Plot month-to-month inflation using data from https://download.bls.gov/pub/time.series/cu/

plot_monthly_inflation.py

#!/usr/bin/env python
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

data = pd.read_csv("data/cu.data.1.AllItems.txt", sep=r"[ \t]+", engine="python")

# Get 'All items in U.S. city average, all urban consumers, seasonally adjusted'
overall = data[data["series_id"] == "CUSR0000SA0"]
# NB: pct_change() computes fractional change.
monthly_pct = overall["value"].pct_change() * 100.0

period_str = overall["period"].str.removeprefix("M")
year_str = overall["year"].astype(str)
dates = pd.to_datetime(year_str + "-" + period_str + "-01")

num_periods = 36
value_label = "Inflation %"
date_label = "Month"
monthly_df = pd.DataFrame({date_label: dates, value_label: monthly_pct}).tail(num_periods)

# https://stackoverflow.com/a/30135182
tick_labels = [""] * len(monthly_df[date_label])
tick_labels[-1:0:-4] = [curr_date.strftime("%b\n%Y") for curr_date in monthly_df[date_label][-1:0:-4]]

ax = monthly_df.plot(x=date_label, y=value_label, kind="bar")
ax.xaxis.set_major_formatter(ticker.FixedFormatter(tick_labels))
ax.annotate("Source: https://download.bls.gov/pub/time.series/cu/", xy=(0, -0.25), xycoords="axes fraction")
ax.set(xlabel=None)

plt.gcf().autofmt_xdate()
plt.savefig("cost_vs_time.png")
plt.show()