Overview

Dataset statistics

Number of variables12
Number of observations332
Missing cells232
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.6 KiB
Average record size in memory97.4 B

Variable types

Categorical6
Text6

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-2732/F/1/datasetView.do

Alerts

권고기준.3 is highly overall correlated with 호선 and 4 other fieldsHigh correlation
측정 지점 is highly overall correlated with 권고기준.3High correlation
권고기준.4 is highly overall correlated with 권고기준 and 1 other fieldsHigh correlation
유지기준.3 is highly overall correlated with 권고기준.3High correlation
호선 is highly overall correlated with 권고기준.3High correlation
권고기준 is highly overall correlated with 권고기준.3 and 1 other fieldsHigh correlation
권고기준.3 is highly imbalanced (96.3%)Imbalance
시 설 명 (역사명) has 232 (69.9%) missing valuesMissing

Reproduction

Analysis started2024-04-29 21:59:54.010539
Analysis finished2024-04-29 21:59:54.791459
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2
120 
3
108 
4
69 
1
33 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.0180723
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 120
36.1%
3 108
32.5%
4 69
20.8%
1 33
 
9.9%
<NA> 2
 
0.6%

Length

2024-04-30T06:59:54.851593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T06:59:54.953515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 120
36.1%
3 108
32.5%
4 69
20.8%
1 33
 
9.9%
na 2
 
0.6%

시 설 명 (역사명)
Text

MISSING 

Distinct91
Distinct (%)91.0%
Missing232
Missing (%)69.9%
Memory size2.7 KiB
2024-04-30T06:59:55.203180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length4.41
Min length3

Characters and Unicode

Total characters441
Distinct characters125
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

Unique82 ?
Unique (%)82.0%

Sample

1st row서울역
2nd row시 청
3rd row종 각
4th row종로3가
5th row종로5가
ValueCountFrequency (%)
대 5
 
3.3%
신 5
 
3.3%
사 3
 
2.0%
청 3
 
2.0%
수 3
 
2.0%
당 3
 
2.0%
현 2
 
1.3%
삼 2
 
1.3%
금 2
 
1.3%
서 2
 
1.3%
Other values (103) 121
80.1%
2024-04-30T06:59:55.580874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
137
31.1%
대 17
 
3.9%
신 13
 
2.9%
동 11
 
2.5%
구 11
 
2.5%
로 10
 
2.3%
문 9
 
2.0%
가 7
 
1.6%
입 7
 
1.6%
청 6
 
1.4%
Other values (115) 213
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 295
66.9%
Space Separator 137
31.1%
Decimal Number 6
 
1.4%
Control 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 17
 
5.8%
신 13
 
4.4%
동 11
 
3.7%
구 11
 
3.7%
로 10
 
3.4%
문 9
 
3.1%
가 7
 
2.4%
입 7
 
2.4%
청 6
 
2.0%
사 5
 
1.7%
Other values (110) 199
67.5%
Decimal Number
ValueCountFrequency (%)
3 4
66.7%
5 1
 
16.7%
4 1
 
16.7%
Space Separator
ValueCountFrequency (%)
137
100.0%
Control
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 295
66.9%
Common 146
33.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 17
 
5.8%
신 13
 
4.4%
동 11
 
3.7%
구 11
 
3.7%
로 10
 
3.4%
문 9
 
3.1%
가 7
 
2.4%
입 7
 
2.4%
청 6
 
2.0%
사 5
 
1.7%
Other values (110) 199
67.5%
Common
ValueCountFrequency (%)
137
93.8%
3 4
 
2.7%
3
 
2.1%
5 1
 
0.7%
4 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 295
66.9%
ASCII 146
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
137
93.8%
3 4
 
2.7%
3
 
2.1%
5 1
 
0.7%
4 1
 
0.7%
Hangul
ValueCountFrequency (%)
대 17
 
5.8%
신 13
 
4.4%
동 11
 
3.7%
구 11
 
3.7%
로 10
 
3.4%
문 9
 
3.1%
가 7
 
2.4%
입 7
 
2.4%
청 6
 
2.0%
사 5
 
1.7%
Other values (110) 199
67.5%

측정 지점
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
평 균
100 
승강장
100 
대합실
82 
대합실-1
18 
대합실-2
18 
Other values (2)
14 

Length

Max length5
Median length3
Mean length3.560241
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row평 균
4th row승강장
5th row대합실-1

Common Values

ValueCountFrequency (%)
평 균 100
30.1%
승강장 100
30.1%
대합실 82
24.7%
대합실-1 18
 
5.4%
대합실-2 18
 
5.4%
환승통로 12
 
3.6%
<NA> 2
 
0.6%

Length

2024-04-30T06:59:55.958567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T06:59:56.070937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평 100
23.1%
균 100
23.1%
승강장 100
23.1%
대합실 82
19.0%
대합실-1 18
 
4.2%
대합실-2 18
 
4.2%
환승통로 12
 
2.8%
na 2
 
0.5%

유지기준
Text

Distinct191
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-04-30T06:59:56.411014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1174699
Min length2

Characters and Unicode

Total characters1367
Distinct characters16
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

Unique113 ?
Unique (%)34.0%

Sample

1st rowPM10
2nd row140㎍/㎥
3rd row92.9
4th row103.8
5th row88.3
ValueCountFrequency (%)
90.2 7
 
2.1%
88.4 6
 
1.8%
93.2 6
 
1.8%
90.1 5
 
1.5%
91.6 5
 
1.5%
93.6 5
 
1.5%
98.7 4
 
1.2%
93.1 4
 
1.2%
91.7 4
 
1.2%
88.9 4
 
1.2%
Other values (181) 282
84.9%
2024-04-30T06:59:56.899037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 323
23.6%
9 195
14.3%
8 194
14.2%
1 149
10.9%
4 82
 
6.0%
7 79
 
5.8%
0 74
 
5.4%
2 74
 
5.4%
6 71
 
5.2%
3 65
 
4.8%
Other values (6) 61
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1039
76.0%
Other Punctuation 324
 
23.7%
Other Symbol 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 195
18.8%
8 194
18.7%
1 149
14.3%
4 82
7.9%
7 79
7.6%
0 74
 
7.1%
2 74
 
7.1%
6 71
 
6.8%
3 65
 
6.3%
5 56
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 323
99.7%
/ 1
 
0.3%
Other Symbol
ValueCountFrequency (%)
㎍ 1
50.0%
㎥ 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
M 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1365
99.9%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 323
23.7%
9 195
14.3%
8 194
14.2%
1 149
10.9%
4 82
 
6.0%
7 79
 
5.8%
0 74
 
5.4%
2 74
 
5.4%
6 71
 
5.2%
3 65
 
4.8%
Other values (4) 59
 
4.3%
Latin
ValueCountFrequency (%)
P 1
50.0%
M 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1365
99.9%
CJK Compat 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 323
23.7%
9 195
14.3%
8 194
14.2%
1 149
10.9%
4 82
 
6.0%
7 79
 
5.8%
0 74
 
5.4%
2 74
 
5.4%
6 71
 
5.2%
3 65
 
4.8%
Other values (4) 59
 
4.3%
CJK Compat
ValueCountFrequency (%)
㎍ 1
50.0%
㎥ 1
50.0%

유지기준.1
Text

Distinct150
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-04-30T06:59:57.203691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0120482
Min length2

Characters and Unicode

Total characters1000
Distinct characters15
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

Unique74 ?
Unique (%)22.3%

Sample

1st rowCO2
2nd row1,000ppm
3rd row628
4th row684
5th row584
ValueCountFrequency (%)
498 10
 
3.0%
584 8
 
2.4%
511 7
 
2.1%
482 7
 
2.1%
534 7
 
2.1%
472 7
 
2.1%
512 7
 
2.1%
492 6
 
1.8%
538 6
 
1.8%
471 6
 
1.8%
Other values (140) 261
78.6%
2024-04-30T06:59:57.607488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 227
22.7%
5 188
18.8%
8 110
11.0%
6 91
9.1%
2 80
 
8.0%
7 73
 
7.3%
1 71
 
7.1%
3 61
 
6.1%
9 57
 
5.7%
0 36
 
3.6%
Other values (5) 6
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 994
99.4%
Lowercase Letter 3
 
0.3%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 227
22.8%
5 188
18.9%
8 110
11.1%
6 91
9.2%
2 80
 
8.0%
7 73
 
7.3%
1 71
 
7.1%
3 61
 
6.1%
9 57
 
5.7%
0 36
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
p 2
66.7%
m 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
O 1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 995
99.5%
Latin 5
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 227
22.8%
5 188
18.9%
8 110
11.1%
6 91
9.1%
2 80
 
8.0%
7 73
 
7.3%
1 71
 
7.1%
3 61
 
6.1%
9 57
 
5.7%
0 36
 
3.6%
Latin
ValueCountFrequency (%)
p 2
40.0%
m 1
20.0%
C 1
20.0%
O 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 227
22.7%
5 188
18.8%
8 110
11.0%
6 91
9.1%
2 80
 
8.0%
7 73
 
7.3%
1 71
 
7.1%
3 61
 
6.1%
9 57
 
5.7%
0 36
 
3.6%
Other values (5) 6
 
0.6%

유지기준.2
Text

Distinct111
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-04-30T06:59:57.859516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.7198795
Min length1

Characters and Unicode

Total characters1235
Distinct characters17
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

Unique39 ?
Unique (%)11.7%

Sample

1st rowHCHO
2nd row100㎍/㎥
3rd row14.2
4th row14.6
5th row15.5
ValueCountFrequency (%)
13.2 11
 
3.3%
12.7 10
 
3.0%
13 10
 
3.0%
13.5 9
 
2.7%
13.7 8
 
2.4%
14.3 8
 
2.4%
12.6 8
 
2.4%
13.4 7
 
2.1%
14 7
 
2.1%
11.4 7
 
2.1%
Other values (101) 247
74.4%
2024-04-30T06:59:58.223197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 363
29.4%
. 293
23.7%
2 105
 
8.5%
3 94
 
7.6%
4 77
 
6.2%
5 67
 
5.4%
6 58
 
4.7%
7 52
 
4.2%
8 50
 
4.0%
9 46
 
3.7%
Other values (7) 30
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 935
75.7%
Other Punctuation 294
 
23.8%
Uppercase Letter 4
 
0.3%
Other Symbol 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 363
38.8%
2 105
 
11.2%
3 94
 
10.1%
4 77
 
8.2%
5 67
 
7.2%
6 58
 
6.2%
7 52
 
5.6%
8 50
 
5.3%
9 46
 
4.9%
0 23
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
H 2
50.0%
C 1
25.0%
O 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 293
99.7%
/ 1
 
0.3%
Other Symbol
ValueCountFrequency (%)
㎍ 1
50.0%
㎥ 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1231
99.7%
Latin 4
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 363
29.5%
. 293
23.8%
2 105
 
8.5%
3 94
 
7.6%
4 77
 
6.3%
5 67
 
5.4%
6 58
 
4.7%
7 52
 
4.2%
8 50
 
4.1%
9 46
 
3.7%
Other values (4) 26
 
2.1%
Latin
ValueCountFrequency (%)
H 2
50.0%
C 1
25.0%
O 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1233
99.8%
CJK Compat 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 363
29.4%
. 293
23.8%
2 105
 
8.5%
3 94
 
7.6%
4 77
 
6.2%
5 67
 
5.4%
6 58
 
4.7%
7 52
 
4.2%
8 50
 
4.1%
9 46
 
3.7%
Other values (5) 28
 
2.3%
CJK Compat
ValueCountFrequency (%)
㎍ 1
50.0%
㎥ 1
50.0%

유지기준.3
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.9
68 
1
67 
0.8
48 
1.1
46 
0.7
39 
Other values (8)
64 

Length

Max length4
Median length3
Mean length2.5963855
Min length1

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st rowCO
2nd row9ppm
3rd row0.8
4th row1
5th row0.7

Common Values

ValueCountFrequency (%)
0.9 68
20.5%
1 67
20.2%
0.8 48
14.5%
1.1 46
13.9%
0.7 39
11.7%
1.2 23
 
6.9%
0.6 15
 
4.5%
1.3 13
 
3.9%
0.5 7
 
2.1%
1.4 3
 
0.9%
Other values (3) 3
 
0.9%

Length

2024-04-30T06:59:58.351149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.9 68
20.5%
1 67
20.2%
0.8 48
14.5%
1.1 46
13.9%
0.7 39
11.7%
1.2 23
 
6.9%
0.6 15
 
4.5%
1.3 13
 
3.9%
0.5 7
 
2.1%
1.4 3
 
0.9%
Other values (3) 3
 
0.9%

권고기준
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.015
34 
0.018
29 
0.02
25 
0.019
22 
0.021
22 
Other values (20)
200 

Length

Max length7
Median length5
Mean length4.9126506
Min length3

Unique

Unique5 ?
Unique (%)1.5%

Sample

1st rowNO2
2nd row0.05ppm
3rd row0.024
4th row0.028
5th row0.02

Common Values

ValueCountFrequency (%)
0.015 34
 
10.2%
0.018 29
 
8.7%
0.02 25
 
7.5%
0.019 22
 
6.6%
0.021 22
 
6.6%
0.014 22
 
6.6%
0.024 20
 
6.0%
0.017 19
 
5.7%
0.012 18
 
5.4%
0.016 17
 
5.1%
Other values (15) 104
31.3%

Length

2024-04-30T06:59:58.466680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.015 34
 
10.2%
0.018 29
 
8.7%
0.02 25
 
7.5%
0.019 22
 
6.6%
0.021 22
 
6.6%
0.014 22
 
6.6%
0.024 20
 
6.0%
0.017 19
 
5.7%
0.012 18
 
5.4%
0.016 17
 
5.1%
Other values (15) 104
31.3%

권고기준.1
Text

Distinct64
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-04-30T06:59:58.663309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.0090361
Min length1

Characters and Unicode

Total characters667
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)5.4%

Sample

1st rowRn
2nd row148Bq/㎥
3rd row22
4th row10
5th row22
ValueCountFrequency (%)
21 16
 
4.8%
37 15
 
4.5%
23 13
 
3.9%
34 13
 
3.9%
20 13
 
3.9%
39 13
 
3.9%
41 12
 
3.6%
19 11
 
3.3%
28 11
 
3.3%
26 11
 
3.3%
Other values (54) 204
61.4%
2024-04-30T06:59:59.001091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 137
20.5%
3 131
19.6%
1 86
12.9%
4 81
12.1%
6 47
 
7.0%
5 39
 
5.8%
7 36
 
5.4%
0 36
 
5.4%
9 34
 
5.1%
8 34
 
5.1%
Other values (6) 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 661
99.1%
Uppercase Letter 2
 
0.3%
Lowercase Letter 2
 
0.3%
Other Punctuation 1
 
0.1%
Other Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 137
20.7%
3 131
19.8%
1 86
13.0%
4 81
12.3%
6 47
 
7.1%
5 39
 
5.9%
7 36
 
5.4%
0 36
 
5.4%
9 34
 
5.1%
8 34
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
R 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
q 1
50.0%
n 1
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%
Other Symbol
ValueCountFrequency (%)
㎥ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 663
99.4%
Latin 4
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 137
20.7%
3 131
19.8%
1 86
13.0%
4 81
12.2%
6 47
 
7.1%
5 39
 
5.9%
7 36
 
5.4%
0 36
 
5.4%
9 34
 
5.1%
8 34
 
5.1%
Other values (2) 2
 
0.3%
Latin
ValueCountFrequency (%)
B 1
25.0%
q 1
25.0%
R 1
25.0%
n 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 666
99.9%
CJK Compat 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 137
20.6%
3 131
19.7%
1 86
12.9%
4 81
12.2%
6 47
 
7.1%
5 39
 
5.9%
7 36
 
5.4%
0 36
 
5.4%
9 34
 
5.1%
8 34
 
5.1%
Other values (5) 5
 
0.8%
CJK Compat
ValueCountFrequency (%)
㎥ 1
100.0%

권고기준.2
Text

Distinct314
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-04-30T06:59:59.340056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.2951807
Min length2

Characters and Unicode

Total characters1426
Distinct characters18
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

Unique296 ?
Unique (%)89.2%

Sample

1st rowTVOC
2nd row500㎍/㎥
3rd row282
4th row440.4
5th row55.6
ValueCountFrequency (%)
55.4 2
 
0.6%
83.2 2
 
0.6%
59.5 2
 
0.6%
55.7 2
 
0.6%
248 2
 
0.6%
99.7 2
 
0.6%
157.4 2
 
0.6%
186.4 2
 
0.6%
85.2 2
 
0.6%
99.2 2
 
0.6%
Other values (304) 312
94.0%
2024-04-30T06:59:59.832959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 288
20.2%
1 172
12.1%
2 145
10.2%
5 123
8.6%
3 121
8.5%
4 107
 
7.5%
9 107
 
7.5%
8 102
 
7.2%
7 100
 
7.0%
6 98
 
6.9%
Other values (8) 63
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1131
79.3%
Other Punctuation 289
 
20.3%
Uppercase Letter 4
 
0.3%
Other Symbol 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 172
15.2%
2 145
12.8%
5 123
10.9%
3 121
10.7%
4 107
9.5%
9 107
9.5%
8 102
9.0%
7 100
8.8%
6 98
8.7%
0 56
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
T 1
25.0%
V 1
25.0%
O 1
25.0%
C 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 288
99.7%
/ 1
 
0.3%
Other Symbol
ValueCountFrequency (%)
㎍ 1
50.0%
㎥ 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1422
99.7%
Latin 4
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
. 288
20.3%
1 172
12.1%
2 145
10.2%
5 123
8.6%
3 121
8.5%
4 107
 
7.5%
9 107
 
7.5%
8 102
 
7.2%
7 100
 
7.0%
6 98
 
6.9%
Other values (4) 59
 
4.1%
Latin
ValueCountFrequency (%)
T 1
25.0%
V 1
25.0%
O 1
25.0%
C 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1424
99.9%
CJK Compat 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 288
20.2%
1 172
12.1%
2 145
10.2%
5 123
8.6%
3 121
8.5%
4 107
 
7.5%
9 107
 
7.5%
8 102
 
7.2%
7 100
 
7.0%
6 98
 
6.9%
Other values (6) 61
 
4.3%
CJK Compat
ValueCountFrequency (%)
㎍ 1
50.0%
㎥ 1
50.0%

권고기준.3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.01미만
330 
석면
 
1
0.01개/cc
 
1

Length

Max length8
Median length6
Mean length5.9939759
Min length2

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row석면
2nd row0.01개/cc
3rd row0.01미만
4th row0.01미만
5th row0.01미만

Common Values

ValueCountFrequency (%)
0.01미만 330
99.4%
석면 1
 
0.3%
0.01개/cc 1
 
0.3%

Length

2024-04-30T06:59:59.964604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:00:00.078476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01미만 330
99.4%
석면 1
 
0.3%
0.01개/cc 1
 
0.3%

권고기준.4
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.009
45 
0.011
40 
0.008
35 
0.007
34 
0.01
34 
Other values (12)
144 

Length

Max length7
Median length5
Mean length4.8945783
Min length2

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st rowO3
2nd row0.06ppm
3rd row0.012
4th row0.013
5th row0.011

Common Values

ValueCountFrequency (%)
0.009 45
13.6%
0.011 40
12.0%
0.008 35
10.5%
0.007 34
10.2%
0.01 34
10.2%
0.012 26
7.8%
0.005 26
7.8%
0.006 26
7.8%
0.013 25
7.5%
0.014 18
 
5.4%
Other values (7) 23
6.9%

Length

2024-04-30T07:00:00.188018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.009 45
13.6%
0.011 40
12.0%
0.008 35
10.5%
0.007 34
10.2%
0.01 34
10.2%
0.012 26
7.8%
0.005 26
7.8%
0.006 26
7.8%
0.013 25
7.5%
0.014 18
 
5.4%
Other values (7) 23
6.9%

Correlations

2024-04-30T07:00:00.268970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선시 설 명 (역사명)측정 지점유지기준.3권고기준권고기준.1권고기준.3권고기준.4
호선1.0000.0000.0000.0000.3860.582NaN0.372
시 설 명\n(역사명)0.0001.000NaN0.8960.0000.823NaN0.000
측정\n지점0.000NaN1.0000.3750.0910.468NaN0.000
유지기준.30.0000.8960.3751.0000.8590.8571.0000.829
권고기준0.3860.0000.0910.8591.0000.8501.0000.893
권고기준.10.5820.8230.4680.8570.8501.0001.0000.880
권고기준.3NaNNaNNaN1.0001.0001.0001.0001.000
권고기준.40.3720.0000.0000.8290.8930.8801.0001.000
2024-04-30T07:00:00.387020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권고기준.3측정 지점권고기준.4유지기준.3호선권고기준
권고기준.31.0001.0000.9780.9851.0000.966
측정\n지점1.0001.0000.0000.2010.0000.037
권고기준.40.9780.0001.0000.4690.2150.501
유지기준.30.9850.2010.4691.0000.0000.476
호선1.0000.0000.2150.0001.0000.208
권고기준0.9660.0370.5010.4760.2081.000
2024-04-30T07:00:00.492696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선측정 지점유지기준.3권고기준권고기준.3권고기준.4
호선1.0000.0000.0000.2081.0000.215
측정\n지점0.0001.0000.2010.0371.0000.000
유지기준.30.0000.2011.0000.4760.9850.469
권고기준0.2080.0370.4761.0000.9660.501
권고기준.31.0001.0000.9850.9661.0000.978
권고기준.40.2150.0000.4690.5010.9781.000

Missing values

2024-04-30T06:59:54.560344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:59:54.725408image/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.

Sample

호선시 설 명 (역사명)측정 지점유지기준유지기준.1유지기준.2유지기준.3권고기준권고기준.1권고기준.2권고기준.3권고기준.4
0<NA><NA><NA>PM10CO2HCHOCONO2RnTVOC석면O3
1<NA><NA><NA>140㎍/㎥1,000ppm100㎍/㎥9ppm0.05ppm148Bq/㎥500㎍/㎥0.01개/cc0.06ppm
21서울역평 균92.962814.20.80.024222820.01미만0.012
31<NA>승강장103.868414.610.02810440.40.01미만0.013
41<NA>대합실-188.358415.50.70.022255.60.01미만0.011
51<NA>대합실-286.761612.70.70.02535350.10.01미만0.013
61시 청평 균101.560112.90.80.02534428.50.01미만0.013
71<NA>승강장117.562212.40.90.024363980.01미만0.013
81<NA>대합실-190.460312.60.70.02631433.10.01미만0.015
91<NA>대합실-296.857813.90.90.02636454.40.01미만0.013
호선시 설 명 (역사명)측정 지점유지기준유지기준.1유지기준.2유지기준.3권고기준권고기준.1권고기준.2권고기준.3권고기준.4
3224총신대 입구평 균88.448318.40.80.01739177.70.01미만0.007
3234<NA>승강장89.848514.70.90.01635174.60.01미만0.007
3244<NA>대합실87.148122.20.70.01843180.90.01미만0.007
3254사 당평 균91.553813.51.10.0244638.90.01미만0.011
3264<NA>승강장101.557711.21.30.02973400.01미만0.012
3274<NA>대합실84.849215.210.0234658.40.01미만0.011
3284<NA>환승통로88.354614.110.0222218.30.01미만0.01
3294남태령평 균98.747311.60.80.014109167.10.01미만0.005
3304<NA>승강장99.248210.610.0159779.10.01미만0.006
3314<NA>대합실98.346512.70.70.014122255.10.01미만0.005