def get_column_types(self):
if getattr(self.args, 'blanks', None):
type_kwargs = {'null_values': ()}
else:
type_kwargs = {}
text_type = agate.Text(**type_kwargs)
if self.args.no_inference:
types = [text_type]
else:
number_type = agate.Number(locale=self.args.locale, **type_kwargs)
# See the order in the `agate.TypeTester` class.
types = [
agate.Boolean(**type_kwargs),
agate.TimeDelta(**type_kwargs),
agate.Date(date_format=self.args.date_format, **type_kwargs),
agate.DateTime(datetime_format=self.args.datetime_format,
**type_kwargs),
text_type,
]
# In order to parse dates like "20010101".
if self.args.date_format or self.args.datetime_format:
types.insert(-1, number_type)
else:
types.insert(1, number_type)
return agate.TypeTester(types=types)
def from_sql(cls, connection_or_string, table_name):
"""
Create a new :class:`agate.Table` from a given SQL table. Types will be
inferred from the database schema.
Monkey patched as class method :meth:`Table.from_sql`.
:param connection_or_string:
An existing sqlalchemy connection or connection string.
:param table_name:
The name of a table in the referenced database.
"""
engine, connection = get_engine_and_connection(connection_or_string)
metadata = MetaData(connection)
sql_table = Table(table_name,
metadata,
autoload=True,
autoload_with=connection)
column_names = []
column_types = []
for sql_column in sql_table.columns:
column_names.append(sql_column.name)
if type(sql_column.type) in INTERVAL_MAP.values():
py_type = datetime.timedelta
else:
py_type = sql_column.type.python_type
if py_type in [int, float, decimal.Decimal]:
if py_type is float:
sql_column.type.asdecimal = True
column_types.append(agate.Number())
elif py_type is bool:
column_types.append(agate.Boolean())
elif issubclass(py_type, six.string_types):
column_types.append(agate.Text())
elif py_type is datetime.date:
column_types.append(agate.Date())
elif py_type is datetime.datetime:
column_types.append(agate.DateTime())
elif py_type is datetime.timedelta:
column_types.append(agate.TimeDelta())
else:
raise ValueError('Unsupported sqlalchemy column type: %s' %
type(sql_column.type))
s = select([sql_table])
rows = connection.execute(s)
try:
return agate.Table(rows, column_names, column_types)
finally:
if engine is not None:
connection.close()
engine.dispose()
def _get_tester_for(self, column_type):
from dbt.clients import agate_helper
if column_type is agate.TimeDelta: # dbt never makes this!
return agate.TimeDelta()
for instance in agate_helper.DEFAULT_TYPE_TESTER._possible_types:
if type(instance) is column_type:
return instance
raise ValueError(f'no tester for {column_type}')
def get_column_types(self):
if getattr(self.args, 'blanks', None):
text_type = agate.Text(cast_nulls=False)
else:
text_type = agate.Text()
if self.args.no_inference:
return agate.TypeTester(types=[text_type])
else:
return agate.TypeTester(types=[
agate.Boolean(),
agate.Number(locale=self.args.locale),
agate.TimeDelta(),
agate.Date(date_format=self.args.date_format),
agate.DateTime(datetime_format=self.args.datetime_format),
text_type
])
def get_column_types(self):
if getattr(self.args, 'blanks', None):
type_kwargs = {'null_values': ()}
else:
type_kwargs = {}
types = [agate.Text(**type_kwargs)]
if not self.args.no_inference:
types = [
agate.Boolean(**type_kwargs),
agate.Number(locale=self.args.locale, **type_kwargs),
agate.TimeDelta(**type_kwargs),
agate.Date(date_format=self.args.date_format, **type_kwargs),
agate.DateTime(datetime_format=self.args.datetime_format, **type_kwargs),
] + types
return agate.TypeTester(types=types)
def get_column_types(self):
if getattr(self.args, 'blanks', None):
type_kwargs = {'null_values': ()}
else:
type_kwargs = {}
types = [agate.Text(**type_kwargs)]
if not self.args.no_inference:
types = [
agate.Boolean(**type_kwargs),
agate.TimeDelta(**type_kwargs),
agate.Date(date_format=self.args.date_format, **type_kwargs),
agate.DateTime(datetime_format=self.args.datetime_format,
**type_kwargs),
# This is a different order than agate's default, in order to parse dates like "20010101".
agate.Number(locale=self.args.locale, **type_kwargs),
] + types
return agate.TypeTester(types=types)
def setUp(self):
self.rows = ((1, 'a', True, '11/4/2015', '11/4/2015 12:22 PM', '4:15'),
(2, u'👍', False, '11/5/2015', '11/4/2015 12:45 PM',
'6:18'), (None, 'b', None, None, None, None))
self.column_names = [
'number', 'text', 'boolean', 'date', 'datetime', 'timedelta'
]
self.column_types = [
agate.Number(),
agate.Text(),
agate.Boolean(),
agate.Date(),
agate.DateTime(),
agate.TimeDelta()
]
self.table = agate.Table(self.rows, self.column_names,
self.column_types)
def setUp(self):
self.rows = (('1', 'a', 'True', '10/01/2015', '10/01/2015 12:30 PM',
'4h45m'), ('2', 'b', 'False', '11/01/2015',
'11/01/2015 12:45 PM', '3h25m'), ('', '', '',
'', '', ''))
self.number_type = agate.Number()
self.text_type = agate.Text()
self.boolean_type = agate.Boolean()
self.date_type = agate.Date()
self.datetime_type = agate.DateTime()
self.timedelta_type = agate.TimeDelta()
self.column_names = ('number', 'text', 'boolean', 'date', 'datetime',
'timedelta')
self.column_types = (self.number_type, self.text_type,
self.boolean_type, self.date_type,
self.datetime_type, self.timedelta_type)
self.table = agate.Table(self.rows,
zip(self.column_names, self.column_types))
def from_xls(cls,
path,
sheet=None,
skip_lines=0,
header=True,
encoding_override=None,
**kwargs):
"""
Parse an XLS file.
:param path:
Path to an XLS file to load or a file-like object for one.
:param sheet:
The names or integer indices of the worksheets to load. If not specified
then the first sheet will be used.
:param skip_lines:
The number of rows to skip from the top of the sheet.
:param header:
If :code:`True`, the first row is assumed to contain column names.
"""
if not isinstance(skip_lines, int):
raise ValueError('skip_lines argument must be an int')
if hasattr(path, 'read'):
book = xlrd.open_workbook(file_contents=path.read(),
encoding_override=encoding_override)
else:
with open(path, 'rb') as f:
book = xlrd.open_workbook(file_contents=f.read(),
encoding_override=encoding_override)
multiple = agate.utils.issequence(sheet)
if multiple:
sheets = sheet
else:
sheets = [sheet]
tables = OrderedDict()
for i, sheet in enumerate(sheets):
if isinstance(sheet, six.string_types):
sheet = book.sheet_by_name(sheet)
elif isinstance(sheet, int):
sheet = book.sheet_by_index(sheet)
else:
sheet = book.sheet_by_index(0)
if header:
offset = 1
column_names = []
else:
offset = 0
column_names = None
columns = []
column_types = []
for i in range(sheet.ncols):
data = sheet.col_values(i)
values = data[skip_lines + offset:]
types = sheet.col_types(i)[skip_lines + offset:]
excel_type = determine_excel_type(types)
agate_type = determine_agate_type(excel_type)
if excel_type == xlrd.biffh.XL_CELL_BOOLEAN:
values = normalize_booleans(values)
elif excel_type == xlrd.biffh.XL_CELL_DATE:
values, with_date, with_time = normalize_dates(
values, book.datemode)
if not with_date:
agate_type = agate.TimeDelta()
if not with_time:
agate_type = agate.Date()
if header:
name = six.text_type(data[skip_lines]) or None
column_names.append(name)
columns.append(values)
column_types.append(agate_type)
rows = []
if columns:
for i in range(len(columns[0])):
rows.append([c[i] for c in columns])
if 'column_names' in kwargs:
if not header:
column_names = kwargs['column_names']
del kwargs['column_names']
if 'column_types' in kwargs:
column_types = kwargs['column_types']
del kwargs['column_types']
tables[sheet.name] = agate.Table(rows, column_names, column_types,
**kwargs)
if multiple:
return agate.MappedSequence(tables.values(), tables.keys())
else:
return tables.popitem()[1]
def from_xls(cls,
path,
sheet=None,
skip_lines=0,
header=True,
encoding_override=None,
row_limit=None,
column_names=None,
column_types=None,
**kwargs):
"""
Parse an XLS file.
:param path:
Path to an XLS file to load or a file-like object for one.
:param sheet:
The names or integer indices of the worksheets to load. If not specified
then the first sheet will be used.
:param skip_lines:
The number of rows to skip from the top of the sheet.
:param header:
If :code:`True`, the first row is assumed to contain column names.
:param row_limit:
Limit how many rows of data will be read
:param column_names:
See :meth:`.Table.__init__`.
:param column_types:
See :meth:`.Table.__init__`.
"""
if not isinstance(skip_lines, int):
raise ValueError('skip_lines argument must be an int')
def open_workbook(f):
try:
book = xlrd.open_workbook(file_contents=f.read(),
encoding_override=encoding_override,
on_demand=True)
except xlrd.compdoc.CompDocError:
# This is not a pure XLS file; we'll try to read it though.
# Let's try the Compound File Binary Format:
ole = olefile.OleFileIO(f)
if ole.exists('Workbook'):
d = ole.openstream('Workbook')
book = xlrd.open_workbook(file_contents=d.read(),
on_demand=True)
else:
raise IOError('No Workbook stream found in OLE file')
return book
if hasattr(path, 'read'):
book = open_workbook(path)
else:
with open(path, 'rb') as f:
book = open_workbook(f)
try:
multiple = agate.utils.issequence(sheet)
if multiple:
sheets = sheet
else:
sheets = [sheet]
tables = OrderedDict()
for i, sheet in enumerate(sheets):
if isinstance(sheet, six.string_types):
sheet = book.sheet_by_name(sheet)
elif isinstance(sheet, int):
sheet = book.sheet_by_index(sheet)
else:
sheet = book.sheet_by_index(0)
if header:
offset = 1
column_names_detected = []
else:
offset = 0
column_names_detected = None
columns = []
column_types_detected = []
for i in range(sheet.ncols):
if row_limit is None:
values = sheet.col_values(i, skip_lines + offset)
types = sheet.col_types(i, skip_lines + offset)
else:
values = sheet.col_values(i, skip_lines + offset,
skip_lines + offset + row_limit)
types = sheet.col_types(i, skip_lines + offset,
skip_lines + offset + row_limit)
excel_type = determine_excel_type(types)
agate_type = determine_agate_type(excel_type)
if excel_type == xlrd.biffh.XL_CELL_BOOLEAN:
values = normalize_booleans(values)
elif excel_type == xlrd.biffh.XL_CELL_DATE:
values, with_date, with_time = normalize_dates(
values, book.datemode)
if not with_date:
agate_type = agate.TimeDelta()
if not with_time:
agate_type = agate.Date()
if header:
name = six.text_type(sheet.cell_value(skip_lines,
i)) or None
column_names_detected.append(name)
columns.append(values)
column_types_detected.append(agate_type)
rows = []
if columns:
for i in range(len(columns[0])):
rows.append([c[i] for c in columns])
if column_names is None:
sheet_column_names = column_names_detected
else:
sheet_column_names = column_names
sheet_column_types = column_types
if isinstance(column_types,
dict) and sheet_column_names is not None:
sheet_column_types = dict(
zip(sheet_column_names, column_types_detected))
sheet_column_types.update(column_types)
tables[sheet.name] = agate.Table(rows, sheet_column_names,
sheet_column_types, **kwargs)
finally:
book.release_resources()
if multiple:
return agate.MappedSequence(tables.values(), tables.keys())
else:
return tables.popitem()[1]
