Overview

Dataset statistics

Number of variables16
Number of observations2383
Missing cells5038
Missing cells (%)13.2%
Duplicate rows139
Duplicate rows (%)5.8%
Total size in memory312.0 KiB
Average record size in memory134.1 B

Variable types

Categorical7
Text3
Numeric4
Unsupported2

Dataset

Description경기도 역사내 승강기 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=T02U2VP9MQ87PHS73C9832445229&infSeq=1

Alerts

Dataset has 139 (5.8%) duplicate rowsDuplicates
철도운영기관코드 is highly overall correlated with 철도운영기관명 and 4 other fieldsHigh correlation
선코드 is highly overall correlated with 철도운영기관명 and 4 other fieldsHigh correlation
철도운영기관명 is highly overall correlated with 선명 and 4 other fieldsHigh correlation
선명 is highly overall correlated with 철도운영기관명 and 4 other fieldsHigh correlation
정원수 is highly overall correlated with 정원무게High correlation
정원무게 is highly overall correlated with 정원수High correlation
시작층명 is highly overall correlated with 철도운영기관명 and 3 other fieldsHigh correlation
종료층명 is highly overall correlated with 철도운영기관명 and 3 other fieldsHigh correlation
출입구번호 is highly imbalanced (51.7%)Imbalance
위치 has 63 (2.6%) missing valuesMissing
정원수 has 92 (3.9%) missing valuesMissing
정원무게 has 95 (4.0%) missing valuesMissing
승강기상태 has 2383 (100.0%) missing valuesMissing
승강기번호 has 2383 (100.0%) missing valuesMissing
승강기상태 is an unsupported type, check if it needs cleaning or further analysisUnsupported
승강기번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-01-14 07:58:09.908911
Analysis finished2024-01-14 07:58:14.816651
Duration4.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
서울교통공사
824 
코레일
776 
인천교통공사
253 
서울메트로9호선주식회사
156 
네오트랜스주식회사
 
74
Other values (7)
300 

Length

Max length12
Median length11
Mean length5.9530004
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울교통공사 824
34.6%
코레일 776
32.6%
인천교통공사 253
 
10.6%
서울메트로9호선주식회사 156
 
6.5%
네오트랜스주식회사 74
 
3.1%
공항철도주식회사 70
 
2.9%
의정부경량전철주식회사 54
 
2.3%
우이신설경전철주식회사 52
 
2.2%
용인경량전철주식회사 50
 
2.1%
서해철도주식회사 47
 
2.0%
Other values (2) 27
 
1.1%

Length

2024-01-14T16:58:14.923591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울교통공사 824
34.6%
코레일 776
32.6%
인천교통공사 253
 
10.6%
서울메트로9호선주식회사 156
 
6.5%
네오트랜스주식회사 74
 
3.1%
공항철도주식회사 70
 
2.9%
의정부경량전철주식회사 54
 
2.3%
우이신설경전철주식회사 52
 
2.2%
용인경량전철주식회사 50
 
2.1%
서해철도주식회사 47
 
2.0%
Other values (2) 27
 
1.1%

선명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
1호선
351 
7호선
179 
5호선
175 
경의중앙
157 
9호선
156 
Other values (18)
1365 

Length

Max length6
Median length3
Mean length3.4968527
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1호선 351
14.7%
7호선 179
 
7.5%
5호선 175
 
7.3%
경의중앙 157
 
6.6%
9호선 156
 
6.5%
4호선 149
 
6.3%
2호선 148
 
6.2%
3호선 125
 
5.2%
수인분당 121
 
5.1%
인천2호선 114
 
4.8%
Other values (13) 708
29.7%

Length

2024-01-14T16:58:15.103354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1호선 351
14.7%
7호선 179
 
7.5%
5호선 175
 
7.3%
경의중앙 157
 
6.6%
9호선 156
 
6.5%
4호선 149
 
6.3%
2호선 148
 
6.2%
3호선 125
 
5.2%
수인분당 121
 
5.1%
인천2호선 114
 
4.8%
Other values (13) 708
29.7%

역명
Text

Distinct607
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
2024-01-14T16:58:15.543336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length3.7528326
Min length2

Characters and Unicode

Total characters8943
Distinct characters320
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)1.1%

Sample

1st row신논현
2nd row신논현
3rd row신논현
4th row신논현
5th row강남
ValueCountFrequency (%)
공덕 20
 
0.8%
디지털미디어시티 19
 
0.8%
서울역 17
 
0.7%
김포공항 13
 
0.5%
홍대입구 13
 
0.5%
광명 12
 
0.5%
수원 12
 
0.5%
영등포 12
 
0.5%
금정 12
 
0.5%
주안 12
 
0.5%
Other values (597) 2241
94.0%
2024-01-14T16:58:16.412820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 297
 
3.3%
) 297
 
3.3%
대 279
 
3.1%
구 225
 
2.5%
신 192
 
2.1%
동 182
 
2.0%
산 176
 
2.0%
원 173
 
1.9%
청 167
 
1.9%
천 162
 
1.8%
Other values (310) 6793
76.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8282
92.6%
Open Punctuation 297
 
3.3%
Close Punctuation 297
 
3.3%
Decimal Number 40
 
0.4%
Other Punctuation 27
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 279
 
3.4%
구 225
 
2.7%
신 192
 
2.3%
동 182
 
2.2%
산 176
 
2.1%
원 173
 
2.1%
청 167
 
2.0%
천 162
 
2.0%
정 161
 
1.9%
시 145
 
1.8%
Other values (299) 6420
77.5%
Decimal Number
ValueCountFrequency (%)
3 10
25.0%
1 8
20.0%
4 8
20.0%
2 7
17.5%
9 4
 
10.0%
5 3
 
7.5%
Other Punctuation
ValueCountFrequency (%)
· 19
70.4%
? 4
 
14.8%
. 4
 
14.8%
Open Punctuation
ValueCountFrequency (%)
( 297
100.0%
Close Punctuation
ValueCountFrequency (%)
) 297
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8282
92.6%
Common 661
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 279
 
3.4%
구 225
 
2.7%
신 192
 
2.3%
동 182
 
2.2%
산 176
 
2.1%
원 173
 
2.1%
청 167
 
2.0%
천 162
 
2.0%
정 161
 
1.9%
시 145
 
1.8%
Other values (299) 6420
77.5%
Common
ValueCountFrequency (%)
( 297
44.9%
) 297
44.9%
· 19
 
2.9%
3 10
 
1.5%
1 8
 
1.2%
4 8
 
1.2%
2 7
 
1.1%
? 4
 
0.6%
. 4
 
0.6%
9 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8282
92.6%
ASCII 642
 
7.2%
None 19
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 297
46.3%
) 297
46.3%
3 10
 
1.6%
1 8
 
1.2%
4 8
 
1.2%
2 7
 
1.1%
? 4
 
0.6%
. 4
 
0.6%
9 4
 
0.6%
5 3
 
0.5%
Hangul
ValueCountFrequency (%)
대 279
 
3.4%
구 225
 
2.7%
신 192
 
2.3%
동 182
 
2.2%
산 176
 
2.1%
원 173
 
2.1%
청 167
 
2.0%
천 162
 
2.0%
정 161
 
1.9%
시 145
 
1.8%
Other values (299) 6420
77.5%
None
ValueCountFrequency (%)
· 19
100.0%

철도운영기관코드
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
S1
824 
KR
776 
IC
253 
S9
156 
DX
 
74
Other values (7)
300 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
S1 824
34.6%
KR 776
32.6%
IC 253
 
10.6%
S9 156
 
6.5%
DX 74
 
3.1%
AR 70
 
2.9%
UL 54
 
2.3%
UI 52
 
2.2%
EV 50
 
2.1%
SW 47
 
2.0%
Other values (2) 27
 
1.1%

Length

2024-01-14T16:58:16.632749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s1 824
34.6%
kr 776
32.6%
ic 253
 
10.6%
s9 156
 
6.5%
dx 74
 
3.1%
ar 70
 
2.9%
ul 54
 
2.3%
ui 52
 
2.2%
ev 50
 
2.1%
sw 47
 
2.0%
Other values (2) 27
 
1.1%

선코드
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
1
351 
7
179 
5
175 
K4
157 
9
156 
Other values (18)
1365 

Length

Max length2
Median length1
Mean length1.3902644
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD1
2nd rowD1
3rd rowD1
4th rowD1
5th rowD1

Common Values

ValueCountFrequency (%)
1 351
14.7%
7 179
 
7.5%
5 175
 
7.3%
K4 157
 
6.6%
9 156
 
6.5%
4 149
 
6.3%
2 148
 
6.2%
3 125
 
5.2%
K1 121
 
5.1%
I2 114
 
4.8%
Other values (13) 708
29.7%

Length

2024-01-14T16:58:16.810647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 351
14.7%
7 179
 
7.5%
5 175
 
7.3%
k4 157
 
6.6%
9 156
 
6.5%
4 149
 
6.3%
2 148
 
6.2%
3 125
 
5.2%
k1 121
 
5.1%
i2 114
 
4.8%
Other values (13) 708
29.7%

역코드
Text

Distinct635
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
2024-01-14T16:58:17.337662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.6000839
Min length3

Characters and Unicode

Total characters8579
Distinct characters19
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

Unique32 ?
Unique (%)1.3%

Sample

1st row4306
2nd row4306
3rd row4306
4th row4306
5th row4307
ValueCountFrequency (%)
k410 16
 
0.7%
139 16
 
0.7%
0118 12
 
0.5%
p149 12
 
0.5%
221 12
 
0.5%
761 11
 
0.5%
156 11
 
0.5%
219 10
 
0.4%
a01 10
 
0.4%
117 10
 
0.4%
Other values (625) 2263
95.0%
2024-01-14T16:58:18.160490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1691
19.7%
2 1617
18.8%
3 861
10.0%
0 746
8.7%
4 724
8.4%
5 634
 
7.4%
6 444
 
5.2%
7 442
 
5.2%
9 388
 
4.5%
8 318
 
3.7%
Other values (9) 714
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7865
91.7%
Uppercase Letter 708
 
8.3%
Lowercase Letter 4
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1691
21.5%
2 1617
20.6%
3 861
10.9%
0 746
9.5%
4 724
9.2%
5 634
 
8.1%
6 444
 
5.6%
7 442
 
5.6%
9 388
 
4.9%
8 318
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
K 291
41.1%
P 170
24.0%
S 99
 
14.0%
A 70
 
9.9%
Y 50
 
7.1%
D 18
 
2.5%
G 10
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
p 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7867
91.7%
Latin 712
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1691
21.5%
2 1617
20.6%
3 861
10.9%
0 746
9.5%
4 724
9.2%
5 634
 
8.1%
6 444
 
5.6%
7 442
 
5.6%
9 388
 
4.9%
8 318
 
4.0%
Latin
ValueCountFrequency (%)
K 291
40.9%
P 170
23.9%
S 99
 
13.9%
A 70
 
9.8%
Y 50
 
7.0%
D 18
 
2.5%
G 10
 
1.4%
p 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1691
19.7%
2 1617
18.8%
3 861
10.0%
0 746
8.7%
4 724
8.4%
5 634
 
7.4%
6 444
 
5.2%
7 442
 
5.2%
9 388
 
4.5%
8 318
 
3.7%
Other values (9) 714
8.3%

출입구번호
Categorical

IMBALANCE 

Distinct42
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
<NA>
1265 
1
342 
2
215 
3
 
120
4
 
80
Other values (37)
361 

Length

Max length9
Median length4
Mean length2.7784305
Min length1

Unique

Unique11 ?
Unique (%)0.5%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row3,4

Common Values

ValueCountFrequency (%)
<NA> 1265
53.1%
1 342
 
14.4%
2 215
 
9.0%
3 120
 
5.0%
4 80
 
3.4%
1,2 58
 
2.4%
6 55
 
2.3%
5 49
 
2.1%
7 27
 
1.1%
3,4 26
 
1.1%
Other values (32) 146
 
6.1%

Length

2024-01-14T16:58:18.438799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1265
53.1%
1 342
 
14.4%
2 215
 
9.0%
3 120
 
5.0%
4 80
 
3.4%
1,2 59
 
2.5%
6 55
 
2.3%
5 49
 
2.1%
7 27
 
1.1%
3,4 26
 
1.1%
Other values (31) 145
 
6.1%

위치
Text

MISSING 

Distinct1672
Distinct (%)72.1%
Missing63
Missing (%)2.6%
Memory size18.7 KiB
2024-01-14T16:58:18.919768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length102
Median length76
Mean length27.058621
Min length2

Characters and Unicode

Total characters62776
Distinct characters403
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1506 ?
Unique (%)64.9%

Sample

1st row(B5) 강남 방면(하행) 승강장 3-4 출입문 앞
2nd row(B5) 논현 방면(상행) 승강장 4-1출입문 앞
3rd row(B5) 강남 방면(하행) 승강장 6-4 출입문 앞
4th row(B5) 논현 방면(상행) 승강장 1-1 출입문 앞
5th row (1) 3번 출입구 근처, (B1) 상가층 중앙, (B3) 고객센터 앞 대합실
ValueCountFrequency (%)
승강장 962
 
6.6%
방향 942
 
6.4%
출입구 899
 
6.1%
앞 757
 
5.2%
1f 607
 
4.1%
옆 516
 
3.5%
출입문 509
 
3.5%
b1 487
 
3.3%
b2 282
 
1.9%
2f 274
 
1.9%
Other values (1635) 8408
57.4%
2024-01-14T16:58:19.645587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12911
20.6%
( 3572
 
5.7%
) 3569
 
5.7%
1 3318
 
5.3%
B 2461
 
3.9%
2 1952
 
3.1%
출 1810
 
2.9%
- 1780
 
2.8%
F 1755
 
2.8%
입 1712
 
2.7%
Other values (393) 27936
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27823
44.3%
Space Separator 12911
20.6%
Decimal Number 8106
 
12.9%
Uppercase Letter 4357
 
6.9%
Open Punctuation 3572
 
5.7%
Close Punctuation 3569
 
5.7%
Dash Punctuation 1780
 
2.8%
Other Punctuation 555
 
0.9%
Math Symbol 92
 
0.1%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
출 1810
 
6.5%
입 1712
 
6.2%
번 1483
 
5.3%
구 1426
 
5.1%
방 1425
 
5.1%
승 1236
 
4.4%
장 1225
 
4.4%
향 1129
 
4.1%
강 1124
 
4.0%
앞 823
 
3.0%
Other values (340) 14430
51.9%
Uppercase Letter
ValueCountFrequency (%)
B 2461
56.5%
F 1755
40.3%
E 30
 
0.7%
S 26
 
0.6%
M 16
 
0.4%
T 9
 
0.2%
K 9
 
0.2%
A 9
 
0.2%
X 8
 
0.2%
V 8
 
0.2%
Other values (11) 26
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 3318
40.9%
2 1952
24.1%
3 1161
 
14.3%
4 788
 
9.7%
5 278
 
3.4%
6 185
 
2.3%
7 172
 
2.1%
8 130
 
1.6%
9 71
 
0.9%
0 51
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 325
58.6%
/ 191
34.4%
· 13
 
2.3%
# 10
 
1.8%
. 9
 
1.6%
? 5
 
0.9%
; 1
 
0.2%
& 1
 
0.2%
Math Symbol
ValueCountFrequency (%)
> 36
39.1%
~ 29
31.5%
< 14
 
15.2%
↔ 10
 
10.9%
→ 2
 
2.2%
+ 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
m 8
80.0%
v 1
 
10.0%
e 1
 
10.0%
Space Separator
ValueCountFrequency (%)
12911
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3572
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3569
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1780
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30586
48.7%
Hangul 27823
44.3%
Latin 4367
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
출 1810
 
6.5%
입 1712
 
6.2%
번 1483
 
5.3%
구 1426
 
5.1%
방 1425
 
5.1%
승 1236
 
4.4%
장 1225
 
4.4%
향 1129
 
4.1%
강 1124
 
4.0%
앞 823
 
3.0%
Other values (340) 14430
51.9%
Common
ValueCountFrequency (%)
12911
42.2%
( 3572
 
11.7%
) 3569
 
11.7%
1 3318
 
10.8%
2 1952
 
6.4%
- 1780
 
5.8%
3 1161
 
3.8%
4 788
 
2.6%
, 325
 
1.1%
5 278
 
0.9%
Other values (19) 932
 
3.0%
Latin
ValueCountFrequency (%)
B 2461
56.4%
F 1755
40.2%
E 30
 
0.7%
S 26
 
0.6%
M 16
 
0.4%
T 9
 
0.2%
K 9
 
0.2%
A 9
 
0.2%
X 8
 
0.2%
m 8
 
0.2%
Other values (14) 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34928
55.6%
Hangul 27823
44.3%
None 13
 
< 0.1%
Arrows 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12911
37.0%
( 3572
 
10.2%
) 3569
 
10.2%
1 3318
 
9.5%
B 2461
 
7.0%
2 1952
 
5.6%
- 1780
 
5.1%
F 1755
 
5.0%
3 1161
 
3.3%
4 788
 
2.3%
Other values (40) 1661
 
4.8%
Hangul
ValueCountFrequency (%)
출 1810
 
6.5%
입 1712
 
6.2%
번 1483
 
5.3%
구 1426
 
5.1%
방 1425
 
5.1%
승 1236
 
4.4%
장 1225
 
4.4%
향 1129
 
4.1%
강 1124
 
4.0%
앞 823
 
3.0%
Other values (340) 14430
51.9%
None
ValueCountFrequency (%)
· 13
100.0%
Arrows
ValueCountFrequency (%)
↔ 10
83.3%
→ 2
 
16.7%

시작층명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
지하
1429 
지상
942 
<NA>
 
12

Length

Max length4
Median length2
Mean length2.0100713
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지하 1429
60.0%
지상 942
39.5%
<NA> 12
 
0.5%

Length

2024-01-14T16:58:19.868339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:58:20.050591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하 1429
60.0%
지상 942
39.5%
na 12
 
0.5%

시작운행층수
Real number (ℝ)

Distinct7
Distinct (%)0.3%
Missing12
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.5744412
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-01-14T16:58:20.178119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92266609
Coefficient of variation (CV)0.58602767
Kurtosis3.939393
Mean1.5744412
Median Absolute Deviation (MAD)0
Skewness1.8857775
Sum3733
Variance0.85131271
MonotonicityNot monotonic
2024-01-14T16:58:20.335151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 1507
63.2%
2 532
 
22.3%
3 208
 
8.7%
4 91
 
3.8%
5 28
 
1.2%
7 4
 
0.2%
6 1
 
< 0.1%
(Missing) 12
 
0.5%
ValueCountFrequency (%)
1 1507
63.2%
2 532
 
22.3%
3 208
 
8.7%
4 91
 
3.8%
5 28
 
1.2%
6 1
 
< 0.1%
7 4
 
0.2%
ValueCountFrequency (%)
7 4
 
0.2%
6 1
 
< 0.1%
5 28
 
1.2%
4 91
 
3.8%
3 208
 
8.7%
2 532
 
22.3%
1 1507
63.2%

종료층명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.7 KiB
지상
1262 
지하
1115 
<NA>
 
6

Length

Max length4
Median length2
Mean length2.0050357
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지하
2nd row지하
3rd row지하
4th row지하
5th row지상

Common Values

ValueCountFrequency (%)
지상 1262
53.0%
지하 1115
46.8%
<NA> 6
 
0.3%

Length

2024-01-14T16:58:20.555945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:58:20.714921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 1262
53.0%
지하 1115
46.8%
na 6
 
0.3%

종료운행층수
Real number (ℝ)

Distinct7
Distinct (%)0.3%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.82933
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-01-14T16:58:20.842839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90991323
Coefficient of variation (CV)0.49740246
Kurtosis2.6165256
Mean1.82933
Median Absolute Deviation (MAD)1
Skewness1.2402021
Sum4341
Variance0.82794209
MonotonicityNot monotonic
2024-01-14T16:58:21.011551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 1030
43.2%
2 854
35.8%
3 389
 
16.3%
4 75
 
3.1%
5 18
 
0.8%
6 5
 
0.2%
8 2
 
0.1%
(Missing) 10
 
0.4%
ValueCountFrequency (%)
1 1030
43.2%
2 854
35.8%
3 389
 
16.3%
4 75
 
3.1%
5 18
 
0.8%
6 5
 
0.2%
8 2
 
0.1%
ValueCountFrequency (%)
8 2
 
0.1%
6 5
 
0.2%
5 18
 
0.8%
4 75
 
3.1%
3 389
 
16.3%
2 854
35.8%
1 1030
43.2%

정원수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)0.7%
Missing92
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean15.47883
Minimum8
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-01-14T16:58:21.170404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q115
median15
Q315
95-th percentile24
Maximum40
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1146732
Coefficient of variation (CV)0.20122149
Kurtosis23.740656
Mean15.47883
Median Absolute Deviation (MAD)0
Skewness3.7582612
Sum35462
Variance9.7011892
MonotonicityNot monotonic
2024-01-14T16:58:21.343103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
15 1544
64.8%
17 267
 
11.2%
13 154
 
6.5%
11 138
 
5.8%
24 101
 
4.2%
20 22
 
0.9%
18 22
 
0.9%
40 12
 
0.5%
21 9
 
0.4%
9 6
 
0.3%
Other values (5) 16
 
0.7%
(Missing) 92
 
3.9%
ValueCountFrequency (%)
8 3
 
0.1%
9 6
 
0.3%
10 5
 
0.2%
11 138
 
5.8%
12 2
 
0.1%
13 154
 
6.5%
15 1544
64.8%
16 3
 
0.1%
17 267
 
11.2%
18 22
 
0.9%
ValueCountFrequency (%)
40 12
 
0.5%
38 3
 
0.1%
24 101
 
4.2%
21 9
 
0.4%
20 22
 
0.9%
18 22
 
0.9%
17 267
 
11.2%
16 3
 
0.1%
15 1544
64.8%
13 154
 
6.5%

정원무게
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)1.0%
Missing95
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1044.5686
Minimum450
Maximum2600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 KiB
2024-01-14T16:58:21.510618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum450
5-th percentile750
Q11000
median1000
Q31000
95-th percentile1600
Maximum2600
Range2150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation203.20391
Coefficient of variation (CV)0.1945338
Kurtosis22.05863
Mean1044.5686
Median Absolute Deviation (MAD)0
Skewness3.6886833
Sum2389973
Variance41291.827
MonotonicityNot monotonic
2024-01-14T16:58:21.659910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1000 1558
65.4%
1150 279
 
11.7%
750 130
 
5.5%
1600 102
 
4.3%
900 81
 
3.4%
1050 27
 
1.1%
1190 22
 
0.9%
1350 17
 
0.7%
1080 13
 
0.5%
1040 12
 
0.5%
Other values (14) 47
 
2.0%
(Missing) 95
 
4.0%
ValueCountFrequency (%)
450 2
 
0.1%
500 1
 
< 0.1%
600 7
 
0.3%
750 130
 
5.5%
900 81
 
3.4%
1000 1558
65.4%
1001 1
 
< 0.1%
1002 1
 
< 0.1%
1005 4
 
0.2%
1040 12
 
0.5%
ValueCountFrequency (%)
2600 12
 
0.5%
2500 3
 
0.1%
2000 1
 
< 0.1%
1800 4
 
0.2%
1600 102
 
4.3%
1500 2
 
0.1%
1350 17
 
0.7%
1275 6
 
0.3%
1190 22
 
0.9%
1150 279
11.7%

승강기상태
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2383
Missing (%)100.0%
Memory size21.1 KiB

승강기번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2383
Missing (%)100.0%
Memory size21.1 KiB

Interactions

2024-01-14T16:58:13.550303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:11.542851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:12.105743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:12.990907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:13.694857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:11.671201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:12.233237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:13.149825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:13.829640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:11.822895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:12.370892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:13.281958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:13.961100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:11.995902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:12.495362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:58:13.422144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T16:58:21.795822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명선명철도운영기관코드선코드출입구번호시작층명시작운행층수종료층명종료운행층수정원수정원무게
철도운영기관명1.0000.9971.0000.9970.2190.6970.4210.6720.3870.5390.515
선명0.9971.0000.9971.0000.2100.6700.4950.6800.5270.6640.649
철도운영기관코드1.0000.9971.0000.9970.2190.6970.4210.6720.3870.5390.515
선코드0.9971.0000.9971.0000.2100.6700.4950.6800.5270.6640.649
출입구번호0.2190.2100.2190.2101.0000.3470.1600.2110.3800.0830.000
시작층명0.6970.6700.6970.6700.3471.0000.3290.5930.3990.1050.128
시작운행층수0.4210.4950.4210.4950.1600.3291.0000.3780.5680.2900.200
종료층명0.6720.6800.6720.6800.2110.5930.3781.0000.1410.1450.151
종료운행층수0.3870.5270.3870.5270.3800.3990.5680.1411.0000.2880.183
정원수0.5390.6640.5390.6640.0830.1050.2900.1450.2881.0000.891
정원무게0.5150.6490.5150.6490.0000.1280.2000.1510.1830.8911.000
2024-01-14T16:58:21.976444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관코드선코드시작층명종료층명출입구번호철도운영기관명선명
철도운영기관코드1.0000.9780.5490.5290.0731.0000.978
선코드0.9781.0000.5900.6000.0510.9781.000
시작층명0.5490.5901.0000.4040.2860.5490.590
종료층명0.5290.6000.4041.0000.1740.5290.600
출입구번호0.0730.0510.2860.1741.0000.0730.051
철도운영기관명1.0000.9780.5490.5290.0731.0000.978
선명0.9781.0000.5900.6000.0510.9781.000
2024-01-14T16:58:22.148653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시작운행층수종료운행층수정원수정원무게철도운영기관명선명철도운영기관코드선코드출입구번호시작층명종료층명
시작운행층수1.0000.0100.1290.1120.2200.2380.2200.2380.0730.3520.405
종료운행층수0.0101.000-0.195-0.2090.2000.2580.2000.2580.1980.4270.151
정원수0.129-0.1951.0000.8930.3280.3840.3280.3840.0640.0980.147
정원무게0.112-0.2090.8931.0000.2640.3140.2640.3140.0000.1300.152
철도운영기관명0.2200.2000.3280.2641.0000.9781.0000.9780.0730.5490.529
선명0.2380.2580.3840.3140.9781.0000.9781.0000.0510.5900.600
철도운영기관코드0.2200.2000.3280.2641.0000.9781.0000.9780.0730.5490.529
선코드0.2380.2580.3840.3140.9781.0000.9781.0000.0510.5900.600
출입구번호0.0730.1980.0640.0000.0730.0510.0730.0511.0000.2860.174
시작층명0.3520.4270.0980.1300.5490.5900.5490.5900.2861.0000.404
종료층명0.4050.1510.1470.1520.5290.6000.5290.6000.1740.4041.000

Missing values

2024-01-14T16:58:14.180952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T16:58:14.486846image/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.
2024-01-14T16:58:14.689840image/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

철도운영기관명선명역명철도운영기관코드선코드역코드출입구번호위치시작층명시작운행층수종료층명종료운행층수정원수정원무게승강기상태승강기번호
0네오트랜스주식회사신분당선신논현DXD14306<NA>(B5) 강남 방면(하행) 승강장 3-4 출입문 앞지하5지하3171150<NA><NA>
1네오트랜스주식회사신분당선신논현DXD14306<NA>(B5) 논현 방면(상행) 승강장 4-1출입문 앞지하5지하3171150<NA><NA>
2네오트랜스주식회사신분당선신논현DXD14306<NA>(B5) 강남 방면(하행) 승강장 6-4 출입문 앞지하5지하2171150<NA><NA>
3네오트랜스주식회사신분당선신논현DXD14306<NA>(B5) 논현 방면(상행) 승강장 1-1 출입문 앞지하5지하2171150<NA><NA>
4네오트랜스주식회사신분당선강남DXD143073,4(1) 3번 출입구 근처, (B1) 상가층 중앙, (B3) 고객센터 앞 대합실지하3지상1151000<NA><NA>
5네오트랜스주식회사신분당선강남DXD143075,6(1) 6번 출입구 근처, (B1) 상가층 중앙, (B3) 대합실 수유실 옆지하3지상1151000<NA><NA>
6네오트랜스주식회사신분당선강남DXD14307<NA>(B3) 대합실 중앙 표 내는 곳 근처, (B4) 양재방향 승강장 5-2 출입문 앞지하4지하3151000<NA><NA>
7네오트랜스주식회사신분당선강남DXD14307<NA>(B3) 대합실 중앙 표 내는 곳 근처, (B4) 신논현방향 승강장 2-1 출입문 앞지하4지하3151000<NA><NA>
8네오트랜스주식회사신분당선강남DXD14307<NA>(B2) 2호선 역삼 방향 승강장 7-1 출입문 근처, (B3) 환승통로 잠실방면 근처지하3지하2151000<NA><NA>
9네오트랜스주식회사신분당선강남DXD14307<NA>(B2) 2호선 교대 방향 승강장 4-4 출입문 근처, (B3) 환승통로 교대방면 근처지하3지하2151000<NA><NA>
철도운영기관명선명역명철도운영기관코드선코드역코드출입구번호위치시작층명시작운행층수종료층명종료운행층수정원수정원무게승강기상태승강기번호
2373서울교통공사3호선대청S133378(B2-F1) (B2) 대합실 / (B1) 대합실 / (1F) 8번 출입구 옆지하2지상1151000<NA><NA>
2374서울교통공사4호선당고개S144091,4(F1-F2)1-4번 출입구사이지상1지상2151000<NA><NA>
2375서울교통공사4호선미아사거리S14416<NA>길음역 방향 승강장 8-3 출입문 앞 (내부#2)지하1지하212900<NA><NA>
2376서울메트로9호선주식회사9호선샛강S990916<NA>(B2) 표내는 곳(9호선, 신림선) 방향, 1번~4번 출입구 방향 (B3) 여의도역 방향 승강장 1-1 출입문 옆지하3지하2151000<NA><NA>
2377서울메트로9호선주식회사9호선동작(현충원)S990920<NA>(B2) 4,9호선 환승통로, 1번~9번 출입구 방향 (B3) 구반포역방향 승강장 1-2 출입문 앞지하3지하2151000<NA><NA>
2378서울메트로9호선주식회사9호선신논현S9909251,2<NA>지하2지상1151000<NA><NA>
2379서울메트로9호선주식회사9호선봉은사S9992955번 출구와 6번 출구 사이지하2지상1151000<NA><NA>
2380서울메트로9호선주식회사9호선석촌고분S9993233번 출입구 근처<NA><NA>지하1<NA><NA><NA><NA>
2381서울메트로9호선주식회사9호선올림픽공원(한국체대)S9993644번 출입구 근처지상1지하1<NA><NA><NA><NA>
2382서해철도주식회사서해선소새울SWWSS17<NA>지하 1층 대합실 개찰구 통과 후 대야방면 승강장 방향 계단 옆지하1지하3<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

철도운영기관명선명역명철도운영기관코드선코드역코드출입구번호위치시작층명시작운행층수종료층명종료운행층수정원수정원무게# duplicates
103서울메트로9호선주식회사9호선선정릉S9992711번 출구와 4번 출구 사이지하3지하11510004
116서해철도주식회사서해선초지SWWSS26<NA><NA><NA><NA><NA><NA><NA><NA>3
117우이신설경전철주식회사우이신설성신여대입구UIUIS120<NA>(B4) 한성대입구역 방향 환승통로 (B2) 보문역 방향 환승통로지하4지하23825003
0공항철도주식회사공항철도공덕ARA1A02<NA>(B3) 운임구역대합실 화장실 맞은편 (B5) 서울방향 승강장 3-4 출입문 앞지하5지하31711502
1공항철도주식회사공항철도김포공항ARA1A05<NA>(B1) 표내는곳 앞 (B2) 김포선 방향 (B3) 서울역 방향 5-4 출입문 앞 (B4) 인천공항 방향 2-1 출입문 앞지하4지하11510002
2공항철도주식회사공항철도인천공항2터미널ARA1A11<NA>(B1) 제2여객터미널 (B3) 인천공항1터미널역 방향 승강장 3-2 출입문 앞지하2지하12416002
3공항철도주식회사공항철도인천공항2터미널ARA1A11<NA>(B1) 제2여객터미널 (B3) 인천공항1터미널역 방향 승강장 4-2 출입문 앞지하2지하12416002
4서울교통공사3호선일원S133381(B2-F1)1번 출입구측지하2지상11711502
5서울교통공사5호선강일S152562<NA>(B3-B1) 승강장지하3지하12416002
6서울교통공사5호선강일S152562<NA>(B3-B2) 승강장지하3지하22416002