def get_outliers(year_str, master_dict):
# # make a local reference of the master dictionary of all the data
# master = allYears_listData_dict()
# #make the complete dataframe of all years
df_allSalary = df_allYears_salary(master_dict)
# Find out what the upper bound for top outliers is:
std = df_allSalary.loc[:, year_str].std()
mean = df_allSalary.loc[:, year_str].mean()
upper_bound = mean + (2 * std)
# making a copy
# df_copy = df_allSalary.loc[:,year_str].copy()
# df_copy = [df_copy > upper_bound]
# print df_copy
# Initialize variables for outliers
index_outliers = [] # collects the index of outlier
salary_outliers = [] #gets the float salary of outliers
positions_outliers = []
name_outliers = []
# get the list of salary data, & see if salary is greater than upper_bound
# This gets the outliers & upl employees
year_salary = master_dict[year_str]['salary_float']
for i in range(len(year_salary)):
if ((year_salary[i] > upper_bound) and (year_salary[i] != "" or 0)):
salary_outliers.append(year_salary[i])
index_outliers.append(i)
else:
pass
# get the names of the outliers
year_names = master_dict[year_str]['employee_list']
for i in range(len(index_outliers)):
index = index_outliers[i]
name_outliers.append(year_names[index])
# pprint.pprint(name_outliers)
# get the positions of the outliers
year_positions = master_dict[year_str]['position_list']
for i in range(len(index_outliers)):
index = index_outliers[i]
positions_outliers.append(year_positions[index])
# pprint.pprint(positions_outliers)
######## DEBUGGING #################
# print len(positions_outliers)
# print len(index_outliers)
# print len(salary_outliers)
# print len(name_outliers)
# print positions_outliers[-1]
# print index_outliers[-1]
# print salary_outliers[-1]
# print name_outliers[-1]
# create a dataframe
data = zip(name_outliers, positions_outliers, salary_outliers)
pprint.pprint(data)
df = pd.DataFrame(data, columns=["Name", "Position", "Salary"])
df = df.sort(['Salary'], ascending=False)
return df
def get_outliers(year_str, master_dict):
# # make a local reference of the master dictionary of all the data
# master = allYears_listData_dict()
# #make the complete dataframe of all years
df_allSalary= df_allYears_salary(master_dict)
# Find out what the upper bound for top outliers is:
std = df_allSalary.loc[:, year_str].std()
mean = df_allSalary.loc[:, year_str].mean()
upper_bound = mean + (2*std)
# making a copy
# df_copy = df_allSalary.loc[:,year_str].copy()
# df_copy = [df_copy > upper_bound]
# print df_copy
# Initialize variables for outliers
index_outliers = [] # collects the index of outlier
salary_outliers = [] #gets the float salary of outliers
positions_outliers = []
name_outliers = []
# get the list of salary data, & see if salary is greater than upper_bound
# This gets the outliers & upl employees
year_salary = master_dict[year_str]['salary_float']
for i in range(len(year_salary)):
if ((year_salary[i] > upper_bound) and (year_salary[i]!= "" or 0)) :
salary_outliers.append(year_salary[i])
index_outliers.append(i)
else:
pass
# get the names of the outliers
year_names = master_dict[year_str]['employee_list']
for i in range(len(index_outliers)):
index = index_outliers[i]
name_outliers.append(year_names[index])
# pprint.pprint(name_outliers)
# get the positions of the outliers
year_positions = master_dict[year_str]['position_list']
for i in range(len(index_outliers)):
index = index_outliers[i]
positions_outliers.append(year_positions[index])
# pprint.pprint(positions_outliers)
######## DEBUGGING #################
# print len(positions_outliers)
# print len(index_outliers)
# print len(salary_outliers)
# print len(name_outliers)
# print positions_outliers[-1]
# print index_outliers[-1]
# print salary_outliers[-1]
# print name_outliers[-1]
# create a dataframe
data = zip(name_outliers, positions_outliers, salary_outliers)
pprint.pprint(data)
df = pd.DataFrame(data, columns=["Name", "Position", "Salary"])
df = df.sort(['Salary'], ascending=False)
return df
def boxplot_salary_allYears():
# make the master dictionary of all the data
master = allYears_listData_dict()
# make a dataframe of all the salaries for a year, for all the data
df = df_allYears_salary(master)
plt.figure()
df.boxplot(return_type='axes')
## add title and label axis
plt.ylabel('Salary (USD)')
plt.xlabel("School Year")
plt.title("Distribution of UVM employee salary from 1996 - 2014")
plt.show(block=True) # this is code is needed if run outside of canopy
def boxplot_salary_allYears():
# make the master dictionary of all the data
master = allYears_listData_dict()
# make a dataframe of all the salaries for a year, for all the data
df = df_allYears_salary(master)
plt.figure()
df.boxplot(return_type='axes')
## add title and label axis
plt.ylabel('Salary (USD)')
plt.xlabel("School Year")
plt.title("Distribution of UVM employee salary from 1996 - 2014")
plt.show(block=True) # this is code is needed if run outside of canopy
