KHM personnel data

scripts/personnel/khm_personnel.py

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+import geopandas as gpd
+import pandas as pd
+import os
+import numpy as np
+
+ # Get geo ID data for KHM secondary schools
+DATA_PATH = os.path.join(os.getcwd(),"../../files_for_db/geo/khm_geo.csv")
+khm_csv_data = pd.read_csv(DATA_PATH)
+sec_schools = khm_csv_data[khm_csv_data['deped_id'].str.contains("Lycee G7-12|Lycee G10-12", na=False)]
+
+# Get raw secondary school data
+DATA_PATH1 = os.path.join(os.getcwd(),"../../data/KHM/lycee-g7-12/Lycee G7-12.shp")
+DATA_PATH2 = os.path.join(os.getcwd(),"../../data/KHM/lycee-g10-12/Lycee G10-12.shp")
+g7_12 = gpd.read_file(DATA_PATH1)
+g10_12 = gpd.read_file(DATA_PATH2)
+
+# Add dependency IDs to map to geo IDs
+# Secondary schools 7-12
+deped_id_ls = ["Lycee G7-12" + f".{idx}" for idx in range(1,g7_12.shape[0]+1)]
+g7_12["deped_id"] = deped_id_ls
+# Secondary schools 10-12
+deped_id_ls = ["Lycee G10-12" + f".{idx}" for idx in range(1,g10_12.shape[0]+1)]
+g10_12["deped_id"] = deped_id_ls
+
+# Combine secondary school data
+g7_10_12 = pd.concat([g7_12, g10_12], ignore_index=True)
+# Join geo IDs to personnel data
+per_schools = pd.merge(sec_schools, g7_10_12, on='deped_id')
+
+# Filter, add, and restructure features
+per_schools['year'] = np.nan
+per_schools['total_teacher_male'] = np.nan
+per_schools['total_teacher_female'] = np.nan
+per_schools['total_student_male'] = np.nan
+per_schools['total_student_female'] = np.nan
+per_schools.rename(columns={'Teach_staf': 'total_teachers','Enrol':'total_student_enrollment'},inplace=True)
+per_schools = per_schools[['geo_id','deped_id','total_teacher_male','total_teacher_female','total_teachers','total_student_male','total_student_female','total_student_enrollment']]
+
+per_schools.to_csv('../../files_for_db/personnel/khm_personnel.csv',index=False)