Overview

Dataset statistics

Number of variables17
Number of observations1454
Missing cells150
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory203.2 KiB
Average record size in memory143.1 B

Variable types

Text6
Categorical7
DateTime1
Numeric3

Dataset

Description메인 키,코스 카테고리,강북강남구분코드,행정시,행정구,행정동,거리,소요시간,코스레벨,추천수,연계지하철,PDF파일경로,코스명,작성시간,포인트순번,X 좌표,Y 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13100/S/1/datasetView.do

Alerts

추천수 has constant value ""Constant
행정시 is highly overall correlated with 행정구High correlation
행정구 is highly overall correlated with 포인트순번 and 4 other fieldsHigh correlation
강북강남구분코드 is highly overall correlated with 포인트순번 and 2 other fieldsHigh correlation
포인트순번 is highly overall correlated with 코스 카테고리 and 2 other fieldsHigh correlation
X 좌표 is highly overall correlated with 행정구High correlation
Y 좌표 is highly overall correlated with 행정구High correlation
코스 카테고리 is highly overall correlated with 포인트순번 and 1 other fieldsHigh correlation
소요시간 is highly overall correlated with 코스 카테고리 and 2 other fieldsHigh correlation
코스레벨 is highly overall correlated with 소요시간High correlation
행정시 is highly imbalanced (87.3%)Imbalance
연계지하철 has 87 (6.0%) missing valuesMissing
PDF파일경로 has 62 (4.3%) missing valuesMissing
메인 키 has unique valuesUnique
포인트순번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 07:16:46.684602
Analysis finished2023-12-11 07:16:49.756878
Duration3.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

메인 키
Text

UNIQUE 

Distinct1454
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-12-11T16:16:49.957867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters17448
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1454 ?
Unique (%)100.0%

Sample

1st rowBE_IW43-1024
2nd rowBE_IW43-1025
3rd rowBE_IW43-1026
4th rowBE_IW43-1027
5th rowBE_IW43-1028
ValueCountFrequency (%)
be_iw43-1024 1
 
0.1%
be_iw43-0257 1
 
0.1%
be_iw43-1185 1
 
0.1%
be_iw43-1184 1
 
0.1%
be_iw43-1183 1
 
0.1%
be_iw43-1182 1
 
0.1%
be_iw43-1181 1
 
0.1%
be_iw43-1196 1
 
0.1%
be_iw43-1195 1
 
0.1%
be_iw43-1194 1
 
0.1%
Other values (1444) 1444
99.3%
2023-12-11T16:16:50.371362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1950
11.2%
4 1905
10.9%
0 1492
8.6%
B 1454
8.3%
E 1454
8.3%
_ 1454
8.3%
I 1454
8.3%
W 1454
8.3%
- 1454
8.3%
1 951
 
5.5%
Other values (6) 2426
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8724
50.0%
Uppercase Letter 5816
33.3%
Connector Punctuation 1454
 
8.3%
Dash Punctuation 1454
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1950
22.4%
4 1905
21.8%
0 1492
17.1%
1 951
10.9%
2 496
 
5.7%
5 390
 
4.5%
6 385
 
4.4%
8 385
 
4.4%
9 385
 
4.4%
7 385
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
B 1454
25.0%
E 1454
25.0%
I 1454
25.0%
W 1454
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1454
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11632
66.7%
Latin 5816
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1950
16.8%
4 1905
16.4%
0 1492
12.8%
_ 1454
12.5%
- 1454
12.5%
1 951
8.2%
2 496
 
4.3%
5 390
 
3.4%
6 385
 
3.3%
8 385
 
3.3%
Other values (2) 770
 
6.6%
Latin
ValueCountFrequency (%)
B 1454
25.0%
E 1454
25.0%
I 1454
25.0%
W 1454
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1950
11.2%
4 1905
10.9%
0 1492
8.6%
B 1454
8.3%
E 1454
8.3%
_ 1454
8.3%
I 1454
8.3%
W 1454
8.3%
- 1454
8.3%
1 951
 
5.5%
Other values (6) 2426
13.9%

코스 카테고리
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1000
853 
5000
381 
2000
116 
3000
 
62
4000
 
42

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000
2nd row3000
3rd row3000
4th row3000
5th row3000

Common Values

ValueCountFrequency (%)
1000 853
58.7%
5000 381
26.2%
2000 116
 
8.0%
3000 62
 
4.3%
4000 42
 
2.9%

Length

2023-12-11T16:16:50.565390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:16:50.702888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1000 853
58.7%
5000 381
26.2%
2000 116
 
8.0%
3000 62
 
4.3%
4000 42
 
2.9%

강북강남구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
812 
1
642 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 812
55.8%
1 642
44.2%

Length

2023-12-11T16:16:50.834470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:16:50.986212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 812
55.8%
1 642
44.2%

행정시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Seoul
1395 
Gyeonggi-do
 
57
<NA>
 
1
Incheon
 
1

Length

Max length11
Median length5
Mean length5.235901
Min length4

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowSeoul
2nd rowSeoul
3rd rowSeoul
4th rowSeoul
5th rowSeoul

Common Values

ValueCountFrequency (%)
Seoul 1395
95.9%
Gyeonggi-do 57
 
3.9%
<NA> 1
 
0.1%
Incheon 1
 
0.1%

Length

2023-12-11T16:16:51.137563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:16:51.280697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 1395
95.9%
gyeonggi-do 57
 
3.9%
na 1
 
0.1%
incheon 1
 
0.1%

행정구
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Jongno-gu
133 
Dongjak-gu
 
90
Seongbuk-gu
 
76
Songpa-gu
 
76
Gwanak-gu
 
75
Other values (32)
1004 

Length

Max length22
Median length21
Mean length10.037139
Min length4

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowGwanak-gu
2nd rowGwanak-gu
3rd rowGwanak-gu
4th rowGwanak-gu
5th rowGwanak-gu

Common Values

ValueCountFrequency (%)
Jongno-gu 133
 
9.1%
Dongjak-gu 90
 
6.2%
Seongbuk-gu 76
 
5.2%
Songpa-gu 76
 
5.2%
Gwanak-gu 75
 
5.2%
Mapo-gu 75
 
5.2%
Gangseo-gu 71
 
4.9%
Nowon-gu 69
 
4.7%
Gangbuk-gu 66
 
4.5%
Seodaemun-gu 64
 
4.4%
Other values (27) 659
45.3%

Length

2023-12-11T16:16:51.425937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jongno-gu 133
 
9.0%
dongjak-gu 90
 
6.1%
seongbuk-gu 76
 
5.1%
songpa-gu 76
 
5.1%
gwanak-gu 75
 
5.1%
mapo-gu 75
 
5.1%
gangseo-gu 71
 
4.8%
nowon-gu 69
 
4.7%
gangbuk-gu 66
 
4.5%
seodaemun-gu 64
 
4.3%
Other values (31) 682
46.2%

행정동
Text

Distinct313
Distinct (%)21.5%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
2023-12-11T16:16:51.724840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length12.480385
Min length7

Characters and Unicode

Total characters18134
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)6.1%

Sample

1st rowDaehak-dong
2nd rowDaehak-dong
3rd rowDaehak-dong
4th rowDaehak-dong
5th rowDaehak-dong
ValueCountFrequency (%)
cheongunhyoja-dong 34
 
2.3%
oryun-dong 31
 
2.1%
daehak-dong 28
 
1.9%
sangam-dong 28
 
1.9%
sadang2-dong 25
 
1.7%
jongno1.2.3.4ga-dong 24
 
1.7%
beon3-dong 22
 
1.5%
mok2-dong 22
 
1.5%
buam-dong 22
 
1.5%
seongsan2-dong 21
 
1.4%
Other values (303) 1196
82.3%
2023-12-11T16:16:52.207690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 2980
16.4%
g 2578
14.2%
o 2500
13.8%
d 1532
 
8.4%
- 1453
 
8.0%
a 1085
 
6.0%
e 789
 
4.4%
u 467
 
2.6%
S 394
 
2.2%
2 367
 
2.0%
Other values (38) 3989
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14239
78.5%
Dash Punctuation 1453
 
8.0%
Uppercase Letter 1453
 
8.0%
Decimal Number 894
 
4.9%
Other Punctuation 95
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2980
20.9%
g 2578
18.1%
o 2500
17.6%
d 1532
10.8%
a 1085
 
7.6%
e 789
 
5.5%
u 467
 
3.3%
h 354
 
2.5%
i 276
 
1.9%
y 273
 
1.9%
Other values (11) 1405
9.9%
Uppercase Letter
ValueCountFrequency (%)
S 394
27.1%
J 149
 
10.3%
G 143
 
9.8%
B 138
 
9.5%
D 117
 
8.1%
M 97
 
6.7%
H 81
 
5.6%
C 73
 
5.0%
Y 56
 
3.9%
N 54
 
3.7%
Other values (7) 151
 
10.4%
Decimal Number
ValueCountFrequency (%)
2 367
41.1%
1 221
24.7%
3 150
16.8%
4 93
 
10.4%
5 20
 
2.2%
7 19
 
2.1%
6 19
 
2.1%
8 5
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 1453
100.0%
Other Punctuation
ValueCountFrequency (%)
. 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15692
86.5%
Common 2442
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2980
19.0%
g 2578
16.4%
o 2500
15.9%
d 1532
9.8%
a 1085
 
6.9%
e 789
 
5.0%
u 467
 
3.0%
S 394
 
2.5%
h 354
 
2.3%
i 276
 
1.8%
Other values (28) 2737
17.4%
Common
ValueCountFrequency (%)
- 1453
59.5%
2 367
 
15.0%
1 221
 
9.0%
3 150
 
6.1%
. 95
 
3.9%
4 93
 
3.8%
5 20
 
0.8%
7 19
 
0.8%
6 19
 
0.8%
8 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2980
16.4%
g 2578
14.2%
o 2500
13.8%
d 1532
 
8.4%
- 1453
 
8.0%
a 1085
 
6.0%
e 789
 
4.4%
u 467
 
2.6%
S 394
 
2.2%
2 367
 
2.0%
Other values (38) 3989
22.0%

거리
Text

Distinct135
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-12-11T16:16:52.630887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0158184
Min length3

Characters and Unicode

Total characters8747
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3km
2nd row1.3km
3rd row1.3km
4th row1.3km
5th row1.3km
ValueCountFrequency (%)
20.74km 28
 
1.9%
5.21km 27
 
1.9%
8.12km 27
 
1.9%
2.23km 25
 
1.7%
30.21km 25
 
1.7%
8.60km 21
 
1.4%
15.57km 21
 
1.4%
2.61km 20
 
1.4%
3.31km 19
 
1.3%
34.5km 18
 
1.2%
Other values (121) 1223
84.1%
2023-12-11T16:16:53.254629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 1454
16.6%
. 1421
16.2%
k 1066
12.2%
1 702
8.0%
2 609
7.0%
3 485
 
5.5%
7 402
 
4.6%
4 400
 
4.6%
5 395
 
4.5%
K 388
 
4.4%
Other values (4) 1425
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4418
50.5%
Lowercase Letter 2520
28.8%
Other Punctuation 1421
 
16.2%
Uppercase Letter 388
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 702
15.9%
2 609
13.8%
3 485
11.0%
7 402
9.1%
4 400
9.1%
5 395
8.9%
0 362
8.2%
6 361
8.2%
9 361
8.2%
8 341
7.7%
Lowercase Letter
ValueCountFrequency (%)
m 1454
57.7%
k 1066
42.3%
Other Punctuation
ValueCountFrequency (%)
. 1421
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5839
66.8%
Latin 2908
33.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1421
24.3%
1 702
12.0%
2 609
10.4%
3 485
 
8.3%
7 402
 
6.9%
4 400
 
6.9%
5 395
 
6.8%
0 362
 
6.2%
6 361
 
6.2%
9 361
 
6.2%
Latin
ValueCountFrequency (%)
m 1454
50.0%
k 1066
36.7%
K 388
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1454
16.6%
. 1421
16.2%
k 1066
12.2%
1 702
8.0%
2 609
7.0%
3 485
 
5.5%
7 402
 
4.6%
4 400
 
4.6%
5 395
 
4.5%
K 388
 
4.4%
Other values (4) 1425
16.3%

소요시간
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2시간
299 
3시간
222 
1시간
160 
1시간30분
149 
2시간30분
66 
Other values (24)
558 

Length

Max length8
Median length3
Mean length4.196011
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30분
2nd row30분
3rd row30분
4th row30분
5th row30분

Common Values

ValueCountFrequency (%)
2시간 299
20.6%
3시간 222
15.3%
1시간 160
11.0%
1시간30분 149
10.2%
2시간30분 66
 
4.5%
4시간30분 55
 
3.8%
8시간 53
 
3.6%
3시간30분 49
 
3.4%
4시간 48
 
3.3%
1시간 30분 47
 
3.2%
Other values (19) 306
21.0%

Length

2023-12-11T16:16:53.438926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2시간 330
20.4%
3시간 236
14.6%
1시간 232
14.3%
1시간30분 149
9.2%
30분 147
9.1%
2시간30분 66
 
4.1%
4시간 60
 
3.7%
6시간 59
 
3.6%
4시간30분 55
 
3.4%
8시간 53
 
3.3%
Other values (10) 230
14.2%

코스레벨
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
783 
2
567 
3
104 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 783
53.9%
2 567
39.0%
3 104
 
7.2%

Length

2023-12-11T16:16:53.608464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:16:53.724697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 783
53.9%
2 567
39.0%
3 104
 
7.2%

추천수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1454 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1454
100.0%

Length

2023-12-11T16:16:53.864536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:16:53.969822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1454
100.0%

연계지하철
Text

MISSING 

Distinct62
Distinct (%)4.5%
Missing87
Missing (%)6.0%
Memory size11.5 KiB
2023-12-11T16:16:54.202224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.2004389
Min length3

Characters and Unicode

Total characters8476
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2호선
2nd row2호선
3rd row2호선
4th row2호선
5th row2호선
ValueCountFrequency (%)
3호선 142
 
10.4%
4호선 73
 
5.3%
6호선 71
 
5.2%
5호선 60
 
4.4%
1호선 48
 
3.5%
7호선 45
 
3.3%
9호선 41
 
3.0%
1호선4호선 39
 
2.9%
2호선분당선 36
 
2.6%
2호선 35
 
2.6%
Other values (52) 777
56.8%
2023-12-11T16:16:54.600597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
선 2785
32.9%
호 2557
30.2%
1 406
 
4.8%
2 359
 
4.2%
3 354
 
4.2%
5 327
 
3.9%
4 309
 
3.6%
7 270
 
3.2%
6 259
 
3.1%
9 196
 
2.3%
Other values (12) 654
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5919
69.8%
Decimal Number 2557
30.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 2785
47.1%
호 2557
43.2%
당 115
 
1.9%
분 115
 
1.9%
중 87
 
1.5%
앙 87
 
1.5%
공 26
 
0.4%
항 26
 
0.4%
철 26
 
0.4%
도 26
 
0.4%
Other values (3) 69
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 406
15.9%
2 359
14.0%
3 354
13.8%
5 327
12.8%
4 309
12.1%
7 270
10.6%
6 259
10.1%
9 196
7.7%
8 77
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5919
69.8%
Common 2557
30.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 2785
47.1%
호 2557
43.2%
당 115
 
1.9%
분 115
 
1.9%
중 87
 
1.5%
앙 87
 
1.5%
공 26
 
0.4%
항 26
 
0.4%
철 26
 
0.4%
도 26
 
0.4%
Other values (3) 69
 
1.2%
Common
ValueCountFrequency (%)
1 406
15.9%
2 359
14.0%
3 354
13.8%
5 327
12.8%
4 309
12.1%
7 270
10.6%
6 259
10.1%
9 196
7.7%
8 77
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5919
69.8%
ASCII 2557
30.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
선 2785
47.1%
호 2557
43.2%
당 115
 
1.9%
분 115
 
1.9%
중 87
 
1.5%
앙 87
 
1.5%
공 26
 
0.4%
항 26
 
0.4%
철 26
 
0.4%
도 26
 
0.4%
Other values (3) 69
 
1.2%
ASCII
ValueCountFrequency (%)
1 406
15.9%
2 359
14.0%
3 354
13.8%
5 327
12.8%
4 309
12.1%
7 270
10.6%
6 259
10.1%
9 196
7.7%
8 77
 
3.0%

PDF파일경로
Text

MISSING 

Distinct134
Distinct (%)9.6%
Missing62
Missing (%)4.3%
Memory size11.5 KiB
2023-12-11T16:16:54.939459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters91872
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://gil.seoul.go.kr/view/course/2014/06/11/1366650430676180.zip
2nd rowhttp://gil.seoul.go.kr/view/course/2014/06/11/1366650430676180.zip
3rd rowhttp://gil.seoul.go.kr/view/course/2014/06/11/1366650430676180.zip
4th rowhttp://gil.seoul.go.kr/view/course/2014/06/11/1366650430676180.zip
5th rowhttp://gil.seoul.go.kr/view/course/2014/06/11/1366650430676180.zip
ValueCountFrequency (%)
http://gil.seoul.go.kr/view/course/2015/01/05/8971995036544843.zip 28
 
2.0%
http://gil.seoul.go.kr/view/course/2014/07/07/3626443249696511.zip 27
 
1.9%
http://gil.seoul.go.kr/view/course/2014/07/16/4386096059217687.zip 25
 
1.8%
http://gil.seoul.go.kr/view/course/2015/01/05/8971943631002713.zip 21
 
1.5%
http://gil.seoul.go.kr/view/course/2014/07/16/4385485356306540.zip 21
 
1.5%
http://gil.seoul.go.kr/view/course/2014/07/16/4385779691663178.zip 20
 
1.4%
http://gil.seoul.go.kr/view/course/2014/07/16/4385985561460964.zip 19
 
1.4%
http://gil.seoul.go.kr/view/course/2015/04/13/1688265612221430.zip 18
 
1.3%
http://gil.seoul.go.kr/view/course/2014/06/30/3026769462632822.zip 18
 
1.3%
http://gil.seoul.go.kr/view/course/2014/07/16/4386207928914306.zip 17
 
1.2%
Other values (124) 1178
84.6%
2023-12-11T16:16:55.495637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 11136
 
12.1%
0 5748
 
6.3%
. 5568
 
6.1%
1 4397
 
4.8%
o 4176
 
4.5%
i 4176
 
4.5%
e 4176
 
4.5%
4 3806
 
4.1%
2 3556
 
3.9%
6 3183
 
3.5%
Other values (19) 41950
45.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40368
43.9%
Decimal Number 33408
36.4%
Other Punctuation 18096
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4176
10.3%
i 4176
10.3%
e 4176
10.3%
t 2784
 
6.9%
r 2784
 
6.9%
u 2784
 
6.9%
s 2784
 
6.9%
l 2784
 
6.9%
g 2784
 
6.9%
p 2784
 
6.9%
Other values (6) 8352
20.7%
Decimal Number
ValueCountFrequency (%)
0 5748
17.2%
1 4397
13.2%
4 3806
11.4%
2 3556
10.6%
6 3183
9.5%
3 3019
9.0%
8 2745
8.2%
7 2669
8.0%
5 2439
7.3%
9 1846
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/ 11136
61.5%
. 5568
30.8%
: 1392
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 51504
56.1%
Latin 40368
43.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4176
10.3%
i 4176
10.3%
e 4176
10.3%
t 2784
 
6.9%
r 2784
 
6.9%
u 2784
 
6.9%
s 2784
 
6.9%
l 2784
 
6.9%
g 2784
 
6.9%
p 2784
 
6.9%
Other values (6) 8352
20.7%
Common
ValueCountFrequency (%)
/ 11136
21.6%
0 5748
11.2%
. 5568
10.8%
1 4397
 
8.5%
4 3806
 
7.4%
2 3556
 
6.9%
6 3183
 
6.2%
3 3019
 
5.9%
8 2745
 
5.3%
7 2669
 
5.2%
Other values (3) 5677
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 11136
 
12.1%
0 5748
 
6.3%
. 5568
 
6.1%
1 4397
 
4.8%
o 4176
 
4.5%
i 4176
 
4.5%
e 4176
 
4.5%
4 3806
 
4.1%
2 3556
 
3.9%
6 3183
 
3.5%
Other values (19) 41950
45.7%

코스명
Text

Distinct143
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-12-11T16:16:55.816373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length40
Mean length27.581155
Min length10

Characters and Unicode

Total characters40103
Distinct characters61
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGwanaksan jarakgil(Mujangaesupgil)
2nd rowGwanaksan jarakgil(Mujangaesupgil)
3rd rowGwanaksan jarakgil(Mujangaesupgil)
4th rowGwanaksan jarakgil(Mujangaesupgil)
5th rowGwanaksan jarakgil(Mujangaesupgil)
ValueCountFrequency (%)
outing 579
 
12.2%
road 577
 
12.1%
mountain 497
 
10.4%
trails 175
 
3.7%
walking 172
 
3.6%
forest 156
 
3.3%
course 116
 
2.4%
park 76
 
1.6%
dongjak 64
 
1.3%
gil 53
 
1.1%
Other values (196) 2295
48.2%
2023-12-11T16:16:56.366106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 4529
 
11.3%
o 3584
 
8.9%
a 3535
 
8.8%
3306
 
8.2%
g 2989
 
7.5%
i 2495
 
6.2%
u 2122
 
5.3%
e 2048
 
5.1%
t 1539
 
3.8%
r 1310
 
3.3%
Other values (51) 12646
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31291
78.0%
Uppercase Letter 4837
 
12.1%
Space Separator 3306
 
8.2%
Dash Punctuation 254
 
0.6%
Decimal Number 180
 
0.4%
Open Punctuation 82
 
0.2%
Close Punctuation 82
 
0.2%
Connector Punctuation 64
 
0.2%
Other Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4529
14.5%
o 3584
11.5%
a 3535
11.3%
g 2989
9.6%
i 2495
8.0%
u 2122
 
6.8%
e 2048
 
6.5%
t 1539
 
4.9%
r 1310
 
4.2%
l 1268
 
4.1%
Other values (14) 5872
18.8%
Uppercase Letter
ValueCountFrequency (%)
M 596
12.3%
O 585
12.1%
R 579
12.0%
C 467
9.7%
S 449
9.3%
D 273
 
5.6%
B 261
 
5.4%
T 233
 
4.8%
G 213
 
4.4%
W 211
 
4.4%
Other values (13) 970
20.1%
Decimal Number
ValueCountFrequency (%)
1 28
15.6%
4 26
14.4%
7 25
13.9%
3 23
12.8%
6 23
12.8%
2 19
10.6%
8 18
10.0%
5 18
10.0%
Space Separator
ValueCountFrequency (%)
3306
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 254
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 64
100.0%
Other Punctuation
ValueCountFrequency (%)
' 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36128
90.1%
Common 3975
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4529
12.5%
o 3584
 
9.9%
a 3535
 
9.8%
g 2989
 
8.3%
i 2495
 
6.9%
u 2122
 
5.9%
e 2048
 
5.7%
t 1539
 
4.3%
r 1310
 
3.6%
l 1268
 
3.5%
Other values (37) 10709
29.6%
Common
ValueCountFrequency (%)
3306
83.2%
- 254
 
6.4%
( 82
 
2.1%
) 82
 
2.1%
_ 64
 
1.6%
1 28
 
0.7%
4 26
 
0.7%
7 25
 
0.6%
3 23
 
0.6%
6 23
 
0.6%
Other values (4) 62
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4529
 
11.3%
o 3584
 
8.9%
a 3535
 
8.8%
3306
 
8.2%
g 2989
 
7.5%
i 2495
 
6.2%
u 2122
 
5.3%
e 2048
 
5.1%
t 1539
 
3.8%
r 1310
 
3.3%
Other values (51) 12646
31.5%

작성시간
Date

Distinct143
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Minimum2013-09-02 14:20:30
Maximum2014-06-17 20:21:52
2023-12-11T16:16:56.551027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:56.718218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

포인트순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1454
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean778.51719
Minimum1
Maximum1554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T16:16:56.891351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile76.65
Q1405.25
median775.5
Q31158.75
95-th percentile1452.35
Maximum1554
Range1553
Interquartile range (IQR)753.5

Descriptive statistics

Standard deviation434.96062
Coefficient of variation (CV)0.55870393
Kurtosis-1.1739808
Mean778.51719
Median Absolute Deviation (MAD)377
Skewness-0.0045124202
Sum1131964
Variance189190.74
MonotonicityNot monotonic
2023-12-11T16:16:57.073485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1469 1
 
0.1%
352 1
 
0.1%
350 1
 
0.1%
360 1
 
0.1%
1202 1
 
0.1%
1200 1
 
0.1%
1199 1
 
0.1%
1198 1
 
0.1%
1197 1
 
0.1%
1196 1
 
0.1%
Other values (1444) 1444
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1554 1
0.1%
1553 1
0.1%
1552 1
0.1%
1551 1
0.1%
1550 1
0.1%
1549 1
0.1%
1548 1
0.1%
1547 1
0.1%
1546 1
0.1%
1545 1
0.1%

X 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1444
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199355.2
Minimum179599.9
Maximum454500.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T16:16:57.263136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum179599.9
5-th percentile186664.05
Q1193874.32
median199193.12
Q3204415.17
95-th percentile211180.93
Maximum454500.66
Range274900.75
Interquartile range (IQR)10540.853

Descriptive statistics

Standard deviation10022.204
Coefficient of variation (CV)0.050273103
Kurtosis288.03665
Mean199355.2
Median Absolute Deviation (MAD)5279.5233
Skewness11.315736
Sum2.8986245 × 108
Variance1.0044458 × 108
MonotonicityNot monotonic
2023-12-11T16:16:57.701919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200519.881272275 2
 
0.1%
206382.840384416 2
 
0.1%
197039.390996897 2
 
0.1%
192879.0074 2
 
0.1%
200167.65468939 2
 
0.1%
200562.208260008 2
 
0.1%
214035.0227 2
 
0.1%
193471.314732012 2
 
0.1%
200327.65468939 2
 
0.1%
197109.206070053 2
 
0.1%
Other values (1434) 1434
98.6%
ValueCountFrequency (%)
179599.903969036 1
0.1%
180496.373654072 1
0.1%
180674.561522817 1
0.1%
180903.917398594 1
0.1%
181175.076134841 1
0.1%
181295.988800517 1
0.1%
181901.456917568 1
0.1%
182146.005919201 1
0.1%
182195.029323082 1
0.1%
182219.705783659 1
0.1%
ValueCountFrequency (%)
454500.655207824 1
0.1%
218001.589432497 1
0.1%
216836.601149545 1
0.1%
215806.507671187 1
0.1%
215729.520226368 1
0.1%
215484.212549485 1
0.1%
215319.600526181 1
0.1%
215287.239716526 1
0.1%
215284.926514792 1
0.1%
215087.795644165 1
0.1%

Y 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1445
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450706.33
Minimum203879.82
Maximum466722.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-12-11T16:16:57.867029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203879.82
5-th percentile440821.59
Q1445717.25
median451168.64
Q3454730.87
95-th percentile462401.19
Maximum466722.49
Range262842.67
Interquartile range (IQR)9013.6211

Descriptive statistics

Standard deviation9144.9436
Coefficient of variation (CV)0.020290249
Kurtosis364.35358
Mean450706.33
Median Absolute Deviation (MAD)4596.2771
Skewness-13.468739
Sum6.5532701 × 108
Variance83629993
MonotonicityNot monotonic
2023-12-11T16:16:58.053036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
454314.224911173 2
 
0.1%
445930.463106746 2
 
0.1%
459599.1853 2
 
0.1%
453708.768912227 2
 
0.1%
451030.484199484 2
 
0.1%
445980.908178033 2
 
0.1%
454742.431982646 2
 
0.1%
454782.432330428 2
 
0.1%
454258.768912227 2
 
0.1%
438549.6436 1
 
0.1%
Other values (1435) 1435
98.7%
ValueCountFrequency (%)
203879.821809826 1
0.1%
437173.091509343 1
0.1%
437204.5481 1
0.1%
437214.826002505 1
0.1%
437220.7767 1
0.1%
437240.754009181 1
0.1%
437325.464519087 1
0.1%
437468.179820936 1
0.1%
437908.4268 1
0.1%
438176.003 1
0.1%
ValueCountFrequency (%)
466722.4895 1
0.1%
466656.4256 1
0.1%
466638.1519 1
0.1%
466182.6014 1
0.1%
465942.1763 1
0.1%
465681.472 1
0.1%
465628.4768 1
0.1%
465551.8931 1
0.1%
465532.4264 1
0.1%
465509.29421499 1
0.1%

Interactions

2023-12-11T16:16:48.809445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:47.969067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:48.423157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:48.927108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:48.101604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:48.560674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:49.044735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:48.282259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:16:48.687916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:16:58.180456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코스 카테고리강북강남구분코드행정시행정구소요시간코스레벨연계지하철포인트순번X 좌표Y 좌표
코스 카테고리1.0000.1310.0310.5870.9000.5000.9370.9510.1760.079
강북강남구분코드0.1311.0000.0000.9990.6500.1000.9760.9070.0950.342
행정시0.0310.0001.0001.0000.2950.0970.3600.1300.0460.048
행정구0.5870.9991.0001.0000.8560.6440.9690.8650.9620.811
소요시간0.9000.6500.2950.8561.0000.7940.9870.8090.5660.374
코스레벨0.5000.1000.0970.6440.7941.0000.9190.6290.4590.054
연계지하철0.9370.9760.3600.9690.9870.9191.0000.9430.8020.632
포인트순번0.9510.9070.1300.8650.8090.6290.9431.0000.4620.319
X 좌표0.1760.0950.0460.9620.5660.4590.8020.4621.0000.076
Y 좌표0.0790.3420.0480.8110.3740.0540.6320.3190.0761.000
2023-12-11T16:16:58.364029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코스 카테고리코스레벨행정시소요시간행정구강북강남구분코드
코스 카테고리1.0000.4350.0230.6860.3160.160
코스레벨0.4351.0000.0290.5830.3800.166
행정시0.0230.0291.0000.1550.9890.000
소요시간0.6860.5830.1551.0000.3500.559
행정구0.3160.3800.9890.3501.0000.967
강북강남구분코드0.1600.1660.0000.5590.9671.000
2023-12-11T16:16:58.502045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
포인트순번X 좌표Y 좌표코스 카테고리강북강남구분코드행정시행정구소요시간코스레벨
포인트순번1.0000.1330.2120.6950.7440.0770.5210.4440.474
X 좌표0.1331.0000.1720.1340.1560.0770.8520.3460.179
Y 좌표0.2120.1721.0000.0660.2250.0790.6660.2410.060
코스 카테고리0.6950.1340.0661.0000.1600.0230.3160.6860.435
강북강남구분코드0.7440.1560.2250.1601.0000.0000.9670.5590.166
행정시0.0770.0770.0790.0230.0001.0000.9890.1550.029
행정구0.5210.8520.6660.3160.9670.9891.0000.3500.380
소요시간0.4440.3460.2410.6860.5590.1550.3501.0000.583
코스레벨0.4740.1790.0600.4350.1660.0290.3800.5831.000

Missing values

2023-12-11T16:16:49.218456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:16:49.493433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T16:16:49.666387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

메인 키코스 카테고리강북강남구분코드행정시행정구행정동거리소요시간코스레벨추천수연계지하철PDF파일경로코스명작성시간포인트순번X 좌표Y 좌표
0BE_IW43-102430001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01469195233.775863439603.680075
1BE_IW43-102530001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01470195275.999643439605.550574
2BE_IW43-102630001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01471195448.643146439470.042321
3BE_IW43-102730001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01472195406.657711439393.840679
4BE_IW43-102830001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01473195334.58107439514.214414
5BE_IW43-102930001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01474195241.599234440986.600309
6BE_IW43-103030001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01475195385.483302440835.702858
7BE_IW43-103130001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01476195325.526029439883.75637
8BE_IW43-102330001SeoulGwanak-guDaehak-dong1.3km30분102호선<NA>Gwanaksan jarakgil(Mujangaesupgil)2014-06-17 20:21:52.01468195190.403054439598.519376
9BE_IW43-106840002SeoulJongno-guJongno5.6ga-dong1.8km1시간101호선2호선3호선4호선5호선http://gil.seoul.go.kr/view/course/2014/06/11/1366650430676180.zipHeunginjimun Section2014-06-09 15:35:46.01444200856.544504452395.219458
메인 키코스 카테고리강북강남구분코드행정시행정구행정동거리소요시간코스레벨추천수연계지하철PDF파일경로코스명작성시간포인트순번X 좌표Y 좌표
1444BE_IW43-086220002SeoulNowon-guGongneung2-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.09207447.019255457883.837581
1445BE_IW43-085420002SeoulDobong-guDobong2-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.01204113.489311465475.962367
1446BE_IW43-086020002SeoulNowon-guJunggyebon-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.07207664.266461258.9684
1447BE_IW43-085520002SeoulDobong-guDobong2-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.02204239.7557465532.4264
1448BE_IW43-086120002SeoulNowon-guGongneung2-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.08208165.9087459700.4169
1449BE_IW43-085920002SeoulNowon-guSanggye3.4-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.06207067.9227462587.3225
1450BE_IW43-085820002SeoulNowon-guSanggye3.4-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.05207451.3711463012.2987
1451BE_IW43-085720002SeoulNowon-guSanggye3.4-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.04207359.0804463077.0889
1452BE_IW43-085620002SeoulNowon-guSanggye3.4-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.03208152.4698464185.2523
1453BE_IW43-086820002SeoulNowon-guSanggye3.4-dong14.3km6시간 30분301호선4호선6호선7호선http://gil.seoul.go.kr/view/course/2015/04/13/1688177149964338.zip1Course-Surak Bulam Mountain Course2013-09-02 14:20:30.01491207381.865618463197.473197