Climate change has become a more prevalent problem in recent years, and one contributor that is overlooked is food waste. Data scientists have found that food waste is responsible for 6% of total greenhouse emissions globally (Our World in Data). Currently, over 300 million people globally face food insecurity and we lose about 1.3 billions tons of food per year, which is about a third of the world's food (The World Counts). Many of the things that contribute to climate change and greenhouse emissions aren't actionable items by individuals, but food waste is. Though it will take a lot of people to make a difference, one person can start the process. This is also something that the entire agriculture industry, including small farms and big corporations, can take action on. By finding out what is specifically causing food waste, people can make more thoughtful choices about their food.
import pandas as pd
data = pd.read_csv('foodwaste.csv')
df_foodwaste = pd.DataFrame(data)
df_foodwaste.head()
| m49_code | country | region | cpc_code | commodity | year | loss_percentage | loss_percentage_original | loss_quantity | activity | food_supply_stage | treatment | cause_of_loss | sample_size | method_data_collection | reference | url | notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 104 | Myanmar | NaN | 0142 | Groundnuts, excluding shelled | 2009 | 5.22 | 5.22% | 68100 | NaN | Whole supply chain | NaN | NaN | NaN | FAO's annual Agriculture Production Questionna... | FAO Sources | NaN | NaN |
| 1 | 104 | Myanmar | NaN | 0142 | Groundnuts, excluding shelled | 2008 | 5.43 | 5.43% | 65240 | NaN | Whole supply chain | NaN | NaN | NaN | FAO's annual Agriculture Production Questionna... | FAO Sources | NaN | NaN |
| 2 | 104 | Myanmar | NaN | 0142 | Groundnuts, excluding shelled | 2007 | 5.61 | 5.61% | 61080 | NaN | Whole supply chain | NaN | NaN | NaN | FAO's annual Agriculture Production Questionna... | FAO Sources | NaN | NaN |
| 3 | 104 | Myanmar | NaN | 0142 | Groundnuts, excluding shelled | 2006 | 5.40 | 5.4% | 55270 | NaN | Whole supply chain | NaN | NaN | NaN | FAO's annual Agriculture Production Questionna... | FAO Sources | NaN | NaN |
| 4 | 104 | Myanmar | NaN | 0142 | Groundnuts, excluding shelled | 2005 | 5.00 | 5% | 51970 | NaN | Whole supply chain | NaN | NaN | NaN | FAO's annual Agriculture Production Questionna... | FAO Sources | NaN | NaN |
| feature | description |
|---|---|
| m49 code | code for areas that may emcompass more than one country |
| country | country where food waste occured |
| cpc code | customs procedure codes |
| commodity | type of food |
| year | year food waste occured |
| loss percentage | percentage of food lost |
| loss quantity | amount of food lost |
| activity | activity that caused food waste |
| food supply stage | stage food was in when lost |
Some features are not present because they have many NaN values throughout the dataset/they are not relevant to the problem.
dataset : https://www.fao.org/platform-food-loss-waste/flw-data/en/
Two of the features in the dataset, activity and food supply stage, can really provide an insight into what specifically is causing food to be wasted. By grouping different foods by activity and food supply stage, this dataset can provide information on how to make those processes more efficient and cause less food to go to waste in real life.