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first commit with solution
oussamadz Dec 31, 2022
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README file edited
oussamadz Dec 31, 2022
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30 changes: 15 additions & 15 deletions README.md
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# Task

1. Fork this project
2. Create a python script that reads all of the rows from `homework.csv` and outputs them to a new file `formatted.csv` using the headers from `example.csv` as a guideline. (See `Transformations` below for more details.)
3. You may you any libraries you wish, but you must include a `requirements.txt` if you import anything outside of the standard library.
4. There is no time limit for this assignment.
5. You may ask any clarifying questions via email.
6. Create a pull request against this repository with an English description of how your code works when you are complete
# etl_homework Solution

## Transformations
The approach taken for this solution was using pandas to manipulate
data easier and make the script recyclable as much as possible
by creating a new dataframe from the homework dataset
that respects the example dataset given
## Requirements
pandas
pycountry
## comments
Some of the data was hardcoded and pulled from description column such as fill color, weave....
Since that was the only solution apparent.

## Authors

- [@oussamadz](https://www.github.com/oussamadz)

Follow industry standards for each data type when decided on the final format for cells.

* Dates should use ISO 8601
* Currency should be rounded to unit of accounting. Assume USD for currency and round to cents.
* For dimensions without units, assume inches. Convert anything which isn't in inches to inches.
* For weights without units, assume pounds. Convert anything which isn't in pounds to pounds.
* UPC / Gtin / EAN should be handled as strings
* Floating point and decimal numbers should preserve as much precision as possible
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