See https://sfba.social/@drahardja/111741612198029604 and https://data.bls.gov/timeseries/JTS510000000000000LDL

bls.py

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

plt.ion()

# URL for the data
url = "https://data.bls.gov/timeseries/JTS510000000000000LDL"

# Creating a date range from 2001 to 2023
dates = pd.date_range(start='2001-01-01', end='2023-11-01', freq='MS')

# Creating a mock-up data for 'Value'
import numpy as np

values = np.array([
    44, 43, 23, 55, 47, 52, 46, 48, 57, 55, 62, 54, 52, 31, 42, 62, 44, 47, 45,
    41, 37, 40, 45, 38, 42, 47, 45, 36, 47, 47, 41, 42, 43, 37, 33, 43, 32, 35,
    40, 33, 37, 44, 24, 25, 49, 31, 18, 19, 26, 15, 27, 21, 29, 21, 38, 38, 32,
    26, 41, 28, 25, 48, 28, 41, 21, 23, 24, 28, 23, 31, 34, 33, 40, 45, 28, 26,
    32, 26, 35, 43, 27, 21, 19, 22, 25, 22, 35, 28, 19, 37, 18, 20, 40, 52, 43,
    42, 37, 38, 39, 47, 41, 37, 46, 34, 28, 40, 33, 45, 36, 23, 24, 23, 34, 24,
    27, 26, 29, 22, 23, 30, 24, 21, 29, 20, 26, 25, 24, 20, 21, 21, 22, 26, 24,
    29, 28, 26, 26, 20, 20, 38, 24, 18, 20, 20, 22, 24, 12, 26, 29, 42, 45, 58,
    44, 43, 23, 43, 40, 55, 47, 39, 30, 28, 47, 26, 33, 40, 39, 32, 31, 27, 30,
    36, 27, 30, 26, 26, 25, 30, 31, 22, 25, 30, 24, 31, 33, 26, 29, 27, 14, 20,
    27, 21, 26, 25, 31, 28, 39, 38, 26, 32, 42, 41, 26, 43, 40, 37, 36, 35, 32,
    37, 29, 36, 36, 23, 28, 39, 35, 38, 36, 37, 38, 29, 58, 38, 34, 37, 34, 51,
    27, 33, 191, 169, 36, 35, 27, 29, 21, 32, 32, 30, 25, 27, 27, 23, 26, 31,
    29, 30, 42, 31, 33, 36, 61, 11, 19, 28, 30, 32, 37, 59, 31, 47, 46, 46, 48,
    49, 49, 14, 41, 26, 46, 28, 24, 15, 32
])

# Creating the DataFrame
data = pd.DataFrame({'Date': dates, 'Value': values})

# Preparing the data for the heatmap
data['Year'] = data['Date'].dt.year
data['Month'] = data['Date'].dt.month
heatmap_data = data.pivot('Year', 'Month', 'Value')

# Plotting the heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(heatmap_data,
            cmap="YlGnBu",
            annot=True,
            fmt=".0f",
            vmin=10,
            vmax=60)
plt.title(
    "Information industry, layoffs and discharges: thousands (BLS: JTS510000000000000LDL)"
)
plt.tight_layout()

layoffs.svg