microdata-tools

Tools for the microdata.no platform

Installation

microdata-tools can be installed from PyPI using pip:

pip install microdata-tools

Usage

Once you have your metadata and data files ready to go, they should be named and stored like this:

my-input-directory/
    MY_DATASET_NAME/
        MY_DATASET_NAME.csv
        MY_DATASET_NAME.json

The CSV file is optional in some cases.

Package dataset

The package_dataset() function will encrypt and package your dataset as a tar archive. The process is as follows:

Generate the symmetric key for a dataset.
Encrypt the dataset data (CSV) using the symmetric key and store the encrypted file as <DATASET_NAME>.csv.encr
Encrypt the symmetric key using the asymmetric RSA public key microdata_public_key.pem and store the encrypted file as <DATASET_NAME>.symkey.encr
Gather the encrypted CSV, encrypted symmetric key and metadata (JSON) file in one tar file.

Unpackage dataset

The unpackage_dataset() function will untar and decrypt your dataset using the microdata_private_key.pem RSA private key.

The packaged file has to have the <DATASET_NAME>.tar extension. Its contents should be as follows:

<DATASET_NAME>.json : Required medata file.

<DATASET_NAME>.csv.encr : Optional encrypted dataset file.

<DATASET_NAME>.symkey.encr : Optional encrypted file containing the symmetrical key used to decrypt the dataset file. Required if the .csv.encr file is present.

Decryption uses the RSA private key located at RSA_KEY_DIR.

The packaged file is then stored in output_dir/archive/unpackaged after a successful run or output_dir/archive/failed after an unsuccessful run.

Example

Python script that uses a RSA public key named microdata_public_key.pem and packages a dataset:

from pathlib import Path
from microdata_tools import package_dataset

RSA_KEYS_DIRECTORY = Path("tests/resources/rsa_keys")
DATASET_DIRECTORY = Path("tests/resources/input_package/DATASET_1")
OUTPUT_DIRECTORY = Path("tests/resources/output")

package_dataset(
   rsa_keys_dir=RSA_KEYS_DIRECTORY,
   dataset_dir=DATASET_DIRECTORY,
   output_dir=OUTPUT_DIRECTORY,
)

Validation

Once you have your metadata and data files ready to go, they should be named and stored like this:

my-input-directory/
    MY_DATASET_NAME/
        MY_DATASET_NAME.csv
        MY_DATASET_NAME.json

Note that the filename only allows upper case letters A-Z, number 0-9 and underscores.

Import microdata-tools in your script and validate your files:

from microdata_tools import validate_dataset

validation_errors = validate_dataset(
    "MY_DATASET_NAME",
    input_directory="path/to/my-input-directory"
)

if not validation_errors:
    print("My dataset is valid")
else:
    print("Dataset is invalid :(")
    # You can print your errors like this:
    for error in validation_errors:
        print(error)

For a more in-depth explanation of usage visit the usage documentation.

Data format description

A dataset as defined in microdata consists of one data file, and one metadata file.

The data file is a csv file seperated by semicolons. A valid example would be:

000000000000001;123;2020-01-01;2020-12-31;
000000000000002;123;2020-01-01;2020-12-31;
000000000000003;123;2020-01-01;2020-12-31;
000000000000004;123;2020-01-01;2020-12-31;

Read more about the data format and columns in the documentation.

The metadata files should be in json format. The requirements for the metadata is best described through the Pydantic model, the examples, and the documentation.

statisticsnorway / microdata-tools