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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.

#task1
# 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]

#task2: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]))]

#task3: Where do most avocados come from?
# Filtering your DataFrame by a particular country
avocado_uk=avocado[(avocado['countries']=='United Kingdom')]
#Returning counts of unique values in a column
print('**avocado origins**:', '\n', avocado_uk['origins_tags'].value_counts(),  '\n')
avocado_origin = 'Peru'

#task4:Don't Repeat Yourself (DRY)
# Create a user-defined function to read and filter data
def read_and_filter_data(filepath, subset_columns, relevant_categories, country):
  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_tags'])

  df = df[df['categories_list'].apply(lambda x: any([i for i in x if i in relevant_categories]))]
    
  df = df[(df['countries']==country)]
  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',subset_columns, relevant_lemon_categories, 'United Kingdom')
lemon_origin = 'South Africa'

#task5:Call your function on the remaining datasets
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',subset_columns, relevant_olive_oil_categories, 'Greece')
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',subset_columns, relevant_sourdough_categories, 'United Kingdom')
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',subset_columns, relevant_salt_categories, 'United Kingdom')