import plotly.io as pio
pio.renderers.default = "notebook_connected"Choropleth
Choropleth of US Unemployment Data for 2023
Plot the Unemployment Rate given in the dataset on the map of America.
We use the data set available in kaggle.
import pandas as pd
data=pd.read_csv('/home/stephy/Documents/class/Python/Sem 4/UnemploymentUSA.csv')
data.head(3)| FIPS Code | State/Area | code | Year | Month | Total Civilian Non-Institutional Population in State/Area | Total Civilian Labor Force in State/Area | Percent (%) of State/Area's Population | Total Employment in State/Area | Percent (%) of Labor Force Employed in State/Area | Total Unemployment in State/Area | Percent of Labor Force Unemployed in State/Area | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Alabama | AL | 1976 | 1 | 2605000 | 1484555 | 57.0 | 1386023 | 53.2 | 98532 | 6.6 |
| 1 | 2 | Alaska | AK | 1976 | 1 | 232000 | 160183 | 69.0 | 148820 | 64.1 | 11363 | 7.1 |
| 2 | 4 | Arizona | AZ | 1976 | 1 | 1621000 | 964120 | 59.5 | 865871 | 53.4 | 98249 | 10.2 |
data.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 28200 entries, 0 to 28199
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 FIPS Code 28200 non-null int64
1 State/Area 28200 non-null object
2 code 28200 non-null object
3 Year 28200 non-null int64
4 Month 28200 non-null int64
5 Total Civilian Non-Institutional Population in State/Area 28200 non-null int64
6 Total Civilian Labor Force in State/Area 28200 non-null int64
7 Percent (%) of State/Area's Population 28200 non-null float64
8 Total Employment in State/Area 28200 non-null int64
9 Percent (%) of Labor Force Employed in State/Area 28200 non-null float64
10 Total Unemployment in State/Area 28200 non-null int64
11 Percent of Labor Force Unemployed in State/Area 28200 non-null float64
dtypes: float64(3), int64(7), object(2)
memory usage: 2.6+ MBdata.columnsIndex(['FIPS Code', 'State/Area', 'code', 'Year', 'Month',
'Total Civilian Non-Institutional Population in State/Area',
'Total Civilian Labor Force in State/Area',
'Percent (%) of State/Area's Population',
'Total Employment in State/Area',
'Percent (%) of Labor Force Employed in State/Area',
'Total Unemployment in State/Area',
'Percent of Labor Force Unemployed in State/Area'],
dtype='object')data.Year.dtype
data.Month.dtypedtype('int64')We we are going to select only relevant columns from the data viz., ‘State/Area’, ‘Year’, ‘Month’, ‘Percent of Labor Force Unemployed in State/Area’. We store the new data with the name df.
df=data[['State/Area','code', 'Year', 'Month', 'Percent of Labor Force Unemployed in State/Area']]
df.shape
df.columnsIndex(['State/Area', 'code', 'Year', 'Month',
'Percent of Labor Force Unemployed in State/Area'],
dtype='object')df.columnsIndex(['State/Area', 'code', 'Year', 'Month',
'Percent of Labor Force Unemployed in State/Area'],
dtype='object')df['Percent of Labor Force Unemployed in State/Area'].dtypedtype('float64')Now select the unemployment rate for the December 2022.
df_22=df[(df['Year']==2022) & (df['Month']==12)]
df_22.head(3)| State/Area | code | Year | Month | Percent of Labor Force Unemployed in State/Area | |
|---|---|---|---|---|---|
| 28150 | Alabama | AL | 2022 | 12 | 2.6 |
| 28151 | Alaska | AK | 2022 | 12 | 3.7 |
| 28152 | Arizona | AZ | 2022 | 12 | 4.0 |
import plotly.express as px
import numpy as np
fig = px.choropleth(df_22, locations='code',color=df_22['Percent of Labor Force Unemployed in State/Area'].astype(float), # Column containing country names for hover information
locationmode='USA-states',labels={'Percent of Labor Force Unemployed in State/Area': 'Unemployment Rate'})
fig.show()The current choropleth displays data for US states on a world map. To focus on the Americas, we can use the scope='usa' argument in the choropleth function. This will switch the map projection to encompass only the North and South American continents.
fig = px.choropleth(df_22, locations='code',color=df_22['Percent of Labor Force Unemployed in State/Area'].astype(float), # Column containing country names for hover information
locationmode='USA-states', scope='usa',labels={'Percent of Labor Force Unemployed in State/Area':'Unemployment'})
fig.show()Currently, the choropleth utilizes distinct colors to represent unemployment rates across states. However, for a more intuitive representation of unemployment severity, we can employ a continuous color scale. This approach utilizes a single color that gradually intensifies as unemployment rates increase, providing a clearer visual gradient for unemployment magnitude across different states.
fig = px.choropleth(df_22, locations='code',color=df_22['Percent of Labor Force Unemployed in State/Area'].astype(float), # Column containing country names for hover information
locationmode='USA-states', scope='usa',color_continuous_scale='Blues',labels={'Percent of Labor Force Unemployed in State/Area':'Unemployment Rate'})
fig.show()Some commonly used options include:
viridis(default): Green to yellow to redplasma: Purple to green to yellowcividis: Blue to green to yellow to redinferno: Black to red to yellowmagma: Black to red to whiteturbo: Blue to violet to red to orangeReds: Red in varying intensityBlues: Red in varying intensityGreens: Red in varying intensity
Now we will assign heading to the colour bar as unemployment rate using the function fig.update_layout(coloraxis_colorbar_title_text = 'Unemployment Rate')
fig = px.choropleth(df_22, locations='code',color=df_22['Percent of Labor Force Unemployed in State/Area'].astype(float), # Column containing country names for hover information
locationmode='USA-states',
scope='usa',color_continuous_scale='Reds',labels={'Percent of Labor Force Unemployed in State/Area','Unemployment Rate'}
)
fig.update_layout(coloraxis_colorbar_title_text = 'Unemployment Rate')
# Show the map
fig.show()Currently, the hover text for the choropleth displays basic information like “Code” and “Color”, color representing the unemployment rate. To provide more context and insights, let’s enrich the hover text to include the following details:
State Name: This identifies the specific geographic location represented by the data point.
Unemployment Rate: This is the core data value visualized using the color gradient, allowing users to easily see the unemployment rate for each state upon hovering.
By incorporating these elements, the hover text becomes more informative and user-friendly, enhancing the user’s understanding of the data they’re interacting with.
import numpy as np
fig = px.choropleth(df_22, locations='code',color=df_22['Percent of Labor Force Unemployed in State/Area'].astype(float), # Column containing country names for hover information
locationmode='USA-states',
scope='usa',color_continuous_scale='Reds',labels={'Percent of Labor Force Unemployed in State/Area':'Unemployment Rate'}
)
fig.update_layout(coloraxis_colorbar_title_text = 'Unemployment Rate')
fig.update_traces(
customdata= np.stack((df_22['State/Area'], df_22['Percent of Labor Force Unemployed in State/Area']), axis=-1),
hovertemplate=(
"<b>%{customdata[0]}</b><br>"+\
"Unemployment Rate: %{customdata[1]}"
)
)
# Show the map
fig.show()Lets customize the the borders separating the states.
import numpy as np
fig = px.choropleth(df_22, locations='code',color=df_22['Percent of Labor Force Unemployed in State/Area'].astype(float), # Column containing country names for hover information
locationmode='USA-states',
scope='usa',color_continuous_scale='Reds',title="Unemplyment Rate of Various States of America"
)
fig.update_traces(marker_line_width=3,marker_line_color='white')
fig.update_layout(coloraxis_colorbar_title_text = 'Unemployment Rate')
fig.update_traces(
customdata= np.stack((df_22['State/Area'], df_22['Percent of Labor Force Unemployed in State/Area']), axis=-1),
hovertemplate=(
"<b>%{customdata[0]}</b><br>"+\
"Unemployment Rate: %{customdata[1]}"
)
)
fig.show()Exercise
Use the GDP data to plot the map of the USA.
• Plot the unemployment rate during June 2020 on the map of USA. Give hover text with state name and the unemployment rate. Give boundaries to each state, boundary color should be white with width 1. Use red color to scale the value. Give appropriate title to the plot and the colorbar. Use plotly
Choropleth of Inflation Rate of India
Now we will plot a choropleth using map of India. We use the inflation rate (CPI) data available in RBI website. The geojson file of India is downloaded https://un-mapped.carto.com/tables/states_india/public/map
import json
india=json.load(open('/home/stephy/Documents/class/Python/Sem 4/states_india.geojson'))
india['features'][0]{'type': 'Feature',
'geometry': {'type': 'MultiPolygon',
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[80.010876, 16.710578],
[80.002156, 16.702075],
[80.000012, 16.70018],
[79.981524, 16.667181],
[79.966655, 16.649711],
[79.951061, 16.640285],
[79.93213, 16.638374],
[79.922285, 16.640748],
[79.910529, 16.645427],
[79.899336, 16.657106],
[79.889049, 16.68365],
[79.881671, 16.696152],
[79.870192, 16.705634],
[79.852891, 16.703855],
[79.834241, 16.698843],
[79.818561, 16.697832],
[79.791606, 16.732085],
[79.783121, 16.736741],
[79.771314, 16.734339],
[79.758693, 16.727323],
[79.742004, 16.705228],
[79.730935, 16.699809],
[79.691195, 16.697522],
[79.676786, 16.69113],
[79.663644, 16.681218],
[79.656176, 16.669137],
...]]]},
'properties': {'cartodb_id': 1, 'state_code': 0, 'st_nm': 'Telangana'}}We’ll now proceed to utilize the inflation data collected from the RBI website. This data is stored in a CSV file, which we can conveniently access using the Pandas library in Python.
import pandas as pd
data=pd.read_csv('/home/stephy/Documents/class/Python/Sem 4/inflationIndia.csv')
data.shape(35, 10)data.columnsIndex(['State/Union Territory', '2014-15', '2015-16', '2016-17', '2017-18',
'2018-19', '2019-20', '2020-21', '2021-22', '2022-23'],
dtype='object')data['2022-23'].dtypedtype('float64')Now we need to assign an identifier which will connect the states in json file with that of CSV file.
india['features'][0]['properties']{'cartodb_id': 1, 'state_code': 0, 'st_nm': 'Telangana'}state_id_map={}
for feature in india['features']:
feature["id"]=feature['properties']['state_code']
state_id_map[feature['properties']['st_nm']]=feature["id"]
state_id_map{'Telangana': 0,
'Andaman & Nicobar Island': 35,
'Andhra Pradesh': 28,
'Arunanchal Pradesh': 12,
'Assam': 18,
'Bihar': 10,
'Chhattisgarh': 22,
'Daman & Diu': 25,
'Goa': 30,
'Gujarat': 24,
'Haryana': 6,
'Himachal Pradesh': 2,
'Jammu & Kashmir': 1,
'Jharkhand': 20,
'Karnataka': 29,
'Kerala': 32,
'Lakshadweep': 31,
'Madhya Pradesh': 23,
'Maharashtra': 27,
'Manipur': 14,
'Chandigarh': 4,
'Puducherry': 34,
'Punjab': 3,
'Rajasthan': 8,
'Sikkim': 11,
'Tamil Nadu': 33,
'Tripura': 16,
'Uttar Pradesh': 9,
'Uttarakhand': 5,
'West Bengal': 19,
'Odisha': 21,
'Dadara & Nagar Havelli': 26,
'Meghalaya': 17,
'Mizoram': 15,
'Nagaland': 13,
'NCT of Delhi': 7}Now we will create a new column in data file which will be the identifier.
data['ID']=data['State/Union Territory'].apply(lambda x: state_id_map[x])Now we will proceed with plotting our choropleth map.
import plotly.express as px
fig=px.choropleth(data,locations='ID',geojson=india,color='2022-23')
fig.show()Now we have the choropleth is shown on a very wide canvas. Now we will reduce the canvas to continent Asia.
import plotly.express as px
fig=px.choropleth(data,locations='ID',geojson=india,color='2022-23',scope='asia')
fig.show()