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| #### function to show map for loaction of the job | |
| import time | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| 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 | |
| import pandas as pd | |
| 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 | |