diff --git a/analysis/diversity_db.py b/analysis/diversity_db.py
index fd8fe01..a9934e6 100644
--- a/analysis/diversity_db.py
+++ b/analysis/diversity_db.py
@@ -60,7 +60,7 @@ def shannon(data, stair=0, opts=True):
 # ====================================
 
 
-def save_to_mongo_local(localId: int, factor: str, stair=0, opts=False) -> None:
+def save_to_mongo_local(localId: int, factor: str, stair=0, opts=True) -> None:
     factor_field = {"age": "age", "gender": "gender", "party": "jdName"}
     data = [
         councilor[factor_field[factor]]
@@ -166,7 +166,7 @@ def calculate_age_diversity_rank_history_local() -> None:
 # ====================================
 
 
-def save_to_mongo_metro(metroId: int, factor: str, stair=0, opts=False) -> None:
+def save_to_mongo_metro(metroId: int, factor: str, stair=0, opts=True) -> None:
     factor_field = {"age": "age", "gender": "gender", "party": "jdName"}
     data = [
         councilor[factor_field[factor]]
@@ -267,23 +267,115 @@ def calculate_age_diversity_rank_history_metro() -> None:
                 )
 
 
-if __name__ == "__main__":
-    # for localId in range(1, 227):
-    #     save_to_mongo_local(localId, "age", stair=10)
-    #     save_to_mongo_local(localId, "gender")
-    #     save_to_mongo_local(localId, "party")
-    # calculate_rank_local("age")
-    # calculate_rank_local("gender")
-    # calculate_rank_local("party")
-    # calculate_age_diversity_rank_history_local()
-
-    # for metroId in range(1, 18):
-    #     if metroId in [8, 17]:
-    #         continue
-    #     save_to_mongo_metro(metroId, "age", stair=10)
-    #     save_to_mongo_metro(metroId, "gender")
-    #     save_to_mongo_metro(metroId, "party")
-    # calculate_rank_metro("age")
-    # calculate_rank_metro("gender")
-    # calculate_rank_metro("party")
+# =====================================
+# National council diversity statistics
+# =====================================
+
+
+def save_to_mongo_national(factor: str, stair=0, opts=True) -> None:
+    factor_field = {"age": "age", "gender": "gender", "party": "jdName"}
+    data = [
+        councilor[factor_field[factor]]
+        for councilor in client["council"]["national_councilor"].find()
+    ] + [
+        councilor[factor_field[factor]]
+        for councilor in client["council"]["national_councilor_global"].find()
+    ]
+    print(f"National {factor}")
+    print(data)
+    client["stats"].get_collection("diversity_index").update_one(
+        {"national": True},
+        {"$set": {f"{factor}DiversityIndex": gini_simpson(data, stair, opts)}},
+        upsert=True,
+    )
+
+
+def calculate_age_diversity_rank_history_national() -> None:
+    for is_elected in [True, False]:
+        docs = client["stats"]["age_hist"].find(
+            {
+                "councilorType": "national_councilor",
+                "method": "equal",
+                "is_elected": is_elected,
+            }
+        )
+        for doc in docs:
+            diversity_index = gini_simpson(
+                [
+                    group["minAge"]
+                    for group in doc["data"]
+                    for _ in range(group["count"])
+                ],
+                stair=10,
+            )
+            client["stats"]["age_hist"].find_one_and_update(
+                {
+                    "councilorType": "national_councilor",
+                    "method": "equal",
+                    "is_elected": is_elected,
+                    "year": doc["year"],
+                },
+                {"$set": {"diversityIndex": diversity_index}},
+            )
+
+        years = list({doc["year"] for doc in client["stats"]["age_hist"].find()})
+
+        for year in years:
+            result = client["stats"]["age_hist"].aggregate(
+                [
+                    {
+                        "$match": {
+                            "councilorType": "national_councilor",
+                            "method": "equal",
+                            "is_elected": is_elected,
+                            "year": year,
+                        }
+                    },
+                    {"$sort": {"diversityIndex": -1}},
+                    {"$group": {"_id": "", "items": {"$push": "$$ROOT"}}},
+                    {"$unwind": {"path": "$items", "includeArrayIndex": "items.rank"}},
+                    {"$replaceRoot": {"newRoot": "$items"}},
+                    {"$addFields": {"rank": {"$add": ["$rank", 1]}}},
+                ]
+            )
+            for doc in result:
+                client["stats"]["age_hist"].find_one_and_update(
+                    {
+                        "councilorType": "national_councilor",
+                        "method": "equal",
+                        "is_elected": is_elected,
+                        "year": year,
+                    },
+                    {"$set": {"diversityRank": int(doc["rank"])}},
+                )
+
+
+def main():
+    for localId in range(1, 227):
+        save_to_mongo_local(localId, "age", stair=10)
+        save_to_mongo_local(localId, "gender")
+        save_to_mongo_local(localId, "party")
+    calculate_rank_local("age")
+    calculate_rank_local("gender")
+    calculate_rank_local("party")
+    calculate_age_diversity_rank_history_local()
+
+    for metroId in range(1, 18):
+        if metroId in [17]:
+            continue
+        save_to_mongo_metro(metroId, "age", stair=10)
+        save_to_mongo_metro(metroId, "gender")
+        save_to_mongo_metro(metroId, "party")
+    calculate_rank_metro("age")
+    calculate_rank_metro("gender")
+    calculate_rank_metro("party")
     calculate_age_diversity_rank_history_metro()
+
+    save_to_mongo_national("age", stair=10)
+    save_to_mongo_national("gender")
+    save_to_mongo_national("party")
+    calculate_age_diversity_rank_history_national()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/analysis/gender_party_hist.py b/analysis/gender_party_hist.py
new file mode 100644
index 0000000..6b813e5
--- /dev/null
+++ b/analysis/gender_party_hist.py
@@ -0,0 +1,354 @@
+# coding=utf-8
+import pandas as pd
+import os
+import warnings
+from db.client import client
+from analysis.age.hist_groups import (
+    local_to_metro_list,
+    change_local_name,
+)
+
+# 경고 무시
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+BASE_DIR = os.path.join(os.path.dirname(__file__), os.pardir)
+
+
+# ===================================
+#     Gender history calculations
+# ===================================
+
+
+def gender_hist(
+    councilor_type: str, level: int, is_elected: bool, filenames: list[str]
+):
+    ## TO-DO: excel말고 mongodb에서 받아오도록 합니다.
+    assert (councilor_type, level) in [
+        ("local_councilor", 2),
+        ("metro_councilor", 1),
+        ("national_councilor", 0),
+    ]
+    datadir = os.path.join(BASE_DIR, "_data")
+    df = pd.DataFrame()
+
+    for d in filenames:
+        df_new = pd.read_excel(os.path.join(datadir, d))
+        df = pd.concat([df, df_new])
+
+    district_db = client["district"]
+    gender_hist_collection = client["stats"].get_collection("gender_hist")
+
+    df["wiwName"] = df["wiwName"].apply(lambda x: x if isinstance(x, str) else "")
+    df["sdName"] = df[["sdName", "wiwName"]].apply(
+        lambda x: local_to_metro_list(*x), axis=1
+    )
+    df["wiwName"] = df[["sdName", "wiwName"]].apply(
+        lambda x: change_local_name(*x), axis=1
+    )
+
+    if level == 0:
+        df = df[["sgId", "name", "gender"]].groupby(by=["sgId", "gender"]).count()
+        for idx in df.index:
+            year = int(str(idx[0])[:4])
+            print(f"{year=}")
+            gender_hist_collection.find_one_and_update(
+                {
+                    "councilorType": "national_councilor",
+                    "is_elected": is_elected,
+                    "level": 0,
+                    "year": year,
+                },
+                {"$set": {idx[1]: int(df["name"][idx])}},
+                upsert=True,
+            )
+
+    elif level == 1:
+        df = (
+            df[["sgId", "sdName", "name", "gender"]]
+            .groupby(by=["sgId", "sdName", "gender"])
+            .count()
+        )
+        for idx in df.index:
+            year = int(str(idx[0])[:4])
+            print(f"{year=} sdName={idx[1]}")
+            metroId = district_db.get_collection("metro_district").find_one(
+                {"sdName": idx[1]}
+            )["metroId"]
+
+            gender_hist_collection.find_one_and_update(
+                {
+                    "councilorType": "metro_councilor",
+                    "is_elected": is_elected,
+                    "level": 1,
+                    "metroId": metroId,
+                    "year": year,
+                },
+                {"$set": {idx[2]: int(df["name"][idx])}},
+                upsert=True,
+            )
+
+    elif level == 2:
+        df = (
+            df[["sgId", "sdName", "wiwName", "name", "gender"]]
+            .groupby(by=["sgId", "sdName", "wiwName", "gender"])
+            .count()
+        )
+        for idx in df.index:
+            year = int(str(idx[0])[:4])
+            print(f"{year=} sdName={idx[1]} wiwName={idx[2]}")
+            doc = district_db["local_district"].find_one(
+                {
+                    "sdName": idx[1],
+                    "wiwName": idx[2] if idx[1] != "세종특별자치시" else "세종특별자치시",
+                }
+            )
+            metroId, localId = doc["metroId"], doc["localId"]
+
+            gender_hist_collection.find_one_and_update(
+                {
+                    "councilorType": "local_councilor",
+                    "is_elected": is_elected,
+                    "level": 2,
+                    "metroId": metroId,
+                    "localId": localId,
+                    "year": year,
+                },
+                {"$set": {idx[3]: int(df["name"][idx])}},
+                upsert=True,
+            )
+
+
+def gender_hist_add_zero():
+    gender_hist_collection = client["stats"].get_collection("gender_hist")
+    gender_hist_collection.update_many({"남": {"$exists": False}}, {"$set": {"남": 0}})
+    gender_hist_collection.update_many({"여": {"$exists": False}}, {"$set": {"여": 0}})
+
+
+# ===================================
+#     Party history calculations
+# ===================================
+
+
+def party_hist(councilor_type: str, level: int, is_elected: bool, filenames: list[str]):
+    ## TO-DO: excel말고 mongodb에서 받아오도록 합니다.
+    assert (councilor_type, level) in [
+        ("local_councilor", 2),
+        ("metro_councilor", 1),
+        ("national_councilor", 0),
+    ]
+    datadir = os.path.join(BASE_DIR, "_data")
+    df = pd.DataFrame()
+
+    for d in filenames:
+        df_new = pd.read_excel(os.path.join(datadir, d))
+        df = pd.concat([df, df_new])
+
+    district_db = client["district"]
+    party_hist_collection = client["stats"].get_collection("party_hist")
+
+    df["wiwName"] = df["wiwName"].apply(lambda x: x if isinstance(x, str) else "")
+    df["sdName"] = df[["sdName", "wiwName"]].apply(
+        lambda x: local_to_metro_list(*x), axis=1
+    )
+    df["wiwName"] = df[["sdName", "wiwName"]].apply(
+        lambda x: change_local_name(*x), axis=1
+    )
+
+    if level == 0:
+        df = df[["sgId", "name", "jdName"]].groupby(by=["sgId", "jdName"]).count()
+        for idx in df.index:
+            year = int(str(idx[0])[:4])
+            print(f"{year=}")
+            party_hist_collection.find_one_and_update(
+                {
+                    "councilorType": "national_councilor",
+                    "is_elected": is_elected,
+                    "level": 0,
+                    "year": year,
+                },
+                {"$set": {idx[1]: int(df["name"][idx])}},
+                upsert=True,
+            )
+
+    elif level == 1:
+        df = (
+            df[["sgId", "sdName", "name", "jdName"]]
+            .groupby(by=["sgId", "sdName", "jdName"])
+            .count()
+        )
+        for idx in df.index:
+            year = int(str(idx[0])[:4])
+            print(f"{year=} sdName={idx[1]}")
+            metroId = district_db.get_collection("metro_district").find_one(
+                {"sdName": idx[1]}
+            )["metroId"]
+
+            party_hist_collection.find_one_and_update(
+                {
+                    "councilorType": "metro_councilor",
+                    "is_elected": is_elected,
+                    "level": 1,
+                    "metroId": metroId,
+                    "year": year,
+                },
+                {"$set": {idx[2]: int(df["name"][idx])}},
+                upsert=True,
+            )
+
+    elif level == 2:
+        df = (
+            df[["sgId", "sdName", "wiwName", "name", "jdName"]]
+            .groupby(by=["sgId", "sdName", "wiwName", "jdName"])
+            .count()
+        )
+        for idx in df.index:
+            year = int(str(idx[0])[:4])
+            print(f"{year=} sdName={idx[1]} wiwName={idx[2]}")
+            doc = district_db["local_district"].find_one(
+                {
+                    "sdName": idx[1],
+                    "wiwName": idx[2] if idx[1] != "세종특별자치시" else "세종특별자치시",
+                }
+            )
+            metroId, localId = doc["metroId"], doc["localId"]
+
+            party_hist_collection.find_one_and_update(
+                {
+                    "councilorType": "local_councilor",
+                    "is_elected": is_elected,
+                    "level": 2,
+                    "metroId": metroId,
+                    "localId": localId,
+                    "year": year,
+                },
+                {"$set": {idx[3]: int(df["name"][idx])}},
+                upsert=True,
+            )
+
+
+# ===================================================
+#  Age history calculations for national councilors
+# ===================================================
+
+
+def age_hist_national(is_elected: bool, filenames: list[str]):
+    datadir = os.path.join(BASE_DIR, "_data")
+    df = pd.DataFrame()
+
+    for d in filenames:
+        df_new = pd.read_excel(os.path.join(datadir, d))
+        df = pd.concat([df, df_new])
+
+    df = df[["sgId", "name", "age"]].groupby(by=["sgId", "age"]).count().reset_index()
+    df["cumsum"] = df.groupby(by="sgId")["name"].cumsum()
+    df["quintile"] = pd.qcut(df["cumsum"], q=5, labels=[0, 1, 2, 3, 4])
+
+    total = (
+        df[["sgId", "name"]]
+        .groupby(by=["sgId"])
+        .sum()
+        .rename(columns={"name": "total"})
+    )
+    first_quintile = (
+        df.loc[df["quintile"] == 0]
+        .groupby(by=["sgId"])
+        .max()[["age"]]
+        .rename(columns={"age": "q1"})
+    )
+    last_quintile = (
+        df.loc[df["quintile"] == 4]
+        .groupby(by=["sgId"])
+        .min()[["age"]]
+        .rename(columns={"age": "q5"})
+    )
+    quintiles = pd.concat([total, first_quintile, last_quintile], axis=1)
+
+    hist_data: dict[int, list[dict]] = {}
+    for _, row in df.iterrows():
+        year = int(str(row["sgId"])[:4])
+        age = int(row["age"])
+        cnt = int(row["name"])
+        age_group = int(row["quintile"])
+
+        if year in hist_data:
+            hist_data[year].append(
+                {"minAge": age, "maxAge": age + 1, "count": cnt, "ageGroup": age_group}
+            )
+        else:
+            hist_data[year] = [
+                {"minAge": age, "maxAge": age + 1, "count": cnt, "ageGroup": age_group}
+            ]
+
+    stat_data: dict[int, dict] = {}
+    for sgId, row in quintiles.iterrows():
+        year = int(str(sgId)[:4])
+        stat_data[year] = {
+            "firstquintile": int(row["q1"]),
+            "lastquintile": int(row["q5"]),
+            "population": int(row["total"]),
+        }
+
+    age_hist_collection = client["stats"].get_collection("age_hist")
+    for year, docs in hist_data.items():
+        age_hist_collection.find_one_and_update(
+            {
+                "councilorType": "national_councilor",
+                "is_elected": is_elected,
+                "level": 0,
+                "method": "equal",
+                "year": year,
+            },
+            {"$set": {"data": docs}},
+            upsert=True,
+        )
+
+    age_stat_collection = client["stats"].get_collection("age_stat")
+    for year, doc in stat_data.items():
+        age_stat_collection.find_one_and_update(
+            {
+                "councilorType": "national_councilor",
+                "is_elected": is_elected,
+                "level": 0,
+                "method": "equal",
+                "year": year,
+            },
+            {"$set": {"data": [doc]}},
+            upsert=True,
+        )
+
+
+def main():
+    gender_hist(
+        "local_councilor", 2, True, ["[당선][구시군의회의원].xlsx", "[당선][기초의원비례대표].xlsx"]
+    )
+    gender_hist(
+        "local_councilor", 2, False, ["[후보][구시군의회의원].xlsx", "[후보][기초의원비례대표].xlsx"]
+    )
+
+    gender_hist("metro_councilor", 1, True, ["[당선][시도의원].xlsx", "[당선][광역의원비례대표].xlsx"])
+    gender_hist("metro_councilor", 1, False, ["[후보][시도의원].xlsx", "[후보][광역의원비례대표].xlsx"])
+
+    gender_hist("national_councilor", 0, True, ["[당선][국회의원].xlsx"])
+    gender_hist("national_councilor", 0, False, ["[후보][국회의원].xlsx"])
+
+    gender_hist_add_zero()
+
+    party_hist(
+        "local_councilor", 2, True, ["[당선][구시군의회의원].xlsx", "[당선][기초의원비례대표].xlsx"]
+    )
+    party_hist(
+        "local_councilor", 2, False, ["[후보][구시군의회의원].xlsx", "[후보][기초의원비례대표].xlsx"]
+    )
+
+    party_hist("metro_councilor", 1, True, ["[당선][시도의원].xlsx", "[당선][광역의원비례대표].xlsx"])
+    party_hist("metro_councilor", 1, False, ["[후보][시도의원].xlsx", "[후보][광역의원비례대표].xlsx"])
+
+    party_hist("national_councilor", 0, True, ["[당선][국회의원].xlsx"])
+    party_hist("national_councilor", 0, False, ["[후보][국회의원].xlsx"])
+
+    age_hist_national(True, ["[당선][국회의원].xlsx"])
+    age_hist_national(False, ["[후보][국회의원].xlsx"])
+
+
+if __name__ == "__main__":
+    main()