Retail Sales by 04-01-2023

The charts below depict the historical retail sales and the year-over-year percent change in retail sales. Notably, the retail sales experienced a significant surge during the pandemic period, while the year-over-year percent change exhibited a rapid decline. It is worth mentioning that the latest value for the year-over-year percent change stands at 0.54%.

Python codes to generate - 

import pandas as pd
import pandas_datareader.data as web
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np

# Define the date range
start = pd.to_datetime('1992-01-01')
end = pd.to_datetime('2023-04-30')

# Download the retail sales data from FRED
retail_sales = web.DataReader('RSXFS', 'fred', start, end)

# Plot the data
plt.figure(figsize=(10, 5))
plt.plot(retail_sales.index, retail_sales.values, label='Retail Sales')

# Fit a trend line using linear regression
x = np.arange(len(retail_sales))
y = retail_sales.values.flatten()
coefficients = np.polyfit(x, y, 1)
trend_line = np.polyval(coefficients, x)
plt.plot(retail_sales.index, trend_line, color='red', linestyle='--', label='Trend Line')

# Display the latest value
latest_date = retail_sales.index[-1].strftime('%Y-%m-%d')
latest_value = retail_sales.values[-1][0]
latest_date_num = mdates.date2num(retail_sales.index[-1])
plt.annotate(f'Latest Value\n({latest_date}, {latest_value})', xy=(latest_date_num, latest_value),
             xytext=(latest_date_num, latest_value+100), arrowprops=dict(facecolor='black', arrowstyle='->'))

# Configure x-axis date formatting
date_fmt = mdates.DateFormatter('%Y-%m-%d')
plt.gca().xaxis.set_major_formatter(date_fmt)
plt.gca().xaxis.set_major_locator(mdates.YearLocator())

plt.title('Retail Sales from FRED')
plt.ylabel('Retail Sales')
plt.legend()
plt.grid(True)

# Rotate the x-axis label vertically
plt.xticks(rotation='vertical')

plt.show()


import pandas as pd
import pandas_datareader.data as web
import matplotlib.pyplot as plt

# Define the date range
start = pd.to_datetime('1993-01-01')
end = pd.to_datetime('2023-04-30')

# Download the retail sales data from FRED
retail_sales = web.DataReader('RSXFS', 'fred', start, end)

# Calculate the year-over-year percent change
retail_sales_change = retail_sales.pct_change(periods=12) * 100

# Calculate the average
average_change = retail_sales_change.mean()[0]  # Extract the value from the Series

# Plot the data
plt.figure(figsize=(10,5))
plt.plot(retail_sales_change.index, retail_sales_change.values, label='Percent Change')
plt.axhline(y=average_change, color='r', linestyle='-', label='Average')
plt.title('Year-over-Year Percent Change in Retail Sales from FRED')
plt.xlabel('Year')
plt.ylabel('Percent Change')
plt.legend()
plt.grid(True)

# Display the latest value
latest_date = retail_sales_change.index[-1].strftime('%Y-%m-%d')
latest_value = retail_sales_change.iloc[-1].values[0]
print(f'The latest value on {latest_date} is {latest_value:.2f}%')

plt.show()