import plotly.io as pio
pio.renderers.default = "notebook_connected"Choropleth
Choropleth Map of U.S. Unemployment Data (2023)
The objective of this webpage is to guide you in plotting the Unemployment Rate from a dataset onto a map of the United States using Python and Plotly. The dataset used for this analysis is sourced from 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 |
The code imports the Pandas library, which is used for data manipulation and analysis and now on we refer the pandas libray by pd. Also pd.read_csv() is used to load the CSV (Comma-Separated Values) file into a DataFrame, called data. .head(n) displays the first n rows of the dataset, data. This will help to preview the data structure and values.
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+ MBThe above code provides a summary of the dataset, including:
- The number of rows and columns
- The data types of each column
- The count of non-null (non-empty) values
Now, let’s examine the column names in the dataset. This will help us identify and select the relevant variables for further analysis.
data.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')Now, we will check the data types of the columns Year and Month. This step is crucial when we need to filter the dataset based on a specific year or month.
If the Year column is stored as a string (character data type) instead of a numeric type (integer), filtering operations will require different syntax. For instance:
If Year is stored as an integer (int), we can directly apply conditions like data[data.Year == 2023] to filter the data for the year 2023.
However, if Year is stored as a string (object in Pandas), we must compare it as a string: data[data.Year == "2023"].Similarly, the Month column may be stored as a string or a categorical variable. If it’s in string format, converting it into a datetime format or a categorical variable will make filtering and analysis easier.
By checking the data types beforehand, we ensure that our filtering and analysis operations run smoothly without unexpected errors.
data.Year.dtype
data.Month.dtypedtype('int64')We are going to select only relevant columns from the data namely, ‘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 |
Now, we will visualize the unemployment rate across various states in the United States using a choropleth map. For this analysis, we will specifically plot the data for December 2022 to observe state-wise unemployment variations during that period.
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 map displays data for U.S. states on a world map. To focus specifically on the United States, we can use the scope=‘usa’ argument in the choropleth function. This will adjust the map projection to display only the United States, providing a clearer and more relevant visualization of the data.
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 map uses distinct colors to represent unemployment rates across different states. However, to create a more intuitive and visually meaningful representation, we can implement a continuous color scale.
This approach uses a single color that gradually intensifies as unemployment rates increase, forming a smooth gradient. As a result, areas with higher unemployment rates will appear darker or more saturated, while those with lower rates will appear lighter. This continuous scale provides a clearer and more effective visual representation of unemployment severity across 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 for continuous colour scale 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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'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()