def from_csv(cls, dir_path, column_info, header=True, **kwargs):
"""
Create a new :class:`TableSet` from a directory of CSVs. This method
will use csvkit if it is available, otherwise it will use Python's
builtin csv module.
``kwargs`` will be passed through to :meth:`csv.reader`.
If you are using Python 2 and not using csvkit, this method is not
unicode-safe.
:param dir_path: Path to a directory full of CSV files. All CSV files
in this directory will be loaded.
:param column_info: See :class:`.Table` constructor.
:param header: If `True`, the first row of the CSV is assumed to contains
headers and will be skipped.
"""
from agate.table import Table
if not os.path.isdir(dir_path):
raise IOError(
'Specified path doesn\'t exist or isn\'t a directory.')
tables = OrderedDict()
for path in glob(os.path.join(dir_path, '*.csv')):
name = os.path.split(path)[1].strip('.csv')
table = Table.from_csv(path, column_info, header=header, **kwargs)
tables[name] = table
return TableSet(tables)
def from_csv(cls, dir_path, column_names=None, column_types=None, row_names=None, header=True, **kwargs):
"""
Create a new :class:`TableSet` from a directory of CSVs.
See :meth:`.Table.from_csv` for additional details.
:param dir_path:
Path to a directory full of CSV files. All CSV files in this
directory will be loaded.
:param column_names:
See :meth:`Table.__init__`.
:param column_types:
See :meth:`Table.__init__`.
:param row_names:
See :meth:`Table.__init__`.
:param header:
See :meth:`Table.from_csv`.
"""
if not os.path.isdir(dir_path):
raise IOError('Specified path doesn\'t exist or isn\'t a directory.')
tables = OrderedDict()
for path in glob(os.path.join(dir_path, '*.csv')):
name = os.path.split(path)[1].strip('.csv')
tables[name] = Table.from_csv(path, column_names, column_types, row_names=row_names, header=header, **kwargs)
return TableSet(tables.values(), tables.keys())
def from_csv(cls, dir_path, column_names=None, column_types=None, row_names=None, header=True, **kwargs):
"""
Create a new :class:`TableSet` from a directory of CSVs.
See :meth:`.Table.from_csv` for additional details.
:param dir_path:
Path to a directory full of CSV files. All CSV files in this
directory will be loaded.
:param column_names:
See :meth:`Table.__init__`.
:param column_types:
See :meth:`Table.__init__`.
:param row_names:
See :meth:`Table.__init__`.
:param header:
See :meth:`Table.from_csv`.
"""
if not os.path.isdir(dir_path):
raise IOError('Specified path doesn\'t exist or isn\'t a directory.')
tables = OrderedDict()
for path in glob(os.path.join(dir_path, '*.csv')):
name = os.path.split(path)[1].strip('.csv')
tables[name] = Table.from_csv(path, column_names, column_types, row_names=row_names, header=header, **kwargs)
return TableSet(tables.values(), tables.keys())
def test_table_from_csv(self):
import csvkit
from agate import table
table.csv = csvkit
if six.PY2:
table = Table.from_csv('examples/test.csv', self.tester, encoding='utf8')
else:
table = Table.from_csv('examples/test.csv', self.tester)
self.assertSequenceEqual(table.get_column_names(), ['one', 'two', 'three'])
self.assertSequenceEqual(tuple(map(type, table.get_column_types())), [Number, Number, Text])
self.assertEqual(len(table.columns), 3)
self.assertSequenceEqual(table.rows[0], [1, 4, 'a'])
self.assertSequenceEqual(table.rows[1], [2, 3, 'b'])
self.assertSequenceEqual(table.rows[2], [None, 2, u'👍'])
def from_csv(cls,
dir_path,
column_info,
row_names=None,
header=True,
**kwargs):
"""
Create a new :class:`TableSet` from a directory of CSVs. This method
will use csvkit if it is available, otherwise it will use Python's
builtin csv module.
``kwargs`` will be passed through to :meth:`csv.reader`.
If you are using Python 2 and not using csvkit, this method is not
unicode-safe.
:param dir_path: Path to a directory full of CSV files. All CSV files
in this directory will be loaded.
:param column_info: A sequence of pairs of column names and types. The latter
must be instances of :class:`.DataType`. Or, an instance of
:class:`.TypeTester` to infer types.
:param row_names: See :meth:`Table.__init__`.
:param header: If `True`, the first row of the CSV is assumed to contains
headers and will be skipped.
"""
use_inference = isinstance(column_info, TypeTester)
if use_inference and not header:
raise ValueError(
'Can not apply TypeTester to a CSV without headers.')
if not os.path.isdir(dir_path):
raise IOError(
'Specified path doesn\'t exist or isn\'t a directory.')
tables = OrderedDict()
if use_inference:
has_inferred_columns = False
for path in glob(os.path.join(dir_path, '*.csv')):
name = os.path.split(path)[1].strip('.csv')
table = Table.from_csv(path,
column_info,
row_names=row_names,
header=header,
**kwargs)
if use_inference and not has_inferred_columns:
column_info = tuple(zip(table.column_names,
table.column_types))
has_inferred_columns = True
tables[name] = table
return TableSet(tables.values(), tables.keys())
def test_table_from_csv(self):
table = Table.from_csv('examples/test.csv', self.tester)
self.assertSequenceEqual(table.column_names, ['one', 'two', 'three'])
self.assertSequenceEqual(tuple(map(type, table.column_types)), [Number, Number, Text])
self.assertEqual(len(table.columns), 3)
self.assertSequenceEqual(table.rows[0], [1, 4, 'a'])
self.assertSequenceEqual(table.rows[1], [2, 3, 'b'])
self.assertSequenceEqual(table.rows[2], [None, 2, u'👍'])
def test_table_from_csv(self):
table = Table.from_csv('examples/test.csv', column_types=self.tester)
self.assertSequenceEqual(table.column_names, ['one', 'two', 'three'])
self.assertSequenceEqual(tuple(map(type, table.column_types)), [Number, Number, Text])
self.assertEqual(len(table.columns), 3)
self.assertSequenceEqual(table.rows[0], [1, 4, 'a'])
self.assertSequenceEqual(table.rows[1], [2, 3, 'b'])
self.assertSequenceEqual(table.rows[2], [None, 2, u'👍'])
def test_table_from_csv(self):
import csvkit
from agate import table
table.csv = csvkit
if six.PY2:
table = Table.from_csv('examples/test.csv',
self.tester,
encoding='utf8')
else:
table = Table.from_csv('examples/test.csv', self.tester)
self.assertSequenceEqual(table.column_names, ['one', 'two', 'three'])
self.assertSequenceEqual(tuple(map(type, table.column_types)),
[Number, Number, Text])
self.assertEqual(len(table.columns), 3)
self.assertSequenceEqual(table.rows[0], [1, 4, 'a'])
self.assertSequenceEqual(table.rows[1], [2, 3, 'b'])
self.assertSequenceEqual(table.rows[2], [None, 2, u'👍'])
def from_csv(cls, dir_path, column_info, header=True, **kwargs):
"""
Create a new :class:`TableSet` from a directory of CSVs. This method
will use csvkit if it is available, otherwise it will use Python's
builtin csv module.
``kwargs`` will be passed through to :meth:`csv.reader`.
If you are using Python 2 and not using csvkit, this method is not
unicode-safe.
:param dir_path: Path to a directory full of CSV files. All CSV files
in this directory will be loaded.
:param column_info: A sequence of pairs of column names and types. The latter
must be instances of :class:`.DataType`. Or, an instance of
:class:`.TypeTester` to infer types.
:param header: If `True`, the first row of the CSV is assumed to contains
headers and will be skipped.
"""
from agate.table import Table
use_inference = isinstance(column_info, TypeTester)
if use_inference and not header:
raise ValueError('Can not apply TypeTester to a CSV without headers.')
if not os.path.isdir(dir_path):
raise IOError('Specified path doesn\'t exist or isn\'t a directory.')
tables = OrderedDict()
if use_inference:
has_inferred_columns = False
for path in glob(os.path.join(dir_path, '*.csv')):
name = os.path.split(path)[1].strip('.csv')
table = Table.from_csv(path, column_info, header=header, **kwargs)
if use_inference and not has_inferred_columns:
column_info = tuple(zip(table.get_column_names(), table.get_column_types()))
has_inferred_columns = True
tables[name] = table
return TableSet(tables)
def from_csv(cls, dir_path, column_info, row_names=None, header=True, **kwargs):
"""
Create a new :class:`TableSet` from a directory of CSVs.
See :meth:`.Table.from_csv` for additional details.
:param dir_path:
Path to a directory full of CSV files. All CSV files in this
directory will be loaded.
:param column_info:
A sequence of pairs of column names and types. The latter must be
instances of :class:`.DataType`. Or, an instance of
:class:`.TypeTester` to infer types.
:param row_names:
See :meth:`Table.__init__`.
:param header:
If `True`, the first row of the CSV is assumed to contains headers
and will be skipped.
"""
use_inference = isinstance(column_info, TypeTester)
if use_inference and not header:
raise ValueError('Can not apply TypeTester to a CSV without headers.')
if not os.path.isdir(dir_path):
raise IOError('Specified path doesn\'t exist or isn\'t a directory.')
tables = OrderedDict()
if use_inference:
has_inferred_columns = False
for path in glob(os.path.join(dir_path, '*.csv')):
name = os.path.split(path)[1].strip('.csv')
table = Table.from_csv(path, column_info, row_names=row_names, header=header, **kwargs)
if use_inference and not has_inferred_columns:
column_info = tuple(zip(table.column_names, table.column_types))
has_inferred_columns = True
tables[name] = table
return TableSet(tables.values(), tables.keys())
