Overview

Dataset statistics

Number of variables4
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory33.7 B

Variable types

Categorical1
Text2
Numeric1

Dataset

Description정보보호 관리체계(ISMS) 및 개인정보보호 관리체계(PIMS)에 관련한 자료입니다. o 컬럼명 : 통제분야, 통제영역, 통제항목, 결함수
URLhttps://www.data.go.kr/data/15039354/fileData.do

Alerts

통제항목 has unique valuesUnique
결함수 has 138 (72.6%) zerosZeros

Reproduction

Analysis started2023-12-12 01:54:42.862928
Analysis finished2023-12-12 01:54:43.334141
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통제분야
Categorical

Distinct23
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
8. 기술적 보호조치
32 
11. 운영보안
22 
5. 개인정보 생명주기 관리
16 
10. 접근통제
14 
0. 정보보호관리과정
12 
Other values (18)
94 

Length

Max length15
Median length12
Mean length10.389474
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
8. 기술적 보호조치 32
16.8%
11. 운영보안 22
11.6%
5. 개인정보 생명주기 관리 16
 
8.4%
10. 접근통제 14
 
7.4%
0. 정보보호관리과정 12
 
6.3%
8. 시스템 개발보안 10
 
5.3%
7. 관리적 보호조치 10
 
5.3%
7. 물리적 보안 9
 
4.7%
9. 물리적 보호조치 8
 
4.2%
12. 침해사고 관리 7
 
3.7%
Other values (13) 50
26.3%

Length

2023-12-12T10:54:43.413546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
보호조치 50
 
9.2%
8 42
 
7.7%
기술적 32
 
5.9%
관리 23
 
4.2%
11 22
 
4.0%
운영보안 22
 
4.0%
5 20
 
3.7%
7 19
 
3.5%
보안 17
 
3.1%
물리적 17
 
3.1%
Other values (40) 281
51.6%
Distinct67
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T10:54:43.732014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length12.478947
Min length6

Characters and Unicode

Total characters2371
Distinct characters137
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

Unique13 ?
Unique (%)6.8%

Sample

1st row1. 정보보호 정책수립 및 범위설정
2nd row1. 정보보호 정책수립 및 범위설정
3rd row2. 경영진 책임 및 조직구성
4th row2. 경영진 책임 및 조직구성
5th row3. 위험관리
ValueCountFrequency (%)
57
 
8.6%
관리 29
 
4.4%
운영보안 20
 
3.0%
보안 19
 
2.9%
개인정보 18
 
2.7%
접근통제 13
 
2.0%
시스템 12
 
1.8%
보호조치 12
 
1.8%
5.1 11
 
1.7%
8.1 11
 
1.7%
Other values (133) 457
69.3%
2023-12-12T10:54:44.179139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
469
19.8%
. 190
 
8.0%
1 143
 
6.0%
118
 
5.0%
75
 
3.2%
2 70
 
3.0%
66
 
2.8%
57
 
2.4%
56
 
2.4%
47
 
2.0%
Other values (127) 1080
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1298
54.7%
Space Separator 469
 
19.8%
Decimal Number 414
 
17.5%
Other Punctuation 190
 
8.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
118
 
9.1%
75
 
5.8%
66
 
5.1%
57
 
4.4%
56
 
4.3%
47
 
3.6%
39
 
3.0%
32
 
2.5%
31
 
2.4%
30
 
2.3%
Other values (115) 747
57.6%
Decimal Number
ValueCountFrequency (%)
1 143
34.5%
2 70
16.9%
8 42
 
10.1%
3 41
 
9.9%
4 30
 
7.2%
5 27
 
6.5%
7 19
 
4.6%
6 18
 
4.3%
0 14
 
3.4%
9 10
 
2.4%
Space Separator
ValueCountFrequency (%)
469
100.0%
Other Punctuation
ValueCountFrequency (%)
. 190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1298
54.7%
Common 1073
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
118
 
9.1%
75
 
5.8%
66
 
5.1%
57
 
4.4%
56
 
4.3%
47
 
3.6%
39
 
3.0%
32
 
2.5%
31
 
2.4%
30
 
2.3%
Other values (115) 747
57.6%
Common
ValueCountFrequency (%)
469
43.7%
. 190
17.7%
1 143
 
13.3%
2 70
 
6.5%
8 42
 
3.9%
3 41
 
3.8%
4 30
 
2.8%
5 27
 
2.5%
7 19
 
1.8%
6 18
 
1.7%
Other values (2) 24
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1298
54.7%
ASCII 1073
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
469
43.7%
. 190
17.7%
1 143
 
13.3%
2 70
 
6.5%
8 42
 
3.9%
3 41
 
3.8%
4 30
 
2.8%
5 27
 
2.5%
7 19
 
1.8%
6 18
 
1.7%
Other values (2) 24
 
2.2%
Hangul
ValueCountFrequency (%)
118
 
9.1%
75
 
5.8%
66
 
5.1%
57
 
4.4%
56
 
4.3%
47
 
3.6%
39
 
3.0%
32
 
2.5%
31
 
2.4%
30
 
2.3%
Other values (115) 747
57.6%

통제항목
Text

UNIQUE 

Distinct190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T10:54:44.771344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length15.715789
Min length8

Characters and Unicode

Total characters2986
Distinct characters221
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)100.0%

Sample

1st row1.1 정보보호정책의 수립
2nd row1.2 범위설정
3rd row2.1 경영진 참여
4th row2.2 정보보호 조직 구성 및 자원 할당
5th row3.1 위험관리 방법 및 계획 수립
ValueCountFrequency (%)
41
 
5.8%
관리 22
 
3.1%
보안 20
 
2.8%
수립 15
 
2.1%
개인정보 13
 
1.8%
접근 10
 
1.4%
침해사고 9
 
1.3%
교육 7
 
1.0%
지정 5
 
0.7%
정책 5
 
0.7%
Other values (401) 562
79.3%
2023-12-12T10:54:45.209319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
519
 
17.4%
. 368
 
12.3%
1 212
 
7.1%
2 124
 
4.2%
87
 
2.9%
73
 
2.4%
3 71
 
2.4%
52
 
1.7%
8 45
 
1.5%
4 44
 
1.5%
Other values (211) 1391
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1479
49.5%
Decimal Number 607
20.3%
Space Separator 519
 
17.4%
Other Punctuation 375
 
12.6%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
87
 
5.9%
73
 
4.9%
52
 
3.5%
41
 
2.8%
40
 
2.7%
37
 
2.5%
36
 
2.4%
26
 
1.8%
25
 
1.7%
23
 
1.6%
Other values (194) 1039
70.3%
Decimal Number
ValueCountFrequency (%)
1 212
34.9%
2 124
20.4%
3 71
 
11.7%
8 45
 
7.4%
4 44
 
7.2%
5 36
 
5.9%
6 24
 
4.0%
7 23
 
3.8%
0 16
 
2.6%
9 12
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 368
98.1%
· 7
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
519
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1505
50.4%
Hangul 1479
49.5%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
87
 
5.9%
73
 
4.9%
52
 
3.5%
41
 
2.8%
40
 
2.7%
37
 
2.5%
36
 
2.4%
26
 
1.8%
25
 
1.7%
23
 
1.6%
Other values (194) 1039
70.3%
Common
ValueCountFrequency (%)
519
34.5%
. 368
24.5%
1 212
14.1%
2 124
 
8.2%
3 71
 
4.7%
8 45
 
3.0%
4 44
 
2.9%
5 36
 
2.4%
6 24
 
1.6%
7 23
 
1.5%
Other values (5) 39
 
2.6%
Latin
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
50.2%
Hangul 1479
49.5%
None 7
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
519
34.6%
. 368
24.5%
1 212
14.1%
2 124
 
8.3%
3 71
 
4.7%
8 45
 
3.0%
4 44
 
2.9%
5 36
 
2.4%
6 24
 
1.6%
7 23
 
1.5%
Other values (6) 34
 
2.3%
Hangul
ValueCountFrequency (%)
87
 
5.9%
73
 
4.9%
52
 
3.5%
41
 
2.8%
40
 
2.7%
37
 
2.5%
36
 
2.4%
26
 
1.8%
25
 
1.7%
23
 
1.6%
Other values (194) 1039
70.3%
None
ValueCountFrequency (%)
· 7
100.0%

결함수
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91578947
Minimum0
Maximum19
Zeros138
Zeros (%)72.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T10:54:45.354288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.357755
Coefficient of variation (CV)2.5745601
Kurtosis24.163101
Mean0.91578947
Median Absolute Deviation (MAD)0
Skewness4.3366309
Sum174
Variance5.5590086
MonotonicityNot monotonic
2023-12-12T10:54:45.567314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 138
72.6%
1 21
 
11.1%
2 9
 
4.7%
4 6
 
3.2%
5 4
 
2.1%
3 4
 
2.1%
7 3
 
1.6%
14 1
 
0.5%
6 1
 
0.5%
9 1
 
0.5%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
0 138
72.6%
1 21
 
11.1%
2 9
 
4.7%
3 4
 
2.1%
4 6
 
3.2%
5 4
 
2.1%
6 1
 
0.5%
7 3
 
1.6%
9 1
 
0.5%
10 1
 
0.5%
ValueCountFrequency (%)
19 1
 
0.5%
14 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
7 3
 
1.6%
6 1
 
0.5%
5 4
2.1%
4 6
3.2%
3 4
2.1%
2 9
4.7%

Interactions

2023-12-12T10:54:43.108813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:54:45.660234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통제분야통제영역결함수
통제분야1.0001.0000.198
통제영역1.0001.0000.482
결함수0.1980.4821.000
2023-12-12T10:54:45.756233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결함수통제분야
결함수1.0000.073
통제분야0.0731.000

Missing values

2023-12-12T10:54:43.223915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:54:43.301343image/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

통제분야통제영역통제항목결함수
00. 정보보호관리과정1. 정보보호 정책수립 및 범위설정1.1 정보보호정책의 수립0
10. 정보보호관리과정1. 정보보호 정책수립 및 범위설정1.2 범위설정0
20. 정보보호관리과정2. 경영진 책임 및 조직구성2.1 경영진 참여0
30. 정보보호관리과정2. 경영진 책임 및 조직구성2.2 정보보호 조직 구성 및 자원 할당0
40. 정보보호관리과정3. 위험관리3.1 위험관리 방법 및 계획 수립1
50. 정보보호관리과정3. 위험관리3.2 위험 식별 및 평가5
60. 정보보호관리과정3. 위험관리3.3 정보보호대책 선정 및 이행계획 수립4
70. 정보보호관리과정4. 정보보호대책 구현4.1 정보보호대책의 효과적 구현2
80. 정보보호관리과정4. 정보보호대책 구현4.2 내부 공유 및 교육0
90. 정보보호관리과정5. 사후관리5.1 법적요구사항 준수검토14
통제분야통제영역통제항목결함수
1808. 기술적 보호조치8.6 개발 보안8.6.4 시험 데이터 및 소스 프로그램 보안0
1818. 기술적 보호조치8.6 개발 보안8.6.5 외주개발 보안0
1829. 물리적 보호조치9.1 영상정보처리기기 관리9.1.1 영상정보처리기기 설치·운영 제한0
1839. 물리적 보호조치9.1 영상정보처리기기 관리9.1.2 영상정보처리기기설치·운영 사무의 위탁 관리0
1849. 물리적 보호조치9.2 물리적 보안관리9.2.1 보호구역의 지정 및 관리0
1859. 물리적 보호조치9.2 물리적 보안관리9.2.2 출입통제 및 사무실 보안0
1869. 물리적 보호조치9.2 물리적 보안관리9.2.3 개인업무 환경 보안0
1879. 물리적 보호조치9.3 매체 관리9.3.1 개인정보처리시스템 저장매체 관리0
1889. 물리적 보호조치9.3 매체 관리9.3.2 휴대용 저장매체 관리0
1899. 물리적 보호조치9.3 매체 관리9.3.3 이동 컴퓨팅관리0