""" Collection of query wrappers / abstractions to both facilitate data retrieval and to reduce dependency on DB-specific API. """ from __future__ import annotations from abc import ( ABC, abstractmethod, ) from contextlib import ( ExitStack, contextmanager, ) from datetime import ( date, datetime, time, ) from functools import partial import re from typing import ( TYPE_CHECKING, Any, Iterator, Literal, cast, overload, ) import warnings import numpy as np from pandas._libs import lib from pandas._typing import ( DateTimeErrorChoices, DtypeArg, DtypeBackend, IndexLabel, ) from pandas.compat._optional import import_optional_dependency from pandas.errors import ( AbstractMethodError, DatabaseError, ) from pandas.util._exceptions import find_stack_level from pandas.util._validators import check_dtype_backend from pandas.core.dtypes.common import ( is_datetime64tz_dtype, is_dict_like, is_integer, 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.arrays import ArrowExtensionArray from pandas.core.base import PandasObject import pandas.core.common as com from pandas.core.internals.construction import convert_object_array from pandas.core.tools.datetimes import to_datetime if TYPE_CHECKING: from sqlalchemy import Table from sqlalchemy.sql.expression import ( Select, TextClause, ) # ----------------------------------------------------------------------------- # -- Helper functions 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 = False, 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: DateTimeErrorChoices = 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 _convert_arrays_to_dataframe( data, columns, coerce_float: bool = True, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame: content = lib.to_object_array_tuples(data) arrays = convert_object_array( list(content.T), dtype=None, coerce_float=coerce_float, dtype_backend=dtype_backend, ) if dtype_backend == "pyarrow": pa = import_optional_dependency("pyarrow") arrays = [ ArrowExtensionArray(pa.array(arr, from_pandas=True)) for arr in arrays ] if arrays: df = DataFrame(dict(zip(list(range(len(columns))), arrays))) df.columns = columns return df else: return DataFrame(columns=columns) def _wrap_result( data, columns, index_col=None, coerce_float: bool = True, parse_dates=None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ): """Wrap result set of query in a DataFrame.""" frame = _convert_arrays_to_dataframe(data, columns, coerce_float, dtype_backend) if dtype: frame = frame.astype(dtype) frame = _parse_date_columns(frame, parse_dates) if index_col is not None: frame = frame.set_index(index_col) 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 connection or sqlite3 connection 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 """ warnings.warn( "`pandas.io.sql.execute` is deprecated and " "will be removed in the future version.", FutureWarning, stacklevel=find_stack_level(), ) # GH50185 sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore") if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Engine)): raise TypeError("pandas.io.sql.execute requires a connection") # GH50185 with pandasSQL_builder(con, need_transaction=True) as pandas_sql: return pandas_sql.execute(sql, params) # ----------------------------------------------------------------------------- # -- Read and write to DataFrames @overload def read_sql_table( table_name, con, schema=..., index_col: str | list[str] | None = ..., coerce_float=..., parse_dates: list[str] | dict[str, str] | None = ..., columns: list[str] | None = ..., chunksize: None = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., ) -> DataFrame: ... @overload def read_sql_table( table_name, con, schema=..., index_col: str | list[str] | None = ..., coerce_float=..., parse_dates: list[str] | dict[str, str] | None = ..., columns: list[str] | None = ..., chunksize: int = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., ) -> Iterator[DataFrame]: ... def read_sql_table( table_name: str, con, schema: str | None = None, index_col: str | list[str] | None = None, coerce_float: bool = True, parse_dates: list[str] | dict[str, str] | None = None, columns: list[str] | None = None, chunksize: int | None = None, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, ) -> 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. dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when "numpy_nullable" is set, pyarrow is used for all dtypes if "pyarrow" is set. The dtype_backends are still experimential. .. versionadded:: 2.0 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 """ check_dtype_backend(dtype_backend) if dtype_backend is lib.no_default: dtype_backend = "numpy" # type: ignore[assignment] with pandasSQL_builder(con, schema=schema, need_transaction=True) as pandas_sql: 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, dtype_backend=dtype_backend, ) 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: str | list[str] | None = ..., coerce_float=..., params: list[str] | dict[str, str] | None = ..., parse_dates: list[str] | dict[str, str] | None = ..., chunksize: None = ..., dtype: DtypeArg | None = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., ) -> DataFrame: ... @overload def read_sql_query( sql, con, index_col: str | list[str] | None = ..., coerce_float=..., params: list[str] | dict[str, str] | None = ..., parse_dates: list[str] | dict[str, str] | None = ..., chunksize: int = ..., dtype: DtypeArg | None = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., ) -> Iterator[DataFrame]: ... def read_sql_query( sql, con, index_col: str | list[str] | None = None, coerce_float: bool = True, params: list[str] | dict[str, str] | None = None, parse_dates: list[str] | dict[str, str] | None = None, chunksize: int | None = None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, ) -> 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 dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when "numpy_nullable" is set, pyarrow is used for all dtypes if "pyarrow" is set. The dtype_backends are still experimential. .. versionadded:: 2.0 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. """ check_dtype_backend(dtype_backend) if dtype_backend is lib.no_default: dtype_backend = "numpy" # type: ignore[assignment] with pandasSQL_builder(con) as pandas_sql: return pandas_sql.read_query( sql, index_col=index_col, params=params, coerce_float=coerce_float, parse_dates=parse_dates, chunksize=chunksize, dtype=dtype, dtype_backend=dtype_backend, ) @overload def read_sql( sql, con, index_col: str | list[str] | None = ..., coerce_float=..., params=..., parse_dates=..., columns: list[str] = ..., chunksize: None = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., dtype: DtypeArg | None = None, ) -> DataFrame: ... @overload def read_sql( sql, con, index_col: str | list[str] | None = ..., coerce_float=..., params=..., parse_dates=..., columns: list[str] = ..., chunksize: int = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., dtype: DtypeArg | None = None, ) -> Iterator[DataFrame]: ... def read_sql( sql, con, index_col: str | list[str] | None = None, coerce_float: bool = True, params=None, parse_dates=None, columns: list[str] | None = None, chunksize: int | None = None, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, dtype: DtypeArg | 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 `_. 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. dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when "numpy_nullable" is set, pyarrow is used for all dtypes if "pyarrow" is set. The dtype_backends are still experimential. .. versionadded:: 2.0 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’}. The argument is ignored if a table is passed instead of a query. .. versionadded:: 2.0.0 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) 2 >>> 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 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: >>> 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 """ check_dtype_backend(dtype_backend) if dtype_backend is lib.no_default: dtype_backend = "numpy" # type: ignore[assignment] with pandasSQL_builder(con) as pandas_sql: 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, dtype_backend=dtype_backend, # type: ignore[arg-type] dtype=dtype, ) 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: return pandas_sql.read_table( sql, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, columns=columns, chunksize=chunksize, dtype_backend=dtype_backend, ) else: return pandas_sql.read_query( sql, index_col=index_col, params=params, coerce_float=coerce_float, parse_dates=parse_dates, chunksize=chunksize, dtype_backend=dtype_backend, dtype=dtype, ) def to_sql( frame, name: str, con, schema: str | None = None, if_exists: Literal["fail", "replace", "append"] = "fail", index: bool = True, index_label: IndexLabel = None, chunksize: int | None = None, dtype: DtypeArg | None = None, method: str | None = None, engine: str = "auto", **engine_kwargs, ) -> int | 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) -> int | None``. Details and a sample callable implementation can be found in the section :ref:`insert 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. Returns ------- None or int Number of rows affected by to_sql. None is returned if the callable passed into ``method`` does not return an integer number of rows. .. versionadded:: 1.4.0 Notes ----- The returned rows affected is the sum of the ``rowcount`` attribute of ``sqlite3.Cursor`` or SQLAlchemy connectable. The returned value may not reflect the exact number of written rows as stipulated in the `sqlite3 `__ or `SQLAlchemy `__ """ # noqa:E501 if if_exists not in ("fail", "replace", "append"): raise ValueError(f"'{if_exists}' is not valid for if_exists") 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" ) with pandasSQL_builder(con, schema=schema, need_transaction=True) as pandas_sql: return 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) -> bool: """ 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 """ with pandasSQL_builder(con, schema=schema) as pandas_sql: return pandas_sql.has_table(table_name) table_exists = has_table def pandasSQL_builder( con, schema: str | None = None, need_transaction: bool = False, ) -> PandasSQL: """ Convenience function to return the correct PandasSQL subclass based on the provided parameters. Also creates a sqlalchemy connection and transaction if necessary. """ import sqlite3 if isinstance(con, sqlite3.Connection) or con is None: return SQLiteDatabase(con) sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore") if isinstance(con, str) and sqlalchemy is None: raise ImportError("Using URI string without sqlalchemy installed.") if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Connectable)): return SQLDatabase(con, schema, need_transaction) warnings.warn( "pandas only supports SQLAlchemy connectable (engine/connection) or " "database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 " "objects are not tested. Please consider using SQLAlchemy.", UserWarning, stacklevel=find_stack_level(), ) return SQLiteDatabase(con) 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: bool | str | list[str] | None = True, if_exists: Literal["fail", "replace", "append"] = "fail", prefix: str = "pandas", index_label=None, schema=None, keys=None, dtype: DtypeArg | None = 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) -> str: from sqlalchemy.schema import CreateTable return str(CreateTable(self.table).compile(self.pd_sql.con)) def _execute_create(self) -> None: # Inserting table into database, add to MetaData object self.table = self.table.to_metadata(self.pd_sql.meta) with self.pd_sql.run_transaction(): self.table.create(bind=self.pd_sql.con) def create(self) -> None: if self.exists(): if self.if_exists == "fail": raise ValueError(f"Table '{self.name}' already exists.") if 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) -> int: """ 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] result = conn.execute(self.table.insert(), data) return result.rowcount def _execute_insert_multi(self, conn, keys: list[str], data_iter) -> int: """ 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) result = conn.execute(stmt) return result.rowcount def insert_data(self) -> tuple[list[str], list[np.ndarray]]: 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) # this just pre-allocates the list: None's will be replaced with ndarrays # error: List item 0 has incompatible type "None"; expected "ndarray" data_list: list[np.ndarray] = [None] * ncols # type: ignore[list-item] for i, (_, ser) in enumerate(temp.items()): if ser.dtype.kind == "M": d = ser.dt.to_pydatetime() elif ser.dtype.kind == "m": vals = ser._values if isinstance(vals, ArrowExtensionArray): vals = vals.to_numpy(dtype=np.dtype("m8[ns]")) # store as integers, see GH#6921, GH#7076 d = vals.view("i8").astype(object) else: d = ser._values.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 data_list[i] = d return column_names, data_list def insert( self, chunksize: int | None = None, method: str | None = None ) -> int | 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 0 if chunksize is None: chunksize = nrows elif chunksize == 0: raise ValueError("chunksize argument should be non-zero") chunks = (nrows // chunksize) + 1 total_inserted = None 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)) num_inserted = exec_insert(conn, keys, chunk_iter) # GH 46891 if is_integer(num_inserted): if total_inserted is None: total_inserted = num_inserted else: total_inserted += num_inserted return total_inserted def _query_iterator( self, result, exit_stack: ExitStack, chunksize: str | None, columns, coerce_float: bool = True, parse_dates=None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ): """Return generator through chunked result set.""" has_read_data = False with exit_stack: 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 has_read_data = True self.frame = _convert_arrays_to_dataframe( data, columns, coerce_float, dtype_backend ) self._harmonize_columns( parse_dates=parse_dates, dtype_backend=dtype_backend ) if self.index is not None: self.frame.set_index(self.index, inplace=True) yield self.frame def read( self, exit_stack: ExitStack, coerce_float: bool = True, parse_dates=None, columns=None, chunksize=None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame | Iterator[DataFrame]: 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) else: sql_select = select(self.table) result = self.pd_sql.execute(sql_select) column_names = result.keys() if chunksize is not None: return self._query_iterator( result, exit_stack, chunksize, column_names, coerce_float=coerce_float, parse_dates=parse_dates, dtype_backend=dtype_backend, ) else: data = result.fetchall() self.frame = _convert_arrays_to_dataframe( data, column_names, coerce_float, dtype_backend ) self._harmonize_columns( parse_dates=parse_dates, dtype_backend=dtype_backend ) 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}" ) 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 com.fill_missing_names(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: list[Any] = [ 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, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> 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 dtype_backend == "numpy" and col_type is float: # floats support NA, can always convert! self.frame[col_name] = df_col.astype(col_type, copy=False) elif dtype_backend == "numpy" and 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 in ("datetime64", "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=find_stack_level(), ) 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, ABC): """ Subclasses Should define read_query and to_sql. """ def __enter__(self): return self def __exit__(self, *args) -> None: pass def read_table( self, table_name: str, index_col: str | list[str] | None = None, coerce_float: bool = True, parse_dates=None, columns=None, schema: str | None = None, chunksize: int | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame | Iterator[DataFrame]: raise NotImplementedError @abstractmethod def read_query( self, sql: str, index_col: str | list[str] | None = None, coerce_float: bool = True, parse_dates=None, params=None, chunksize: int | None = None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame | Iterator[DataFrame]: pass @abstractmethod def to_sql( self, frame, name, if_exists: Literal["fail", "replace", "append"] = "fail", index: bool = True, index_label=None, schema=None, chunksize=None, dtype: DtypeArg | None = None, method=None, engine: str = "auto", **engine_kwargs, ) -> int | None: pass @abstractmethod def execute(self, sql: str | Select | TextClause, params=None): pass @abstractmethod def has_table(self, name: str, schema: str | None = None) -> bool: pass @abstractmethod def _create_sql_schema( self, frame: DataFrame, table_name: str, keys: list[str] | None = None, dtype: DtypeArg | None = None, schema: str | None = None, ): pass class BaseEngine: def insert_records( self, table: SQLTable, con, frame, name, index: bool | str | list[str] | None = True, schema=None, chunksize=None, method=None, **engine_kwargs, ) -> int | None: """ Inserts data into already-prepared table """ raise AbstractMethodError(self) class SQLAlchemyEngine(BaseEngine): def __init__(self) -> None: import_optional_dependency( "sqlalchemy", extra="sqlalchemy is required for SQL support." ) def insert_records( self, table: SQLTable, con, frame, name, index: bool | str | list[str] | None = True, schema=None, chunksize=None, method=None, **engine_kwargs, ) -> int | None: from sqlalchemy import exc try: return table.insert(chunksize=chunksize, method=method) except exc.StatementError 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 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}" ) if 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 ---------- con : SQLAlchemy Connectable or URI string. 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). need_transaction : bool, default False If True, SQLDatabase will create a transaction. """ def __init__( self, con, schema: str | None = None, need_transaction: bool = False ) -> None: from sqlalchemy import create_engine from sqlalchemy.engine import Engine from sqlalchemy.schema import MetaData # self.exit_stack cleans up the Engine and Connection and commits the # transaction if any of those objects was created below. # Cleanup happens either in self.__exit__ or at the end of the iterator # returned by read_sql when chunksize is not None. self.exit_stack = ExitStack() if isinstance(con, str): con = create_engine(con) self.exit_stack.callback(con.dispose) if isinstance(con, Engine): con = self.exit_stack.enter_context(con.connect()) if need_transaction and not con.in_transaction(): self.exit_stack.enter_context(con.begin()) self.con = con self.meta = MetaData(schema=schema) self.returns_generator = False def __exit__(self, *args) -> None: if not self.returns_generator: self.exit_stack.close() @contextmanager def run_transaction(self): if not self.con.in_transaction(): with self.con.begin(): yield self.con else: yield self.con def execute(self, sql: str | Select | TextClause, params=None): """Simple passthrough to SQLAlchemy connectable""" args = [] if params is None else [params] if isinstance(sql, str): return self.con.exec_driver_sql(sql, *args) return self.con.execute(sql, *args) def read_table( self, table_name: str, index_col: str | list[str] | None = None, coerce_float: bool = True, parse_dates=None, columns=None, schema: str | None = None, chunksize: int | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame | Iterator[DataFrame]: """ 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. dtype_backend : {{"numpy_nullable", "pyarrow"}}, defaults to NumPy dtypes Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when "numpy_nullable" is set, pyarrow is used for all dtypes if "pyarrow" is set. The dtype_backends are still experimential. .. versionadded:: 2.0 Returns ------- DataFrame See Also -------- pandas.read_sql_table SQLDatabase.read_query """ self.meta.reflect(bind=self.con, only=[table_name]) table = SQLTable(table_name, self, index=index_col, schema=schema) if chunksize is not None: self.returns_generator = True return table.read( self.exit_stack, coerce_float=coerce_float, parse_dates=parse_dates, columns=columns, chunksize=chunksize, dtype_backend=dtype_backend, ) @staticmethod def _query_iterator( result, exit_stack: ExitStack, chunksize: int, columns, index_col=None, coerce_float: bool = True, parse_dates=None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ): """Return generator through chunked result set""" has_read_data = False with exit_stack: 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, dtype=dtype, dtype_backend=dtype_backend, ) break has_read_data = True yield _wrap_result( data, columns, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, dtype=dtype, dtype_backend=dtype_backend, ) def read_query( self, sql: str, index_col: str | list[str] | None = None, coerce_float: bool = True, parse_dates=None, params=None, chunksize: int | None = None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame | Iterator[DataFrame]: """ 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:: 1.3.0 Returns ------- DataFrame See Also -------- read_sql_table : Read SQL database table into a DataFrame. read_sql """ result = self.execute(sql, params) columns = result.keys() if chunksize is not None: self.returns_generator = True return self._query_iterator( result, self.exit_stack, chunksize, columns, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, dtype=dtype, dtype_backend=dtype_backend, ) else: data = result.fetchall() frame = _wrap_result( data, columns, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, dtype=dtype, dtype_backend=dtype_backend, ) return frame read_sql = read_query def prep_table( self, frame, name, if_exists: Literal["fail", "replace", "append"] = "fail", index: bool | str | list[str] | None = True, index_label=None, schema=None, dtype: DtypeArg | None = None, ) -> SQLTable: """ Prepares table in the database for data insertion. Creates it if needed, etc. """ if dtype: if not is_dict_like(dtype): # error: Value expression in dictionary comprehension has incompatible # type "Union[ExtensionDtype, str, dtype[Any], Type[object], # Dict[Hashable, Union[ExtensionDtype, Union[str, dtype[Any]], # Type[str], Type[float], Type[int], Type[complex], Type[bool], # Type[object]]]]"; expected type "Union[ExtensionDtype, str, # dtype[Any], Type[object]]" dtype = {col_name: dtype for col_name in frame} # type: ignore[misc] else: dtype = cast(dict, dtype) from sqlalchemy.types import TypeEngine for col, my_type in dtype.items(): if isinstance(my_type, type) and issubclass(my_type, TypeEngine): pass elif isinstance(my_type, TypeEngine): pass else: raise ValueError(f"The type of {col} is not a SQLAlchemy type") table = SQLTable( name, self, frame=frame, index=index, if_exists=if_exists, index_label=index_label, schema=schema, dtype=dtype, ) table.create() return table def check_case_sensitive( self, name: str, schema: str | None, ) -> None: """ Checks table name for issues with case-sensitivity. Method is called after data is inserted. """ if not name.isdigit() and not name.islower(): # check for potentially case sensitivity issues (GH7815) # Only check when name is not a number and name is not lower case from sqlalchemy import inspect as sqlalchemy_inspect insp = sqlalchemy_inspect(self.con) table_names = insp.get_table_names(schema=schema or self.meta.schema) if name not in table_names: msg = ( f"The provided table name '{name}' is not found exactly as " "such in the database after writing the table, possibly " "due to case sensitivity issues. Consider using lower " "case table names." ) warnings.warn( msg, UserWarning, stacklevel=find_stack_level(), ) def to_sql( self, frame, name: str, if_exists: Literal["fail", "replace", "append"] = "fail", index: bool = True, index_label=None, schema: str | None = None, chunksize=None, dtype: DtypeArg | None = None, method=None, engine: str = "auto", **engine_kwargs, ) -> int | None: """ Write records stored in a DataFrame to a SQL database. Parameters ---------- frame : DataFrame name : string Name of SQL table. 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 : boolean, default True Write DataFrame index as a column. index_label : string or sequence, default None 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. schema : string, default None Name of SQL schema in database to write to (if database flavor supports this). If specified, this overwrites the default schema of the SQLDatabase object. chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. dtype : single type or dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type. If all columns are of the same type, one single value can be used. method : {None', 'multi', callable}, default None 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 `. 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. """ sql_engine = get_engine(engine) table = self.prep_table( frame=frame, name=name, if_exists=if_exists, index=index, index_label=index_label, schema=schema, dtype=dtype, ) total_inserted = sql_engine.insert_records( table=table, con=self.con, frame=frame, name=name, index=index, schema=schema, chunksize=chunksize, method=method, **engine_kwargs, ) self.check_case_sensitive(name=name, schema=schema) return total_inserted @property def tables(self): return self.meta.tables def has_table(self, name: str, schema: str | None = None) -> bool: from sqlalchemy import inspect as sqlalchemy_inspect insp = sqlalchemy_inspect(self.con) return insp.has_table(name, schema or self.meta.schema) def get_table(self, table_name: str, schema: str | None = None) -> Table: from sqlalchemy import ( Numeric, Table, ) schema = schema or self.meta.schema tbl = Table(table_name, self.meta, autoload_with=self.con, schema=schema) for column in tbl.columns: if isinstance(column.type, Numeric): column.type.asdecimal = False return tbl def drop_table(self, table_name: str, schema: str | None = None) -> None: schema = schema or self.meta.schema if self.has_table(table_name, schema): self.meta.reflect(bind=self.con, only=[table_name], schema=schema) with self.run_transaction(): self.get_table(table_name, schema).drop(bind=self.con) self.meta.clear() def _create_sql_schema( self, frame: DataFrame, table_name: str, keys: list[str] | None = None, dtype: DtypeArg | None = None, schema: str | None = None, ): table = SQLTable( table_name, self, frame=frame, index=False, keys=keys, dtype=dtype, schema=schema, ) return str(table.sql_schema()) # ---- SQL without SQLAlchemy --- # sqlite-specific sql strings and handler class # dictionary used for readability purposes _SQL_TYPES = { "string": "TEXT", "floating": "REAL", "integer": "INTEGER", "datetime": "TIMESTAMP", "date": "DATE", "time": "TIME", "boolean": "INTEGER", } def _get_unicode_name(name): try: uname = str(name).encode("utf-8", "strict").decode("utf-8") except UnicodeError as err: raise ValueError(f"Cannot convert identifier to UTF-8: '{name}'") from err return uname def _get_valid_sqlite_name(name): # See https://stackoverflow.com/questions/6514274/how-do-you-escape-strings\ # -for-sqlite-table-column-names-in-python # Ensure the string can be encoded as UTF-8. # Ensure the string does not include any NUL characters. # Replace all " with "". # Wrap the entire thing in double quotes. uname = _get_unicode_name(name) if not len(uname): raise ValueError("Empty table or column name specified") nul_index = uname.find("\x00") if nul_index >= 0: raise ValueError("SQLite identifier cannot contain NULs") return '"' + uname.replace('"', '""') + '"' class SQLiteTable(SQLTable): """ Patch the SQLTable for fallback support. Instead of a table variable just use the Create Table statement. """ def __init__(self, *args, **kwargs) -> None: # GH 8341 # register an adapter callable for datetime.time object import sqlite3 # this will transform time(12,34,56,789) into '12:34:56.000789' # (this is what sqlalchemy does) def _adapt_time(t) -> str: # This is faster than strftime return f"{t.hour:02d}:{t.minute:02d}:{t.second:02d}.{t.microsecond:06d}" sqlite3.register_adapter(time, _adapt_time) super().__init__(*args, **kwargs) def sql_schema(self) -> str: return str(";\n".join(self.table)) def _execute_create(self) -> None: with self.pd_sql.run_transaction() as conn: for stmt in self.table: conn.execute(stmt) def insert_statement(self, *, num_rows: int) -> str: names = list(map(str, self.frame.columns)) wld = "?" # wildcard char escape = _get_valid_sqlite_name if self.index is not None: for idx in self.index[::-1]: names.insert(0, idx) bracketed_names = [escape(column) for column in names] col_names = ",".join(bracketed_names) row_wildcards = ",".join([wld] * len(names)) wildcards = ",".join([f"({row_wildcards})" for _ in range(num_rows)]) insert_statement = ( f"INSERT INTO {escape(self.name)} ({col_names}) VALUES {wildcards}" ) return insert_statement def _execute_insert(self, conn, keys, data_iter) -> int: data_list = list(data_iter) conn.executemany(self.insert_statement(num_rows=1), data_list) return conn.rowcount def _execute_insert_multi(self, conn, keys, data_iter) -> int: data_list = list(data_iter) flattened_data = [x for row in data_list for x in row] conn.execute(self.insert_statement(num_rows=len(data_list)), flattened_data) return conn.rowcount def _create_table_setup(self): """ Return a list of SQL statements that creates a table reflecting the structure of a DataFrame. The first entry will be a CREATE TABLE statement while the rest will be CREATE INDEX statements. """ column_names_and_types = self._get_column_names_and_types(self._sql_type_name) escape = _get_valid_sqlite_name create_tbl_stmts = [ escape(cname) + " " + ctype for cname, ctype, _ in column_names_and_types ] if self.keys is not None and len(self.keys): if not is_list_like(self.keys): keys = [self.keys] else: keys = self.keys cnames_br = ", ".join([escape(c) for c in keys]) create_tbl_stmts.append( f"CONSTRAINT {self.name}_pk PRIMARY KEY ({cnames_br})" ) if self.schema: schema_name = self.schema + "." else: schema_name = "" create_stmts = [ "CREATE TABLE " + schema_name + escape(self.name) + " (\n" + ",\n ".join(create_tbl_stmts) + "\n)" ] ix_cols = [cname for cname, _, is_index in column_names_and_types if is_index] if len(ix_cols): cnames = "_".join(ix_cols) cnames_br = ",".join([escape(c) for c in ix_cols]) create_stmts.append( "CREATE INDEX " + escape("ix_" + self.name + "_" + cnames) + "ON " + escape(self.name) + " (" + cnames_br + ")" ) return create_stmts def _sql_type_name(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) 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=find_stack_level(), ) col_type = "integer" elif col_type == "datetime64": col_type = "datetime" elif col_type == "empty": col_type = "string" elif col_type == "complex": raise ValueError("Complex datatypes not supported") if col_type not in _SQL_TYPES: col_type = "string" return _SQL_TYPES[col_type] class SQLiteDatabase(PandasSQL): """ Version of SQLDatabase to support SQLite connections (fallback without SQLAlchemy). This should only be used internally. Parameters ---------- con : sqlite connection object """ def __init__(self, con) -> None: self.con = con @contextmanager def run_transaction(self): cur = self.con.cursor() try: yield cur self.con.commit() except Exception: self.con.rollback() raise finally: cur.close() def execute(self, sql: str | Select | TextClause, params=None): if not isinstance(sql, str): raise TypeError("Query must be a string unless using sqlalchemy.") args = [] if params is None else [params] cur = self.con.cursor() try: cur.execute(sql, *args) return cur except Exception as exc: try: self.con.rollback() except Exception as inner_exc: # pragma: no cover ex = DatabaseError( f"Execution failed on sql: {sql}\n{exc}\nunable to rollback" ) raise ex from inner_exc ex = DatabaseError(f"Execution failed on sql '{sql}': {exc}") raise ex from exc @staticmethod def _query_iterator( cursor, chunksize: int, columns, index_col=None, coerce_float: bool = True, parse_dates=None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ): """Return generator through chunked result set""" has_read_data = False while True: data = cursor.fetchmany(chunksize) if type(data) == tuple: data = list(data) if not data: cursor.close() if not has_read_data: result = DataFrame.from_records( [], columns=columns, coerce_float=coerce_float ) if dtype: result = result.astype(dtype) yield result break has_read_data = True yield _wrap_result( data, columns, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, dtype=dtype, dtype_backend=dtype_backend, ) def read_query( self, sql, index_col=None, coerce_float: bool = True, parse_dates=None, params=None, chunksize: int | None = None, dtype: DtypeArg | None = None, dtype_backend: DtypeBackend | Literal["numpy"] = "numpy", ) -> DataFrame | Iterator[DataFrame]: cursor = self.execute(sql, params) columns = [col_desc[0] for col_desc in cursor.description] if chunksize is not None: return self._query_iterator( cursor, chunksize, columns, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, dtype=dtype, dtype_backend=dtype_backend, ) else: data = self._fetchall_as_list(cursor) cursor.close() frame = _wrap_result( data, columns, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, dtype=dtype, dtype_backend=dtype_backend, ) return frame def _fetchall_as_list(self, cur): result = cur.fetchall() if not isinstance(result, list): result = list(result) return result def to_sql( self, frame, name, if_exists: str = "fail", index: bool = True, index_label=None, schema=None, chunksize=None, dtype: DtypeArg | None = None, method=None, engine: str = "auto", **engine_kwargs, ) -> int | None: """ Write records stored in a DataFrame to a SQL database. Parameters ---------- frame: DataFrame name: string Name of SQL table. 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 it does not exist. index : bool, default True Write DataFrame index as a column index_label : string or sequence, default None 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. schema : string, default None Ignored parameter included for compatibility with SQLAlchemy version of ``to_sql``. chunksize : int, default None If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once. dtype : single type or dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a string. If all columns are of the same type, one single value can be used. method : {None, 'multi', callable}, default None 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 `. """ if dtype: if not is_dict_like(dtype): # error: Value expression in dictionary comprehension has incompatible # type "Union[ExtensionDtype, str, dtype[Any], Type[object], # Dict[Hashable, Union[ExtensionDtype, Union[str, dtype[Any]], # Type[str], Type[float], Type[int], Type[complex], Type[bool], # Type[object]]]]"; expected type "Union[ExtensionDtype, str, # dtype[Any], Type[object]]" dtype = {col_name: dtype for col_name in frame} # type: ignore[misc] else: dtype = cast(dict, dtype) for col, my_type in dtype.items(): if not isinstance(my_type, str): raise ValueError(f"{col} ({my_type}) not a string") table = SQLiteTable( name, self, frame=frame, index=index, if_exists=if_exists, index_label=index_label, dtype=dtype, ) table.create() return table.insert(chunksize, method) def has_table(self, name: str, schema: str | None = None) -> bool: wld = "?" query = f"SELECT name FROM sqlite_master WHERE type='table' AND name={wld};" return len(self.execute(query, [name]).fetchall()) > 0 def get_table(self, table_name: str, schema: str | None = None) -> None: return None # not supported in fallback mode def drop_table(self, name: str, schema: str | None = None) -> None: drop_sql = f"DROP TABLE {_get_valid_sqlite_name(name)}" self.execute(drop_sql) def _create_sql_schema( self, frame, table_name: str, keys=None, dtype: DtypeArg | None = None, schema: str | None = None, ): table = SQLiteTable( table_name, self, frame=frame, index=False, keys=keys, dtype=dtype, schema=schema, ) return str(table.sql_schema()) def get_schema( frame, name: str, keys=None, con=None, dtype: DtypeArg | None = None, schema: str | None = None, ) -> str: """ Get the SQL db table schema for the given frame. Parameters ---------- frame : DataFrame name : str name of SQL table keys : string or sequence, default: None columns to use a primary key con: an open SQL database connection object or a SQLAlchemy connectable Using SQLAlchemy makes it possible to use any DB supported by that library, default: None If a DBAPI2 object, only sqlite3 is supported. dtype : dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. schema: str, default: None Optional specifying the schema to be used in creating the table. .. versionadded:: 1.2.0 """ with pandasSQL_builder(con=con) as pandas_sql: return pandas_sql._create_sql_schema( frame, name, keys=keys, dtype=dtype, schema=schema )