아주 유용 1.지역별 인구 통계 분석 2. 서울 지하철 노선별 시간별 사용

as_형준 2022. 10. 25. 10:01

전국 지역별 인구 시각화¶

In [3]:

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import csv

In [4]:

f= open('age.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
f.close()

In [ ]:

In [25]:

f= open('age.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
age=[]
age_ok=[]
sinlim=[]
for row in data:
    if '송하동' in row[0]:
        for i in row[3:]:
            age.append(int(i))
    elif '안동시 옥동' in row[0]:
        for i in row[3:]:
            age_ok.append(int(i))
    elif '신도림' in row[0]:
        for i in row[3:]:
            sinlim.append(int(i))
            
print(age)
f.close()
plt.style.use('ggplot')
plt.plot(age,color='r')
plt.plot(age_ok,color='y')
plt.plot(sinlim,color='b')
plt.show()

[128, 136, 159, 171, 150, 159, 183, 179, 170, 169, 165, 196, 155, 155, 155, 163, 152, 213, 198, 213, 197, 230, 204, 205, 204, 193, 186, 175, 167, 197, 163, 195, 203, 169, 185, 229, 251, 238, 248, 216, 243, 236, 246, 305, 256, 294, 255, 267, 271, 288, 283, 254, 278, 240, 253, 258, 294, 269, 339, 284, 219, 214, 231, 172, 183, 137, 133, 142, 118, 102, 134, 85, 75, 49, 103, 73, 104, 80, 65, 71, 59, 60, 67, 56, 56, 34, 37, 27, 25, 15, 20, 7, 6, 11, 7, 5, 4, 3, 3, 0, 5]

In [19]:

f= open('age.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
age_ok=[]
for row in data:
    if '안동시 옥동' in row[0]:
        for i in row[3:]:
            age_ok.append(int(i))
print(age_ok)
f.close()

[157, 177, 205, 251, 221, 251, 309, 278, 287, 264, 265, 280, 281, 271, 270, 265, 279, 301, 324, 326, 310, 332, 311, 333, 257, 279, 264, 260, 226, 230, 234, 217, 270, 216, 233, 301, 318, 370, 375, 387, 355, 389, 358, 431, 389, 417, 442, 390, 409, 429, 411, 340, 373, 353, 340, 317, 354, 376, 388, 393, 293, 288, 304, 247, 246, 207, 207, 195, 144, 161, 147, 132, 111, 91, 96, 109, 165, 103, 99, 103, 92, 84, 81, 68, 60, 59, 45, 41, 40, 26, 19, 18, 14, 15, 6, 2, 7, 2, 1, 2, 2]

In [24]:

plt.style.use('ggplot')
plt.plot(age,color='r')
plt.plot(age_ok,color='y')
plt.show()

In [38]:

f= open('age.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
city=[]
name=input('검색하실 지역의 이름 (읍면동)단위로 검색하시길 바랍니다.>>>')
for row in data:
    if name in row[0]:
        for i in row[3:]:
            city.append(int(i))

f.close()

plt.rc('font', family='Malgun Gothic')
# range필수 !
plt.bar(range(101),city)
plt.show()

검색하실 지역의 이름 (읍면동)단위로 검색하시길 바랍니다.>>>신도림

In [3]:

f= open('age.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
city=[]
name=input('검색하실 지역의 이름 (읍면동)단위로 검색하시길 바랍니다.>>>')
for row in data:
    if name in row[0]:
        for i in row[3:]:
            city.append(int(i))

f.close()
print(city)
plt.rc('font', family='Malgun Gothic')
# range필수 !

plt.barh(range(101),city)
plt.show()

검색하실 지역의 이름 (읍면동)단위로 검색하시길 바랍니다.>>>송하동
[128, 136, 159, 171, 150, 159, 183, 179, 170, 169, 165, 196, 155, 155, 155, 163, 152, 213, 198, 213, 197, 230, 204, 205, 204, 193, 186, 175, 167, 197, 163, 195, 203, 169, 185, 229, 251, 238, 248, 216, 243, 236, 246, 305, 256, 294, 255, 267, 271, 288, 283, 254, 278, 240, 253, 258, 294, 269, 339, 284, 219, 214, 231, 172, 183, 137, 133, 142, 118, 102, 134, 85, 75, 49, 103, 73, 104, 80, 65, 71, 59, 60, 67, 56, 56, 34, 37, 27, 25, 15, 20, 7, 6, 11, 7, 5, 4, 3, 3, 0, 5]

In [47]:

f= open('gender.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
men=[]
women=[]
for row in data:
    if '신도림' in row[0]:
        for i in row[3:104]:
            men.append(-int(i))
        for i in row[106:]:
            women.append(int(i))

plt.barh(range(101),men,label='men')
plt.barh(range(101),women,label='women')
plt.legend()
plt.show()

C:\Users\82105\anaconda3\lib\site-packages\IPython\core\pylabtools.py:151: UserWarning: Glyph 8722 (\N{MINUS SIGN}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)

In [56]:

f= open('gender.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
men=[]
women=[]
name=input('검색하실 지역의 이름 (읍면동)단위로 검색하시길 바랍니다.>>>')
for row in data:
    if name in row[0]:
        for i in row[3:104]:
            men.append(-int(i))
        for i in row[106:]:
            women.append(int(i))
            
          
plt.rcParams['axes.unicode_minus']= False
plt.barh(range(101),men,label='men')
plt.barh(range(101),women,label='women')
plt.legend()
plt.show()

검색하실 지역의 이름 (읍면동)단위로 검색하시길 바랍니다.>>>송하동

In [ ]:

break를 이용해서 안동시 전체인구 출력 
break가 없으면 shape 오류 발생

In [63]:

f= open('gender.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
men=[]
women=[]
for row in data:
    if '옥동' in row[0]:
        for i in row[3:104]:
            men.append(-int(i))
        for i in row[106:]:
            women.append(int(i))
        break

plt.barh(range(101),men,label='men')
plt.barh(range(101),women,label='women')
plt.legend()
plt.show()

파이 그래프¶

무조건 시작점은 오후 3시위치에서 시작이며 반시계방향으로 데이터가 출력된다.

In [69]:

plt.rc('font', family = 'Malgun Gothic')
size = [2441, 2312, 1031, 1233]
label = ['A형','B형','AB형', 'O형']
color = ['darkmagenta', 'deeppink', 'hotpink', 'pink']
plt.axis('equal')
                #AUTOpct은 데이터퍼센트 표시   #3번째 데이터만 띄어서 표시
plt.pie(size, labels = label,autopct = '%.1f%%', explode = (0,0,0.1,0), colors = color)
plt.legend()
plt.show()

In [81]:

f= open('gender.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
ingu=[]


name=input('도시이름이나 구읍동면을 입력해주세요')
for row in data:
    if name in row[0]:
        m=0
        w=0
        for i in range(101):
            m +=int(row[i+3])
            w +=int(row[i+106])
        break
ingu.append(m)
ingu.append(w)


plt.rc('font', family = 'Malgun Gothic')
size = [2441, 2312, 1031, 1233]
label = ['men', 'women']
color = ['skyblue', 'pink']
plt.axis('equal')
                                                     #3번째 데이터만 띄어서 표시
plt.pie(ingu, labels = label,autopct = '%.1f%%', colors = color,startangle=90)
plt.legend()
plt.show()

도시이름이나 구읍동면을 입력해주세요안동시

In [ ]:

컬러 rgb표

In [71]:

from matplotlib import colors as mcolors


colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)

# Sort colors by hue, saturation, value and name.
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
                for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]

n = len(sorted_names)
ncols = 4
nrows = n // ncols

fig, ax = plt.subplots(figsize=(9, 8), dpi = 300)

# Get height and width
X, Y = fig.get_dpi() * fig.get_size_inches()
h = Y / (nrows + 1)
w = X / ncols

for i, name in enumerate(sorted_names):
    row = i % nrows
    col = i // nrows
    y = Y - (row * h) - h

    xi_line = w * (col + 0.05)
    xf_line = w * (col + 0.25)
    xi_text = w * (col + 0.3)

    ax.text(xi_text, y, name, fontsize=(10),
            horizontalalignment='left',
            verticalalignment='center')

    ax.hlines(y + h * 0.1, xi_line, xf_line,
              color=colors[name], linewidth=(6))

ax.set_xlim(0, X)
ax.set_ylim(0, Y)
ax.set_axis_off()

fig.subplots_adjust(left=0, right=1,
                    top=1, bottom=0,
                    hspace=0, wspace=0)
plt.show()

In [ ]:

지역별 인구수 선그래프 선생님버전

In [86]:

import csv
f = open('gender.csv')
data = csv.reader(f)
m = []
f = []
name = input('궁금한 동네를 입력해주세요 : ')
for row in data :
    if name in row[0] :
        for i in range(3,104) :
            m.append(int(row[i]))
            f.append(int(row[i+103]))
        break
import matplotlib.pyplot as plt
plt.plot(m, label = 'Male')
plt.plot(f, label = 'Female')
plt.legend()

궁금한 동네를 입력해주세요 : 안동시

Out[86]:

<matplotlib.legend.Legend at 0x2893755b6d0>

In [ ]:

지역별 인구수 내꺼버전

In [91]:

f= open('gender.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
m=[]
w=[]


name=input('도시이름이나 구읍동면을 입력해주세요')
for row in data:
    if name in row[0]:
        for i in range(3,104):
            m.append(int(row[i]))
            w.append(int(row[i+103]))
        break


plt.plot(m, label = 'Male')
plt.plot(w, label = 'Female')
plt.legend()
plt.show()

도시이름이나 구읍동면을 입력해주세요안동시

In [99]:

f= open('gender.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
result=[]



name=input('도시이름이나 구읍동면을 입력해주세요')
for row in data:
    if name in row[0]:
        for i in range(3,104):
            result.append(int(row[i])- int(row[i+103]))
        break




plt.bar(range(101), result)
plt.show()

도시이름이나 구읍동면을 입력해주세요서울특별시

대중교통 데이터 시각화¶

In [ ]:

알고리즘 생각
데이터 읽기
모든역 데이터 바탕으로 비율
유임승차 무임승차 비율
승차가 많은 역

In [4]:

import csv

f= open('subwayfee.csv', 'r', encoding='cp949')
data = csv.reader(f,delimiter=',')
header=next(data)
for row in data:
    for i in range(4,8):
        row[i]= int(row[i])
    print(row)
f.close()

['2019-01', '1호선', '0150', '서울역', 1516452, 1400464, 221180, 211764]
['2019-01', '1호선', '0151', '시청', 676932, 676266, 102388, 101033]
['2019-01', '1호선', '0152', '종각', 1240810, 1177643, 162410, 152062]
['2019-01', '1호선', '0153', '종로3가', 703110, 674148, 387062, 362735]
['2019-01', '1호선', '0154', '종로5가', 571824, 583955, 275447, 263687]
['2019-01', '1호선', '0155', '동대문', 308301, 342898, 134071, 138633]
['2019-01', '1호선', '0156', '신설동', 360561, 353834, 126466, 120147]
['2019-01', '1호선', '0157', '제기동', 293540, 288217, 374571, 400607]
['2019-01', '1호선', '0158', '청량리(서울시립대입구)', 463456, 482960, 352121, 357571]
['2019-01', '1호선', '0159', '동묘앞', 174683, 188711, 146034, 147551]
['2019-01', '2호선', '0201', '시청', 706097, 679503, 61058, 55387]
['2019-01', '2호선', '0202', '을지로입구', 1415414, 1414980, 120425, 111592]
['2019-01', '2호선', '0203', '을지로3가', 608979, 601323, 91668, 91016]
['2019-01', '2호선', '0204', '을지로4가', 278663, 281834, 98689, 94261]
['2019-01', '2호선', '0205', '동대문역사문화공원', 470548, 540467, 58918, 62861]
['2019-01', '2호선', '0206', '신당', 350840, 370121, 105001, 106934]
['2019-01', '2호선', '0207', '상왕십리', 370292, 352021, 70677, 71280]
['2019-01', '2호선', '0208', '왕십리(성동구청)', 525543, 439506, 61311, 52124]
['2019-01', '2호선', '0209', '한양대', 233785, 256079, 15380, 16652]
['2019-01', '2호선', '0210', '뚝섬', 474396, 505844, 62356, 63026]
['2019-01', '2호선', '0211', '성수', 767696, 836948, 109790, 111635]
['2019-01', '2호선', '0212', '건대입구', 1238362, 1367192, 96044, 100954]
['2019-01', '2호선', '0213', '구의(광진구청)', 640208, 617578, 110505, 111865]
['2019-01', '2호선', '0214', '강변(동서울터미널)', 1234132, 1210954, 143139, 137791]
['2019-01', '2호선', '0215', '잠실나루', 419874, 401622, 93395, 93825]
['2019-01', '2호선', '0216', '잠실(송파구청)', 2416810, 2311873, 209079, 198791]
['2019-01', '2호선', '0217', '잠실새내', 653006, 666914, 115042, 113965]
['2019-01', '2호선', '0218', '종합운동장', 249732, 274799, 46155, 48143]
['2019-01', '2호선', '0219', '삼성(무역센터)', 1708765, 1751738, 130382, 122279]
['2019-01', '2호선', '0220', '선릉', 1512507, 1330307, 231177, 202344]
['2019-01', '2호선', '0221', '역삼', 1381548, 1529313, 170313, 171825]
['2019-01', '2호선', '0222', '강남', 3153418, 3210437, 186486, 167666]
['2019-01', '2호선', '0223', '교대(법원.검찰청)', 946530, 1067419, 169873, 189952]
['2019-01', '2호선', '0224', '서초', 610589, 616036, 85793, 86672]
['2019-01', '2호선', '0225', '방배', 484175, 495686, 82941, 81796]
['2019-01', '2호선', '0226', '사당', 1114495, 1281460, 211281, 198971]
['2019-01', '2호선', '0227', '낙성대', 795786, 764958, 116511, 117808]
['2019-01', '2호선', '0228', '서울대입구(관악구청)', 1424677, 1391043, 211560, 201922]
['2019-01', '2호선', '0229', '봉천', 605635, 552653, 156696, 157860]
['2019-01', '2호선', '0230', '신림', 1928249, 1883865, 249174, 254427]
['2019-01', '2호선', '0231', '신대방', 741010, 699826, 129858, 131180]
['2019-01', '2호선', '0232', '구로디지털단지', 1790096, 1784632, 182537, 185035]
['2019-01', '2호선', '0233', '대림(구로구청)', 765065, 788044, 117840, 122048]
['2019-01', '2호선', '0234', '신도림', 1601042, 1580905, 215960, 204974]
['2019-01', '2호선', '0235', '문래', 533563, 541539, 86593, 85835]
['2019-01', '2호선', '0236', '영등포구청', 569540, 578864, 107339, 105413]
['2019-01', '2호선', '0237', '당산', 593074, 651170, 67769, 73323]
['2019-01', '2호선', '0238', '합정', 963554, 1037217, 73350, 73712]
['2019-01', '2호선', '0239', '홍대입구', 2351935, 2507561, 114832, 111488]
['2019-01', '2호선', '0240', '신촌', 1293198, 1324547, 146482, 143369]
['2019-01', '2호선', '0241', '이대', 476235, 498130, 56990, 57268]
['2019-01', '2호선', '0242', '아현', 249121, 237525, 65983, 65834]
['2019-01', '2호선', '0243', '충정로(경기대입구)', 283094, 302304, 52048, 51604]
['2019-01', '2호선', '0244', '용답', 62637, 62609, 17274, 17125]
['2019-01', '2호선', '0245', '신답', 38031, 42538, 10674, 11506]
['2019-01', '2호선', '0246', '신설동', 100784, 96388, 39618, 41247]
['2019-01', '2호선', '0247', '도림천', 28512, 30572, 6495, 7268]
['2019-01', '2호선', '0248', '양천구청', 164311, 174273, 45637, 46805]
['2019-01', '2호선', '0249', '신정네거리', 224687, 231648, 67361, 68036]
['2019-01', '2호선', '0250', '용두(동대문구청)', 48254, 49405, 25872, 27089]
['2019-01', '3호선', '0309', '지축', 23573, 25825, 8828, 11700]
['2019-01', '3호선', '0310', '구파발', 514949, 475587, 102695, 95809]
['2019-01', '3호선', '0311', '연신내', 998373, 946477, 275949, 268661]
['2019-01', '3호선', '0312', '불광', 420524, 457410, 151438, 166749]
['2019-01', '3호선', '0313', '녹번', 339600, 289711, 67841, 65985]
['2019-01', '3호선', '0314', '홍제', 423880, 395539, 152183, 151005]
['2019-01', '3호선', '0315', '무악재', 87734, 87462, 30692, 32301]
['2019-01', '3호선', '0316', '독립문', 162939, 169496, 75288, 74780]
['2019-01', '3호선', '0317', '경복궁(정부서울청사)', 632058, 647453, 91432, 84303]
['2019-01', '3호선', '0318', '안국', 602643, 623450, 91847, 96600]
['2019-01', '3호선', '0319', '종로3가', 285401, 386960, 66004, 74526]
['2019-01', '3호선', '0320', '을지로3가', 267923, 291747, 63164, 61895]
['2019-01', '3호선', '0321', '충무로', 55, 0, 2, 0]
['2019-01', '3호선', '0322', '동대입구', 273760, 299610, 35986, 38633]
['2019-01', '3호선', '0323', '약수', 347979, 337106, 88077, 84353]
['2019-01', '3호선', '0324', '금호', 207393, 189021, 57696, 56729]
['2019-01', '3호선', '0325', '옥수', 237529, 247315, 59129, 59822]
['2019-01', '3호선', '0326', '압구정', 900459, 993949, 150205, 148455]
['2019-01', '3호선', '0327', '신사', 969740, 1044254, 76590, 73692]
['2019-01', '3호선', '0328', '잠원', 154384, 148266, 37920, 37127]
['2019-01', '3호선', '0329', '고속터미널', 1672593, 1594038, 192170, 182793]
['2019-01', '3호선', '0330', '교대(법원.검찰청)', 375604, 249984, 73887, 45715]
['2019-01', '3호선', '0331', '남부터미널(예술의전당)', 964159, 987210, 128393, 128863]
['2019-01', '3호선', '0332', '양재(서초구청)', 1082067, 1167403, 170638, 171222]
['2019-01', '3호선', '0333', '매봉', 293172, 281731, 60062, 55114]
['2019-01', '3호선', '0334', '도곡', 145454, 148508, 28466, 24665]
['2019-01', '3호선', '0335', '대치', 328634, 313771, 63912, 61647]
['2019-01', '3호선', '0336', '학여울', 58420, 59233, 18531, 17919]
['2019-01', '3호선', '0337', '대청', 243544, 217014, 54187, 52060]
['2019-01', '3호선', '0338', '일원', 229657, 234627, 64134, 64834]
['2019-01', '3호선', '0339', '수서', 455919, 453416, 104207, 101676]
['2019-01', '3호선', '0340', '가락시장', 234180, 226833, 78795, 77067]
['2019-01', '3호선', '0341', '경찰병원', 198576, 197570, 43877, 44845]
['2019-01', '3호선', '0342', '오금', 178619, 158041, 29523, 27548]
['2019-01', '4호선', '0409', '당고개', 290085, 266616, 93657, 92102]
['2019-01', '4호선', '0410', '상계', 516628, 457332, 117224, 115582]
['2019-01', '4호선', '0411', '노원', 578020, 669142, 94962, 93019]
['2019-01', '4호선', '0412', '창동', 674935, 718930, 202889, 209219]
['2019-01', '4호선', '0413', '쌍문', 831100, 723822, 172066, 167596]
['2019-01', '4호선', '0414', '수유(강북구청)', 945148, 934275, 214439, 213726]
['2019-01', '4호선', '0415', '미아(서울사이버대학)', 382942, 349712, 122691, 121209]
['2019-01', '4호선', '0416', '미아사거리', 707702, 686868, 185575, 183967]
['2019-01', '4호선', '0417', '길음', 547211, 514959, 125391, 126604]
['2019-01', '4호선', '0418', '성신여대입구(돈암)', 609703, 581600, 93628, 93054]
['2019-01', '4호선', '0419', '한성대입구(삼선교)', 375205, 359881, 77755, 77186]
['2019-01', '4호선', '0420', '혜화', 1128434, 1169583, 139638, 137908]
['2019-01', '4호선', '0421', '동대문', 591297, 606127, 150149, 140211]
['2019-01', '4호선', '0422', '동대문역사문화공원', 604325, 545170, 63849, 57652]
['2019-01', '4호선', '0423', '충무로', 778649, 804659, 129893, 131057]
['2019-01', '4호선', '0424', '명동', 1013193, 1090078, 74354, 76387]
['2019-01', '4호선', '0425', '회현(남대문시장)', 705485, 758424, 198383, 197336]
['2019-01', '4호선', '0426', '서울역', 367040, 634814, 42165, 53241]
['2019-01', '4호선', '0427', '숙대입구(갈월)', 408452, 402270, 58246, 54196]
['2019-01', '4호선', '0428', '삼각지', 123852, 147453, 28013, 29833]
['2019-01', '4호선', '0429', '신용산', 449542, 469059, 52330, 52195]
['2019-01', '4호선', '0430', '이촌(국립중앙박물관)', 240562, 263802, 48131, 51639]
['2019-01', '4호선', '0431', '동작(현충원)', 48849, 52497, 18799, 19144]
['2019-01', '4호선', '0432', '총신대입구(이수)', 505634, 569524, 154374, 161734]
['2019-01', '4호선', '0433', '사당', 797948, 668979, 96377, 98957]
['2019-01', '4호선', '0434', '남태령', 38898, 27113, 8469, 7607]
['2019-01', '경부선', '1001', '서울역', 178223, 29980, 10325, 1612]
['2019-01', '경부선', '1002', '남영', 277702, 292630, 39574, 41277]
['2019-01', '경부선', '1003', '용산', 1117965, 1137861, 130281, 129064]
['2019-01', '경부선', '1004', '노량진', 354067, 325632, 46863, 43876]
['2019-01', '경부선', '1005', '대방', 375785, 377701, 71034, 70986]
['2019-01', '경부선', '1006', '영등포', 1227641, 1292898, 291705, 289797]
['2019-01', '경부선', '1007', '신도림', 93312, 95198, 21308, 24436]
['2019-01', '경부선', '1032', '신길', 252784, 206136, 46402, 42844]
['2019-01', '경부선', '1701', '구로', 530917, 553635, 98540, 95357]
['2019-01', '경부선', '1702', '가산디지털단지', 470248, 552752, 31014, 33827]
['2019-01', '경부선', '1703', '금천구청', 279888, 268537, 79353, 80253]
['2019-01', '경부선', '1704', '석수', 287434, 251734, 48593, 46324]
['2019-01', '경부선', '1705', '관악', 199191, 185653, 55505, 54374]
['2019-01', '경부선', '1706', '안양', 628613, 618670, 119887, 121870]
['2019-01', '경부선', '1707', '명학', 241180, 240142, 43655, 43885]
['2019-01', '경부선', '1708', '금정', 707795, 712943, 109113, 110210]
['2019-01', '경부선', '1709', '군포', 188981, 185580, 34841, 33522]
['2019-01', '경부선', '1710', '의왕', 227093, 220280, 38975, 39248]
['2019-01', '경부선', '1711', '성균관대', 409799, 397352, 54424, 54532]
['2019-01', '경부선', '1712', '화서', 206201, 198549, 42502, 42475]
['2019-01', '경부선', '1713', '수원', 1149442, 1218291, 147589, 155225]
['2019-01', '경부선', '1714', '독산', 425046, 448103, 68162, 71765]
['2019-01', '경부선', '1715', '세류', 95555, 90492, 18211, 18161]
['2019-01', '경부선', '1716', '병점', 383141, 383446, 60726, 60726]
['2019-01', '경부선', '1717', '세마', 74316, 66566, 14617, 13612]
['2019-01', '경부선', '1718', '오산대', 71667, 66774, 13425, 12941]
['2019-01', '경부선', '1719', '오산', 255002, 248128, 43309, 43344]
['2019-01', '경부선', '1720', '진위', 40682, 34644, 7636, 7285]
['2019-01', '경부선', '1721', '송탄', 117953, 116502, 24326, 23812]
['2019-01', '경부선', '1722', '서정리', 110688, 111264, 24237, 23587]
['2019-01', '경부선', '1723', '지제', 63312, 58765, 7242, 7112]
['2019-01', '경부선', '1724', '평택', 320815, 315485, 67892, 66292]
['2019-01', '경부선', '1725', '성환', 78338, 78393, 20313, 20251]
['2019-01', '경부선', '1726', '직산', 20397, 21029, 5366, 5284]
['2019-01', '경부선', '1727', '두정', 129268, 131487, 22310, 22910]
['2019-01', '경부선', '1728', '천안', 130002, 128484, 55877, 55197]
['2019-01', '경부선', '1729', '당정', 174805, 166606, 30604, 29260]
['2019-01', '경부선', '1749', '서동탄', 42902, 36644, 10134, 9282]
['2019-01', '경부선', '1750', '광명', 78196, 61098, 6431, 5470]
['2019-01', '경인선', '1801', '개봉', 567257, 567401, 160030, 166520]
['2019-01', '경인선', '1802', '오류동', 281322, 258116, 95635, 93883]
['2019-01', '경인선', '1803', '역곡', 750795, 725247, 152804, 154853]
['2019-01', '경인선', '1804', '부천', 983623, 994438, 218519, 220820]
['2019-01', '경인선', '1805', '송내', 848291, 830885, 122961, 121409]
['2019-01', '경인선', '1806', '부평', 1013723, 1108119, 149188, 152451]
['2019-01', '경인선', '1807', '백운', 192357, 185035, 65110, 66354]
['2019-01', '경인선', '1808', '동암', 444540, 440339, 102095, 102926]
['2019-01', '경인선', '1809', '주안', 592278, 536706, 96798, 88549]
['2019-01', '경인선', '1810', '제물포', 253761, 235858, 60147, 60088]
['2019-01', '경인선', '1811', '동인천', 386868, 367520, 125985, 121657]
['2019-01', '경인선', '1812', '인천', 83933, 62133, 31716, 29348]
['2019-01', '경인선', '1813', '구일', 242468, 271261, 37355, 36989]
['2019-01', '경인선', '1814', '소사', 255661, 236488, 72231, 71031]
['2019-01', '경인선', '1815', '부개', 222869, 205618, 65846, 64691]
['2019-01', '경인선', '1816', '간석', 152262, 143703, 45549, 44132]
['2019-01', '경인선', '1817', '도원', 64486, 64863, 35274, 36192]
['2019-01', '경인선', '1821', '온수(성공회대입구)', 157853, 154281, 47094, 45877]
['2019-01', '경인선', '1822', '중동', 231711, 212763, 69190, 68129]
['2019-01', '경인선', '1823', '도화', 87519, 76548, 37883, 37025]
['2019-01', '경원선', '1008', '이촌(국립중앙박물관)', 102572, 76092, 29477, 23974]
['2019-01', '경원선', '1009', '서빙고', 43668, 43508, 15757, 15152]
['2019-01', '경원선', '1010', '한남', 98040, 96128, 34754, 32168]
['2019-01', '경원선', '1011', '옥수', 1445, 788, 121, 91]
['2019-01', '경원선', '1012', '응봉', 38351, 31967, 17072, 16050]
['2019-01', '경원선', '1013', '왕십리(성동구청)', 422894, 480774, 62879, 67011]
['2019-01', '경원선', '1014', '청량리(서울시립대입구)', 367681, 399974, 158007, 165310]
['2019-01', '경원선', '1016', '외대앞', 246211, 233082, 52337, 51437]
['2019-01', '경원선', '1017', '신이문', 215287, 187164, 65998, 64898]
['2019-01', '경원선', '1018', '석계', 229136, 257645, 64959, 69124]
['2019-01', '경원선', '1019', '광운대', 205537, 192338, 53320, 53257]
['2019-01', '경원선', '1020', '월계', 111870, 100025, 56640, 53811]
['2019-01', '경원선', '1021', '녹천', 109676, 88711, 39218, 35962]
['2019-01', '경원선', '1022', '창동', 19, 0, 0, 0]
['2019-01', '경원선', '1901', '방학', 197239, 188478, 109242, 107190]
['2019-01', '경원선', '1902', '도봉', 122533, 114295, 69522, 69718]
['2019-01', '경원선', '1903', '도봉산', 163088, 83849, 92078, 71779]
['2019-01', '경원선', '1904', '망월사', 167313, 166632, 70977, 73305]
['2019-01', '경원선', '1905', '회룡', 249439, 235179, 144082, 145171]
['2019-01', '경원선', '1906', '의정부', 515625, 543580, 123588, 125444]
['2019-01', '경원선', '1907', '가능', 152256, 140676, 58533, 56804]
['2019-01', '경원선', '1908', '녹양', 113568, 103216, 34658, 32717]
['2019-01', '경원선', '1909', '양주', 209812, 185253, 55754, 53508]
['2019-01', '경원선', '1910', '덕계', 40320, 36405, 15483, 14960]
['2019-01', '경원선', '1911', '덕정', 148065, 142591, 34959, 35462]
['2019-01', '경원선', '1912', '지행', 166269, 161449, 46103, 45369]
['2019-01', '경원선', '1913', '동두천중앙', 70022, 65811, 43089, 43205]
['2019-01', '경원선', '1914', '보산', 38762, 36526, 13880, 12911]
['2019-01', '경원선', '1915', '동두천', 43099, 50274, 20108, 25594]
['2019-01', '경원선', '1916', '소요산', 48337, 37020, 62372, 54630]
['2019-01', '안산선', '1751', '산본', 448304, 484455, 106437, 108079]
['2019-01', '안산선', '1752', '대야미', 111880, 96430, 24935, 23395]
['2019-01', '안산선', '1753', '반월', 113530, 105344, 28230, 27928]
['2019-01', '안산선', '1754', '상록수', 510513, 495290, 84450, 85664]
['2019-01', '안산선', '1755', '한대앞', 216583, 210888, 37547, 37770]
['2019-01', '안산선', '1756', '중앙', 480226, 511117, 57930, 56256]
['2019-01', '안산선', '1757', '고잔', 225464, 216806, 32769, 31721]
['2019-01', '안산선', '1758', '초지', 88817, 86237, 12651, 13039]
['2019-01', '안산선', '1759', '안산', 410566, 371622, 38369, 35947]
['2019-01', '안산선', '1760', '신길온천', 42401, 37258, 8165, 7822]
['2019-01', '안산선', '1761', '정왕', 211750, 228532, 23897, 25505]
['2019-01', '안산선', '1762', '오이도', 256207, 244259, 36209, 35361]
['2019-01', '안산선', '1763', '수리산', 98889, 73299, 32076, 29771]
['2019-01', '과천선', '1450', '선바위', 249683, 195677, 31393, 30069]
['2019-01', '과천선', '1451', '경마공원', 135284, 138630, 60965, 60991]
['2019-01', '과천선', '1452', '대공원', 59162, 62171, 31234, 33183]
['2019-01', '과천선', '1453', '과천', 120456, 103727, 34320, 32896]
['2019-01', '과천선', '1454', '정부과천청사', 308487, 301902, 66282, 63635]
['2019-01', '과천선', '1455', '인덕원', 680720, 676281, 106311, 110092]
['2019-01', '과천선', '1456', '평촌', 464834, 457435, 66101, 66163]
['2019-01', '과천선', '1457', '범계', 790403, 769393, 109095, 106063]
['2019-01', '분당선', '1023', '선릉', 278427, 500378, 34768, 51679]
['2019-01', '분당선', '1024', '한티', 396374, 413225, 69706, 70771]
['2019-01', '분당선', '1025', '도곡', 155781, 155154, 42063, 42070]
['2019-01', '분당선', '1026', '구룡', 30857, 30299, 12434, 12239]
['2019-01', '분당선', '1027', '개포동', 76658, 78688, 21538, 22152]
['2019-01', '분당선', '1028', '대모산입구', 68346, 61760, 29234, 28706]
['2019-01', '분당선', '1030', '수서', 379294, 382530, 65134, 66861]
['2019-01', '분당선', '1031', '복정', 25, 0, 0, 0]
['2019-01', '분당선', '1846', '수원', 249890, 169909, 27800, 17215]
['2019-01', '분당선', '1847', '서울숲', 207076, 207131, 22533, 21791]
['2019-01', '분당선', '1848', '압구정로데오', 451755, 516671, 42330, 41182]
['2019-01', '분당선', '1849', '강남구청', 241666, 281747, 41928, 45124]
['2019-01', '분당선', '1850', '선정릉', 203804, 224793, 32804, 32257]
['2019-01', '분당선', '1851', '가천대', 186832, 216023, 27334, 28811]
['2019-01', '분당선', '1852', '태평', 352629, 338209, 91732, 95998]
['2019-01', '분당선', '1853', '모란', 540743, 531985, 169136, 185734]
['2019-01', '분당선', '1854', '야탑', 730803, 769807, 172832, 170673]
['2019-01', '분당선', '1855', '서현', 759497, 773561, 137206, 134207]
['2019-01', '분당선', '1856', '수내', 364964, 360114, 82906, 82092]
['2019-01', '분당선', '1857', '정자', 451669, 484328, 76882, 77577]
['2019-01', '분당선', '1858', '미금', 422530, 446971, 113374, 122794]
['2019-01', '분당선', '1859', '오리', 319097, 270314, 84995, 72171]
['2019-01', '분당선', '1860', '이매', 141591, 135340, 42041, 41000]
['2019-01', '분당선', '1861', '보정', 57611, 45834, 24755, 23232]
['2019-01', '분당선', '1862', '죽전', 377195, 428482, 80001, 88064]
['2019-01', '분당선', '1863', '구성', 111594, 112048, 38488, 38862]
['2019-01', '분당선', '1864', '신갈', 115462, 110544, 34087, 33729]
['2019-01', '분당선', '1865', '기흥', 259142, 246816, 42350, 41874]
['2019-01', '분당선', '1866', '상갈', 141070, 132410, 33606, 31461]
['2019-01', '분당선', '1867', '청명', 114105, 104334, 18842, 19140]
['2019-01', '분당선', '1868', '영통', 254011, 260149, 31617, 31676]
['2019-01', '분당선', '1869', '망포', 390803, 361759, 34846, 33268]
['2019-01', '분당선', '1870', '매탄권선', 122476, 114975, 16517, 15976]
['2019-01', '분당선', '1871', '수원시청', 339525, 385826, 38330, 39329]
['2019-01', '분당선', '1872', '매교', 54412, 53571, 17447, 17541]
['2019-01', '일산선', '1948', '원흥', 222464, 197811, 44512, 42321]
['2019-01', '일산선', '1949', '지축', 2, 0, 0, 0]
['2019-01', '일산선', '1950', '삼송', 407823, 384125, 87401, 86745]
['2019-01', '일산선', '1951', '원당', 275272, 260445, 82585, 82374]
['2019-01', '일산선', '1952', '화정', 462731, 474664, 130390, 129775]
['2019-01', '일산선', '1953', '대곡', 40040, 28528, 9210, 8295]
['2019-01', '일산선', '1954', '백석', 277088, 277139, 80740, 78852]
['2019-01', '일산선', '1955', '마두', 179985, 186549, 77167, 79437]
['2019-01', '일산선', '1956', '정발산', 208869, 225720, 56527, 52936]
['2019-01', '일산선', '1957', '주엽', 200870, 202482, 98914, 103338]
['2019-01', '일산선', '1958', '대화', 352572, 271133, 89650, 80896]
['2019-01', '중앙선', '1015', '회기', 684067, 666710, 129129, 128734]
['2019-01', '중앙선', '1201', '중랑', 122101, 115472, 51649, 50612]
['2019-01', '중앙선', '1202', '상봉(시외버스터미널)', 143000, 137073, 39237, 36867]
['2019-01', '중앙선', '1203', '망우', 172521, 167062, 69556, 70878]
['2019-01', '중앙선', '1204', '양원', 28772, 29685, 15782, 15786]
['2019-01', '중앙선', '1205', '구리', 347058, 357649, 87719, 89160]
['2019-01', '중앙선', '1206', '도농', 216211, 187739, 58716, 55895]
['2019-01', '중앙선', '1207', '양정', 18096, 14747, 6967, 5937]
['2019-01', '중앙선', '1208', '덕소', 139275, 135740, 50107, 50007]
['2019-01', '중앙선', '1209', '도심', 58817, 44680, 20003, 18908]
['2019-01', '중앙선', '1210', '팔당', 19184, 19206, 6176, 5803]
['2019-01', '중앙선', '1211', '운길산', 14701, 13007, 5812, 5749]
['2019-01', '중앙선', '1212', '양수', 38629, 38962, 16598, 16754]
['2019-01', '중앙선', '1213', '신원', 3187, 2825, 2438, 2179]
['2019-01', '중앙선', '1214', '국수', 14325, 13917, 8576, 8544]
['2019-01', '중앙선', '1215', '아신', 12518, 12536, 5118, 5017]
['2019-01', '중앙선', '1216', '오빈', 5489, 5252, 3794, 3829]
['2019-01', '중앙선', '1217', '양평', 68513, 69220, 32615, 32589]
['2019-01', '중앙선', '1218', '원덕', 6045, 5796, 5061, 4933]
['2019-01', '중앙선', '1219', '용문', 36259, 36316, 34572, 34257]
['2019-01', '중앙선', '1220', '지평', 615, 566, 501, 479]
['2019-01', '장항선', '1401', '봉명', 25197, 24703, 13835, 13382]
['2019-01', '장항선', '1402', '쌍용(나사렛대)', 48606, 44773, 14666, 13807]
['2019-01', '장항선', '1403', '아산', 73834, 65751, 10005, 9277]
['2019-01', '장항선', '1405', '배방', 22947, 21645, 8139, 8021]
['2019-01', '장항선', '1407', '온양온천', 84623, 85690, 73363, 73626]
['2019-01', '장항선', '1408', '신창(순천향대)', 27393, 26879, 7314, 6964]
['2019-01', '경의선', '1251', '서울역', 94475, 118708, 12831, 16661]
['2019-01', '경의선', '1252', '신촌', 21246, 28103, 3191, 3903]
['2019-01', '경의선', '1261', '효창공원앞', 64044, 69279, 13047, 14475]
['2019-01', '경의선', '1262', '공덕', 97249, 79394, 12603, 11922]
['2019-01', '경의선', '1263', '서강대', 54116, 53339, 9629, 8898]
['2019-01', '경의선', '1264', '홍대입구', 51771, 64757, 5906, 6468]
['2019-01', '경의선', '1265', '가좌', 109237, 97211, 47219, 47573]
['2019-01', '경의선', '1266', '디지털미디어시티', 51241, 48032, 8623, 8640]
['2019-01', '경의선', '1267', '수색', 53120, 54293, 15180, 15169]
['2019-01', '경의선', '1268', '화전', 42874, 39439, 8114, 8020]
['2019-01', '경의선', '1269', '강매', 68817, 55740, 16219, 15458]
['2019-01', '경의선', '1270', '행신', 199484, 191845, 35522, 35886]
['2019-01', '경의선', '1271', '능곡', 82285, 73147, 28701, 27790]
['2019-01', '경의선', '1272', '곡산', 11758, 11719, 4209, 4073]
['2019-01', '경의선', '1273', '백마', 171339, 157674, 31842, 31126]
['2019-01', '경의선', '1274', '풍산', 131192, 119253, 30873, 30730]
['2019-01', '경의선', '1275', '일산', 218799, 212721, 56204, 56786]
['2019-01', '경의선', '1276', '탄현', 194496, 187025, 36597, 36267]
['2019-01', '경의선', '1277', '야당', 123062, 115251, 26760, 26021]
['2019-01', '경의선', '1278', '운정', 141244, 130069, 32911, 31481]
['2019-01', '경의선', '1279', '금릉', 114351, 112078, 23783, 23617]
['2019-01', '경의선', '1280', '금촌', 169243, 166186, 44895, 45722]
['2019-01', '경의선', '1282', '월롱', 61746, 55813, 7194, 8019]
['2019-01', '경의선', '1283', '파주', 29543, 20352, 7325, 5784]
['2019-01', '경의선', '1284', '문산', 138756, 135982, 42569, 42177]
['2019-01', '경의선', '1291', '서울역', 11, 0, 0, 0]
['2019-01', '경의선', '1293', '홍대입구', 2, 0, 0, 0]
['2019-01', '경의선', '1295', '김포공항', 10, 0, 0, 0]
['2019-01', '경의선', '1296', '계양', 7, 0, 0, 0]
['2019-01', '경의선', '1297', '검암', 5, 0, 0, 0]
['2019-01', '경춘선', '1311', '신내', 13900, 14886, 6998, 6745]
['2019-01', '경춘선', '1312', '갈매', 63226, 54339, 16415, 15231]
['2019-01', '경춘선', '1313', '별내', 56467, 55160, 17701, 16543]
['2019-01', '경춘선', '1314', '퇴계원', 80984, 73242, 25996, 24471]
['2019-01', '경춘선', '1315', '사릉', 82111, 77969, 18861, 18487]
['2019-01', '경춘선', '1316', '금곡', 29582, 28461, 15558, 14369]
['2019-01', '경춘선', '1317', '평내호평', 131330, 128279, 41797, 39593]
['2019-01', '경춘선', '1318', '천마산', 29047, 24827, 16520, 16454]
['2019-01', '경춘선', '1319', '마석', 76587, 76036, 32473, 32259]
['2019-01', '경춘선', '1320', '대성리', 13377, 12880, 4473, 4005]
['2019-01', '경춘선', '1321', '청평', 45320, 41255, 15084, 14790]
['2019-01', '경춘선', '1322', '상천', 5205, 4423, 3812, 3739]
['2019-01', '경춘선', '1323', '가평', 43342, 45953, 12227, 11760]
['2019-01', '경춘선', '1324', '굴봉산', 3123, 3071, 1501, 1534]
['2019-01', '경춘선', '1325', '백양리', 8614, 8259, 720, 766]
['2019-01', '경춘선', '1326', '강촌', 9716, 8853, 3707, 3635]
['2019-01', '경춘선', '1327', '김유정', 7769, 8199, 2731, 2742]
['2019-01', '경춘선', '1328', '남춘천', 58343, 60051, 24195, 25346]
['2019-01', '경춘선', '1329', '춘천', 33885, 29626, 17392, 14323]
['2019-01', '수인선', '1878', '달월', 1038, 747, 865, 431]
['2019-01', '수인선', '1879', '월곶', 59105, 55042, 13378, 13156]
['2019-01', '수인선', '1880', '소래포구', 97856, 90366, 38607, 37484]
['2019-01', '수인선', '1881', '인천논현', 149882, 156861, 34100, 33963]
['2019-01', '수인선', '1882', '호구포', 90870, 90716, 18151, 17957]
['2019-01', '수인선', '1883', '남동인더스파크', 54317, 60283, 5948, 5811]
['2019-01', '수인선', '1884', '원인재', 109977, 111720, 27923, 28306]
['2019-01', '수인선', '1885', '연수', 126045, 126660, 29483, 29338]
['2019-01', '수인선', '1886', '송도', 64785, 59378, 22506, 22191]
['2019-01', '수인선', '1888', '인하대', 134223, 125177, 23818, 22629]
['2019-01', '수인선', '1889', '숭의', 82885, 76089, 28383, 27531]
['2019-01', '수인선', '1890', '신포', 43861, 45326, 12921, 13331]
['2019-01', '수인선', '1891', '인천', 20868, 40378, 11458, 14049]
['2019-01', '경강선', '1501', '판교', 107396, 108340, 12114, 10040]
['2019-01', '경강선', '1502', '이매', 28715, 23087, 6985, 5585]
['2019-01', '경강선', '1503', '삼동', 53152, 44616, 9321, 9192]
['2019-01', '경강선', '1504', '경기광주', 176154, 164708, 25708, 26256]
['2019-01', '경강선', '1505', '초월', 66411, 62995, 11406, 11479]
['2019-01', '경강선', '1506', '곤지암', 49314, 47245, 11014, 10654]
['2019-01', '경강선', '1507', '신둔도예촌', 27790, 25181, 5470, 5173]
['2019-01', '경강선', '1508', '이천', 64628, 67915, 12433, 13231]
['2019-01', '경강선', '1509', '부발', 45186, 42466, 6157, 5974]
['2019-01', '경강선', '1510', '세종대왕릉', 5423, 5391, 2781, 2686]
['2019-01', '경강선', '1511', '여주', 43963, 42968, 13529, 13267]
['2019-01', '5호선', '2511', '방화', 171979, 160910, 48362, 46312]
['2019-01', '5호선', '2512', '개화산', 147874, 139989, 35495, 33823]
['2019-01', '5호선', '2513', '김포공항', 216911, 182963, 30949, 30878]
['2019-01', '5호선', '2514', '송정', 216201, 205432, 60599, 60431]
['2019-01', '5호선', '2515', '마곡', 176763, 159755, 26922, 25724]
['2019-01', '5호선', '2516', '발산', 448838, 457389, 72839, 70273]
['2019-01', '5호선', '2517', '우장산', 380499, 373682, 87503, 90995]
['2019-01', '5호선', '2518', '화곡', 764917, 704486, 146048, 140077]
['2019-01', '5호선', '2519', '까치산', 769021, 724296, 158304, 159488]
['2019-01', '5호선', '2520', '신정(은행정)', 373681, 290837, 61645, 60011]
['2019-01', '5호선', '2521', '목동', 468654, 506184, 88507, 89947]
['2019-01', '5호선', '2522', '오목교(목동운동장앞)', 666082, 722651, 86962, 86033]
['2019-01', '5호선', '2523', '양평', 177617, 176162, 28711, 28330]
['2019-01', '5호선', '2524', '영등포구청', 88181, 92186, 20672, 20358]
['2019-01', '5호선', '2525', '영등포시장', 221814, 238594, 81767, 83945]
['2019-01', '5호선', '2526', '신길', 89495, 103940, 11733, 12869]
['2019-01', '5호선', '2527', '여의도', 683494, 729474, 52715, 51298]
['2019-01', '5호선', '2528', '여의나루', 193422, 203267, 24966, 23992]
['2019-01', '5호선', '2529', '마포', 372675, 386467, 65600, 65229]
['2019-01', '5호선', '2530', '공덕', 378952, 409930, 46749, 44068]
['2019-01', '5호선', '2531', '애오개', 179895, 183018, 40186, 39053]
['2019-01', '5호선', '2532', '충정로(경기대입구)', 117489, 117469, 15945, 16796]
['2019-01', '5호선', '2533', '서대문', 449372, 467665, 74055, 71241]
['2019-01', '5호선', '2534', '광화문(세종문화회관)', 1022109, 1062447, 87987, 81909]
['2019-01', '5호선', '2535', '종로3가', 441289, 409078, 122897, 128616]
['2019-01', '5호선', '2536', '을지로4가', 113913, 131046, 45925, 46569]
['2019-01', '5호선', '2537', '동대문역사문화공원', 77768, 90305, 8975, 9733]
['2019-01', '5호선', '2538', '청구', 97714, 92167, 28002, 21303]
['2019-01', '5호선', '2539', '신금호', 157349, 154501, 38695, 42963]
['2019-01', '5호선', '2540', '행당', 212258, 185808, 49484, 45339]
['2019-01', '5호선', '2541', '왕십리(성동구청)', 131362, 157349, 27177, 26204]
['2019-01', '5호선', '2542', '마장', 143267, 134912, 49634, 48537]
['2019-01', '5호선', '2543', '답십리', 413270, 373975, 86799, 84604]
['2019-01', '5호선', '2544', '장한평', 469205, 465274, 97119, 98163]
['2019-01', '5호선', '2545', '군자(능동)', 295969, 330997, 40193, 43687]
['2019-01', '5호선', '2546', '아차산(어린이대공원후문)', 346110, 335278, 95520, 92686]
['2019-01', '5호선', '2547', '광나루(장신대)', 366611, 328766, 68102, 66033]
['2019-01', '5호선', '2548', '천호(풍납토성)', 484232, 496590, 140935, 149958]
['2019-01', '5호선', '2549', '강동', 502069, 433173, 104126, 94346]
['2019-01', '5호선', '2550', '길동', 165882, 175877, 66177, 67974]
['2019-01', '5호선', '2551', '굽은다리(강동구민회관앞)', 233202, 221182, 66208, 61275]
['2019-01', '5호선', '2552', '명일', 186301, 194472, 60969, 63838]
['2019-01', '5호선', '2553', '고덕', 221638, 215230, 65089, 63403]
['2019-01', '5호선', '2554', '상일동', 318982, 262373, 65253, 61805]
['2019-01', '5호선', '2555', '둔촌동', 209380, 215358, 61708, 63195]
['2019-01', '5호선', '2556', '올림픽공원(한국체대)', 111922, 104819, 30864, 28555]
['2019-01', '5호선', '2557', '방이', 185340, 190186, 46436, 46038]
['2019-01', '5호선', '2558', '오금', 90009, 89326, 18685, 18267]
['2019-01', '5호선', '2559', '개롱', 161324, 165432, 46963, 47841]
['2019-01', '5호선', '2560', '거여', 170625, 158520, 56371, 55024]
['2019-01', '5호선', '2561', '마천', 109860, 107635, 49789, 52202]
['2019-01', '6호선', '2611', '응암', 432574, 426356, 116207, 115202]
['2019-01', '6호선', '2612', '역촌', 86735, 107201, 36031, 34774]
['2019-01', '6호선', '2613', '불광', 123414, 123733, 42571, 34561]
['2019-01', '6호선', '2614', '독바위', 67135, 68946, 21527, 25819]
['2019-01', '6호선', '2615', '연신내', 25, 0, 2, 0]
['2019-01', '6호선', '2616', '구산', 183515, 124393, 55766, 54438]
['2019-01', '6호선', '2617', '새절(신사)', 309167, 290669, 87089, 88281]
['2019-01', '6호선', '2618', '증산(명지대앞)', 242882, 227254, 71063, 71069]
['2019-01', '6호선', '2619', '디지털미디어시티', 348858, 385939, 53858, 53860]
['2019-01', '6호선', '2620', '월드컵경기장(성산)', 111535, 120983, 46349, 46460]
['2019-01', '6호선', '2621', '마포구청', 363189, 322426, 82469, 75604]
['2019-01', '6호선', '2622', '망원', 353805, 385868, 85752, 90169]
['2019-01', '6호선', '2623', '합정', 436483, 386049, 42570, 37588]
['2019-01', '6호선', '2624', '상수', 291531, 335798, 23172, 23503]
['2019-01', '6호선', '2625', '광흥창(서강)', 232495, 225081, 57776, 56950]
['2019-01', '6호선', '2626', '대흥(서강대앞)', 209511, 220277, 48004, 47678]
['2019-01', '6호선', '2627', '공덕', 489399, 482818, 76137, 71833]
['2019-01', '6호선', '2628', '효창공원앞', 175640, 160486, 40637, 38227]
['2019-01', '6호선', '2629', '삼각지', 184221, 152208, 18108, 14570]
['2019-01', '6호선', '2630', '녹사평(용산구청)', 157383, 165381, 19494, 18782]
['2019-01', '6호선', '2631', '이태원', 448763, 498553, 34989, 36760]
['2019-01', '6호선', '2632', '한강진', 243064, 281963, 15723, 15679]
['2019-01', '6호선', '2633', '버티고개', 57730, 51894, 15137, 17314]
['2019-01', '6호선', '2634', '약수', 93236, 95145, 25775, 24240]
['2019-01', '6호선', '2635', '청구', 94594, 98991, 19175, 24116]
['2019-01', '6호선', '2636', '신당', 232009, 238760, 41243, 39818]
['2019-01', '6호선', '2637', '동묘앞', 214228, 225214, 70909, 64736]
['2019-01', '6호선', '2638', '창신', 89098, 69059, 18861, 17698]
['2019-01', '6호선', '2639', '보문', 194680, 175465, 33715, 32423]
['2019-01', '6호선', '2640', '안암(고대병원앞)', 317118, 317938, 37134, 35107]
['2019-01', '6호선', '2641', '고려대(종암)', 232126, 221839, 35457, 35372]
['2019-01', '6호선', '2642', '월곡(동덕여대)', 293000, 280282, 81768, 80883]
['2019-01', '6호선', '2643', '상월곡(한국과학기술연구원)', 146489, 128535, 42556, 41455]
['2019-01', '6호선', '2644', '돌곶이', 193137, 179733, 59198, 59109]
['2019-01', '6호선', '2645', '석계', 315137, 318909, 78340, 79194]
['2019-01', '6호선', '2646', '태릉입구', 136700, 154325, 29525, 30899]
['2019-01', '6호선', '2647', '화랑대(서울여대입구)', 322367, 242084, 47593, 42333]
['2019-01', '6호선', '2648', '봉화산(서울의료원)', 250098, 223017, 54586, 52951]
['2019-01', '7호선', '2711', '장암', 61254, 21459, 7103, 4372]
['2019-01', '7호선', '2712', '도봉산', 229794, 268815, 38236, 55905]
['2019-01', '7호선', '2713', '수락산', 324221, 313250, 88802, 88833]
['2019-01', '7호선', '2714', '마들', 287854, 264283, 79579, 79420]
['2019-01', '7호선', '2715', '노원', 536292, 575598, 122516, 121965]
['2019-01', '7호선', '2716', '중계', 378203, 341527, 95196, 92966]
['2019-01', '7호선', '2717', '하계', 477632, 447057, 109895, 104934]
['2019-01', '7호선', '2718', '공릉(서울과학기술대)', 304216, 303167, 69246, 69926]
['2019-01', '7호선', '2719', '태릉입구', 213689, 195986, 40450, 38134]
['2019-01', '7호선', '2720', '먹골', 267295, 257794, 64030, 64624]
['2019-01', '7호선', '2721', '중화', 288775, 276940, 64937, 64815]
['2019-01', '7호선', '2722', '상봉(시외버스터미널)', 461901, 441190, 73224, 70184]
['2019-01', '7호선', '2723', '면목', 380729, 362795, 90652, 92994]
['2019-01', '7호선', '2724', '사가정', 391640, 387249, 98172, 97058]
['2019-01', '7호선', '2725', '용마산', 156543, 142057, 35613, 38290]
['2019-01', '7호선', '2726', '중곡', 247484, 224647, 62753, 63505]
['2019-01', '7호선', '2727', '군자(능동)', 379876, 283621, 82273, 69241]
['2019-01', '7호선', '2728', '어린이대공원(세종대)', 341050, 332765, 40738, 40259]
['2019-01', '7호선', '2729', '건대입구', 429463, 462509, 59256, 54166]
['2019-01', '7호선', '2730', '뚝섬유원지', 183531, 175221, 37416, 37379]
['2019-01', '7호선', '2731', '청담', 532995, 623876, 75498, 73092]
['2019-01', '7호선', '2732', '강남구청', 414491, 464248, 49264, 49362]
['2019-01', '7호선', '2733', '학동', 605493, 669069, 60736, 57602]
['2019-01', '7호선', '2734', '논현', 551090, 612054, 53375, 53425]
['2019-01', '7호선', '2735', '반포', 161316, 157023, 34431, 32417]
['2019-01', '7호선', '2736', '고속터미널', 570975, 468885, 82867, 75227]
['2019-01', '7호선', '2737', '내방', 363822, 377367, 67797, 66878]
['2019-01', '7호선', '2738', '이수', 439109, 431646, 82224, 78703]
['2019-01', '7호선', '2739', '남성', 286612, 262736, 73598, 73401]
['2019-01', '7호선', '2740', '숭실대입구(살피재)', 350189, 344849, 62572, 64159]
['2019-01', '7호선', '2741', '상도', 271885, 256237, 59009, 56522]
['2019-01', '7호선', '2742', '장승배기', 264030, 254138, 76345, 76811]
['2019-01', '7호선', '2743', '신대방삼거리', 421756, 399763, 98088, 99238]
['2019-01', '7호선', '2744', '보라매', 242722, 243947, 47051, 46305]
['2019-01', '7호선', '2745', '신풍', 283897, 265440, 67385, 67472]
['2019-01', '7호선', '2746', '대림(구로구청)', 290072, 295419, 38373, 37061]
['2019-01', '7호선', '2747', '남구로', 438822, 466546, 62909, 63384]
['2019-01', '7호선', '2748', '가산디지털단지', 1200693, 1191942, 82182, 77361]
['2019-01', '7호선', '2749', '철산', 629391, 649382, 101508, 101762]
['2019-01', '7호선', '2750', '광명사거리', 656538, 636941, 156569, 160065]
['2019-01', '7호선', '2751', '천왕', 224928, 204043, 49186, 48686]
['2019-01', '7호선', '2752', '온수(성공회대입구)', 269728, 253933, 37163, 36681]
['2019-01', '7호선', '2753', '까치울', 195618, 182252, 24974, 25054]
['2019-01', '7호선', '2754', '부천종합운동장', 130012, 106501, 17234, 16660]
['2019-01', '7호선', '2755', '춘의', 250016, 244063, 30632, 30195]
['2019-01', '7호선', '2756', '신중동', 399683, 395461, 57421, 55130]
['2019-01', '7호선', '2757', '부천시청', 341611, 343460, 51722, 49756]
['2019-01', '7호선', '2758', '상동', 401090, 417332, 51825, 51965]
['2019-01', '7호선', '2759', '삼산체육관', 135739, 119472, 26373, 25125]
['2019-01', '7호선', '2760', '굴포천', 249917, 260776, 36430, 37145]
['2019-01', '7호선', '2761', '부평구청', 330328, 262246, 31027, 27512]
['2019-01', '8호선', '2811', '암사', 478470, 414407, 99780, 98455]
['2019-01', '8호선', '2812', '천호(풍납토성)', 482110, 560223, 94403, 86933]
['2019-01', '8호선', '2813', '강동구청', 264480, 269595, 66036, 66708]
['2019-01', '8호선', '2814', '몽촌토성(평화의문)', 153437, 163804, 38289, 38408]
['2019-01', '8호선', '2815', '잠실(송파구청)', 413415, 486278, 71595, 68215]
['2019-01', '8호선', '2816', '석촌', 198692, 236680, 54118, 58706]
['2019-01', '8호선', '2817', '송파', 144085, 143856, 43655, 43150]
['2019-01', '8호선', '2818', '가락시장', 188435, 228265, 63539, 65840]
['2019-01', '8호선', '2819', '문정', 526918, 547695, 55650, 55591]
['2019-01', '8호선', '2820', '장지', 505065, 485384, 74945, 71327]
['2019-01', '8호선', '2821', '복정', 292774, 238941, 48071, 44076]
['2019-01', '8호선', '2822', '산성', 136393, 128901, 33395, 35788]
['2019-01', '8호선', '2823', '남한산성입구(성남법원.검찰청)', 336308, 295457, 77979, 74953]
['2019-01', '8호선', '2824', '단대오거리', 262050, 253423, 69327, 66722]
['2019-01', '8호선', '2825', '신흥', 102217, 110447, 44236, 46228]
['2019-01', '8호선', '2826', '수진', 121272, 109186, 50492, 49387]
['2019-01', '8호선', '2827', '모란', 92614, 72139, 54120, 39575]
['2019-01', '9호선', '4101', '개화', 169271, 110797, 35930, 27430]
['2019-01', '9호선', '4102', '김포공항', 268159, 340576, 21012, 22720]
['2019-01', '9호선', '4103', '공항시장', 65907, 75438, 21652, 24306]
['2019-01', '9호선', '4104', '신방화', 178187, 173429, 43282, 43322]
['2019-01', '9호선', '4105', '마곡나루', 261506, 257586, 22246, 22293]
['2019-01', '9호선', '4106', '양천향교', 216273, 220728, 42628, 41972]
['2019-01', '9호선', '4107', '가양', 506045, 487879, 125108, 121141]
['2019-01', '9호선', '4108', '증미', 172552, 175833, 42953, 43663]
['2019-01', '9호선', '4109', '등촌', 235166, 236756, 80121, 80685]
['2019-01', '9호선', '4110', '염창', 442095, 426641, 66057, 65433]
['2019-01', '9호선', '4111', '신목동', 100971, 88880, 18519, 17277]
['2019-01', '9호선', '4112', '선유도', 184101, 180644, 25486, 24685]
['2019-01', '9호선', '4113', '당산', 503716, 489145, 67667, 63128]
['2019-01', '9호선', '4114', '국회의사당', 432110, 428086, 54157, 46794]
['2019-01', '9호선', '4115', '여의도', 605162, 596361, 44306, 41096]
['2019-01', '9호선', '4116', '샛강', 130709, 135230, 27307, 27254]
['2019-01', '9호선', '4117', '노량진', 809737, 845116, 96158, 97812]
['2019-01', '9호선', '4118', '노들', 108438, 92267, 32103, 30095]
['2019-01', '9호선', '4119', '흑석(중앙대입구)', 214559, 224713, 56278, 58197]
['2019-01', '9호선', '4120', '동작(현충원)', 44466, 32426, 13400, 11585]
['2019-01', '9호선', '4121', '구반포', 72639, 77493, 25160, 25714]
['2019-01', '9호선', '4122', '신반포', 68729, 69838, 21393, 22085]
['2019-01', '9호선', '4123', '고속터미널', 341832, 554030, 51496, 61919]
['2019-01', '9호선', '4124', '사평', 75728, 73217, 16687, 16793]
['2019-01', '9호선', '4125', '신논현', 965930, 995451, 57013, 54557]
['2019-01', '공항철도 1호선', '4201', '서울역', 361418, 297358, 34330, 31219]
['2019-01', '공항철도 1호선', '4202', '공덕', 62886, 84969, 7191, 8384]
['2019-01', '공항철도 1호선', '4203', '홍대입구', 336328, 335507, 17545, 17112]
['2019-01', '공항철도 1호선', '4204', '디지털미디어시티', 364334, 319490, 23367, 22022]
['2019-01', '공항철도 1호선', '4206', '마곡나루', 133488, 119389, 8060, 8192]
['2019-01', '공항철도 1호선', '4207', '김포공항', 300654, 196229, 18640, 14332]
['2019-01', '공항철도 1호선', '4208', '계양', 299093, 275349, 29832, 28898]
['2019-01', '공항철도 1호선', '4209', '검암', 234050, 225849, 18332, 17974]
['2019-01', '공항철도 1호선', '4210', '청라국제도시', 174320, 164483, 9921, 9676]
['2019-01', '공항철도 1호선', '4211', '운서', 181510, 187995, 22540, 22223]
['2019-01', '공항철도 1호선', '4212', '공항화물청사', 66328, 77391, 8364, 8128]
['2019-01', '공항철도 1호선', '4213', '인천공항1터미널', 442059, 448913, 32280, 30925]
['2019-01', '공항철도 1호선', '4215', '인천공항2터미널', 156879, 139212, 15720, 14308]
['2019-01', '공항철도 1호선', '4217', '영종', 56266, 56533, 14101, 14391]
['2019-01', '9호선2~3단계', '4126', '언주', 252387, 250373, 27918, 26982]
['2019-01', '9호선2~3단계', '4127', '선정릉', 227032, 244880, 22287, 22837]
['2019-01', '9호선2~3단계', '4128', '삼성중앙', 151060, 155520, 23091, 22945]
['2019-01', '9호선2~3단계', '4129', '봉은사', 458359, 451178, 47044, 44944]
['2019-01', '9호선2~3단계', '4130', '종합운동장', 123363, 110135, 16937, 14810]
['2019-01', '9호선2~3단계', '4131', '삼전', 115510, 113979, 27484, 26938]
['2019-01', '9호선2~3단계', '4132', '석촌고분', 97684, 98527, 26503, 25808]
['2019-01', '9호선2~3단계', '4133', '석촌', 134697, 99683, 24074, 18577]
['2019-01', '9호선2~3단계', '4134', '송파나루', 78484, 81312, 20228, 20157]
['2019-01', '9호선2~3단계', '4135', '한성백제', 45622, 39639, 10769, 9318]
['2019-01', '9호선2~3단계', '4136', '올림픽공원(한국체대)', 155506, 154127, 14235, 14305]
['2019-01', '9호선2~3단계', '4137', '둔촌오륜', 16917, 16995, 5226, 4987]
['2019-01', '9호선2~3단계', '4138', '중앙보훈병원', 126493, 113313, 57402, 58027]
['2019-01', '우이신설선', '4701', '북한산우이', 48522, 39613, 32154, 32256]
['2019-01', '우이신설선', '4702', '솔밭공원', 44120, 40598, 25998, 26357]
['2019-01', '우이신설선', '4703', '4.19민주묘지', 49789, 48250, 28647, 26730]
['2019-01', '우이신설선', '4704', '가오리', 77993, 73611, 44978, 46677]
['2019-01', '우이신설선', '4705', '화계', 59426, 52190, 39531, 41027]
['2019-01', '우이신설선', '4706', '삼양', 35022, 36823, 25791, 28584]
['2019-01', '우이신설선', '4707', '삼양사거리', 37572, 38401, 35436, 36998]
['2019-01', '우이신설선', '4708', '솔샘', 133044, 118786, 34119, 34990]
['2019-01', '우이신설선', '4709', '북한산보국문', 131839, 117790, 40396, 40629]
['2019-01', '우이신설선', '4710', '정릉', 83527, 73090, 43457, 41092]
['2019-01', '우이신설선', '4711', '성신여대입구(돈암)', 77040, 89799, 31555, 29648]
['2019-01', '우이신설선', '4712', '보문', 31502, 32724, 15900, 16109]
['2019-01', '우이신설선', '4713', '신설동', 37248, 40743, 19037, 17418]

In [ ]:

유임 승차 비율이 가장 높은 역은 어디?

In [130]:

f= open('subwayfee.csv')
data = csv.reader(f)
next(data)

mx=0
rate=0
for row in data:
    for i in range(4,8):
        row[i]= int(row[i])
    
    if row[6] !=0:
        rate = row[4]/row[6]
        if rate> mx:
            mx=rate
            print(row, round(rate,2))

['2019-01', '1호선', '0150', '서울역', 1516452, 1400464, 221180, 211764] 6.86
['2019-01', '1호선', '0152', '종각', 1240810, 1177643, 162410, 152062] 7.64
['2019-01', '2호선', '0201', '시청', 706097, 679503, 61058, 55387] 11.56
['2019-01', '2호선', '0202', '을지로입구', 1415414, 1414980, 120425, 111592] 11.75
['2019-01', '2호선', '0209', '한양대', 233785, 256079, 15380, 16652] 15.2
['2019-01', '2호선', '0222', '강남', 3153418, 3210437, 186486, 167666] 16.91
['2019-01', '2호선', '0239', '홍대입구', 2351935, 2507561, 114832, 111488] 20.48
['2019-01', '3호선', '0321', '충무로', 55, 0, 2, 0] 27.5

In [ ]:

In [132]:

import csv
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
mx = 0
rate = 0
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
    if row[6] !=0 and (row[4] + row[6]) >100000 :
        rate = row[4] / (row[4] + row[6])
        if rate > mx :
            mx = rate
            print(row, round(rate,2))

['2019-01', '1호선', '0150', '서울역', 1516452, 1400464, 221180, 211764] 0.87
['2019-01', '1호선', '0152', '종각', 1240810, 1177643, 162410, 152062] 0.88
['2019-01', '2호선', '0201', '시청', 706097, 679503, 61058, 55387] 0.92
['2019-01', '2호선', '0202', '을지로입구', 1415414, 1414980, 120425, 111592] 0.92
['2019-01', '2호선', '0209', '한양대', 233785, 256079, 15380, 16652] 0.94
['2019-01', '2호선', '0222', '강남', 3153418, 3210437, 186486, 167666] 0.94
['2019-01', '2호선', '0239', '홍대입구', 2351935, 2507561, 114832, 111488] 0.95

In [133]:

import csv
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
mx = 0
rate = 0
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
    if row[6] !=0 and (row[4] + row[6]) >100000 :
        rate = row[4] / (row[4] + row[6])
        if rate > 0.94 :
            mx = rate
            print(row, round(rate,2))

['2019-01', '2호선', '0222', '강남', 3153418, 3210437, 186486, 167666] 0.94
['2019-01', '2호선', '0239', '홍대입구', 2351935, 2507561, 114832, 111488] 0.95
['2019-01', '경부선', '1001', '서울역', 178223, 29980, 10325, 1612] 0.95
['2019-01', '9호선', '4125', '신논현', 965930, 995451, 57013, 54557] 0.94
['2019-01', '공항철도 1호선', '4203', '홍대입구', 336328, 335507, 17545, 17112] 0.95
['2019-01', '공항철도 1호선', '4206', '마곡나루', 133488, 119389, 8060, 8192] 0.94
['2019-01', '공항철도 1호선', '4207', '김포공항', 300654, 196229, 18640, 14332] 0.94
['2019-01', '공항철도 1호선', '4210', '청라국제도시', 174320, 164483, 9921, 9676] 0.95

In [ ]:

유임승차비율이 가장 높은 역 찾기 
row[6] !=0 and (row[4] + row[6]) >100000 

In [134]:

import csv
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
mx = 0
rate = 0
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
    if row[6] !=0 and (row[4] + row[6]) >100000 :
        rate = row[4] / (row[4] + row[6])
        if rate > mx :
            mx = rate
            mx_station = row[3] + ' ' + row[1]
            print(mx_station, round(mx * 100,2))

서울역 1호선 87.27
종각 1호선 88.43
시청 2호선 92.04
을지로입구 2호선 92.16
한양대 2호선 93.83
강남 2호선 94.42
홍대입구 2호선 95.34

In [136]:

import csv
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
mx = [0] * 4
mx_station = [''] * 4
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
        if row[i] > mx[i-4] :
            mx_station[i-4] = row[i]
            mx_station[i-4] = row[3] + ' ' + row[1]
for i in range(4) :
    print(mx_station[i], mx[i])

신설동 우이신설선 0
신설동 우이신설선 0
신설동 우이신설선 0
신설동 우이신설선 0

In [138]:

import csv
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
mx = [0] * 4
mx_station = [''] * 4
label = ['유임승차','유임하차','무임승차','무임하차']
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
        if row[i] > mx[i-4] :
            mx[i-4] = row[i]
            mx_station[i-4] = row[3] + ' ' + row[1]
for i in range(4) :
    print(label[i] + ' : ' + mx_station[i], mx[i])

유임승차 : 강남 2호선 3153418
유임하차 : 강남 2호선 3210437
무임승차 : 종로3가 1호선 387062
무임하차 : 제기동 1호선 400607

In [139]:

import csv
import matplotlib.pyplot as plt
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
label = ['유임승차','유임하차','무임승차','무임하차']
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
#plt.figure(dpi = 300)
plt.pie(row[4:8])
plt.axis('equal')
plt.show()

In [1]:

import matplotlib.pyplot as plt
f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
label = ['유임승차','유임하차','무임승차','무임하차']
c = ['#14CCC0', '#389993', '#FF1C6A', '#CC14AF']
plt.rc('font', family = 'Malgun Gothic')
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
plt.figure(dpi = 300)
plt.title(row[3] + ' ' + row[1])
plt.pie(row[4:8], labels = label, colors = c, autopct = '%1.f%%')
plt.savefig(row[3] + ' ' + row[1] + '.png') # png로 저장 코딩한 파일에 저장
plt.axis('equal')
plt.show()

  Input In [1]
    
    ^
SyntaxError: unexpected EOF while parsing

In [143]:

f = open('subwayfee.csv')
data = csv.reader(f)
next(data)
label = ['유임승차','유임하차','무임승차','무임하차']
c = ['#14CCC0', '#389993', '#FF1C6A', '#CC14AF']
plt.rc('font', family = 'Malgun Gothic')
for row in data :
    for i in range(4,8) :
        row[i] = int(row[i])
        
        
plt.figure(dpi = 300)
plt.title(row[3] + ' ' + row[1])
plt.pie(row[4:8], labels = label, colors = c, autopct = '%1.f%%')
plt.axis('equal')
plt.savefig(row[3] + ' ' + row[1] + '.png')
plt.show()

In [ ]:

지하철 시간대별 이용현황
    3컬럼부터 출력
    7시 모든 역 승차 데이터만 출력

In [149]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)
result=[]
for row in data:
    row[4:]= map(int,row[4:])
    result.append(row[10])
print(len(result))
print(result)

598
[39110, 7703, 6733, 6066, 5143, 14893, 20077, 22615, 46015, 6088, 5822, 8459, 5822, 3225, 7973, 27835, 38199, 31012, 6313, 23263, 25458, 59821, 70814, 99368, 45639, 130638, 59461, 18787, 17974, 23502, 16391, 48487, 27365, 14946, 26301, 78413, 89675, 146181, 84974, 243083, 99608, 154641, 77272, 153133, 46364, 45277, 56578, 48068, 75744, 40444, 18646, 26197, 18343, 7965, 6917, 3353, 3160, 32301, 43628, 4860, 3988, 91650, 148741, 58325, 55607, 62354, 14773, 16297, 15231, 6464, 1992, 2337, 0, 5340, 25921, 27033, 24997, 17015, 13731, 16790, 28669, 8261, 27157, 36344, 15093, 8325, 25900, 3695, 25425, 21481, 38038, 22032, 23508, 26639, 49477, 101709, 59128, 108209, 141391, 127573, 54631, 92454, 88501, 52815, 33607, 17800, 5662, 7510, 10424, 5612, 6458, 12348, 18564, 5669, 11193, 13780, 4651, 49720, 78726, 3340, 6380, 12719, 21239, 19333, 31594, 42323, 9949, 24815, 39212, 15241, 47110, 44522, 37137, 89122, 28664, 89750, 19514, 28555, 71907, 42380, 77372, 43514, 16219, 48837, 9716, 9749, 21289, 3085, 11855, 9157, 5282, 24051, 5263, 1584, 12138, 8757, 28239, 11118, 13191, 102383, 51233, 139670, 127056, 133782, 104104, 33578, 67792, 73755, 39575, 48303, 5734, 28465, 40308, 43925, 27432, 7721, 22025, 45976, 15548, 8773, 2872, 5009, 34, 6761, 26713, 26535, 23224, 38643, 28922, 37100, 18199, 22515, 0, 33032, 20720, 27233, 28023, 60318, 45613, 26295, 19146, 37268, 8535, 22065, 21735, 10092, 4825, 5691, 5942, 64390, 15216, 16974, 86991, 30974, 51600, 30417, 11801, 26504, 7218, 18571, 35129, 20607, 21542, 1801, 673, 18617, 17858, 90202, 61519, 96163, 3688, 19126, 7004, 2419, 6744, 8430, 24977, 1, 28855, 14358, 5296, 7136, 4318, 18701, 45127, 62115, 57586, 35997, 17844, 29899, 26836, 33968, 14915, 10012, 43681, 20719, 19247, 37243, 19538, 18026, 18213, 47885, 17277, 22889, 7745, 39208, 0, 61144, 48268, 59077, 6137, 24609, 17547, 8953, 27554, 68616, 60949, 23633, 20676, 29526, 5547, 61624, 44038, 2480, 25941, 13618, 1264, 1384, 5843, 562, 2101, 1997, 801, 9690, 860, 3631, 187, 2195, 7123, 5753, 2837, 7925, 1972, 1687, 278, 4437, 4816, 2251, 1408, 18493, 5675, 2661, 3726, 18196, 43358, 16154, 1378, 35684, 24249, 52718, 42395, 21572, 27350, 16902, 20344, 2227, 2872, 18826, 0, 0, 0, 0, 0, 2051, 16695, 7884, 19369, 17273, 5176, 26488, 7130, 14939, 955, 3424, 532, 1996, 184, 73, 349, 431, 3911, 1997, 101, 8596, 12323, 14272, 6082, 1176, 14385, 14276, 9536, 19705, 11038, 3319, 2012, 3731, 3974, 10584, 29898, 10588, 5894, 3898, 6568, 4360, 833, 5265, 33410, 24572, 7401, 31836, 26095, 29477, 62971, 128061, 139522, 69298, 67001, 53755, 22228, 7654, 19069, 10431, 10399, 5459, 26163, 20404, 19821, 5579, 17043, 10566, 3606, 1411, 867, 6723, 22626, 33836, 6478, 16863, 56080, 49375, 32646, 43392, 43138, 28727, 67499, 24028, 37876, 33549, 35491, 75094, 25725, 10832, 20144, 10094, 26384, 32721, 22065, 72019, 17318, 12121, 13324, 1, 30752, 53100, 40142, 38344, 8515, 39459, 26662, 16590, 10188, 17413, 14834, 22427, 15392, 6875, 4892, 14369, 3236, 5585, 4568, 7413, 7727, 8849, 14905, 20553, 8862, 21609, 41534, 22912, 27561, 53407, 19499, 67381, 55748, 18286, 57793, 56751, 64546, 41621, 68168, 76395, 40606, 21401, 40404, 41591, 54399, 59324, 50925, 26108, 31636, 35718, 17282, 13275, 20581, 11981, 6990, 8657, 9914, 8329, 10577, 17631, 27280, 38829, 38570, 32099, 33041, 53375, 19842, 36148, 15804, 33675, 20278, 93286, 100278, 38233, 51028, 30096, 20759, 25287, 50700, 44400, 44977, 26143, 39162, 69923, 81944, 50030, 32446, 10599, 8611, 17587, 13303, 9910, 23995, 50900, 37594, 20480, 52891, 39400, 12815, 14597, 8904, 43458, 13832, 9051, 30341, 26742, 27634, 78872, 20434, 37541, 75256, 19186, 18591, 52644, 4252, 8655, 4056, 60869, 16764, 16984, 4976, 4600, 5107, 14830, 8584, 35371, 13461, 5827, 11519, 16128, 5458, 8804, 66113, 45980, 31076, 17140, 2165, 17466, 6331, 8188, 5728, 8758, 5401, 9939, 10595, 12092, 10227, 15152, 7309, 4438, 11517, 2068, 20599, 8566, 8940, 7188, 15720, 11517, 5945, 6395, 27463, 20034, 12345, 3373, 1868, 1098]

In [ ]:

7시 승차 오름차순 비교

In [159]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)
result=[]
for row in data:
    row[4:]= map(int,row[4:])
    result.append(row[10])
print(len(result))

result.sort()
plt.figure(figsize=(16,10))
plt.bar(range(len(result)),result)

Out[159]:

<BarContainer object of 598 artists>

In [ ]:

승차역 최대값 찾기

In [163]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)

mx_station=0
stay=''
for row in data:
    row[4:]=map(int,row[4:])
    mx=row[4+(10-4)*2]     # 10이 시간 나머지는 4+(시간-4)*2 시간 = 해당시간 인덱스공식이다
    if mx>mx_station:
        mx_station=mx
        stay=row[3]+'('+row[1]+')'
print(stay, mx_station)

        
    

신림(2호선) 113830

In [3]:

f= open('subwayfee.csv')
data = csv.reader(f)
next(data)

mx=0
rate=0
for row in data:
    for i in range(4,8):
        row[i]= int(row[i])
    
    if row[6] !=0:
        rate = row[4]/row[6]
        if rate> mx:
            mx=rate
            print(row, round(rate,2))

['2019-01', '1호선', '0150', '서울역', 1516452, 1400464, 221180, 211764] 6.86
['2019-01', '1호선', '0152', '종각', 1240810, 1177643, 162410, 152062] 7.64
['2019-01', '2호선', '0201', '시청', 706097, 679503, 61058, 55387] 11.56
['2019-01', '2호선', '0202', '을지로입구', 1415414, 1414980, 120425, 111592] 11.75
['2019-01', '2호선', '0209', '한양대', 233785, 256079, 15380, 16652] 15.2
['2019-01', '2호선', '0222', '강남', 3153418, 3210437, 186486, 167666] 16.91
['2019-01', '2호선', '0239', '홍대입구', 2351935, 2507561, 114832, 111488] 20.48
['2019-01', '3호선', '0321', '충무로', 55, 0, 2, 0] 27.5

In [9]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)
time= int(input('시간 검색>>>'))
mx_station=0
stay=''
for row in data:
    row[4:]=map(int,row[4:])
    mx=row[4+(time-4)*2]     # 10이 시간 나머지는 4+(시간-4)*2 시간 = 해당시간 인덱스공식이다
    if mx>mx_station:
        mx_station=mx
        stay=row[3]+'('+row[1]+')'
print(time ,'시에 가장많은 승차역은 :',stay, mx_station,'명')

        
    

시간 검색>>>23
23 시에 가장많은 승차역은 : 강남(2호선) 145504 명

In [ ]:

새벽 4시~3시 (24시간) 총 승차역 중에 가장 많은 시간대와 승차역

In [17]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)

mx=[0]*24
mx_station=['']*24

for row in data:
    row[4:]=map(int,row[4:])
    for j in range(24):
        a= row[j*2+4]
        if a> mx[j]: 
            mx[j]=a
            mx_station[j]= row[3]
            
print(mx_station)
print(mx)

plt.figure(figsize=(12,10))
plt.rc('font', family = 'Malgun Gothic')
plt.bar(range(24),mx)
plt.xticks(range(24), mx_station, rotation=90)
plt.show()

['구로', '홍대입구', '신림', '신림', '신림', '신림', '신림', '신림', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '강남', '성신여대입구(돈암)', '신방화']
[8418, 42966, 80407, 243083, 355172, 211286, 113830, 98765, 126159, 170216, 169097, 203483, 227268, 291623, 431115, 292521, 235489, 295326, 314609, 145504, 27203, 36, 3, 1]

In [ ]:

새벽 4시~3시 (24시간) 총 승차역 중에 가장 많은 시간대와 승차역
 역이름 + 시간대 추가하는 코드 추가

In [18]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)

mx=[0]*24
mx_station=['']*24

for row in data:
    row[4:]=map(int,row[4:])
    for j in range(24):
        a= row[j*2+4]
        if a> mx[j]: 
            mx[j]=a
            mx_station[j]= row[3]+ '('+str(j+4)+')'
            #역이름 + 시간대 추가하는 코드 추가
            
print(mx_station)
print(mx)

plt.figure(figsize=(12,10))
plt.rc('font', family = 'Malgun Gothic')
plt.bar(range(24),mx)
plt.xticks(range(24), mx_station, rotation=90)
plt.show()

['구로(4)', '홍대입구(5)', '신림(6)', '신림(7)', '신림(8)', '신림(9)', '신림(10)', '신림(11)', '강남(12)', '강남(13)', '강남(14)', '강남(15)', '강남(16)', '강남(17)', '강남(18)', '강남(19)', '강남(20)', '강남(21)', '강남(22)', '강남(23)', '강남(24)', '강남(25)', '성신여대입구(돈암)(26)', '신방화(27)']
[8418, 42966, 80407, 243083, 355172, 211286, 113830, 98765, 126159, 170216, 169097, 203483, 227268, 291623, 431115, 292521, 235489, 295326, 314609, 145504, 27203, 36, 3, 1]

In [31]:

f = open('subwaytime.csv')
data = csv.reader(f)
next(data)
next(data)

in_=[0]*24
out_=[0]*24
mx_station=['']*24
for row in data:
    row[4:]=map(int,row[4:])
    for j in range(24):
        in_[j]+=row[4+j*2]
        out_[j]+= row[5+j*2]
        mx_station[j]= j+4
            
print(in_)
print(out_)

plt.figure(dpi=300)
plt.rc('font', family = 'Malgun Gothic')
plt.plot(in_, label='승차')
plt.plot(out_, label='하차')
plt.xticks(range(24),mx_station, rotation=90)
plt.legend()
plt.show()

[105055, 3004777, 6109712, 16485348, 19843950, 12357620, 9438699, 9673116, 10609249, 11262850, 11106713, 12041962, 13343105, 16889514, 23883151, 13366319, 10190610, 9754728, 8167505, 3701401, 512101, 506, 22, 1]
[1616, 769865, 4063753, 9027520, 23309313, 15352447, 9873017, 9611044, 10183646, 11171310, 10979461, 11400318, 12321795, 14629199, 21088185, 18874835, 11095402, 9759638, 9230667, 6107653, 1981268, 66402, 21, 0]