def bin_salary_data(self, data, **kwargs):
float_data = np.asarray(data, dtype='float')
max_value = np.amax(float_data)
bin_size = DISTRIBUTION_MAX / DISTRIBUTION_BIN_NUM
bin_edges = np.array(
[i * bin_size for i in range(DISTRIBUTION_BIN_NUM + 1)],
dtype='float'
)
if max_value > bin_edges[-1]:
bin_edges = np.append(bin_edges, max_value)
values, edges = np.histogram(float_data, bins=bin_edges)
salary_json = []
for i, value in enumerate(values):
lower, upper = int(edges[i]), int(edges[i + 1])
salary_json.append({
'value': int(value), # number of salaries in given bin
'lower_edge': format_ballpark_number(lower),
'upper_edge': format_ballpark_number(upper),
'color': self._get_bar_color(lower, upper, **kwargs),
})
return salary_json
def get_department_salaries(self, obj):
formatted_salaries = []
for salary in self.department_statistics[:5]:
formatted_salaries.append({
'name':
salary.name,
'slug':
salary.slug,
'headcount':
format_exact_number(salary.headcount),
'median_tp':
format_salary(salary.median_tp),
'entity_bp':
format_ballpark_number(salary.entity_bp),
'entity_ep':
format_ballpark_number(salary.entity_ep),
'total_expenditure':
format_ballpark_number(salary.total_expenditure),
})
return formatted_salaries
def get_total_expenditure(self, obj):
return format_ballpark_number(self.employer_payroll['base_pay'] +
self.employer_payroll['extra_pay'])
def get_headcount(self, obj):
return format_ballpark_number(self.employer_salary_count)