Load in and display unemployment data

choropleth_example1.py

import geopandas as gpd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import plotly.graph_objects as go

emp_df = pd.read_csv('./data/ABS_C16_G43_LGA_26072020234812892.csv') #read in the data
emp_df = emp_df[['LGA_2016', 'Labour force status', 'Region', 'Value']] #select only the columns we need
emp_df['LGA_2016'] = emp_df['LGA_2016'].astype('str') # we will join on this axis, so both dataframes need this to be the same type
emp_df = emp_df.pivot(index='LGA_2016', columns='Labour force status', values='Value').reset_index().rename_axis(None, axis=1) #pivot the dataframe to make the spatial location the index
emp_df['percent_unemployed'] = emp_df['Total Unemployed']/(emp_df['Total Unemployed']+emp_df['Total Employed']) #calculate unemployment rate
emp_df.head()