Tag maintentance

Scope

As shown in the basics, a dataset gets a few mandatory technical attributes on creation.

Additional descriptive metadata may be provided, that is the dataset and its series may be tagged. The tagging is a one time operation that neeed should not need to be repeated. That is, unless mistakes or omissions have been made, or new series are added to the set.

For calculations that derive new data. Some functions will automaticly update the metadata. Others will require that to be handled by the user.

Setup

from datetime import timedelta

from ssb_timeseries.sample_data import create_df,date_ranges
from ssb_timeseries.dates import ensure_datetime, date_utc

import polars as pl
from datetime import datetime

from ssb_timeseries.types import SeriesType, Versioning, Temporality

from ssb_timeseries.dataset import Dataset

Manually tagging set and series

def some_simple_data_from_file_or_query(
    start='2020-01-01',
    end='2025-06-01',
):
    """create some sample data"""
    return create_df(
        ['p','q','r'],
        start_date=start,
        end_date=end,
        freq='D'
    )

pqr_df = some_simple_data_from_file_or_query()

pqr = Dataset(
    name = 'PQR',
    data_type = SeriesType('NONE', 'AT'),
    data = pqr_df,
)

The technical metadata is added at creation time.

pqr.tags

{'name': 'PQR',
 'product group': ['essential', 'essentials'],
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'product': 'coffee',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'product': 'crispbread',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'knekkebrød',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'product': 'brown cheese',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 'variable': 'price',
 'versioning': 'NONE'}

pqr.tag_dataset(tags={'variabel': 'pris','varegruppe': 'nødvendigheter'})

pqr.tag_series('p',tags={'vare': 'kaffe'})
pqr.tag_series('q',tags={'vare': 'knekkebrød'})
pqr.tag_series('r',tags={'vare': 'brunost'})

pqr.tags

{'name': 'PQR',
 'product group': ['essential', 'essentials'],
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'product': 'coffee',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'product': 'crispbread',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'knekkebrød',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'product': 'brown cheese',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 'variable': 'price',
 'versioning': 'NONE'}

Tags can be used immediately.

pqr[{'vare': 'kaffe'}].data

valid_at	p
2019-12-31 23:00:00+00:00	100.0
2020-01-01 23:00:00+00:00	100.0
2020-01-02 23:00:00+00:00	120.0
2020-01-03 23:00:00+00:00	80.0
2020-01-04 23:00:00+00:00	90.0
…	…
2025-05-27 22:00:00+00:00	100.0
2025-05-28 22:00:00+00:00	90.0
2025-05-29 22:00:00+00:00	110.0
2025-05-30 22:00:00+00:00	90.0
2025-05-31 22:00:00+00:00	100.0

pqr.save()

Autotagging

interval_data = SeriesType('NONE', 'FROM_TO')

def mock_interval_data_from_file_or_query(start, end):
    a_to_z = [chr(i) for i in range(ord('a'), ord('z') + 1)]
    variables = ['volume', 'price']
    products = ['coffee', 'tea', 'soft-drinks', 'beer', 'wine']
    regions = ['N', 'E', 'W', 'S', 'NE', 'NW', 'SE', 'SW']
    return create_df(
        a_to_z, variables, products, regions,
        start_date=start,
        end_date=end,
        freq='M',
        temporality='FROM_TO',
        implementation='polars'
    )

bigger_data = mock_interval_data_from_file_or_query(start='2025-01-01', end='2025-06-01')

bigger_data

valid_from	valid_to	a_volume_coffee_N	a_volume_coffee_E	a_volume_coffee_W	a_volume_coffee_S	a_volume_coffee_NE	a_volume_coffee_NW	a_volume_coffee_SE	a_volume_coffee_SW	a_volume_tea_N	a_volume_tea_E	a_volume_tea_W	a_volume_tea_S	a_volume_tea_NE	a_volume_tea_NW	a_volume_tea_SE	a_volume_tea_SW	a_volume_soft-drinks_N	a_volume_soft-drinks_E	a_volume_soft-drinks_W	a_volume_soft-drinks_S	a_volume_soft-drinks_NE	a_volume_soft-drinks_NW	a_volume_soft-drinks_SE	a_volume_soft-drinks_SW	a_volume_beer_N	a_volume_beer_E	a_volume_beer_W	a_volume_beer_S	a_volume_beer_NE	a_volume_beer_NW	a_volume_beer_SE	a_volume_beer_SW	a_volume_wine_N	a_volume_wine_E	a_volume_wine_W	…	z_price_coffee_S	z_price_coffee_NE	z_price_coffee_NW	z_price_coffee_SE	z_price_coffee_SW	z_price_tea_N	z_price_tea_E	z_price_tea_W	z_price_tea_S	z_price_tea_NE	z_price_tea_NW	z_price_tea_SE	z_price_tea_SW	z_price_soft-drinks_N	z_price_soft-drinks_E	z_price_soft-drinks_W	z_price_soft-drinks_S	z_price_soft-drinks_NE	z_price_soft-drinks_NW	z_price_soft-drinks_SE	z_price_soft-drinks_SW	z_price_beer_N	z_price_beer_E	z_price_beer_W	z_price_beer_S	z_price_beer_NE	z_price_beer_NW	z_price_beer_SE	z_price_beer_SW	z_price_wine_N	z_price_wine_E	z_price_wine_W	z_price_wine_S	z_price_wine_NE	z_price_wine_NW	z_price_wine_SE	z_price_wine_SW
datetime[μs, Europe/Oslo]	datetime[μs, Europe/Oslo]	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	…	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
2025-01-01 00:00:00 CET	2025-02-01 00:00:00 CET	100.0	90.0	90.0	100.0	100.0	100.0	100.0	90.0	110.0	100.0	120.0	90.0	90.0	90.0	100.0	120.0	130.0	100.0	110.0	100.0	110.0	70.0	90.0	80.0	100.0	100.0	100.0	90.0	80.0	90.0	110.0	110.0	120.0	110.0	100.0	…	110.0	100.0	100.0	100.0	100.0	90.0	120.0	100.0	110.0	110.0	100.0	90.0	100.0	110.0	90.0	100.0	110.0	110.0	100.0	90.0	110.0	90.0	100.0	100.0	90.0	110.0	110.0	100.0	100.0	100.0	100.0	90.0	80.0	120.0	90.0	80.0	100.0
2025-02-01 00:00:00 CET	2025-03-01 00:00:00 CET	100.0	110.0	70.0	80.0	100.0	80.0	100.0	80.0	90.0	100.0	90.0	110.0	100.0	80.0	100.0	80.0	100.0	110.0	110.0	90.0	90.0	120.0	100.0	80.0	110.0	90.0	90.0	120.0	100.0	80.0	110.0	100.0	80.0	100.0	80.0	…	120.0	90.0	120.0	90.0	110.0	110.0	120.0	110.0	100.0	90.0	110.0	100.0	100.0	90.0	90.0	110.0	90.0	100.0	100.0	100.0	100.0	100.0	110.0	100.0	110.0	100.0	110.0	100.0	110.0	100.0	90.0	90.0	90.0	100.0	110.0	100.0	100.0
2025-03-01 00:00:00 CET	2025-04-01 00:00:00 CEST	100.0	110.0	100.0	120.0	110.0	90.0	110.0	110.0	100.0	90.0	90.0	100.0	110.0	90.0	120.0	100.0	80.0	90.0	90.0	110.0	110.0	100.0	100.0	90.0	110.0	100.0	100.0	90.0	100.0	100.0	110.0	100.0	100.0	90.0	110.0	…	80.0	100.0	120.0	120.0	80.0	110.0	100.0	90.0	90.0	90.0	100.0	110.0	90.0	110.0	90.0	120.0	110.0	110.0	110.0	100.0	110.0	100.0	100.0	90.0	110.0	90.0	100.0	110.0	90.0	100.0	110.0	90.0	100.0	110.0	120.0	90.0	120.0
2025-04-01 00:00:00 CEST	2025-05-01 00:00:00 CEST	90.0	100.0	100.0	110.0	100.0	120.0	90.0	90.0	80.0	90.0	100.0	90.0	120.0	90.0	110.0	110.0	120.0	80.0	100.0	100.0	100.0	110.0	80.0	100.0	100.0	110.0	120.0	100.0	90.0	90.0	90.0	110.0	110.0	80.0	110.0	…	90.0	100.0	100.0	80.0	120.0	110.0	110.0	100.0	100.0	100.0	100.0	100.0	80.0	80.0	110.0	90.0	90.0	110.0	80.0	100.0	90.0	100.0	120.0	80.0	100.0	100.0	100.0	120.0	110.0	100.0	90.0	100.0	100.0	100.0	80.0	90.0	90.0
2025-05-01 00:00:00 CEST	2025-06-01 00:00:00 CEST	100.0	100.0	110.0	90.0	90.0	110.0	100.0	90.0	100.0	100.0	90.0	120.0	100.0	110.0	110.0	100.0	90.0	80.0	90.0	110.0	90.0	80.0	120.0	100.0	100.0	110.0	100.0	90.0	100.0	100.0	90.0	110.0	100.0	100.0	100.0	…	100.0	90.0	100.0	100.0	90.0	100.0	100.0	100.0	90.0	100.0	100.0	90.0	120.0	80.0	100.0	100.0	90.0	90.0	100.0	90.0	90.0	110.0	80.0	110.0	100.0	120.0	90.0	110.0	90.0	90.0	100.0	100.0	90.0	110.0	80.0	100.0	110.0
2025-06-01 00:00:00 CEST	2025-07-01 00:00:00 CEST	110.0	90.0	110.0	90.0	100.0	100.0	100.0	90.0	100.0	90.0	110.0	90.0	100.0	90.0	100.0	90.0	110.0	100.0	110.0	110.0	110.0	110.0	110.0	100.0	90.0	100.0	110.0	100.0	100.0	110.0	100.0	110.0	90.0	100.0	110.0	…	100.0	70.0	110.0	100.0	90.0	100.0	130.0	100.0	100.0	100.0	90.0	120.0	100.0	100.0	110.0	110.0	90.0	110.0	100.0	100.0	100.0	100.0	110.0	110.0	100.0	90.0	100.0	100.0	110.0	90.0	100.0	100.0	100.0	100.0	110.0	100.0	70.0

az = Dataset(
    name = 'AZ_drinks',
    data_type = interval_data,
    data = bigger_data,
    attributes=['store','variable','product', 'region'], # <-- this is the clever part
)
az.save()

len(az.series)

2080

az_selection = az[{'product': 'tea', 'variable': 'price'}]
az_selection.data

valid_from	valid_to	a_price_tea_E	a_price_tea_N	a_price_tea_NE	a_price_tea_NW	a_price_tea_S	a_price_tea_SE	a_price_tea_SW	a_price_tea_W	b_price_tea_E	b_price_tea_N	b_price_tea_NE	b_price_tea_NW	b_price_tea_S	b_price_tea_SE	b_price_tea_SW	b_price_tea_W	c_price_tea_E	c_price_tea_N	c_price_tea_NE	c_price_tea_NW	c_price_tea_S	c_price_tea_SE	c_price_tea_SW	c_price_tea_W	d_price_tea_E	d_price_tea_N	d_price_tea_NE	d_price_tea_NW	d_price_tea_S	d_price_tea_SE	d_price_tea_SW	d_price_tea_W	e_price_tea_E	e_price_tea_N	e_price_tea_NE	…	v_price_tea_NW	v_price_tea_S	v_price_tea_SE	v_price_tea_SW	v_price_tea_W	w_price_tea_E	w_price_tea_N	w_price_tea_NE	w_price_tea_NW	w_price_tea_S	w_price_tea_SE	w_price_tea_SW	w_price_tea_W	x_price_tea_E	x_price_tea_N	x_price_tea_NE	x_price_tea_NW	x_price_tea_S	x_price_tea_SE	x_price_tea_SW	x_price_tea_W	y_price_tea_E	y_price_tea_N	y_price_tea_NE	y_price_tea_NW	y_price_tea_S	y_price_tea_SE	y_price_tea_SW	y_price_tea_W	z_price_tea_E	z_price_tea_N	z_price_tea_NE	z_price_tea_NW	z_price_tea_S	z_price_tea_SE	z_price_tea_SW	z_price_tea_W
datetime[ns, UTC]	datetime[ns, UTC]	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	…	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
2024-12-31 23:00:00 UTC	2025-01-31 23:00:00 UTC	80.0	90.0	110.0	110.0	100.0	90.0	110.0	110.0	90.0	90.0	100.0	120.0	120.0	100.0	100.0	100.0	90.0	110.0	100.0	90.0	100.0	100.0	100.0	120.0	100.0	90.0	90.0	90.0	110.0	90.0	90.0	100.0	100.0	110.0	100.0	…	110.0	100.0	100.0	120.0	100.0	100.0	90.0	100.0	100.0	100.0	90.0	100.0	110.0	90.0	90.0	80.0	100.0	100.0	90.0	90.0	100.0	110.0	100.0	110.0	110.0	100.0	120.0	110.0	100.0	120.0	90.0	110.0	100.0	110.0	90.0	100.0	100.0
2025-01-31 23:00:00 UTC	2025-02-28 23:00:00 UTC	90.0	100.0	90.0	80.0	90.0	130.0	100.0	120.0	110.0	100.0	90.0	110.0	120.0	100.0	90.0	120.0	110.0	110.0	90.0	100.0	100.0	100.0	100.0	110.0	90.0	120.0	110.0	100.0	100.0	90.0	110.0	90.0	80.0	90.0	100.0	…	90.0	100.0	100.0	90.0	90.0	90.0	100.0	100.0	120.0	100.0	100.0	90.0	80.0	100.0	110.0	90.0	90.0	100.0	90.0	100.0	90.0	110.0	110.0	100.0	100.0	120.0	100.0	100.0	90.0	120.0	110.0	90.0	110.0	100.0	100.0	100.0	110.0
2025-02-28 23:00:00 UTC	2025-03-31 22:00:00 UTC	110.0	110.0	110.0	80.0	100.0	110.0	100.0	80.0	90.0	100.0	100.0	100.0	110.0	80.0	100.0	90.0	100.0	100.0	120.0	80.0	80.0	100.0	90.0	100.0	100.0	90.0	100.0	90.0	100.0	100.0	100.0	110.0	100.0	100.0	100.0	…	100.0	90.0	100.0	100.0	100.0	100.0	100.0	130.0	100.0	100.0	100.0	90.0	90.0	110.0	100.0	90.0	80.0	100.0	80.0	100.0	90.0	80.0	100.0	100.0	100.0	100.0	90.0	80.0	100.0	100.0	110.0	90.0	100.0	90.0	110.0	90.0	90.0
2025-03-31 22:00:00 UTC	2025-04-30 22:00:00 UTC	110.0	90.0	110.0	80.0	110.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	110.0	100.0	80.0	120.0	100.0	90.0	110.0	120.0	100.0	80.0	90.0	90.0	100.0	110.0	100.0	130.0	90.0	110.0	80.0	100.0	100.0	120.0	…	100.0	80.0	100.0	90.0	110.0	110.0	110.0	100.0	90.0	90.0	100.0	90.0	100.0	100.0	110.0	100.0	90.0	100.0	110.0	90.0	100.0	100.0	110.0	120.0	90.0	110.0	110.0	100.0	110.0	110.0	110.0	100.0	100.0	100.0	100.0	80.0	100.0
2025-04-30 22:00:00 UTC	2025-05-31 22:00:00 UTC	110.0	90.0	90.0	130.0	110.0	110.0	90.0	90.0	100.0	110.0	90.0	90.0	110.0	90.0	110.0	100.0	110.0	100.0	90.0	80.0	110.0	100.0	100.0	80.0	90.0	110.0	90.0	100.0	100.0	100.0	110.0	120.0	100.0	90.0	90.0	…	90.0	100.0	100.0	100.0	100.0	100.0	100.0	120.0	90.0	100.0	80.0	110.0	90.0	110.0	110.0	100.0	90.0	100.0	90.0	110.0	100.0	100.0	100.0	110.0	100.0	110.0	90.0	120.0	110.0	100.0	100.0	100.0	100.0	90.0	90.0	120.0	100.0
2025-05-31 22:00:00 UTC	2025-06-30 22:00:00 UTC	100.0	100.0	100.0	100.0	110.0	110.0	110.0	70.0	110.0	110.0	110.0	100.0	100.0	100.0	90.0	110.0	110.0	100.0	110.0	110.0	110.0	110.0	100.0	100.0	90.0	100.0	110.0	110.0	80.0	110.0	100.0	100.0	80.0	100.0	90.0	…	100.0	90.0	100.0	110.0	110.0	110.0	110.0	90.0	90.0	100.0	100.0	100.0	110.0	100.0	100.0	90.0	100.0	100.0	120.0	120.0	90.0	130.0	100.0	110.0	110.0	90.0	100.0	110.0	110.0	130.0	100.0	100.0	90.0	100.0	120.0	100.0	100.0

len(az_selection.series)

208

By supplying the attributes parameter, we utilised the fact that names were structured as underscore separated strings. This way, we managed to tag 26 * 2 * 5 attributes across 260 series.

The autotagging is quite powerful. Additional parameters may be supplied to specify other separators, substitutions, or more complex patterns with regexes.

Updating tags after calculations

prices = Dataset('AZ_drinks')[{'variable':'price'}]
volumes = Dataset('AZ_drinks')[{'variable':'volume'}]
revenues = prices * volumes

The new Dataset instance revenues gets an autogenerated name. The series names are also inherited from the inputs to the calculation.

print(revenues.name)
print(len(revenues.series), 'series, first five:')
print(revenues.series[:5])

(COPY of(AZ_drinks SELECTED by names (), pattern: , regex:  tags: [{'variable': 'price'}]).multiply.COPY of(AZ_drinks SELECTED by names (), pattern: , regex:  tags: [{'variable': 'volume'}]))
1040 series, first five:
['a_price_beer_E', 'a_price_beer_N', 'a_price_beer_NE', 'a_price_beer_NW', 'a_price_beer_S']

revenues.rename('AZ Revenue', ('price', 'revenue'))
print(revenues.name)
print(len(revenues.series), 'series, first five:')
print(revenues.series[:5])

AZ Revenue
1040 series, first five:
['a_revenue_beer_E', 'a_revenue_beer_N', 'a_revenue_beer_NE', 'a_revenue_beer_NW', 'a_revenue_beer_S']

A similar operation is required for tags:

# DEBUG: tags are lost in selects above, hence not flowing through
revenues.tags["series"]["a_revenue_beer_E"]

{'dataset': 'AZ Revenue',
 'name': 'a_revenue_beer_E',
 'product': 'beer',
 'region': 'E',
 'repository': 'tutorials',
 'store': 'a',
 'temporality': 'FROM_TO',
 'variable': 'price',
 'versioning': 'NONE'}

# ... tag maintenance is likely to be necessary after calculations:
revenues.replace_tags(({'variable':'price'},{'variable':'revenue'}))
revenues.tags["series"]["a_revenue_beer_E"]

{'dataset': 'AZ Revenue',
 'name': 'a_revenue_beer_E',
 'product': 'beer',
 'region': 'E',
 'repository': 'tutorials',
 'store': 'a',
 'temporality': 'FROM_TO',
 'variable': 'revenue',
 'versioning': 'NONE'}

Detagging

If mistakes have been made, it may be necessary to remove tags.

from copy import deepcopy
deepcopy(pqr.tags)

{'name': 'PQR',
 'product group': ['essential', 'essentials'],
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'product': 'coffee',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'product': 'crispbread',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'knekkebrød',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'product': 'brown cheese',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 'variable': 'price',
 'versioning': 'NONE'}

pqr.detag_series('varegruppe', vare='knekkebrød')

pqr.detag_series( vare='knekkebrød' )
pqr.tags

{'name': 'PQR',
 'product group': ['essential', 'essentials'],
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'product': 'coffee',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'product': 'crispbread',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'product': 'brown cheese',
                  'product group': ['essential', 'essentials'],
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'variabel': 'pris',
                  'variable': 'price',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 'variable': 'price',
 'versioning': 'NONE'}

The catalog

Allows inspection and analysis of tags across all sets and series. Actual maintenance via Dataset methods.

from ssb_timeseries import get_catalog

our_timeseries_database = get_catalog()

all_sets = our_timeseries_database.datasets()
[s.object_name for s in all_sets]

['AZ_beverages',
 'AZ_drinks',
 'BNO',
 'More Prices and Volumes',
 'POPU06',
 'PQR',
 'Prices and Volumes',
 'SampleDataset',
 'XYZ']

series_in_xyz = our_timeseries_database.series(tags={'dataset': 'XYZ'})
[s.object_name for s in series_in_xyz]

['x', 'y', 'z']

import pandas as pd
everything = our_timeseries_database.items()
pd.DataFrame(everything)

repository_name	object_name	object_type	object_tags	parent	children
tutorials	AZ_beverages	dataset	{‘name’: ‘AZ_beverages’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘series’: {‘a_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘a_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘a_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘a_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘a_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘a_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘a_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘a_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘a_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘a_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘b_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘b_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘b_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘b_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘b_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘b_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘b_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘b_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘b_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘b_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘b_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘b’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘c_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘c_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘c_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘c_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘c_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘c_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘c_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘c_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘c_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘c_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘c_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘c’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘d_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘d_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘d_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘d_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘d_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘d_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘d_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘d_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘d_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘d_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘d_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘d’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘e_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘e_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘e_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘e_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘e_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘e_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘e_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘e_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘e_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘e_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘e_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘e’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘f_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘f_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘f_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘f_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘f_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘f_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘f_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘f_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘f_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘f_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘f_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘f’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘g_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘g_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘g_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘g_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘g_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘g_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘g_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘g_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘g_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘g_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘g_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘g’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘h_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘h_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘h_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘h_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘h_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘h_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘h_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘h_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘h_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘h_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘h_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘h’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘i_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘i_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘i_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘i_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘i_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘i_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘i_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘i_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘i_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘i_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘i_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘i’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, ‘j_price_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘price’, ‘product’: ‘beer’}, ‘j_price_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘price’, ‘product’: ‘coffee’}, ‘j_price_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘price’, ‘product’: ‘soda’}, ‘j_price_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘price’, ‘product’: ‘tea’}, ‘j_price_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_price_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘price’, ‘product’: ‘wine’}, ‘j_quantity_beer’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_quantity_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘quantity’, ‘product’: ‘beer’}, ‘j_quantity_coffee’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_quantity_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘quantity’, ‘product’: ‘coffee’}, ‘j_quantity_soda’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_quantity_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘quantity’, ‘product’: ‘soda’}, ‘j_quantity_tea’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_quantity_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘quantity’, ‘product’: ‘tea’}, ‘j_quantity_wine’: {‘dataset’: ‘AZ_beverages’, ‘name’: ‘j_quantity_wine’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘j’, ‘variable’: ‘quantity’, ‘product’: ‘wine’}, …}, ‘repository’: ‘tutorials’}		None
tutorials	a_price_beer	series	{‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_beer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘beer’}	AZ_beverages	None
tutorials	a_price_coffee	series	{‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_coffee’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘coffee’}	AZ_beverages	None
tutorials	a_price_soda	series	{‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_soda’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘soda’}	AZ_beverages	None
tutorials	a_price_tea	series	{‘dataset’: ‘AZ_beverages’, ‘name’: ‘a_price_tea’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘a’, ‘variable’: ‘price’, ‘product’: ‘tea’}	AZ_beverages	None
…	…	…	…	…	…
tutorials	z	series	{‘dataset’: ‘SampleDataset’, ‘name’: ‘z’}	SampleDataset	None
tutorials	XYZ	dataset	{‘name’: ‘XYZ’, ‘versioning’: ‘NONE’, ‘temporality’: ‘AT’, ‘series’: {‘x’: {‘dataset’: ‘XYZ’, ‘name’: ‘x’}, ‘y’: {‘dataset’: ‘XYZ’, ‘name’: ‘y’}, ‘z’: {‘dataset’: ‘XYZ’, ‘name’: ‘z’}}, ‘repository’: ‘tutorials’}		None
tutorials	x	series	{‘dataset’: ‘XYZ’, ‘name’: ‘x’}	XYZ	None
tutorials	y	series	{‘dataset’: ‘XYZ’, ‘name’: ‘y’}	XYZ	None
tutorials	z	series	{‘dataset’: ‘XYZ’, ‘name’: ‘z’}	XYZ	None

Consuming KLASS

The Dataset and Series attributes are technically just key-value pairs. It is, however, possible (even recommended) to rely on more formal taxonomies top structure these.

from klass import get_classification
from klass import KlassClassification # Import the class for KlassClassifications

print(get_classification(157))

Classification 157: Standard for klassifisering av energibalanseposter
        Owning Section: 425 - Seksjon for energi-, miljø- og ​transportstatistikk
        Contact Person:
	name: Bjelvert, Malin
	email: Malin.Bjelvert@ssb.no
	phone:

        Statistical Units: Foretak, Aktivitet/produkt/tjeneste/vare, Person, Bedrift
        Number of versions: 1

Klassifisering av energibalanseposter er en klassifisering av tilgang og anvendelse av energiprodukter. Energibalansen følger en territorial avgrensing og omfatter all
flyt av energiprodukter på norsk jord, uavhengig av nasjonalitet.

from ssb_timeseries.meta import Taxonomy

print('"Energy balance posts" - KLASS 157 is an example of a taxonomy with hierarchical structure.')
klass157 = Taxonomy(klass_id=157)
klass157.entities # <-- arrow table, with an extra row 0

"Energy balance posts" - KLASS 157 is an example of a taxonomy with hierarchical structure.

pyarrow.Table
code: string not null
parentCode: string
name: string not null
level: string
shortName: string
presentationName: string
validFrom: string
validTo: string
notes: string
----
code: [["0","1","1.1","1.1.1","1.1.2",...,"8.6","8.7","8.8","8.9","9"]]
parentCode: [[null,"0","1","1.1","1.1",...,"8","8","8","8","0"]]
name: [["KLASS-157","Produksjon av primære energiprodukter","Av dette fra fornybare kilder","Av dette i vannkraftverk","Av dette i vindkraftverk",...,"Vindkraftstasjoner","Varmekraftverk","Kraftvarmeverk","Fjernvarmeverk","Svinn"]]
level: [["0","1","2","3","3",...,"2","2","2","2","1"]]
shortName: [["","","","","",...,"","","","",""]]
presentationName: [["","","","","",...,"","","","",""]]
validFrom: [["",null,null,null,null,...,null,null,null,null,null]]
validTo: [["",null,null,null,null,...,null,null,null,null,null]]
notes: [["","Produksjon av primære energiprodukter omfatter utvinning av brensel eller energi fra naturlige fo (... 239 chars omitted)","Produksjon av primære energiprodukter fra fornybare energikilder, for eksempel produksjon av biod (... 72 chars omitted)","Elektrisitet produsert i verk som drives av frisk, flytende eller fallende vann.","Elektrisitet produsert i verk som drives av vindkraft",...,"","I varmekraftverk omvandles termisk energi til elektrisitet ved bruk av brennbare energiprodukter,  (... 107 chars omitted)","Forbruk av energi i anlegg som produserer både varme og elektrisk kraft.","I fjernvarmeverk omvandles termisk energi til varme ved bruk av brennbare energiprodukter, dvs. en (... 100 chars omitted)","Svinn er tap under overføring, fordeling og transport av brensel, varme og elektrisitet."]]

# ... is inserted by the Taxonomy() bto create a tree structure with a single root node
klass157.print_tree()

╙── 0
    ├─╼ 1
    │   ├─╼ 1.1
    │   │   ├─╼ 1.1.1
    │   │   ├─╼ 1.1.2
    │   │   └─╼ 1.1.3
    │   └─╼ 1.2
    ├─╼ 11
    │   ├─╼ 11.1
    │   └─╼ 11.2
    ├─╼ 12
    │   ├─╼ 12.1
    │   │   ├─╼ 12.1.1
    │   │   ├─╼ 12.1.10
    │   │   ├─╼ 12.1.11
    │   │   ├─╼ 12.1.12
    │   │   ├─╼ 12.1.13
    │   │   ├─╼ 12.1.2
    │   │   ├─╼ 12.1.3
    │   │   ├─╼ 12.1.4
    │   │   ├─╼ 12.1.5
    │   │   ├─╼ 12.1.6
    │   │   ├─╼ 12.1.7
    │   │   ├─╼ 12.1.8
    │   │   └─╼ 12.1.9
    │   ├─╼ 12.2
    │   │   ├─╼ 12.2.1
    │   │   ├─╼ 12.2.2
    │   │   ├─╼ 12.2.3
    │   │   ├─╼ 12.2.4
    │   │   └─╼ 12.2.5
    │   └─╼ 12.3
    │       ├─╼ 12.3.1
    │       ├─╼ 12.3.2
    │       ├─╼ 12.3.3
    │       └─╼ 12.3.4
    ├─╼ 13
    ├─╼ 14
    ├─╼ 15
    ├─╼ 2
    ├─╼ 3
    ├─╼ 4
    │   ├─╼ 4.1
    │   └─╼ 4.2
    ├─╼ 5
    ├─╼ 6
    ├─╼ 7
    │   ├─╼ 7.1
    │   ├─╼ 7.2
    │   ├─╼ 7.3
    │   ├─╼ 7.4
    │   ├─╼ 7.5
    │   └─╼ 7.6
    ├─╼ 8
    │   ├─╼ 8.1
    │   ├─╼ 8.2
    │   ├─╼ 8.3
    │   ├─╼ 8.4
    │   ├─╼ 8.5
    │   ├─╼ 8.6
    │   ├─╼ 8.7
    │   ├─╼ 8.8
    │   └─╼ 8.9
    └─╼ 9

print(klass157.leaf_nodes)

['1.1.1', '1.1.2', '1.1.3', '1.2', '11.1', '11.2', '12.1.1', '12.1.10', '12.1.11', '12.1.12', '12.1.13', '12.1.2', '12.1.3', '12.1.4', '12.1.5', '12.1.6', '12.1.7', '12.1.8', '12.1.9', '12.2.1', '12.2.2', '12.2.3', '12.2.4', '12.2.5', '12.3.1', '12.3.2', '12.3.3', '12.3.4', '13', '14', '15', '2', '3', '4.1', '4.2', '5', '6', '7.1', '7.2', '7.3', '7.4', '7.5', '7.6', '8.1', '8.2', '8.3', '8.4', '8.5', '8.6', '8.7', '8.8', '8.9', '9']

print(klass157.parent_nodes)

['1', '0', '1.1', '11', '12', '12.1', '12.2', '12.3', '4', '7', '8']

# read/write to file -> taxonomies can be defined outside KLASS
#klass157.save('klass157.json')
#file157 = Taxonomy(path='klass157.json')

#klass157 == file157