"""
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 <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.
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 <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.
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 <https://docs.python.org/3/library/sqlite3.html#sqlite3.Cursor.rowcount>`__ or
`SQLAlchemy <https://docs.sqlalchemy.org/en/14/core/connections.html#sqlalchemy.engine.BaseCursorResult.rowcount>`__
""" # 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 <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.
"""
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 <io.sql.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
)