Project Description

In this project, we used our data analysis skills to understand where the ingredients in an avocado toast come from. We used data from the Open Food Facts database, which has detailed information about many food items, including their countries of origin.

We focused on three main ingredients:

• Avocados
• Olive oil
• Sourdough bread (represented by lemons in the data as an example)

Each ingredient came with:

• A CSV file (like avocado.csv) that has information about the product and where it comes from.
• A TXT file (like relevant_avocado_categories.txt) that lists the important food categories we need to focus on.

What We Did

• We filtered the data to keep only the rows that matched the relevant food categories (like fruits, oils, etc.) using the categories_tags column.
• Each product often had more than one category, so we had to check all the tags in each row.
• We then looked at the origins_tags column to find out the country of origin of each item.

Goal

By the end of this project, we created a list showing which countries the ingredients in avocado toast most commonly come from. This helps us better understand the global supply chain behind just one simple dish.

Our final task was to find the most common country of origin for:

• Avocados
• Olive oil
• Sourdough bread

# import library and read tab-delimited data
import pandas as pd
avocado = pd.read_csv('avocado.csv', sep='\t')
avocado.head()

	code	lc	product_name_de	product_name_el	product_name_en	product_name_es	product_name_fi	product_name_fr	product_name_id	product_name_it	...	off:ecoscore_data.adjustments.packaging.non_recyclable_and_non_biodegradable_materials	off:ecoscore_data.adjustments.production_system.value	off:ecoscore_data.adjustments.threatened_species.value	sources_fields:org-database-usda:available_date	sources_fields:org-database-usda:fdc_category	sources_fields:org-database-usda:fdc_data_source	sources_fields:org-database-usda:fdc_id	sources_fields:org-database-usda:modified_date	sources_fields:org-database-usda:publication_date	data_sources
0	0059749979702	fr	NaN	NaN	NaN	NaN	NaN	Naturalia Avocado Oil	NaN	NaN	...	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - yuka, Apps
1	7610095131409	en	NaN	NaN	NaN	NaN	NaN	Avocado Bowl chips	NaN	NaN	...	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - Yuka, Apps, Producers, Producer - zweifel
2	4005514005578	en	NaN	NaN	Gelbe Linse Avocado Brotaufstrich	NaN	NaN	NaN	NaN	NaN	...	1.0	15.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - yuka, Apps, App - smoothie-openfoodfacts
3	0879890002513	en	NaN	NaN	Avocado toast chili lime	NaN	NaN	NaN	NaN	NaN	...	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - Yuka, Apps, App - InFood
4	0223086613685	en	NaN	NaN	Avocado	NaN	NaN	NaN	NaN	NaN	...	1.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - Yuka, Apps

5 rows × 184 columns

# Subset large 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]
avocado.head()

	code	lc	product_name_en	quantity	serving_size	packaging_tags	brands	brands_tags	categories_tags	labels_tags	countries	countries_tags	origins	origins_tags
0	0059749979702	fr	NaN	NaN	NaN	NaN	Naturalia	naturalia	en:plant-based-foods-and-beverages,en:plant-ba...	NaN	Canada	en:canada	NaN	NaN
1	7610095131409	en	NaN	NaN	NaN	NaN	Zweifel	zweifel	en:snacks,en:salty-snacks,en:appetizers,en:chi...	en:vegetarian,en:vegan	Switzerland, World	en:switzerland,en:world	NaN	NaN
2	4005514005578	en	Gelbe Linse Avocado Brotaufstrich	NaN	NaN	NaN	Tartex	tartex	de:abendbrotsufstrich	en:organic,en:eu-organic,en:eg-oko-verordnung	Germany	en:germany	NaN	NaN
3	0879890002513	en	Avocado toast chili lime	NaN	NaN	NaN	NaN	NaN	NaN	NaN	United States	en:united-states	NaN	NaN
4	0223086613685	en	Avocado	NaN	NaN	NaN	NaN	NaN	NaN	NaN	United States	en:united-states	NaN	NaN

# Gather relevant categories data for avocados
with open("relevant_avocado_categories.txt", "r") as file:
    relevant_avocado_categories = file.read().splitlines()
    file.close()

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']

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

# Drop rows with null values in the newly created column
avocado = avocado.dropna(subset = 'categories_list')

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

avocado.head()

	code	lc	product_name_de	product_name_el	product_name_en	product_name_es	product_name_fi	product_name_fr	product_name_id	product_name_it	...	off:ecoscore_data.adjustments.production_system.value	off:ecoscore_data.adjustments.threatened_species.value	sources_fields:org-database-usda:available_date	sources_fields:org-database-usda:fdc_category	sources_fields:org-database-usda:fdc_data_source	sources_fields:org-database-usda:fdc_id	sources_fields:org-database-usda:modified_date	sources_fields:org-database-usda:publication_date	data_sources	categories_list
5	3662994002063	fr	NaN	NaN	NaN	NaN	NaN	Avocat hass	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - smoothie-openfoodfacts, Apps, App - Yuka	[en:plant-based-foods-and-beverages, en:plant-...
6	8437013031011	fr	NaN	NaN	NaN	NaN	NaN	Avocat	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - Yuka, Apps	[en:plant-based-foods-and-beverages, en:plant-...
14	4016249238155	de	Saatenliebe Mohn Avocado	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	15.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - yuka, Apps	[en:plant-based-foods-and-beverages, en:plant-...
17	8718963381532	de	EAT ME AVOCADO	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - Yuka, Apps, App - smoothie-openfoodfacts	[en:plant-based-foods-and-beverages, en:plant-...
23	8436002746707	es	NaN	NaN	NaN	AGUACATES CASADO PREMIUM	NaN	NaN	NaN	NaN	...	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	App - elcoco, Apps, App - yuka, App - El CoCo	[en:plant-based-foods-and-beverages, en:plant-...

5 rows × 185 columns

Where do most avocados come from?

# Filter DataFrame for UK data
avocados_uk = avocado[(avocado['countries']=='United Kingdom')]


# Find most common country for avocado origin
avocado_origin = (avocados_uk['origins_tags'].value_counts().index[0])
avocado_origin = avocado_origin.lstrip("en:")

avocado_origin

'peru'

Lets create a general function to read and filter data for a particular ingredient, and return the top origin country for that food item

def read_and_filter_data(filename, relevant_categories):
    df = pd.read_csv(filename, sep='\t', low_memory=False)
  
  # Subset large 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']
    df = df[subset_columns]

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

    # Drop rows with null categories data
    df = df.dropna(subset = 'categories_list')

    # Filter data for relevant categories
    df = df[df['categories_list'].apply(lambda x: any([i for i in x if i in relevant_categories]))]
    
    # Filter data for the UK
    df_uk = df[(df['countries']=='United Kingdom')]

    # Find top origin country string with the highest count
    top_origin_string = (df_uk['origins_tags'].value_counts().index[0])

    # Clean up top origin country string
    top_origin_country = top_origin_string.lstrip("en:")
    top_origin_country = top_origin_country.replace('-', ' ')

    print(f'**{filename[:-4]} origins**','\n', top_origin_country, '\n')

    print ("Top origin country: ", top_origin_country)
    print ("\n")

    # End of function - return top origin country for this ingredient
    return top_origin_country

# Analyze avocado origins again, this time by calling function
top_avocado_origin = read_and_filter_data('avocado.csv',relevant_avocado_categories)

**avocado origins** 
 peru 

Top origin country:  peru

Repeat process above using function for the other 2 ingredients

# Read the file with UTF-8 encoding
with open("relevant_olive_oil_categories.txt", "r", encoding="utf-8") as file:
    relevant_olive_oil_categories = file.read().splitlines()
    file.close()

# Calling our function on olive_oil.csv
top_olive_oil_origin = read_and_filter_data('olive_oil.csv',relevant_olive_oil_categories)

**olive_oil origins** 
 greece 

Top origin country:  greece

# Gather relevant categories data for sourdough
with open("relevant_sourdough_categories.txt", "r") as file:
    relevant_sourdough_categories = file.read().splitlines()
    file.close()

# Call our function on sourdough.csv
top_sourdough_origin = read_and_filter_data('sourdough.csv',relevant_sourdough_categories)

**sourdough origins** 
 united kingdom 

Top origin country:  united kingdom

We can explore other questions also using the available food data!