def test_to_string_with_formatters(self):
df = DataFrame({'int': [1, 2, 3],
'float': [1.0, 2.0, 3.0],
'object': [(1,2), True, False]},
columns=['int', 'float', 'object'])
formatters = [('int', lambda x: '0x%x' % x),
('float', lambda x: '[% 4.1f]' % x),
('object', lambda x: '-%s-' % str(x))]
result = df.to_string(formatters=dict(formatters))
result2 = df.to_string(formatters=lzip(*formatters)[1])
self.assertEqual(result, (' int float object\n'
'0 0x1 [ 1.0] -(1, 2)-\n'
'1 0x2 [ 2.0] -True-\n'
'2 0x3 [ 3.0] -False-'))
self.assertEqual(result, result2)
def test_to_string_with_formatters(self):
df = DataFrame(
{"int": [1, 2, 3], "float": [1.0, 2.0, 3.0], "object": [(1, 2), True, False]},
columns=["int", "float", "object"],
)
formatters = [
("int", lambda x: "0x%x" % x),
("float", lambda x: "[% 4.1f]" % x),
("object", lambda x: "-%s-" % str(x)),
]
result = df.to_string(formatters=dict(formatters))
result2 = df.to_string(formatters=lzip(*formatters)[1])
self.assertEqual(
result,
(" int float object\n" "0 0x1 [ 1.0] -(1, 2)-\n" "1 0x2 [ 2.0] -True-\n" "2 0x3 [ 3.0] -False-"),
)
self.assertEqual(result, result2)
def test_to_string_with_formatters(self):
df = DataFrame(
{
'int': [1, 2, 3],
'float': [1.0, 2.0, 3.0],
'object': [(1, 2), True, False]
},
columns=['int', 'float', 'object'])
formatters = [('int', lambda x: '0x%x' % x),
('float', lambda x: '[% 4.1f]' % x),
('object', lambda x: '-%s-' % str(x))]
result = df.to_string(formatters=dict(formatters))
result2 = df.to_string(formatters=lzip(*formatters)[1])
self.assertEqual(result, (' int float object\n'
'0 0x1 [ 1.0] -(1, 2)-\n'
'1 0x2 [ 2.0] -True-\n'
'2 0x3 [ 3.0] -False-'))
self.assertEqual(result, result2)
def _get_schema(self, data, tblname, format_file=None, keys=None):
"""
Return a CREATE TABLE statement to suit the contents of a DataFrame.
Extending existing pandas sql api to add support for MSSQL
"""
column_types = []
if self.dbFlavor == 'mssql' or self.dbFlavor == 'postgres':
if format_file != None:
column_types = self.parse_format_file(data, format_file)
else:
lookup_type = lambda dtype: self._get_type(dtype.type)
# Replace spaces in DataFrame column names with _.
if self.dbFlavor == 'mssql':
safe_columns = [
s.replace(' ', '_').replace('.', '_').replace(
'\n', '_').strip().rstrip('_')[:127]
for s in data.dtypes.index
]
elif self.dbFlavor == 'postgres':
safe_columns = make_psql_compatible_column(
data.dtypes.index)
column_types = lzip(safe_columns, map(lookup_type,
data.dtypes))
if self.dbFlavor == 'mssql':
columns = ',\n '.join('[%s] %s' % x for x in column_types)
elif self.dbFlavor == 'postgres':
column_list = []
column_name_list = []
#Checking column names for reserved words and non-ascii values
for x in column_types:
col_name = x[0]
col_type = x[1]
column_list.append(col_name + ' ' + col_type)
column_name_list.append(col_name)
columns = ',\n '.join('%s' % x for x in column_list)
keystr = ''
if keys is not None:
if isinstance(keys, pandas.compat.string_types):
keys = (keys, )
keystr = ', PRIMARY KEY (%s)' % ','.join(keys)
template = """CREATE TABLE %(name)s (
%(columns)s
%(keystr)s
);"""
create_statement = template % {
'name': tblname,
'columns': columns,
'keystr': keystr
}
else:
raise NotImplementedError('This module is not implemented')
if self.dbFlavor == 'mssql':
return create_statement
elif self.dbFlavor == 'postgres':
return create_statement, column_name_list