Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Categorical2
Text1
Boolean1

Dataset

Description아임셀러 제휴회사브랜드 삭제여부 정보를 제공합니다. 기준연도, 기준월, 제휴브랜드 번호 및 삭제여부에 대해 제공합니다.
Author(주)중소기업유통센터
URLhttps://www.data.go.kr/data/15067599/fileData.do

Alerts

기준연도 has constant value ""Constant
기준월 has constant value ""Constant
삭제여부 has constant value ""Constant
제휴브랜드명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:06:47.508475
Analysis finished2023-12-12 13:06:47.776848
Duration0.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 10000
100.0%

Length

2023-12-12T22:06:47.826921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:06:47.899646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10000
100.0%

기준월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
9
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 10000
100.0%

Length

2023-12-12T22:06:47.975487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:06:48.042707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9 10000
100.0%

제휴브랜드명
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:06:48.274716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.7932
Min length4

Characters and Unicode

Total characters67932
Distinct characters36
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

Unique10000 ?
Unique (%)100.0%

Sample

1st rowC3064001
2nd rowE9404000
3rd row82670000
4th row143712
5th row142421
ValueCountFrequency (%)
c3064001 1
 
< 0.1%
x7732001 1
 
< 0.1%
130234 1
 
< 0.1%
81022000 1
 
< 0.1%
114112 1
 
< 0.1%
20746 1
 
< 0.1%
61584 1
 
< 0.1%
118918 1
 
< 0.1%
96985000 1
 
< 0.1%
x1354001 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-12T22:06:48.722520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18020
26.5%
1 8968
13.2%
9 5303
 
7.8%
5 5054
 
7.4%
8 5019
 
7.4%
2 5018
 
7.4%
3 4883
 
7.2%
4 4746
 
7.0%
7 4530
 
6.7%
6 4221
 
6.2%
Other values (26) 2170
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65762
96.8%
Uppercase Letter 2170
 
3.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 364
16.8%
B 208
 
9.6%
C 164
 
7.6%
D 160
 
7.4%
E 147
 
6.8%
G 132
 
6.1%
F 105
 
4.8%
J 79
 
3.6%
I 71
 
3.3%
H 69
 
3.2%
Other values (16) 671
30.9%
Decimal Number
ValueCountFrequency (%)
0 18020
27.4%
1 8968
13.6%
9 5303
 
8.1%
5 5054
 
7.7%
8 5019
 
7.6%
2 5018
 
7.6%
3 4883
 
7.4%
4 4746
 
7.2%
7 4530
 
6.9%
6 4221
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 65762
96.8%
Latin 2170
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 364
16.8%
B 208
 
9.6%
C 164
 
7.6%
D 160
 
7.4%
E 147
 
6.8%
G 132
 
6.1%
F 105
 
4.8%
J 79
 
3.6%
I 71
 
3.3%
H 69
 
3.2%
Other values (16) 671
30.9%
Common
ValueCountFrequency (%)
0 18020
27.4%
1 8968
13.6%
9 5303
 
8.1%
5 5054
 
7.7%
8 5019
 
7.6%
2 5018
 
7.6%
3 4883
 
7.4%
4 4746
 
7.2%
7 4530
 
6.9%
6 4221
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18020
26.5%
1 8968
13.2%
9 5303
 
7.8%
5 5054
 
7.4%
8 5019
 
7.4%
2 5018
 
7.4%
3 4883
 
7.2%
4 4746
 
7.0%
7 4530
 
6.7%
6 4221
 
6.2%
Other values (26) 2170
 
3.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
10000 
ValueCountFrequency (%)
False 10000
100.0%
2023-12-12T22:06:48.827966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T22:06:47.675599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:06:47.747229image/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

기준연도기준월제휴브랜드명삭제여부
2811420209C3064001N
2751020209E9404000N
54712020982670000N
5402320209143712N
5668920209142421N
3890520209116794N
4993720209155459N
2510720209L0769000N
5293920209134251N
2601320209V6601001N
기준연도기준월제휴브랜드명삭제여부
57489202094893N
3083120209D0316000N
3168620209E5929000N
104952020998088000N
4447120209153685N
1181220209B3281000N
1270620209A6183000N
179292020996782000N
4095520209120504N
2518820209P4086000N