csv-etl

A rules based approach to performing ETL operations on csv files.

Installation

Requires Python 3

Latest Release = 0.1.3

Using pip

python -m venv venv
source venv/bin/activate
pip install csv-etl

Locally

git clone https://github.com/winslowdibona/csv-etl.git
cd csv-etl
python -m venv venv
source venv/bin/activate
pip install --editable .

Developing

git clone https://github.com/winslowdibona/csv-etl.git
cd csv-etl
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Overview

The goal of this project is to provide a re-usable way to perform ETL operations on csv files. This implementation takes a given row of data from a csv file and applies a set of rules to generate a new format of the data.

Rule Overview

The Rule in this project has the following properties

• target
• source
• type
• input_type
• output_type
• operations

Using different combinations of values for these properties can give us a flexible toolset for extracting data.

target - str

Once we have the extracted the value from the csv and applied our rule to it, the resulting value will appear under this value as the key in the result

source - str/list

This is where we want to pull the data from in the csv. If the source is a string, it will fetch that single column. If the source is a list, it will fetch all of the values.

type - RuleType

This can be one of two options

• Static
• Calculation

If RuleType.Static, the rule will simply return the value stored in Rule.source under Rule.target

If RuleType.Calculation, the rule will fetch the value(s) defined in Rule.source, and perform the operations on them

input_type - InputType

The data type you would like to read the value in from the csv as.

This can be one of three options

• String
• Integer
• Decimal

output_type - OutputType

The data type you would like the resulting value to be.

This can be one of four options

• String
• Integer
• Decimal
• Date

operations - list

A list of strings that will be run through python eval statement. The value(s) extracted from the csv will be available for use in these operations. If there is only a single source, the value will be assigned to the variable s. If there are multiple sources, the values will be passed in as a list under the variable s.

Defining Rules

These rules can be defined programmatically, or via a YAML configuration. The configuraiton follows the below structure

rules:
  -
    target: target_name
    type: Static || Calculation
    input_type: String || Integer || Decimal
    output_type: String || Integer || Decimal || Date
    source: source_name || [source, names]
    operations: ['operations', 'to', 'run']

Converting CSV Data

Once we have a set of rules, we can use the CSVConverter class to execute our rules on a data set.

from csv_etl import CSVConverter

csv_converter = CSVConverter(rules)

result = csv_converter.convert('path/to/csv_file')

This will give us back a list of dictionaries, with each item in the list representing the modified data for each row in the initial csv file.

We can also get the result fed back to us in json/csv format as a string

result = csv_converter.convert('path/to/csv/file', to='json')
result = csv_converter.convert('path/to/csv/file', to='csv')

Usage

CLI

$ csv-etl ./examples/order_data/config.yaml ./examples/order_data/test_data.csv
[
    {
        "OrderId": 1000,
        "OrderDate": "2018-01-01",
        "ProductId": "P-10001",
        "ProductName": "Arugola",
        "Quantity": 5250.5,
        "Unit": "kg"
    },
    {
        "OrderId": 1001,
        "OrderDate": "2017-12-12",
        "ProductId": "P-10002",
        "ProductName": "Iceberg Lettuce",
        "Quantity": 500.0,
        "Unit": "kg"
    }
]

$ csv-etl --help
Usage: csv-etl [OPTIONS] CONFIG CSV

Options:
  --outfile TEXT  File path to write the result to
  --format TEXT   Format the result should be. "json" or "csv"
  --help          Show this message and exit.

Examples

More detailed usage and programmatic examples are provided

• Order Data Example
• Example Rule Configs

Helpful Make Commands

Generating Documentation

make docs
open html/csv_etl/index.html

Running Tests

make pytest

Getting Test Coverage

make test-cov
open cov_html/index.html

What Next?

Better error handling

Currently the errors are just printed to the console. Might be nice to have an option to gather them and have them represented in the resulting data set somehow.

Better handling of eval statements

Right now the use of eval statements is a little tailored for the original problem and could use some more exploration on how they can be further utilized and how to handle potential errors.

Also right now the ability to perform multiple operations on a single extracted value is doable, but performing multiple operations on multiple extracted values will not. eval statements don't allow assignment of variables, and this prevents us from performing some logic, and being able to use the resulting values again in a list. Further exploration here may find a way to do multiple operations with multiple extracted values.