def generate_chart(year):
data = get_data(year)
barchart = Bar(data,
values=blend(*SCORED_EVENTS, labels_name='event'),
label=cat(columns='Team', sort=False),
stack=cat(columns='event', sort=False),
color=color(columns='event', palette=Spectral9, sort=False),
xgrid=False, ygrid=False, legend='top_right',
plot_width=1000, plot_height=625,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + str(year) + " Total Scores by Place"
barchart._xaxis.axis_label = "Teams"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._xaxis.major_label_text_font_size = '0.6em'
barchart._yaxis.axis_label = "Total Score"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
return barchart
def fnCreate_Chart_Bar(df):
pd.options.html.border=0
plot = Bar(df, 'Sub_Attbt', values='FY2013', title="Total DISPL by Attribute", bar_width=0.4)
plot.logo=None
script, div = components(plot,CDN)
return script, div
def fnCreate_Chart_Bar1(df,strS):
pd.options.html.border=1
label1=CatAttr(columns=['Airline'], sort=False)
plot = Bar(df, label=label1, values='Ticket Price',group='Airline', title="Average of Ticket Price- Airline wise" + strS, legend=False, bar_width=100, xlabel='Airlines',ylabel='Average Ticket Price')
plot.logo=None
script, div = components(plot,CDN)
return script, div
def fnCreate_Chart_Bar(df):
pd.options.html.border=1
# pd.options.display.html.border=1
label1=CatAttr(columns=['Routes'], sort=False)
plot = Bar(df, label=label1, values='Ticket Price',group='Routes', title="Average of Ticket Price- Routes wise", legend=False, bar_width=50, xlabel='Routes',ylabel='Average Ticket Price')
plot.logo=None
script, div = components(plot,CDN)
return script, div
def fnCreate_Chart_Bar_Full(df):
pd.options.html.border=0
plot = Bar(df, label='Attbt', values='FY2013', agg='sum', group='Attbt',
title="Total Sum, grouped by Attribute", legend='top_left')
# plot = Bar(df, 'Sub_Attbt', values='FY2013', title="Total DISPL by Attribute", bar_width=0.4)
plot.logo=None
script, div = components(plot,CDN)
return script, div
def fnCreate_Chart_Bar2(df):
output_file("line_bar.html")
pd.options.html.border=0
plot = Bar(df, label='Attbt', values='FY2013', agg='sum', group='Attbt',
title="Total Sum, grouped by Attribute", legend='top_left')
# plot = Bar(df, 'Sub_Attbt', values='FY2013', title="Total DISPL by Attribute", bar_width=0.4)
plot.logo=None
show(plot)
return True
def plot(df):
print("-" * 10)
print(df)
print("-" * 10)
from bokeh.charts import Bar, show
p = Bar(df, label=["model", "dim reduction"], \
values="accuracy", agg="mean", color="model",\
legend=None, bar_width=0.3, plot_width=600, plot_height=600)
p.logo = None
# will open a browser to show the plot.
show(p)
def fnCreate_Stacked_Bar(df):
# label1=CatAttr(columns=['Aspects'], sort=True)
tmp=df
plot = Bar(tmp, stack=tmp.columns[0],
label=tmp.columns[1], values=tmp.columns[2],
legend=True,title="Sentiment segmentation based on top 7 Aspects",
xlabel='Polarity',ylabel='Count')
plot.logo=None
plot.legend.label_text_font_size = "7pt"
plot.legend.orientation = "horizontal"
script, div = components(plot,CDN)
return script, div
def fnCreate_Chart_Bar(df):
plot = Bar(df, label='Aspects', values='Count',group='Polarity',
title="Average Sentiment for various Aspects", legend='top_left',
bar_width=0.9,color=["green","red"],
xlabel='Aspects',ylabel='Count')
plot.logo=None
plot.legend.label_text_font_size = "7pt"
plot.legend.orientation = "horizontal"
# plot.legend.click_policy="hide"
script, div = components(plot,CDN)
return script, div
def generate_chart(event):
data = get_data(TIMED_EVENTS[event])
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
values='percentage_dnf',
label='year',
color="FireBrick",
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan DNFs - " + event
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Percentage DNF"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart._yaxis.formatter = NumeralTickFormatter(format="0%")
barchart.y_range = Range1d(0, 1)
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
for renderer in barchart.select(GlyphRenderer):
if renderer.data_source.data['height'] != [0]:
year = renderer.data_source.data['year']
num_dnf = data['dnfs'].loc[data['year'] == year]
num_entries = data['entries'].loc[data['year'] == year]
percent_dnf = data['percentage_dnf'].loc[data['year'] == year]
hover = HoverTool(renderers=[renderer],
tooltips=[
("# DNFs", '%d' % num_dnf.values[0]),
("# Entries", '%d' % num_entries.values[0]),
("% DNF",
'%.2f%%' % (100 * percent_dnf.values[0]))
])
barchart.add_tools(hover)
return barchart
def fnCreate_Chart_Bar_wf(df,stS= "all type reviews"):
label1=CatAttr(columns=['word'], sort=False)
plot = Bar(df, label=label1, values='freq',group='word',
title="Top 10 Frequent Words for " + stS, legend=None,
bar_width=0.9,xlabel='word',ylabel='freq') #,plot_width=450, plot_height=450
plot.logo=None
plot.legend.label_text_font_size = "7pt"
plot.legend.orientation = "horizontal"
script, div = components(plot,CDN)
return script, div
def generate_chart(team):
data = generate_data(team)
selectable_years = list(map(str, data.index.unique()))
# Generate the chart UNFORTUNATELY using the high level plots. Streaming
# a bunch of quads resulted in lots of graphics corruption when switching
# teams. I would rather have the plots work all the time but really slow
# then have the plot show the wrong information.
barchart = Bar(data,
values=blend(*SCORED_EVENTS, labels_name='event'),
label=cat(columns='Year', sort=False),
stack=cat(columns='event', sort=False),
color=color(columns='event', palette=Spectral9, sort=False),
xgrid=False, ygrid=False,
plot_width=1000, plot_height=625,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan - " + team
barchart._xaxis.axis_label = "Teams"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Total Score"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
# Hacky tooltips
for renderer in barchart.select(GlyphRenderer):
if renderer.data_source.data['height'] != [0]:
year = renderer.data_source.data['Year']
place = data['Place'].loc[data['Year'] == year]
score = data['Total Score'].loc[data['Year'] == year]
hover = HoverTool(renderers=[renderer],
tooltips=[("Year", '@Year'),
("Selection", '@event'),
("Event Score", '@height'),
("Total Score", '%.2f' % score.values[0]),
("Overall Place", '%d' % place.values[0])])
barchart.add_tools(hover)
return barchart
def output_chart(issues_df, output_mode='static'):
import datetime
import bokeh
from bokeh.models import HoverTool
# Add timestamp to title
issues_chart = Bar(issues_df,
label='value_delivered',
values='status',
agg='count',
stack='status',
title=ISSUES_TITLE + " (Updated " +
datetime.datetime.now().strftime('%m/%d/%Y') + ")",
xlabel="Value Delivered",
ylabel="Number of Use Cases",
legend='top_right',
tools='hover',
color=brewer["GnBu"][3])
issues_chart.plot_width = DESTINATION_FRAME_WIDTH - (HTML_BODY_MARGIN * 2)
issues_chart.plot_height = DESTINATION_FRAME_HEIGHT - (HTML_BODY_MARGIN *
2)
issues_chart.logo = None
issues_chart.toolbar_location = None
hover = issues_chart.select(dict(type=HoverTool))
hover.tooltips = [("Value Delivered", "$x")]
#--- Configure output ---
reset_output()
if output_mode == 'static':
# Static file. CDN is most space efficient
output_file(ISSUES_FILE,
title=ISSUES_TITLE,
autosave=False,
mode='cdn',
root_dir=None) # Generate file
save(issues_chart, filename=ISSUES_FILE)
elif output_mode == 'notebook':
output_notebook() # Show inline
show(issues_chart)
else:
# Server (using internal server IP, rather than localhost or external)
session = bokeh.session.Session(root_url=BOKEH_SERVER_IP,
load_from_config=False)
output_server("ddod_chart", session=session)
show(issues_chart)
def fnCreate_Chart_Bar(df):
pd.options.html.border = 1
plot = Bar(df,
label='ID',
values='Tweet Count',
group='ID',
title="Top 5 contributors",
legend=False,
bar_width=1.8,
xlabel='Users',
ylabel='Tweets Count')
plot.logo = None
script, div = components(plot, CDN)
return script, div
def generate_chart(event):
data = get_data(TIMED_EVENTS[event])
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
values='percentage_dnf',
label='year',
color="FireBrick",
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan DNFs - " + event
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Percentage DNF"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart._yaxis.formatter = NumeralTickFormatter(format="0%")
barchart.y_range = Range1d(0, 1)
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
for renderer in barchart.select(GlyphRenderer):
if renderer.data_source.data['height'] != [0]:
year = renderer.data_source.data['year']
num_dnf = data['dnfs'].loc[data['year'] == year]
num_entries = data['entries'].loc[data['year'] == year]
percent_dnf = data['percentage_dnf'].loc[data['year'] == year]
hover = HoverTool(renderers=[renderer],
tooltips=[("# DNFs", '%d' % num_dnf.values[0]),
("# Entries", '%d' % num_entries.values[0]),
("% DNF", '%.2f%%' % (100 * percent_dnf.values[0]))])
barchart.add_tools(hover)
return barchart
def output_chart(issues_df,output_mode='static'):
import datetime
import bokeh
from bokeh.models import HoverTool
# Add timestamp to title
issues_chart = Bar(issues_df, label='value_delivered',
values='status', agg='count', stack='status',
title=ISSUES_TITLE+" (Updated "+datetime.datetime.now().strftime('%m/%d/%Y')+")",
xlabel="Value Delivered",ylabel="Number of Use Cases",
legend='top_right',
tools='hover',
color=brewer["GnBu"][3]
)
issues_chart.plot_width = DESTINATION_FRAME_WIDTH - (HTML_BODY_MARGIN * 2)
issues_chart.plot_height = DESTINATION_FRAME_HEIGHT - (HTML_BODY_MARGIN * 2)
issues_chart.logo = None
issues_chart.toolbar_location = None
hover = issues_chart.select(dict(type=HoverTool))
hover.tooltips = [ ("Value Delivered", "$x")]
#--- Configure output ---
reset_output()
if output_mode == 'static':
# Static file. CDN is most space efficient
output_file(ISSUES_FILE, title=ISSUES_TITLE,
autosave=False, mode='cdn',
root_dir=None
) # Generate file
save(issues_chart,filename=ISSUES_FILE)
elif output_mode == 'notebook':
output_notebook() # Show inline
show(issues_chart)
else:
# Server (using internal server IP, rather than localhost or external)
session = bokeh.session.Session(root_url = BOKEH_SERVER_IP, load_from_config=False)
output_server("ddod_chart", session=session)
show(issues_chart)
def generate_chart(year):
data = get_data(year)
plot_data = {
'country': data.index.tolist(),
'count': [float(i) for i in data.values.tolist()]
}
barchart = Bar(plot_data,
label='country',
values='count',
color="red",
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + year + " Countries"
barchart.x_range = FactorRange(factors=data.index.tolist())
barchart._xaxis.axis_label = "Country"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Number of Teams"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Country", '@x'), ("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart():
data = get_data()
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
label='year',
values='count',
group=cat(columns='size', ascending=True, sort=True),
color=color(columns='size', palette=Spectral4),
legend='top_left',
xgrid=False,
ygrid=False,
plot_width=800,
plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan Engine Cylinders"
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Frequency"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Engine", '@size'), ("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart(year):
data = get_data(year)
plot_data = {'country': data.index.tolist(),
'count': [float(i) for i in data.values.tolist()]}
barchart = Bar(plot_data,
label='country',
values='count',
color="red",
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + year + " Countries"
barchart.x_range = FactorRange(factors=data.index.tolist())
barchart._xaxis.axis_label = "Country"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Number of Teams"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Country", '@x'),
("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart():
data = get_data()
# Bokeh doesn't let me control the order of the grouping! This is
# frustrating since it will be different on every server launch
barchart = Bar(data,
label='year',
values='count',
group=cat(columns='size', ascending=True, sort=True),
color=color(columns='size', palette=Spectral4),
legend='top_left',
xgrid=False, ygrid=False,
plot_width=800, plot_height=500,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan Engine Cylinders"
barchart._xaxis.axis_label = "Year"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._yaxis.axis_label = "Frequency"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
hover = HoverTool(tooltips=[("Engine", '@size'),
("# Teams", '@height')])
barchart.add_tools(hover)
return barchart
def generate_chart(year):
data = get_data(year)
barchart = Bar(data,
values=blend(*SCORED_EVENTS, labels_name='event'),
label=cat(columns='Team', sort=False),
stack=cat(columns='event', sort=False),
color=color(columns='event', palette=Spectral9, sort=False),
xgrid=False,
ygrid=False,
legend='top_right',
plot_width=1000,
plot_height=625,
tools="pan,wheel_zoom,box_zoom,reset,resize")
barchart.title = "Formula SAE Michigan " + str(
year) + " Total Scores by Place"
barchart._xaxis.axis_label = "Teams"
barchart._xaxis.axis_line_color = None
barchart._xaxis.major_tick_line_color = None
barchart._xaxis.minor_tick_line_color = None
barchart._xaxis.major_label_text_font_size = '0.6em'
barchart._yaxis.axis_label = "Total Score"
barchart._yaxis.axis_line_color = None
barchart._yaxis.major_tick_line_color = None
barchart._yaxis.minor_tick_line_color = None
barchart.outline_line_color = None
barchart.toolbar_location = 'right'
barchart.logo = None
return barchart
portions_df = pd.DataFrame.from_records(portions, columns=["Name", "Portion","HasFeature","SaleStatus", "Feature"] )
# Make the chart
TOOLS = ''
plt2 = Bar(portions_df,
label="Name",
values="Portion",
stack = "SaleStatus",
legend="bottom_right",
color=['Green','Red'],
xlabel="Listing feature",
ylabel="Portion sold or unsold",
title="Impact of listing features on likelihood of sale",
tools=TOOLS)
plt2.logo = None
plt2.toolbar_location = None
plt2.y_range = Range1d(start=0,end=1)
# Save the plot
output_file("./templates/plot3_new.html")
# show(plt2)
#######################################################
# Look at dependence of sale outcome on seller feedback score
#######################################################
# hist = Histogram(data_sold, values='feedbackScore', color='listingType',
# title="Distribution of feedback scores for sold items", legend='top_left')
def summarize_plans(state, age, npi):
todaysdate = str(datetime.now())
age = str(age)
if age > '65':
age = '65'
elif age < '20':
age = '20'
# filter based on what plans are current (not expired) and age
filteredplans = pd.read_hdf('webapp/data/plan-rates.h5', state, where=['(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'],
columns = ['IndividualRate', 'MetalLevel', 'Age','URLForSummaryofBenefitsCoverage','PlanMarketingName'])
stateave = filteredplans.IndividualRate.mean()
myave = stateave
# plot it
#filteredplans.groupby('MetalLevel').IndividualRate.mean().plot(kind='bar')
#statebardf = filteredplans.groupby('MetalLevel').IndividualRate.mean()
statebardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
#p = mpl.to_bokeh()
if npi == '':
p = Bar(filteredplans.groupby('MetalLevel').IndividualRate.mean(), values='IndividualRate',
xlabel="", ylabel="Montly Premium ($)")
else:
print 'input npi is ' + npi
# read hdf5 of provider-plan pairings
provnplanpd = pd.read_hdf('webapp/data/plan_providers.h5', state)
# make a list of planids for the input npi
planlst = provnplanpd[provnplanpd.npi == npi].plan_id.values
print planlst
# read in hdf5 of plan info
planinfo = pd.read_hdf('webapp/data/plan-rates.h5', state, where=['(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'])
# for each planid, get the first entry and concatenate them together.
filteredplans = planinfo[planinfo.PlanId==planlst[0]].groupby('PlanId', as_index=False).first()
filteredplans.head()
#pd.concat([filteredplans, filteredplans])
for ii in range(1, len(planlst)):
filteredplans = pd.concat([filteredplans, planinfo[planinfo.PlanId==planlst[ii]].groupby('PlanId', as_index=False).first()])
provbardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
provbardf['average'] = ['provider rates']*len(provbardf.MetalLevel.values)
statebardf['average'] = ['state average']*len(provbardf.MetalLevel.values)
myave = statebardf.IndividualRate.mean()
#provbardf['average'] = [1, 1]
#statebardf['average'] = [2, 2]
#plotdf = pd.merge(statebardf, provbardf, on='MetalLevel')
plotdf = pd.concat([provbardf, statebardf])
#plotdf['state average'] = statebardf['IndividualRate']
#plotdf['provider rates'] = provbardf['IndividualRate']
print plotdf
p = Bar(plotdf, label='MetalLevel', values='IndividualRate', group='average', legend='top_right',
xlabel="", ylabel="Montly Premium ($)")
p.logo = None
p.plot_height=400
p.toolbar_location = None
script,div = components(p)
print filteredplans.to_dict(orient='records')
return {'num_plans':len(filteredplans), 'script': script, 'plot_div': div,
'national_comp': format_price_comp(float(age2nationalAverage(age))-myave),
'state_comp': format_price_comp(float(stateave)-myave),
'plans': render_template('plans.html', plans=filteredplans.sort_values(by='IndividualRate').to_dict(orient='records'))}
def summarize_plans(state, age, npi):
todaysdate = str(datetime.now())
age = str(age)
if age > '65':
age = '65'
elif age < '20':
age = '20'
# filter based on what plans are current (not expired) and age
filteredplans = pd.read_hdf(
'webapp/data/plan-rates.h5',
state,
where=[
'(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'
],
columns=[
'IndividualRate', 'MetalLevel', 'Age',
'URLForSummaryofBenefitsCoverage', 'PlanMarketingName'
])
stateave = filteredplans.IndividualRate.mean()
myave = stateave
# plot it
#filteredplans.groupby('MetalLevel').IndividualRate.mean().plot(kind='bar')
#statebardf = filteredplans.groupby('MetalLevel').IndividualRate.mean()
statebardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
#p = mpl.to_bokeh()
if npi == '':
p = Bar(filteredplans.groupby('MetalLevel').IndividualRate.mean(),
values='IndividualRate',
xlabel="",
ylabel="Montly Premium ($)")
else:
print 'input npi is ' + npi
# read hdf5 of provider-plan pairings
provnplanpd = pd.read_hdf('webapp/data/plan_providers.h5', state)
# make a list of planids for the input npi
planlst = provnplanpd[provnplanpd.npi == npi].plan_id.values
print planlst
# read in hdf5 of plan info
planinfo = pd.read_hdf(
'webapp/data/plan-rates.h5',
state,
where=[
'(Age==age) & (RateExpirationDate > todaysdate) & (RateEffectiveDate < todaysdate)'
])
# for each planid, get the first entry and concatenate them together.
filteredplans = planinfo[planinfo.PlanId == planlst[0]].groupby(
'PlanId', as_index=False).first()
filteredplans.head()
#pd.concat([filteredplans, filteredplans])
for ii in range(1, len(planlst)):
filteredplans = pd.concat([
filteredplans,
planinfo[planinfo.PlanId == planlst[ii]].groupby(
'PlanId', as_index=False).first()
])
provbardf = filteredplans.groupby('MetalLevel', as_index=False).mean()
provbardf['average'] = ['provider rates'] * len(
provbardf.MetalLevel.values)
statebardf['average'] = ['state average'] * len(
provbardf.MetalLevel.values)
myave = statebardf.IndividualRate.mean()
#provbardf['average'] = [1, 1]
#statebardf['average'] = [2, 2]
#plotdf = pd.merge(statebardf, provbardf, on='MetalLevel')
plotdf = pd.concat([provbardf, statebardf])
#plotdf['state average'] = statebardf['IndividualRate']
#plotdf['provider rates'] = provbardf['IndividualRate']
print plotdf
p = Bar(plotdf,
label='MetalLevel',
values='IndividualRate',
group='average',
legend='top_right',
xlabel="",
ylabel="Montly Premium ($)")
p.logo = None
p.plot_height = 400
p.toolbar_location = None
script, div = components(p)
print filteredplans.to_dict(orient='records')
return {
'num_plans':
len(filteredplans),
'script':
script,
'plot_div':
div,
'national_comp':
format_price_comp(float(age2nationalAverage(age)) - myave),
'state_comp':
format_price_comp(float(stateave) - myave),
'plans':
render_template('plans.html',
plans=filteredplans.sort_values(
by='IndividualRate').to_dict(orient='records'))
}
portions,
columns=["Name", "Portion", "HasFeature", "SaleStatus", "Feature"])
# Make the chart
TOOLS = ''
plt2 = Bar(portions_df,
label="Name",
values="Portion",
stack="SaleStatus",
legend="bottom_right",
color=['Green', 'Red'],
xlabel="Listing feature",
ylabel="Portion sold or unsold",
title="Impact of listing features on likelihood of sale",
tools=TOOLS)
plt2.logo = None
plt2.toolbar_location = None
plt2.y_range = Range1d(start=0, end=1)
# Save the plot
output_file("./templates/plot3_new.html")
# show(plt2)
#######################################################
# Look at dependence of sale outcome on seller feedback score
#######################################################
# hist = Histogram(data_sold, values='feedbackScore', color='listingType',
# title="Distribution of feedback scores for sold items", legend='top_left')
#
# # Save the plot
