def badass_plot(newpoint, new_df):
"""
Used for Fraud Case Study
This function will add the data of a new point (json or csv)
to the graphs that have been build in bokeh
Input:
newpoint in json form or csv, as long as it's a dictionary, we're good!
Return:
A tab through visualization of bokeh plots, showing the data points
location on these plots
"""
color_list = ['#FF8C00', '#FAFCCC', '#EA653B', '#EE993D', \
'#EA653B', '#ED9239', '#A4A7AA', '#676767']
output_file("Check-Graphs.html")
bins = 15
p1 = figure(width=500,
height=500,
x_range=(new_df['event_created_to_end'].min(),
new_df[u'event_created_to_end'].max()))
p1 = Histogram(new_df,
values='event_created_to_end',
label='label',
color='label',
title='Duration of \n Event Created to End',
density=True,
legend='top_right',
bins=bins,
palette=color_list)
vline = Span(location=newpoint[u'event_created_to_end'],
dimension='height',
line_color='red',
line_width=3)
p1.xaxis.axis_label = ''
p1.xaxis.axis_label_text_font_style = 'normal'
p1.yaxis.axis_label_text_font_style = 'normal'
p1.xaxis.axis_label_text_font_size = '18pt'
p1.yaxis.axis_label_text_font_size = '18pt'
p1.background_fill_color = "#acaaa8"
p1.background_fill_alpha = 0.5
p1.title.text_font = 'helvetica'
p1.title.text_font_size = '18pt'
p1.renderers.extend([vline])
bins = 2
p2 = figure(width=500,
height=500,
x_range=(new_df['total_tickets_sold'].min(),
new_df['total_tickets_sold'].max()))
p2 = Histogram(new_df,
values='total_tickets_sold',
label='label',
color='label',
title='Total Tickets Sold',
density=True,
legend='top_right',
bins=bins,
palette=color_list)
vline = Span(location=newpoint[u'total_tickets_sold'],
dimension='height',
line_color='red',
line_width=3)
p2.xaxis.axis_label = ''
p2.xaxis.axis_label_text_font_style = 'normal'
p2.yaxis.axis_label_text_font_style = 'normal'
p2.xaxis.axis_label_text_font_size = '18pt'
p2.yaxis.axis_label_text_font_size = '18pt'
p2.background_fill_color = "#acaaa8"
p2.background_fill_alpha = 0.5
p2.title.text_font = 'helvetica'
p2.title.text_font_size = '18pt'
p2.renderers.extend([vline])
bins = 2
p3 = figure(width=500,
height=500,
x_range=(new_df['payout_type_MISSING'].min(),
new_df['payout_type_MISSING'].max()))
p3 = Histogram(new_df,
values='payout_type_MISSING',
label='label',
color='label',
title='Payout Type Missing \n (Yes/No)',
density=True,
legend='top_right',
bins=bins,
palette=color_list)
vline = Span(location=newpoint[u'payout_type_MISSING'],
dimension='height',
line_color='red',
line_width=3)
p3.xaxis.axis_label = ''
p3.xaxis.axis_label_text_font_style = 'normal'
p3.yaxis.axis_label_text_font_style = 'normal'
p3.xaxis.axis_label_text_font_size = '18pt'
p3.yaxis.axis_label_text_font_size = '18pt'
p3.background_fill_color = "#acaaa8"
p3.background_fill_alpha = 0.5
p3.title.text_font = 'helvetica'
p3.title.text_font_size = '18pt'
p3.renderers.extend([vline])
bins = 30
p4 = figure(width=500,
height=500,
x_range=(new_df['median_ticket_cost'].min(),
new_df['median_ticket_cost'].max()))
p4 = Histogram(new_df,
values='median_ticket_cost',
label='label',
color='label',
title='Median Ticket Cost',
density=True,
legend='top_right',
bins=bins,
palette=color_list)
vline = Span(location=newpoint[u'median_ticket_cost'],
dimension='height',
line_color='red',
line_width=3)
p4.xaxis.axis_label = ''
p4.xaxis.axis_label_text_font_style = 'normal'
p4.yaxis.axis_label_text_font_style = 'normal'
p4.xaxis.axis_label_text_font_size = '18pt'
p4.yaxis.axis_label_text_font_size = '18pt'
p4.background_fill_color = "#acaaa8"
p4.background_fill_alpha = 0.5
p4.title.text_font = 'helvetica'
p4.title.text_font_size = '18pt'
p4.renderers.extend([vline])
tab1 = Panel(child=p1, title='Event Create to End')
tab2 = Panel(child=p2, title='Total Tickets Sold')
tab3 = Panel(child=p3, title='Payout Type Missing (yes/no)')
tab4 = Panel(child=p4, title='Median Ticket Cost')
tabs = Tabs(tabs=[tab1, tab2, tab3, tab4])
return file_html(tabs, CDN, 'plot')
