuple or dict, optional, default: None
            List of parameters to pass to execute method.  The syntax used
            to pass parameters is database driver dependent. Check your
            database driver documentation for which of the five syntax styles,
            described in PEP 249's paramstyle, is supported.
            Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}
        parse_dates : list or dict, default: None
            - List of column names to parse as dates.
            - Dict of ``{column_name: format string}`` where format string is
              strftime compatible in case of parsing string times, or is one of
              (D, s, ns, ms, us) in case of parsing integer timestamps.
            - Dict of ``{column_name: arg dict}``, where the arg dict
              corresponds to the keyword arguments of
              :func:`pandas.to_datetime` Especially useful with databases
              without native Datetime support, such as SQLite.
        chunksize : int, default None
            If specified, return an iterator where `chunksize` is the number
            of rows to include in each chunk.
        dtype : Type name or dict of columns
            Data type for data or columns. E.g. np.float64 or
            {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}

            .. versionadded…