인문대생의 데이터 직군 취업로그 34

7. Population

1. 배경

• 목표
  • 1. 인구 소멸 위기 지역 파악
  • 2. 인구 소멸 위기 지역의 지도 표현
  • 3. 지도 표현에 대한 카르토그램 표현

2. 데이터 읽고 인구 소멸 지역 계산하기

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import set_matplotlib_hangul
import warnings

warnings.filterwarnings(action="ignore")
%matplotlib inline 

- fillna()
datas = {
    "A": np.random.randint(1, 45, 8), 
    "B": np.random.randint(1, 45, 8),
    "C": np.random.randint(1, 45, 8),
}

datas

fillna_df = pd.DataFrame(datas)
fillna_df

fillna_df.loc[2:4, ["A"]] = np.nan
fillna_df.loc[3:5, ["B"]] = np.nan
fillna_df.loc[4:7, ["C"]] = np.nan
fillna_df

method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
    Method to use for filling holes in reindexed Series
    pad / ffill: propagate last valid observation forward to next valid
    backfill / bfill: use next valid observation to fill gap.

fillna_df.fillna(method="pad")

population = pd.read_excel("../data/07_population_raw_data.xlsx", header=1)
population.fillna(method="pad", inplace=True)
population

population.info()
# 컬럼 이름 변경 

population.rename(
    columns={
        "행정구역(동읍면)별(1)": "광역시도",
        "행정구역(동읍면)별(2)": "시도",
        "계": "인구수"
    }, inplace=True
)
population.tail()

population.head()

# 소계 제거 
population = population[population["시도"] != "소계"]
population.head()

population.is_copy = False 

population.rename(
    columns={"항목": "구분"}, inplace=True
)

population.head()

population.loc[population["구분"] == "총인구수 (명)", "구분"] = "합계"
population.loc[population["구분"] == "남자인구수 (명)", "구분"] = "남자"
population.loc[population["구분"] == "여자인구수 (명)", "구분"] = "여자“

population

# 소멸지역을 조사하기 위한 데이터 

population["20-39세"] = (
    population["20 - 24세"] + population["25 - 29세"] + population["30 - 34세"] + population["35 - 39세"]
)

population["65세이상"] = (
    population["65 - 69세"] + population["70 - 74세"] + population["75 - 79세"] + population["80 - 84세"] + population["85 - 89세"] + population["90 - 94세"] + population["95 - 99세"] + population["100+"]
)

population.tail()

# pivot_table 

pop = pd.pivot_table(
    data=population,
    index=["광역시도", "시도"], 
    columns=["구분"],
    values=["인구수", "20-39세", "65세이상"]
)

pop

# 소멸 비율 계산 

pop["소멸비율"] = pop["20-39세", "여자"] / (pop["65세이상", "합계"] / 2)
pop.tail()

# 소멸위기지역 컬럼 생성 

pop["소멸위기지역"] = pop["소멸비율"] < 1.0 
pop

# 소멸위기지역 조회 
pop[pop["소멸위기지역"] == True].index.get_level_values(1)

pop.reset_index(inplace=True)
pop.head()

pop.columns.get_level_values(0)[5]

tmp_columns = [
    pop.columns.get_level_values(0)[n] + pop.columns.get_level_values(1)[n]
    for n in range(0, len(pop.columns.get_level_values(0)))
]

pop.columns = tmp_columns
pop.head()
3. 지도 시각화를 위한 지역별 ID 만들기
pop.info()
pop.head()
pop["시도"].unique()
si_name = [None] * len(pop)
si_name

tmp_gu_dict = {
    "수원": ["장안구", "권선구", "팔달구", "영통구"],
    "성남": ["수정구", "중원구", "분당구"],
    "안양": ["만안구", "동안구"],
    "안산": ["상록구", "단원구"],
    "고양": ["덕양구", "일산동구", "일산서구"],
    "용인": ["처인구", "기흥구", "수지구"],
    "청주": ["상당구", "서원구", "흥덕구", "청원구"],
    "천안": ["동남구", "서북구"],
    "전주": ["완산구", "덕진구"],
    "포항": ["남구", "북구"],
    "창원": ["의창구", "성산구", "진해구", "마산합포구", "마산회원구"],
    "부천": ["오정구", "원미구", "소사구"],
}
pop.head()
pop["광역시도"].unique()
pop["시도"].unique()

• 만들고자 하는 ID의 형태
  • 서울 중구
  • 서울 서초
  • 통영
  • 남양주
  • 포항 북구
  • 인천 남동
  • 안양 만안
  • 안양 동안
  • 안산 단원

(1) 일반 시 이름과 세종시, 광역시도 일반 구 정리
pop["광역시도"]
pop[pop["광역시도"] == "세종특별자치시"]
text1 = "서울특별시"
text2 = "경산시"

text1[:2] + " " + text2[:-1]

for idx, row in pop.iterrows():
    if row["광역시도"][-3:] not in ["광역시", "특별시", "자치시"]:
        si_name[idx] = row["시도"][:-1]
    
    elif row["광역시도"] == "세종특별자치시":
        si_name[idx] = "세종"
    
    else:
        if len(row["시도"]) == 2:
            si_name[idx] = row["광역시도"][:2] + " " + row["시도"]
        else:
            si_name[idx] = row["광역시도"][:2] + " " + row["시도"][:-1]

text = "마산회원구"
text[2:-1]

(2) 행정구 
for idx, row in pop.iterrows():
    if row["광역시도"][-3:] not in ["광역시", "특별시", "자치시"]:
        for keys, values in tmp_gu_dict.items():
            if row["시도"] in values:
                if len(row["시도"]) == 2:
                    si_name[idx] = keys + " " + row["시도"]
                    
                elif row["시도"] in ["마산합포구", "마산회원구"]:
                    si_name[idx] = keys + " " + row["시도"][2:-1]
                
                else:
                    si_name[idx] = keys + " " + row["시도"][:-1]
(3) 고성군
for idx, row in pop.iterrows():
    if row["광역시도"][-3:] not in ["광역시", "특별시", "자치시"]:
        if row["시도"][:-1] == "고성" and row["광역시도"] == "강원도": 
            si_name[idx] = "고성(강원)"
        elif row["시도"][:-1] == "고성" and row["광역시도"] == "경상남도": 
            si_name[idx] = "고성(경남)“
si_name
pop["ID"] = si_name
pop
del pop["20-39세남자"]
del pop["65세이상남자"]
del pop["65세이상여자"]
pop.head()


4. 지도 그리기(카르토그램)
draw_korea_raw = pd.read_excel("../data/07_draw_korea_raw.xlsx")
draw_korea_raw

draw_korea_raw_stacked = pd.DataFrame(draw_korea_raw.stack())
draw_korea_raw_stacked

draw_korea_raw_stacked.reset_index(inplace=True)
draw_korea_raw_stacked

draw_korea_raw_stacked.rename(
    columns={
        "level_0": "y",
        "level_1": "x",
        0: "ID"
    }, inplace=True
)
draw_korea_raw_stacked

draw_korea = draw_korea_raw_stacked

BORDER_LINES = [
    [(5, 1), (5, 2), (7, 2), (7, 3), (11, 3), (11, 0)], # 인천
    [(5, 4), (5, 5), (2, 5), (2, 7), (4, 7), (4, 9), (7, 9), (7, 7), (9, 7), (9, 5), (10, 5), (10, 4), (5, 4)], # 서울
    [(1, 7), (1, 8), (3, 8), (3, 10), (10, 10), (10, 7), (12, 7), (12, 6), (11, 6), (11, 5), (12, 5), (12, 4), (11, 4), (11, 3)], # 경기도
    [(8, 10), (8, 11), (6, 11), (6, 12)], # 강원도
    [(12, 5), (13, 5), (13, 4), (14, 4), (14, 5), (15, 5), (15, 4), (16, 4), (16, 2)], # 충청북도
    [(16, 4), (17, 4), (17, 5), (16, 5), (16, 6), (19, 6), (19, 5), (20, 5), (20, 4), (21, 4), (21, 3), (19, 3), (19, 1)], # 전라북도
    [(13, 5), (13, 6), (16, 6)], 
    [(13, 5), (14, 5)], # 대전시 # 세종시
    [(21, 2), (21, 3), (22, 3), (22, 4), (24, 4), (24, 2), (21, 2)], # 광주
    [(20, 5), (21, 5), (21, 6), (23, 6)], # 전라남도
    [(10, 8), (12, 8), (12, 9), (14, 9), (14, 8), (16, 8), (16, 6)], # 충청북도
    [(14, 9), (14, 11), (14, 12), (13, 12), (13, 13)], # 경상북도
    [(15, 8), (17, 8), (17, 10), (16, 10), (16, 11), (14, 11)], # 대구
    [(17, 9), (18, 9), (18, 8), (19, 8), (19, 9), (20, 9), (20, 10), (21, 10)], # 부산
    [(16, 11), (16, 13)],
    [(27, 5), (27, 6), (25, 6)]
]

draw_korea["ID"][13].split()[1]

def plot_text_simple(draw_korea):
    for idx, row in draw_korea.iterrows():
        if len(row["ID"].split()) == 2:
            dispname = "{}\n{}".format(row["ID"].split()[0], row["ID"].split()[1])
        elif row["ID"][:2] == "고성":
            dispname = "고성"
        else:
            dispname = row["ID"]
            
        if len(dispname.splitlines()[-1]) >= 3:
            fontsize, linespacing = 9.5, 1.5
        else:
            fontsize, linespacing = 11, 1.2


        plt.annotate(
            dispname,
            (row["x"] + 0.5, row["y"] + 0.5),
            weight="bold",
            fontsize=fontsize,
            linespacing=linespacing,
            ha="center", # 수평 정렬
            va="center", # 수직 정렬 
        )

def simpleDraw(draw_korea):
    plt.figure(figsize=(8, 11))
    
    plot_text_simple(draw_korea)
    
    for path in BORDER_LINES:
        ys, xs = zip(*path)
        plt.plot(xs, ys, c="black", lw=1.5)
    
    plt.gca().invert_yaxis()
    plt.axis("off")
    plt.tight_layout()
    plt.show()

simpleDraw(draw_korea)

pop.head()

draw_korea.head()

pop.head()

draw_korea.head()

검증 작업

set(draw_korea["ID"].unique()) - set(pop["ID"].unique())
set(pop["ID"].unique()) - set(draw_korea["ID"].unique())

tmp_list = list(set(pop["ID"].unique()) - set(draw_korea["ID"].unique()))

for tmp in tmp_list:
    pop = pop.drop(pop[pop["ID"] == tmp].index)
print(set(pop["ID"].unique()) - set(draw_korea["ID"].unique()))

merge (병합)
pop = pd.merge(pop, draw_korea, how="left", on="ID")
pop.head()

그림을 그리기 위한 데이터를 계산하는 함수 
- 색상을 만들 때, 최소값을 흰색 
- blockedMap: 인구현황(pop) 
- targetData: 그리고 싶은 컬럼


def get_data_info(targetData, blockedMap):
    whitelabelmin = (
        max(blockedMap[targetData]) - min(blockedMap[targetData])
    ) * 0.25 + min(blockedMap[targetData])
    vmin = min(blockedMap[targetData])
    vmax = max(blockedMap[targetData])
    
    mapdata = blockedMap.pivot_table(index="y", columns="x", values=targetData)

    return mapdata, vmax, vmin, whitelabelmin

def get_data_info_for_zero_center(targetData, blockedMap):
    whitelabelmin = 5 
    tmp_max = max(
        [np.abs(min(blockedMap[targetData])), np.abs(max(blockedMap[targetData]))]
    )
    vmin, vmax = -tmp_max, tmp_max
    mapdata = blockedMap.pivot_table(index="y", columns="x", values=targetData)
    return mapdata, vmax, vmin, whitelabelmin

def plot_text(targetData, blockedMap, whitelabelmin):
    for idx, row in blockedMap.iterrows():
        if len(row["ID"].split()) == 2:
            dispname = "{}\n{}".format(row["ID"].split()[0], row["ID"].split()[1])
        elif row["ID"][:2] == "고성":
            dispname = "고성"
        else:
            dispname = row["ID"]
            
        if len(dispname.splitlines()[-1]) >= 3:
            fontsize, linespacing = 9.5, 1.5
        else:
            fontsize, linespacing = 11, 1.2


        annocolor = "white" if np.abs(row[targetData]) > whitelabelmin else "black"
        plt.annotate(
            dispname,
            (row["x"] + 0.5, row["y"] + 0.5),
            weight="bold",
            color=annocolor,
            fontsize=fontsize,
            linespacing=linespacing,
            ha="center", # 수평 정렬
            va="center", # 수직 정렬 
        )

def drawKorea(targetData, blockedMap, cmapname, zeroCenter=False):
    if zeroCenter:
        masked_mapdata, vmax, vmin, whitelabelmin = get_data_info_for_zero_center(targetData, blockedMap)
    
    if not zeroCenter:
        masked_mapdata, vmax, vmin, whitelabelmin = get_data_info(targetData, blockedMap)
        
    plt.figure(figsize=(8, 11))
    plt.pcolor(masked_mapdata, vmin=vmin, vmax=vmax, cmap=cmapname, edgecolor="#aaaaaa", linewidth=0.5)
    
    plot_text(targetData, blockedMap, whitelabelmin)
    
    for path in BORDER_LINES:
        ys, xs = zip(*path)
        plt.plot(xs, ys, c="black", lw=1.5)
    
    plt.gca().invert_yaxis()
    plt.axis("off")
    plt.tight_layout()
    cb = plt.colorbar(shrink=0.1, aspect=10)
    cb.set_label(targetData)
    plt.show()

pop.head()

drawKorea("인구수합계", pop, "Blues")

pop

pop["소멸위기지역"] = [1 if con else 0 for con in pop["소멸위기지역"]]
drawKorea("소멸위기지역", pop, "Reds")

pop.head()

pop["여성비"] = (pop["인구수여자"] / pop["인구수합계"] - 0.5) * 100
drawKorea("여성비", pop, "RdBu", zeroCenter=True)

pop["2030여성비"] = (pop["20-39세여자"] / pop["20-39세합계"] - 0.5) * 100
drawKorea("2030여성비", pop, "RdBu", zeroCenter=True)

import folium
import json 

pop_folium = pop.set_index("ID")
pop_folium.head()

geo_path = "../data/07_skorea_municipalities_geo_simple.json"
geo_str = json.load(open(geo_path, encoding="utf-8"))

# 인구수합계 지도 시각화

mymap = folium.Map(location=[36.2002, 127.054], zoom_start=7)
mymap.choropleth(
    geo_data=geo_str,
    data=pop_folium["인구수합계"],
    key_on="feature.id",
    columns=[pop_folium.index, pop_folium["인구수합계"]],
    fill_color="YlGnBu"
)

mymap

# 소멸위기지역 지도 시각화
mymap = folium.Map(location=[36.2002, 127.054], zoom_start=7)
mymap.choropleth(
    geo_data=geo_str,
    data=pop_folium["소멸위기지역"],
    key_on="feature.id",
    columns=[pop_folium.index, pop_folium["소멸위기지역"]],
    fill_color="PuRd"
)

mymap
# 데이터 저장 

draw_korea.to_csv("../data/07_draw_korea.csv", encoding="utf-8", sep=",")