Overview

Dataset statistics

Number of variables8
Number of observations1935
Missing cells109
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.8 KiB
Average record size in memory66.1 B

Variable types

Numeric2
Categorical2
Text4

Dataset

Description파일 다운로드
Author서울 교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13242/F/1/datasetView.do

Alerts

장비 has constant value ""Constant
연번 is highly overall correlated with 호선High correlation
호선 is highly overall correlated with 연번High correlation
운행구간 has 35 (1.8%) missing valuesMissing
설치위치 has 49 (2.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:43:07.569513
Analysis finished2023-12-11 04:43:09.097292
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean968
Minimum1
Maximum1935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2023-12-11T13:43:09.208361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile97.7
Q1484.5
median968
Q31451.5
95-th percentile1838.3
Maximum1935
Range1934
Interquartile range (IQR)967

Descriptive statistics

Standard deviation558.7307
Coefficient of variation (CV)0.57720114
Kurtosis-1.2
Mean968
Median Absolute Deviation (MAD)484
Skewness0
Sum1873080
Variance312180
MonotonicityStrictly increasing
2023-12-11T13:43:09.411691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1272 1
 
0.1%
1300 1
 
0.1%
1299 1
 
0.1%
1298 1
 
0.1%
1297 1
 
0.1%
1296 1
 
0.1%
1295 1
 
0.1%
1294 1
 
0.1%
1293 1
 
0.1%
Other values (1925) 1925
99.5%
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 (%)
1935 1
0.1%
1934 1
0.1%
1933 1
0.1%
1932 1
0.1%
1931 1
0.1%
1930 1
0.1%
1929 1
0.1%
1928 1
0.1%
1927 1
0.1%
1926 1
0.1%

호선
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
7
384 
5
349 
6
301 
2
231 
3
200 
Other values (5)
470 

Length

Max length4
Median length1
Mean length1.351938
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
7 384
19.8%
5 349
18.0%
6 301
15.6%
2 231
11.9%
3 200
10.3%
4 123
 
6.4%
7(연) 119
 
6.1%
5(연) 108
 
5.6%
8 87
 
4.5%
1 33
 
1.7%

Length

2023-12-11T13:43:09.910010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:43:10.099359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 384
19.8%
5 349
18.0%
6 301
15.6%
2 231
11.9%
3 200
10.3%
4 123
 
6.4%
7(연 119
 
6.1%
5(연 108
 
5.6%
8 87
 
4.5%
1 33
 
1.7%

역명
Text

Distinct236
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
2023-12-11T13:43:10.586336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length3.2160207
Min length2

Characters and Unicode

Total characters6223
Distinct characters210
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row서울(1)
2nd row서울(1)
3rd row서울(1)
4th row서울(1)
5th row서울(1)
ValueCountFrequency (%)
고속터미널 45
 
2.3%
가락시장 34
 
1.8%
하남시청 28
 
1.4%
하남풍산 24
 
1.2%
동묘앞 24
 
1.2%
이수 23
 
1.2%
태릉입구 23
 
1.2%
충무로(4 22
 
1.1%
합정 22
 
1.1%
오금 22
 
1.1%
Other values (226) 1668
86.2%
2023-12-11T13:43:11.191111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 198
 
3.2%
대 188
 
3.0%
동 169
 
2.7%
산 157
 
2.5%
신 149
 
2.4%
청 146
 
2.3%
남 124
 
2.0%
장 103
 
1.7%
시 97
 
1.6%
( 96
 
1.5%
Other values (200) 4796
77.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5900
94.8%
Decimal Number 131
 
2.1%
Open Punctuation 96
 
1.5%
Close Punctuation 96
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 198
 
3.4%
대 188
 
3.2%
동 169
 
2.9%
산 157
 
2.7%
신 149
 
2.5%
청 146
 
2.5%
남 124
 
2.1%
장 103
 
1.7%
시 97
 
1.6%
가 95
 
1.6%
Other values (194) 4474
75.8%
Decimal Number
ValueCountFrequency (%)
4 62
47.3%
3 32
24.4%
2 20
 
15.3%
1 17
 
13.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5900
94.8%
Common 323
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 198
 
3.4%
대 188
 
3.2%
동 169
 
2.9%
산 157
 
2.7%
신 149
 
2.5%
청 146
 
2.5%
남 124
 
2.1%
장 103
 
1.7%
시 97
 
1.6%
가 95
 
1.6%
Other values (194) 4474
75.8%
Common
ValueCountFrequency (%)
( 96
29.7%
) 96
29.7%
4 62
19.2%
3 32
 
9.9%
2 20
 
6.2%
1 17
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5900
94.8%
ASCII 323
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 198
 
3.4%
대 188
 
3.2%
동 169
 
2.9%
산 157
 
2.7%
신 149
 
2.5%
청 146
 
2.5%
남 124
 
2.1%
장 103
 
1.7%
시 97
 
1.6%
가 95
 
1.6%
Other values (194) 4474
75.8%
ASCII
ValueCountFrequency (%)
( 96
29.7%
) 96
29.7%
4 62
19.2%
3 32
 
9.9%
2 20
 
6.2%
1 17
 
5.3%

장비
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
E/S
1935 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE/S
2nd rowE/S
3rd rowE/S
4th rowE/S
5th rowE/S

Common Values

ValueCountFrequency (%)
E/S 1935
100.0%

Length

2023-12-11T13:43:11.383110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:43:11.536877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e/s 1935
100.0%

호기
Real number (ℝ)

Distinct28
Distinct (%)1.5%
Missing8
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6.0275039
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2023-12-11T13:43:11.693686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile15
Maximum28
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6905011
Coefficient of variation (CV)0.77818301
Kurtosis1.6963688
Mean6.0275039
Median Absolute Deviation (MAD)3
Skewness1.3085104
Sum11615
Variance22.000801
MonotonicityNot monotonic
2023-12-11T13:43:11.850924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 253
13.1%
2 251
13.0%
3 214
11.1%
4 209
10.8%
5 158
8.2%
6 153
7.9%
7 110
 
5.7%
8 106
 
5.5%
9 84
 
4.3%
10 80
 
4.1%
Other values (18) 309
16.0%
ValueCountFrequency (%)
1 253
13.1%
2 251
13.0%
3 214
11.1%
4 209
10.8%
5 158
8.2%
6 153
7.9%
7 110
5.7%
8 106
5.5%
9 84
 
4.3%
10 80
 
4.1%
ValueCountFrequency (%)
28 1
 
0.1%
27 1
 
0.1%
26 1
 
0.1%
25 1
 
0.1%
24 3
 
0.2%
23 4
 
0.2%
22 7
0.4%
21 8
0.4%
20 10
0.5%
19 11
0.6%

승강기번호
Text

Distinct1910
Distinct (%)99.6%
Missing17
Missing (%)0.9%
Memory size15.2 KiB
2023-12-11T13:43:12.284129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.0036496
Min length7

Characters and Unicode

Total characters15351
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1908 ?
Unique (%)99.5%

Sample

1st row1800-448
2nd row1806-600
3rd row1806-599
4th row1810-816
5th row1810-817
ValueCountFrequency (%)
0000-000 8
 
0.4%
1810-568 2
 
0.1%
3811-984 1
 
0.1%
1809-105 1
 
0.1%
1800-448 1
 
0.1%
1809-111 1
 
0.1%
1809-104 1
 
0.1%
1809-103 1
 
0.1%
1803-403 1
 
0.1%
1810-502 1
 
0.1%
Other values (1900) 1900
99.1%
2023-12-11T13:43:12.885970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 2792
18.2%
1 2790
18.2%
0 2545
16.6%
- 1917
12.5%
3 1067
 
7.0%
9 887
 
5.8%
7 713
 
4.6%
4 703
 
4.6%
6 679
 
4.4%
2 640
 
4.2%
Other values (2) 618
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13426
87.5%
Dash Punctuation 1917
 
12.5%
Space Separator 8
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 2792
20.8%
1 2790
20.8%
0 2545
19.0%
3 1067
 
7.9%
9 887
 
6.6%
7 713
 
5.3%
4 703
 
5.2%
6 679
 
5.1%
2 640
 
4.8%
5 610
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 1917
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 2792
18.2%
1 2790
18.2%
0 2545
16.6%
- 1917
12.5%
3 1067
 
7.0%
9 887
 
5.8%
7 713
 
4.6%
4 703
 
4.6%
6 679
 
4.4%
2 640
 
4.2%
Other values (2) 618
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 2792
18.2%
1 2790
18.2%
0 2545
16.6%
- 1917
12.5%
3 1067
 
7.0%
9 887
 
5.8%
7 713
 
4.6%
4 703
 
4.6%
6 679
 
4.4%
2 640
 
4.2%
Other values (2) 618
 
4.0%

운행구간
Text

MISSING 

Distinct174
Distinct (%)9.2%
Missing35
Missing (%)1.8%
Memory size15.2 KiB
2023-12-11T13:43:13.245544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length6.7215789
Min length1

Characters and Unicode

Total characters12771
Distinct characters73
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)3.8%

Sample

1st rowB2(대)-B1(연결통로)
2nd row섬석 B1(연결통로)-B2(승)
3rd row섬식 B2(승)-B1(연결통로)
4th row지상-B1(대)
5th rowB1(대)-지상
ValueCountFrequency (%)
f1-b1 397
20.5%
b1-b2 179
 
9.3%
f1-bm1 137
 
7.1%
bm1-b1 135
 
7.0%
b1(대)-지상 107
 
5.5%
지상-b1(대 102
 
5.3%
b2-b3 101
 
5.2%
b1-b3 81
 
4.2%
b2-b4 44
 
2.3%
b3-b4 32
 
1.7%
Other values (169) 619
32.0%
2023-12-11T13:43:13.808545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 2682
21.0%
1 2182
17.1%
- 1883
14.7%
( 855
 
6.7%
) 855
 
6.7%
2 718
 
5.6%
F 645
 
5.1%
대 563
 
4.4%
3 391
 
3.1%
상 345
 
2.7%
Other values (63) 1652
12.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3687
28.9%
Decimal Number 3498
27.4%
Other Letter 1930
15.1%
Dash Punctuation 1883
14.7%
Open Punctuation 855
 
6.7%
Close Punctuation 855
 
6.7%
Space Separator 34
 
0.3%
Other Punctuation 23
 
0.2%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 563
29.2%
상 345
17.9%
지 327
16.9%
승 205
 
10.6%
환 65
 
3.4%
통 59
 
3.1%
로 59
 
3.1%
선 47
 
2.4%
호 37
 
1.9%
연 34
 
1.8%
Other values (39) 189
 
9.8%
Uppercase Letter
ValueCountFrequency (%)
B 2682
72.7%
F 645
 
17.5%
M 312
 
8.5%
D 11
 
0.3%
N 9
 
0.2%
P 9
 
0.2%
U 9
 
0.2%
A 6
 
0.2%
X 3
 
0.1%
C 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 2182
62.4%
2 718
 
20.5%
3 391
 
11.2%
4 138
 
3.9%
5 51
 
1.5%
8 9
 
0.3%
6 7
 
0.2%
7 2
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1883
100.0%
Open Punctuation
ValueCountFrequency (%)
( 855
100.0%
Close Punctuation
ValueCountFrequency (%)
) 855
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7154
56.0%
Latin 3687
28.9%
Hangul 1930
 
15.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 563
29.2%
상 345
17.9%
지 327
16.9%
승 205
 
10.6%
환 65
 
3.4%
통 59
 
3.1%
로 59
 
3.1%
선 47
 
2.4%
호 37
 
1.9%
연 34
 
1.8%
Other values (39) 189
 
9.8%
Common
ValueCountFrequency (%)
1 2182
30.5%
- 1883
26.3%
( 855
 
12.0%
) 855
 
12.0%
2 718
 
10.0%
3 391
 
5.5%
4 138
 
1.9%
5 51
 
0.7%
34
 
0.5%
, 23
 
0.3%
Other values (4) 24
 
0.3%
Latin
ValueCountFrequency (%)
B 2682
72.7%
F 645
 
17.5%
M 312
 
8.5%
D 11
 
0.3%
N 9
 
0.2%
P 9
 
0.2%
U 9
 
0.2%
A 6
 
0.2%
X 3
 
0.1%
C 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10841
84.9%
Hangul 1930
 
15.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 2682
24.7%
1 2182
20.1%
- 1883
17.4%
( 855
 
7.9%
) 855
 
7.9%
2 718
 
6.6%
F 645
 
5.9%
3 391
 
3.6%
M 312
 
2.9%
4 138
 
1.3%
Other values (14) 180
 
1.7%
Hangul
ValueCountFrequency (%)
대 563
29.2%
상 345
17.9%
지 327
16.9%
승 205
 
10.6%
환 65
 
3.4%
통 59
 
3.1%
로 59
 
3.1%
선 47
 
2.4%
호 37
 
1.9%
연 34
 
1.8%
Other values (39) 189
 
9.8%

설치위치
Text

MISSING 

Distinct201
Distinct (%)10.7%
Missing49
Missing (%)2.5%
Memory size15.2 KiB
2023-12-11T13:43:14.180066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length2
Mean length3.6638388
Min length2

Characters and Unicode

Total characters6910
Distinct characters83
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)6.4%

Sample

1st row4호선연결통로 상행
2nd row남영방향 하행 2-3
3rd row시청방향 상행 9-3
4th row4번출구 하행
5th row4번출구 상행
ValueCountFrequency (%)
내부 738
29.1%
외부 566
22.3%
상행 266
 
10.5%
하행 248
 
9.8%
1번출구 78
 
3.1%
3번출구 54
 
2.1%
2번출구 51
 
2.0%
5번출구 42
 
1.7%
4번출구 40
 
1.6%
6번출구 29
 
1.1%
Other values (101) 426
16.8%
2023-12-11T13:43:14.846019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 1306
18.9%
내 761
11.0%
657
9.5%
외 594
8.6%
행 518
 
7.5%
출 360
 
5.2%
구 360
 
5.2%
번 359
 
5.2%
상 268
 
3.9%
하 250
 
3.6%
Other values (73) 1477
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5160
74.7%
Decimal Number 800
 
11.6%
Space Separator 657
 
9.5%
Dash Punctuation 203
 
2.9%
Uppercase Letter 38
 
0.5%
Close Punctuation 16
 
0.2%
Open Punctuation 16
 
0.2%
Other Punctuation 14
 
0.2%
Math Symbol 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 1306
25.3%
내 761
14.7%
외 594
11.5%
행 518
 
10.0%
출 360
 
7.0%
구 360
 
7.0%
번 359
 
7.0%
상 268
 
5.2%
하 250
 
4.8%
선 59
 
1.1%
Other values (52) 325
 
6.3%
Decimal Number
ValueCountFrequency (%)
1 179
22.4%
2 144
18.0%
3 140
17.5%
4 114
14.2%
5 63
 
7.9%
7 47
 
5.9%
6 40
 
5.0%
8 36
 
4.5%
9 25
 
3.1%
0 12
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
X 22
57.9%
B 8
 
21.1%
A 4
 
10.5%
E 2
 
5.3%
H 2
 
5.3%
Space Separator
ValueCountFrequency (%)
657
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 203
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5160
74.7%
Common 1712
 
24.8%
Latin 38
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 1306
25.3%
내 761
14.7%
외 594
11.5%
행 518
 
10.0%
출 360
 
7.0%
구 360
 
7.0%
번 359
 
7.0%
상 268
 
5.2%
하 250
 
4.8%
선 59
 
1.1%
Other values (52) 325
 
6.3%
Common
ValueCountFrequency (%)
657
38.4%
- 203
 
11.9%
1 179
 
10.5%
2 144
 
8.4%
3 140
 
8.2%
4 114
 
6.7%
5 63
 
3.7%
7 47
 
2.7%
6 40
 
2.3%
8 36
 
2.1%
Other values (6) 89
 
5.2%
Latin
ValueCountFrequency (%)
X 22
57.9%
B 8
 
21.1%
A 4
 
10.5%
E 2
 
5.3%
H 2
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5160
74.7%
ASCII 1750
 
25.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 1306
25.3%
내 761
14.7%
외 594
11.5%
행 518
 
10.0%
출 360
 
7.0%
구 360
 
7.0%
번 359
 
7.0%
상 268
 
5.2%
하 250
 
4.8%
선 59
 
1.1%
Other values (52) 325
 
6.3%
ASCII
ValueCountFrequency (%)
657
37.5%
- 203
 
11.6%
1 179
 
10.2%
2 144
 
8.2%
3 140
 
8.0%
4 114
 
6.5%
5 63
 
3.6%
7 47
 
2.7%
6 40
 
2.3%
8 36
 
2.1%
Other values (11) 127
 
7.3%

Interactions

2023-12-11T13:43:08.374236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:43:08.079023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:43:08.498932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:43:08.236755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T13:43:14.996839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선호기
연번1.0000.9750.287
호선0.9751.0000.412
호기0.2870.4121.000
2023-12-11T13:43:15.123457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호기호선
연번1.0000.1600.726
호기0.1601.0000.137
호선0.7260.1371.000

Missing values

2023-12-11T13:43:08.669437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:43:08.863523image/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.
2023-12-11T13:43:09.008717image/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

연번호선역명장비호기승강기번호운행구간설치위치
011서울(1)E/S11800-448B2(대)-B1(연결통로)4호선연결통로 상행
121서울(1)E/S21806-600섬석 B1(연결통로)-B2(승)남영방향 하행 2-3
231서울(1)E/S31806-599섬식 B2(승)-B1(연결통로)시청방향 상행 9-3
341서울(1)E/S41810-816지상-B1(대)4번출구 하행
451서울(1)E/S51810-817B1(대)-지상4번출구 상행
561시청(1)E/S11808-119지상(시청광장)-B1(대)5번출구 하행
671시청(1)E/S21808-120B1(대)-지상(시청광장)5번출구 상행
781시청(1)E/S31809-239B1(대)-지상(덕수궁)1번출구 상행
891종각E/S11810-730지상-B1(대)1번출구 상행
9101종각E/S21810-729지상-B1(대)1번출구 상행
연번호선역명장비호기승강기번호운행구간설치위치
192519268남한산성입구E/S33805-603B2-B1내부
192619278남한산성입구E/S43805-604B2-B2내부
192719288단대오거리E/S13802-053F1-B2외부
192819298단대오거리E/S23802-054F1-B2외부
192919308단대오거리E/S33805-601B1-B2내부
193019318단대오거리E/S43805-602B1-B2내부
193119328단대오거리E/S53811-916F1-B1외부
193219338단대오거리E/S63811-917F1-B1외부
193319348모란E/S13802-183B1-B2내부
193419358모란E/S23802-184B1-B2내부