Overview

Dataset statistics

Number of variables11
Number of observations1203
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.7%
Total size in memory105.9 KiB
Average record size in memory90.1 B

Variable types

Text6
Numeric2
Categorical3

Dataset

DescriptionHRD4U(www.hrd4u.or.kr)에서 제공하는 콘텐츠 강의관련 정보로, 강의명과 NCS코드등의 정보가 있음
Author한국산업인력공단
URLhttps://www.data.go.kr/data/15121944/fileData.do

Alerts

Dataset has 9 (0.7%) duplicate rowsDuplicates
NCS코드2 is highly overall correlated with NCS코드번호 and 3 other fieldsHigh correlation
NCS체계2 is highly overall correlated with NCS코드번호 and 3 other fieldsHigh correlation
NCS코드번호 is highly overall correlated with NCS체계1 and 2 other fieldsHigh correlation
NCS코드번호1 is highly overall correlated with NCS체계1 and 2 other fieldsHigh correlation
NCS체계1 is highly overall correlated with NCS코드번호 and 3 other fieldsHigh correlation
NCS코드번호 has 83 (6.9%) zerosZeros
NCS코드번호1 has 83 (6.9%) zerosZeros

Reproduction

Analysis started2023-12-12 00:56:57.484169
Analysis finished2023-12-12 00:56:59.665311
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

콘텐츠명
Text

Distinct1169
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-12-12T09:56:59.896555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length30
Mean length13.737323
Min length2

Characters and Unicode

Total characters16526
Distinct characters518
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1139 ?
Unique (%)94.7%

Sample

1st rowFun 경영
2nd row기초승마
3rd row말조련사 자격
4th row재활승마지도사 자격
5th row콘텐츠관리테스트용
ValueCountFrequency (%)
실기 56
 
1.9%
자동차전기·전차장치정비 35
 
1.2%
시각디자인 34
 
1.1%
co2용접 34
 
1.1%
위한 34
 
1.1%
자동차엔진정비 32
 
1.1%
자동차차체정비 31
 
1.0%
배우는 30
 
1.0%
1 30
 
1.0%
2 30
 
1.0%
Other values (1427) 2664
88.5%
2023-12-12T09:57:00.461011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1763
 
10.7%
기 498
 
3.0%
자 402
 
2.4%
차 305
 
1.8%
정 276
 
1.7%
비 268
 
1.6%
전 238
 
1.4%
2 230
 
1.4%
동 222
 
1.3%
용 221
 
1.3%
Other values (508) 12103
73.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12143
73.5%
Space Separator 1763
 
10.7%
Decimal Number 798
 
4.8%
Uppercase Letter 656
 
4.0%
Lowercase Letter 288
 
1.7%
Other Punctuation 277
 
1.7%
Dash Punctuation 158
 
1.0%
Connector Punctuation 149
 
0.9%
Open Punctuation 104
 
0.6%
Close Punctuation 104
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 498
 
4.1%
자 402
 
3.3%
차 305
 
2.5%
정 276
 
2.3%
비 268
 
2.2%
전 238
 
2.0%
동 222
 
1.8%
용 221
 
1.8%
장 202
 
1.7%
실 193
 
1.6%
Other values (437) 9318
76.7%
Uppercase Letter
ValueCountFrequency (%)
C 134
20.4%
S 100
15.2%
W 76
11.6%
D 54
8.2%
A 49
 
7.5%
O 45
 
6.9%
M 37
 
5.6%
N 33
 
5.0%
P 23
 
3.5%
L 18
 
2.7%
Other values (13) 87
13.3%
Lowercase Letter
ValueCountFrequency (%)
e 125
43.4%
o 18
 
6.2%
n 17
 
5.9%
i 15
 
5.2%
r 14
 
4.9%
a 13
 
4.5%
t 12
 
4.2%
s 11
 
3.8%
l 9
 
3.1%
u 9
 
3.1%
Other values (13) 45
 
15.6%
Decimal Number
ValueCountFrequency (%)
2 230
28.8%
1 180
22.6%
0 167
20.9%
3 107
13.4%
4 30
 
3.8%
5 22
 
2.8%
7 18
 
2.3%
6 17
 
2.1%
8 14
 
1.8%
9 13
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 143
51.6%
· 98
35.4%
, 13
 
4.7%
& 10
 
3.6%
! 6
 
2.2%
/ 5
 
1.8%
' 2
 
0.7%
Open Punctuation
ValueCountFrequency (%)
[ 69
66.3%
( 35
33.7%
Close Punctuation
ValueCountFrequency (%)
] 69
66.3%
) 35
33.7%
Space Separator
ValueCountFrequency (%)
1763
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 158
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 149
100.0%
Control
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12142
73.5%
Common 3439
 
20.8%
Latin 944
 
5.7%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 498
 
4.1%
자 402
 
3.3%
차 305
 
2.5%
정 276
 
2.3%
비 268
 
2.2%
전 238
 
2.0%
동 222
 
1.8%
용 221
 
1.8%
장 202
 
1.7%
실 193
 
1.6%
Other values (436) 9317
76.7%
Latin
ValueCountFrequency (%)
C 134
14.2%
e 125
13.2%
S 100
 
10.6%
W 76
 
8.1%
D 54
 
5.7%
A 49
 
5.2%
O 45
 
4.8%
M 37
 
3.9%
N 33
 
3.5%
P 23
 
2.4%
Other values (36) 268
28.4%
Common
ValueCountFrequency (%)
1763
51.3%
2 230
 
6.7%
1 180
 
5.2%
0 167
 
4.9%
- 158
 
4.6%
_ 149
 
4.3%
. 143
 
4.2%
3 107
 
3.1%
· 98
 
2.8%
86
 
2.5%
Other values (15) 358
 
10.4%
Han
ValueCountFrequency (%)
美 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12142
73.5%
ASCII 4285
 
25.9%
None 98
 
0.6%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1763
41.1%
2 230
 
5.4%
1 180
 
4.2%
0 167
 
3.9%
- 158
 
3.7%
_ 149
 
3.5%
. 143
 
3.3%
C 134
 
3.1%
e 125
 
2.9%
3 107
 
2.5%
Other values (60) 1129
26.3%
Hangul
ValueCountFrequency (%)
기 498
 
4.1%
자 402
 
3.3%
차 305
 
2.5%
정 276
 
2.3%
비 268
 
2.2%
전 238
 
2.0%
동 222
 
1.8%
용 221
 
1.8%
장 202
 
1.7%
실 193
 
1.6%
Other values (436) 9317
76.7%
None
ValueCountFrequency (%)
· 98
100.0%
CJK
ValueCountFrequency (%)
美 1
100.0%

NCS코드번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct394
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6498168 × 108
Minimum0
Maximum2.0020203 × 109
Zeros83
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-12-12T09:57:00.641748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median7010301
Q31.5060301 × 109
95-th percentile2.0010202 × 109
Maximum2.0020203 × 109
Range2.0020203 × 109
Interquartile range (IQR)1.5060301 × 109

Descriptive statistics

Standard deviation7.8183489 × 108
Coefficient of variation (CV)1.3838234
Kurtosis-1.1842138
Mean5.6498168 × 108
Median Absolute Deviation (MAD)7010301
Skewness0.79201645
Sum6.7967297 × 1011
Variance6.112658 × 1017
MonotonicityNot monotonic
2023-12-12T09:57:00.815052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 101
 
8.4%
0 83
 
6.9%
402 65
 
5.4%
19 31
 
2.6%
18 27
 
2.2%
20010202 26
 
2.2%
7010301 25
 
2.1%
16 24
 
2.0%
2202 22
 
1.8%
802 16
 
1.3%
Other values (384) 783
65.1%
ValueCountFrequency (%)
0 83
6.9%
1 6
 
0.5%
2 10
 
0.8%
4 1
 
0.1%
6 3
 
0.2%
8 2
 
0.2%
9 1
 
0.1%
11 1
 
0.1%
14 4
 
0.3%
15 101
8.4%
ValueCountFrequency (%)
2002020310 3
0.2%
2002020309 3
0.2%
2002020306 3
0.2%
2002020305 2
0.2%
2002020210 3
0.2%
2002020208 3
0.2%
2002020207 3
0.2%
2002020110 3
0.2%
2002020108 3
0.2%
2002020106 2
0.2%

NCS체계
Text

Distinct396
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:01.069638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length47
Mean length23.906899
Min length2

Characters and Unicode

Total characters28760
Distinct characters321
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)17.0%

Sample

1st row교육·자연·사회과학> 평생교육
2nd row농림어업> 축산> 사육관리> 말사육
3rd row농림어업> 축산> 사육관리> 말사육
4th row농림어업> 축산> 사육관리> 말사육
5th row경비·청소> 경비> 경비·경호> 보안> 경계방비
ValueCountFrequency (%)
기계 343
 
7.2%
미분류 312
 
6.6%
정보통신 175
 
3.7%
자동차 175
 
3.7%
자동차정비 162
 
3.4%
디자인 126
 
2.6%
정보기술 117
 
2.5%
재료 93
 
2.0%
정보기술개발 84
 
1.8%
교육·자연·사회과학 83
 
1.7%
Other values (489) 3087
64.9%
2023-12-12T09:57:01.517795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3554
 
12.4%
> 2921
 
10.2%
기 1125
 
3.9%
· 1106
 
3.8%
자 1047
 
3.6%
정 795
 
2.8%
계 654
 
2.3%
차 576
 
2.0%
동 559
 
1.9%
전 502
 
1.7%
Other values (311) 15921
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20382
70.9%
Space Separator 3554
 
12.4%
Math Symbol 2921
 
10.2%
Other Punctuation 1111
 
3.9%
Uppercase Letter 479
 
1.7%
Open Punctuation 108
 
0.4%
Close Punctuation 108
 
0.4%
Decimal Number 94
 
0.3%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 1125
 
5.5%
자 1047
 
5.1%
정 795
 
3.9%
계 654
 
3.2%
차 576
 
2.8%
동 559
 
2.7%
전 502
 
2.5%
보 465
 
2.3%
비 438
 
2.1%
신 374
 
1.8%
Other values (286) 13847
67.9%
Uppercase Letter
ValueCountFrequency (%)
W 110
23.0%
S 102
21.3%
C 91
19.0%
D 48
10.0%
O 47
9.8%
M 19
 
4.0%
N 18
 
3.8%
A 17
 
3.5%
P 9
 
1.9%
L 7
 
1.5%
Other values (4) 11
 
2.3%
Other Punctuation
ValueCountFrequency (%)
· 1106
99.5%
? 3
 
0.3%
/ 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 47
50.0%
3 46
48.9%
5 1
 
1.1%
Space Separator
ValueCountFrequency (%)
3554
100.0%
Math Symbol
ValueCountFrequency (%)
> 2921
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20382
70.9%
Common 7899
 
27.5%
Latin 479
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 1125
 
5.5%
자 1047
 
5.1%
정 795
 
3.9%
계 654
 
3.2%
차 576
 
2.8%
동 559
 
2.7%
전 502
 
2.5%
보 465
 
2.3%
비 438
 
2.1%
신 374
 
1.8%
Other values (286) 13847
67.9%
Latin
ValueCountFrequency (%)
W 110
23.0%
S 102
21.3%
C 91
19.0%
D 48
10.0%
O 47
9.8%
M 19
 
4.0%
N 18
 
3.8%
A 17
 
3.5%
P 9
 
1.9%
L 7
 
1.5%
Other values (4) 11
 
2.3%
Common
ValueCountFrequency (%)
3554
45.0%
> 2921
37.0%
· 1106
 
14.0%
( 108
 
1.4%
) 108
 
1.4%
2 47
 
0.6%
3 46
 
0.6%
? 3
 
< 0.1%
- 3
 
< 0.1%
/ 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20357
70.8%
ASCII 7272
 
25.3%
None 1106
 
3.8%
Compat Jamo 25
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3554
48.9%
> 2921
40.2%
W 110
 
1.5%
( 108
 
1.5%
) 108
 
1.5%
S 102
 
1.4%
C 91
 
1.3%
D 48
 
0.7%
2 47
 
0.6%
O 47
 
0.6%
Other values (14) 136
 
1.9%
Hangul
ValueCountFrequency (%)
기 1125
 
5.5%
자 1047
 
5.1%
정 795
 
3.9%
계 654
 
3.2%
차 576
 
2.8%
동 559
 
2.7%
전 502
 
2.5%
보 465
 
2.3%
비 438
 
2.2%
신 374
 
1.8%
Other values (285) 13822
67.9%
None
ValueCountFrequency (%)
· 1106
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 25
100.0%

NCS코드번호1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.256858
Minimum0
Maximum25
Zeros83
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2023-12-12T09:57:01.643488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median15
Q319
95-th percentile22
Maximum25
Range25
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.665881
Coefficient of variation (CV)0.50282511
Kurtosis-0.73803012
Mean13.256858
Median Absolute Deviation (MAD)5
Skewness-0.67043075
Sum15948
Variance44.433969
MonotonicityNot monotonic
2023-12-12T09:57:01.781286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15 343
28.5%
20 175
14.5%
16 92
 
7.6%
4 83
 
6.9%
0 83
 
6.9%
8 77
 
6.4%
19 71
 
5.9%
2 51
 
4.2%
22 47
 
3.9%
7 36
 
3.0%
Other values (15) 145
12.1%
ValueCountFrequency (%)
0 83
6.9%
1 7
 
0.6%
2 51
4.2%
4 83
6.9%
5 10
 
0.8%
6 5
 
0.4%
7 36
3.0%
8 77
6.4%
9 13
 
1.1%
10 2
 
0.2%
ValueCountFrequency (%)
25 2
 
0.2%
24 6
 
0.5%
23 16
 
1.3%
22 47
 
3.9%
21 8
 
0.7%
20 175
14.5%
19 71
5.9%
18 30
 
2.5%
17 11
 
0.9%
16 92
7.6%

NCS체계1
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
기계
343 
정보통신
175 
재료
92 
미분류
85 
교육·자연·사회과학
83 
Other values (19)
425 

Length

Max length15
Median length14
Mean length4.9359933
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육·자연·사회과학
2nd row농림어업
3rd row농림어업
4th row농림어업
5th row경비·청소

Common Values

ValueCountFrequency (%)
기계 343
28.5%
정보통신 175
14.5%
재료 92
 
7.6%
미분류 85
 
7.1%
교육·자연·사회과학 83
 
6.9%
문화·예술·디자인·방송 77
 
6.4%
전기·전자 71
 
5.9%
경영·회계·사무 51
 
4.2%
인쇄·목재·가구·공예 47
 
3.9%
사회복지·종교 36
 
3.0%
Other values (14) 143
11.9%

Length

2023-12-12T09:57:01.932714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기계 343
28.5%
정보통신 175
14.5%
재료 92
 
7.6%
미분류 85
 
7.1%
교육·자연·사회과학 83
 
6.9%
문화·예술·디자인·방송 77
 
6.4%
전기·전자 71
 
5.9%
경영·회계·사무 51
 
4.2%
인쇄·목재·가구·공예 47
 
3.9%
사회복지·종교 36
 
3.0%
Other values (14) 143
11.9%

NCS코드2
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
미분류
270 
1506
170 
2001
117 
402
70 
1601
68 
Other values (39)
508 

Length

Max length4
Median length4
Mean length3.4380715
Min length1

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st row402
2nd row2402
3rd row2402
4th row2402
5th row1101

Common Values

ValueCountFrequency (%)
미분류 270
22.4%
1506 170
14.1%
2001 117
9.7%
402 70
 
5.8%
1601 68
 
5.7%
802 64
 
5.3%
0 49
 
4.1%
2002 49
 
4.1%
1502 38
 
3.2%
701 36
 
3.0%
Other values (34) 272
22.6%

Length

2023-12-12T09:57:02.070326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미분류 270
22.4%
1506 170
14.1%
2001 117
9.7%
402 70
 
5.8%
1601 68
 
5.7%
802 64
 
5.3%
0 49
 
4.1%
2002 49
 
4.1%
1502 38
 
3.2%
701 36
 
3.0%
Other values (34) 272
22.6%

NCS체계2
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
미분류
322 
자동차
160 
정보기술
117 
평생교육
70 
금속재료
68 
Other values (37)
466 

Length

Max length9
Median length7
Mean length3.599335
Min length2

Unique

Unique6 ?
Unique (%)0.5%

Sample

1st row평생교육
2nd row축산
3rd row축산
4th row축산
5th row경비

Common Values

ValueCountFrequency (%)
미분류 322
26.8%
자동차 160
13.3%
정보기술 117
 
9.7%
평생교육 70
 
5.8%
금속재료 68
 
5.7%
디자인 64
 
5.3%
통신기술 49
 
4.1%
기계가공 38
 
3.2%
사회복지 36
 
3.0%
공예 34
 
2.8%
Other values (32) 245
20.4%

Length

2023-12-12T09:57:02.197745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미분류 322
26.8%
자동차 160
13.3%
정보기술 117
 
9.7%
평생교육 70
 
5.8%
금속재료 68
 
5.7%
디자인 64
 
5.3%
통신기술 49
 
4.1%
기계가공 38
 
3.2%
사회복지 36
 
3.0%
공예 34
 
2.8%
Other values (32) 245
20.4%

NCS코드3
Text

Distinct61
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:02.378461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.599335
Min length1

Characters and Unicode

Total characters5533
Distinct characters13
Distinct categories2 ?
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 (%)1.1%

Sample

1st row미분류
2nd row240202
3rd row240202
4th row240202
5th row110101
ValueCountFrequency (%)
미분류 424
35.2%
150603 162
 
13.5%
200102 84
 
7.0%
160105 53
 
4.4%
0 49
 
4.1%
80201 48
 
4.0%
200202 36
 
3.0%
150201 35
 
2.9%
200101 31
 
2.6%
70103 29
 
2.4%
Other values (51) 252
20.9%
2023-12-12T09:57:02.728311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1654
29.9%
1 903
16.3%
2 620
 
11.2%
미 424
 
7.7%
분 424
 
7.7%
류 424
 
7.7%
5 329
 
5.9%
3 312
 
5.6%
6 232
 
4.2%
4 69
 
1.2%
Other values (3) 142
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4261
77.0%
Other Letter 1272
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1654
38.8%
1 903
21.2%
2 620
 
14.6%
5 329
 
7.7%
3 312
 
7.3%
6 232
 
5.4%
4 69
 
1.6%
8 60
 
1.4%
9 44
 
1.0%
7 38
 
0.9%
Other Letter
ValueCountFrequency (%)
미 424
33.3%
분 424
33.3%
류 424
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4261
77.0%
Hangul 1272
 
23.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1654
38.8%
1 903
21.2%
2 620
 
14.6%
5 329
 
7.7%
3 312
 
7.3%
6 232
 
5.4%
4 69
 
1.6%
8 60
 
1.4%
9 44
 
1.0%
7 38
 
0.9%
Hangul
ValueCountFrequency (%)
미 424
33.3%
분 424
33.3%
류 424
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4261
77.0%
Hangul 1272
 
23.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1654
38.8%
1 903
21.2%
2 620
 
14.6%
5 329
 
7.7%
3 312
 
7.3%
6 232
 
5.4%
4 69
 
1.6%
8 60
 
1.4%
9 44
 
1.0%
7 38
 
0.9%
Hangul
ValueCountFrequency (%)
미 424
33.3%
분 424
33.3%
류 424
33.3%

NCS체계3
Text

Distinct55
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:02.959541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length4.4779717
Min length2

Characters and Unicode

Total characters5387
Distinct characters116
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.7%

Sample

1st row미분류
2nd row사육관리
3rd row사육관리
4th row사육관리
5th row경비·경호
ValueCountFrequency (%)
미분류 479
39.8%
자동차정비 162
 
13.5%
정보기술개발 84
 
7.0%
용접 53
 
4.4%
디자인 48
 
4.0%
무선통신구축(이동통신포함 36
 
3.0%
절삭가공 35
 
2.9%
정보기술전략·계획 31
 
2.6%
직업상담서비스 29
 
2.4%
3d프린터개발 22
 
1.8%
Other values (45) 224
18.6%
2023-12-12T09:57:03.322397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 483
 
9.0%
류 479
 
8.9%
분 479
 
8.9%
정 281
 
5.2%
자 216
 
4.0%
비 209
 
3.9%
동 203
 
3.8%
기 182
 
3.4%
차 166
 
3.1%
보 122
 
2.3%
Other values (106) 2567
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5209
96.7%
Other Punctuation 62
 
1.2%
Close Punctuation 36
 
0.7%
Open Punctuation 36
 
0.7%
Uppercase Letter 22
 
0.4%
Decimal Number 22
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 483
 
9.3%
류 479
 
9.2%
분 479
 
9.2%
정 281
 
5.4%
자 216
 
4.1%
비 209
 
4.0%
동 203
 
3.9%
기 182
 
3.5%
차 166
 
3.2%
보 122
 
2.3%
Other values (101) 2389
45.9%
Other Punctuation
ValueCountFrequency (%)
· 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 22
100.0%
Decimal Number
ValueCountFrequency (%)
3 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5209
96.7%
Common 156
 
2.9%
Latin 22
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 483
 
9.3%
류 479
 
9.2%
분 479
 
9.2%
정 281
 
5.4%
자 216
 
4.1%
비 209
 
4.0%
동 203
 
3.9%
기 182
 
3.5%
차 166
 
3.2%
보 122
 
2.3%
Other values (101) 2389
45.9%
Common
ValueCountFrequency (%)
· 62
39.7%
) 36
23.1%
( 36
23.1%
3 22
 
14.1%
Latin
ValueCountFrequency (%)
D 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5209
96.7%
ASCII 116
 
2.2%
None 62
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 483
 
9.3%
류 479
 
9.2%
분 479
 
9.2%
정 281
 
5.4%
자 216
 
4.1%
비 209
 
4.0%
동 203
 
3.9%
기 182
 
3.5%
차 166
 
3.2%
보 122
 
2.3%
Other values (101) 2389
45.9%
None
ValueCountFrequency (%)
· 62
100.0%
ASCII
ValueCountFrequency (%)
) 36
31.0%
( 36
31.0%
D 22
19.0%
3 22
19.0%

NCS코드4
Text

Distinct92
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:03.544122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.6267664
Min length1

Characters and Unicode

Total characters6769
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)2.2%

Sample

1st row미분류
2nd row24020205
3rd row24020205
4th row24020205
5th row11010101
ValueCountFrequency (%)
미분류 470
39.1%
20010202 54
 
4.5%
0 49
 
4.1%
15060305 36
 
3.0%
15060301 35
 
2.9%
16010502 34
 
2.8%
15060302 32
 
2.7%
15060304 30
 
2.5%
8020101 30
 
2.5%
15060306 26
 
2.2%
Other values (82) 407
33.8%
2023-12-12T09:57:03.949797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2252
33.3%
1 1039
15.3%
2 788
 
11.6%
미 470
 
6.9%
분 470
 
6.9%
류 470
 
6.9%
5 374
 
5.5%
3 351
 
5.2%
6 262
 
3.9%
4 150
 
2.2%
Other values (3) 143
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5359
79.2%
Other Letter 1410
 
20.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2252
42.0%
1 1039
19.4%
2 788
 
14.7%
5 374
 
7.0%
3 351
 
6.5%
6 262
 
4.9%
4 150
 
2.8%
8 64
 
1.2%
9 42
 
0.8%
7 37
 
0.7%
Other Letter
ValueCountFrequency (%)
미 470
33.3%
분 470
33.3%
류 470
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5359
79.2%
Hangul 1410
 
20.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2252
42.0%
1 1039
19.4%
2 788
 
14.7%
5 374
 
7.0%
3 351
 
6.5%
6 262
 
4.9%
4 150
 
2.8%
8 64
 
1.2%
9 42
 
0.8%
7 37
 
0.7%
Hangul
ValueCountFrequency (%)
미 470
33.3%
분 470
33.3%
류 470
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5359
79.2%
Hangul 1410
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2252
42.0%
1 1039
19.4%
2 788
 
14.7%
5 374
 
7.0%
3 351
 
6.5%
6 262
 
4.9%
4 150
 
2.8%
8 64
 
1.2%
9 42
 
0.8%
7 37
 
0.7%
Hangul
ValueCountFrequency (%)
미 470
33.3%
분 470
33.3%
류 470
33.3%

NCS체계4
Text

Distinct85
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
2023-12-12T09:57:04.294674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length5.0049875
Min length2

Characters and Unicode

Total characters6021
Distinct characters161
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)1.9%

Sample

1st row미분류
2nd row말사육
3rd row말사육
4th row말사육
5th row보안
ValueCountFrequency (%)
미분류 537
43.8%
응용sw엔지니어링 54
 
4.4%
자동차도장 36
 
2.9%
자동차전기·전자장치정비 35
 
2.9%
co2용접 34
 
2.8%
자동차엔진정비 32
 
2.6%
시각디자인 30
 
2.4%
자동차차체정비 30
 
2.4%
자동차정비검사 26
 
2.1%
직업상담 25
 
2.0%
Other values (78) 386
31.5%
2023-12-12T09:57:04.777470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
분 543
 
9.0%
미 539
 
9.0%
류 537
 
8.9%
자 246
 
4.1%
차 192
 
3.2%
동 162
 
2.7%
기 136
 
2.3%
정 135
 
2.2%
지 129
 
2.1%
비 128
 
2.1%
Other values (151) 3274
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5579
92.7%
Uppercase Letter 318
 
5.3%
Decimal Number 56
 
0.9%
Other Punctuation 46
 
0.8%
Space Separator 22
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
분 543
 
9.7%
미 539
 
9.7%
류 537
 
9.6%
자 246
 
4.4%
차 192
 
3.4%
동 162
 
2.9%
기 136
 
2.4%
정 135
 
2.4%
지 129
 
2.3%
비 128
 
2.3%
Other values (139) 2832
50.8%
Uppercase Letter
ValueCountFrequency (%)
W 93
29.2%
S 87
27.4%
C 48
15.1%
O 34
 
10.7%
D 22
 
6.9%
M 14
 
4.4%
A 14
 
4.4%
N 6
 
1.9%
Decimal Number
ValueCountFrequency (%)
2 34
60.7%
3 22
39.3%
Other Punctuation
ValueCountFrequency (%)
· 46
100.0%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5579
92.7%
Latin 318
 
5.3%
Common 124
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
분 543
 
9.7%
미 539
 
9.7%
류 537
 
9.6%
자 246
 
4.4%
차 192
 
3.4%
동 162
 
2.9%
기 136
 
2.4%
정 135
 
2.4%
지 129
 
2.3%
비 128
 
2.3%
Other values (139) 2832
50.8%
Latin
ValueCountFrequency (%)
W 93
29.2%
S 87
27.4%
C 48
15.1%
O 34
 
10.7%
D 22
 
6.9%
M 14
 
4.4%
A 14
 
4.4%
N 6
 
1.9%
Common
ValueCountFrequency (%)
· 46
37.1%
2 34
27.4%
22
17.7%
3 22
17.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5579
92.7%
ASCII 396
 
6.6%
None 46
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
분 543
 
9.7%
미 539
 
9.7%
류 537
 
9.6%
자 246
 
4.4%
차 192
 
3.4%
동 162
 
2.9%
기 136
 
2.4%
정 135
 
2.4%
지 129
 
2.3%
비 128
 
2.3%
Other values (139) 2832
50.8%
ASCII
ValueCountFrequency (%)
W 93
23.5%
S 87
22.0%
C 48
12.1%
O 34
 
8.6%
2 34
 
8.6%
22
 
5.6%
D 22
 
5.6%
3 22
 
5.6%
M 14
 
3.5%
A 14
 
3.5%
None
ValueCountFrequency (%)
· 46
100.0%

Interactions

2023-12-12T09:56:59.184535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:56:58.969848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:56:59.283645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:56:59.072626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:57:04.902012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NCS코드번호NCS코드번호1NCS체계1NCS코드2NCS체계2NCS코드3NCS체계3NCS코드4NCS체계4
NCS코드번호1.0000.7300.8700.9260.9240.9840.9630.9930.949
NCS코드번호10.7301.0000.9990.9910.9880.9750.9690.9640.950
NCS체계10.8700.9991.0000.9930.9920.9870.9850.9850.977
NCS코드20.9260.9910.9931.0001.0000.9970.9950.9960.992
NCS체계20.9240.9880.9921.0001.0000.9960.9950.9950.993
NCS코드30.9840.9750.9870.9970.9961.0001.0000.9990.998
NCS체계30.9630.9690.9850.9950.9951.0001.0000.9990.998
NCS코드40.9930.9640.9850.9960.9950.9990.9991.0001.000
NCS체계40.9490.9500.9770.9920.9930.9980.9981.0001.000
2023-12-12T09:57:05.057591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NCS코드2NCS체계1NCS체계2
NCS코드21.0000.8570.986
NCS체계10.8571.0000.846
NCS체계20.9860.8461.000
2023-12-12T09:57:05.168894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NCS코드번호NCS코드번호1NCS체계1NCS코드2NCS체계2
NCS코드번호1.0000.4430.5160.6630.669
NCS코드번호10.4431.0000.9890.9120.893
NCS체계10.5160.9891.0000.8570.846
NCS코드20.6630.9120.8571.0000.986
NCS체계20.6690.8930.8460.9861.000

Missing values

2023-12-12T09:56:59.424979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:56:59.606369image/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

콘텐츠명NCS코드번호NCS체계NCS코드번호1NCS체계1NCS코드2NCS체계2NCS코드3NCS체계3NCS코드4NCS체계4
0Fun 경영402교육·자연·사회과학> 평생교육4교육·자연·사회과학402평생교육미분류미분류미분류미분류
1기초승마24020205농림어업> 축산> 사육관리> 말사육24농림어업2402축산240202사육관리24020205말사육
2말조련사 자격24020205농림어업> 축산> 사육관리> 말사육24농림어업2402축산240202사육관리24020205말사육
3재활승마지도사 자격24020205농림어업> 축산> 사육관리> 말사육24농림어업2402축산240202사육관리24020205말사육
4콘텐츠관리테스트용1101010104경비·청소> 경비> 경비·경호> 보안> 경계방비11경비·청소1101경비110101경비·경호11010101보안
5청년에게 고함-권영설402교육·자연·사회과학> 평생교육4교육·자연·사회과학402평생교육미분류미분류미분류미분류
6직업기행그곳에가면7010301사회복지·종교> 사회복지> 직업상담서비스> 직업상담7사회복지·종교701사회복지70103직업상담서비스7010301직업상담
7온라인 쇼핑몰 창업가이드 [1]20020308정보통신> 통신기술> 통신서비스> 콘텐츠네트워크서비스20정보통신2002통신기술200203통신서비스20020308콘텐츠네트워크서비스
8아주특별한창업특강4020201교육·자연·사회과학> 평생교육> 평생교육운영> 평생교육프로그램 기획·개발·평가4교육·자연·사회과학402평생교육40202평생교육운영4020201평생교육프로그램 기획·개발·평가
9쉽게 배우는 철강 [2]16010202재료> 금속재료> 금속재료제조> 제강16재료1601금속재료160102금속재료제조16010202제강
콘텐츠명NCS코드번호NCS체계NCS코드번호1NCS체계1NCS코드2NCS체계2NCS코드3NCS체계3NCS코드4NCS체계4
1193보안엔지니어링_05.물리적 보안 구축2001020605정보통신> 정보기술> 정보기술개발> 보안엔지니어링> 물리적 보안 구축20정보통신2001정보기술200102정보기술개발20010206보안엔지니어링
1194보안엔지니어링_07.보안체계 운영관리2001020607정보통신> 정보기술> 정보기술개발> 보안엔지니어링> 보안체계 운영관리20정보통신2001정보기술200102정보기술개발20010206보안엔지니어링
1195보안엔지니어링_09.보안감사 수행2001020609정보통신> 정보기술> 정보기술개발> 보안엔지니어링> 보안감사 수행20정보통신2001정보기술200102정보기술개발20010206보안엔지니어링
1196보안엔지니어링_10. 보안인증 관리2001020610정보통신> 정보기술> 정보기술개발> 보안엔지니어링> 보안인증 관리20정보통신2001정보기술200102정보기술개발20010206보안엔지니어링
1197임베디드SW엔지니어링_04.운영체제 이식2001020304정보통신> 정보기술> 정보기술개발> 임베디드SW엔지니어링> 운영체제 이식(구버전)20정보통신2001정보기술200102정보기술개발20010203임베디드SW엔지니어링
1198임베디드SW엔지니어링_05.디바이스 드라이버 분석 설계2001020305정보통신> 정보기술> 정보기술개발> 임베디드SW엔지니어링> 디바이스 드라이버 분석 설계(구버전)20정보통신2001정보기술200102정보기술개발20010203임베디드SW엔지니어링
1199임베디드SW엔지니어링_06.디바이스 드라이버 구현2001020306정보통신> 정보기술> 정보기술개발> 임베디드SW엔지니어링> 디바이스 드라이버 구현(구버전)20정보통신2001정보기술200102정보기술개발20010203임베디드SW엔지니어링
1200임베디드SW엔지니어링_07.임베디드 애플리케이션 분석 설계2001020307정보통신> 정보기술> 정보기술개발> 임베디드SW엔지니어링> 임베디드 애플리케이션 분석 설계(구버전)20정보통신2001정보기술200102정보기술개발20010203임베디드SW엔지니어링
12012023 Best HRD 우수기관 인증심사_대중소기업용25010101미분류> 미분류> 미분류> 미분류25미분류2501미분류250101미분류25010101미분류
12022023 Best HRD 우수기관 인증심사_선취업후학습용25010101미분류> 미분류> 미분류> 미분류25미분류2501미분류250101미분류25010101미분류

Duplicate rows

Most frequently occurring

콘텐츠명NCS코드번호NCS체계NCS코드번호1NCS체계1NCS코드2NCS체계2NCS코드3NCS체계3NCS코드4NCS체계4# duplicates
03D 모델링 실무802문화·예술·디자인·방송> 디자인8문화·예술·디자인·방송802디자인미분류미분류미분류미분류2
1건축시공140302건설> 건축> 건축시공14건설1403건축140302건축시공미분류미분류2
2기계제도150102기계> 기계설계> 기계설계15기계1501기계설계150102기계설계미분류미분류2
3기초전자실기19전기·전자19전기·전자미분류미분류미분류미분류미분류미분류2
4비파괴검사116재료16재료미분류미분류미분류미분류미분류미분류2
5비파괴검사216재료16재료미분류미분류미분류미분류미분류미분류2
6용접일반15기계15기계미분류미분류미분류미분류미분류미분류2
7제품디자인8020102문화·예술·디자인·방송> 디자인> 디자인> 제품디자인8문화·예술·디자인·방송802디자인80201디자인8020102제품디자인2
8창업 홀로서기2경영·회계·사무2경영·회계·사무미분류미분류미분류미분류미분류미분류2