2258 lines
74 KiB
Python
2258 lines
74 KiB
Python
"""
|
||
Collection of query wrappers / abstractions to both facilitate data
|
||
retrieval and to reduce dependency on DB-specific API.
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
from contextlib import contextmanager
|
||
from datetime import (
|
||
date,
|
||
datetime,
|
||
time,
|
||
)
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||
from functools import partial
|
||
import re
|
||
from typing import (
|
||
TYPE_CHECKING,
|
||
Any,
|
||
Iterator,
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||
cast,
|
||
overload,
|
||
)
|
||
import warnings
|
||
|
||
import numpy as np
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||
|
||
import pandas._libs.lib as lib
|
||
from pandas._typing import (
|
||
DateTimeErrorChoices,
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||
DtypeArg,
|
||
IndexLabel,
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||
)
|
||
from pandas.compat._optional import import_optional_dependency
|
||
from pandas.errors import (
|
||
AbstractMethodError,
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||
DatabaseError,
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||
)
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||
from pandas.util._exceptions import find_stack_level
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||
|
||
from pandas.core.dtypes.common import (
|
||
is_datetime64tz_dtype,
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||
is_dict_like,
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||
is_integer,
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||
is_list_like,
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||
)
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||
from pandas.core.dtypes.dtypes import DatetimeTZDtype
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||
from pandas.core.dtypes.missing import isna
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||
|
||
from pandas import get_option
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||
from pandas.core.api import (
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||
DataFrame,
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||
Series,
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||
)
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||
from pandas.core.base import PandasObject
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||
import pandas.core.common as com
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||
from pandas.core.tools.datetimes import to_datetime
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||
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||
if TYPE_CHECKING:
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||
from sqlalchemy import Table
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||
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||
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||
# -----------------------------------------------------------------------------
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||
# -- Helper functions
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||
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||
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||
def _convert_params(sql, params):
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||
"""Convert SQL and params args to DBAPI2.0 compliant format."""
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||
args = [sql]
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||
if params is not None:
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||
if hasattr(params, "keys"): # test if params is a mapping
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||
args += [params]
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||
else:
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||
args += [list(params)]
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||
return args
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||
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||
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||
def _process_parse_dates_argument(parse_dates):
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||
"""Process parse_dates argument for read_sql functions"""
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||
# handle non-list entries for parse_dates gracefully
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||
if parse_dates is True or parse_dates is None or parse_dates is False:
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||
parse_dates = []
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||
|
||
elif not hasattr(parse_dates, "__iter__"):
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||
parse_dates = [parse_dates]
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||
return parse_dates
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||
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||
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||
def _handle_date_column(
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||
col, utc: bool | None = None, format: str | dict[str, Any] | None = None
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||
):
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||
if isinstance(format, dict):
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||
# GH35185 Allow custom error values in parse_dates argument of
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||
# read_sql like functions.
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||
# Format can take on custom to_datetime argument values such as
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||
# {"errors": "coerce"} or {"dayfirst": True}
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||
error: DateTimeErrorChoices = format.pop("errors", None) or "ignore"
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||
return to_datetime(col, errors=error, **format)
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||
else:
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||
# Allow passing of formatting string for integers
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||
# GH17855
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||
if format is None and (
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||
issubclass(col.dtype.type, np.floating)
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||
or issubclass(col.dtype.type, np.integer)
|
||
):
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||
format = "s"
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||
if format in ["D", "d", "h", "m", "s", "ms", "us", "ns"]:
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||
return to_datetime(col, errors="coerce", unit=format, utc=utc)
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||
elif is_datetime64tz_dtype(col.dtype):
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||
# coerce to UTC timezone
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||
# GH11216
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||
return to_datetime(col, utc=True)
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||
else:
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||
return to_datetime(col, errors="coerce", format=format, utc=utc)
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||
|
||
|
||
def _parse_date_columns(data_frame, parse_dates):
|
||
"""
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||
Force non-datetime columns to be read as such.
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||
Supports both string formatted and integer timestamp columns.
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||
"""
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||
parse_dates = _process_parse_dates_argument(parse_dates)
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||
|
||
# we want to coerce datetime64_tz dtypes for now to UTC
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||
# we could in theory do a 'nice' conversion from a FixedOffset tz
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||
# GH11216
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||
for col_name, df_col in data_frame.items():
|
||
if is_datetime64tz_dtype(df_col.dtype) or col_name in parse_dates:
|
||
try:
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||
fmt = parse_dates[col_name]
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||
except TypeError:
|
||
fmt = None
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||
data_frame[col_name] = _handle_date_column(df_col, format=fmt)
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||
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||
return data_frame
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||
|
||
|
||
def _wrap_result(
|
||
data,
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||
columns,
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||
index_col=None,
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||
coerce_float: bool = True,
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||
parse_dates=None,
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||
dtype: DtypeArg | None = None,
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||
):
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||
"""Wrap result set of query in a DataFrame."""
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||
frame = DataFrame.from_records(data, columns=columns, coerce_float=coerce_float)
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||
|
||
if dtype:
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||
frame = frame.astype(dtype)
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||
|
||
frame = _parse_date_columns(frame, parse_dates)
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||
|
||
if index_col is not None:
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||
frame.set_index(index_col, inplace=True)
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||
|
||
return frame
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||
|
||
|
||
def execute(sql, con, params=None):
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||
"""
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||
Execute the given SQL query using the provided connection object.
|
||
|
||
Parameters
|
||
----------
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||
sql : string
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||
SQL query to be executed.
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||
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
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||
Using SQLAlchemy makes it possible to use any DB supported by the
|
||
library.
|
||
If a DBAPI2 object, only sqlite3 is supported.
|
||
params : list or tuple, optional, default: None
|
||
List of parameters to pass to execute method.
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||
|
||
Returns
|
||
-------
|
||
Results Iterable
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||
"""
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||
pandas_sql = pandasSQL_builder(con)
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||
args = _convert_params(sql, params)
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||
return pandas_sql.execute(*args)
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||
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||
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||
# -----------------------------------------------------------------------------
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||
# -- Read and write to DataFrames
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||
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||
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||
@overload
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||
def read_sql_table(
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||
table_name,
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||
con,
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||
schema=...,
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||
index_col: str | list[str] | None = ...,
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||
coerce_float=...,
|
||
parse_dates: list[str] | dict[str, str] | None = ...,
|
||
columns: list[str] | None = ...,
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||
chunksize: None = ...,
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||
) -> DataFrame:
|
||
...
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||
|
||
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||
@overload
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||
def read_sql_table(
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||
table_name,
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||
con,
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||
schema=...,
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||
index_col: str | list[str] | None = ...,
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||
coerce_float=...,
|
||
parse_dates: list[str] | dict[str, str] | None = ...,
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||
columns: list[str] | None = ...,
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||
chunksize: int = ...,
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||
) -> Iterator[DataFrame]:
|
||
...
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||
|
||
|
||
def read_sql_table(
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||
table_name: str,
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||
con,
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||
schema: str | None = None,
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||
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,
|
||
) -> DataFrame | Iterator[DataFrame]:
|
||
"""
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||
Read SQL database table into a DataFrame.
|
||
|
||
Given a table name and a SQLAlchemy connectable, returns a DataFrame.
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||
This function does not support DBAPI connections.
|
||
|
||
Parameters
|
||
----------
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||
table_name : str
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||
Name of SQL table in database.
|
||
con : SQLAlchemy connectable or str
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||
A database URI could be provided as str.
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||
SQLite DBAPI connection mode not supported.
|
||
schema : str, default None
|
||
Name of SQL schema in database to query (if database flavor
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||
supports this). Uses default schema if None (default).
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||
index_col : str or list of str, optional, default: None
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||
Column(s) to set as index(MultiIndex).
|
||
coerce_float : bool, default True
|
||
Attempts to convert values of non-string, non-numeric objects (like
|
||
decimal.Decimal) to floating point. Can result in loss of Precision.
|
||
parse_dates : list or dict, default None
|
||
- List of column names to parse as dates.
|
||
- Dict of ``{column_name: format string}`` where format string is
|
||
strftime compatible in case of parsing string times or is one of
|
||
(D, s, ns, ms, us) in case of parsing integer timestamps.
|
||
- Dict of ``{column_name: arg dict}``, where the arg dict corresponds
|
||
to the keyword arguments of :func:`pandas.to_datetime`
|
||
Especially useful with databases without native Datetime support,
|
||
such as SQLite.
|
||
columns : list, default None
|
||
List of column names to select from SQL table.
|
||
chunksize : int, default None
|
||
If specified, returns an iterator where `chunksize` is the number of
|
||
rows to include in each chunk.
|
||
|
||
Returns
|
||
-------
|
||
DataFrame or Iterator[DataFrame]
|
||
A SQL table is returned as two-dimensional data structure with labeled
|
||
axes.
|
||
|
||
See Also
|
||
--------
|
||
read_sql_query : Read SQL query into a DataFrame.
|
||
read_sql : Read SQL query or database table into a DataFrame.
|
||
|
||
Notes
|
||
-----
|
||
Any datetime values with time zone information will be converted to UTC.
|
||
|
||
Examples
|
||
--------
|
||
>>> pd.read_sql_table('table_name', 'postgres:///db_name') # doctest:+SKIP
|
||
"""
|
||
pandas_sql = pandasSQL_builder(con, schema=schema)
|
||
if not pandas_sql.has_table(table_name):
|
||
raise ValueError(f"Table {table_name} not found")
|
||
|
||
# error: Item "SQLiteDatabase" of "Union[SQLDatabase, SQLiteDatabase]"
|
||
# has no attribute "read_table"
|
||
table = pandas_sql.read_table( # type: ignore[union-attr]
|
||
table_name,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
columns=columns,
|
||
chunksize=chunksize,
|
||
)
|
||
|
||
if table is not None:
|
||
return table
|
||
else:
|
||
raise ValueError(f"Table {table_name} not found", con)
|
||
|
||
|
||
@overload
|
||
def read_sql_query(
|
||
sql,
|
||
con,
|
||
index_col: 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 = ...,
|
||
) -> 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 = ...,
|
||
) -> 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,
|
||
) -> DataFrame | Iterator[DataFrame]:
|
||
"""
|
||
Read SQL query into a DataFrame.
|
||
|
||
Returns a DataFrame corresponding to the result set of the query
|
||
string. Optionally provide an `index_col` parameter to use one of the
|
||
columns as the index, otherwise default integer index will be used.
|
||
|
||
Parameters
|
||
----------
|
||
sql : str SQL query or SQLAlchemy Selectable (select or text object)
|
||
SQL query to be executed.
|
||
con : SQLAlchemy connectable, str, or sqlite3 connection
|
||
Using SQLAlchemy makes it possible to use any DB supported by that
|
||
library. If a DBAPI2 object, only sqlite3 is supported.
|
||
index_col : str or list of str, optional, default: None
|
||
Column(s) to set as index(MultiIndex).
|
||
coerce_float : bool, default True
|
||
Attempts to convert values of non-string, non-numeric objects (like
|
||
decimal.Decimal) to floating point. Useful for SQL result sets.
|
||
params : list, tuple or dict, optional, default: None
|
||
List of parameters to pass to execute method. The syntax used
|
||
to pass parameters is database driver dependent. Check your
|
||
database driver documentation for which of the five syntax styles,
|
||
described in PEP 249's paramstyle, is supported.
|
||
Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
|
||
parse_dates : list or dict, default: None
|
||
- List of column names to parse as dates.
|
||
- Dict of ``{column_name: format string}`` where format string is
|
||
strftime compatible in case of parsing string times, or is one of
|
||
(D, s, ns, ms, us) in case of parsing integer timestamps.
|
||
- Dict of ``{column_name: arg dict}``, where the arg dict corresponds
|
||
to the keyword arguments of :func:`pandas.to_datetime`
|
||
Especially useful with databases without native Datetime support,
|
||
such as SQLite.
|
||
chunksize : int, default None
|
||
If specified, return an iterator where `chunksize` is the number of
|
||
rows to include in each chunk.
|
||
dtype : Type name or dict of columns
|
||
Data type for data or columns. E.g. np.float64 or
|
||
{‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}.
|
||
|
||
.. versionadded:: 1.3.0
|
||
|
||
Returns
|
||
-------
|
||
DataFrame or Iterator[DataFrame]
|
||
|
||
See Also
|
||
--------
|
||
read_sql_table : Read SQL database table into a DataFrame.
|
||
read_sql : Read SQL query or database table into a DataFrame.
|
||
|
||
Notes
|
||
-----
|
||
Any datetime values with time zone information parsed via the `parse_dates`
|
||
parameter will be converted to UTC.
|
||
"""
|
||
pandas_sql = pandasSQL_builder(con)
|
||
return pandas_sql.read_query(
|
||
sql,
|
||
index_col=index_col,
|
||
params=params,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
chunksize=chunksize,
|
||
dtype=dtype,
|
||
)
|
||
|
||
|
||
@overload
|
||
def read_sql(
|
||
sql,
|
||
con,
|
||
index_col: str | list[str] | None = ...,
|
||
coerce_float=...,
|
||
params=...,
|
||
parse_dates=...,
|
||
columns: list[str] = ...,
|
||
chunksize: None = ...,
|
||
) -> DataFrame:
|
||
...
|
||
|
||
|
||
@overload
|
||
def read_sql(
|
||
sql,
|
||
con,
|
||
index_col: str | list[str] | None = ...,
|
||
coerce_float=...,
|
||
params=...,
|
||
parse_dates=...,
|
||
columns: list[str] = ...,
|
||
chunksize: int = ...,
|
||
) -> 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,
|
||
) -> DataFrame | Iterator[DataFrame]:
|
||
"""
|
||
Read SQL query or database table into a DataFrame.
|
||
|
||
This function is a convenience wrapper around ``read_sql_table`` and
|
||
``read_sql_query`` (for backward compatibility). It will delegate
|
||
to the specific function depending on the provided input. A SQL query
|
||
will be routed to ``read_sql_query``, while a database table name will
|
||
be routed to ``read_sql_table``. Note that the delegated function might
|
||
have more specific notes about their functionality not listed here.
|
||
|
||
Parameters
|
||
----------
|
||
sql : str or SQLAlchemy Selectable (select or text object)
|
||
SQL query to be executed or a table name.
|
||
con : SQLAlchemy connectable, str, or sqlite3 connection
|
||
Using SQLAlchemy makes it possible to use any DB supported by that
|
||
library. If a DBAPI2 object, only sqlite3 is supported. The user is responsible
|
||
for engine disposal and connection closure for the SQLAlchemy connectable; str
|
||
connections are closed automatically. See
|
||
`here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_.
|
||
index_col : str or list of str, optional, default: None
|
||
Column(s) to set as index(MultiIndex).
|
||
coerce_float : bool, default True
|
||
Attempts to convert values of non-string, non-numeric objects (like
|
||
decimal.Decimal) to floating point, useful for SQL result sets.
|
||
params : list, tuple or dict, optional, default: None
|
||
List of parameters to pass to execute method. The syntax used
|
||
to pass parameters is database driver dependent. Check your
|
||
database driver documentation for which of the five syntax styles,
|
||
described in PEP 249's paramstyle, is supported.
|
||
Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}.
|
||
parse_dates : list or dict, default: None
|
||
- List of column names to parse as dates.
|
||
- Dict of ``{column_name: format string}`` where format string is
|
||
strftime compatible in case of parsing string times, or is one of
|
||
(D, s, ns, ms, us) in case of parsing integer timestamps.
|
||
- Dict of ``{column_name: arg dict}``, where the arg dict corresponds
|
||
to the keyword arguments of :func:`pandas.to_datetime`
|
||
Especially useful with databases without native Datetime support,
|
||
such as SQLite.
|
||
columns : list, default: None
|
||
List of column names to select from SQL table (only used when reading
|
||
a table).
|
||
chunksize : int, default None
|
||
If specified, return an iterator where `chunksize` is the
|
||
number of rows to include in each chunk.
|
||
|
||
Returns
|
||
-------
|
||
DataFrame or Iterator[DataFrame]
|
||
|
||
See Also
|
||
--------
|
||
read_sql_table : Read SQL database table into a DataFrame.
|
||
read_sql_query : Read SQL query into a DataFrame.
|
||
|
||
Examples
|
||
--------
|
||
Read data from SQL via either a SQL query or a SQL tablename.
|
||
When using a SQLite database only SQL queries are accepted,
|
||
providing only the SQL tablename will result in an error.
|
||
|
||
>>> from sqlite3 import connect
|
||
>>> conn = connect(':memory:')
|
||
>>> df = pd.DataFrame(data=[[0, '10/11/12'], [1, '12/11/10']],
|
||
... columns=['int_column', 'date_column'])
|
||
>>> df.to_sql('test_data', conn)
|
||
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
|
||
|
||
>>> pd.read_sql('SELECT int_column, date_column FROM test_data',
|
||
... conn,
|
||
... parse_dates=["date_column"])
|
||
int_column date_column
|
||
0 0 2012-10-11
|
||
1 1 2010-12-11
|
||
|
||
The ``parse_dates`` argument calls ``pd.to_datetime`` on the provided columns.
|
||
Custom argument values for applying ``pd.to_datetime`` on a column are specified
|
||
via a dictionary format:
|
||
1. Ignore errors while parsing the values of "date_column"
|
||
|
||
>>> pd.read_sql('SELECT int_column, date_column FROM test_data',
|
||
... conn,
|
||
... parse_dates={"date_column": {"errors": "ignore"}})
|
||
int_column date_column
|
||
0 0 2012-10-11
|
||
1 1 2010-12-11
|
||
|
||
2. Apply a dayfirst date parsing order on the values of "date_column"
|
||
|
||
>>> pd.read_sql('SELECT int_column, date_column FROM test_data',
|
||
... conn,
|
||
... parse_dates={"date_column": {"dayfirst": True}})
|
||
int_column date_column
|
||
0 0 2012-11-10
|
||
1 1 2010-11-12
|
||
|
||
3. Apply custom formatting when date parsing the values of "date_column"
|
||
|
||
>>> pd.read_sql('SELECT int_column, date_column FROM test_data',
|
||
... conn,
|
||
... parse_dates={"date_column": {"format": "%d/%m/%y"}})
|
||
int_column date_column
|
||
0 0 2012-11-10
|
||
1 1 2010-11-12
|
||
"""
|
||
pandas_sql = pandasSQL_builder(con)
|
||
|
||
if isinstance(pandas_sql, SQLiteDatabase):
|
||
return pandas_sql.read_query(
|
||
sql,
|
||
index_col=index_col,
|
||
params=params,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
chunksize=chunksize,
|
||
)
|
||
|
||
try:
|
||
_is_table_name = pandas_sql.has_table(sql)
|
||
except Exception:
|
||
# using generic exception to catch errors from sql drivers (GH24988)
|
||
_is_table_name = False
|
||
|
||
if _is_table_name:
|
||
pandas_sql.meta.reflect(bind=pandas_sql.connectable, only=[sql])
|
||
return pandas_sql.read_table(
|
||
sql,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
columns=columns,
|
||
chunksize=chunksize,
|
||
)
|
||
else:
|
||
return pandas_sql.read_query(
|
||
sql,
|
||
index_col=index_col,
|
||
params=params,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
chunksize=chunksize,
|
||
)
|
||
|
||
|
||
def to_sql(
|
||
frame,
|
||
name: str,
|
||
con,
|
||
schema: str | None = None,
|
||
if_exists: str = "fail",
|
||
index: bool = True,
|
||
index_label: 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")
|
||
|
||
pandas_sql = pandasSQL_builder(con, schema=schema)
|
||
|
||
if isinstance(frame, Series):
|
||
frame = frame.to_frame()
|
||
elif not isinstance(frame, DataFrame):
|
||
raise NotImplementedError(
|
||
"'frame' argument should be either a Series or a DataFrame"
|
||
)
|
||
|
||
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
|
||
"""
|
||
pandas_sql = pandasSQL_builder(con, schema=schema)
|
||
return pandas_sql.has_table(table_name)
|
||
|
||
|
||
table_exists = has_table
|
||
|
||
|
||
def pandasSQL_builder(con, schema: str | None = None) -> SQLDatabase | SQLiteDatabase:
|
||
"""
|
||
Convenience function to return the correct PandasSQL subclass based on the
|
||
provided parameters.
|
||
"""
|
||
import sqlite3
|
||
import warnings
|
||
|
||
if isinstance(con, sqlite3.Connection) or con is None:
|
||
return SQLiteDatabase(con)
|
||
|
||
sqlalchemy = import_optional_dependency("sqlalchemy", errors="ignore")
|
||
|
||
if isinstance(con, str):
|
||
if sqlalchemy is None:
|
||
raise ImportError("Using URI string without sqlalchemy installed.")
|
||
else:
|
||
con = sqlalchemy.create_engine(con)
|
||
|
||
if sqlalchemy is not None and isinstance(con, sqlalchemy.engine.Connectable):
|
||
return SQLDatabase(con, schema=schema)
|
||
|
||
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: str = "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.connectable))
|
||
|
||
def _execute_create(self):
|
||
# Inserting table into database, add to MetaData object
|
||
self.table = self.table.to_metadata(self.pd_sql.meta)
|
||
self.table.create(bind=self.pd_sql.connectable)
|
||
|
||
def create(self) -> None:
|
||
if self.exists():
|
||
if self.if_exists == "fail":
|
||
raise ValueError(f"Table '{self.name}' already exists.")
|
||
elif self.if_exists == "replace":
|
||
self.pd_sql.drop_table(self.name, self.schema)
|
||
self._execute_create()
|
||
elif self.if_exists == "append":
|
||
pass
|
||
else:
|
||
raise ValueError(f"'{self.if_exists}' is not valid for if_exists")
|
||
else:
|
||
self._execute_create()
|
||
|
||
def _execute_insert(self, conn, keys: list[str], data_iter) -> 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()):
|
||
vals = ser._values
|
||
if vals.dtype.kind == "M":
|
||
d = vals.to_pydatetime()
|
||
elif vals.dtype.kind == "m":
|
||
# store as integers, see GH#6921, GH#7076
|
||
d = vals.view("i8").astype(object)
|
||
else:
|
||
d = vals.astype(object)
|
||
|
||
assert isinstance(d, np.ndarray), type(d)
|
||
|
||
if ser._can_hold_na:
|
||
# Note: this will miss timedeltas since they are converted to int
|
||
mask = isna(d)
|
||
d[mask] = None
|
||
|
||
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,
|
||
chunksize: str | None,
|
||
columns,
|
||
coerce_float: bool = True,
|
||
parse_dates=None,
|
||
):
|
||
"""Return generator through chunked result set."""
|
||
has_read_data = False
|
||
while True:
|
||
data = result.fetchmany(chunksize)
|
||
if not data:
|
||
if not has_read_data:
|
||
yield DataFrame.from_records(
|
||
[], columns=columns, coerce_float=coerce_float
|
||
)
|
||
break
|
||
else:
|
||
has_read_data = True
|
||
self.frame = DataFrame.from_records(
|
||
data, columns=columns, coerce_float=coerce_float
|
||
)
|
||
|
||
self._harmonize_columns(parse_dates=parse_dates)
|
||
|
||
if self.index is not None:
|
||
self.frame.set_index(self.index, inplace=True)
|
||
|
||
yield self.frame
|
||
|
||
def read(
|
||
self,
|
||
coerce_float: bool = True,
|
||
parse_dates=None,
|
||
columns=None,
|
||
chunksize=None,
|
||
) -> 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,
|
||
chunksize,
|
||
column_names,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
)
|
||
else:
|
||
data = result.fetchall()
|
||
self.frame = DataFrame.from_records(
|
||
data, columns=column_names, coerce_float=coerce_float
|
||
)
|
||
|
||
self._harmonize_columns(parse_dates=parse_dates)
|
||
|
||
if self.index is not None:
|
||
self.frame.set_index(self.index, inplace=True)
|
||
|
||
return self.frame
|
||
|
||
def _index_name(self, index, index_label):
|
||
# for writing: index=True to include index in sql table
|
||
if index is True:
|
||
nlevels = self.frame.index.nlevels
|
||
# if index_label is specified, set this as index name(s)
|
||
if index_label is not None:
|
||
if not isinstance(index_label, list):
|
||
index_label = [index_label]
|
||
if len(index_label) != nlevels:
|
||
raise ValueError(
|
||
"Length of 'index_label' should match number of "
|
||
f"levels, which is {nlevels}"
|
||
)
|
||
else:
|
||
return index_label
|
||
# return the used column labels for the index columns
|
||
if (
|
||
nlevels == 1
|
||
and "index" not in self.frame.columns
|
||
and self.frame.index.name is None
|
||
):
|
||
return ["index"]
|
||
else:
|
||
return 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 = [
|
||
Column(name, typ, index=is_index)
|
||
for name, typ, is_index in column_names_and_types
|
||
]
|
||
|
||
if self.keys is not None:
|
||
if not is_list_like(self.keys):
|
||
keys = [self.keys]
|
||
else:
|
||
keys = self.keys
|
||
pkc = PrimaryKeyConstraint(*keys, name=self.name + "_pk")
|
||
columns.append(pkc)
|
||
|
||
schema = self.schema or self.pd_sql.meta.schema
|
||
|
||
# At this point, attach to new metadata, only attach to self.meta
|
||
# once table is created.
|
||
meta = MetaData()
|
||
return Table(self.name, meta, *columns, schema=schema)
|
||
|
||
def _harmonize_columns(self, parse_dates=None):
|
||
"""
|
||
Make the DataFrame's column types align with the SQL table
|
||
column types.
|
||
Need to work around limited NA value support. Floats are always
|
||
fine, ints must always be floats if there are Null values.
|
||
Booleans are hard because converting bool column with None replaces
|
||
all Nones with false. Therefore only convert bool if there are no
|
||
NA values.
|
||
Datetimes should already be converted to np.datetime64 if supported,
|
||
but here we also force conversion if required.
|
||
"""
|
||
parse_dates = _process_parse_dates_argument(parse_dates)
|
||
|
||
for sql_col in self.table.columns:
|
||
col_name = sql_col.name
|
||
try:
|
||
df_col = self.frame[col_name]
|
||
|
||
# Handle date parsing upfront; don't try to convert columns
|
||
# twice
|
||
if col_name in parse_dates:
|
||
try:
|
||
fmt = parse_dates[col_name]
|
||
except TypeError:
|
||
fmt = None
|
||
self.frame[col_name] = _handle_date_column(df_col, format=fmt)
|
||
continue
|
||
|
||
# the type the dataframe column should have
|
||
col_type = self._get_dtype(sql_col.type)
|
||
|
||
if (
|
||
col_type is datetime
|
||
or col_type is date
|
||
or col_type is DatetimeTZDtype
|
||
):
|
||
# Convert tz-aware Datetime SQL columns to UTC
|
||
utc = col_type is DatetimeTZDtype
|
||
self.frame[col_name] = _handle_date_column(df_col, utc=utc)
|
||
elif col_type is float:
|
||
# floats support NA, can always convert!
|
||
self.frame[col_name] = df_col.astype(col_type, copy=False)
|
||
|
||
elif len(df_col) == df_col.count():
|
||
# No NA values, can convert ints and bools
|
||
if col_type is np.dtype("int64") or col_type is bool:
|
||
self.frame[col_name] = df_col.astype(col_type, copy=False)
|
||
except KeyError:
|
||
pass # this column not in results
|
||
|
||
def _sqlalchemy_type(self, col):
|
||
|
||
dtype: DtypeArg = self.dtype or {}
|
||
if is_dict_like(dtype):
|
||
dtype = cast(dict, dtype)
|
||
if col.name in dtype:
|
||
return dtype[col.name]
|
||
|
||
# Infer type of column, while ignoring missing values.
|
||
# Needed for inserting typed data containing NULLs, GH 8778.
|
||
col_type = lib.infer_dtype(col, skipna=True)
|
||
|
||
from sqlalchemy.types import (
|
||
TIMESTAMP,
|
||
BigInteger,
|
||
Boolean,
|
||
Date,
|
||
DateTime,
|
||
Float,
|
||
Integer,
|
||
SmallInteger,
|
||
Text,
|
||
Time,
|
||
)
|
||
|
||
if col_type == "datetime64" or col_type == "datetime":
|
||
# GH 9086: TIMESTAMP is the suggested type if the column contains
|
||
# timezone information
|
||
try:
|
||
if col.dt.tz is not None:
|
||
return TIMESTAMP(timezone=True)
|
||
except AttributeError:
|
||
# The column is actually a DatetimeIndex
|
||
# GH 26761 or an Index with date-like data e.g. 9999-01-01
|
||
if getattr(col, "tz", None) is not None:
|
||
return TIMESTAMP(timezone=True)
|
||
return DateTime
|
||
if col_type == "timedelta64":
|
||
warnings.warn(
|
||
"the 'timedelta' type is not supported, and will be "
|
||
"written as integer values (ns frequency) to the database.",
|
||
UserWarning,
|
||
stacklevel=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):
|
||
"""
|
||
Subclasses Should define read_sql and to_sql.
|
||
"""
|
||
|
||
def read_sql(self, *args, **kwargs):
|
||
raise ValueError(
|
||
"PandasSQL must be created with an SQLAlchemy "
|
||
"connectable or sqlite connection"
|
||
)
|
||
|
||
def to_sql(
|
||
self,
|
||
frame,
|
||
name,
|
||
if_exists: str = "fail",
|
||
index: bool = True,
|
||
index_label=None,
|
||
schema=None,
|
||
chunksize=None,
|
||
dtype: DtypeArg | None = None,
|
||
method=None,
|
||
) -> int | None:
|
||
raise ValueError(
|
||
"PandasSQL must be created with an SQLAlchemy "
|
||
"connectable or sqlite connection"
|
||
)
|
||
|
||
|
||
class BaseEngine:
|
||
def insert_records(
|
||
self,
|
||
table: SQLTable,
|
||
con,
|
||
frame,
|
||
name,
|
||
index=True,
|
||
schema=None,
|
||
chunksize=None,
|
||
method=None,
|
||
**engine_kwargs,
|
||
) -> 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=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.SQLAlchemyError as err:
|
||
# GH34431
|
||
# https://stackoverflow.com/a/67358288/6067848
|
||
msg = r"""(\(1054, "Unknown column 'inf(e0)?' in 'field list'"\))(?#
|
||
)|inf can not be used with MySQL"""
|
||
err_text = str(err.orig)
|
||
if re.search(msg, err_text):
|
||
raise ValueError("inf cannot be used with MySQL") from err
|
||
else:
|
||
raise err
|
||
|
||
|
||
def get_engine(engine: str) -> BaseEngine:
|
||
"""return our implementation"""
|
||
if engine == "auto":
|
||
engine = get_option("io.sql.engine")
|
||
|
||
if engine == "auto":
|
||
# try engines in this order
|
||
engine_classes = [SQLAlchemyEngine]
|
||
|
||
error_msgs = ""
|
||
for engine_class in engine_classes:
|
||
try:
|
||
return engine_class()
|
||
except ImportError as err:
|
||
error_msgs += "\n - " + str(err)
|
||
|
||
raise ImportError(
|
||
"Unable to find a usable engine; "
|
||
"tried using: 'sqlalchemy'.\n"
|
||
"A suitable version of "
|
||
"sqlalchemy is required for sql I/O "
|
||
"support.\n"
|
||
"Trying to import the above resulted in these errors:"
|
||
f"{error_msgs}"
|
||
)
|
||
|
||
elif engine == "sqlalchemy":
|
||
return SQLAlchemyEngine()
|
||
|
||
raise ValueError("engine must be one of 'auto', 'sqlalchemy'")
|
||
|
||
|
||
class SQLDatabase(PandasSQL):
|
||
"""
|
||
This class enables conversion between DataFrame and SQL databases
|
||
using SQLAlchemy to handle DataBase abstraction.
|
||
|
||
Parameters
|
||
----------
|
||
engine : SQLAlchemy connectable
|
||
Connectable to connect with the database. Using SQLAlchemy makes it
|
||
possible to use any DB supported by that library.
|
||
schema : string, default None
|
||
Name of SQL schema in database to write to (if database flavor
|
||
supports this). If None, use default schema (default).
|
||
|
||
"""
|
||
|
||
def __init__(self, engine, schema: str | None = None) -> None:
|
||
from sqlalchemy.schema import MetaData
|
||
|
||
self.connectable = engine
|
||
self.meta = MetaData(schema=schema)
|
||
|
||
@contextmanager
|
||
def run_transaction(self):
|
||
from sqlalchemy.engine import Engine
|
||
|
||
if isinstance(self.connectable, Engine):
|
||
with self.connectable.connect() as conn:
|
||
with conn.begin():
|
||
yield conn
|
||
else:
|
||
yield self.connectable
|
||
|
||
def execute(self, *args, **kwargs):
|
||
"""Simple passthrough to SQLAlchemy connectable"""
|
||
return self.connectable.execution_options().execute(*args, **kwargs)
|
||
|
||
def read_table(
|
||
self,
|
||
table_name: str,
|
||
index_col: str | list[str] | None = None,
|
||
coerce_float: bool = True,
|
||
parse_dates=None,
|
||
columns=None,
|
||
schema: str | None = None,
|
||
chunksize: int | None = None,
|
||
) -> 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.
|
||
|
||
Returns
|
||
-------
|
||
DataFrame
|
||
|
||
See Also
|
||
--------
|
||
pandas.read_sql_table
|
||
SQLDatabase.read_query
|
||
|
||
"""
|
||
table = SQLTable(table_name, self, index=index_col, schema=schema)
|
||
return table.read(
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
columns=columns,
|
||
chunksize=chunksize,
|
||
)
|
||
|
||
@staticmethod
|
||
def _query_iterator(
|
||
result,
|
||
chunksize: int,
|
||
columns,
|
||
index_col=None,
|
||
coerce_float=True,
|
||
parse_dates=None,
|
||
dtype: DtypeArg | None = None,
|
||
):
|
||
"""Return generator through chunked result set"""
|
||
has_read_data = False
|
||
while True:
|
||
data = result.fetchmany(chunksize)
|
||
if not data:
|
||
if not has_read_data:
|
||
yield _wrap_result(
|
||
[],
|
||
columns,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
)
|
||
break
|
||
else:
|
||
has_read_data = True
|
||
yield _wrap_result(
|
||
data,
|
||
columns,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
dtype=dtype,
|
||
)
|
||
|
||
def read_query(
|
||
self,
|
||
sql: str,
|
||
index_col: str | list[str] | None = None,
|
||
coerce_float: bool = True,
|
||
parse_dates=None,
|
||
params=None,
|
||
chunksize: int | None = None,
|
||
dtype: DtypeArg | None = None,
|
||
) -> 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
|
||
|
||
"""
|
||
args = _convert_params(sql, params)
|
||
|
||
result = self.execute(*args)
|
||
columns = result.keys()
|
||
|
||
if chunksize is not None:
|
||
return self._query_iterator(
|
||
result,
|
||
chunksize,
|
||
columns,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
dtype=dtype,
|
||
)
|
||
else:
|
||
data = result.fetchall()
|
||
frame = _wrap_result(
|
||
data,
|
||
columns,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
dtype=dtype,
|
||
)
|
||
return frame
|
||
|
||
read_sql = read_query
|
||
|
||
def prep_table(
|
||
self,
|
||
frame,
|
||
name,
|
||
if_exists="fail",
|
||
index=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,
|
||
to_instance,
|
||
)
|
||
|
||
for col, my_type in dtype.items():
|
||
if not isinstance(to_instance(my_type), TypeEngine):
|
||
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
|
||
|
||
with self.connectable.connect() as conn:
|
||
insp = sqlalchemy_inspect(conn)
|
||
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: str = "fail",
|
||
index: bool = True,
|
||
index_label=None,
|
||
schema: str | None = None,
|
||
chunksize=None,
|
||
dtype: DtypeArg | None = None,
|
||
method=None,
|
||
engine="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.connectable,
|
||
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):
|
||
from sqlalchemy import inspect as sqlalchemy_inspect
|
||
|
||
insp = sqlalchemy_inspect(self.connectable)
|
||
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.connectable, 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.connectable, only=[table_name], schema=schema)
|
||
self.get_table(table_name, schema).drop(bind=self.connectable)
|
||
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):
|
||
# 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):
|
||
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, *args, **kwargs):
|
||
cur = self.con.cursor()
|
||
try:
|
||
cur.execute(*args, **kwargs)
|
||
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: {args[0]}\n{exc}\nunable to rollback"
|
||
)
|
||
raise ex from inner_exc
|
||
|
||
ex = DatabaseError(f"Execution failed on sql '{args[0]}': {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,
|
||
):
|
||
"""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:
|
||
yield DataFrame.from_records(
|
||
[], columns=columns, coerce_float=coerce_float
|
||
)
|
||
break
|
||
else:
|
||
has_read_data = True
|
||
yield _wrap_result(
|
||
data,
|
||
columns,
|
||
index_col=index_col,
|
||
coerce_float=coerce_float,
|
||
parse_dates=parse_dates,
|
||
dtype=dtype,
|
||
)
|
||
|
||
def read_query(
|
||
self,
|
||
sql,
|
||
index_col=None,
|
||
coerce_float: bool = True,
|
||
params=None,
|
||
parse_dates=None,
|
||
chunksize: int | None = None,
|
||
dtype: DtypeArg | None = None,
|
||
) -> DataFrame | Iterator[DataFrame]:
|
||
|
||
args = _convert_params(sql, params)
|
||
cursor = self.execute(*args)
|
||
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,
|
||
)
|
||
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,
|
||
)
|
||
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,
|
||
**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
|
||
"""
|
||
pandas_sql = pandasSQL_builder(con=con)
|
||
return pandas_sql._create_sql_schema(
|
||
frame, name, keys=keys, dtype=dtype, schema=schema
|
||
)
|