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README.md

@@ -1,13 +1,19 @@
----
-title: VisualizationDemo
-emoji: 🐨
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.38.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏂 US Population Dashboard
+
+A dashboard web app template built in Python using Streamlit.
+
+## Demo App
+
+[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://population-dashboard.streamlit.app/)
+
+## Colab notebook
+[![Colab Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/dataprofessor/population-dashboard/blob/master/US_Population.ipynb)
+
+## Prerequisite libraries
+Here are the Python libraries used in the creation of this dashboard app
+
+## Data source
+US Population data spanning the duration of 2010-2019 was obtained from the [U.S. Census Bureau](https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html).
+
+## Reference
+A talk entitled [_Crafting a Dashboard App in Python using Streamlit_](https://budapestbi.hu/2023/hu/program/speakers/chanin-nantasenamat/) showing how to build this app is given at the [Budapest BI Forum (Data Visualization track)](https://budapestbi.hu/2023/hu/en/program-data-visualization-track/) on November 22, 2023.

requirements.txt

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+streamlit
+pandas
+altair
+plotly

streamlit_app.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")
+
+#######################
+# CSS styling
+st.markdown("""
+<style>
+
+[data-testid="block-container"] {
+    padding-left: 2rem;
+    padding-right: 2rem;
+    padding-top: 1rem;
+    padding-bottom: 0rem;
+    margin-bottom: -7rem;
+}
+
+[data-testid="stVerticalBlock"] {
+    padding-left: 0rem;
+    padding-right: 0rem;
+}
+
+[data-testid="stMetric"] {
+    background-color: #393939;
+    text-align: center;
+    padding: 15px 0;
+}
+
+[data-testid="stMetricLabel"] {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+}
+
+[data-testid="stMetricDeltaIcon-Up"] {
+    position: relative;
+    left: 38%;
+    -webkit-transform: translateX(-50%);
+    -ms-transform: translateX(-50%);
+    transform: translateX(-50%);
+}
+
+[data-testid="stMetricDeltaIcon-Down"] {
+    position: relative;
+    left: 38%;
+    -webkit-transform: translateX(-50%);
+    -ms-transform: translateX(-50%);
+    transform: translateX(-50%);
+}
+
+</style>
+""", unsafe_allow_html=True)
+
+
+#######################
+# Load data
+df_reshaped = pd.read_csv('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
+            ''')

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
+            ''')

us-population-2010-2019-reshaped.csv

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+,states,states_code,id,year,population
+0,Alabama,AL,1,2010,4785437
+1,Alaska,AK,2,2010,713910
+2,Arizona,AZ,4,2010,6407172
+3,Arkansas,AR,5,2010,2921964
+4,California,CA,6,2010,37319502
+5,Colorado,CO,8,2010,5047349
+6,Connecticut,CT,9,2010,3579114
+7,Delaware,DE,10,2010,899593
+8,District of Columbia,DC,11,2010,605226
+9,Florida,FL,12,2010,18845537
+10,Georgia,GA,13,2010,9711881
+11,Hawaii,HI,15,2010,1363963
+12,Idaho,ID,16,2010,1570746
+13,Illinois,IL,17,2010,12840503
+14,Indiana,IN,18,2010,6490432
+15,Iowa,IA,19,2010,3050745
+16,Kansas,KS,20,2010,2858190
+17,Kentucky,KY,21,2010,4348181
+18,Louisiana,LA,22,2010,4544532
+19,Maine,ME,23,2010,1327629
+20,Maryland,MD,24,2010,5788645
+21,Massachusetts,MA,25,2010,6566307
+22,Michigan,MI,26,2010,9877510
+23,Minnesota,MN,27,2010,5310828
+24,Mississippi,MS,28,2010,2970548
+25,Missouri,MO,29,2010,5995974
+26,Montana,MT,30,2010,990697
+27,Nebraska,NE,31,2010,1829542
+28,Nevada,NV,32,2010,2702405
+29,New Hampshire,NH,33,2010,1316762
+30,New Jersey,NJ,34,2010,8799446
+31,New Mexico,NM,35,2010,2064552
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+508,South Carolina,SC,45,2019,5148714
+509,South Dakota,SD,46,2019,884659
+510,Tennessee,TN,47,2019,6829174
+511,Texas,TX,48,2019,28995881
+512,Utah,UT,49,2019,3205958
+513,Vermont,VT,50,2019,623989
+514,Virginia,VA,51,2019,8535519
+515,Washington,WA,53,2019,7614893
+516,West Virginia,WV,54,2019,1792147
+517,Wisconsin,WI,55,2019,5822434
+518,Wyoming,WY,56,2019,578759
+519,Puerto Rico,PR,72,2019,3193694

us-population-2010-2019-states-code.csv

@@ -0,0 +1,53 @@
+states,states_code,id,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019
+Alabama,AL,1,"4,785,437","4,799,069","4,815,588","4,830,081","4,841,799","4,852,347","4,863,525","4,874,486","4,887,681","4,903,185"
+Alaska,AK,2,"713,910","722,128","730,443","737,068","736,283","737,498","741,456","739,700","735,139","731,545"
+Arizona,AZ,4,"6,407,172","6,472,643","6,554,978","6,632,764","6,730,413","6,829,676","6,941,072","7,044,008","7,158,024","7,278,717"
+Arkansas,AR,5,"2,921,964","2,940,667","2,952,164","2,959,400","2,967,392","2,978,048","2,989,918","3,001,345","3,009,733","3,017,804"
+California,CA,6,"37,319,502","37,638,369","37,948,800","38,260,787","38,596,972","38,918,045","39,167,117","39,358,497","39,461,588","39,512,223"
+Colorado,CO,8,"5,047,349","5,121,108","5,192,647","5,269,035","5,350,101","5,450,623","5,539,215","5,611,885","5,691,287","5,758,736"
+Connecticut,CT,9,"3,579,114","3,588,283","3,594,547","3,594,841","3,594,524","3,587,122","3,578,141","3,573,297","3,571,520","3,565,287"
+Delaware,DE,10,"899,593","907,381","915,179","923,576","932,487","941,252","948,921","956,823","965,479","973,764"
+District of Columbia,DC,11,"605,226","619,800","634,924","650,581","662,328","675,400","685,815","694,906","701,547","705,749"
+Florida,FL,12,"18,845,537","19,053,237","19,297,822","19,545,621","19,845,911","20,209,042","20,613,477","20,963,613","21,244,317","21,477,737"
+Georgia,GA,13,"9,711,881","9,802,431","9,901,430","9,972,479","10,067,278","10,178,447","10,301,890","10,410,330","10,511,131","10,617,423"
+Hawaii,HI,15,"1,363,963","1,379,329","1,394,804","1,408,243","1,414,538","1,422,052","1,427,559","1,424,393","1,420,593","1,415,872"
+Idaho,ID,16,"1,570,746","1,583,910","1,595,324","1,611,206","1,631,112","1,651,059","1,682,380","1,717,715","1,750,536","1,787,065"
+Illinois,IL,17,"12,840,503","12,867,454","12,882,510","12,895,129","12,884,493","12,858,913","12,820,527","12,778,828","12,723,071","12,671,821"
+Indiana,IN,18,"6,490,432","6,516,528","6,537,703","6,568,713","6,593,644","6,608,422","6,634,304","6,658,078","6,695,497","6,732,219"
+Iowa,IA,19,"3,050,745","3,066,336","3,076,190","3,092,997","3,109,350","3,120,960","3,131,371","3,141,550","3,148,618","3,155,070"
+Kansas,KS,20,"2,858,190","2,869,225","2,885,257","2,893,212","2,900,475","2,909,011","2,910,844","2,908,718","2,911,359","2,913,314"
+Kentucky,KY,21,"4,348,181","4,369,821","4,386,346","4,404,659","4,414,349","4,425,976","4,438,182","4,452,268","4,461,153","4,467,673"
+Louisiana,LA,22,"4,544,532","4,575,625","4,600,972","4,624,527","4,644,013","4,664,628","4,678,135","4,670,560","4,659,690","4,648,794"
+Maine,ME,23,"1,327,629","1,328,284","1,327,729","1,328,009","1,330,513","1,328,262","1,331,317","1,334,612","1,339,057","1,344,212"
+Maryland,MD,24,"5,788,645","5,839,419","5,886,992","5,923,188","5,957,283","5,985,562","6,003,323","6,023,868","6,035,802","6,045,680"
+Massachusetts,MA,25,"6,566,307","6,613,583","6,663,005","6,713,315","6,762,596","6,794,228","6,823,608","6,859,789","6,882,635","6,892,503"
+Michigan,MI,26,"9,877,510","9,882,412","9,897,145","9,913,065","9,929,848","9,931,715","9,950,571","9,973,114","9,984,072","9,986,857"
+Minnesota,MN,27,"5,310,828","5,346,143","5,376,643","5,413,479","5,451,079","5,482,032","5,522,744","5,566,230","5,606,249","5,639,632"
+Mississippi,MS,28,"2,970,548","2,978,731","2,983,816","2,988,711","2,990,468","2,988,471","2,987,938","2,988,510","2,981,020","2,976,149"
+Missouri,MO,29,"5,995,974","6,010,275","6,024,367","6,040,715","6,056,202","6,071,732","6,087,135","6,106,670","6,121,623","6,137,428"
+Montana,MT,30,"990,697","997,316","1,003,783","1,013,569","1,021,869","1,030,475","1,040,859","1,052,482","1,060,665","1,068,778"
+Nebraska,NE,31,"1,829,542","1,840,672","1,853,303","1,865,279","1,879,321","1,891,277","1,905,616","1,915,947","1,925,614","1,934,408"
+Nevada,NV,32,"2,702,405","2,712,730","2,743,996","2,775,970","2,817,628","2,866,939","2,917,563","2,969,905","3,027,341","3,080,156"
+New Hampshire,NH,33,"1,316,762","1,320,202","1,324,232","1,326,622","1,333,341","1,336,350","1,342,307","1,348,787","1,353,465","1,359,711"
+New Jersey,NJ,34,"8,799,446","8,828,117","8,844,942","8,856,972","8,864,525","8,867,949","8,870,827","8,885,525","8,886,025","8,882,190"
+New Mexico,NM,35,"2,064,552","2,080,450","2,087,309","2,092,273","2,089,568","2,089,291","2,091,630","2,091,784","2,092,741","2,096,829"
+New York,NY,36,"19,399,878","19,499,241","19,572,932","19,624,447","19,651,049","19,654,666","19,633,428","19,589,572","19,530,351","19,453,561"
+North Carolina,NC,37,"9,574,323","9,657,592","9,749,476","9,843,336","9,932,887","10,031,646","10,154,788","10,268,233","10,381,615","10,488,084"
+North Dakota,ND,38,"674,715","685,225","701,176","722,036","737,401","754,066","754,434","754,942","758,080","762,062"
+Ohio,OH,39,"11,539,336","11,544,663","11,548,923","11,576,684","11,602,700","11,617,527","11,634,370","11,659,650","11,676,341","11,689,100"
+Oklahoma,OK,40,"3,759,944","3,788,379","3,818,814","3,853,214","3,878,187","3,909,500","3,926,331","3,931,316","3,940,235","3,956,971"
+Oregon,OR,41,"3,837,491","3,872,036","3,899,001","3,922,468","3,963,244","4,015,792","4,089,976","4,143,625","4,181,886","4,217,737"
+Pennsylvania,PA,42,"12,711,160","12,745,815","12,767,118","12,776,309","12,788,313","12,784,826","12,782,275","12,787,641","12,800,922","12,801,989"
+Rhode Island,RI,44,"1,053,959","1,053,649","1,054,621","1,055,081","1,055,936","1,056,065","1,056,770","1,055,673","1,058,287","1,059,361"
+South Carolina,SC,45,"4,635,649","4,671,994","4,717,354","4,764,080","4,823,617","4,891,938","4,957,968","5,021,268","5,084,156","5,148,714"
+South Dakota,SD,46,"816,166","823,579","833,566","842,316","849,129","853,988","862,996","872,868","878,698","884,659"
+Tennessee,TN,47,"6,355,311","6,399,291","6,453,898","6,494,340","6,541,223","6,591,170","6,646,010","6,708,799","6,771,631","6,829,174"
+Texas,TX,48,"25,241,971","25,645,629","26,084,481","26,480,266","26,964,333","27,470,056","27,914,410","28,295,273","28,628,666","28,995,881"
+Utah,UT,49,"2,775,332","2,814,384","2,853,375","2,897,640","2,936,879","2,981,835","3,041,868","3,101,042","3,153,550","3,205,958"
+Vermont,VT,50,"625,879","627,049","626,090","626,210","625,214","625,216","623,657","624,344","624,358","623,989"
+Virginia,VA,51,"8,023,699","8,101,155","8,185,080","8,252,427","8,310,993","8,361,808","8,410,106","8,463,587","8,501,286","8,535,519"
+Washington,WA,53,"6,742,830","6,826,627","6,897,058","6,963,985","7,054,655","7,163,657","7,294,771","7,423,362","7,523,869","7,614,893"
+West Virginia,WV,54,"1,854,239","1,856,301","1,856,872","1,853,914","1,849,489","1,842,050","1,831,023","1,817,004","1,804,291","1,792,147"
+Wisconsin,WI,55,"5,690,475","5,705,288","5,719,960","5,736,754","5,751,525","5,760,940","5,772,628","5,790,186","5,807,406","5,822,434"
+Wyoming,WY,56,"564,487","567,299","576,305","582,122","582,531","585,613","584,215","578,931","577,601","578,759"
+Puerto Rico,PR,72,"3,721,525","3,678,732","3,634,488","3,593,077","3,534,874","3,473,232","3,406,672","3,325,286","3,193,354","3,193,694"

us-population-2010-2019.csv

@@ -0,0 +1,53 @@
+states,id,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019
+Alabama,1,"4,785,437","4,799,069","4,815,588","4,830,081","4,841,799","4,852,347","4,863,525","4,874,486","4,887,681","4,903,185"
+Alaska,2,"713,910","722,128","730,443","737,068","736,283","737,498","741,456","739,700","735,139","731,545"
+Arizona,4,"6,407,172","6,472,643","6,554,978","6,632,764","6,730,413","6,829,676","6,941,072","7,044,008","7,158,024","7,278,717"
+Arkansas,5,"2,921,964","2,940,667","2,952,164","2,959,400","2,967,392","2,978,048","2,989,918","3,001,345","3,009,733","3,017,804"
+California,6,"37,319,502","37,638,369","37,948,800","38,260,787","38,596,972","38,918,045","39,167,117","39,358,497","39,461,588","39,512,223"
+Colorado,8,"5,047,349","5,121,108","5,192,647","5,269,035","5,350,101","5,450,623","5,539,215","5,611,885","5,691,287","5,758,736"
+Connecticut,9,"3,579,114","3,588,283","3,594,547","3,594,841","3,594,524","3,587,122","3,578,141","3,573,297","3,571,520","3,565,287"
+Delaware,10,"899,593","907,381","915,179","923,576","932,487","941,252","948,921","956,823","965,479","973,764"
+District of Columbia,11,"605,226","619,800","634,924","650,581","662,328","675,400","685,815","694,906","701,547","705,749"
+Florida,12,"18,845,537","19,053,237","19,297,822","19,545,621","19,845,911","20,209,042","20,613,477","20,963,613","21,244,317","21,477,737"
+Georgia,13,"9,711,881","9,802,431","9,901,430","9,972,479","10,067,278","10,178,447","10,301,890","10,410,330","10,511,131","10,617,423"
+Hawaii,15,"1,363,963","1,379,329","1,394,804","1,408,243","1,414,538","1,422,052","1,427,559","1,424,393","1,420,593","1,415,872"
+Idaho,16,"1,570,746","1,583,910","1,595,324","1,611,206","1,631,112","1,651,059","1,682,380","1,717,715","1,750,536","1,787,065"
+Illinois,17,"12,840,503","12,867,454","12,882,510","12,895,129","12,884,493","12,858,913","12,820,527","12,778,828","12,723,071","12,671,821"
+Indiana,18,"6,490,432","6,516,528","6,537,703","6,568,713","6,593,644","6,608,422","6,634,304","6,658,078","6,695,497","6,732,219"
+Iowa,19,"3,050,745","3,066,336","3,076,190","3,092,997","3,109,350","3,120,960","3,131,371","3,141,550","3,148,618","3,155,070"
+Kansas,20,"2,858,190","2,869,225","2,885,257","2,893,212","2,900,475","2,909,011","2,910,844","2,908,718","2,911,359","2,913,314"
+Kentucky,21,"4,348,181","4,369,821","4,386,346","4,404,659","4,414,349","4,425,976","4,438,182","4,452,268","4,461,153","4,467,673"
+Louisiana,22,"4,544,532","4,575,625","4,600,972","4,624,527","4,644,013","4,664,628","4,678,135","4,670,560","4,659,690","4,648,794"
+Maine,23,"1,327,629","1,328,284","1,327,729","1,328,009","1,330,513","1,328,262","1,331,317","1,334,612","1,339,057","1,344,212"
+Maryland,24,"5,788,645","5,839,419","5,886,992","5,923,188","5,957,283","5,985,562","6,003,323","6,023,868","6,035,802","6,045,680"
+Massachusetts,25,"6,566,307","6,613,583","6,663,005","6,713,315","6,762,596","6,794,228","6,823,608","6,859,789","6,882,635","6,892,503"
+Michigan,26,"9,877,510","9,882,412","9,897,145","9,913,065","9,929,848","9,931,715","9,950,571","9,973,114","9,984,072","9,986,857"
+Minnesota,27,"5,310,828","5,346,143","5,376,643","5,413,479","5,451,079","5,482,032","5,522,744","5,566,230","5,606,249","5,639,632"
+Mississippi,28,"2,970,548","2,978,731","2,983,816","2,988,711","2,990,468","2,988,471","2,987,938","2,988,510","2,981,020","2,976,149"
+Missouri,29,"5,995,974","6,010,275","6,024,367","6,040,715","6,056,202","6,071,732","6,087,135","6,106,670","6,121,623","6,137,428"
+Montana,30,"990,697","997,316","1,003,783","1,013,569","1,021,869","1,030,475","1,040,859","1,052,482","1,060,665","1,068,778"
+Nebraska,31,"1,829,542","1,840,672","1,853,303","1,865,279","1,879,321","1,891,277","1,905,616","1,915,947","1,925,614","1,934,408"
+Nevada,32,"2,702,405","2,712,730","2,743,996","2,775,970","2,817,628","2,866,939","2,917,563","2,969,905","3,027,341","3,080,156"
+New Hampshire,33,"1,316,762","1,320,202","1,324,232","1,326,622","1,333,341","1,336,350","1,342,307","1,348,787","1,353,465","1,359,711"
+New Jersey,34,"8,799,446","8,828,117","8,844,942","8,856,972","8,864,525","8,867,949","8,870,827","8,885,525","8,886,025","8,882,190"
+New Mexico,35,"2,064,552","2,080,450","2,087,309","2,092,273","2,089,568","2,089,291","2,091,630","2,091,784","2,092,741","2,096,829"
+New York,36,"19,399,878","19,499,241","19,572,932","19,624,447","19,651,049","19,654,666","19,633,428","19,589,572","19,530,351","19,453,561"
+North Carolina,37,"9,574,323","9,657,592","9,749,476","9,843,336","9,932,887","10,031,646","10,154,788","10,268,233","10,381,615","10,488,084"
+North Dakota,38,"674,715","685,225","701,176","722,036","737,401","754,066","754,434","754,942","758,080","762,062"
+Ohio,39,"11,539,336","11,544,663","11,548,923","11,576,684","11,602,700","11,617,527","11,634,370","11,659,650","11,676,341","11,689,100"
+Oklahoma,40,"3,759,944","3,788,379","3,818,814","3,853,214","3,878,187","3,909,500","3,926,331","3,931,316","3,940,235","3,956,971"
+Oregon,41,"3,837,491","3,872,036","3,899,001","3,922,468","3,963,244","4,015,792","4,089,976","4,143,625","4,181,886","4,217,737"
+Pennsylvania,42,"12,711,160","12,745,815","12,767,118","12,776,309","12,788,313","12,784,826","12,782,275","12,787,641","12,800,922","12,801,989"
+Rhode Island,44,"1,053,959","1,053,649","1,054,621","1,055,081","1,055,936","1,056,065","1,056,770","1,055,673","1,058,287","1,059,361"
+South Carolina,45,"4,635,649","4,671,994","4,717,354","4,764,080","4,823,617","4,891,938","4,957,968","5,021,268","5,084,156","5,148,714"
+South Dakota,46,"816,166","823,579","833,566","842,316","849,129","853,988","862,996","872,868","878,698","884,659"
+Tennessee,47,"6,355,311","6,399,291","6,453,898","6,494,340","6,541,223","6,591,170","6,646,010","6,708,799","6,771,631","6,829,174"
+Texas,48,"25,241,971","25,645,629","26,084,481","26,480,266","26,964,333","27,470,056","27,914,410","28,295,273","28,628,666","28,995,881"
+Utah,49,"2,775,332","2,814,384","2,853,375","2,897,640","2,936,879","2,981,835","3,041,868","3,101,042","3,153,550","3,205,958"
+Vermont,50,"625,879","627,049","626,090","626,210","625,214","625,216","623,657","624,344","624,358","623,989"
+Virginia,51,"8,023,699","8,101,155","8,185,080","8,252,427","8,310,993","8,361,808","8,410,106","8,463,587","8,501,286","8,535,519"
+Washington,53,"6,742,830","6,826,627","6,897,058","6,963,985","7,054,655","7,163,657","7,294,771","7,423,362","7,523,869","7,614,893"
+West Virginia,54,"1,854,239","1,856,301","1,856,872","1,853,914","1,849,489","1,842,050","1,831,023","1,817,004","1,804,291","1,792,147"
+Wisconsin,55,"5,690,475","5,705,288","5,719,960","5,736,754","5,751,525","5,760,940","5,772,628","5,790,186","5,807,406","5,822,434"
+Wyoming,56,"564,487","567,299","576,305","582,122","582,531","585,613","584,215","578,931","577,601","578,759"
+Puerto Rico,72,"3,721,525","3,678,732","3,634,488","3,593,077","3,534,874","3,473,232","3,406,672","3,325,286","3,193,354","3,193,694"