Overview

Dataset statistics

Number of variables4
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory34.3 B

Variable types

Text3
Categorical1

Alerts

이미지순번 has constant value ""Constant
제품ID has unique valuesUnique
이미지파일명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:04:24.460719
Analysis finished2023-12-10 12:04:25.076285
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제품ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:04:25.389979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters700
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowp000001
2nd rowp000002
3rd rowp000003
4th rowp000004
5th rowp000005
ValueCountFrequency (%)
p000001 1
 
1.0%
p000063 1
 
1.0%
p000074 1
 
1.0%
p000073 1
 
1.0%
p000072 1
 
1.0%
p000071 1
 
1.0%
p000070 1
 
1.0%
p000069 1
 
1.0%
p000068 1
 
1.0%
p000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T21:04:25.975729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 419
59.9%
p 100
 
14.3%
1 21
 
3.0%
3 20
 
2.9%
4 20
 
2.9%
5 20
 
2.9%
6 20
 
2.9%
7 20
 
2.9%
8 20
 
2.9%
9 20
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
85.7%
Lowercase Letter 100
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 419
69.8%
1 21
 
3.5%
3 20
 
3.3%
4 20
 
3.3%
5 20
 
3.3%
6 20
 
3.3%
7 20
 
3.3%
8 20
 
3.3%
9 20
 
3.3%
2 20
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
p 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
85.7%
Latin 100
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 419
69.8%
1 21
 
3.5%
3 20
 
3.3%
4 20
 
3.3%
5 20
 
3.3%
6 20
 
3.3%
7 20
 
3.3%
8 20
 
3.3%
9 20
 
3.3%
2 20
 
3.3%
Latin
ValueCountFrequency (%)
p 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 419
59.9%
p 100
 
14.3%
1 21
 
3.0%
3 20
 
2.9%
4 20
 
2.9%
5 20
 
2.9%
6 20
 
2.9%
7 20
 
2.9%
8 20
 
2.9%
9 20
 
2.9%

이미지순번
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T21:04:26.140341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:04:26.260806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

이미지파일명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:04:26.612579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.92
Min length7

Characters and Unicode

Total characters792
Distinct characters15
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

Unique100 ?
Unique (%)100.0%

Sample

1st row1_1.png
2nd row2_1.png
3rd row3_1.png
4th row4_1.png
5th row5_1.png
ValueCountFrequency (%)
1_1.png 1
 
1.0%
63_1.png 1
 
1.0%
74_1.png 1
 
1.0%
73_1.png 1
 
1.0%
72_1.png 1
 
1.0%
71_1.png 1
 
1.0%
70_1.png 1
 
1.0%
69_1.png 1
 
1.0%
68_1.png 1
 
1.0%
67_1.png 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T21:04:27.217990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 121
15.3%
_ 100
12.6%
. 100
12.6%
p 100
12.6%
n 100
12.6%
g 100
12.6%
3 20
 
2.5%
4 20
 
2.5%
5 20
 
2.5%
6 20
 
2.5%
Other values (5) 91
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 300
37.9%
Decimal Number 292
36.9%
Connector Punctuation 100
 
12.6%
Other Punctuation 100
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 121
41.4%
3 20
 
6.8%
4 20
 
6.8%
5 20
 
6.8%
6 20
 
6.8%
7 20
 
6.8%
8 20
 
6.8%
9 20
 
6.8%
2 20
 
6.8%
0 11
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
p 100
33.3%
n 100
33.3%
g 100
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 100
100.0%
Other Punctuation
ValueCountFrequency (%)
. 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 492
62.1%
Latin 300
37.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 121
24.6%
_ 100
20.3%
. 100
20.3%
3 20
 
4.1%
4 20
 
4.1%
5 20
 
4.1%
6 20
 
4.1%
7 20
 
4.1%
8 20
 
4.1%
9 20
 
4.1%
Other values (2) 31
 
6.3%
Latin
ValueCountFrequency (%)
p 100
33.3%
n 100
33.3%
g 100
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 121
15.3%
_ 100
12.6%
. 100
12.6%
p 100
12.6%
n 100
12.6%
g 100
12.6%
3 20
 
2.5%
4 20
 
2.5%
5 20
 
2.5%
6 20
 
2.5%
Other values (5) 91
11.5%

제품명
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:04:27.740814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length19
Mean length11.67
Min length2

Characters and Unicode

Total characters1167
Distinct characters285
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

Unique98 ?
Unique (%)98.0%

Sample

1st row베이비 세탁 세제
2nd rowRUSTYNO RCL
3rd row디퓨저
4th row소낙스 김서림 방지제
5th row중외다목적세정제
ValueCountFrequency (%)
캔들 5
 
2.2%
디퓨저 4
 
1.7%
고네쉬 4
 
1.7%
방향제 4
 
1.7%
룸스프레이 3
 
1.3%
소낙스 3
 
1.3%
그레이드 3
 
1.3%
라벤더 3
 
1.3%
에어후레쉬너 2
 
0.9%
젤 2
 
0.9%
Other values (187) 199
85.8%
2023-12-10T21:04:28.450867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132
 
11.3%
스 26
 
2.2%
이 21
 
1.8%
리 19
 
1.6%
레 18
 
1.5%
제 18
 
1.5%
드 17
 
1.5%
E 16
 
1.4%
라 16
 
1.4%
트 15
 
1.3%
Other values (275) 869
74.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 720
61.7%
Uppercase Letter 143
 
12.3%
Space Separator 132
 
11.3%
Lowercase Letter 101
 
8.7%
Decimal Number 45
 
3.9%
Open Punctuation 7
 
0.6%
Close Punctuation 7
 
0.6%
Other Punctuation 6
 
0.5%
Dash Punctuation 5
 
0.4%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 26
 
3.6%
이 21
 
2.9%
리 19
 
2.6%
레 18
 
2.5%
제 18
 
2.5%
드 17
 
2.4%
라 16
 
2.2%
트 15
 
2.1%
캔 13
 
1.8%
들 13
 
1.8%
Other values (212) 544
75.6%
Uppercase Letter
ValueCountFrequency (%)
E 16
 
11.2%
R 13
 
9.1%
S 11
 
7.7%
A 10
 
7.0%
L 10
 
7.0%
C 9
 
6.3%
F 8
 
5.6%
N 7
 
4.9%
I 7
 
4.9%
O 7
 
4.9%
Other values (13) 45
31.5%
Lowercase Letter
ValueCountFrequency (%)
e 15
14.9%
r 10
9.9%
n 9
 
8.9%
o 8
 
7.9%
a 8
 
7.9%
t 7
 
6.9%
m 7
 
6.9%
i 5
 
5.0%
l 5
 
5.0%
f 4
 
4.0%
Other values (11) 23
22.8%
Decimal Number
ValueCountFrequency (%)
0 14
31.1%
5 9
20.0%
1 7
15.6%
7 4
 
8.9%
4 4
 
8.9%
3 3
 
6.7%
6 2
 
4.4%
9 1
 
2.2%
2 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
& 2
33.3%
* 1
16.7%
. 1
16.7%
' 1
16.7%
, 1
16.7%
Space Separator
ValueCountFrequency (%)
132
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 720
61.7%
Latin 244
 
20.9%
Common 203
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 26
 
3.6%
이 21
 
2.9%
리 19
 
2.6%
레 18
 
2.5%
제 18
 
2.5%
드 17
 
2.4%
라 16
 
2.2%
트 15
 
2.1%
캔 13
 
1.8%
들 13
 
1.8%
Other values (212) 544
75.6%
Latin
ValueCountFrequency (%)
E 16
 
6.6%
e 15
 
6.1%
R 13
 
5.3%
S 11
 
4.5%
r 10
 
4.1%
A 10
 
4.1%
L 10
 
4.1%
n 9
 
3.7%
C 9
 
3.7%
o 8
 
3.3%
Other values (34) 133
54.5%
Common
ValueCountFrequency (%)
132
65.0%
0 14
 
6.9%
5 9
 
4.4%
1 7
 
3.4%
( 7
 
3.4%
) 7
 
3.4%
- 5
 
2.5%
7 4
 
2.0%
4 4
 
2.0%
3 3
 
1.5%
Other values (9) 11
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 720
61.7%
ASCII 447
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132
29.5%
E 16
 
3.6%
e 15
 
3.4%
0 14
 
3.1%
R 13
 
2.9%
S 11
 
2.5%
r 10
 
2.2%
A 10
 
2.2%
L 10
 
2.2%
5 9
 
2.0%
Other values (53) 207
46.3%
Hangul
ValueCountFrequency (%)
스 26
 
3.6%
이 21
 
2.9%
리 19
 
2.6%
레 18
 
2.5%
제 18
 
2.5%
드 17
 
2.4%
라 16
 
2.2%
트 15
 
2.1%
캔 13
 
1.8%
들 13
 
1.8%
Other values (212) 544
75.6%

Correlations

2023-12-10T21:04:28.636621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제품ID이미지파일명제품명
제품ID1.0001.0001.000
이미지파일명1.0001.0001.000
제품명1.0001.0001.000

Missing values

2023-12-10T21:04:24.889238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:04:25.025949image/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

제품ID이미지순번이미지파일명제품명
0p00000111_1.png베이비 세탁 세제
1p00000212_1.pngRUSTYNO RCL
2p00000313_1.png디퓨저
3p00000414_1.png소낙스 김서림 방지제
4p00000515_1.png중외다목적세정제
5p00000616_1.png유리세정제
6p00000717_1.png퍼스트클래스 유리크리너 550ml
7p00000818_1.png에코스 세탁세제 매그놀리아&릴리향
8p00000919_1.png에스테반리퀴드에어후레쉬너 동백
9p000010110_1.png컴포트
제품ID이미지순번이미지파일명제품명
90p000091191_1.png이니셜 방향제 (라벤더)
91p000092192_1.png이온스톤-탈취박사
92p000093193_1.pngJU-DIFFUSER
93p000094194_1.png글라스캔들- 플레르드마라케시
94p000095195_1.png사각 100 프렌치라벤더
95p000096196_1.png캔들라이트(오션블루미스트)
96p000097197_1.pngTOILET GEL
97p000098198_1.pngACS 에센셜오일 그레이프프루트
98p000099199_1.pngBaby Sleep
99p0001001100_1.png아로마 향기향초