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- from taipy import Gui
- import pandas as pd
- from taipy.gui import builder as tgb
- import plotly.graph_objects as go
- import plotly.offline as pyo
- data = pd.read_csv('data/aggregate/aggregate.csv')
- location_counts = data['location'].value_counts(sort=True)
- location_fig = go.Figure(data=go.Bar(x=location_counts.index, y=location_counts.values))
- location_fig.update_layout(title_text='Location counts', xaxis_title='index', yaxis_title='values')
- # md='''
- # # Analysis of sourced data
- # <|{location_counts}|chart|type=bar|x=index|y=values|>'''
- # Figures are as observed on March 18, 2024
- demand={
- "python developer": 7947,
- "data analyst": 5221,
- "machine learning engineer": 27829,
- "software engineer": 46596,
- "backend developer": 18583,
- "devops engineer": 1785,
- "automation engineer": 12976,
- "network engineer": 10513,
- "vuejs developer": 1444,
- "react developer": 6112,
- "nodejs developer": 4883,
- "frontend developer": 12399,
- "full stack developer": 7006,
- "ui developer": 9303,
- "web application developer": 19582,
- "javascript engineer": 6797,
- "mobile app developer": 4191,
- }
- demand = pd.DataFrame.from_dict(demand, orient = 'index', columns=['demand'])
- demand.reset_index(inplace=True)
- demand.columns=['Query','Demand']
- with tgb.Page() as analysis_page:
- tgb.text('Analysis of sourced data',class_name='h1')
- tgb.html('br')
- tgb.text('Demand of jobs as sourced on 18 March 2024.', class_name='h4')
- with tgb.part('card'):
- tgb.text('Demand of jobs sourced')
- tgb.table('{demand}')
- #tgb.html()
- # todo : add the plotly charts - store as image then use html(md is hard, no docs for py)
- #Gui(analysis_page).run()
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