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Project: What's in an Avocado Toast: A Supply Chain Analysis
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    • 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.

    ### Task 1. Reading in the avocado data

# Reading tab-delimited data
import pandas as pd
avocado = pd.read_csv('data/avocado.csv', sep='\t')

# Subsetting a DataFrame to include only relevant columns
subset_columns = ['code', 'lc', 'product_name_en', 'quantity', 'serving_size', 'packaging_tags', 'brands', 'brands_tags', 'categories_tags', 'labels_tags', 'countries', 'countries_tags', 'origins','origins_tags']
avocado = avocado[subset_columns]

### Task 2. Filter avocado data using relevant category tags

# Dropping rows with null values in a particular column
avocado = avocado.dropna(subset = 'categories_tags')

# Turning a column of comma separated tags into a column of lists
avocado['categories_list'] = avocado['categories_tags'].str.split(',')

# Identifying relevant categories for avocados
relevant_avocado_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'
]


# Filtering a DataFrame based on a column of lists
avocado = avocado[avocado['categories_list'].apply(lambda x: any([i for i in x if i in relevant_avocado_categories]))]

### Task 3. Where do most avocados come from?

# Filtering your DataFrame by a particular country
avocados_uk = avocado[(avocado['countries']=='United Kingdom')]

# Returning counts of unique values in a column
print('**avocado origins**:', '\n', avocados_uk['origins_tags'].value_counts(),  '\n')
avocado_origin = 'Peru'

### Task 4. Don't Repeat Yourself (DRY): Create a user-define function instead!

# Create a user-defined function to read and filter data
def read_and_filter_data(filepath, relevant_categories):
  df = pd.read_csv('data/' + filepath, sep='\t')

  # Subset data
  df = df[subset_columns]

  # Split tags into lists
  df['categories_list'] = df['categories_tags'].str.split(',')

  # Drop null categories and filter data
  df = df.dropna(subset = 'categories_list')

  df = df[df['categories_list'].apply(lambda x: any([i for i in x if i in relevant_categories]))]
    
  df = df[(df['countries']=='United Kingdom')]
  print(f'**{filepath[:-4]} origins**','\n',df['origins_tags'].value_counts(), '\n')

  return df

# Identify relevant categories for lemons
relevant_lemon_categories = [
 'en:aromatic-herbs',
 'en:aromatic-plants',
 'en:citron',
 'en:citrus',
 'en:fresh-fruits',
 'en:fresh-lemons',
 'en:fruits',
 'en:lemons',
 'en:unwaxed-lemons'
]

# Call your user-defined function on lemon.csv
lemons = read_and_filter_data('lemon.csv',relevant_lemon_categories)
lemon_origin = 'South Africa'

## Task 5. Call your user-defined function on the olive oil, salt and sourdough data

# Call your user-defined function on olive_oil.csv

with open("data/relevant_olive_oil_categories.txt", "r") as file:
    relevant_olive_oil_categories = file.read().splitlines()
    file.close()

olive_oil = read_and_filter_data('olive_oil.csv',relevant_olive_oil_categories)
olive_oil_origin = 'Greece'

# Call your user-defined function on sourdough.csv

with open("data/relevant_sourdough_categories.txt", "r") as file:
    relevant_sourdough_categories = file.read().splitlines()
    file.close()
    
sourdough = read_and_filter_data('sourdough.csv',relevant_sourdough_categories)
sourdough_origin = 'United Kingdom'

relevant_salt_categories = [
 'en:edible-common-salt',
 'en:salts',
 'en:sea-salts']

# Call your user-defined function on salt.csv

salt_flakes = read_and_filter_data('salt_flakes.csv',relevant_salt_categories)