def split_dataset(dataset, p_train_data, split_mode):
fields = list(fieldnames(dataset))
size_dataset = len(values(dataset, fields[0]))
size_train_data = int(round(size_dataset * p_train_data))
size_test_data = abs(size_train_data - size_dataset)
if split_mode == 'normal' :
train_data = head(dataset, size_train_data - 1)
if size_test_data == 0:
test_data = []
else:
test_data = tail(dataset, size_test_data - 1)
#################### Falta incluir Shuffle mode ###############
return train_data, test_data
table1 = [['foo', 'bar'],
['a', 1],
['b', 2],
['c', 5],
['d', 7],
['f', 42],
['f', 3],
['h', 90],
['k', 12],
['l', 77],
['q', 2]]
from petl import tail, look
look(table1)
table2 = tail(table1, 4)
look(table2)
# sort
table1 = [['foo', 'bar'],
['C', 2],
['A', 9],
['A', 6],
['F', 1],
['D', 10]]
from petl import sort, look
look(table1)
table2 = sort(table1, 'foo')
table1 = [['foo', 'bar'],
['a', 1],
['b', 2],
['c', 5],
['d', 7],
['f', 42],
['f', 3],
['h', 90],
['k', 12],
['l', 77],
['q', 2]]
from petl import tail, look
look(table1)
table2 = tail(table1, 4)
look(table2)
# sort
table1 = [['foo', 'bar'],
['C', 2],
['A', 9],
['A', 6],
['F', 1],
['D', 10]]
from petl import sort, look
look(table1)
table2 = sort(table1, 'foo')
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--input-csv-filename",
required=True,
help="Input UTF8 CSV to summarize")
parser.add_argument(
"--sep-columns",
required=False,
nargs='*',
default=argparse.SUPPRESS,
help=
"Column names of columns containing comma- or semi-colon-separated values"
)
parser.add_argument("--sep-character", required=False, help="Character used to separate values in multi-value " \
"fields. Defaults to ';' if not specified.")
parser.add_argument("--skip-columns",
required=False,
nargs='*',
default=argparse.SUPPRESS,
help="Column names to NOT generate stats for")
parser.add_argument("--skip-num-rows",
required=False,
type=int,
help="Skip specified number "
"of header rows")
parser.add_argument(
"--first-ccb-column",
required=False,
help="String name of first CCB column. If "
"specified, all preceeding columns will be labeled 'Servant Keeper' and this column "
"and all subsequent will be labeled 'CCB'")
args = parser.parse_args()
if args.first_ccb_column is not None:
column_prefix = 'Servant Keeper '
else:
column_prefix = ''
assert os.path.isfile(
args.input_csv_filename
), "Error: cannot open file '" + args.input_csv_filename + "'"
table = petl.fromcsv(args.input_csv_filename)
# Skip header rows
if args.skip_num_rows:
skip_num = args.skip_num_rows
assert skip_num > 0, "--skip-num-rows value '" + str(
skip_num) + "' is invalid. Must be positive."
it = iter(table)
while skip_num >= 0:
row = next(it)
skip_num -= 1
table = petl.setheader(table, row)
table = petl.tail(table, petl.nrows(table) - args.skip_num_rows)
# Print nicely formatted stats for each column
sep = ''
args_dict = vars(args)
skip_columns_specified = 'skip_columns' in args_dict
sep_char_specified = 'sep_character' in args_dict
for column in petl.header(table):
if args.first_ccb_column is not None and column == args.first_ccb_column:
column_prefix = 'CCB '
if not skip_columns_specified or column not in args.skip_columns:
output_str = column_prefix + "Column '" + column + "'"
print sep + output_str
print >> sys.stderr, output_str
if args.sep_columns is not None and column in args.sep_columns:
if sep_char_specified:
sep_character = args.sep_character
else:
sep_character = ';'
output_str = num_dict2str(
dict_dump(sep_valuecounter(table, column, sep_character)))
print output_str
else:
output_str = num_dict2str(dict_dump(valuecounts(table,
column)))
print output_str
sep = '\n'
# Flush to ensure all output is written
sys.stdout.flush()
sys.stderr.flush()
table1 = etl.addfield(
etl.convertnumbers(
etl.setheader(etl.fromcsv('winequality-red.csv'), table_header)),
"Type", "Red")
table2 = etl.addfield(
etl.convertnumbers(
etl.setheader(etl.fromcsv('winequality-white.csv'), table_header)),
"Type", "White")
#print(etl.head(table1))
#print(etl.head(table2))
table1_filtered = etl.select(table1, "Quality", lambda v: v > 6)
table2_filtered = etl.select(table2, "Quality", lambda v: v > 4)
good_wines = etl.cat(table1_filtered, table2_filtered)
good_wines_enhanced = etl.addfields(
good_wines,
[("Max Acidity",
lambda rec: rec["Fixed Acidity"] + rec["Volatile Acidity"]),
("Locked SO2", lambda rec: rec["Total SO2"] - rec["Free SO2"])])
#print(etl.head(good_wines_enhanced))
#print(etl.tail(good_wines_enhanced))
gwe_sorted = etl.sort(good_wines_enhanced, key=["Quality", "Sugar"])
#print(etl.head(gwe_sorted))
print(etl.lookall(etl.tail(gwe_sorted, 500)))
def main(argv):
parser = argparse.ArgumentParser()
parser.add_argument("--input-csv-filename", required=True, help="Input UTF8 CSV to summarize")
parser.add_argument("--sep-columns", required=False, nargs = '*', default=argparse.SUPPRESS,
help="Column names of columns containing comma- or semi-colon-separated values")
parser.add_argument("--sep-character", required=False, help="Character used to separate values in multi-value " \
"fields. Defaults to ';' if not specified.")
parser.add_argument("--skip-columns", required=False, nargs='*', default=argparse.SUPPRESS,
help="Column names to NOT generate stats for")
parser.add_argument("--skip-num-rows", required=False, type=int, help="Skip specified number "
"of header rows")
parser.add_argument("--first-ccb-column", required=False, help="String name of first CCB column. If "
"specified, all preceeding columns will be labeled 'Servant Keeper' and this column "
"and all subsequent will be labeled 'CCB'")
args = parser.parse_args()
if args.first_ccb_column is not None:
column_prefix = 'Servant Keeper '
else:
column_prefix = ''
assert os.path.isfile(args.input_csv_filename), "Error: cannot open file '" + args.input_csv_filename + "'"
table = petl.fromcsv(args.input_csv_filename)
# Skip header rows
if args.skip_num_rows:
skip_num = args.skip_num_rows
assert skip_num > 0, "--skip-num-rows value '" + str(skip_num) + "' is invalid. Must be positive."
it = iter(table)
while skip_num >= 0:
row = next(it)
skip_num -= 1
table = petl.setheader(table, row)
table = petl.tail(table, petl.nrows(table) - args.skip_num_rows)
# Print nicely formatted stats for each column
sep = ''
args_dict = vars(args)
skip_columns_specified = 'skip_columns' in args_dict
sep_char_specified = 'sep_character' in args_dict
for column in petl.header(table):
if args.first_ccb_column is not None and column == args.first_ccb_column:
column_prefix = 'CCB '
if not skip_columns_specified or column not in args.skip_columns:
output_str = column_prefix + "Column '" + column + "'"
print sep + output_str
print >> sys.stderr, output_str
if args.sep_columns is not None and column in args.sep_columns:
if sep_char_specified:
sep_character = args.sep_character
else:
sep_character = ';'
output_str = num_dict2str(dict_dump(sep_valuecounter(table, column, sep_character)))
print output_str
else:
output_str = num_dict2str(dict_dump(valuecounts(table, column)))
print output_str
sep = '\n'
# Flush to ensure all output is written
sys.stdout.flush()
sys.stderr.flush()
# tail()
########
import petl as etl
table1 = [['foo', 'bar'],
['a', 1],
['b', 2],
['c', 5],
['d', 7],
['f', 42],
['f', 3],
['h', 90],
['k', 12],
['l', 77],
['q', 2]]
table2 = etl.tail(table1, 4)
table2
# skipcomments()
################
import petl as etl
table1 = [['##aaa', 'bbb', 'ccc'],
['##mmm',],
['#foo', 'bar'],
['##nnn', 1],
['a', 1],
['b', 2]]
table2 = etl.skipcomments(table1, '##')
table2
# coding:utf8
import petl as etl
table1 = [('foo', 'bar', 'baz'), ('apple', 1, 2.5), ('orange', 3, 4.5),
('pears', 5, 6.5), ('bananer', 7, 8.5), ('cat', 9, 10.5)]
# head 4
table_head = etl.head(table1, 4)
print(table_head)
# tail 4
table_tail = etl.tail(table1, 4)
print(table_tail)
# rowslice
rowsliceTb = etl.rowslice(table1, 2)
print(rowsliceTb)
rowsliceTb_2_4 = etl.rowslice(table1, 2, 4)
print(rowsliceTb_2_4)
# 从1开始，2作为第一个，步长为2，
rowsliceTb_1_2_5 = etl.rowslice(table1, 1, 5, 2)
print(rowsliceTb_1_2_5)
# cut
cutTb = etl.cut(table1, 'foo', 'bar')
print(cutTb)
# index starts from 0
cutTb_0_2 = etl.cut(table1, 0, 2)