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Exploratory Data Analysis: CPI & PPI Forecast Inflation

Authors
Affiliations
University of California, Berkeley
University of California, Berkeley
University of California, Berkeley
University of California, Berkeley

Introduction

In this notebook, we will explore inflation patterns in the Historical Consumer Price Index (CPI) and Historical Producer Price Index (PPI) forecast series.

Both datasets contain annual percent-change forecasts from 1974–2024 for multiple food-related categories.

Specifically, we will examine:

  • Inflation trends over time via line plots

  • Cross-category comparisons using bar charts

  • The top 5 fastest-inflating categories

  • The most volatile categories, measured by standard deviation

These results will later be summarized in the main narrative notebook.

Imports and Load Data

import os
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append('..')  # Just add the parent directory for utils
from utils.data_loader import load_inflation_data
from utils.transformers import reshape_to_long_format

plt.rcParams["figure.figsize"] = (14, 6)
plt.rcParams["axes.grid"] = True

# Load processed data
cpi, ppi = load_inflation_data()

display(cpi.head())
display(ppi.head())
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Tidy Long Formatting

cpi_long = reshape_to_long_format(cpi)
cpi_long.head()
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ppi_long = reshape_to_long_format(ppi)
ppi_long.head()
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Line plots of inflation over time

Line plots can show how forecast inflation changes year-to-year for each category.

By plotting all categories in each dataset, we will be able to visually inspect:

  • Long-run trends in food price forecasts

  • Periods of high inflation (e.g., 1970s, post-2020)

  • How different categories move together or diverge

We first start with CPI, then repeat for PPI.

CPI Line Plot:

fig, ax = plt.subplots()

for cat, df_cat in cpi_long.groupby("category"):
    ax.plot(
        df_cat["Year"],
        df_cat["pct_change"],
        alpha=0.4,
        label=cat
    )

ax.set_title("CPI Forecast: Annual Percent Change by Category (1974–2024)")
ax.set_xlabel("Year")
ax.set_ylabel("Percent change")

ax.legend(
    title="Category",
    bbox_to_anchor=(1.02, 1),
    loc="upper left"
)
plt.savefig('../figures/cpi_annual_change_by_category.png')
plt.show()
<Figure size 1400x600 with 1 Axes>

PPI Line Plot:

fig, ax = plt.subplots()

for cat, df_cat in ppi_long.groupby("category"):
    ax.plot(
        df_cat["Year"],
        df_cat["pct_change"],
        alpha=0.4,
        label=cat
    )

ax.set_title("PPI Forecast: Annual Percent Change by Category (1974–2024)")
ax.set_xlabel("Year")
ax.set_ylabel("Percent change")

ax.legend(
    title="Category",
    bbox_to_anchor=(1.02, 1),
    loc="upper left"
)
plt.savefig('../figures/ppi_annual_change_by_category.png')
plt.show()
<Figure size 1400x600 with 1 Axes>

Bar charts comparing categories

To compare categories more directly, we will now collapse the time dimension and compute the average annual percent change for each category.

This will give us a single summary number for each category, which we visualize with bar charts. Higher means indicate categories that, on average, are forecasted to inflate more quickly.

1. Compute mean inflation per category

cpi_mean = (
    cpi_long
    .groupby("category")["pct_change"]
    .mean()
    .sort_values(ascending=False)
)

display(cpi_mean)

ppi_mean = (
    ppi_long
    .groupby("category")["pct_change"]
    .mean()
    .sort_values(ascending=False)
)

display(ppi_mean)
category Sugar_and_sweets 4.588235 Fresh_fruits 4.576471 Cereals_and_bakery_products 4.321569 Food_away_from_home 4.278431 Fresh_fruits_and_vegetables 4.268627 Fish_and_seafood 4.250980 Nonalcoholic_beverages 4.239216 Beef_and_veal 4.078431 Fruits_and_vegetables 4.076471 Fresh_vegetables 4.072549 Fats_and_oils 4.060784 All_food 3.882353 Other_foods 3.864706 Food_at_home 3.682353 Meats 3.519608 Meats_poultry_and_fish 3.458824 Dairy_products 3.390196 Other_meats 3.270588 Eggs 3.225490 Pork 3.009804 Poultry 2.849020 Processed_fruits_and_vegetables 2.723077 Name: pct_change, dtype: float64
category Farm_level_eggs 6.534694 Wholesale_fats_and_oils 4.131373 Farm_level_milk 3.476471 Farm_level_vegetables 3.450980 Wholesale_beef 3.409804 Farm_level_cattle 3.247059 Finished_consumer_foods 3.207843 Wholesale_dairy 3.200000 Wholesale_wheat_flour 2.996078 Farm_level_wheat 2.978431 Processed_foods_and_feeds 2.672549 Wholesale_pork 2.654902 Unprocessed_foodstuffs_and_feedstuffs 2.623529 Farm_level_fruit 2.621569 Farm_level_soybeans 2.317647 Wholesale_poultry 1.429412 Name: pct_change, dtype: float64

2. Bar charts of all categories

# CPI:
cpi_mean.plot(kind="barh")
plt.gca().invert_yaxis() # top to bottom
plt.title("CPI: Average Annual Forecast Inflation by Category")
plt.xlabel("Average percent change (1974–2024)")
plt.tight_layout()
plt.savefig('../figures/cpi_avg_annual_change_by_category.png')
plt.show()

# PPI:
ppi_mean.plot(kind="barh")
plt.gca().invert_yaxis() # top to bottom
plt.title("PPI: Average Annual Forecast Inflation by Category")
plt.xlabel("Average percent change (1974–2024)")
plt.tight_layout()
plt.savefig('../figures/ppi_avg_annual_change_by_category.png')
plt.show()
<Figure size 1400x600 with 1 Axes>
<Figure size 1400x600 with 1 Axes>

Top 5 fastest-inflating categories

Next, we explicitly rank categories by their average annual forecast inflation and highlight the top 5 for each dataset.

These categories represent the food items with the most persistent upward price pressure in the forecast data.

Extract top 5

cpi_top5 = cpi_mean.head(5)
ppi_top5 = ppi_mean.head(5)

display(cpi_top5)
display(ppi_top5)
category Sugar_and_sweets 4.588235 Fresh_fruits 4.576471 Cereals_and_bakery_products 4.321569 Food_away_from_home 4.278431 Fresh_fruits_and_vegetables 4.268627 Name: pct_change, dtype: float64
category Farm_level_eggs 6.534694 Wholesale_fats_and_oils 4.131373 Farm_level_milk 3.476471 Farm_level_vegetables 3.450980 Wholesale_beef 3.409804 Name: pct_change, dtype: float64

Plot top 5 (CPI & PPI)

# CPI:
cpi_top5.plot(kind="barh")
plt.gca().invert_yaxis() # top to bottom
plt.title("CPI: Top 5 Fastest-Inflating Categories (Avg Forecast)")
plt.xlabel("Average percent change (1974–2024)")
plt.tight_layout()
plt.savefig('../figures/cpi_top_5_fastest_inflating_categories.png')
plt.show()

# PPI:
ppi_top5.plot(kind="barh")
plt.gca().invert_yaxis() # top to bottom
plt.title("PPI: Top 5 Fastest-Inflating Categories (Avg Forecast)")
plt.xlabel("Average percent change (1974–2024)")
plt.tight_layout()
plt.savefig('../figures/ppi_top_5_fastest_inflating_categories.png')
plt.show()
<Figure size 1400x600 with 1 Axes>
<Figure size 1400x600 with 1 Axes>

Most volatile categories (year-to-year)

To measure how unstable inflation forecasts are, we will look at the standard deviation of the annual percent change for each category:

A higher standard deviation means the category’s inflation forecast fluctuates more from year to year (higher volatility).

Compute volatility per category

cpi_vol = (
    cpi_long
    .groupby("category")["pct_change"]
    .std()
    .sort_values(ascending=False)
)

ppi_vol = (
    ppi_long
    .groupby("category")["pct_change"]
    .std()
    .sort_values(ascending=False)
)

display(cpi_vol)
display(ppi_vol)
category Eggs 10.696875 Sugar_and_sweets 8.731842 Nonalcoholic_beverages 8.114507 Fats_and_oils 7.371705 Beef_and_veal 5.891666 Pork 5.629574 Fresh_fruits 5.314719 Cereals_and_bakery_products 5.037909 Fresh_vegetables 4.717079 Meats 4.407041 Dairy_products 4.298477 Fresh_fruits_and_vegetables 4.078014 Other_foods 4.033501 Poultry 4.014343 Other_meats 3.773502 Fish_and_seafood 3.756827 Fruits_and_vegetables 3.730769 Meats_poultry_and_fish 3.722804 Processed_fruits_and_vegetables 3.258995 Food_at_home 3.232504 All_food 2.886500 Food_away_from_home 2.644263 Name: pct_change, dtype: float64
category Farm_level_eggs 31.169660 Farm_level_wheat 19.420044 Farm_level_soybeans 17.729610 Wholesale_fats_and_oils 16.889482 Farm_level_milk 15.097080 Farm_level_vegetables 12.094914 Wholesale_wheat_flour 12.071768 Wholesale_pork 11.294287 Farm_level_cattle 10.915628 Unprocessed_foodstuffs_and_feedstuffs 9.721823 Farm_level_fruit 9.421535 Wholesale_beef 9.167383 Wholesale_poultry 7.644143 Wholesale_dairy 6.664203 Processed_foods_and_feeds 6.581309 Finished_consumer_foods 3.545580 Name: pct_change, dtype: float64

Plot most volatile categories (top 5)

# CPI:
cpi_vol.head(5).plot(kind="barh")
plt.gca().invert_yaxis() # top to bottom
plt.title("CPI: Most Volatile Categories (Std Dev of Forecast Inflation)")
plt.xlabel("Standard deviation of percent change")
plt.tight_layout()
plt.savefig('../figures/cpi_most_volatile_categories.png')
plt.show()

# PPI:
ppi_vol.head(5).plot(kind="barh")
plt.gca().invert_yaxis() # top to bottom
plt.title("PPI: Most Volatile Categories (Std Dev of Forecast Inflation)")
plt.xlabel("Standard deviation of percent change")
plt.tight_layout()
plt.savefig('../figures/ppi_most_volatile_categories.png')
plt.show()
<Figure size 1400x600 with 1 Axes>
<Figure size 1400x600 with 1 Axes>

Saving Summary Tables for Later Use

# make a directory called eda_summary under ../outputs/
save_directory = "../outputs/eda_summary/"
os.makedirs(save_directory, exist_ok=True)

cpi_mean.to_csv(save_directory + "cpi_mean_inflation.csv")
cpi_vol.to_csv(save_directory + "cpi_volatility.csv")
ppi_mean.to_csv(save_directory + "ppi_mean_inflation.csv")
ppi_vol.to_csv(save_directory + "ppi_volatility.csv")
STAT 159/259 Final Project: An Empirical Analysis of U.S. Food Price Dynamics Using CPI and PPI Data
Food Price Inflation in the United States: Evidence from CPI and PPI Data
Notebooks
Analysis: Fastest-Inflating Food Categories (CPI & PPI)