Skip to main content

Utility Tools for Population Estimates

Project description

estpop is a Python package providing population forecasting from historical data. This method is based on cohort change ratio[1].

Sample Code

Change Ratio

import numpy as np
import openpyxl
import estpop

sheet = openpyxl.load_workbook('data.xlsx').worksheets[0]

pops = {}
for i in range(1, sheet.max_row):
    code = sheet.cell(i+1, 3).value

    if not code in pops:
        pops[code] = []

    males, females = [], []
    for j in range(29, 50):
        males.append(sheet.cell(i+1, j).value)
        females.append(sheet.cell(i+1, j+22).value)
    pops[code].append([males, females])

ratios = {}
for k, v in pops.items():
    change_ratios, baby_ratios, tail_ratios = [], [], []
        for i in range(len(v) - 5):
            change_ratio, baby_ratio, tail_ratio = estpop.ratios(v[i], v[i+5])

        ratios[k] = {
            'change_ratio': np.mean(change_ratios, axis=0).tolist(),
            'baby_ratio': float(np.mean(baby_ratios)),
            'tail_ratio': float(np.mean(tail_ratios))


import openpyxl
import estpop

f = open('result.csv', mode='w')

for k, v in pops.items():
    if k in [411, 421, 521]:
        change_ratio = ratios[0]['change_ratio']
        baby_ratio = ratios[0]['baby_ratio']
        tail_ratio = ratios[0]['tail_ratio']
        change_ratio = ratios[k]['change_ratio']
        baby_ratio = ratios[k]['baby_ratio']
        tail_ratio = ratios[k]['tail_ratio']

        year = 2020
        estimates = v[5]

        for i in range(7):
            estimates = estpop.simulate(estimates, change_ratio,
                                        baby_ratio, tail_ratio)
            f.write('%s,%s,%s,%s\n' % (k, year+i*5,
                                       ','.join(map(str, estimates[0])),
                                       ','.join(map(str, estimates[1]))))



  1. Einoshin SUZUKI, Kaoru MORI, Koichi NAGASE, Masatoshi TAMAMURA, Ikuyo KANEKO: The Development of the Future Predictive Model of 'Potentially Disappearing Neighborhood Associations' Using Demographic Data of the Neighborhood Association Base, Journal of the Japan Association of Regional Development and Vitalization, Vol.6, pp.20-30, 2015.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Built Distribution

estpop-0.0.4-py3-none-any.whl (3.7 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page