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random_numbers2.py
import agate
import numpy.random as npr
import isodate
from faker import Factory
random_groups = npr.choice(3, 100, p=[.25, .65, .10])
column_names = ['id', 'name', 'hire_date', 'pb', 'salary']
column_types = [
agate.Number(),
agate.Text(),
agate.Date(),
agate.Number(),
agate.Number()
]
def generate_test_data():
# Set seed to generate consistent test data
npr.seed(1)
data_lists = []
n_recs = 110
fk = Factory.create()
for i in range(n_recs):
payband = npr.choice([1, 2, 3], p=[0.7, 0.25, 0.05])
payband = int(payband)
salary = 0,
if payband == 1:
salary = fk.random_int(min=60000, max=120000)
elif payband == 2:
salary = fk.random_int(min=120000, max=160000)
else:
salary = fk.random_int(min=160000, max=200000)
list_record = [
i + 1,
'{}, {}'.format(fk.last_name(), fk.first_name()),
isodate.parse_date(fk.date()),
payband,
salary
]
data_lists.append(list_record)
return data_lists
def _generate_random(row):
'''Generate random number according to the specified distribution'''
r_n = npr.choice(3, 1, p=[.25, .65, .10])
return int(r_n + 1)
def _add_random_column(data_tbl):
# Reset seed to produce random numbers
npr.seed()
new_table = data_tbl.compute([
('random_group', agate.Formula(agate.Number(), _generate_random))
])
return new_table
if __name__ == '__main__':
data_lists = generate_test_data()
# Create data table
tbl = agate.Table(data_lists, column_names=column_names, column_types=column_types)
# Produce summary table
by_payband = tbl.group_by('pb')
summary_tbl = by_payband.aggregate([
('count', agate.Count()),
('sal_min', agate.Min('salary')),
('sal_max', agate.Max('salary')),
('sal_median', agate.Median('salary'))
])
# Display summary of generated test data
print('Model data summary:\n')
summary_tbl.print_table()
print()
summary_tbl.print_bars('pb', 'count', width=40)
# ---Generate random numbers for simulation
new_table = _add_random_column(tbl)
# Show distributions of new table
rand_tbl_count = new_table.pivot('random_group')
rand_tbl_count = rand_tbl_count.order_by('random_group')
rand_tbl_count = rand_tbl_count.compute([
('Percent', agate.Percent('Count'))
])
print('\n Group distribution (count):\n')
rand_tbl_count.print_table()
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