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Download, cache, collate, filter and extrapolate UK Population estimates and projections

Project description

Build Status License status Version 1.0.0

ukpopulation: UK Demographic Projections

The statistical agencies of the United Kingdom, that is: ONS, StatsWales, NR Scotland, and NISRA, all produce annual population estimates and projection data. Although the data are essentially the same, the quantity, format, and availability varies between agencies and datasets. All of the projection data is available by (single year of) age and gender.

National population projections (NPP) are the responsibility of ONS who provide the data for each country within the UK, including 15 variants covering a number of possible future scenarios. The current data is based on 2016 population estimates and project a century to 2116.

Subnational population projections (SNPP) are the responsibility of each country's agencies (ONS for England), and project 25 years from a base year that depends on the country in question:

Country Latest SNPP year range (as of June 2018)
England 2016-2041
Wales 2014-2039
Scotland 2016-2041
Northern Ireland 2016-2041

Mid-year population estimates (MYE) are available for the entire UK by local authority, single year of age and gender, from 1991 to 2016 inclusive.

Projection Coverage

The countries within the UK produce their own SNPP data, and also produce some (patchy) variant projections. The ONS currently regard these (the England ones at least) as "experimental".

Scenario/Variant Code E S W N NPP
Principal ppp x x x x x
High fertility hpp x x x
Low fertility lpp x x
High life expectancy php x x
Low life expectancy plp x x
Moderately high life expectancy pjp x
Moderately low life expectancy plp x
High migration pph x x
Low migration ppl x x
High population hhh x x
Low population lll x x
0% future EU migration ppq x
50% future EU migration ppr x
150% future EU migration pps x
Zero net migration ppz x x x x
Young age structure hlh
Old age structure lhl
Replacement fertility rpp
Constant fertility cpp
No mortality improvement pnp
No change cnp
Long term balanced net migration ppb x


Nomisweb provides an API which allows relatively easy programmatic access the to data, and by far the preferred source of data. Currently not all the data is available from this source but this may change.

Nomisweb currently hosts the ONS principal NPP data for the UK, the SNPP data for England, and all of the MYE data.

All other data: ONS NPP variants, SNPP data for Wales, Scotland and Northern Ireland are available in different formats from the appropriate agency's website.


The purpose of this package is to provide a unified interface to both SNPP and NPP data, including variants:

  • encapsulating the downloading, processing and caching of the NPP and SNPP data from the various sources.
  • consistently differentiating by age (single year, up to 90) and gender over the various datasets.
  • providing a unified format for all the data.
  • providing a method of synthesising SNPP variant projections using SNPP principal and NPP principal/variant projections
  • providing a method of extrapolating SNPP data using NPP data
  • enabling easy filtering and aggregating of of the data, e.g. extracting projections of the working-age population.

Methodology and Detail

Data Sources

Data Processing

  • Note that SNPP data is 2014-based while NPP data is 2016-based.
  • NPP data is broken down by country (England/Wales/Scotland/Northern Ireland), for all the variant projections indicated in the table above.
  • Column headings and category values follow the nomisweb/census conventions:
    • GEOGRAPHY_CODE: ONS country, LAD, or LAD-equivalent code
    • GENDER: 1=Male, 2=Female
    • C_AGE: 0-90, where 90 represents 90 or over. To avoid ambiguity, this is an exception - nomisweb census values are typically age+1)
    • PROJECTED_YEAR_NAME: 2014-2116
    • OBS_VALUE: count of persons
  • All data are cached for swift retrieval.


The extrapolation methodology is explained by the following equation for the aggregate SNPP S(g,y) for a given geography and year.


where N is the NPP, a is age, s is gender, y bar is a reference year (typically the final year in the SNPP data), and c(g) represents a mapping from a SNPP geography (LAD) to a NPP one (country).

Projection of Variants

Similarly the methodology for synthesising SNPP variants from SNPP and NPP data is:


where the subscripts V and 0 refer to the variant and the principal projections respectively.




This package uses the UKCensusAPI package to obtain some of the projection data. The package requires an API key to function correctly, see here for details.


Requires Python 3.5 or higher. Dependencies should resolve automatically, but if not see troubleshooting

Install from git repo:

$ python3 -m pip install git+

Some of the examples (see below) plot graphs and have a dependency on matplotlib, which can be installed with

$ python3 -m pip install matplotlib


First Clone the repo or a fork of it. The test data cache directory contains a file NOMIS_API_KEY which defines a dummy key for testing purposes only. The test suit can be run from the (project root dir) using:

$ ./ test


Ensure you are using the correct version (>=3) of pip:

$ pip --version
pip 9.0.1 from /usr/lib/python3/dist-packages (python 3.6)

If not replace pip with pip3 or python3 -m pip

To clone the repo and install locally (if you intend to contribute, or if all other installation attempts fail):

$ git clone
$ ./ install

If the installation has missing dependencies, try:

$ pip install -r requirements.txt
$ ./ install

The UKCensusAPI dependency should be resolved automatically, but if not you can force installation using

pip install git+

If (with python 3.5?) you encounter

AttributeError: module 'html5lib.treebuilders' has no attribute '_base'


$ pip install html5lib=0.9999999

should fix it. But better solution is to upgrade to python3.6

If matplotlib fails to install due to a missing dependency (tkinter), this can be fixed on Debian variants by

$ sudo apt install python3-tk

If your problem isn't addressed above, please post an issue including as much supporting information as possible.

Usage Examples

Retrieve SNPP for specific LADs

Detailed data

This example fetches the 2018 projection for Newcastle by gender and age.

>>> import ukpopulation.snppdata as SNPPData
>>> snpp = SNPPData.SNPPData()
Cache directory:  ./raw_data/
using cached LAD codes: ./raw_data/lad_codes.json
Collating SNPP data for England...
./raw_data/NM_2006_1_metadata.json found, using cached metadata...
Using cached data: ./raw_data/NM_2006_1_56aba41fc0fab32f58ead6ae91a867b4.tsv
./raw_data/NM_2006_1_metadata.json found, using cached metadata...
Using cached data: ./raw_data/NM_2006_1_dbe6c087fb46306789f7d54b125482e4.tsv
Collating SNPP data for Wales...
Collating SNPP data for Scotland...
Collating SNPP data for Northern Ireland...
>>> newcastle=snpp.filter("E08000021", 2018)
>>> newcastle.head()
0      0       1      E08000021     1814.0                 2018
1      1       1      E08000021     1780.0                 2018
2      2       1      E08000021     1770.0                 2018
3      3       1      E08000021     1757.0                 2018
4      4       1      E08000021     1747.0                 2018

Aggregated data

This example fetches the total population projections for Newcastle from 2018 to 2039.

>>> import ukpopulation.snppdata as SNPPData
>>> snpp = SNPPData.SNPPData()
Cache directory:  ./raw_data/
using cached LAD codes: ./raw_data/lad_codes.json
Collating SNPP data for England...
./raw_data/NM_2006_1_metadata.json found, using cached metadata...
Using cached data: ./raw_data/NM_2006_1_56aba41fc0fab32f58ead6ae91a867b4.tsv
./raw_data/NM_2006_1_metadata.json found, using cached metadata...
Using cached data: ./raw_data/NM_2006_1_dbe6c087fb46306789f7d54b125482e4.tsv
Collating SNPP data for Wales...
Collating SNPP data for Scotland...
Collating SNPP data for Northern Ireland...
>>> newcastle=snpp.aggregate(["GENDER", "C_AGE"], "E08000021", range(2018,2039))
>>> newcastle.head()
0      E08000021                 2018   299132.0
1      E08000021                 2019   300530.0
2      E08000021                 2020   301699.0
3      E08000021                 2021   302729.0
4      E08000021                 2022   303896.0

Retrieve NPP data filtered by age

Here's how to get the total working-age population by country from 2016 to 2050:

>>> import ukpopulation.nppdata as NPPData
>>> npp = NPPData.NPPData()
Cache directory:  ./raw_data/
using cached LAD codes: ./raw_data/lad_codes.json
Loading NPP principal (ppp) data for England, Wales, Scotland & Northern Ireland
./raw_data/NM_2009_1_metadata.json found, using cached metadata...
Using cached data: ./raw_data/NM_2009_1_444caf1f672f0646722e389963289973.tsv
>>> uk_working_age=npp.aggregate(["GENDER", "C_AGE"], "ppp", NPPData.NPPData.UK, range(2016,2051), ages=range(16,75))
>>> uk_working_age.head()
0      E92000001                 2016   40269470
1      E92000001                 2017   40460118
2      E92000001                 2018   40591965
3      E92000001                 2019   40704521
4      E92000001                 2020   40834471

And this aggregates the figures for Great Britain:

>>> gb_working_age=npp.aggregate(["GEOGRAPHY_CODE", "GENDER", "C_AGE"], "ppp", NPPData.NPPData.GB, range(2016,2051), ages=range(16,75))
>>> gb_working_age.head()
0                 2016   46590014
1                 2017   46801693
2                 2018   46944219
3                 2019   47063069
4                 2020   47201882

NB SNPP data can also be filtered by age and/or gender and/or geography in the same way.

Retrieve NPP variants for England & Wales

First detailed data (by age, gender and country), then aggregated by age and gender.

>>> import ukpopulation.nppdata as NPPData
>>> npp=NPPData.NPPData()
Cache directory:  ./raw_data/
using cached LAD codes: ./raw_data/lad_codes.json
Loading NPP principal (ppp) data for England, Wales, Scotland & Northern Ireland
./raw_data/NM_2009_1_metadata.json found, using cached metadata...
Using cached data: ./raw_data/NM_2009_1_444caf1f672f0646722e389963289973.tsv
>>> high_growth = npp.detail("hhh", NPPData.NPPData.EW)
>>> high_growth.head()
0      0       1     343198                 2016      E92000001
1      0       1     334025                 2017      E92000001
2      0       1     345332                 2018      E92000001
3      0       1     349796                 2019      E92000001
4      0       1     354274                 2020      E92000001
>>> high_growth_agg = npp.aggregate(["GENDER", "C_AGE"], "hhh", NPPData.NPPData.EW)
>>> high_growth_agg.head()
0      E92000001                 2016   55268067
1      E92000001                 2017   55660155
2      E92000001                 2018   56115027
3      E92000001                 2019   56568795
4      E92000001                 2020   57019007

Extrapolate MYE using SNPP and NPP data

Single Area

Construct aggregate data for Exeter from 2011-2065:

  • use MYE data up to 2016, aggregated by age and gender.
  • then use SNPP data up to 2041, aggregated by age and gender.
  • extrapolate using NPP data and Exeter's (2041) age-gender structure.
  • aggregrate the extrapolated data by age and gender
  • plot the data.

Source Code

Extrapolated Newcastle Population Projection

Bulk Calculation

In this example we extrapolate and aggregrate the SNPP for every LAD in Wales:

  • for each area,
    • extrapolate from 2039 to 2050 using the 2039 age-gender structure.
    • aggregate the extrapolated datma by age and gender.
    • append to full dataset.
  • save Wales dataset as csv:
W06000011 2040 262903.24103359133
W06000011 2041 262933.2340468692
W06000011 2042 263162.3661643687
W06000011 2043 263332.96819104964
W06000011 2044 263593.29826455784
W06000011 2045 263923.03553008236
W06000011 2046 264243.6253810904
W06000011 2047 264168.2113917932
W06000011 2048 264211.4576059673
... ... ...

Source Code

Construct an SNPP variant by applying NPP variant to a specific LAD

Here we apply the "hhh" (high growth) and "lll" (low growth) NPP variants to the SNPP data for Newcastle:

  • calculate the principal ("ppp") projection by simply aggregrating the SNPP data for Newcastle, 2018-2039, by age and gender.
  • calculate the variants by weighting the unaggregated data (i.e. by age and gender) by the ratio of the NPP variant/principal.
  • aggregrate the variant data by age and gender.
  • plot the results.

Source Code

Newcastle Population Projection Variants

Extrapolating an SNPP variant

Here we build on the examples above by not only applying the NPP variant, but extrapolating too. The process first involves extrapolating the SNPP by the NPP principal variant. The extrapolated data then has the variant adjustments applied to it.

Source Code

Newcastle Population Projection Variants

Code Documentation

Package documentation can be viewed like so:

import ukpopulation.myedata as MYEData
import ukpopulation.nppdata as NPPData
import ukpopulation.snppdata as SNPPData


This package was developed as a component of the EPSRC-funded MISTRAL programme, part of the Infrastructure Transitions Research Consortium.

Project details

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