Python scripts that generate charts for Income vs Housing Cost in the United States via FRED 1984-2022

full_plot.py

import pandas as pd
import matplotlib.pyplot as plt
data1 = pd.read_csv('MEHOINUSA646N.csv')
data2 = pd.read_csv('ASPUS.csv')
data1['DATE'] = pd.to_datetime(data1['DATE'])
data2['DATE'] = pd.to_datetime(data2['DATE'])

fig, ax1 = plt.subplots(figsize=(12, 6))

ax1.plot(data1['DATE'], data1['MEHOINUSA646N'], label='Median Household Income', color='blue')
ax1.set_xlabel('Year')
ax1.set_ylabel('Median Household Income (USD)', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')

ax2 = ax1.twinx()
ax2.plot(data2['DATE'], data2['ASPUS'], label='Average Sale Price of Houses', color='green')
ax2.set_ylabel('Average Sale Price of Houses (USD)', color='green')
ax2.tick_params(axis='y', labelcolor='green')

plt.title('Median Household Income vs. Average Sale Price of Houses in the USA')
fig.legend(loc="upper left", bbox_to_anchor=(0.1,0.9))
plt.grid(True)
plt.show()

improper_plot.py

import pandas as pd
import matplotlib.pyplot as plt

data1 = pd.read_csv('MEHOINUSA646N.csv')
data2 = pd.read_csv('ASPUS.csv')

data1['DATE'] = pd.to_datetime(data1['DATE'])
data2['DATE'] = pd.to_datetime(data2['DATE'])

plt.figure(figsize=(12, 6))

plt.plot(data1['DATE'], data1['MEHOINUSA646N'], label='Median Household Income', color='blue')
plt.plot(data2['DATE'], data2['ASPUS'], label='Average Sale Price of Houses', color='green')

plt.xlabel('Year')
plt.ylabel('Value (in USD)')
plt.title('Median Household Income vs. Average Sale Price of Houses in the USA')
plt.legend()
plt.grid(True)

plt.show()

normalized_plot.py

import pandas as pd
import matplotlib.pyplot as plt

data1 = pd.read_csv('MEHOINUSA646N.csv')
data2 = pd.read_csv('ASPUS.csv')

data1['DATE'] = pd.to_datetime(data1['DATE'])
data2['DATE'] = pd.to_datetime(data2['DATE'])

data1 = data1[data1['DATE'] >= '1984-01-01']
data2 = data2[data2['DATE'] >= '1984-01-01']

data1['Pct_Increase_Income'] = (data1['MEHOINUSA646N'] / data1['MEHOINUSA646N'].iloc[0]) * 100
data2['Pct_Increase_Price'] = (data2['ASPUS'] / data2['ASPUS'].iloc[0]) * 100

max_percentage = max(data1['Pct_Increase_Income'].max(), data2['Pct_Increase_Price'].max())

fig, ax1 = plt.subplots(figsize=(12, 6))

ax1.plot(data1['DATE'], data1['Pct_Increase_Income'], label='Percentage Increase in Median Household Income', color='blue')
ax1.set_xlabel('Year')
ax1.set_ylabel('Percentage Increase in Median Household Income', color='blue')
ax1.tick_params(axis='y', labelcolor='blue')
ax1.set_ylim(0, max_percentage)  # NORMALIZE SCALES

ax2 = ax1.twinx()
ax2.plot(data2['DATE'], data2['Pct_Increase_Price'], label='Percentage Increase in Average Sale Price of Houses', color='green')
ax2.set_ylabel('Percentage Increase in Average Sale Price of Houses', color='green')
ax2.tick_params(axis='y', labelcolor='green')
ax2.set_ylim(0, max_percentage)  # NORMALIZE SCALES

plt.title('Percentage Increase in Median Household Income vs. Average Sale Price of Houses in the USA (Starting from 1984)')

fig.legend(loc="upper left", bbox_to_anchor=(0.1,0.9))

plt.grid(True)
plt.show()