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population_counts.py
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/
population_counts.py
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import os
import pandas as pd
from datetime import datetime
import os
from collections import Counter
from utilities import *
# get total number of practices by region to look at backend coverage
# source: https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice/metadata#gp-reg-pat-prac-map
practice_df = pd.read_csv('analysis/gp-reg-pat-prac-map.csv')
num_practices_region = practice_df.groupby(by='COMM_REGION_NAME')['PRACTICE_CODE'].nunique().reset_index()
num_practices_region.to_csv('output/practice_region_total_count.csv')
dates_list = []
full_df = pd.read_feather(os.path.join('output', 'input_2019-01-01.feather'))
full_df = full_df.set_index('patient_id')
for file in sorted(os.listdir('output')):
if match_input_files(file):
date = get_date_input_file(file)
datetime_object = pd.to_datetime(date)
dates_list.append(datetime_object)
df = pd.read_feather(os.path.join('output', file))
df = df.set_index('patient_id')
#update existing values in full_df
full_df.update(df)
#add new rows to full_df
existing_id = list(full_df.index)
df = df[~df.index.isin(existing_id)]
full_df = pd.concat([full_df, df])
unique_practices = full_df['practice'].unique()
total_count_df = pd.DataFrame([['total', len(full_df)], ['practice', len(unique_practices)]], columns=['pop', 'count'])
total_count_df.to_csv('output/total_count.csv')
practices_by_region = full_df.groupby(by='region')['practice'].nunique().reset_index()
practices_by_region.to_csv('output/practice_region_count.csv')
for column in ['sex', 'age_band', 'ethnicity', 'imd', 'region']:
count = Counter(full_df[column])
count_df = pd.DataFrame.from_dict(count, orient='index')
count_df.to_csv(f'output/{column}_count.csv')