Python sqlalchemy.types方法代碼示例

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
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 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
def __init__(self, table):
        super(Model, self).__init__()
        self.table = table
        self.schema = table.schema

        # Adapt column types to the most reasonable generic types (ie. VARCHAR -> String)
        for column in table.columns:
            try:
                column.type = self._get_adapted_type(column.type, column.table.bind)
            except:
                # print('Failed to get col type for {}, {}'.format(column, column.type))
                print("#Failed to get col type for {}".format(column)) 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
def render_column_type(cls, coltype):
        args = []
        if isinstance(coltype, Enum):
            args.extend(repr(arg) for arg in coltype.enums)
            if coltype.name is not None:
                args.append("name={0!r}".format(coltype.name))
        else:
            # All other types
            argspec = cls._getargspec_init(coltype.__class__.__init__)
            defaults = dict(zip(argspec.args[-len(argspec.defaults or ()) :], argspec.defaults or ()))
            missing = object()
            use_kwargs = False
            for attr in argspec.args[1:]:
                # Remove annoyances like _warn_on_bytestring
                if attr.startswith("_"):
                    continue

                value = getattr(coltype, attr, missing)
                default = defaults.get(attr, missing)
                if value is missing or value == default:
                    use_kwargs = True
                elif use_kwargs:
                    args.append("{0}={1}".format(attr, repr(value)))
                else:
                    args.append(repr(value))

        rendered = coltype.__class__.__name__
        if args:
            rendered += "({0})".format(", ".join(args))

        return rendered 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
def compare_type(self, ctxt, insp_col, meta_col, insp_type, meta_type):
        """Return True if types are different, False if not.

        Return None to allow the default implementation to compare these types.

        :param ctxt: alembic MigrationContext instance
        :param insp_col: reflected column
        :param meta_col: column from model
        :param insp_type: reflected column type
        :param meta_type: column type from model

        """

        # some backends (e.g. mysql) don't provide native boolean type
        BOOLEAN_METADATA = (types.BOOLEAN, types.Boolean)
        BOOLEAN_SQL = BOOLEAN_METADATA + (types.INTEGER, types.Integer)

        if issubclass(type(meta_type), BOOLEAN_METADATA):
            return not issubclass(type(insp_type), BOOLEAN_SQL)

        # Alembic <=0.8.4 do not contain logic of comparing Variant type with
        # others.
        if isinstance(meta_type, types.Variant):
            orig_type = meta_col.type
            impl_type = meta_type.load_dialect_impl(ctxt.dialect)
            meta_col.type = impl_type
            try:
                return self.compare_type(ctxt, insp_col, meta_col, insp_type,
                                         impl_type)
            finally:
                meta_col.type = orig_type

        return ctxt.impl.compare_type(insp_col, meta_col) 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
def _new_type(self):
            return getattr(sqlalchemy.types, self.new_type) 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
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 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
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 (BigInteger, Integer, Float,
                                      Text, Boolean,
                                      DateTime, Date, Time, TIMESTAMP)

        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
                if col.tz 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 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
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 

# 需要導入模塊: import sqlalchemy [as 別名]
# 或者: from sqlalchemy import types [as 別名]
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