Overview

Dataset statistics

Number of variables3
Number of observations529
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory25.2 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국수력원자력(주) 2021년도 기준 보유 PC 현황에 대한 자료로 구분(PC, 노트북),PC Model명,수량 관련 자료입니다.
URLhttps://www.data.go.kr/data/15086906/fileData.do

Reproduction

Analysis started2023-12-12 22:56:43.548015
Analysis finished2023-12-12 22:56:43.948410
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PC 구분
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
NoteBook
241 
Desktop
184 
AllInOne
104 

Length

Max length8
Median length8
Mean length7.6521739
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
NoteBook 241
45.6%
Desktop 184
34.8%
AllInOne 104
19.7%

Length

2023-12-13T07:56:44.030757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:56:44.151087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
notebook 241
45.6%
desktop 184
34.8%
allinone 104
19.7%

PC 모델명
Text

Distinct517
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2023-12-13T07:56:44.383879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length13.20983
Min length3

Characters and Unicode

Total characters6988
Distinct characters97
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique505 ?
Unique (%)95.5%

Sample

1st row11T540-G.A33BKN
2nd row15N540-RF5SKN
3rd row15N540-RF7SKN
4th row15UG480-GP50KN
5th row15UG50P-GP70KN
ValueCountFrequency (%)
hp 18
 
2.8%
dell 9
 
1.4%
현지 7
 
1.1%
vito 6
 
0.9%
probook 5
 
0.8%
optiplex 5
 
0.8%
450 4
 
0.6%
sens 3
 
0.5%
lg 3
 
0.5%
a8tssabp-bbw87pnaq 2
 
0.3%
Other values (557) 575
90.3%
2023-12-13T07:56:44.821927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 545
 
7.8%
0 541
 
7.7%
A 420
 
6.0%
5 350
 
5.0%
G 322
 
4.6%
1 300
 
4.3%
B 293
 
4.2%
S 281
 
4.0%
P 278
 
4.0%
D 262
 
3.7%
Other values (87) 3396
48.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3550
50.8%
Decimal Number 2310
33.1%
Dash Punctuation 545
 
7.8%
Lowercase Letter 298
 
4.3%
Space Separator 108
 
1.5%
Other Punctuation 75
 
1.1%
Other Letter 71
 
1.0%
Open Punctuation 15
 
0.2%
Close Punctuation 15
 
0.2%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8
 
11.3%
현 8
 
11.3%
로 7
 
9.9%
솔 7
 
9.9%
탑 5
 
7.0%
스 4
 
5.6%
트 3
 
4.2%
네 2
 
2.8%
이 2
 
2.8%
션 2
 
2.8%
Other values (20) 23
32.4%
Uppercase Letter
ValueCountFrequency (%)
A 420
11.8%
G 322
 
9.1%
B 293
 
8.3%
S 281
 
7.9%
P 278
 
7.8%
D 262
 
7.4%
N 205
 
5.8%
T 159
 
4.5%
C 148
 
4.2%
K 142
 
4.0%
Other values (16) 1040
29.3%
Lowercase Letter
ValueCountFrequency (%)
e 43
14.4%
r 39
13.1%
l 31
10.4%
o 27
 
9.1%
p 19
 
6.4%
c 15
 
5.0%
i 15
 
5.0%
s 13
 
4.4%
y 11
 
3.7%
t 11
 
3.7%
Other values (14) 74
24.8%
Decimal Number
ValueCountFrequency (%)
0 541
23.4%
5 350
15.2%
1 300
13.0%
2 238
10.3%
6 215
 
9.3%
7 205
 
8.9%
3 151
 
6.5%
4 122
 
5.3%
8 109
 
4.7%
9 79
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 42
56.0%
. 33
44.0%
Dash Punctuation
ValueCountFrequency (%)
- 545
100.0%
Space Separator
ValueCountFrequency (%)
108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3848
55.1%
Common 3069
43.9%
Hangul 71
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 420
 
10.9%
G 322
 
8.4%
B 293
 
7.6%
S 281
 
7.3%
P 278
 
7.2%
D 262
 
6.8%
N 205
 
5.3%
T 159
 
4.1%
C 148
 
3.8%
K 142
 
3.7%
Other values (40) 1338
34.8%
Hangul
ValueCountFrequency (%)
지 8
 
11.3%
현 8
 
11.3%
로 7
 
9.9%
솔 7
 
9.9%
탑 5
 
7.0%
스 4
 
5.6%
트 3
 
4.2%
네 2
 
2.8%
이 2
 
2.8%
션 2
 
2.8%
Other values (20) 23
32.4%
Common
ValueCountFrequency (%)
- 545
17.8%
0 541
17.6%
5 350
11.4%
1 300
9.8%
2 238
7.8%
6 215
 
7.0%
7 205
 
6.7%
3 151
 
4.9%
4 122
 
4.0%
8 109
 
3.6%
Other values (7) 293
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6917
99.0%
Hangul 71
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 545
 
7.9%
0 541
 
7.8%
A 420
 
6.1%
5 350
 
5.1%
G 322
 
4.7%
1 300
 
4.3%
B 293
 
4.2%
S 281
 
4.1%
P 278
 
4.0%
D 262
 
3.8%
Other values (57) 3325
48.1%
Hangul
ValueCountFrequency (%)
지 8
 
11.3%
현 8
 
11.3%
로 7
 
9.9%
솔 7
 
9.9%
탑 5
 
7.0%
스 4
 
5.6%
트 3
 
4.2%
네 2
 
2.8%
이 2
 
2.8%
션 2
 
2.8%
Other values (20) 23
32.4%

수량
Real number (ℝ)

Distinct127
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.731569
Minimum1
Maximum4089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2023-12-13T07:56:44.995351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q334
95-th percentile247.2
Maximum4089
Range4088
Interquartile range (IQR)32

Descriptive statistics

Standard deviation304.26182
Coefficient of variation (CV)4.3016411
Kurtosis112.39923
Mean70.731569
Median Absolute Deviation (MAD)8
Skewness9.7024846
Sum37417
Variance92575.257
MonotonicityNot monotonic
2023-12-13T07:56:45.176680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 105
19.8%
2 51
 
9.6%
3 28
 
5.3%
5 25
 
4.7%
4 23
 
4.3%
13 16
 
3.0%
6 16
 
3.0%
20 11
 
2.1%
8 9
 
1.7%
17 9
 
1.7%
Other values (117) 236
44.6%
ValueCountFrequency (%)
1 105
19.8%
2 51
9.6%
3 28
 
5.3%
4 23
 
4.3%
5 25
 
4.7%
6 16
 
3.0%
7 6
 
1.1%
8 9
 
1.7%
9 6
 
1.1%
10 7
 
1.3%
ValueCountFrequency (%)
4089 1
0.2%
3937 1
0.2%
2024 1
0.2%
1636 1
0.2%
1511 1
0.2%
1484 1
0.2%
1157 1
0.2%
1044 1
0.2%
765 1
0.2%
742 1
0.2%

Interactions

2023-12-13T07:56:43.694402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:56:45.280919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PC 구분수량
PC 구분1.0000.339
수량0.3391.000
2023-12-13T07:56:45.377354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량PC 구분
수량1.0000.149
PC 구분0.1491.000

Missing values

2023-12-13T07:56:43.824286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:56:43.914176image/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

PC 구분PC 모델명수량
0NoteBook11T540-G.A33BKN2
1NoteBook15N540-RF5SKN4
2NoteBook15N540-RF7SKN13
3NoteBook15UG480-GP50KN60
4NoteBook15UG50P-GP70KN5
5NoteBook15UG50P-KP76KN3
6NoteBook15UG50Q-GP75KN11
7NoteBook15Z960-GR50KN10
8NoteBook15Z960-GX76K2
9NoteBook15Z970-TA50K3
PC 구분PC 모델명수량
519AllInOneDA241-078210-YW27
520AllInOneDA261-256510-QW57
521AllInOneDell Precision 5820 Tower1
522AllInOneDell Precision T36002
523AllInOneDL360 G101
524AllInOneEXA-1071
525AllInOneGLORY SI7-107H1S2PE60
526AllInOneS220-3D4
527AllInOneS220-3D24
528AllInOne스마트솔로S240-3B314