Add bar chart

app.py

@@ -1,6 +1,14 @@
 import panel as pn
 import pandas as pd
-pn.extension()
+import matplotlib
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+matplotlib.use("agg")
+
+pn.extension('ipywidgets')
 
 ### Part 0: Getting data
 
@@ -23,7 +31,8 @@ def get_data():
     df['county_fips'] = df['county_fips'].astype(str).str.zfill(5)
 
     df.columns = ['state_abbr','FIPS','county','family','housing','food','transportation','healthcare',
-                  'other_necessities','childcare','taxes','total','median_family_income','num_counties_in_st','st_cost_rank','st_med_aff_rank','st_income_rank']
+                  'other_necessities','childcare','taxes','total','median_family_income',
+                  'num_counties_in_st','st_cost_rank','st_med_aff_rank','st_income_rank']
     
     #Some counties share names across states; adding their state code makes them unique.
     df['county_state'] = df.county + ', ' + df.state_abbr
@@ -35,9 +44,10 @@ def get_data():
 
 df = get_data()
 
+
 ### Part 1: Find and display model budget
 
-#Function to represent data in US dollars
+#Helper function to represent data in US dollars
 def dol(value,df,row=0):
     return '${:0,}'.format(df[value][row])
 
@@ -46,7 +56,7 @@ def calculate_model(user_county,user_parents,user_children):
     user_family = f'{user_parents}p{user_children}c'
     user_df = df.loc[(df.county_state == user_county) & (df.family == user_family)].reset_index(drop=True)
     return (
-        f'Moderately frugal families of {user_parents} {"adult" if user_parents == 1 else "adults"} and {user_children} {"child" if user_children == 1 else "children"} living in {user_county} '
+        f'Moderately frugal families of {user_parents} {"adult" if user_parents == "1" else "adults"} and {user_children} {"child" if user_children == 1 else "children"} living in {user_county} '
         'tend to have a monthly family budget similar to the following:\n'
         f'\nHousing: {dol("housing",user_df)}'
         f'\nFood: {dol("food",user_df)}'
@@ -66,7 +76,7 @@ user_county = pn.widgets.AutocompleteInput(
     case_sensitive=False,
     placeholder='Input county name')
 
-adult_title = pn.pane.Markdown('Number of adults in household:')
+adult_title = pn.pane.Markdown('Number of adults in household:') #Radio buttons don't have their name displayed as a title, so I add it here
 user_parents = pn.widgets.RadioButtonGroup(
     name='Number of adults', options=['1', '2'], button_type='default', margin=(12,0,0,0))
 
@@ -102,8 +112,10 @@ def calculate_percentage(user_value,column,df):
 
 #Function that takes user budget input and returns how it compares to model.
 def calculate_budget_percentage(user_county,user_parents,user_children,user_income,user_housing,user_food,user_transportation,user_healthcare,user_childcare,user_other,user_taxes):
+    #Repeated code. Could potentially be avoided with more binding, but that wouldn't change performance
     user_family = f'{user_parents}p{user_children}c'
     user_df = df.loc[(df.county_state == user_county) & (df.family == user_family)].reset_index(drop=True)
+    #New code
     income_p = calculate_percentage(user_income,'median_monthly_family_income',user_df)
     housing_p = calculate_percentage(user_housing,'housing',user_df)
     food_p = calculate_percentage(user_food,'food',user_df)
@@ -158,11 +170,13 @@ def budget_result(clicked):
 
 budget_result = pn.pane.Markdown(pn.bind(budget_result,budget_submit))
 
+
 ### Part 3: Calculate and display most afforable counties and budgets
 
 #Function to take user constraints, find most affordable counties, and calculate budgets
 def calculate_comparison(user_county,user_parents,user_children,user_income,user_housing,user_food,user_transportation,user_healthcare,user_childcare,user_other,user_taxes,
                   bring_income,income_cap,income_cap_amount,state_restriction,states_allowed,result_count):
+    #Repeated code:
     user_family = f'{user_parents}p{user_children}c'
     user_df = df.loc[(df.county_state == user_county) & (df.family == user_family)].reset_index(drop=True)
     income_p = calculate_percentage(user_income,'median_monthly_family_income',user_df)
@@ -173,6 +187,7 @@ def calculate_comparison(user_county,user_parents,user_children,user_income,user
     childcare_p = calculate_percentage(user_childcare,'childcare',user_df)
     other_p = calculate_percentage(user_other,'other_necessities',user_df)
     taxes_p = calculate_percentage(user_taxes,'taxes',user_df)
+    #New code:
     new_df = df.loc[(df.family == user_family)].reset_index(drop=True)
     if bring_income:
         new_df.median_monthly_family_income = user_income
@@ -256,7 +271,75 @@ def comparison_result(clicked):
 
 comparison_result = pn.pane.Markdown(pn.bind(comparison_result,comparison_submit))
 
-### Part 4: Templating
+
+### Part 4: Bar chart of results
+def calculate_bar(user_county,user_parents,user_children,user_income,user_housing,user_food,user_transportation,user_healthcare,user_childcare,user_other,user_taxes,
+                  bring_income,income_cap,income_cap_amount,state_restriction,states_allowed,result_count):
+    #Repeated code:
+    user_family = f'{user_parents}p{user_children}c'
+    user_df = df.loc[(df.county_state == user_county) & (df.family == user_family)].reset_index(drop=True)
+    income_p = calculate_percentage(user_income,'median_monthly_family_income',user_df)
+    housing_p = calculate_percentage(user_housing,'housing',user_df)
+    food_p = calculate_percentage(user_food,'food',user_df)
+    transportation_p = calculate_percentage(user_transportation,'transportation',user_df)
+    healthcare_p = calculate_percentage(user_healthcare,'healthcare',user_df)
+    childcare_p = calculate_percentage(user_childcare,'childcare',user_df)
+    other_p = calculate_percentage(user_other,'other_necessities',user_df)
+    taxes_p = calculate_percentage(user_taxes,'taxes',user_df)
+    new_df = df.loc[(df.family == user_family)].reset_index(drop=True)
+    if bring_income:
+        new_df.median_monthly_family_income = user_income
+    else:
+        new_df.median_monthly_family_income = new_df.median_monthly_family_income.mul(income_p).round(0).astype('int64')
+    new_df['median_monthly_family_income_uncapped'] = new_df.median_monthly_family_income
+    if income_cap:
+        new_df.median_monthly_family_income.clip(upper=income_cap_amount,inplace=True)
+    if state_restriction:
+        new_df = new_df.loc[(new_df.state_abbr.isin(states_allowed))].reset_index(drop=True)
+    new_df.housing = new_df.housing.mul(housing_p).round(0).astype('int64')
+    new_df.food = new_df.food.mul(food_p).round(0).astype('int64')
+    new_df.transportation = new_df.transportation.mul(transportation_p).round(0).astype('int64')
+    new_df.healthcare = new_df.healthcare.mul(healthcare_p).round(0).astype('int64')
+    new_df.other_necessities = new_df.other_necessities.mul(other_p).round(0).astype('int64')
+    new_df.childcare = new_df.childcare.mul(childcare_p).round(0).astype('int64')
+    new_df.taxes = new_df.taxes.mul(taxes_p).round(0).astype('int64')
+    new_df.total = new_df.housing + new_df.food + new_df.transportation + new_df.healthcare + new_df.other_necessities + new_df.childcare + new_df.taxes
+    new_df.remaining_money = new_df.median_monthly_family_income - new_df.total
+    new_df = new_df.sort_values('remaining_money',ascending=False).reset_index(drop=True)
+    #New code:
+    new_df = new_df[:result_count]
+    fig = Figure(figsize=(result_count,4))
+    ax = fig.add_subplot(111)
+    ax = new_df.median_monthly_family_income.plot.bar(width=0.4, position=1, ax=ax)
+    ax = new_df[['housing', 'food','transportation','healthcare','childcare','other_necessities','taxes']].plot.bar(stacked=True, width=0.4, position=0, ax=ax, alpha=0.7)
+    ax.legend(labels=['Income','Housing','Food','Transportation','Healthcare','Childcare','Other','Taxes'],loc="upper right",bbox_to_anchor=(1 + 2/result_count, 1))
+    ax.set_xticks(ticks=np.arange(len(new_df)),labels=new_df.county_state.values)
+    ax.yaxis.set_major_formatter('${x:,.0f}')
+    ax.yaxis.grid(linestyle='--',alpha=.4)
+    ax.set_xlim([-.5, len(new_df)-.5])
+    plt.close(fig)
+    return fig
+
+def nothing_fig():
+    fig = Figure(figsize=(5,4))
+    ax = fig.add_subplot(111)
+    return fig
+
+bar = pn.bind(calculate_bar, user_county,user_parents,user_children,
+                     user_income,user_housing,user_food,user_transportation,user_healthcare,user_childcare,user_other,user_taxes,
+                     bring_income,income_cap,income_cap_amount,state_restriction,states_allowed,result_count)
+
+def bar_result(clicked):
+    if clicked:
+        if user_county.value == '':
+            return nothing_fig()
+        return bar()
+    return nothing_fig()
+
+bar_result = pn.pane.Matplotlib(pn.bind(bar_result,comparison_submit), dpi=144, tight=True)
+
+
+### Part 5: Templating
 
 template = pn.template.BootstrapTemplate(title='Affordable County Locator')
 
@@ -288,8 +371,14 @@ main3 = pn.Card(
     styles={'background': 'WhiteSmoke'},
     max_height = 300
 )
+main4 = pn.Card(
+    bar_result,
+    title='Most Affordable Counties, bar chart',
+    styles={'background':'WhiteSmoke'},
+    collapsed=True
+)
 template.main.append(
-    pn.Column(main1,main2,main3)
+    pn.Column(main1,main2,main3,main4)
 )
 
 template.servable();