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laywerrobot/lib/python3.6/site-packages/pandas/io/sql.py

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2020-08-27 21:55:39 +02:00
# -*- coding: utf-8 -*-
"""
Collection of query wrappers / abstractions to both facilitate data
retrieval and to reduce dependency on DB-specific API.
"""
from __future__ import print_function, division
from datetime import datetime, date, time
import warnings
import re
import numpy as np
import pandas._libs.lib as lib
from pandas.core.dtypes.missing import isna
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.common import (
is_list_like, is_dict_like,
is_datetime64tz_dtype)
from pandas.compat import (map, zip, raise_with_traceback,
string_types, text_type)
from pandas.core.api import DataFrame, Series
from pandas.core.base import PandasObject
from pandas.core.tools.datetimes import to_datetime
from contextlib import contextmanager
class SQLAlchemyRequired(ImportError):
pass
class DatabaseError(IOError):
pass
# -----------------------------------------------------------------------------
# -- Helper functions
_SQLALCHEMY_INSTALLED = None
def _is_sqlalchemy_connectable(con):
global _SQLALCHEMY_INSTALLED
if _SQLALCHEMY_INSTALLED is None:
try:
import sqlalchemy
_SQLALCHEMY_INSTALLED = True
from distutils.version import LooseVersion
ver = sqlalchemy.__version__
# For sqlalchemy versions < 0.8.2, the BIGINT type is recognized
# for a sqlite engine, which results in a warning when trying to
# read/write a DataFrame with int64 values. (GH7433)
if LooseVersion(ver) < LooseVersion('0.8.2'):
from sqlalchemy import BigInteger
from sqlalchemy.ext.compiler import compiles
@compiles(BigInteger, 'sqlite')
def compile_big_int_sqlite(type_, compiler, **kw):
return 'INTEGER'
except ImportError:
_SQLALCHEMY_INSTALLED = False
if _SQLALCHEMY_INSTALLED:
import sqlalchemy
return isinstance(con, sqlalchemy.engine.Connectable)
else:
return False
def _convert_params(sql, params):
"""Convert SQL and params args to DBAPI2.0 compliant format."""
args = [sql]
if params is not None:
if hasattr(params, 'keys'): # test if params is a mapping
args += [params]
else:
args += [list(params)]
return args
def _handle_date_column(col, utc=None, format=None):
if isinstance(format, dict):
return to_datetime(col, errors='ignore', **format)
else:
# Allow passing of formatting string for integers
# GH17855
if format is None and (issubclass(col.dtype.type, np.floating) or
issubclass(col.dtype.type, np.integer)):
format = 's'
if format in ['D', 'd', 'h', 'm', 's', 'ms', 'us', 'ns']:
return to_datetime(col, errors='coerce', unit=format, utc=utc)
elif is_datetime64tz_dtype(col):
# coerce to UTC timezone
# GH11216
return (to_datetime(col, errors='coerce')
.astype('datetime64[ns, UTC]'))
else:
return to_datetime(col, errors='coerce', format=format, utc=utc)
def _parse_date_columns(data_frame, parse_dates):
"""
Force non-datetime columns to be read as such.
Supports both string formatted and integer timestamp columns.
"""
# handle non-list entries for parse_dates gracefully
if parse_dates is True or parse_dates is None or parse_dates is False:
parse_dates = []
if not hasattr(parse_dates, '__iter__'):
parse_dates = [parse_dates]
for col_name in parse_dates:
df_col = data_frame[col_name]
try:
fmt = parse_dates[col_name]
except TypeError:
fmt = None
data_frame[col_name] = _handle_date_column(df_col, format=fmt)
# we want to coerce datetime64_tz dtypes for now
# we could in theory do a 'nice' conversion from a FixedOffset tz
# GH11216
for col_name, df_col in data_frame.iteritems():
if is_datetime64tz_dtype(df_col):
data_frame[col_name] = _handle_date_column(df_col)
return data_frame
def _wrap_result(data, columns, index_col=None, coerce_float=True,
parse_dates=None):
"""Wrap result set of query in a DataFrame."""
frame = DataFrame.from_records(data, columns=columns,
coerce_float=coerce_float)
_parse_date_columns(frame, parse_dates)
if index_col is not None:
frame.set_index(index_col, inplace=True)
return frame
def execute(sql, con, cur=None, params=None):
"""
Execute the given SQL query using the provided connection object.
Parameters
----------
sql : string
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
Using SQLAlchemy makes it possible to use any DB supported by the
library.
If a DBAPI2 object, only sqlite3 is supported.
cur : deprecated, cursor is obtained from connection, default: None
params : list or tuple, optional, default: None
List of parameters to pass to execute method.
Returns
-------
Results Iterable
"""
if cur is None:
pandas_sql = pandasSQL_builder(con)
else:
pandas_sql = pandasSQL_builder(cur, is_cursor=True)
args = _convert_params(sql, params)
return pandas_sql.execute(*args)
# -----------------------------------------------------------------------------
# -- Read and write to DataFrames
def read_sql_table(table_name, con, schema=None, index_col=None,
coerce_float=True, parse_dates=None, columns=None,
chunksize=None):
"""Read SQL database table into a DataFrame.
Given a table name and a SQLAlchemy connectable, returns a DataFrame.
This function does not support DBAPI connections.
Parameters
----------
table_name : string
Name of SQL table in database.
con : SQLAlchemy connectable (or database string URI)
SQLite DBAPI connection mode not supported.
schema : string, default None
Name of SQL schema in database to query (if database flavor
supports this). Uses default schema if None (default).
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex).
coerce_float : boolean, 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
Notes
-----
Any datetime values with time zone information will be converted to UTC.
See also
--------
read_sql_query : Read SQL query into a DataFrame.
read_sql
"""
con = _engine_builder(con)
if not _is_sqlalchemy_connectable(con):
raise NotImplementedError("read_sql_table only supported for "
"SQLAlchemy connectable.")
import sqlalchemy
from sqlalchemy.schema import MetaData
meta = MetaData(con, schema=schema)
try:
meta.reflect(only=[table_name], views=True)
except sqlalchemy.exc.InvalidRequestError:
raise ValueError("Table %s not found" % table_name)
pandas_sql = SQLDatabase(con, meta=meta)
table = pandas_sql.read_table(
table_name, index_col=index_col, coerce_float=coerce_float,
parse_dates=parse_dates, columns=columns, chunksize=chunksize)
if table is not None:
return table
else:
raise ValueError("Table %s not found" % table_name, con)
def read_sql_query(sql, con, index_col=None, coerce_float=True, params=None,
parse_dates=None, chunksize=None):
"""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 : string SQL query or SQLAlchemy Selectable (select or text object)
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection), 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.
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex).
coerce_float : boolean, 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
Notes
-----
Any datetime values with time zone information parsed via the `parse_dates`
parameter will be converted to UTC.
See also
--------
read_sql_table : Read SQL database table into a DataFrame.
read_sql
"""
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)
def read_sql(sql, con, index_col=None, coerce_float=True, params=None,
parse_dates=None, columns=None, chunksize=None):
"""
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 : string or SQLAlchemy Selectable (select or text object)
SQL query to be executed or a table name.
con : SQLAlchemy connectable (engine/connection) or database string URI
or DBAPI2 connection (fallback mode)
Using SQLAlchemy makes it possible to use any DB supported by that
library. If a DBAPI2 object, only sqlite3 is supported.
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex).
coerce_float : boolean, 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
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:
_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):
"""
Write records stored in a DataFrame to a SQL database.
Parameters
----------
frame : DataFrame, Series
name : string
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 : string, default None
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 : 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.
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 SQLtype or 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.
If all columns are of the same type, one single value can be used.
"""
if if_exists not in ('fail', 'replace', 'append'):
raise ValueError("'{0}' is not valid for if_exists".format(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)
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, string_types):
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, string_types):
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("Could not init table '%s'" % 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
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("Table '%s' already exists." % self.name)
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(
"'{0}' is not valid for if_exists".format(self.if_exists))
else:
self._execute_create()
def insert_statement(self):
return self.table.insert()
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(
"duplicate name in index/columns: {0}".format(err))
else:
temp = self.frame
column_names = list(map(text_type, temp.columns))
ncols = len(column_names)
data_list = [None] * ncols
blocks = temp._data.blocks
for i in range(len(blocks)):
b = blocks[i]
if b.is_datetime:
# convert to microsecond resolution so this yields
# datetime.datetime
d = b.values.astype('M8[us]').astype(object)
else:
d = np.array(b.get_values(), dtype=object)
# replace NaN with None
if b._can_hold_na:
mask = isna(d)
d[mask] = None
for col_loc, col in zip(b.mgr_locs, d):
data_list[col_loc] = col
return column_names, data_list
def _execute_insert(self, conn, keys, data_iter):
data = [{k: v for k, v in zip(keys, row)} for row in data_iter]
conn.execute(self.insert_statement(), data)
def insert(self, chunksize=None):
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])
self._execute_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:
[cols.insert(0, self.table.c[idx]) for idx in self.index[::-1]]
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 "
"levels, which is {0}".format(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 "level_{0}".format(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, string_types):
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((text_type(idx_label),
idx_type, True))
column_names_and_types += [
(text_type(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 Table, Column, PrimaryKeyConstraint
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.
"""
# handle non-list entries for parse_dates gracefully
if parse_dates is True or parse_dates is None or parse_dates is False:
parse_dates = []
if not hasattr(parse_dates, '__iter__'):
parse_dates = [parse_dates]
for sql_col in self.table.columns:
col_name = sql_col.name
try:
df_col = self.frame[col_name]
# 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)
# Handle date parsing
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)
except KeyError:
pass # this column not in results
def _get_notna_col_dtype(self, col):
"""
Infer datatype of the Series col. In case the dtype of col is 'object'
and it contains NA values, this infers the datatype of the not-NA
values. Needed for inserting typed data containing NULLs, GH8778.
"""
col_for_inference = col
if col.dtype == 'object':
notnadata = col[~isna(col)]
if len(notnadata):
col_for_inference = notnadata
return lib.infer_dtype(col_for_inference)
def _sqlalchemy_type(self, col):
dtype = self.dtype or {}
if col.name in dtype:
return self.dtype[col.name]
col_type = self._get_notna_col_dtype(col)
from sqlalchemy.types import (BigInteger, Integer, Float,
Text, Boolean,
DateTime, Date, Time)
if col_type == 'datetime64' or col_type == 'datetime':
try:
tz = col.tzinfo # noqa
return DateTime(timezone=True)
except:
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 (Integer, Float, Boolean, DateTime,
Date, TIMESTAMP)
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.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):
"""
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.
"""
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 to_instance, TypeEngine
for col, my_type in dtype.items():
if not isinstance(to_instance(my_type), TypeEngine):
raise ValueError('The type of %s is not a SQLAlchemy '
'type ' % col)
table = SQLTable(name, self, frame=frame, index=index,
if_exists=if_exists, index_label=index_label,
schema=schema, dtype=dtype)
table.create()
table.insert(chunksize)
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:
table_names = engine.table_names(
schema=schema or self.meta.schema,
connection=conn,
)
if name not in table_names:
msg = (
"The provided table name '{0}' 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."
).format(name)
warnings.warn(msg, UserWarning)
@property
def tables(self):
return self.meta.tables
def has_table(self, name, schema=None):
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, table_name, keys=None, dtype=None):
table = SQLTable(table_name, self, frame=frame, index=False, keys=keys,
dtype=dtype)
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 = text_type(name).encode("utf-8", "strict").decode("utf-8")
except UnicodeError:
raise ValueError("Cannot convert identifier to UTF-8: '%s'" % name)
return uname
def _get_valid_sqlite_name(name):
# See http://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(SQLiteTable, self).__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):
names = list(map(text_type, self.frame.columns))
wld = '?' # wildcard char
escape = _get_valid_sqlite_name
if self.index is not None:
[names.insert(0, idx) for idx in self.index[::-1]]
bracketed_names = [escape(column) for column in names]
col_names = ','.join(bracketed_names)
wildcards = ','.join([wld] * len(names))
insert_statement = 'INSERT INTO %s (%s) VALUES (%s)' % (
escape(self.name), col_names, wildcards)
return insert_statement
def _execute_insert(self, conn, keys, data_iter):
data_list = list(data_iter)
conn.executemany(self.insert_statement(), data_list)
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(
"CONSTRAINT {tbl}_pk PRIMARY KEY ({cnames_br})".format(
tbl=self.name, cnames_br=cnames_br))
create_stmts = ["CREATE TABLE " + 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]
col_type = self._get_notna_col_dtype(col)
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:
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:
if kwargs:
cur.execute(*args, **kwargs)
else:
cur.execute(*args)
return cur
except Exception as exc:
try:
self.con.rollback()
except Exception: # pragma: no cover
ex = DatabaseError("Execution failed on sql: %s\n%s\nunable"
" to rollback" % (args[0], exc))
raise_with_traceback(ex)
ex = DatabaseError(
"Execution failed on sql '%s': %s" % (args[0], exc))
raise_with_traceback(ex)
@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):
"""
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.
"""
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('%s (%s) not a string' % (
col, str(my_type)))
table = SQLiteTable(name, self, frame=frame, index=index,
if_exists=if_exists, index_label=index_label,
dtype=dtype)
table.create()
table.insert(chunksize)
def has_table(self, name, schema=None):
# TODO(wesm): unused?
# escape = _get_valid_sqlite_name
# esc_name = escape(name)
wld = '?'
query = ("SELECT name FROM sqlite_master "
"WHERE type='table' AND name=%s;") % 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 = "DROP TABLE %s" % _get_valid_sqlite_name(name)
self.execute(drop_sql)
def _create_sql_schema(self, frame, table_name, keys=None, dtype=None):
table = SQLiteTable(table_name, self, frame=frame, index=False,
keys=keys, dtype=dtype)
return str(table.sql_schema())
def get_schema(frame, name, keys=None, con=None, dtype=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.
"""
pandas_sql = pandasSQL_builder(con=con)
return pandas_sql._create_sql_schema(frame, name, keys=keys, dtype=dtype)
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