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CSV export details
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Open Food Facts data is released as Open Data: it can be reused freely by anyone, under the Open Database License (ODBL).
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[[Category:Reuse]]
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Open Food Facts data is released as Open Data: it can be reused freely by anyone, under the Open Database License (ODBL). While this page is related to practical reuse, you must really be aware of [[ODBL License|rights and duties provided by the Open Database]] License (ODBL).
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== Where is the data ==
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== Where is the data? ==
 
You'll find different kind of ways to get the data.
 
You'll find different kind of ways to get the data.
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=== Searching for a selection of product? ===
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=== Looking for a selection of products? ===
 
Then use the advanced search. The Open Food Facts advanced search feature allows to download selections of the data. See: https://world.openfoodfacts.org/cgi/search.pl
 
Then use the advanced search. The Open Food Facts advanced search feature allows to download selections of the data. See: https://world.openfoodfacts.org/cgi/search.pl
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When you search is done, you will be able to download the selection, just give a try!
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When your search is done, you will be able to download the selection in '''CSV or Excel format''', just give a try!
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=== Searching for the whole database? ===
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=== Looking for the whole database? ===
 
The whole database can be downloaded at https://world.openfoodfacts.org/data
 
The whole database can be downloaded at https://world.openfoodfacts.org/data
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It's very big. Open Food Facts hosts more than 1,200,000 products (as of April 2020). So you will probably need skills to reuse the data.
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It's very big. Open Food Facts hosts more than 1,400,000 products (as of July 2020). So you will probably need skills to reuse the data.
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You'll be able to find there different kinds of data.
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You'll be able to find here different kinds of data.
    
==== The MongoDB daily export ====
 
==== The MongoDB daily export ====
 
It represents the most complete data; it's very big and you have to know how to deal with MongoDB.
 
It represents the most complete data; it's very big and you have to know how to deal with MongoDB.
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==== The JSONL daily export ====
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While still undocumented, there is a daily export of the whole database in jsonl format. It represents the same data as the MongoDB export. It's very big! More than 14GB uncompressed.
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You can find it at https://static.openfoodfacts.org/data/openfoodfacts-products.jsonl.gz
    
==== The CSV daily export ====
 
==== The CSV daily export ====
It represents a subset of the database but it is generally fitted to the majority of usages. It's a 2.3GB file (as of April 2020), so it can't be opened by Libre Office or Excel with an 8GB machine.
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It contains all the products, but with a subset of the database fields. [https://world.openfoodfacts.org/data/data-fields.txt This subset is very large] and include main characteristics (EAN, name, brand...), many tags (such as categories, origins, labels, packaging...), ingredients and nutrition facts. Thus, it is generally fitted to the majority of usages. It's a 2.3GB file (as of April 2020), so it can't be opened by Libre Office or Excel with an 8GB machine.
    
== How to reuse? ==
 
== How to reuse? ==
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[https://csvkit.readthedocs.io/en/latest/ csvkit] is a very efficient tool to manipulate huge amounts of CSV data. Here are some useful tips to manipulate Open Food Facts CSV export.
 
[https://csvkit.readthedocs.io/en/latest/ csvkit] is a very efficient tool to manipulate huge amounts of CSV data. Here are some useful tips to manipulate Open Food Facts CSV export.
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'''Selecting 2 columns'''. Selecting two or three columns can be useful for some usages. Extracting two columns produce a smaller CSV file which can be opened by common softwares such as Libre Office or Excel. The following command creates a CSV file (brands.csv) containing two columns from Open Food Facts (code and brands). (It generally takes more than 2 minutes, depending on your computer.)
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''Selecting 2 column''s. Selecting two or three columns can be useful for some usages. Extracting two columns produce a smaller CSV file which can be opened by common softwares such as Libre Office or Excel. The following command creates a CSV file (brands.csv) containing two columns from Open Food Facts (code and brands). (It generally takes more than 2 minutes, depending on your computer.)
    
<code>
 
<code>
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</code>
 
</code>
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==== Import CSV in PostGRE SQL ====
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''Selecting products based on a regular expression''. csvkit can search in some specified fields, allowing to make powerful selections. The following command creates a CSV file (selection.csv) containing all products where the barcode (code) is beginning by 325798 (<code>-r "^325798(.*)"</code>).
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<code>
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$ csvgrep -t -c code -r "^325798(.*)" en.openfoodfacts.org.products.csv > selection.csv
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</code>
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The following command creates a CSV file (calissons.csv) containing all products where the category (categories) is containing "calisson".
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<code>
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$ csvgrep -t -c categories -r "calisson" en.openfoodfacts.org.products.csv > calisson.csv
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</code>
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==== Import CSV in PostgreSQL ====
 
See this article: https://blog-postgresql.verite.pro/2018/12/21/import-openfoodfacts.html (in french, but should be understandable with Google Translator).
 
See this article: https://blog-postgresql.verite.pro/2018/12/21/import-openfoodfacts.html (in french, but should be understandable with Google Translator).
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==== Import CSV to SQLite ====
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The repository [https://github.com/fairdirect/foodrescue-content foodrescue-content] contains Ruby scripts that import Open Food Facts CSV data into a [https://www.sqlite.org/index.html SQLite] database with full table normalization. Only a few fields are imported so far, but this an be extended easily. Data imported so far includes:
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* barcode number
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* product name
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* product categories
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* product countries
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* full categories hierarchy imported from the <code>categories.txt</code> taxonomy ([https://github.com/openfoodfacts/openfoodfacts-server/tree/master/taxonomies see])
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==== Python ====
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There are some articles dealing with using Python language to explore Open Food Facts data.
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Step by step commands: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/prepare_data_2017.html (also in french)
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Python notebooks are great to learn Open Food Facts data, as they mix code and results together:
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* Find [https://www.kaggle.com/openfoodfacts/world-food-facts/kernels?sortBy=hotness&group=everyone&pageSize=20&datasetId=20&language=Python dozens of python notebooks on Kaggle]
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* https://www.datasciencesociety.net/part-1-exploring-food-data/
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==== R stat ====
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For people who have R stat skills, there are [https://www.kaggle.com/openfoodfacts/world-food-facts/kernels?sortBy=hotness&group=everyone&pageSize=20&datasetId=20&language=R more than 50 notebooks from Kaggle community].
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=== JSONL export ===
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JSONL is a huge file! It's not possible to play with it with common editors or common tools. But there is some command line tools that allows interesting things, like [https://stedolan.github.io/jq/manual/v1.6/ jq].
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==== jq ====
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* start decompress the file (be carreful => 14GB after decompression):
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$ gunzip openfoodfacts-products.jsonl.gz
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* work on a small subset to test. E.g. for 100 products:
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$ head -n 100 openfoodfacts-products.jsonl > small.jsonl
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You can start playing with jq. Here are examples.
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$ cat small.jsonl | jq . # print all file in JSON format
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$ cat small.jsonl | jq -r .code # print all products' codes.
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$ cat small.jsonl | jq -r '[.code,.product_name] | @csv' # output CSV data containing code,product_name
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Then you can try on the whole database:
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$ cat openfoodfacts-products.jsonl | jq -r '[.code,.product_name] | @csv' > names.csv # output CSV file (name.csv) containing all products with code,product_name
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If you don't have enough disk place to uncompress the .gz file, you can use zcat directly on the compressed file. Example:
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$ zcat openfoodfacts-products.jsonl.gz | jq -r '[.code,.product_name] | @csv' # output CSV data containing code,product_name

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