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streamlit_app_no_css.py
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streamlit_app_no_css.py
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#######################
# Import libraries
import streamlit as st
import pandas as pd
import altair as alt
import plotly.express as px
#######################
# Page configuration
st.set_page_config(
page_title="US Population Dashboard",
page_icon="🏂",
layout="wide",
initial_sidebar_state="expanded")
alt.themes.enable("dark")
#######################
# Load data
df_reshaped = pd.read_csv('data/us-population-2010-2019-reshaped.csv')
#######################
# Sidebar
with st.sidebar:
st.title('🏂 US Population Dashboard')
year_list = list(df_reshaped.year.unique())[::-1]
selected_year = st.selectbox('Select a year', year_list)
df_selected_year = df_reshaped[df_reshaped.year == selected_year]
df_selected_year_sorted = df_selected_year.sort_values(by="population", ascending=False)
color_theme_list = ['blues', 'cividis', 'greens', 'inferno', 'magma', 'plasma', 'reds', 'rainbow', 'turbo', 'viridis']
selected_color_theme = st.selectbox('Select a color theme', color_theme_list)
#######################
# Plots
# Heatmap
def make_heatmap(input_df, input_y, input_x, input_color, input_color_theme):
heatmap = alt.Chart(input_df).mark_rect().encode(
y=alt.Y(f'{input_y}:O', axis=alt.Axis(title="Year", titleFontSize=18, titlePadding=15, titleFontWeight=900, labelAngle=0)),
x=alt.X(f'{input_x}:O', axis=alt.Axis(title="", titleFontSize=18, titlePadding=15, titleFontWeight=900)),
color=alt.Color(f'max({input_color}):Q',
legend=None,
scale=alt.Scale(scheme=input_color_theme)),
stroke=alt.value('black'),
strokeWidth=alt.value(0.25),
).properties(width=900
).configure_axis(
labelFontSize=12,
titleFontSize=12
)
# height=300
return heatmap
# Choropleth map
def make_choropleth(input_df, input_id, input_column, input_color_theme):
choropleth = px.choropleth(input_df, locations=input_id, color=input_column, locationmode="USA-states",
color_continuous_scale=input_color_theme,
range_color=(0, max(df_selected_year.population)),
scope="usa",
labels={'population':'Population'}
)
choropleth.update_layout(
template='plotly_dark',
plot_bgcolor='rgba(0, 0, 0, 0)',
paper_bgcolor='rgba(0, 0, 0, 0)',
margin=dict(l=0, r=0, t=0, b=0),
height=350
)
return choropleth
# Donut chart
def make_donut(input_response, input_text, input_color):
if input_color == 'blue':
chart_color = ['#29b5e8', '#155F7A']
if input_color == 'green':
chart_color = ['#27AE60', '#12783D']
if input_color == 'orange':
chart_color = ['#F39C12', '#875A12']
if input_color == 'red':
chart_color = ['#E74C3C', '#781F16']
source = pd.DataFrame({
"Topic": ['', input_text],
"% value": [100-input_response, input_response]
})
source_bg = pd.DataFrame({
"Topic": ['', input_text],
"% value": [100, 0]
})
plot = alt.Chart(source).mark_arc(innerRadius=45, cornerRadius=25).encode(
theta="% value",
color= alt.Color("Topic:N",
scale=alt.Scale(
#domain=['A', 'B'],
domain=[input_text, ''],
# range=['#29b5e8', '#155F7A']), # 31333F
range=chart_color),
legend=None),
).properties(width=130, height=130)
text = plot.mark_text(align='center', color="#29b5e8", font="Lato", fontSize=32, fontWeight=700, fontStyle="italic").encode(text=alt.value(f'{input_response} %'))
plot_bg = alt.Chart(source_bg).mark_arc(innerRadius=45, cornerRadius=20).encode(
theta="% value",
color= alt.Color("Topic:N",
scale=alt.Scale(
# domain=['A', 'B'],
domain=[input_text, ''],
range=chart_color), # 31333F
legend=None),
).properties(width=130, height=130)
return plot_bg + plot + text
# Convert population to text
def format_number(num):
if num > 1000000:
if not num % 1000000:
return f'{num // 1000000} M'
return f'{round(num / 1000000, 1)} M'
return f'{num // 1000} K'
# Calculation year-over-year population migrations
def calculate_population_difference(input_df, input_year):
selected_year_data = input_df[input_df['year'] == input_year].reset_index()
previous_year_data = input_df[input_df['year'] == input_year - 1].reset_index()
selected_year_data['population_difference'] = selected_year_data.population.sub(previous_year_data.population, fill_value=0)
return pd.concat([selected_year_data.states, selected_year_data.id, selected_year_data.population, selected_year_data.population_difference], axis=1).sort_values(by="population_difference", ascending=False)
#######################
# Dashboard Main Panel
col = st.columns((1.5, 4.5, 2), gap='medium')
with col[0]:
st.markdown('#### Gains/Losses')
df_population_difference_sorted = calculate_population_difference(df_reshaped, selected_year)
if selected_year > 2010:
first_state_name = df_population_difference_sorted.states.iloc[0]
first_state_population = format_number(df_population_difference_sorted.population.iloc[0])
first_state_delta = format_number(df_population_difference_sorted.population_difference.iloc[0])
else:
first_state_name = '-'
first_state_population = '-'
first_state_delta = ''
st.metric(label=first_state_name, value=first_state_population, delta=first_state_delta)
if selected_year > 2010:
last_state_name = df_population_difference_sorted.states.iloc[-1]
last_state_population = format_number(df_population_difference_sorted.population.iloc[-1])
last_state_delta = format_number(df_population_difference_sorted.population_difference.iloc[-1])
else:
last_state_name = '-'
last_state_population = '-'
last_state_delta = ''
st.metric(label=last_state_name, value=last_state_population, delta=last_state_delta)
st.markdown('#### States Migration')
if selected_year > 2010:
# Filter states with population difference > 50000
# df_greater_50000 = df_population_difference_sorted[df_population_difference_sorted.population_difference_absolute > 50000]
df_greater_50000 = df_population_difference_sorted[df_population_difference_sorted.population_difference > 50000]
df_less_50000 = df_population_difference_sorted[df_population_difference_sorted.population_difference < -50000]
# % of States with population difference > 50000
states_migration_greater = round((len(df_greater_50000)/df_population_difference_sorted.states.nunique())*100)
states_migration_less = round((len(df_less_50000)/df_population_difference_sorted.states.nunique())*100)
donut_chart_greater = make_donut(states_migration_greater, 'Inbound Migration', 'green')
donut_chart_less = make_donut(states_migration_less, 'Outbound Migration', 'red')
else:
states_migration_greater = 0
states_migration_less = 0
donut_chart_greater = make_donut(states_migration_greater, 'Inbound Migration', 'green')
donut_chart_less = make_donut(states_migration_less, 'Outbound Migration', 'red')
migrations_col = st.columns((0.2, 1, 0.2))
with migrations_col[1]:
st.write('Inbound')
st.altair_chart(donut_chart_greater)
st.write('Outbound')
st.altair_chart(donut_chart_less)
with col[1]:
st.markdown('#### Total Population')
choropleth = make_choropleth(df_selected_year, 'states_code', 'population', selected_color_theme)
st.plotly_chart(choropleth, use_container_width=True)
heatmap = make_heatmap(df_reshaped, 'year', 'states', 'population', selected_color_theme)
st.altair_chart(heatmap, use_container_width=True)
with col[2]:
st.markdown('#### Top States')
st.dataframe(df_selected_year_sorted,
column_order=("states", "population"),
hide_index=True,
width=None,
column_config={
"states": st.column_config.TextColumn(
"States",
),
"population": st.column_config.ProgressColumn(
"Population",
format="%f",
min_value=0,
max_value=max(df_selected_year_sorted.population),
)}
)
with st.expander('About', expanded=True):
st.write('''
- Data: [U.S. Census Bureau](https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html).
- :orange[**Gains/Losses**]: states with high inbound/ outbound migration for selected year
- :orange[**States Migration**]: percentage of states with annual inbound/ outbound migration > 50,000
''')