fetcha 0.0.2

Talk to SSB using Python.

fetcha

Talk to SSB using Python.

import fetcha as fetcha
import logging
# Turn off INFO-warnings
logging.getLogger().setLevel(logging.WARNING)

Installation

# >> pip install git+https://github.com/dafeda/fetcha.git --upgrade

# Instantiate object with specific table_id that refers to a SSB-table.
# 10945 refers to Monetary aggregates M1, M2 and M3:
# https://www.ssb.no/en/statbank/table/10945
ssb_10945 = fetcha.SSB("10945", language="en")

# Number of rows in table.
ssb_10945.nrows_tot()

1422

# Number of rows per period.
ssb_10945.nrows_period()

9

# Get all available periods
periods = ssb_10945.periods()
periods[-7:]

['2020M08', '2020M09', '2020M10', '2020M11', '2020M12', '2021M01', '2021M02']

# Fetch latest period.
# Returns a pandas dataframe with its index set with verify_integrity set to True.
# If the dataframe is lacking an index, it means that the index columns do not make up a unique combination.
df_latest = ssb_10945.fetch()
df_latest.head()

		value
contents	month
Monetary aggregate M1. Stocks (NOK million)	2021M02	2526071.0
Monetary aggregate M2. Stocks (NOK million)	2021M02	2695383.0
Monetary aggregate M3. Stocks (NOK million)	2021M02	2697783.0
Monetary aggregate M1. Transactions last 12 months (NOK million)	2021M02	359029.0
Monetary aggregate M2. Transactions last 12 months (NOK million)	2021M02	343687.0

# Fetch list of periods
df_periods = ssb_10945.fetch(["2019M12", "2020M01", "2020M02"])
df_periods.head()

		value
contents	month
Monetary aggregate M1. Stocks (NOK million)	2019M12	2159770.0
2020M01	2182450.0
2020M02	2175681.0
Monetary aggregate M2. Stocks (NOK million)	2019M12	2345545.0
2020M01	2364841.0

# Fetch whole year of data
df_year = ssb_10945.fetch("2020")
df_year.head()

		value
contents	month
Monetary aggregate M1. Stocks (NOK million)	2020M01	2182450.0
2020M02	2175681.0
2020M03	2300443.0
2020M04	2340381.0
2020M05	2374607.0

# Fetch multiple years
df_years = ssb_10945.fetch(["2019", "2020"])
df_year.head()

		value
contents	month
Monetary aggregate M1. Stocks (NOK million)	2020M01	2182450.0
2020M02	2175681.0
2020M03	2300443.0
2020M04	2340381.0
2020M05	2374607.0

# Reset index before pivoting
df_year = df_year.reset_index().pivot(index="month", columns="contents")
df_year.head()

	value
contents	Monetary aggregate M1. 12-month growth (per cent	Monetary aggregate M1. Stocks (NOK million)	Monetary aggregate M1. Transactions last 12 months (NOK million)	Monetary aggregate M2. 12-month growth (per cent)	Monetary aggregate M2. Stocks (NOK million)	Monetary aggregate M2. Transactions last 12 months (NOK million)	Monetary aggregate M3. 12-month growth (per cent)	Monetary aggregate M3. Stocks (NOK million)	Monetary aggregate M3. Transactions last 12 months (NOK million)
month
2020M01	3.1	2182450.0	66236.0	3.9	2364841.0	87622.0	3.7	2368402.0	84912.0
2020M02	3.2	2175681.0	66037.0	3.8	2360484.0	86360.0	3.7	2364033.0	83138.0
2020M03	7.0	2300443.0	148469.0	7.5	2489403.0	170692.0	7.3	2492801.0	167960.0
2020M04	9.8	2340381.0	205486.0	9.5	2522315.0	216155.0	9.4	2525731.0	214558.0
2020M05	10.9	2374607.0	232311.0	10.2	2552508.0	234581.0	10.1	2555817.0	232003.0

ssb_10948 = fetcha.SSB("10948", language="en")
df_10948 = ssb_10948.fetch("2020")

df_10948.head()

			value
holding sector	contents	month
Money holding sector	Monetary aggregate M3. Stocks, seasonally adjusted (NOK million)	2020M01	2374459.0
2020M02	2387955.0
2020M03	2499994.0
2020M04	2543868.0
2020M05	2580435.0

# Fetch and join
# Get another table so we have something to join with.
ssb_10948 = fetcha.SSB("10948", language="en")
df_10948 = ssb_10948.fetch("2020")
df_10948 = df_10948.reset_index().pivot_table(
    index="month", columns="contents", aggfunc="mean"
)

df_10948.join(df_year).head()

	value
contents	1-month growth, seasonally adjusted (per cent)	Monetary aggregate M3. Stocks, seasonally adjusted (NOK million)	Transactions last month, seasonally adjusted (NOK million)	Monetary aggregate M1. 12-month growth (per cent	Monetary aggregate M1. Stocks (NOK million)	Monetary aggregate M1. Transactions last 12 months (NOK million)	Monetary aggregate M2. 12-month growth (per cent)	Monetary aggregate M2. Stocks (NOK million)	Monetary aggregate M2. Transactions last 12 months (NOK million)	Monetary aggregate M3. 12-month growth (per cent)	Monetary aggregate M3. Stocks (NOK million)	Monetary aggregate M3. Transactions last 12 months (NOK million)
month
2020M01	-10.02	949783.6	-1329.8	3.1	2182450.0	66236.0	3.9	2364841.0	87622.0	3.7	2368402.0	84912.0
2020M02	3.32	955182.0	3182.0	3.2	2175681.0	66037.0	3.8	2360484.0	86360.0	3.7	2364033.0	83138.0
2020M03	541.24	999997.4	38556.4	7.0	2300443.0	148469.0	7.5	2489403.0	170692.0	7.3	2492801.0	167960.0
2020M04	19.36	1017547.0	18928.0	9.8	2340381.0	205486.0	9.5	2522315.0	216155.0	9.4	2525731.0	214558.0
2020M05	14.82	1032174.2	17398.2	10.9	2374607.0	232311.0	10.2	2552508.0	234581.0	10.1	2555817.0	232003.0

# SSB has a limit of 300k rows per transaction.
# Some tables have more than that in one period.
ssb_10261 = fetcha.SSB("10261", language="en")

# Gives warning and returns None.
df_10261 = ssb_10261.fetch()

WARNING:fetcha.ssb:Query exceeds SSB limit of 300k rows per transaction. Current query tries to fetch 607104 rows. User a filter

# Can pass filter to fetch(), but first we need to choose what we want.
# Use variable levels to see which options you have.
ssb_10261.levels

0    {'code': 'Region', 'text': 'region', 'values':...
1    {'code': 'Kjonn', 'text': 'sex', 'values': ['0...
2    {'code': 'Alder', 'text': 'age', 'values': ['9...
3    {'code': 'Diagnose3', 'text': 'diagnosis: Chap...
4    {'code': 'ContentsCode', 'text': 'contents', '...
5    {'code': 'Tid', 'text': 'year', 'values': ['20...
Name: variables, dtype: object

# We limit the region to "The whole country".
ssb_10261.levels.iloc[0]

{'code': 'Region',
 'text': 'region',
 'values': ['0',
  '30',
  '01',
  '02',
  '03',
  '34',
  '04',
  '05',
  '06',
  '38',
  '07',
  '08',
  '42',
  '09',
  '10',
  '11',
  '46',
  '12',
  '14',
  '15',
  '50',
  '16',
  '17',
  '18',
  '54',
  '19',
  '20',
  'F00',
  '9',
  'H03',
  'H04',
  'H05',
  'H12',
  'Uoppgitt'],
 'valueTexts': ['The whole country',
  'Viken',
  'Østfold (-2019)',
  'Akershus (-2019)',
  'Oslo',
  'Innlandet',
  'Hedmark (-2019)',
  'Oppland (-2019)',
  'Buskerud (-2019)',
  'Vestfold og Telemark',
  'Vestfold (-2019)',
  'Telemark (-2019)',
  'Agder',
  'Aust-Agder (-2019)',
  'Vest-Agder (-2019)',
  'Rogaland',
  'Vestland',
  'Hordaland (-2019)',
  'Sogn og Fjordane (-2019)',
  'Møre og Romsdal',
  'Trøndelag - Trööndelage',
  'Sør-Trøndelag (-2017)',
  'Nord-Trøndelag (-2017)',
  'Nordland',
  'Troms og Finnmark - Romsa ja Finnmárku',
  'Troms - Romsa (-2019)',
  'Finnmark - Finnmárku (-2019)',
  'Total',
  'Uoppgitt',
  'Helseregion Vest',
  'Helseregion Midt-Norge',
  'Helseregion Nord',
  'Helseregion Sør-Øst',
  'Unknown'],
 'elimination': True}

fltr = [{"code": "Region", "values": ["0"]}]

df_10261 = ssb_10261.fetch(fltr=fltr)

df_10261.shape

(17856, 1)

df_10261.sample(10)

						value
region	sex	age	diagnosis: Chapter in ICD-10	contents	year
The whole country	Males	40-59 years	Influenza and pneumonia	Number of day cases	2019	76.0
Females	20-39 years	Injuries of upper extremities	Patients with day cases	2019	613.0
60-69 years	CONGENITAL MALFORMATIONS	Number of bed-days	2019	290.0
Both sexes	20-39 years	Cardiac dysrhythmias	Patients with day cases	2019	276.0
PREGNANCY, CHILDBIRTH AND THE PUERPERIUM	Number of out-patient consultations	2019	109637.0
Females	60-69 years	Glaucoma	Number of day cases	2019	253.0
Both sexes	Years, total	Other maternal disorders predominantly related to pregnancy	In-patients	2019	1478.0
60-69 years	Diabetes mellitus	In-patients	2019	488.0
70-79 years	Other diseases of oesophagus, stomach and duodenum	Number of bed-days	2019	3265.0
0-9 years	Malignant neoplasms of female genital organs	Out-patients	2019	1.0