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dataVisualization.py
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from mpld3 import fig_to_html, plugins
import matplotlib.pyplot as plt
from tinydb import TinyDB
import interactive
import numpy as np
import dictionary
db=TinyDB('employerData.json')
region=db.table('Orlando')
#region=db.table('Tallahassee')
words=dictionary.keywords()
languages=words.languages
systems=words.systems
softwares=words.softwares
buzzwords=words.buzzwords
academics=words.academics
all_keywords=languages+systems+softwares+buzzwords+academics
attributes=['languages','systems','softwares','buzzwords','academics']
keywords=[languages,systems,softwares,buzzwords,academics]
def getAllStats(str,keywords):
arr=[]
for i in region.all():
if len(i['job_salary'])>2:
if i['job_salary'].find(str)>0:
print("Job Salary: ",i['job_salary']," keywords: ",i[keywords])
x="".join(c for c in i['job_salary'] if c.isdigit() or c==' ' or c=='.').split()
for i in range(len(x)):
x[i]=float(x[i])
arr.append(np.mean(x))
return arr
def getStatsForJob(str,item):
for i in region.all():
if i['job_position']==item:
if len(i['job_salary'])>2:
if i['job_salary'].find(str)>0:
x="".join(c for c in i['job_salary'] if c.isdigit() or c==' ' or c=='.').split()
for i in range(len(x)):
x[i]=float(x[i])
return np.mean(x)
def plotAllOccurences(keywords,attributes):
y=np.zeros(len(keywords),dtype=int)
def recursion(attribute):
for item in items[attribute]:
for keyword in keywords:
if item==keyword:
y[keywords.index(item)]+=1
break
for items in region.all():
for i in range(len(attributes)):
recursion(attributes[i])
print(y)
print("Total entries: ",len(region.all()))
for i in range(len(y)):
percent=y[i]/len(region.all())*100
word=keywords[i]
print(word," ", percent,"%")
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
rects=ax.bar(keywords,y)
for rect, label in zip(rects, keywords):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2, height, label,
ha='center', va='bottom')
plt.show()
def plotOccurences(keywords,attribute):
y=np.zeros(len(keywords),dtype=int)
for items in region.all():
for item in items[attribute]:
for keyword in keywords:
if item==keyword:
y[keywords.index(item)]+=1
break
print(y)
print("Total entries: ",len(region.all()))
for i in range(len(y)):
percent=y[i]/len(region.all())*100
word=keywords[i]
print(word," ", percent,"%")
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
rects=ax.bar(keywords,y)
for rect, label in zip(rects, keywords):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2, height, label,
ha='center', va='bottom')
plt.show()
def clusterJobs(keywords,attribute_1,num):
x=[]
y=[]
words=[]
z=0
all_items=region.all()
fig, ax = plt.subplots()
all_jobs=[job['job_position'] for job in region.all()]
for i in range(len(all_items)):
for j in all_items[i][attribute_1]:
if all_items[i]['job_salary'].find('year')>0:
z=getStatsForJob('year',all_items[i]['job_position'])
print(z)
words.append(keywords.index(j))
if len(words)>num:
feature_1=np.mean(words)
feature_2=np.std(words)
x.append(feature_1)
y.append(feature_2)
ax.scatter(x=feature_1,y=feature_2,c=['#1f77b4'],alpha=0.5,s=z/1000)
#ax.annotate(str(all_items[i]['job_salary']),(feature_1,feature_2))
words.clear()
elif len(words)<=num:
all_jobs.remove(all_items[i]['job_position'])
af = interactive.AnnoteFinder(x,y, all_jobs, ax=ax)
fig.canvas.mpl_connect('button_press_event', af)
plt.xlabel('Mean')
plt.ylabel('Standard Deviation')
plt.title('%s Clustering for Local Job Vaccancies'%attribute_1.capitalize())
plt.show()
def prototype(keywords,attribute_1,attributes,num):
all_items=region.all()
words=[]
def recursion(attribute):
for j in all_items[i][attribute]:
words.append(keywords.index(j))
x=[]
y=[]
words=[]
fig, ax = plt.subplots()
all_jobs=[job[attribute_1] for job in region.all()]
for i in range(len(all_items)):
for j in range(len(attributes)):
recursion(attributes[j])
print(words)
if len(words)>num:
feature_1=np.mean(words)
feature_2=np.std(words)
x.append(feature_1)
y.append(feature_2)
print(all_items[i]['job_salary'])
ax.scatter(x=feature_1,y=feature_2) #,c=colors[j])
words.clear()
elif len(words)<=num:
all_jobs.remove(all_items[i][attribute_1])
af = interactive.AnnoteFinder(x,y, all_jobs, ax=ax)
fig.canvas.mpl_connect('button_press_event', af)
print(len(x),len(y),len(all_jobs))
plt.xlabel('Mean')
plt.ylabel('Standard Deviation')
plt.title('Keywords Clustering for Local Job Vaccancies')
plt.show()
#for attribute in attributes:
# clusterJobs(keywords,'job_position',attribute,2)
for i in range(len(attributes)):
#plotOccurences(keywords[i],attributes[i])
clusterJobs(keywords[i],attributes[i],2)
#prototype(all_keywords,'job_position',attributes,0)
#plotAllOccurences(all_keywords,attributes)
#plotOccurences(keywords,'academics')
#year=getAllStats('year','languages')
year=getAllStats('year','languages')
plt.hist(year,bins=60)
plt.show()
#hour=getAllStats('hour','languages')
#plt.hist(hour,bins=60)
#plt.show()
#print("the average salary: ",np.mean(year))
#print("the average hourly pay: ",np.mean(hour))