Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells6
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory62.7 B

Variable types

Categorical2
Text2
Numeric2
DateTime1

Dataset

Description소재부품종합정보망의 신뢰성 동영상 강의 정보입니다. 신뢰성 강의 관련 과정목록(5개 과정), 과정코드, 강의명 등 강의별 정보를 제공합니다.
Author한국산업기술진흥원
URLhttps://www.data.go.kr/data/15069724/fileData.do

Alerts

과정코드 is highly overall correlated with 강의코드 and 1 other fieldsHigh correlation
과정명 is highly overall correlated with 강의코드 and 1 other fieldsHigh correlation
강의코드 is highly overall correlated with 강의조회수 and 2 other fieldsHigh correlation
강의조회수 is highly overall correlated with 강의코드High correlation
강의시간 has 6 (16.7%) missing valuesMissing
강의명 has unique valuesUnique
강의코드 has unique valuesUnique
강의파일경로 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:43:40.621102
Analysis finished2023-12-12 06:43:41.697927
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과정명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
2. 신뢰성 시험 기초다지기
11 
1. 신뢰성 기초다지기
7 
3. 신뢰성데이터분석 기초다지기
6 
4. 고장분석 기초다지기
6 
5. 신뢰성설계/예측 기초다지기
6 

Length

Max length17
Median length15
Mean length14.75
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1. 신뢰성 기초다지기
2nd row1. 신뢰성 기초다지기
3rd row1. 신뢰성 기초다지기
4th row1. 신뢰성 기초다지기
5th row1. 신뢰성 기초다지기

Common Values

ValueCountFrequency (%)
2. 신뢰성 시험 기초다지기 11
30.6%
1. 신뢰성 기초다지기 7
19.4%
3. 신뢰성데이터분석 기초다지기 6
16.7%
4. 고장분석 기초다지기 6
16.7%
5. 신뢰성설계/예측 기초다지기 6
16.7%

Length

2023-12-12T15:43:41.790082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:43:41.929737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기초다지기 36
30.3%
신뢰성 18
15.1%
2 11
 
9.2%
시험 11
 
9.2%
1 7
 
5.9%
3 6
 
5.0%
신뢰성데이터분석 6
 
5.0%
4 6
 
5.0%
고장분석 6
 
5.0%
5 6
 
5.0%

과정코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
200
11 
100
7 
300
6 
400
6 
500
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
200 11
30.6%
100 7
19.4%
300 6
16.7%
400 6
16.7%
500 6
16.7%

Length

2023-12-12T15:43:42.062914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:43:42.198582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200 11
30.6%
100 7
19.4%
300 6
16.7%
400 6
16.7%
500 6
16.7%

강의명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T15:43:42.512248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length19
Mean length14.638889
Min length4

Characters and Unicode

Total characters527
Distinct characters100
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row1-1. 신뢰성이란?
2nd row1-2. 신뢰성의 필요성
3rd row1-3. 신뢰성의 성공과 실패
4th row1-4. 제품수명주기와 신뢰성
5th row1-5. 고장이란?
ValueCountFrequency (%)
사례 3
 
2.9%
미니탭을 3
 
2.9%
신뢰성의 3
 
2.9%
신뢰성 3
 
2.9%
활용한 3
 
2.9%
고장분석의 2
 
1.9%
환경시험의 2
 
1.9%
개요와 2
 
1.9%
신뢰성데이터분석 2
 
1.9%
fmea 2
 
1.9%
Other values (78) 78
75.7%
2023-12-12T15:43:43.009382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
12.7%
- 38
 
7.2%
. 35
 
6.6%
2 19
 
3.6%
1 18
 
3.4%
이 16
 
3.0%
성 13
 
2.5%
의 12
 
2.3%
3 12
 
2.3%
4 11
 
2.1%
Other values (90) 286
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
52.0%
Decimal Number 79
 
15.0%
Space Separator 67
 
12.7%
Other Punctuation 42
 
8.0%
Dash Punctuation 38
 
7.2%
Uppercase Letter 8
 
1.5%
Open Punctuation 7
 
1.3%
Close Punctuation 7
 
1.3%
Other Number 5
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 16
 
5.8%
성 13
 
4.7%
의 12
 
4.4%
분 11
 
4.0%
시 11
 
4.0%
신 11
 
4.0%
뢰 11
 
4.0%
험 10
 
3.6%
석 10
 
3.6%
가 9
 
3.3%
Other values (65) 160
58.4%
Decimal Number
ValueCountFrequency (%)
2 19
24.1%
1 18
22.8%
3 12
15.2%
4 11
13.9%
5 10
12.7%
6 4
 
5.1%
7 2
 
2.5%
0 1
 
1.3%
8 1
 
1.3%
9 1
 
1.3%
Other Number
ValueCountFrequency (%)
⑤ 1
20.0%
④ 1
20.0%
③ 1
20.0%
① 1
20.0%
② 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
E 2
25.0%
F 2
25.0%
M 2
25.0%
A 2
25.0%
Other Punctuation
ValueCountFrequency (%)
. 35
83.3%
? 7
 
16.7%
Space Separator
ValueCountFrequency (%)
67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
52.0%
Common 245
46.5%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 16
 
5.8%
성 13
 
4.7%
의 12
 
4.4%
분 11
 
4.0%
시 11
 
4.0%
신 11
 
4.0%
뢰 11
 
4.0%
험 10
 
3.6%
석 10
 
3.6%
가 9
 
3.3%
Other values (65) 160
58.4%
Common
ValueCountFrequency (%)
67
27.3%
- 38
15.5%
. 35
14.3%
2 19
 
7.8%
1 18
 
7.3%
3 12
 
4.9%
4 11
 
4.5%
5 10
 
4.1%
( 7
 
2.9%
? 7
 
2.9%
Other values (11) 21
 
8.6%
Latin
ValueCountFrequency (%)
E 2
25.0%
F 2
25.0%
M 2
25.0%
A 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
52.0%
ASCII 248
47.1%
Enclosed Alphanum 5
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
27.0%
- 38
15.3%
. 35
14.1%
2 19
 
7.7%
1 18
 
7.3%
3 12
 
4.8%
4 11
 
4.4%
5 10
 
4.0%
( 7
 
2.8%
? 7
 
2.8%
Other values (10) 24
 
9.7%
Hangul
ValueCountFrequency (%)
이 16
 
5.8%
성 13
 
4.7%
의 12
 
4.4%
분 11
 
4.0%
시 11
 
4.0%
신 11
 
4.0%
뢰 11
 
4.0%
험 10
 
3.6%
석 10
 
3.6%
가 9
 
3.3%
Other values (65) 160
58.4%
Enclosed Alphanum
ValueCountFrequency (%)
⑤ 1
20.0%
④ 1
20.0%
③ 1
20.0%
① 1
20.0%
② 1
20.0%

강의코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280559.92
Minimum100001
Maximum500006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:43.146034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile100002.75
Q1200002.75
median250006
Q3400003.25
95-th percentile500004.25
Maximum500006
Range400005
Interquartile range (IQR)200000.5

Descriptive statistics

Standard deviation139015.23
Coefficient of variation (CV)0.49549213
Kurtosis-1.1829856
Mean280559.92
Median Absolute Deviation (MAD)149995.5
Skewness0.30088584
Sum10100157
Variance1.9325234 × 1010
MonotonicityStrictly increasing
2023-12-12T15:43:43.295379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
100001 1
 
2.8%
300002 1
 
2.8%
300004 1
 
2.8%
300005 1
 
2.8%
300006 1
 
2.8%
400001 1
 
2.8%
400002 1
 
2.8%
400003 1
 
2.8%
400004 1
 
2.8%
400005 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
100001 1
2.8%
100002 1
2.8%
100003 1
2.8%
100004 1
2.8%
100005 1
2.8%
100006 1
2.8%
100007 1
2.8%
200001 1
2.8%
200002 1
2.8%
200003 1
2.8%
ValueCountFrequency (%)
500006 1
2.8%
500005 1
2.8%
500004 1
2.8%
500003 1
2.8%
500002 1
2.8%
500001 1
2.8%
400006 1
2.8%
400005 1
2.8%
400004 1
2.8%
400003 1
2.8%

강의시간
Date

MISSING 

Distinct30
Distinct (%)100.0%
Missing6
Missing (%)16.7%
Memory size420.0 B
Minimum2023-12-12 00:54:00
Maximum2023-12-12 21:59:00
2023-12-12T15:43:43.442898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:43.587182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

강의파일경로
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T15:43:43.853048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters720
Distinct characters21
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

Unique36 ?
Unique (%)100.0%

Sample

1st row/Contents/01/01.html
2nd row/Contents/02/01.html
3rd row/Contents/03/01.html
4th row/Contents/04/01.html
5th row/Contents/05/01.html
ValueCountFrequency (%)
contents/01/01.html 1
 
2.8%
contents/02/01.html 1
 
2.8%
contents/27/01.html 1
 
2.8%
contents/21/01.html 1
 
2.8%
contents/22/01.html 1
 
2.8%
contents/23/01.html 1
 
2.8%
contents/24/01.html 1
 
2.8%
contents/25/01.html 1
 
2.8%
contents/26/01.html 1
 
2.8%
contents/28/01.html 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T15:43:44.281755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 108
15.0%
t 108
15.0%
n 72
10.0%
1 50
 
6.9%
0 48
 
6.7%
. 36
 
5.0%
l 36
 
5.0%
m 36
 
5.0%
C 36
 
5.0%
h 36
 
5.0%
Other values (11) 154
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 396
55.0%
Other Punctuation 144
 
20.0%
Decimal Number 144
 
20.0%
Uppercase Letter 36
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 50
34.7%
0 48
33.3%
2 14
 
9.7%
3 11
 
7.6%
4 4
 
2.8%
5 4
 
2.8%
6 4
 
2.8%
9 3
 
2.1%
7 3
 
2.1%
8 3
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
t 108
27.3%
n 72
18.2%
l 36
 
9.1%
m 36
 
9.1%
h 36
 
9.1%
s 36
 
9.1%
e 36
 
9.1%
o 36
 
9.1%
Other Punctuation
ValueCountFrequency (%)
/ 108
75.0%
. 36
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 432
60.0%
Common 288
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 108
37.5%
1 50
17.4%
0 48
16.7%
. 36
 
12.5%
2 14
 
4.9%
3 11
 
3.8%
4 4
 
1.4%
5 4
 
1.4%
6 4
 
1.4%
9 3
 
1.0%
Other values (2) 6
 
2.1%
Latin
ValueCountFrequency (%)
t 108
25.0%
n 72
16.7%
l 36
 
8.3%
m 36
 
8.3%
C 36
 
8.3%
h 36
 
8.3%
s 36
 
8.3%
e 36
 
8.3%
o 36
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 108
15.0%
t 108
15.0%
n 72
10.0%
1 50
 
6.9%
0 48
 
6.7%
. 36
 
5.0%
l 36
 
5.0%
m 36
 
5.0%
C 36
 
5.0%
h 36
 
5.0%
Other values (11) 154
21.4%

강의조회수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean523.75
Minimum172
Maximum3451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:44.440327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile209.75
Q1319.5
median430
Q3578.25
95-th percentile776.5
Maximum3451
Range3279
Interquartile range (IQR)258.75

Descriptive statistics

Standard deviation531.45452
Coefficient of variation (CV)1.0147103
Kurtosis27.983357
Mean523.75
Median Absolute Deviation (MAD)145
Skewness5.011623
Sum18855
Variance282443.91
MonotonicityNot monotonic
2023-12-12T15:43:44.573016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
340 2
 
5.6%
3451 1
 
2.8%
277 1
 
2.8%
720 1
 
2.8%
449 1
 
2.8%
345 1
 
2.8%
226 1
 
2.8%
531 1
 
2.8%
285 1
 
2.8%
219 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
172 1
2.8%
182 1
2.8%
219 1
2.8%
224 1
2.8%
226 1
2.8%
239 1
2.8%
255 1
2.8%
277 1
2.8%
285 1
2.8%
331 1
2.8%
ValueCountFrequency (%)
3451 1
2.8%
817 1
2.8%
763 1
2.8%
720 1
2.8%
703 1
2.8%
684 1
2.8%
656 1
2.8%
651 1
2.8%
588 1
2.8%
575 1
2.8%

Interactions

2023-12-12T15:43:41.243155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:40.946326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:41.363394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:41.115099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:43:44.661442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과정명과정코드강의명강의코드강의시간강의파일경로강의조회수
과정명1.0001.0001.0001.0001.0001.0000.243
과정코드1.0001.0001.0001.0001.0001.0000.243
강의명1.0001.0001.0001.0001.0001.0001.000
강의코드1.0001.0001.0001.0001.0001.0000.540
강의시간1.0001.0001.0001.0001.0001.0001.000
강의파일경로1.0001.0001.0001.0001.0001.0001.000
강의조회수0.2430.2431.0000.5401.0001.0001.000
2023-12-12T15:43:44.765444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과정코드과정명
과정코드1.0001.000
과정명1.0001.000
2023-12-12T15:43:44.850283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강의코드강의조회수과정명과정코드
강의코드1.000-0.6460.9840.984
강의조회수-0.6461.0000.1980.198
과정명0.9840.1981.0001.000
과정코드0.9840.1981.0001.000

Missing values

2023-12-12T15:43:41.494906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:43:41.639264image/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

과정명과정코드강의명강의코드강의시간강의파일경로강의조회수
01. 신뢰성 기초다지기1001-1. 신뢰성이란?10000121:45/Contents/01/01.html3451
11. 신뢰성 기초다지기1001-2. 신뢰성의 필요성10000221:59/Contents/02/01.html817
21. 신뢰성 기초다지기1001-3. 신뢰성의 성공과 실패10000312:22/Contents/03/01.html488
31. 신뢰성 기초다지기1001-4. 제품수명주기와 신뢰성10000411:16/Contents/04/01.html656
41. 신뢰성 기초다지기1001-5. 고장이란?10000515:42/Contents/05/01.html575
51. 신뢰성 기초다지기1001-6. 신뢰성의 평가척도10000614:16/Contents/06/01.html651
61. 신뢰성 기초다지기1001-7. 중간평가①100007<NA>/Contents/07/01.html383
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