What's in an Avocado Toast: A Supply Chain Analysis
You're in London, making an avocado toast, a quick-to-make dish that has soared in popularity on breakfast menus since the 2010s. A simple smashed avocado toast can be made with five ingredients: one ripe avocado, half a lemon, a big pinch of salt flakes, two slices of sourdough bread and a good drizzle of extra virgin olive oil.
It's no small feat that most of these ingredients are readily available in grocery stores. In this project, you'll conduct a supply chain analysis of the ingredients used in an avocado toast, utilizing the Open Food Facts database. This database contains extensive, openly-sourced information on various foods, including their origins. Through this analysis, you will gain an in-depth understanding of the complex supply chain involved in producing a single dish. The data is contained in .csv files in the data/ folder provided.
After completing this project, you'll be armed with a list of ingredients and their countries of origin, and be well-positioned to launch into other analyses that explore how long, on average, these ingredients spend at sea.
# Setup
import pandas as pd
import numpy as np
from itertools import chain
relevant_categories = ['en:avocadoes', 'en:avocados', 'en:fresh-foods', 'en:fresh-vegetables', 'en:fruchte', 'en:fruits', 'en:raw-green-avocados', 'en:tropical-fruits', 'en:tropische-fruchte', 'en:vegetables-based-foods','fr:hass-avocados']
relevant_categories = ['en:aromatic-plants', 'en:citron', 'en:citrus', 'en:fresh-fruits', 'en:fresh-lemons', 'en:fruits', 'en:lemons', 'en:unwaxed-lemons']
relevant_categories = pd.read_csv("data/relevant_sourdough_categories.txt", header=None)[0].to_list()
# Read data
df = pd.read_csv("./data/sourdough.csv", delimiter="\t")
# Select subset of columns
df = df[[ 'code', 'lc', 'product_name_en', 'quantity', 'serving_size', 'packaging_tags', 'brands', 'brands_tags', 'categories_tags', 'labels_tags', 'countries', 'countries_tags', 'origins','origins_tags']]
# Drop null values in categories
df = df[~pd.isnull(df['categories_tags'])]
# Split each category tag value into list
categories_list = df['categories_tags'].str.split(",")
# Filter for records where a relevant category hits
df = df[categories_list.apply(lambda x: any([category in x for category in relevant_categories]))]
# Split origins and countries tags into lists
countries_list = df['countries'].str.replace(", ", ",").str.split(',').fillna("")
# Filter for records recorded in the United Kingdom
df = df[countries_list.apply(lambda x: "United Kingdom" in x or "Royaume-Uni" in x)]def read_and_filter_data(filepath, relevant_categories):
# Read data
df = pd.read_csv("./data/"+filepath, delimiter="\t")
# Select subset of columns
df = df[[ 'code', 'lc', 'product_name_en', 'quantity', 'serving_size', 'packaging_tags', 'brands', 'brands_tags', 'categories_tags', 'labels_tags', 'countries', 'countries_tags', 'origins','origins_tags']]
# Drop null values in categories
df = df[~pd.isnull(df['categories_tags'])]
# Split each category tag value into list
categories_list = df['categories_tags'].str.split(",")
# Filter for records where a relevant category hits
df = df[categories_list.apply(lambda x: any([category in x for category in relevant_categories]))]
# Split origins and countries tags into lists
countries_list = df['countries'].str.replace(", ", ",").str.split(',').fillna("")
# Filter for records recorded in the United Kingdom
df = df[countries_list.apply(lambda x: "United Kingdom" in x)]
origins_list = df['origins_tags'].str.replace(", ", ",").str.split(',').dropna()
# Flatten the origins list
origins = pd.Series(list(chain.from_iterable(origins_list)))
# Count # of occurrences of each origin tag
df_origins = origins.groupby(origins).size().sort_values(ascending=False).to_frame()
# Clean origin tag strings
df_origins.index = df_origins.index.str.replace(r"^..:", "")
df_origins.index = df_origins.index.str.replace("-", " ")
return df, df_origins[0].idxmax()
avocado, avocado_origin = read_and_filter_data("avocado.csv", ['en:avocadoes', 'en:avocados', 'en:fresh-foods', 'en:fresh-vegetables', 'en:fruchte', 'en:fruits', 'en:raw-green-avocados', 'en:tropical-fruits', 'en:tropische-fruchte', 'en:vegetables-based-foods','fr:hass-avocados'])
lemon, lemon_origin = read_and_filter_data("lemon.csv", ['en:aromatic-plants', 'en:citron', 'en:citrus', 'en:fresh-fruits', 'en:fresh-lemons', 'en:fruits', 'en:lemons', 'en:unwaxed-lemons'])
sourdough, sourdough_origin = read_and_filter_data("sourdough.csv", pd.read_csv("data/relevant_sourdough_categories.txt", header=None)[0].to_list())
olive_oil, olive_oil_origin = read_and_filter_data("olive_oil.csv", pd.read_csv("data/relevant_olive_oil_categories.txt", header=None)[0].to_list())
results = [lemon_origin, sourdough_origin, olive_oil_origin]
#salt, salt_origin = read_and_filter_data("salt_flakes.csv", ['en:edible-common-salt', 'en:salts', 'en:sea-salts'])
print(sourdough_origin)
print(olive_oil_origin)
print(lemon_origin)
print(avocado_origin)