updates

scripts/geo/tan_geo.py

@@ -38,16 +38,156 @@
 # tan_ef.to_csv("tan_coordinates.csv", index=False)
 
 
+
+
+
+
+
+
+
+# import pandas as pd
+
+# tan_ef = pd.read_csv("../../data/TAN/pri-performing-all.csv")
+# tan_ef = tan_ef[["CODE", "NAME", "LONGITUDE", "LATITUDE"]]
+# tan_ef = tan_ef.drop_duplicates(subset = ["CODE"])
+# tan_ef = tan_ef.reset_index()
+# tan_ef['geo_id'] = tan_ef['index'].apply(lambda x: 'TAN-{0:0>6}'.format(x))
+# tan_ef = tan_ef.drop(["index"], axis = 1)
+# # tan_ef = tan_ef[["geo_id", "CODE", "NAME"]].rename(columns = {"CODE": "deped_id", "NAME": "school_name"})
+# tan_ef = tan_ef[["geo_id", "CODE", "NAME", "LONGITUDE", "LATITUDE"]]
+# tan_ef.columns = ["geo_id", "deped_id", "school_name", "longitude", "latitude"]
+# print(tan_ef.head())
+
+# # asga
+
+# longs = tan_ef["longitude"].values
+# lats = tan_ef["latitude"].values
+
+# iso = "TZA"
+# tan_ef["address"] = None
+# tan_ef["adm0"] = iso
+
+# # Geocode to ADM levels
+# cols = ["geo_id", "deped_id", "school_name", "adm0", "address"]
+# for adm in range(1, 4):
+
+#     try:
+
+#         cols += ["adm" + str(adm)]
+#         downloadGB(iso, str(adm), "../../gb")
+#         shp = gpd.read_file(getGBpath(iso, f"ADM{str(adm)}", "../../gb"))
+#         tan_ef = gpd.GeoDataFrame(tan_ef, geometry = gpd.points_from_xy(tan_ef.longitude, tan_ef.latitude))
+#         tan_ef = gpd.tools.sjoin(tan_ef, shp, how = "left").rename(columns = {"shapeName": "adm" + str(adm)})[cols]
+#         tan_ef["longitude"] = longs
+#         tan_ef["latitude"] = lats
+#         print(tan_ef.head())
+
+
+#     except Exception as e:
+
+#         tan_ef["adm" + str(adm)] = None
+#         print(e)
+
+# tan_ef = tan_ef[["geo_id", "deped_id", "school_name", "address", "adm0", "adm1", "adm2", "adm3", "longitude", "latitude"]]
+
+# tan_ef.to_csv("/Users/heatherbaier/Documents/geo_git/files_for_db/geo/tan_geo.csv", index = False)
+
+
+# gdf = gpd.GeoDataFrame(
+#     tan_ef,
+#     geometry = gpd.points_from_xy(
+#         x = tan_ef.longitude,
+#         y = tan_ef.latitude,
+#         crs = 'EPSG:4326', # or: crs = pyproj.CRS.from_user_input(4326)
+#     )
+
+# )
+
+# if not os.path.exists("/Users/heatherbaier/Documents/geo_git/files_for_db/shps/tan/"):
+#     os.mkdir("/Users/heatherbaier/Documents/geo_git/files_for_db/shps/tan/")
+
+# gdf.to_file("/Users/heatherbaier/Documents/geo_git/files_for_db/shps/tan/tan.shp", index = False)
+
+# shutil.make_archive("/Users/heatherbaier/Documents/geo_git/files_for_db/shps/tan", 'zip', "/Users/heatherbaier/Documents/geo_git/files_for_db/shps/tan")
+
+
+
+# Tanzania merge stuff
+
+
 import pandas as pd
 
-tan_ef = pd.read_csv("../../data/TAN/pri-performing-all.csv")
-tan_ef = tan_ef[["CODE", "NAME", "LONGITUDE", "LATITUDE"]]
-tan_ef = tan_ef.drop_duplicates(subset = ["CODE"])
+# The coordinates only appear accuratein pri_performing
+df = pd.read_csv("/Users/heatherbaier/Documents/geo_git/data/TAN/Consolidated_Primary_EnrolmentbyGrade_PTR_2022_PSLE2021.csv")
+df = df[['REGION', 'COUNCIL', 'WARD', 'SCHOOL NAME', 'SCHOOL OWNERSHIP',
+       'SCHOOL REG. NUMBER',
+       'STD 1-BOYS', 'STD 1-GIRLS', 'STD 2-BOYS', 'STD 2-GIRLS', 'STD 3-BOYS',
+       'STD 3-GIRLS', 'STD 4-BOYS', 'STD 4-GIRLS', 'STD 5-BOYS', 'STD 5-GIRLS',
+       'STD 6-BOYS', 'STD 6-GIRLS', 'STD 7-BOYS', 'STD 7-GIRLS', 'TOTAL BOYS',
+       'TOTAL GIRLS', 'ALL TEACHERS MALE', 'ALL TEACHERS FEMALE',
+       'QUALIFIED TEACHERS MALE', 'QUALIFIED TEACHERS FEMALE', 'PTR', 'PQTR',
+       'CLEAN CAND.S 2021', 'NUMBER OF CAND. PASSED (A-C)',
+       'AVERAGE TOTAL MARKS (/300) 2021', 'AVERAGE TOTAL MARKS (/250) 2020',
+       'CHANGE ON AVERAGE TOTAL MARKS FROM 2020', 'BAND OF SCHOOL 2021',
+       'BAND OF SCHOOL 2020', 'RANK OF SCHOOL 2021', 'RANK OF SCHOOL 2020',
+       'NO. OF PSLE CANDATES ']]
+df.columns = [i.title() for i in df.columns]
+df.head()
+print(df.columns)
+
+df2 = pd.read_csv("/Users/heatherbaier/Documents/geo_git/data/TAN/pri-performing-all.csv")
+df2.columns = [i.title() for i in df2.columns]
+df2["School Name"] = df2["Name"].str.split(" PR. ").str[0]
+print(df2.columns)
+print(df.shape)
+df2["Ward"] = df2["Ward"].str.title()
+
+df3 = pd.merge(df, df2, on = ["Ward", "School Name"])
+print(df3.head())
+print(df3.shape)
+
+
+df3.to_csv("./test_merge1.csv")
+
+
+
+df1 = pd.read_csv("/Users/heatherbaier/Documents/geo_git/data/TAN/Consolidated_Secondary_EnrolmentbyGrade_PTR_CSEE2021, 2022.csv")
+df = df1[['Region', 'Council', 'Ward', 'School', 'Reg.No.',
+       'NECTA Exam Centre No.', 'School Ownership',
+       'Form 1-M', 'Form 1-F', 'Form 2-M', 'Form 2-F', 'Form 3-M', 'Form 3-F',
+       'Form 4-M', 'Form 4-F', 'Form 5-M', 'Form 5-F', 'Form 6-M', 'Form 6-F',
+       'M-Total', 'F-Total', 'Total Teachers-Male', 'Total Teachers-Female',
+       'Qualified Teachers-Male', 'Qualified Teachers-Female', 'PTR', 'PQTR',
+       'Regist. Cand. 2021', 'Clean Cand. 2021', 'Passed Cand. (I to IV) 2021',
+       'Average GPA 2021', 'Band of School 2021', 'Band of School 2020',
+       'Rank of School 2021', 'Rank of School 2020', 'Number of Candidates']]
+df1 = df1.rename(columns = {"School": "School Name"})
+df1.columns = [i.title() for i in df1.columns]
+df1.head()
+print(df1.columns)
+
+df3 = pd.merge(df3, df1, on = ["Ward", "School Name"], how = "left")
+print(df3.head())
+print(df3.shape)
+
+df3.to_csv("./test_merge2.csv")
+
+
+# adgsa
+
+tan_ef = df3
+
+# old stuff
+
+
+
+tan_ef = tan_ef[["School Reg. Number", "School Name", "Longitude_x", "Latitude_x"]]
+tan_ef = tan_ef.drop_duplicates(subset = ["School Reg. Number"])
 tan_ef = tan_ef.reset_index()
 tan_ef['geo_id'] = tan_ef['index'].apply(lambda x: 'TAN-{0:0>6}'.format(x))
 tan_ef = tan_ef.drop(["index"], axis = 1)
-# tan_ef = tan_ef[["geo_id", "CODE", "NAME"]].rename(columns = {"CODE": "deped_id", "NAME": "school_name"})
-tan_ef = tan_ef[["geo_id", "CODE", "NAME", "LONGITUDE", "LATITUDE"]]
+# tan_ef = tan_ef[["geo_id", "Code", "NAME"]].rename(columns = {"Code": "deped_id", "NAME": "school_name"})
+tan_ef = tan_ef[["geo_id", "School Reg. Number", "School Name", "Longitude_x", "Latitude_x"]]
 tan_ef.columns = ["geo_id", "deped_id", "school_name", "longitude", "latitude"]
 print(tan_ef.head())
 
@@ -105,4 +245,3 @@
 
 
 
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