Rename app.py to affordable_county_locator.py

affordable_county_locator.py

@@ -0,0 +1,295 @@
+import panel as pn
+import pandas as pd
+pn.extension()
+
+### Part 0: Getting data
+
+@pn.cache # Add caching to only download data once
+def get_data():
+    #df = pd.read_excel('fbc_data_2024.xlsx', sheet_name='County', header=1)
+    df = pd.read_excel('https://files.epi.org/uploads/fbc_data_2024.xlsx', sheet_name='County', header=1)
+    
+    #Drop unneeded columns
+    col_to_drop = list(df.columns[:1]) + list(df.columns[13:21])
+    df = df.drop(columns=col_to_drop)
+
+    #One apparently random county is missing median income data, but is still ranked.
+    #By taking the average of the counties ranked immediately above and below it, a quick estimate of $55,122 can be made.
+    df.fillna(55122.0,inplace=True)
+
+    df['median_family_income'] = df['median_family_income'].astype('int64')
+
+    #Add leading zeroes to FIPS codes makes them readable to Tableau.
+    df['county_fips'] = df['county_fips'].astype(str).str.zfill(5)
+
+    df.columns = ['state_abbr','FIPS','county','family','housing','food','transportation','healthcare',
+                  'other_necessities','childcare','taxes','total','median_family_income','num_counties_in_st','st_cost_rank','st_med_aff_rank','st_income_rank']
+    
+    #Some counties share names across states; adding their state code makes them unique.
+    df['county_state'] = df.county + ', ' + df.state_abbr
+    
+    df['median_monthly_family_income'] = df.median_family_income.div(12).round(0).astype('int64')
+    
+    df['remaining_money'] = df.median_monthly_family_income - df.total
+    return df
+
+df = get_data()
+
+### Part 1: Find and display model budget
+
+#Function to represent data in US dollars
+def dol(value,df,row=0):
+    return '${:0,}'.format(df[value][row])
+
+#Function that takes user input and displays the corresponding row from the df
+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} '
+        'tend to have a monthly family budget similar to the following:\n'
+        f'\nHousing: {dol("housing",user_df)}'
+        f'\nFood: {dol("food",user_df)}'
+        f'\nTransportation: {dol("transportation",user_df)}'
+        f'\nHealthcare: {dol("healthcare",user_df)}'
+        f'\nChildcare: {dol("childcare",user_df)}'
+        f'\nOther necessities: {dol("other_necessities",user_df)}'
+        f'\nTaxes: {dol("taxes",user_df)}'
+        f'\nTotal: {dol("total",user_df)}'
+        f'\n\nThe median monthly family income for {user_county} is {dol("median_monthly_family_income",user_df)}, which would '
+        f'leave {dol("remaining_money",user_df)} each month for emergencies, saving, and discretionary spending.'
+    )
+
+#Widgets for receiving user input
+user_county = pn.widgets.AutocompleteInput(
+    name='County of primary residence:', options=df.county_state.unique().tolist(),
+    case_sensitive=False,
+    placeholder='Input county name')
+
+adult_title = pn.pane.Markdown('Number of adults in household:')
+user_parents = pn.widgets.RadioButtonGroup(
+    name='Number of adults', options=['1', '2'], button_type='default', margin=(12,0,0,0))
+
+user_children = pn.widgets.IntSlider(
+    name='Number of children', start=0, end=4, step=1, value=0)
+
+#Bind widgets to function
+county_model = pn.bind(calculate_model, user_county=user_county,user_parents=user_parents,user_children=user_children)
+
+#Submit button
+county_submit = pn.widgets.Button(name="Submit Family/Location", button_type="primary")
+
+#Function to make model function respond to submit button
+def county_result(clicked):
+    if clicked:
+        if user_county.value == '':
+            return 'County information is required.'
+        return county_model()
+    return "Click Submit Family/Location to see a typical budget."
+
+#Binding button to button function
+county_result = pn.pane.Markdown(pn.bind(county_result,county_submit))
+
+
+### Part 2: Calculate and display user budget's comparison to model
+
+#Function to calculate the user budget's comparison to the model. Accounts for dividing by zero.
+def calculate_percentage(user_value,column,df):
+    if df[column][0] == 0:
+        return 1.0
+    else:
+        return 1+round(((user_value-df[column][0])/df[column][0]),3)
+
+#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):
+    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)
+    total_p = 1+round((((user_housing+user_food+user_transportation+user_healthcare+
+                     user_childcare+user_other+user_taxes)-user_df.total[0])/user_df.total[0]),3)
+    return (
+        f"Your family's income is {income_p:.1%} that of the median family income in your area. "
+        'Your budget compares to the typical model as follows:\n'
+        f'\nHousing: {housing_p:.1%}'
+        f'\nFood: {food_p:.1%}'
+        f'\nTransportation: {transportation_p:.1%}'
+        f'\nHealthcare: {healthcare_p:.1%}'
+        f'\nChildcare: {"N/A" if user_children==0 else str(round(childcare_p*100,1))+"%"}'
+        f'\nOther necessities: {other_p:.1%}'
+        f'\nTaxes: {taxes_p:.1%}'
+        f'\nTotal: {total_p:.1%}'
+    )
+
+#Function for budget-input widgets
+def input_budget(prompt):
+    return pn.widgets.IntInput(name=prompt, value=0, start=0)
+
+#Budget input widgets
+user_income = input_budget('What is your monthly family income? (Pre-tax.)')
+budget_title = pn.pane.Markdown("#### For each item, input your family's monthly spending.")
+                          #How much do you spend on housing per month? Inclu
+user_housing = input_budget('Housing (Include utilities.)')
+user_food = input_budget('Food')
+user_transportation = input_budget('Transportation (Gas, car payment/repair, bus pass, etc.)')
+user_healthcare = input_budget('Healthcare (Both out-of-pocket and premium)')
+user_childcare = input_budget('Childcare')
+user_other = input_budget('Other necessities (Clothes, house/school supplies, etc.)')
+user_taxes = input_budget('Taxes')
+
+#Binding widgets to function
+budget_percentage = pn.bind(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)
+
+#Submit button, function to respond to button, binding to button
+budget_submit = pn.widgets.Button(name="Submit Budget", button_type="primary")
+
+def budget_result(clicked):
+    if clicked:
+        if user_county.value == '':
+            return 'County information is required.'
+        return budget_percentage()
+    return "Click Submit Budget to see how your budget compares."
+
+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):
+    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)
+    county_results = []
+    limit = result_count
+    if limit > len(new_df):
+        limit = len(new_df)
+        county_results.append(f'There are only {limit} counties to show!\n\n')
+    if limit == 1:
+        second_line = 'here is the most affordable county for your family, along with what your budget might look living there:\n'
+    else:
+        second_line = f'here are the {limit} most affordable counties for your family, along with what your budget might look living there:\n'
+    if not bring_income and not income_cap:
+        county_results.append("With both your spending and income recalculated based on each county's norm, "
+                             + second_line)
+    elif bring_income:
+        county_results.append("With your exact income maintained but your spending recalculated based on each county's norm, "
+                          + second_line)
+    else:
+        county_results.append(f"With both your spending and income recalculated based on each county's norm (and an income cap of ${income_cap_amount:,.0f}), "
+                          + second_line)
+    for i in range(limit):
+        current_result = [f'\n#{i+1}\n{new_df.county_state[i]}']
+        for [j,k] in [['housing','Housing'],['food','Food'],['transportation','Transportation'],['healthcare','Healthcare'],
+                      ['childcare','Childcare'],['other_necessities','Other necessities'],['taxes','Taxes'],['total','Total'],
+                      ['median_monthly_family_income','Median family income'],['remaining_money','Remaining money']]:
+            if j == 'median_monthly_family_income' and income_cap == True and new_df.median_monthly_family_income_uncapped[i] > new_df.median_monthly_family_income[i]:
+                current_result.append(f"    {k}: {dol(j,new_df,row=i)} (Uncapped: {dol('median_monthly_family_income_uncapped',new_df,row=i)})")
+            else:
+                current_result.append(f"    {k}: {dol(j,new_df,row=i)}")
+        county_results.append('\n'.join(current_result))
+    return '\n'.join(county_results)
+
+#Constraint widgets
+bring_income_title = pn.pane.Markdown('Maintain income exactly, regardless of county:')
+bring_income = pn.widgets.Switch(margin=(19,0,0,0))
+
+income_cap_title = pn.pane.Markdown('Enforce income cap:') #Would be nice to disable when bring_income is active
+income_cap = pn.widgets.Switch(margin=(19,0,0,0))
+
+income_cap_amount = pn.widgets.IntInput(name='Income cap value:', value=0, start=0)
+
+state_restriction_title = pn.pane.Markdown('Restrict results to particular states:')
+state_restriction = pn.widgets.Switch(margin=(19,0,0,0))
+
+states_allowed = pn.widgets.MultiChoice(name='States to include:', options=df.state_abbr.unique().tolist()) #Would be nice to disable when state_restriction is not active
+
+result_count = pn.widgets.IntInput(name='Results to view:', value=5, start=1)
+
+#Bind to widgets
+comparison = pn.bind(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)
+
+#Submit button, bind
+comparison_submit = pn.widgets.Button(name="Submit Constraints", button_type="primary")
+
+def comparison_result(clicked):
+    if clicked:
+        if user_county.value == '':
+            return 'County information is required.'
+        return comparison()
+    return "Click Submit Constraints to see the final results."
+
+comparison_result = pn.pane.Markdown(pn.bind(comparison_result,comparison_submit))
+
+### Part 4: Templating
+
+template = pn.template.BootstrapTemplate(title='Affordable County Locator')
+
+#Sidebar formatting
+intro = pn.pane.Markdown('If you and your family could maintain your relative income and spending habits,'
+                         'in which US county would you be able to save the most money every month? Use this app to find out!')
+part1_title = pn.pane.Markdown('## Part 1: County and family size')
+part2_title = pn.pane.Markdown('## Part 2: Budget')
+part3_title = pn.pane.Markdown('## Part 3: Calculation constraints')
+
+template.sidebar.extend([intro, part1_title, user_county, pn.Row(adult_title, user_parents), user_children, county_submit,
+                         part2_title, user_income, budget_title, user_housing, user_food, user_transportation, user_healthcare, user_childcare, user_other, user_taxes, budget_submit,
+                         part3_title, pn.Row(bring_income_title, bring_income),pn.Row(income_cap_title, income_cap),income_cap_amount,pn.Row(state_restriction_title, state_restriction),states_allowed,result_count,comparison_submit])
+
+#Main formatting
+main1 = pn.Card(
+    county_result,
+    title='Typical Budget',
+    styles={'background': 'WhiteSmoke'}
+)
+main2 = pn.Card(
+    budget_result,
+    title='Budget Comparison',
+    styles={'background': 'WhiteSmoke'}
+)
+main3 = pn.Card(
+    comparison_result,
+    title='Most Affordable Counties',
+    styles={'background': 'WhiteSmoke'},
+    max_height = 300
+)
+template.main.append(
+    pn.Column(main1,main2,main3)
+)
+
+template.servable();

app.py

@@ -1,108 +0,0 @@
-import panel as pn
-import pandas as pd
-import hvplot.pandas
-
-pn.extension("tabulator")
-
-ACCENT="teal"
-
-styles = {
-    "box-shadow": "rgba(50, 50, 93, 0.25) 0px 6px 12px -2px, rgba(0, 0, 0, 0.3) 0px 3px 7px -3px",
-    "border-radius": "4px",
-    "padding": "10px",
-}
-
-image = pn.pane.JPG("https://assets.holoviz.org/panel/tutorials/wind_turbines_sunset.png")
-
-# Extract Data
-
-@pn.cache()  # only download data once
-def get_data():
-    return pd.read_csv("https://assets.holoviz.org/panel/tutorials/turbines.csv.gz")
-
-# Transform Data
-
-source_data = get_data()
-min_year = int(source_data["p_year"].min())
-max_year = int(source_data["p_year"].max())
-top_manufacturers = (
-    source_data.groupby("t_manu").p_cap.sum().sort_values().iloc[-10:].index.to_list()
-)
-
-def filter_data(t_manu, year):
-    data = source_data[(source_data.t_manu == t_manu) & (source_data.p_year <= year)]
-    return data
-
-# Filters
-
-t_manu = pn.widgets.Select(
-    name="Manufacturer",
-    value="Vestas",
-    options=sorted(top_manufacturers),
-    description="The name of the manufacturer",
-)
-p_year = pn.widgets.IntSlider(name="Year", value=max_year, start=min_year, end=max_year)
-
-# Transform Data 2
-
-df = pn.rx(filter_data)(t_manu=t_manu, year=p_year)
-count = df.rx.len()
-total_capacity = df.t_cap.sum()
-avg_capacity = df.t_cap.mean()
-avg_rotor_diameter = df.t_rd.mean()
-
-# Plot Data
-
-fig = (
-    df[["p_year", "t_cap"]].groupby("p_year").sum() / 10**6
-).hvplot.bar(
-    title="Capacity Change",
-    rot=90,
-    ylabel="Capacity (GW)",
-    xlabel="Year",
-    xlim=(min_year, max_year),
-    color=ACCENT, 
-)
-
-# Display Data
-
-indicators = pn.FlexBox(
-    pn.indicators.Number(
-        value=count, name="Count", format="{value:,.0f}", styles=styles
-    ),
-    pn.indicators.Number(
-        value=total_capacity / 1e6,
-        name="Total Capacity (GW)",
-        format="{value:,.1f}",
-        styles=styles,
-    ),
-    pn.indicators.Number(
-        value=avg_capacity/1e3,
-        name="Avg. Capacity (MW)",
-        format="{value:,.1f}",
-        styles=styles,
-    ),
-    pn.indicators.Number(
-        value=avg_rotor_diameter,
-        name="Avg. Rotor Diameter (m)",
-        format="{value:,.1f}",
-        styles=styles,
-    ),
-)
-
-plot = pn.pane.HoloViews(fig, sizing_mode="stretch_both", name="Plot")
-table = pn.widgets.Tabulator(df, sizing_mode="stretch_both", name="Table")
-
-# Layout Data
-
-tabs = pn.Tabs(
-    plot, table, styles=styles, sizing_mode="stretch_width", height=500, margin=10
-)
-
-pn.template.FastListTemplate(
-    title="Wind Turbine Dashboard",
-    sidebar=[image, t_manu, p_year],
-    main=[pn.Column(indicators, tabs, sizing_mode="stretch_both")],
-    main_layout=None,
-    accent=ACCENT,
-).servable()