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Job.web.scrapping
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#### function to show map for loaction of the job
import time
import matplotlib.pyplot as plt 
import seaborn as sns 
import matplotlib as mpl
import plotly 
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
from plotly.offline import iplot
from plotly.subplots import make_subplots
import plotly.figure_factory as ff



def map_bubble(df):
  
  import requests
  import urllib.parse
  g =[]
  for i  in range(len(df.Location)):

    if  df.Location.loc[i].split(","):
      g.append(df.Location.loc[i].split(",")[0])
    else:
      g.append(df.Location.loc[i])
  df['new_loc']=g 
  if 'country' in df.columns:
    df["full_location"] = df["new_loc"] + ", " +df["country"]
    dict_cities = dict(df.full_location.value_counts())
  else :
    dict_cities = dict(df.new_loc.value_counts())
  lat = []
  lon = []
  bubble_df = pd.DataFrame()
  add=[]
  val=[]
  try:
    for address in dict_cities.keys():
      url = 'https://nominatim.openstreetmap.org/search/' + urllib.parse.quote(address) +'?format=json'

      response = requests.get(url).json()
      lat.append(response[0]["lat"])
      lon.append(response[0]["lon"])
      add.append(address)
      val.append(dict_cities[address])
  except:
    pass

  bubble_df['address'] =add
  bubble_df['lat'] = lat
  bubble_df['lon'] = lon
  bubble_df['value'] = val


  # import the library
  import folium

  # Make an empty map
  m = folium.Map(location=[20,0], tiles="OpenStreetMap", zoom_start=2)
  # add marker one by one on the map
  for i in range(0,len(bubble_df)):
    folium.Circle(
        location=[bubble_df.iloc[i]['lat'], bubble_df.iloc[i]['lon']],

        popup=bubble_df.iloc[i][['address','value']].values,
        radius=float(bubble_df.iloc[i]['value'])*500,
        color='#69b3a2',
        fill=True,
        fill_color='#69b3a2'
    ).add_to(m)
  m
  # Show the map again
  return m


##########################





#########################
#### wuzzuf analysis
def wuzzuf_exp(df1):
  top10_job_title = df1['Title'].value_counts()[:10]
  fig1 = px.bar(y=top10_job_title.values, 
              x=top10_job_title.index, 
              color = top10_job_title.index,
              color_discrete_sequence=px.colors.sequential.deep,
              text=top10_job_title.values,
              title= 'Top 10 Job Titles',
              template= 'plotly_dark')
  fig1.update_layout(height=500,width=500, 
      xaxis_title="Job Titles",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  st.plotly_chart(fig1)

  type_grouped = df1['Career_Level'].value_counts()
  #e_type = ['Full-Time','Part-Time','Contract','Freelance']
  e_type =dict(df1['Career_Level'].value_counts()).keys()
  fig2 = px.bar(x = e_type, y = type_grouped.values, 
        color = type_grouped.index, 
        color_discrete_sequence=px.colors.sequential.dense,
        template = 'plotly_dark',
        text = type_grouped.values, title = 'Career Level Distribution')
  fig2.update_layout( height=500, width=500,
      xaxis_title="Career Level",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  fig2.update_traces(width=0.5)
  st.plotly_chart(fig2)
  residence = df1['Location'].value_counts()
  top10_employee_location = residence[:10]
  fig3 = px.bar(y=top10_employee_location.values, 
              x=top10_employee_location.index, 
              color = top10_employee_location.index,
              color_discrete_sequence=px.colors.sequential.deep,
              text=top10_employee_location.values,
              title= 'Top 10 Location of job',
              template= 'plotly_dark')
  fig3.update_layout(height=500,width=500,
      xaxis_title="Location of job",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  st.plotly_chart(fig3)
    
  type_grouped = df1['Experience_Needed'].value_counts()
  #e_type = ['Full-Time','Part-Time','Contract','Freelance']
  e_type =dict(df1['Experience_Needed'].value_counts()).keys()
  fig4 = px.bar(x = e_type, y = type_grouped.values, 
        color = type_grouped.index, 
        color_discrete_sequence=px.colors.sequential.dense,
        template = 'plotly_dark',
        text = type_grouped.values, title = ' Experience Level Distribution')
  fig4.update_layout(height=500,width=500,
      xaxis_title=" Experience Level (years)",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  fig4.update_traces(width=0.5)
  st.plotly_chart(fig4)
  return 



#########################
### linkedin analysis

def linkedin_exp(df1):
  top10_job_title = df1['Title'].value_counts()[:10]
  fig1 = px.bar(y=top10_job_title.values, 
              x=top10_job_title.index, 
              color = top10_job_title.index,
              color_discrete_sequence=px.colors.sequential.deep,
              text=top10_job_title.values,
              title= 'Top 10 Job Titles',
              template= 'plotly_dark')
  fig1.update_layout(height=500,width=500, 
      xaxis_title="Job Titles",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  st.plotly_chart(fig1)

  type_grouped = df1['Employment type'].value_counts()
  #e_type = ['Full-Time','Part-Time','Contract','Freelance']
  e_type =dict(df1['Employment type'].value_counts()).keys()
  fig2 = px.bar(x = e_type, y = type_grouped.values, 
        color = type_grouped.index, 
        color_discrete_sequence=px.colors.sequential.dense,
        template = 'plotly_dark',
        text = type_grouped.values, title = 'Employment type Distribution')
  fig2.update_layout( height=500, width=500,
      xaxis_title="Employment type",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  fig2.update_traces(width=0.5)
  st.plotly_chart(fig2)
  residence = df1['Location'].value_counts()
  top10_employee_location = residence[:10]
  fig3 = px.bar(y=top10_employee_location.values, 
              x=top10_employee_location.index, 
              color = top10_employee_location.index,
              color_discrete_sequence=px.colors.sequential.deep,
              text=top10_employee_location.values,
              title= 'Top 10 Location of job',
              template= 'plotly_dark')
  fig3.update_layout(height=500,width=500,
      xaxis_title="Location of job",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  st.plotly_chart(fig3)
    
  type_grouped = df1['Seniority level'].value_counts()
  #e_type = ['Full-Time','Part-Time','Contract','Freelance']
  e_type =dict(df1['Seniority level'].value_counts()).keys()
  fig4 = px.bar(x = e_type, y = type_grouped.values, 
        color = type_grouped.index, 
        color_discrete_sequence=px.colors.sequential.dense,
        template = 'plotly_dark',
        text = type_grouped.values, title = 'Seniority level Distribution')
  fig4.update_layout(height=500,width=500,
      xaxis_title="Seniority level",
      yaxis_title="count",
      font = dict(size=17,family="Franklin Gothic"))
  fig4.update_traces(width=0.5)
  st.plotly_chart(fig4)
  return