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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. |
| | | |
− | '''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.)
| + | ''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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| ==== Import CSV in PostGRE SQL ==== | | ==== Import CSV in PostGRE SQL ==== |
| 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). |
| + | |
| + | ==== Python ==== |
| + | There are some articles dealing with using Python language to explore Open Food Facts data. |
| + | |
| + | Step by step commands: http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/prepare_data_2017.html (also in french) |
| + | |
| + | Python notebooks are great to learn Open Food Facts data, as they mix code and results together: |
| + | * 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] |
| + | * https://www.datasciencesociety.net/part-1-exploring-food-data/ |
| + | |
| + | ==== R stat ==== |
| + | 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]. |