1931 lines
63 KiB
Python
1931 lines
63 KiB
Python
"""
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Collection of query wrappers / abstractions to both facilitate data
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retrieval and to reduce dependency on DB-specific API.
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"""
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from contextlib import contextmanager
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from datetime import date, datetime, time
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from distutils.version import LooseVersion
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from functools import partial
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import re
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from typing import Iterator, List, Optional, Union, overload
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import warnings
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import numpy as np
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import pandas._libs.lib as lib
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from pandas.core.dtypes.common import is_datetime64tz_dtype, is_dict_like, is_list_like
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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.core.api import DataFrame, Series
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from pandas.core.base import PandasObject
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from pandas.core.tools.datetimes import to_datetime
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class SQLAlchemyRequired(ImportError):
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pass
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class DatabaseError(IOError):
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pass
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# -----------------------------------------------------------------------------
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# -- Helper functions
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_SQLALCHEMY_INSTALLED = None
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def _is_sqlalchemy_connectable(con):
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global _SQLALCHEMY_INSTALLED
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if _SQLALCHEMY_INSTALLED is None:
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try:
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import sqlalchemy
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_SQLALCHEMY_INSTALLED = True
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except ImportError:
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_SQLALCHEMY_INSTALLED = False
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if _SQLALCHEMY_INSTALLED:
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import sqlalchemy # noqa: F811
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return isinstance(con, sqlalchemy.engine.Connectable)
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else:
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return False
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def _gt14() -> bool:
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"""
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Check if sqlalchemy.__version__ is at least 1.4.0, when several
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deprecations were made.
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"""
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import sqlalchemy
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return LooseVersion(sqlalchemy.__version__) >= LooseVersion("1.4.0")
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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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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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def _handle_date_column(col, utc=None, format=None):
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if isinstance(format, dict):
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return to_datetime(col, errors="ignore", **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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):
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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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"""
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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():
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if is_datetime64tz_dtype(df_col.dtype) or col_name in parse_dates:
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try:
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fmt = parse_dates[col_name]
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except TypeError:
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fmt = None
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data_frame[col_name] = _handle_date_column(df_col, format=fmt)
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return data_frame
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def _wrap_result(data, columns, index_col=None, coerce_float=True, parse_dates=None):
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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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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, cur=None, params=None):
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"""
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Execute the given SQL query using the provided connection object.
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Parameters
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----------
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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
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library.
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If a DBAPI2 object, only sqlite3 is supported.
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cur : deprecated, cursor is obtained from connection, default: None
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params : list or tuple, optional, default: None
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List of parameters to pass to execute method.
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Returns
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-------
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Results Iterable
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"""
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if cur is None:
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pandas_sql = pandasSQL_builder(con)
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else:
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pandas_sql = pandasSQL_builder(cur, is_cursor=True)
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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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# -- Read and write to DataFrames
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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=None,
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index_col=None,
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coerce_float=True,
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parse_dates=None,
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columns=None,
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chunksize: None = 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=None,
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index_col=None,
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coerce_float=True,
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parse_dates=None,
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columns=None,
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chunksize: int = 1,
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) -> Iterator[DataFrame]:
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...
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def read_sql_table(
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table_name,
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con,
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schema=None,
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index_col=None,
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coerce_float=True,
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parse_dates=None,
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columns=None,
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chunksize: Optional[int] = None,
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) -> Union[DataFrame, Iterator[DataFrame]]:
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"""
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Read SQL database table into a DataFrame.
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Given a table name and a SQLAlchemy connectable, returns a DataFrame.
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This function does not support DBAPI connections.
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Parameters
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----------
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table_name : str
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Name of SQL table in database.
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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.
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schema : str, default None
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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).
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coerce_float : bool, default True
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Attempts to convert values of non-string, non-numeric objects (like
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decimal.Decimal) to floating point. Can result in loss of Precision.
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parse_dates : list or dict, default None
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- List of column names to parse as dates.
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- Dict of ``{column_name: format string}`` where format string is
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strftime compatible in case of parsing string times or is one of
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(D, s, ns, ms, us) in case of parsing integer timestamps.
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- Dict of ``{column_name: arg dict}``, where the arg dict corresponds
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to the keyword arguments of :func:`pandas.to_datetime`
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Especially useful with databases without native Datetime support,
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such as SQLite.
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columns : list, default None
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List of column names to select from SQL table.
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chunksize : int, default None
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If specified, returns an iterator where `chunksize` is the number of
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rows to include in each chunk.
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Returns
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-------
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DataFrame or Iterator[DataFrame]
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A SQL table is returned as two-dimensional data structure with labeled
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axes.
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See Also
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--------
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read_sql_query : Read SQL query into a DataFrame.
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read_sql : Read SQL query or database table into a DataFrame.
|
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Notes
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-----
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Any datetime values with time zone information will be converted to UTC.
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Examples
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--------
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>>> pd.read_sql_table('table_name', 'postgres:///db_name') # doctest:+SKIP
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"""
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con = _engine_builder(con)
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if not _is_sqlalchemy_connectable(con):
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raise NotImplementedError(
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"read_sql_table only supported for SQLAlchemy connectable."
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)
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import sqlalchemy
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from sqlalchemy.schema import MetaData
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meta = MetaData(con, schema=schema)
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try:
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meta.reflect(only=[table_name], views=True)
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except sqlalchemy.exc.InvalidRequestError as err:
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raise ValueError(f"Table {table_name} not found") from err
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pandas_sql = SQLDatabase(con, meta=meta)
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table = pandas_sql.read_table(
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table_name,
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index_col=index_col,
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coerce_float=coerce_float,
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parse_dates=parse_dates,
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columns=columns,
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chunksize=chunksize,
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)
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if table is not None:
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return table
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else:
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raise ValueError(f"Table {table_name} not found", con)
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|
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@overload
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def read_sql_query(
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sql,
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con,
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index_col=None,
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coerce_float=True,
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params=None,
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|
parse_dates=None,
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|
chunksize: None = None,
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) -> DataFrame:
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...
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|
|
|
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|
@overload
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|
def read_sql_query(
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sql,
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con,
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index_col=None,
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coerce_float=True,
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params=None,
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parse_dates=None,
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chunksize: int = 1,
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) -> Iterator[DataFrame]:
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|
...
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|
|
|
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|
def read_sql_query(
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sql,
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con,
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index_col=None,
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coerce_float=True,
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params=None,
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parse_dates=None,
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chunksize: Optional[int] = None,
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) -> Union[DataFrame, Iterator[DataFrame]]:
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"""
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Read SQL query into a DataFrame.
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Returns a DataFrame corresponding to the result set of the query
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string. Optionally provide an `index_col` parameter to use one of the
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columns as the index, otherwise default integer index will be used.
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|
|
|
Parameters
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|
----------
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|
sql : str SQL query or SQLAlchemy Selectable (select or text object)
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|
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.
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@overload
|
|
def read_sql(
|
|
sql,
|
|
con,
|
|
index_col=None,
|
|
coerce_float=True,
|
|
params=None,
|
|
parse_dates=None,
|
|
columns=None,
|
|
chunksize: None = None,
|
|
) -> DataFrame:
|
|
...
|
|
|
|
|
|
@overload
|
|
def read_sql(
|
|
sql,
|
|
con,
|
|
index_col=None,
|
|
coerce_float=True,
|
|
params=None,
|
|
parse_dates=None,
|
|
columns=None,
|
|
chunksize: int = 1,
|
|
) -> Iterator[DataFrame]:
|
|
...
|
|
|
|
|
|
def read_sql(
|
|
sql,
|
|
con,
|
|
index_col=None,
|
|
coerce_float=True,
|
|
params=None,
|
|
parse_dates=None,
|
|
columns=None,
|
|
chunksize: Optional[int] = None,
|
|
) -> Union[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.
|
|
"""
|
|
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(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,
|
|
con,
|
|
schema=None,
|
|
if_exists="fail",
|
|
index=True,
|
|
index_label=None,
|
|
chunksize=None,
|
|
dtype=None,
|
|
method=None,
|
|
) -> 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 : boolean, default True
|
|
Write DataFrame index as a column.
|
|
index_label : str or sequence, optional
|
|
Column label for index column(s). If None is given (default) and
|
|
`index` is True, then the index names are used.
|
|
A sequence should be given if the DataFrame uses MultiIndex.
|
|
chunksize : int, optional
|
|
Specify the number of rows in each batch to be written at a time.
|
|
By default, all rows will be written at once.
|
|
dtype : dict or scalar, optional
|
|
Specifying the datatype for columns. If a dictionary is used, the
|
|
keys should be the column names and the values should be the
|
|
SQLAlchemy types or strings for the sqlite3 fallback mode. If a
|
|
scalar is provided, it will be applied to all columns.
|
|
method : {None, 'multi', callable}, optional
|
|
Controls the SQL insertion clause used:
|
|
|
|
- None : Uses standard SQL ``INSERT`` clause (one per row).
|
|
- 'multi': Pass multiple values in a single ``INSERT`` clause.
|
|
- callable with signature ``(pd_table, conn, keys, data_iter)``.
|
|
|
|
Details and a sample callable implementation can be found in the
|
|
section :ref:`insert method <io.sql.method>`.
|
|
|
|
.. versionadded:: 0.24.0
|
|
"""
|
|
if if_exists not in ("fail", "replace", "append"):
|
|
raise ValueError(f"'{if_exists}' is not valid for if_exists")
|
|
|
|
pandas_sql = pandasSQL_builder(con, schema=schema)
|
|
|
|
if isinstance(frame, Series):
|
|
frame = frame.to_frame()
|
|
elif not isinstance(frame, DataFrame):
|
|
raise NotImplementedError(
|
|
"'frame' argument should be either a Series or a DataFrame"
|
|
)
|
|
|
|
pandas_sql.to_sql(
|
|
frame,
|
|
name,
|
|
if_exists=if_exists,
|
|
index=index,
|
|
index_label=index_label,
|
|
schema=schema,
|
|
chunksize=chunksize,
|
|
dtype=dtype,
|
|
method=method,
|
|
)
|
|
|
|
|
|
def has_table(table_name, con, schema=None):
|
|
"""
|
|
Check if DataBase has named table.
|
|
|
|
Parameters
|
|
----------
|
|
table_name: string
|
|
Name of SQL table.
|
|
con: SQLAlchemy connectable(engine/connection) or sqlite3 DBAPI2 connection
|
|
Using SQLAlchemy makes it possible to use any DB supported by that
|
|
library.
|
|
If a DBAPI2 object, only sqlite3 is supported.
|
|
schema : string, default None
|
|
Name of SQL schema in database to write to (if database flavor supports
|
|
this). If None, use default schema (default).
|
|
|
|
Returns
|
|
-------
|
|
boolean
|
|
"""
|
|
pandas_sql = pandasSQL_builder(con, schema=schema)
|
|
return pandas_sql.has_table(table_name)
|
|
|
|
|
|
table_exists = has_table
|
|
|
|
|
|
def _engine_builder(con):
|
|
"""
|
|
Returns a SQLAlchemy engine from a URI (if con is a string)
|
|
else it just return con without modifying it.
|
|
"""
|
|
global _SQLALCHEMY_INSTALLED
|
|
if isinstance(con, str):
|
|
try:
|
|
import sqlalchemy
|
|
except ImportError:
|
|
_SQLALCHEMY_INSTALLED = False
|
|
else:
|
|
con = sqlalchemy.create_engine(con)
|
|
return con
|
|
|
|
return con
|
|
|
|
|
|
def pandasSQL_builder(con, schema=None, meta=None, is_cursor=False):
|
|
"""
|
|
Convenience function to return the correct PandasSQL subclass based on the
|
|
provided parameters.
|
|
"""
|
|
# When support for DBAPI connections is removed,
|
|
# is_cursor should not be necessary.
|
|
con = _engine_builder(con)
|
|
if _is_sqlalchemy_connectable(con):
|
|
return SQLDatabase(con, schema=schema, meta=meta)
|
|
elif isinstance(con, str):
|
|
raise ImportError("Using URI string without sqlalchemy installed.")
|
|
else:
|
|
return SQLiteDatabase(con, is_cursor=is_cursor)
|
|
|
|
|
|
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,
|
|
pandas_sql_engine,
|
|
frame=None,
|
|
index=True,
|
|
if_exists="fail",
|
|
prefix="pandas",
|
|
index_label=None,
|
|
schema=None,
|
|
keys=None,
|
|
dtype=None,
|
|
):
|
|
self.name = name
|
|
self.pd_sql = pandas_sql_engine
|
|
self.prefix = prefix
|
|
self.frame = frame
|
|
self.index = self._index_name(index, index_label)
|
|
self.schema = schema
|
|
self.if_exists = if_exists
|
|
self.keys = keys
|
|
self.dtype = dtype
|
|
|
|
if frame is not None:
|
|
# We want to initialize based on a dataframe
|
|
self.table = self._create_table_setup()
|
|
else:
|
|
# no data provided, read-only mode
|
|
self.table = self.pd_sql.get_table(self.name, self.schema)
|
|
|
|
if self.table is None:
|
|
raise ValueError(f"Could not init table '{name}'")
|
|
|
|
def exists(self):
|
|
return self.pd_sql.has_table(self.name, self.schema)
|
|
|
|
def sql_schema(self):
|
|
from sqlalchemy.schema import CreateTable
|
|
|
|
return str(CreateTable(self.table).compile(self.pd_sql.connectable))
|
|
|
|
def _execute_create(self):
|
|
# Inserting table into database, add to MetaData object
|
|
if _gt14():
|
|
self.table = self.table.to_metadata(self.pd_sql.meta)
|
|
else:
|
|
self.table = self.table.tometadata(self.pd_sql.meta)
|
|
self.table.create()
|
|
|
|
def create(self):
|
|
if self.exists():
|
|
if self.if_exists == "fail":
|
|
raise ValueError(f"Table '{self.name}' already exists.")
|
|
elif self.if_exists == "replace":
|
|
self.pd_sql.drop_table(self.name, self.schema)
|
|
self._execute_create()
|
|
elif self.if_exists == "append":
|
|
pass
|
|
else:
|
|
raise ValueError(f"'{self.if_exists}' is not valid for if_exists")
|
|
else:
|
|
self._execute_create()
|
|
|
|
def _execute_insert(self, conn, keys, data_iter):
|
|
"""
|
|
Execute SQL statement inserting data
|
|
|
|
Parameters
|
|
----------
|
|
conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
|
|
keys : list of str
|
|
Column names
|
|
data_iter : generator of list
|
|
Each item contains a list of values to be inserted
|
|
"""
|
|
data = [dict(zip(keys, row)) for row in data_iter]
|
|
conn.execute(self.table.insert(), data)
|
|
|
|
def _execute_insert_multi(self, conn, keys, data_iter):
|
|
"""
|
|
Alternative to _execute_insert for DBs support multivalue INSERT.
|
|
|
|
Note: multi-value insert is usually faster for analytics DBs
|
|
and tables containing a few columns
|
|
but performance degrades quickly with increase of columns.
|
|
"""
|
|
data = [dict(zip(keys, row)) for row in data_iter]
|
|
conn.execute(self.table.insert(data))
|
|
|
|
def insert_data(self):
|
|
if self.index is not None:
|
|
temp = self.frame.copy()
|
|
temp.index.names = self.index
|
|
try:
|
|
temp.reset_index(inplace=True)
|
|
except ValueError as err:
|
|
raise ValueError(f"duplicate name in index/columns: {err}") from err
|
|
else:
|
|
temp = self.frame
|
|
|
|
column_names = list(map(str, temp.columns))
|
|
ncols = len(column_names)
|
|
data_list = [None] * ncols
|
|
|
|
for i, (_, ser) in enumerate(temp.items()):
|
|
vals = ser._values
|
|
if vals.dtype.kind == "M":
|
|
d = vals.to_pydatetime()
|
|
elif vals.dtype.kind == "m":
|
|
# store as integers, see GH#6921, GH#7076
|
|
d = vals.view("i8").astype(object)
|
|
else:
|
|
d = vals.astype(object)
|
|
|
|
assert isinstance(d, np.ndarray), type(d)
|
|
|
|
if ser._can_hold_na:
|
|
# Note: this will miss timedeltas since they are converted to int
|
|
mask = isna(d)
|
|
d[mask] = None
|
|
|
|
data_list[i] = d
|
|
|
|
return column_names, data_list
|
|
|
|
def insert(self, chunksize=None, method=None):
|
|
|
|
# set insert method
|
|
if method is None:
|
|
exec_insert = self._execute_insert
|
|
elif method == "multi":
|
|
exec_insert = self._execute_insert_multi
|
|
elif callable(method):
|
|
exec_insert = partial(method, self)
|
|
else:
|
|
raise ValueError(f"Invalid parameter `method`: {method}")
|
|
|
|
keys, data_list = self.insert_data()
|
|
|
|
nrows = len(self.frame)
|
|
|
|
if nrows == 0:
|
|
return
|
|
|
|
if chunksize is None:
|
|
chunksize = nrows
|
|
elif chunksize == 0:
|
|
raise ValueError("chunksize argument should be non-zero")
|
|
|
|
chunks = int(nrows / chunksize) + 1
|
|
|
|
with self.pd_sql.run_transaction() as conn:
|
|
for i in range(chunks):
|
|
start_i = i * chunksize
|
|
end_i = min((i + 1) * chunksize, nrows)
|
|
if start_i >= end_i:
|
|
break
|
|
|
|
chunk_iter = zip(*[arr[start_i:end_i] for arr in data_list])
|
|
exec_insert(conn, keys, chunk_iter)
|
|
|
|
def _query_iterator(
|
|
self, result, chunksize, columns, coerce_float=True, parse_dates=None
|
|
):
|
|
"""Return generator through chunked result set."""
|
|
while True:
|
|
data = result.fetchmany(chunksize)
|
|
if not data:
|
|
break
|
|
else:
|
|
self.frame = DataFrame.from_records(
|
|
data, columns=columns, coerce_float=coerce_float
|
|
)
|
|
|
|
self._harmonize_columns(parse_dates=parse_dates)
|
|
|
|
if self.index is not None:
|
|
self.frame.set_index(self.index, inplace=True)
|
|
|
|
yield self.frame
|
|
|
|
def read(self, coerce_float=True, parse_dates=None, columns=None, chunksize=None):
|
|
|
|
if columns is not None and len(columns) > 0:
|
|
from sqlalchemy import select
|
|
|
|
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 = self.table.select()
|
|
|
|
result = self.pd_sql.execute(sql_select)
|
|
column_names = result.keys()
|
|
|
|
if chunksize is not None:
|
|
return self._query_iterator(
|
|
result,
|
|
chunksize,
|
|
column_names,
|
|
coerce_float=coerce_float,
|
|
parse_dates=parse_dates,
|
|
)
|
|
else:
|
|
data = result.fetchall()
|
|
self.frame = DataFrame.from_records(
|
|
data, columns=column_names, coerce_float=coerce_float
|
|
)
|
|
|
|
self._harmonize_columns(parse_dates=parse_dates)
|
|
|
|
if self.index is not None:
|
|
self.frame.set_index(self.index, inplace=True)
|
|
|
|
return self.frame
|
|
|
|
def _index_name(self, index, index_label):
|
|
# for writing: index=True to include index in sql table
|
|
if index is True:
|
|
nlevels = self.frame.index.nlevels
|
|
# if index_label is specified, set this as index name(s)
|
|
if index_label is not None:
|
|
if not isinstance(index_label, list):
|
|
index_label = [index_label]
|
|
if len(index_label) != nlevels:
|
|
raise ValueError(
|
|
"Length of 'index_label' should match number of "
|
|
f"levels, which is {nlevels}"
|
|
)
|
|
else:
|
|
return index_label
|
|
# return the used column labels for the index columns
|
|
if (
|
|
nlevels == 1
|
|
and "index" not in self.frame.columns
|
|
and self.frame.index.name is None
|
|
):
|
|
return ["index"]
|
|
else:
|
|
return [
|
|
l if l is not None else f"level_{i}"
|
|
for i, l in enumerate(self.frame.index.names)
|
|
]
|
|
|
|
# for reading: index=(list of) string to specify column to set as index
|
|
elif isinstance(index, str):
|
|
return [index]
|
|
elif isinstance(index, list):
|
|
return index
|
|
else:
|
|
return None
|
|
|
|
def _get_column_names_and_types(self, dtype_mapper):
|
|
column_names_and_types = []
|
|
if self.index is not None:
|
|
for i, idx_label in enumerate(self.index):
|
|
idx_type = dtype_mapper(self.frame.index._get_level_values(i))
|
|
column_names_and_types.append((str(idx_label), idx_type, True))
|
|
|
|
column_names_and_types += [
|
|
(str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
|
|
for i in range(len(self.frame.columns))
|
|
]
|
|
|
|
return column_names_and_types
|
|
|
|
def _create_table_setup(self):
|
|
from sqlalchemy import Column, PrimaryKeyConstraint, Table
|
|
|
|
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.
|
|
from sqlalchemy.schema import MetaData
|
|
|
|
meta = MetaData(self.pd_sql, schema=schema)
|
|
|
|
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 = self.dtype or {}
|
|
if col.name in dtype:
|
|
return self.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,
|
|
Text,
|
|
Time,
|
|
)
|
|
|
|
if col_type == "datetime64" or col_type == "datetime":
|
|
# GH 9086: TIMESTAMP is the suggested type if the column contains
|
|
# timezone information
|
|
try:
|
|
if col.dt.tz is not None:
|
|
return TIMESTAMP(timezone=True)
|
|
except AttributeError:
|
|
# The column is actually a DatetimeIndex
|
|
# GH 26761 or an Index with date-like data e.g. 9999-01-01
|
|
if getattr(col, "tz", None) is not None:
|
|
return TIMESTAMP(timezone=True)
|
|
return DateTime
|
|
if col_type == "timedelta64":
|
|
warnings.warn(
|
|
"the 'timedelta' type is not supported, and will be "
|
|
"written as integer values (ns frequency) to the database.",
|
|
UserWarning,
|
|
stacklevel=8,
|
|
)
|
|
return BigInteger
|
|
elif col_type == "floating":
|
|
if col.dtype == "float32":
|
|
return Float(precision=23)
|
|
else:
|
|
return Float(precision=53)
|
|
elif col_type == "integer":
|
|
if col.dtype == "int32":
|
|
return Integer
|
|
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, *args, **kwargs):
|
|
raise ValueError(
|
|
"PandasSQL must be created with an SQLAlchemy "
|
|
"connectable or sqlite connection"
|
|
)
|
|
|
|
|
|
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).
|
|
meta : SQLAlchemy MetaData object, default None
|
|
If provided, this MetaData object is used instead of a newly
|
|
created. This allows to specify database flavor specific
|
|
arguments in the MetaData object.
|
|
|
|
"""
|
|
|
|
def __init__(self, engine, schema=None, meta=None):
|
|
self.connectable = engine
|
|
if not meta:
|
|
from sqlalchemy.schema import MetaData
|
|
|
|
meta = MetaData(self.connectable, schema=schema)
|
|
|
|
self.meta = meta
|
|
|
|
@contextmanager
|
|
def run_transaction(self):
|
|
with self.connectable.begin() as tx:
|
|
if hasattr(tx, "execute"):
|
|
yield tx
|
|
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,
|
|
index_col=None,
|
|
coerce_float=True,
|
|
parse_dates=None,
|
|
columns=None,
|
|
schema=None,
|
|
chunksize=None,
|
|
):
|
|
"""
|
|
Read SQL database table into a DataFrame.
|
|
|
|
Parameters
|
|
----------
|
|
table_name : string
|
|
Name of SQL table in database.
|
|
index_col : string, optional, default: None
|
|
Column to set as index.
|
|
coerce_float : boolean, 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, columns, index_col=None, coerce_float=True, parse_dates=None
|
|
):
|
|
"""Return generator through chunked result set"""
|
|
while True:
|
|
data = result.fetchmany(chunksize)
|
|
if not data:
|
|
break
|
|
else:
|
|
yield _wrap_result(
|
|
data,
|
|
columns,
|
|
index_col=index_col,
|
|
coerce_float=coerce_float,
|
|
parse_dates=parse_dates,
|
|
)
|
|
|
|
def read_query(
|
|
self,
|
|
sql,
|
|
index_col=None,
|
|
coerce_float=True,
|
|
parse_dates=None,
|
|
params=None,
|
|
chunksize=None,
|
|
):
|
|
"""
|
|
Read SQL query into a DataFrame.
|
|
|
|
Parameters
|
|
----------
|
|
sql : string
|
|
SQL query to be executed.
|
|
index_col : string, optional, default: None
|
|
Column name to use as index for the returned DataFrame object.
|
|
coerce_float : boolean, 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.
|
|
|
|
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,
|
|
)
|
|
else:
|
|
data = result.fetchall()
|
|
frame = _wrap_result(
|
|
data,
|
|
columns,
|
|
index_col=index_col,
|
|
coerce_float=coerce_float,
|
|
parse_dates=parse_dates,
|
|
)
|
|
return frame
|
|
|
|
read_sql = read_query
|
|
|
|
def to_sql(
|
|
self,
|
|
frame,
|
|
name,
|
|
if_exists="fail",
|
|
index=True,
|
|
index_label=None,
|
|
schema=None,
|
|
chunksize=None,
|
|
dtype=None,
|
|
method=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>`.
|
|
|
|
.. versionadded:: 0.24.0
|
|
"""
|
|
if dtype and not is_dict_like(dtype):
|
|
dtype = {col_name: dtype for col_name in frame}
|
|
|
|
if dtype is not None:
|
|
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()
|
|
|
|
from sqlalchemy import exc
|
|
|
|
try:
|
|
table.insert(chunksize, method=method)
|
|
except exc.SQLAlchemyError as err:
|
|
# GH34431
|
|
msg = "(1054, \"Unknown column 'inf' in 'field list'\")"
|
|
err_text = str(err.orig)
|
|
if re.search(msg, err_text):
|
|
raise ValueError("inf cannot be used with MySQL") from err
|
|
else:
|
|
raise err
|
|
|
|
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
|
|
engine = self.connectable.engine
|
|
with self.connectable.connect() as conn:
|
|
if _gt14():
|
|
from sqlalchemy import inspect
|
|
|
|
insp = inspect(conn)
|
|
table_names = insp.get_table_names(
|
|
schema=schema or self.meta.schema
|
|
)
|
|
else:
|
|
table_names = engine.table_names(
|
|
schema=schema or self.meta.schema, connection=conn
|
|
)
|
|
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)
|
|
|
|
@property
|
|
def tables(self):
|
|
return self.meta.tables
|
|
|
|
def has_table(self, name, schema=None):
|
|
if _gt14():
|
|
import sqlalchemy as sa
|
|
|
|
insp = sa.inspect(self.connectable)
|
|
return insp.has_table(name, schema or self.meta.schema)
|
|
else:
|
|
return self.connectable.run_callable(
|
|
self.connectable.dialect.has_table, name, schema or self.meta.schema
|
|
)
|
|
|
|
def get_table(self, table_name, schema=None):
|
|
schema = schema or self.meta.schema
|
|
if schema:
|
|
tbl = self.meta.tables.get(".".join([schema, table_name]))
|
|
else:
|
|
tbl = self.meta.tables.get(table_name)
|
|
|
|
# Avoid casting double-precision floats into decimals
|
|
from sqlalchemy import Numeric
|
|
|
|
for column in tbl.columns:
|
|
if isinstance(column.type, Numeric):
|
|
column.type.asdecimal = False
|
|
|
|
return tbl
|
|
|
|
def drop_table(self, table_name, schema=None):
|
|
schema = schema or self.meta.schema
|
|
if self.has_table(table_name, schema):
|
|
self.meta.reflect(only=[table_name], schema=schema)
|
|
self.get_table(table_name, schema).drop()
|
|
self.meta.clear()
|
|
|
|
def _create_sql_schema(
|
|
self,
|
|
frame: DataFrame,
|
|
table_name: str,
|
|
keys: Optional[List[str]] = None,
|
|
dtype: Optional[dict] = None,
|
|
schema: Optional[str] = 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('"', '""') + '"'
|
|
|
|
|
|
_SAFE_NAMES_WARNING = (
|
|
"The spaces in these column names will not be changed. "
|
|
"In pandas versions < 0.14, spaces were converted to underscores."
|
|
)
|
|
|
|
|
|
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):
|
|
# 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)
|
|
sqlite3.register_adapter(time, lambda _: _.strftime("%H:%M:%S.%f"))
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def sql_schema(self):
|
|
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):
|
|
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):
|
|
data_list = list(data_iter)
|
|
conn.executemany(self.insert_statement(num_rows=1), data_list)
|
|
|
|
def _execute_insert_multi(self, conn, keys, data_iter):
|
|
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)
|
|
|
|
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)
|
|
|
|
pat = re.compile(r"\s+")
|
|
column_names = [col_name for col_name, _, _ in column_names_and_types]
|
|
if any(map(pat.search, column_names)):
|
|
warnings.warn(_SAFE_NAMES_WARNING, stacklevel=6)
|
|
|
|
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 = self.dtype or {}
|
|
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=8,
|
|
)
|
|
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, is_cursor=False):
|
|
self.is_cursor = is_cursor
|
|
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):
|
|
if self.is_cursor:
|
|
cur = self.con
|
|
else:
|
|
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, columns, index_col=None, coerce_float=True, parse_dates=None
|
|
):
|
|
"""Return generator through chunked result set"""
|
|
while True:
|
|
data = cursor.fetchmany(chunksize)
|
|
if type(data) == tuple:
|
|
data = list(data)
|
|
if not data:
|
|
cursor.close()
|
|
break
|
|
else:
|
|
yield _wrap_result(
|
|
data,
|
|
columns,
|
|
index_col=index_col,
|
|
coerce_float=coerce_float,
|
|
parse_dates=parse_dates,
|
|
)
|
|
|
|
def read_query(
|
|
self,
|
|
sql,
|
|
index_col=None,
|
|
coerce_float=True,
|
|
params=None,
|
|
parse_dates=None,
|
|
chunksize=None,
|
|
):
|
|
|
|
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,
|
|
)
|
|
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,
|
|
)
|
|
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="fail",
|
|
index=True,
|
|
index_label=None,
|
|
schema=None,
|
|
chunksize=None,
|
|
dtype=None,
|
|
method=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 : 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
|
|
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>`.
|
|
|
|
.. versionadded:: 0.24.0
|
|
"""
|
|
if dtype and not is_dict_like(dtype):
|
|
dtype = {col_name: dtype for col_name in frame}
|
|
|
|
if dtype is not None:
|
|
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()
|
|
table.insert(chunksize, method)
|
|
|
|
def has_table(self, name, schema=None):
|
|
# TODO(wesm): unused?
|
|
# escape = _get_valid_sqlite_name
|
|
# esc_name = escape(name)
|
|
|
|
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, schema=None):
|
|
return None # not supported in fallback mode
|
|
|
|
def drop_table(self, name, schema=None):
|
|
drop_sql = f"DROP TABLE {_get_valid_sqlite_name(name)}"
|
|
self.execute(drop_sql)
|
|
|
|
def _create_sql_schema(self, frame, table_name, keys=None, dtype=None, schema=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, keys=None, con=None, dtype=None, schema=None):
|
|
"""
|
|
Get the SQL db table schema for the given frame.
|
|
|
|
Parameters
|
|
----------
|
|
frame : DataFrame
|
|
name : string
|
|
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
|
|
)
|