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',
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'knekkebrød',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 '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',
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'knekkebrød',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 'versioning': 'NONE'}

Tags can be used immediately.

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

valid_at	p
2020-01-01	90.0
2020-01-02	90.0
2020-01-03	110.0
2020-01-04	110.0
2020-01-05	100.0
…	…
2025-05-28	90.0
2025-05-29	90.0
2025-05-30	100.0
2025-05-31	110.0
2025-06-01	90.0

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']
    goods = ['coffe', 'tea', 'softdrinks', 'beer', 'wine']
    return create_df(
        a_to_z, variables, goods,
        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_coffe	a_volume_tea	a_volume_softdrinks	a_volume_beer	a_volume_wine	a_price_coffe	a_price_tea	a_price_softdrinks	a_price_beer	a_price_wine	b_volume_coffe	b_volume_tea	b_volume_softdrinks	b_volume_beer	b_volume_wine	b_price_coffe	b_price_tea	b_price_softdrinks	b_price_beer	b_price_wine	c_volume_coffe	c_volume_tea	c_volume_softdrinks	c_volume_beer	c_volume_wine	c_price_coffe	c_price_tea	c_price_softdrinks	c_price_beer	c_price_wine	d_volume_coffe	d_volume_tea	d_volume_softdrinks	d_volume_beer	d_volume_wine	…	w_volume_beer	w_volume_wine	w_price_coffe	w_price_tea	w_price_softdrinks	w_price_beer	w_price_wine	x_volume_coffe	x_volume_tea	x_volume_softdrinks	x_volume_beer	x_volume_wine	x_price_coffe	x_price_tea	x_price_softdrinks	x_price_beer	x_price_wine	y_volume_coffe	y_volume_tea	y_volume_softdrinks	y_volume_beer	y_volume_wine	y_price_coffe	y_price_tea	y_price_softdrinks	y_price_beer	y_price_wine	z_volume_coffe	z_volume_tea	z_volume_softdrinks	z_volume_beer	z_volume_wine	z_price_coffe	z_price_tea	z_price_softdrinks	z_price_beer	z_price_wine
datetime[μs]	datetime[μs]	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	2025-02-01 00:00:00	100.0	100.0	110.0	100.0	110.0	110.0	100.0	90.0	90.0	100.0	90.0	110.0	90.0	90.0	90.0	110.0	100.0	90.0	100.0	100.0	120.0	100.0	110.0	80.0	100.0	110.0	110.0	100.0	100.0	100.0	90.0	90.0	110.0	90.0	100.0	…	100.0	110.0	90.0	100.0	110.0	90.0	100.0	100.0	80.0	110.0	100.0	80.0	120.0	110.0	100.0	100.0	110.0	100.0	80.0	120.0	110.0	100.0	80.0	90.0	100.0	90.0	100.0	100.0	90.0	110.0	90.0	90.0	100.0	80.0	100.0	90.0	110.0
2025-02-01 00:00:00	2025-03-01 00:00:00	100.0	90.0	80.0	120.0	100.0	80.0	110.0	100.0	90.0	100.0	110.0	100.0	100.0	100.0	100.0	110.0	110.0	110.0	100.0	100.0	120.0	70.0	110.0	80.0	90.0	120.0	90.0	100.0	110.0	70.0	110.0	90.0	90.0	100.0	100.0	…	100.0	100.0	90.0	100.0	110.0	120.0	90.0	110.0	110.0	110.0	100.0	100.0	110.0	90.0	100.0	90.0	110.0	110.0	100.0	90.0	100.0	110.0	100.0	100.0	100.0	80.0	100.0	110.0	110.0	100.0	100.0	100.0	110.0	110.0	90.0	80.0	110.0
2025-03-01 00:00:00	2025-04-01 00:00:00	100.0	100.0	100.0	80.0	90.0	90.0	100.0	90.0	110.0	100.0	110.0	80.0	100.0	100.0	110.0	110.0	90.0	100.0	90.0	100.0	100.0	100.0	100.0	110.0	90.0	90.0	100.0	100.0	100.0	80.0	130.0	110.0	80.0	100.0	110.0	…	100.0	100.0	100.0	110.0	80.0	90.0	120.0	80.0	90.0	100.0	100.0	110.0	70.0	90.0	120.0	90.0	90.0	110.0	110.0	110.0	100.0	90.0	70.0	100.0	100.0	100.0	90.0	110.0	90.0	90.0	110.0	100.0	100.0	110.0	100.0	100.0	110.0
2025-04-01 00:00:00	2025-05-01 00:00:00	100.0	100.0	80.0	100.0	100.0	100.0	100.0	80.0	110.0	100.0	110.0	110.0	100.0	100.0	100.0	80.0	120.0	90.0	90.0	100.0	80.0	90.0	110.0	110.0	90.0	100.0	90.0	110.0	100.0	90.0	90.0	110.0	90.0	120.0	110.0	…	120.0	130.0	100.0	80.0	90.0	110.0	90.0	100.0	100.0	110.0	110.0	110.0	100.0	90.0	100.0	110.0	100.0	90.0	100.0	100.0	90.0	90.0	100.0	120.0	90.0	90.0	100.0	100.0	100.0	90.0	90.0	110.0	90.0	100.0	100.0	110.0	90.0
2025-05-01 00:00:00	2025-06-01 00:00:00	100.0	90.0	110.0	100.0	90.0	100.0	80.0	110.0	110.0	100.0	120.0	100.0	100.0	80.0	80.0	90.0	110.0	90.0	90.0	110.0	100.0	110.0	100.0	100.0	110.0	120.0	110.0	110.0	110.0	100.0	100.0	90.0	100.0	110.0	110.0	…	100.0	110.0	100.0	100.0	100.0	90.0	100.0	100.0	100.0	100.0	90.0	90.0	90.0	100.0	90.0	100.0	90.0	100.0	90.0	90.0	90.0	110.0	100.0	100.0	100.0	110.0	90.0	120.0	100.0	110.0	100.0	90.0	120.0	90.0	100.0	120.0	90.0
2025-06-01 00:00:00	2025-07-01 00:00:00	90.0	110.0	90.0	100.0	100.0	100.0	110.0	110.0	100.0	90.0	90.0	110.0	90.0	110.0	70.0	100.0	110.0	110.0	80.0	110.0	120.0	90.0	110.0	90.0	100.0	120.0	80.0	90.0	80.0	90.0	100.0	110.0	100.0	110.0	100.0	…	100.0	100.0	110.0	120.0	90.0	90.0	110.0	110.0	100.0	90.0	90.0	100.0	100.0	100.0	90.0	90.0	80.0	100.0	110.0	120.0	90.0	110.0	90.0	90.0	110.0	90.0	110.0	90.0	100.0	90.0	110.0	90.0	90.0	100.0	110.0	80.0	110.0

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

len(az.series)

260

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

valid_from	valid_to	a_price_tea	b_price_tea	c_price_tea	d_price_tea	e_price_tea	f_price_tea	g_price_tea	h_price_tea	i_price_tea	j_price_tea	k_price_tea	l_price_tea	m_price_tea	n_price_tea	o_price_tea	p_price_tea	q_price_tea	r_price_tea	s_price_tea	t_price_tea	u_price_tea	v_price_tea	w_price_tea	x_price_tea	y_price_tea	z_price_tea
datetime[μs]	datetime[μs]	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	2025-02-01 00:00:00	100.0	100.0	110.0	110.0	80.0	100.0	120.0	100.0	100.0	100.0	90.0	100.0	110.0	110.0	100.0	100.0	90.0	100.0	90.0	100.0	100.0	90.0	100.0	110.0	90.0	80.0
2025-02-01 00:00:00	2025-03-01 00:00:00	110.0	110.0	90.0	110.0	110.0	100.0	130.0	90.0	110.0	100.0	90.0	80.0	100.0	110.0	120.0	90.0	110.0	100.0	100.0	80.0	90.0	120.0	100.0	90.0	100.0	110.0
2025-03-01 00:00:00	2025-04-01 00:00:00	100.0	90.0	100.0	100.0	110.0	110.0	100.0	90.0	100.0	100.0	90.0	100.0	110.0	80.0	100.0	110.0	90.0	100.0	100.0	120.0	110.0	110.0	110.0	90.0	100.0	110.0
2025-04-01 00:00:00	2025-05-01 00:00:00	100.0	120.0	90.0	100.0	100.0	120.0	110.0	100.0	100.0	120.0	120.0	110.0	90.0	100.0	110.0	90.0	100.0	90.0	80.0	90.0	110.0	100.0	80.0	90.0	120.0	100.0
2025-05-01 00:00:00	2025-06-01 00:00:00	80.0	110.0	110.0	90.0	100.0	100.0	90.0	90.0	120.0	100.0	100.0	110.0	90.0	100.0	90.0	90.0	90.0	100.0	80.0	80.0	90.0	110.0	100.0	100.0	100.0	90.0
2025-06-01 00:00:00	2025-07-01 00:00:00	110.0	110.0	80.0	90.0	100.0	100.0	100.0	90.0	90.0	90.0	110.0	110.0	110.0	80.0	100.0	100.0	110.0	110.0	110.0	100.0	90.0	90.0	120.0	100.0	90.0	100.0

len(az_selection.series)

26

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(revenues.series)

(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'}]))
['a_price_beer', 'a_price_coffe', 'a_price_softdrinks', 'a_price_tea', 'a_price_wine', 'b_price_beer', 'b_price_coffe', 'b_price_softdrinks', 'b_price_tea', 'b_price_wine', 'c_price_beer', 'c_price_coffe', 'c_price_softdrinks', 'c_price_tea', 'c_price_wine', 'd_price_beer', 'd_price_coffe', 'd_price_softdrinks', 'd_price_tea', 'd_price_wine', 'e_price_beer', 'e_price_coffe', 'e_price_softdrinks', 'e_price_tea', 'e_price_wine', 'f_price_beer', 'f_price_coffe', 'f_price_softdrinks', 'f_price_tea', 'f_price_wine', 'g_price_beer', 'g_price_coffe', 'g_price_softdrinks', 'g_price_tea', 'g_price_wine', 'h_price_beer', 'h_price_coffe', 'h_price_softdrinks', 'h_price_tea', 'h_price_wine', 'i_price_beer', 'i_price_coffe', 'i_price_softdrinks', 'i_price_tea', 'i_price_wine', 'j_price_beer', 'j_price_coffe', 'j_price_softdrinks', 'j_price_tea', 'j_price_wine', 'k_price_beer', 'k_price_coffe', 'k_price_softdrinks', 'k_price_tea', 'k_price_wine', 'l_price_beer', 'l_price_coffe', 'l_price_softdrinks', 'l_price_tea', 'l_price_wine', 'm_price_beer', 'm_price_coffe', 'm_price_softdrinks', 'm_price_tea', 'm_price_wine', 'n_price_beer', 'n_price_coffe', 'n_price_softdrinks', 'n_price_tea', 'n_price_wine', 'o_price_beer', 'o_price_coffe', 'o_price_softdrinks', 'o_price_tea', 'o_price_wine', 'p_price_beer', 'p_price_coffe', 'p_price_softdrinks', 'p_price_tea', 'p_price_wine', 'q_price_beer', 'q_price_coffe', 'q_price_softdrinks', 'q_price_tea', 'q_price_wine', 'r_price_beer', 'r_price_coffe', 'r_price_softdrinks', 'r_price_tea', 'r_price_wine', 's_price_beer', 's_price_coffe', 's_price_softdrinks', 's_price_tea', 's_price_wine', 't_price_beer', 't_price_coffe', 't_price_softdrinks', 't_price_tea', 't_price_wine', 'u_price_beer', 'u_price_coffe', 'u_price_softdrinks', 'u_price_tea', 'u_price_wine', 'v_price_beer', 'v_price_coffe', 'v_price_softdrinks', 'v_price_tea', 'v_price_wine', 'w_price_beer', 'w_price_coffe', 'w_price_softdrinks', 'w_price_tea', 'w_price_wine', 'x_price_beer', 'x_price_coffe', 'x_price_softdrinks', 'x_price_tea', 'x_price_wine', 'y_price_beer', 'y_price_coffe', 'y_price_softdrinks', 'y_price_tea', 'y_price_wine', 'z_price_beer', 'z_price_coffe', 'z_price_softdrinks', 'z_price_tea', 'z_price_wine']

revenues.rename('AZ Revenue', ('price', 'revenue'))
print(revenues.name)
print(revenues.series)

AZ Revenue
['a_revenue_beer', 'a_revenue_coffe', 'a_revenue_softdrinks', 'a_revenue_tea', 'a_revenue_wine', 'b_revenue_beer', 'b_revenue_coffe', 'b_revenue_softdrinks', 'b_revenue_tea', 'b_revenue_wine', 'c_revenue_beer', 'c_revenue_coffe', 'c_revenue_softdrinks', 'c_revenue_tea', 'c_revenue_wine', 'd_revenue_beer', 'd_revenue_coffe', 'd_revenue_softdrinks', 'd_revenue_tea', 'd_revenue_wine', 'e_revenue_beer', 'e_revenue_coffe', 'e_revenue_softdrinks', 'e_revenue_tea', 'e_revenue_wine', 'f_revenue_beer', 'f_revenue_coffe', 'f_revenue_softdrinks', 'f_revenue_tea', 'f_revenue_wine', 'g_revenue_beer', 'g_revenue_coffe', 'g_revenue_softdrinks', 'g_revenue_tea', 'g_revenue_wine', 'h_revenue_beer', 'h_revenue_coffe', 'h_revenue_softdrinks', 'h_revenue_tea', 'h_revenue_wine', 'i_revenue_beer', 'i_revenue_coffe', 'i_revenue_softdrinks', 'i_revenue_tea', 'i_revenue_wine', 'j_revenue_beer', 'j_revenue_coffe', 'j_revenue_softdrinks', 'j_revenue_tea', 'j_revenue_wine', 'k_revenue_beer', 'k_revenue_coffe', 'k_revenue_softdrinks', 'k_revenue_tea', 'k_revenue_wine', 'l_revenue_beer', 'l_revenue_coffe', 'l_revenue_softdrinks', 'l_revenue_tea', 'l_revenue_wine', 'm_revenue_beer', 'm_revenue_coffe', 'm_revenue_softdrinks', 'm_revenue_tea', 'm_revenue_wine', 'n_revenue_beer', 'n_revenue_coffe', 'n_revenue_softdrinks', 'n_revenue_tea', 'n_revenue_wine', 'o_revenue_beer', 'o_revenue_coffe', 'o_revenue_softdrinks', 'o_revenue_tea', 'o_revenue_wine', 'p_revenue_beer', 'p_revenue_coffe', 'p_revenue_softdrinks', 'p_revenue_tea', 'p_revenue_wine', 'q_revenue_beer', 'q_revenue_coffe', 'q_revenue_softdrinks', 'q_revenue_tea', 'q_revenue_wine', 'r_revenue_beer', 'r_revenue_coffe', 'r_revenue_softdrinks', 'r_revenue_tea', 'r_revenue_wine', 's_revenue_beer', 's_revenue_coffe', 's_revenue_softdrinks', 's_revenue_tea', 's_revenue_wine', 't_revenue_beer', 't_revenue_coffe', 't_revenue_softdrinks', 't_revenue_tea', 't_revenue_wine', 'u_revenue_beer', 'u_revenue_coffe', 'u_revenue_softdrinks', 'u_revenue_tea', 'u_revenue_wine', 'v_revenue_beer', 'v_revenue_coffe', 'v_revenue_softdrinks', 'v_revenue_tea', 'v_revenue_wine', 'w_revenue_beer', 'w_revenue_coffe', 'w_revenue_softdrinks', 'w_revenue_tea', 'w_revenue_wine', 'x_revenue_beer', 'x_revenue_coffe', 'x_revenue_softdrinks', 'x_revenue_tea', 'x_revenue_wine', 'y_revenue_beer', 'y_revenue_coffe', 'y_revenue_softdrinks', 'y_revenue_tea', 'y_revenue_wine', 'z_revenue_beer', 'z_revenue_coffe', 'z_revenue_softdrinks', 'z_revenue_tea', 'z_revenue_wine']

A similar operation is required for tags:

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

{'dataset': 'AZ Revenue',
 'name': 'a_revenue_beer',
 'product': 'beer',
 '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"]

{'dataset': 'AZ Revenue',
 'name': 'a_revenue_beer',
 'product': 'beer',
 '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',
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'knekkebrød',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'varegruppe': 'nødvendigheter',
                  'variabel': 'pris',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 'versioning': 'NONE'}

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

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

{'name': 'PQR',
 'repository': 'tutorials',
 'series': {'p': {'dataset': 'PQR',
                  'name': 'p',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'kaffe',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'q': {'dataset': 'PQR',
                  'name': 'q',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'variabel': 'pris',
                  'versioning': 'NONE'},
            'r': {'dataset': 'PQR',
                  'name': 'r',
                  'repository': 'tutorials',
                  'temporality': 'AT',
                  'vare': 'brunost',
                  'variabel': 'pris',
                  'versioning': 'NONE'}},
 'temporality': 'AT',
 'varegruppe': 'nødvendigheter',
 'variabel': 'pris',
 '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_drikkevarer',
 'Prices and Volumes',
 'A Sample Dataset',
 'PQR',
 'Sample Data',
 'AZ Drinks',
 'XYZ',
 'More Prices and Volumes',
 'AZ_omsetning',
 'AZ_drinks']

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_drikkevarer	dataset	{‘name’: ‘AZ_drikkevarer’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘series’: {‘a_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘a_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘a_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘a_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘a_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘a_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘a_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘a_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘a_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘a_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘b_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘b_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘b_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘b_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘b_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘b_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘b_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘b_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘b_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘b_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘b_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘b’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘c_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘c_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘c_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘c_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘c_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘c_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘c_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘c_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘c_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘c_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘c_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘c’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘d_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘d_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘d_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘d_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘d_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘d_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘d_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘d_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘d_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘d_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘d_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘d’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘e_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘e_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘e_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘e_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘e_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘e_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘e_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘e_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘e_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘e_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘e_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘e’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘f_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘f_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘f_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘f_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘f_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘f_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘f_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘f_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘f_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘f_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘f_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘f’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘g_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘g_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘g_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘g_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘g_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘g_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘g_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘g_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘g_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘g_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘g_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘g’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘h_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘h_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘h_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘h_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘h_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘h_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘h_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘h_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘h_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘h_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘h_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘h’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘i_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘i_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘i_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘i_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘i_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘i_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘i_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘i_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘i_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘i_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘i_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘i’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, ‘j_antall_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}, ‘j_antall_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}, ‘j_antall_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}, ‘j_antall_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}, ‘j_antall_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_antall_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘antall’, ‘vare’: ‘øl’}, ‘j_pris_brus’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_pris_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘pris’, ‘vare’: ‘brus’}, ‘j_pris_kaffe’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_pris_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘pris’, ‘vare’: ‘kaffe’}, ‘j_pris_te’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_pris_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘pris’, ‘vare’: ‘te’}, ‘j_pris_vin’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_pris_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘pris’, ‘vare’: ‘vin’}, ‘j_pris_øl’: {‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘j_pris_øl’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘j’, ‘variabel’: ‘pris’, ‘vare’: ‘øl’}, …}, ‘repository’: ‘tutorials’}		None
tutorials	a_antall_brus	series	{‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_brus’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘brus’}	AZ_drikkevarer	None
tutorials	a_antall_kaffe	series	{‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_kaffe’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘kaffe’}	AZ_drikkevarer	None
tutorials	a_antall_te	series	{‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_te’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘te’}	AZ_drikkevarer	None
tutorials	a_antall_vin	series	{‘dataset’: ‘AZ_drikkevarer’, ‘name’: ‘a_antall_vin’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘butikk’: ‘a’, ‘variabel’: ‘antall’, ‘vare’: ‘vin’}	AZ_drikkevarer	None
…	…	…	…	…	…
tutorials	z_volume_wine_NW	series	{‘dataset’: ‘AZ_drinks’, ‘name’: ‘z_volume_wine_NW’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘z’, ‘variable’: ‘volume’, ‘product’: ‘wine’, ‘region’: ‘NW’}	AZ_drinks	None
tutorials	z_volume_wine_S	series	{‘dataset’: ‘AZ_drinks’, ‘name’: ‘z_volume_wine_S’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘z’, ‘variable’: ‘volume’, ‘product’: ‘wine’, ‘region’: ‘S’}	AZ_drinks	None
tutorials	z_volume_wine_SE	series	{‘dataset’: ‘AZ_drinks’, ‘name’: ‘z_volume_wine_SE’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘z’, ‘variable’: ‘volume’, ‘product’: ‘wine’, ‘region’: ‘SE’}	AZ_drinks	None
tutorials	z_volume_wine_SW	series	{‘dataset’: ‘AZ_drinks’, ‘name’: ‘z_volume_wine_SW’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘z’, ‘variable’: ‘volume’, ‘product’: ‘wine’, ‘region’: ‘SW’}	AZ_drinks	None
tutorials	z_volume_wine_W	series	{‘dataset’: ‘AZ_drinks’, ‘name’: ‘z_volume_wine_W’, ‘versioning’: ‘NONE’, ‘temporality’: ‘FROM_TO’, ‘repository’: ‘tutorials’, ‘store’: ‘z’, ‘variable’: ‘volume’, ‘product’: ‘wine’, ‘region’: ‘W’}	AZ_drinks	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