Overview

Dataset statistics

Number of variables2
Number of observations1064
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.8 KiB
Average record size in memory17.1 B

Variable types

Numeric1
Text1

Dataset

Description학점은행제정보시스템의 평가인정항목의 지표관리에 대한 데이터로 평가인정 차수와 지표제목 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15089706/fileData.do

Reproduction

Analysis started2023-12-12 06:20:49.840186
Analysis finished2023-12-12 06:20:50.152211
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차수
Real number (ℝ)

Distinct31
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.542293
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2023-12-12T15:20:50.211936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median14
Q321
95-th percentile29
Maximum32
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.7887258
Coefficient of variation (CV)0.5355913
Kurtosis-0.82403783
Mean14.542293
Median Absolute Deviation (MAD)6
Skewness0.35963465
Sum15473
Variance60.664249
MonotonicityIncreasing
2023-12-12T15:20:50.361063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8 73
 
6.9%
10 54
 
5.1%
11 53
 
5.0%
4 52
 
4.9%
5 52
 
4.9%
7 52
 
4.9%
17 52
 
4.9%
12 51
 
4.8%
15 49
 
4.6%
18 44
 
4.1%
Other values (21) 532
50.0%
ValueCountFrequency (%)
2 16
 
1.5%
3 36
3.4%
4 52
4.9%
5 52
4.9%
6 16
 
1.5%
7 52
4.9%
8 73
6.9%
9 36
3.4%
10 54
5.1%
11 53
5.0%
ValueCountFrequency (%)
32 14
1.3%
31 14
1.3%
30 14
1.3%
29 14
1.3%
28 14
1.3%
27 21
2.0%
26 23
2.2%
25 18
1.7%
24 28
2.6%
23 28
2.6%

지표제목
Text

Distinct186
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
2023-12-12T15:20:50.853744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length13.404135
Min length4

Characters and Unicode

Total characters14262
Distinct characters180
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

Unique37 ?
Unique (%)3.5%

Sample

1st row강의평가의 실시와 활용
2nd row교/강사 근무조건의 적절성
3rd row교/강사 자격의 적법성
4th row교육 기자재 확보의 적절성
5th row교육 및 실습 시설 확보의 적절성
ValueCountFrequency (%)
적절성 450
 
12.5%
확보의 148
 
4.1%
및 100
 
2.8%
적합성 81
 
2.2%
평가의 70
 
1.9%
합리성 65
 
1.8%
운영의 63
 
1.7%
자격의 59
 
1.6%
적법성 56
 
1.6%
노력 51
 
1.4%
Other values (216) 2469
68.4%
2023-12-12T15:20:51.487534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2550
 
17.9%
성 1053
 
7.4%
의 1024
 
7.2%
적 710
 
5.0%
절 465
 
3.3%
교 317
 
2.2%
정 312
 
2.2%
학 289
 
2.0%
자 235
 
1.6%
습 232
 
1.6%
Other values (170) 7075
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11466
80.4%
Space Separator 2550
 
17.9%
Other Punctuation 101
 
0.7%
Close Punctuation 45
 
0.3%
Open Punctuation 45
 
0.3%
Lowercase Letter 44
 
0.3%
Uppercase Letter 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 1053
 
9.2%
의 1024
 
8.9%
적 710
 
6.2%
절 465
 
4.1%
교 317
 
2.8%
정 312
 
2.7%
학 289
 
2.5%
자 235
 
2.0%
습 232
 
2.0%
업 197
 
1.7%
Other values (159) 6632
57.8%
Lowercase Letter
ValueCountFrequency (%)
u 11
25.0%
r 11
25.0%
o 11
25.0%
t 11
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 59
58.4%
, 22
 
21.8%
· 20
 
19.8%
Space Separator
ValueCountFrequency (%)
2550
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11466
80.4%
Common 2741
 
19.2%
Latin 55
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 1053
 
9.2%
의 1024
 
8.9%
적 710
 
6.2%
절 465
 
4.1%
교 317
 
2.8%
정 312
 
2.7%
학 289
 
2.5%
자 235
 
2.0%
습 232
 
2.0%
업 197
 
1.7%
Other values (159) 6632
57.8%
Common
ValueCountFrequency (%)
2550
93.0%
/ 59
 
2.2%
) 45
 
1.6%
( 45
 
1.6%
, 22
 
0.8%
· 20
 
0.7%
Latin
ValueCountFrequency (%)
u 11
20.0%
r 11
20.0%
o 11
20.0%
t 11
20.0%
T 11
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11466
80.4%
ASCII 2776
 
19.5%
None 20
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2550
91.9%
/ 59
 
2.1%
) 45
 
1.6%
( 45
 
1.6%
, 22
 
0.8%
u 11
 
0.4%
r 11
 
0.4%
o 11
 
0.4%
t 11
 
0.4%
T 11
 
0.4%
Hangul
ValueCountFrequency (%)
성 1053
 
9.2%
의 1024
 
8.9%
적 710
 
6.2%
절 465
 
4.1%
교 317
 
2.8%
정 312
 
2.7%
학 289
 
2.5%
자 235
 
2.0%
습 232
 
2.0%
업 197
 
1.7%
Other values (159) 6632
57.8%
None
ValueCountFrequency (%)
· 20
100.0%

Interactions

2023-12-12T15:20:49.946945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T15:20:50.059678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:20:50.123107image/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

차수지표제목
02강의평가의 실시와 활용
12교/강사 근무조건의 적절성
22교/강사 자격의 적법성
32교육 기자재 확보의 적절성
42교육 및 실습 시설 확보의 적절성
52교육·행정 시스템 질 개선 노력 여부 및 적절성
62교육목표의 적합성
72수업계획의 충실성
82전공교육 중장기 발전계획의 적절성
92전공교육과정 편성·운영의 적절성
차수지표제목
105432수업계획의 적절성
105532인적자원 자격의 적법성
105632인적자원 확보의 적정성
105732재정 건전성
105832학습 지원시설 확보의 적정성
105932학습과정 운영시수의 적합성
106032학습목표의 적절성
106132학습시설·설비 확보의 적절성
106232학업 성취도 평가의 엄정성
106332학업 성취도 평가의 적절성