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NewWays-TechForImpactKAIST · Nov 27, 2023 · c795ec6 · c795ec6
1 parent b6bc27f
commit c795ec6
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Showing 7 changed files with 38 additions and 26 deletions.
diff --git a/API/candidate.py b/API/candidate.py
@@ -91,6 +91,4 @@ def fetch_all_data(
         if args.save_method == "excel":
             save_to_excel(data_list, sgTypecode, is_elected=False)
         elif args.save_method == "mongo":
-            save_to_mongo(
-                data_list, sgTypecode, CANDIDATE_TYPECODE_TYPE[sgTypecode]
-            )
+            save_to_mongo(data_list, sgTypecode, CANDIDATE_TYPECODE_TYPE[sgTypecode])
diff --git a/API/elected.py b/API/elected.py
@@ -54,7 +54,7 @@ def fetch_all_data(
     sgIds: List[str], sgTypecodes: str, drop_columns: List[str]
 ) -> List[dict]:
     data_list = []
-    for sgTypecode in sgTypecodes.split(","):    
+    for sgTypecode in sgTypecodes.split(","):
         for sgId in sgIds:
             data_list.extend(fetch_data(sgId, sgTypecode, drop_columns=drop_columns))
 
@@ -90,6 +90,4 @@ def fetch_all_data(
         if args.save_method == "excel":
             save_to_excel(data_list, sgTypecode, is_elected=True)
         elif args.save_method == "mongo":
-            save_to_mongo(
-                data_list, sgTypecode, ELECTED_TYPECODE_TYPE[sgTypecode]
-            )
+            save_to_mongo(data_list, sgTypecode, ELECTED_TYPECODE_TYPE[sgTypecode])
diff --git a/analysis/age/__init__.py b/analysis/age/__init__.py
@@ -1,6 +1,8 @@
 """
 공공데이터포털 API로 수집한 데이터를 분석하기 위한 패키지입니다.
 """
+
+
 class BasicArgument:
     def __init__(self, **kwargs):
         self.__dict__.update(kwargs)
diff --git a/analysis/age/hist_groups.py b/analysis/age/hist_groups.py
@@ -9,6 +9,7 @@
 from db.client import client
 from analysis.age import BasicArgument
 
+
 def plot_young_and_old(youngest_cluster, oldest_cluster):
     try:
         sns.histplot(
@@ -184,7 +185,7 @@ def cluster(df_original, n_clst, basedic):
     """구역별 그룹을 만듭니다.
     df_original: 데이터프레임
     n_clst: 그룹 수
-    basedic: 기본 정보가 담긴 딕셔너리 
+    basedic: 기본 정보가 담긴 딕셔너리
     """
     distdb = client["district"]
     statdb = client["stats"]
@@ -230,7 +231,9 @@ def cluster(df_original, n_clst, basedic):
             for i in range(n_clst):
                 clst_data = df_clst[df_clst["cluster_label"] == i]
                 # print(f"Cluster {i} in {area}: {clst_data['age'].min()} - {clst_data['age'].max()}")
-                cluster_center_age = round(clst_data["age"].mean(), 2)  # 나이를 소수점 2자리까지 반올림
+                cluster_center_age = round(
+                    clst_data["age"].mean(), 2
+                )  # 나이를 소수점 2자리까지 반올림
                 clst_age_mean.append(cluster_center_age)
             clst_of_young = 0
             clst_of_old = n_clst - 1
@@ -243,7 +246,9 @@ def cluster(df_original, n_clst, basedic):
             # 지역의 가장 젊은, 나이든 그룹을 찾습니다
             yb_clst = df_clst[df_clst["cluster_label"] == clst_of_young]
             ob_clst = df_clst[df_clst["cluster_label"] == clst_of_old]
-            print(f"Youngest in {area}: {yb_clst['age'].min()} - {yb_clst['age'].max()}")
+            print(
+                f"Youngest in {area}: {yb_clst['age'].min()} - {yb_clst['age'].max()}"
+            )
             print(f"Oldest in {area}: {ob_clst['age'].min()} - {ob_clst['age'].max()}")
             # 그룹의 성비를 계산합니다.
             young_group_sexratio = (
@@ -266,7 +271,9 @@ def cluster(df_original, n_clst, basedic):
                     "minAge": int(age),
                     "maxAge": int(age) + 1,
                     "count": df_clst[df_clst["age"] == age].shape[0],
-                    "ageGroup": int(df_clst.loc[df_clst["age"] == age].iloc[0]["cluster_label"])
+                    "ageGroup": int(
+                        df_clst.loc[df_clst["age"] == age].iloc[0]["cluster_label"]
+                    ),
                 }
                 for age in df_clst["age"].unique()
             ]
@@ -312,9 +319,9 @@ def cluster(df_original, n_clst, basedic):
             else:
                 localname = df_clst["wiwName"].iloc[0]
                 print("sdName is ", metroname, "wiwName is", localname)
-                localId = localIds.find_one({"sdName": metroname, "wiwName": localname})[
-                    "localId"
-                ]
+                localId = localIds.find_one(
+                    {"sdName": metroname, "wiwName": localname}
+                )["localId"]
                 dic = basedic.__dict__.copy()
                 dic["metroId"] = metroId
                 insert_data_to_mongo(
@@ -326,11 +333,13 @@ def cluster(df_original, n_clst, basedic):
                     localId=localId,
                 )
 
-            print(f"Number of data points per cluster for {area}, method {basedic.method}")
+            print(
+                f"Number of data points per cluster for {area}, method {basedic.method}"
+            )
             for cluster_label in range(n_clst):
                 closest_data_count = sum(df_clst["cluster_label"] == cluster_label)
                 print(
                     f"Cluster {cluster_label}: Age {clst_age_mean[cluster_label]}, {closest_data_count} closest data points"
                 )
         print(f"Youngest in {youngest_age[0]}: {youngest_age[1]}")
-        print(f"Oldest in {oldest_age[0]}: {oldest_age[1]}")
+        print(f"Oldest in {oldest_age[0]}: {oldest_age[1]}")
diff --git a/analysis/age/main.py b/analysis/age/main.py
@@ -23,6 +23,7 @@
     "기초의원비례대표": "local_councilor",
 }
 
+
 def main(N=5, folder_name="To_be_filled"):
     ## TO-DO: excel말고 mongodb에서 받아오도록 합니다.
     ## 이 링크에 구현될 save_to_mongo함수 참고 : https://github.com/NewWays-TechForImpactKAIST/API-scrap-and-analysis//blob/bd817e9a15086d313d9615b2515a81e0dbd73850/API/utils.py#L34
@@ -33,9 +34,9 @@ def main(N=5, folder_name="To_be_filled"):
     level = 1 if cluster_by == "sdName" else 2
     datadir = os.path.join(BASE_DIR, "_data", folder_name)
     # for d in os.listdir(datadir):
-        # xlsx 파일을 읽어옵니다.
-        # if not d.endswith(".xlsx"):
-        #     continue
+    # xlsx 파일을 읽어옵니다.
+    # if not d.endswith(".xlsx"):
+    #     continue
     # df = pd.read_excel(os.path.join(datadir, d))
     # d = "[당선][시도의원].xlsx"
     d = "[당선][구시군의회의원].xlsx"
@@ -57,12 +58,15 @@ def main(N=5, folder_name="To_be_filled"):
         if "후보" in d
         else ValueError("엑셀파일 이름에 '당선'이든지 '후보'가 있어야 합니다.")
     )
-    councilorType = councilordict[d.split('[')[-1].split(']')[0]]
+    councilorType = councilordict[d.split("[")[-1].split("]")[0]]
     for method in ["kmeans", "equal"]:
-        basedic = BasicArgument(councilorType=councilorType, is_elected=is_elected, level=level, method=method)
-        cluster(
-            df, N, basedic
+        basedic = BasicArgument(
+            councilorType=councilorType,
+            is_elected=is_elected,
+            level=level,
+            method=method,
         )
+        cluster(df, N, basedic)
     ## 2. 광역의회
 
 

diff --git a/scrap/group_head.py b/scrap/group_head.py
@@ -65,13 +65,13 @@ def scrap_group_leaders(
 
         browser.get(url)
     results = dict()
-    for (area, councilor) in metro_heads:
+    for area, councilor in metro_heads:
         results[area] = ScrapResult(
             council_id=area,
             council_type=CouncilType.METRO_LEADER,
             councilors=councilor,
         )
-    for (local_area_name, councilor) in local_heads:
+    for local_area_name, councilor in local_heads:
         print(local_area_name)
         results[local_area_name] = ScrapResult(
             council_id=local_area_name,

diff --git a/scrap/utils/runner.py b/scrap/utils/runner.py
@@ -265,7 +265,8 @@ def parse_cids(cids_str: Optional[str], where: str) -> Optional[List[int]]:
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="지방의회 / 광역의회 / 국회 / 단체장 스크랩 스크립트 실행")
-    parser.add_argument( "-w", 
+    parser.add_argument(
+        "-w",
         "--where",
         help="스크랩할 의회 종류 (지방의회: 'local', 광역의회: 'metro', 국회: 'national', 단체장: 'leaders')",
         choices=["local", "metro", "national", "leaders"],