Overview

Dataset statistics

Number of variables5
Number of observations1391
Missing cells22
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.8 KiB
Average record size in memory41.1 B

Variable types

Categorical1
Text3
Numeric1

Dataset

Description학점은행제 학점인정 학점원인 자격 학점기준 정보에 대한 데이터로 분야명, 자격증명, 발행기관, 중분류, 직무번호 등의 항목을 제공합니다.
Author국가평생교육진흥원
URLhttps://www.data.go.kr/data/15089510/fileData.do

Alerts

중분류 has 22 (1.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:02:37.773634
Analysis finished2023-12-12 20:02:38.946534
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분야명
Categorical

Distinct21
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
이미용/관광/스포츠
426 
기계
129 
공통/기초사무
110 
경영/회계/사무
101 
환경/안전관리
80 
Other values (16)
545 

Length

Max length11
Median length9
Mean length6.907261
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경영/회계/사무
2nd row경영/회계/사무
3rd row경영/회계/사무
4th row경영/회계/사무
5th row경영/회계/사무

Common Values

ValueCountFrequency (%)
이미용/관광/스포츠 426
30.6%
기계 129
 
9.3%
공통/기초사무 110
 
7.9%
경영/회계/사무 101
 
7.3%
환경/안전관리 80
 
5.8%
농림/해양·수산/광업 65
 
4.7%
건설 64
 
4.6%
정보/통신 64
 
4.6%
문화/예술 43
 
3.1%
전기/전자 41
 
2.9%
Other values (11) 268
19.3%

Length

2023-12-13T05:02:39.027131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이미용/관광/스포츠 426
30.6%
기계 129
 
9.3%
공통/기초사무 110
 
7.9%
경영/회계/사무 101
 
7.3%
환경/안전관리 80
 
5.8%
농림/해양·수산/광업 65
 
4.7%
건설 64
 
4.6%
정보/통신 64
 
4.6%
문화/예술 43
 
3.1%
전기/전자 41
 
2.9%
Other values (11) 268
19.3%

자격증이름
Text

Distinct1390
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2023-12-13T05:02:39.328179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length11.109993
Min length3

Characters and Unicode

Total characters15454
Distinct characters430
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1389 ?
Unique (%)99.9%

Sample

1st row공정관리기사
2nd row공정관리산업기사
3rd row전산회계1급
4th row경제경영이해력인증시험 매경TEST 최우수
5th row경제경영이해력인증시험 매경TEST 우수
ValueCountFrequency (%)
1급 12
 
0.8%
2급 11
 
0.8%
mate 4
 
0.3%
경제이해력검증시험(tesat 4
 
0.3%
3급 4
 
0.3%
및 4
 
0.3%
실용영어(pelt 3
 
0.2%
실용수학 3
 
0.2%
철도차량산업기사 2
 
0.1%
한자 2
 
0.1%
Other values (1390) 1405
96.6%
2023-12-13T05:02:39.871559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 1197
 
7.7%
기 716
 
4.6%
( 681
 
4.4%
) 679
 
4.4%
도 560
 
3.6%
급 518
 
3.4%
스 508
 
3.3%
지 496
 
3.2%
포 416
 
2.7%
츠 413
 
2.7%
Other values (420) 9270
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12733
82.4%
Decimal Number 720
 
4.7%
Open Punctuation 683
 
4.4%
Close Punctuation 681
 
4.4%
Uppercase Letter 381
 
2.5%
Lowercase Letter 123
 
0.8%
Space Separator 63
 
0.4%
Dash Punctuation 37
 
0.2%
Other Punctuation 23
 
0.1%
Connector Punctuation 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1197
 
9.4%
기 716
 
5.6%
도 560
 
4.4%
급 518
 
4.1%
스 508
 
4.0%
지 496
 
3.9%
포 416
 
3.3%
츠 413
 
3.2%
산 283
 
2.2%
전 270
 
2.1%
Other values (360) 7356
57.8%
Uppercase Letter
ValueCountFrequency (%)
E 55
14.4%
T 44
11.5%
L 37
9.7%
S 37
9.7%
F 31
8.1%
A 29
7.6%
X 28
7.3%
P 28
7.3%
C 19
 
5.0%
B 13
 
3.4%
Other values (10) 60
15.7%
Lowercase Letter
ValueCountFrequency (%)
e 17
13.8%
t 13
10.6%
i 13
10.6%
s 12
9.8%
a 12
9.8%
n 10
8.1%
r 8
 
6.5%
g 7
 
5.7%
o 6
 
4.9%
m 4
 
3.3%
Other values (9) 21
17.1%
Decimal Number
ValueCountFrequency (%)
2 273
37.9%
1 256
35.6%
0 75
 
10.4%
3 53
 
7.4%
6 30
 
4.2%
4 22
 
3.1%
8 5
 
0.7%
9 3
 
0.4%
5 3
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 681
99.7%
[ 2
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 679
99.7%
] 2
 
0.3%
Other Punctuation
ValueCountFrequency (%)
· 13
56.5%
/ 10
43.5%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12733
82.4%
Common 2215
 
14.3%
Latin 506
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1197
 
9.4%
기 716
 
5.6%
도 560
 
4.4%
급 518
 
4.1%
스 508
 
4.0%
지 496
 
3.9%
포 416
 
3.3%
츠 413
 
3.2%
산 283
 
2.2%
전 270
 
2.1%
Other values (360) 7356
57.8%
Latin
ValueCountFrequency (%)
E 55
 
10.9%
T 44
 
8.7%
L 37
 
7.3%
S 37
 
7.3%
F 31
 
6.1%
A 29
 
5.7%
X 28
 
5.5%
P 28
 
5.5%
C 19
 
3.8%
e 17
 
3.4%
Other values (31) 181
35.8%
Common
ValueCountFrequency (%)
( 681
30.7%
) 679
30.7%
2 273
12.3%
1 256
 
11.6%
0 75
 
3.4%
63
 
2.8%
3 53
 
2.4%
- 37
 
1.7%
6 30
 
1.4%
4 22
 
1.0%
Other values (9) 46
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12733
82.4%
ASCII 2706
 
17.5%
None 13
 
0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 1197
 
9.4%
기 716
 
5.6%
도 560
 
4.4%
급 518
 
4.1%
스 508
 
4.0%
지 496
 
3.9%
포 416
 
3.3%
츠 413
 
3.2%
산 283
 
2.2%
전 270
 
2.1%
Other values (360) 7356
57.8%
ASCII
ValueCountFrequency (%)
( 681
25.2%
) 679
25.1%
2 273
10.1%
1 256
 
9.5%
0 75
 
2.8%
63
 
2.3%
E 55
 
2.0%
3 53
 
2.0%
T 44
 
1.6%
L 37
 
1.4%
Other values (47) 490
18.1%
None
ValueCountFrequency (%)
· 13
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%

발행기관
Text

Distinct91
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2023-12-13T05:02:40.170888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length8
Mean length7.790798
Min length3

Characters and Unicode

Total characters10837
Distinct characters161
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)2.0%

Sample

1st row한국산업인력공단
2nd row한국산업인력공단
3rd row한국세무사회
4th row매일경제신문사
5th row매일경제신문사
ValueCountFrequency (%)
한국산업인력공단 489
35.2%
체육지도자연수원 418
30.1%
대한상공회의소 69
 
5.0%
국토교통부 36
 
2.6%
문화재청 31
 
2.2%
한국방송통신전파진흥원 27
 
1.9%
한국생산성본부 16
 
1.2%
문화체육관광부 16
 
1.2%
해양수산부 15
 
1.1%
중소기업청 15
 
1.1%
Other values (81) 259
18.6%
2023-12-13T05:02:40.635880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 771
 
7.1%
국 737
 
6.8%
공 597
 
5.5%
산 541
 
5.0%
원 524
 
4.8%
업 519
 
4.8%
단 507
 
4.7%
인 504
 
4.7%
력 498
 
4.6%
육 460
 
4.2%
Other values (151) 5179
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10655
98.3%
Close Punctuation 77
 
0.7%
Open Punctuation 77
 
0.7%
Uppercase Letter 28
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 771
 
7.2%
국 737
 
6.9%
공 597
 
5.6%
산 541
 
5.1%
원 524
 
4.9%
업 519
 
4.9%
단 507
 
4.8%
인 504
 
4.7%
력 498
 
4.7%
육 460
 
4.3%
Other values (143) 4997
46.9%
Uppercase Letter
ValueCountFrequency (%)
S 8
28.6%
K 4
14.3%
P 4
14.3%
E 4
14.3%
B 4
14.3%
T 4
14.3%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10655
98.3%
Common 154
 
1.4%
Latin 28
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 771
 
7.2%
국 737
 
6.9%
공 597
 
5.6%
산 541
 
5.1%
원 524
 
4.9%
업 519
 
4.9%
단 507
 
4.8%
인 504
 
4.7%
력 498
 
4.7%
육 460
 
4.3%
Other values (143) 4997
46.9%
Latin
ValueCountFrequency (%)
S 8
28.6%
K 4
14.3%
P 4
14.3%
E 4
14.3%
B 4
14.3%
T 4
14.3%
Common
ValueCountFrequency (%)
) 77
50.0%
( 77
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10655
98.3%
ASCII 182
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 771
 
7.2%
국 737
 
6.9%
공 597
 
5.6%
산 541
 
5.1%
원 524
 
4.9%
업 519
 
4.9%
단 507
 
4.8%
인 504
 
4.7%
력 498
 
4.7%
육 460
 
4.3%
Other values (143) 4997
46.9%
ASCII
ValueCountFrequency (%)
) 77
42.3%
( 77
42.3%
S 8
 
4.4%
K 4
 
2.2%
P 4
 
2.2%
E 4
 
2.2%
B 4
 
2.2%
T 4
 
2.2%

중분류
Text

MISSING 

Distinct108
Distinct (%)7.9%
Missing22
Missing (%)1.6%
Memory size11.0 KiB
2023-12-13T05:02:40.917042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length4.4485026
Min length2

Characters and Unicode

Total characters6090
Distinct characters160
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row경영
2nd row경영
3rd row회계
4th row경영·경제
5th row경영·경제
ValueCountFrequency (%)
오락/체육 408
27.3%
및 55
 
3.7%
외국어 44
 
2.9%
경영 44
 
2.9%
한글/한자 38
 
2.5%
운송장비정비 33
 
2.2%
문화 32
 
2.1%
기초사무 23
 
1.5%
산업안전 22
 
1.5%
회계 22
 
1.5%
Other values (107) 773
51.7%
2023-12-13T05:02:41.340879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 448
 
7.4%
육 411
 
6.7%
체 410
 
6.7%
오 408
 
6.7%
락 408
 
6.7%
비 180
 
3.0%
· 174
 
2.9%
125
 
2.1%
공 117
 
1.9%
기 114
 
1.9%
Other values (150) 3295
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5343
87.7%
Other Punctuation 622
 
10.2%
Space Separator 125
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
육 411
 
7.7%
체 410
 
7.7%
오 408
 
7.6%
락 408
 
7.6%
비 180
 
3.4%
공 117
 
2.2%
기 114
 
2.1%
경 104
 
1.9%
장 95
 
1.8%
한 76
 
1.4%
Other values (147) 3020
56.5%
Other Punctuation
ValueCountFrequency (%)
/ 448
72.0%
· 174
 
28.0%
Space Separator
ValueCountFrequency (%)
125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5343
87.7%
Common 747
 
12.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
육 411
 
7.7%
체 410
 
7.7%
오 408
 
7.6%
락 408
 
7.6%
비 180
 
3.4%
공 117
 
2.2%
기 114
 
2.1%
경 104
 
1.9%
장 95
 
1.8%
한 76
 
1.4%
Other values (147) 3020
56.5%
Common
ValueCountFrequency (%)
/ 448
60.0%
· 174
 
23.3%
125
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5343
87.7%
ASCII 573
 
9.4%
None 174
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 448
78.2%
125
 
21.8%
Hangul
ValueCountFrequency (%)
육 411
 
7.7%
체 410
 
7.7%
오 408
 
7.6%
락 408
 
7.6%
비 180
 
3.4%
공 117
 
2.2%
기 114
 
2.1%
경 104
 
1.9%
장 95
 
1.8%
한 76
 
1.4%
Other values (147) 3020
56.5%
None
ValueCountFrequency (%)
· 174
100.0%

직무번호
Real number (ℝ)

Distinct15
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9863408
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.4 KiB
2023-12-13T05:02:41.504463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum38
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.3904397
Coefficient of variation (CV)1.2034389
Kurtosis90.287098
Mean1.9863408
Median Absolute Deviation (MAD)0
Skewness7.8790959
Sum2763
Variance5.7142018
MonotonicityNot monotonic
2023-12-13T05:02:41.644442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 841
60.5%
2 265
 
19.1%
3 126
 
9.1%
4 57
 
4.1%
5 37
 
2.7%
6 23
 
1.7%
8 17
 
1.2%
7 14
 
1.0%
9 3
 
0.2%
28 3
 
0.2%
Other values (5) 5
 
0.4%
ValueCountFrequency (%)
1 841
60.5%
2 265
 
19.1%
3 126
 
9.1%
4 57
 
4.1%
5 37
 
2.7%
6 23
 
1.7%
7 14
 
1.0%
8 17
 
1.2%
9 3
 
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
38 1
 
0.1%
32 1
 
0.1%
28 3
 
0.2%
25 1
 
0.1%
13 1
 
0.1%
10 1
 
0.1%
9 3
 
0.2%
8 17
1.2%
7 14
1.0%
6 23
1.7%

Interactions

2023-12-13T05:02:38.625193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:02:41.735043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야명발행기관직무번호
분야명1.0000.9540.401
발행기관0.9541.0000.453
직무번호0.4010.4531.000
2023-12-13T05:02:41.818844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직무번호분야명
직무번호1.0000.185
분야명0.1851.000

Missing values

2023-12-13T05:02:38.790510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:02:38.901592image/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

분야명자격증이름발행기관중분류직무번호
0경영/회계/사무공정관리기사한국산업인력공단경영1
1경영/회계/사무공정관리산업기사한국산업인력공단경영1
2경영/회계/사무전산회계1급한국세무사회회계1
3경영/회계/사무경제경영이해력인증시험 매경TEST 최우수매일경제신문사경영·경제1
4경영/회계/사무경제경영이해력인증시험 매경TEST 우수매일경제신문사경영·경제1
5경영/회계/사무경제이해력검증시험(TESAT) S급한국경제신문사경영·경제1
6경영/회계/사무경제이해력검증시험(TESAT) 1급한국경제신문사경영·경제1
7경영/회계/사무경제이해력검증시험(TESAT) 2급한국경제신문사경영·경제1
8경영/회계/사무경제이해력검증시험(TESAT) 3급한국경제신문사경영·경제1
9경영/회계/사무빌딩경영관리사(재)한국산업교육원주택관리 및 경비1
분야명자격증이름발행기관중분류직무번호
1381<NA>생활체육지도자3급(육상)체육지도자연수원<NA>2
1382<NA>생활체육지도자3급(킥복싱)체육지도자연수원<NA>2
1383<NA>생활체육지도자3급(태수도)체육지도자연수원<NA>2
1384<NA>생활체육지도자3급(통일무술)문화체육관광부<NA>2
1385<NA>생활체육지도자3급(특공무술)문화체육관광부<NA>2
1386<NA>생활체육지도자3급(한무도)문화체육관광부<NA>2
1387<NA>생활체육지도자3급(핸드볼)문화체육관광부<NA>2
1388<NA>생활체육지도자3급(회전무술)문화체육관광부<NA>2
1389<NA>생활체육지도자3급(화랑도)문화체육관광부<NA>2
1390<NA>생활체육지도자3급(활기도)문화체육관광부<NA>2