Creating the Beverage Curve Plot
Recreating the Beverage Curve Plot from the BLS website
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import os
from matplotlib.ticker import FormatStrFormatter
def categories(df):
colors = []
label = []
for date in df['month']:
if date <= datetime(2001,2,1):
colors.append("darkblue")
label.append("Dec 2000 to Feb 2001")
elif datetime(2001,3,1) <= date <= datetime(2001,11,1):
colors.append("cornflowerblue")
label.append("Mar 2001 to Nov 2001*")
elif datetime(2001,12,1) <= date <= datetime(2007,11,1):
colors.append("red")
label.append("Dec 2001 to Nov 2007")
elif datetime(2007,12,1) <= date <= datetime(2009,6,1):
colors.append("gold")
label.append("Dec 2007 to June 2009*")
elif datetime(2009,7,1) <= date <= datetime(2020,2,1):
colors.append("gray")
label.append("July 2009 to Feb 2020")
elif datetime(2020,3,1) <= date <= datetime(2020,4,1):
colors.append("pink")
label.append("Mar 2020 to Apr 2020*")
else:
colors.append("black")
label.append("May 2020 to Apr 2022")
return df.assign(color = pd.Categorical(colors,
categories=['darkblue',
'cornflowerblue',
'red',
'gold',
'gray',
'pink',
'black'],
ordered=True),
label = pd.Categorical(label,
categories=['Dec 2000 to Feb 2001',
'Mar 2001 to Nov 2001*',
'Dec 2001 to Nov 2007',
'Dec 2007 to June 2009*',
'July 2009 to Feb 2020',
'Mar 2020 to Apr 2020*',
'May 2020 to Apr 2022'],
ordered=True),
)
def plotter(df, ax):
for (col,group), dfx in df.groupby(['color','label']):
ax.plot(dfx['unemployment_rate'], dfx['job_openings_rate'], color = col, label = group, marker = "o")
plt.legend(bbox_to_anchor=(1.25, 1.10), frameon =False)
url = "https://www.bls.gov/charts/job-openings-and-labor-turnover/job-openings-unemployment-beveridge-curve.htm"
fig, ax = plt.subplots(figsize=(12,9))
(pd.read_html(url)[0]
.rename(columns=lambda cols: cols.lower().replace(" ","_"))
.assign(month = lambda x: pd.to_datetime(x['month']),
job_openings_rate = lambda df: df['job_openings_rate'].str.replace("%","").astype(float),
unemployment_rate = lambda df: df['unemployment_rate'].str.replace("%","").astype(float)
)
.pipe(categories)
.pipe(plotter, ax)
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.grid(axis = 'y', linestyle ="--", alpha = 0.3)
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax.set_ylim(1,8)
ax.set_xlim(3.4,14.8)
ax.set_xlabel("Unemployment Rate", size=13)
plt.text(3.0, 8.2, "Job openings rate",size=13)
plt.text(2.0, 9.0, "The Beverage Curve (job openings rate vs. unemployment rate), seasonally adjusted",size=18)
plt.text(2.0, 8.7, "Click and drag within the chart to zoom in on time periods",size=13)
plt.text(15, 1, "@MichaelHotaling",size=8)
