Overview

Dataset statistics

Number of variables5
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory41.7 B

Variable types

Text1
Boolean4

Dataset

Description해당 파일은 신용보증기금이 운용하는 CRM 컨설팅에 대한 동의정보 데이터로, 컨설팅 번호별 상세 동의 여부 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15121493/fileData.do

Alerts

선택식별정보동의여부 is highly overall correlated with 선택수집조회동의여부 and 2 other fieldsHigh correlation
선택제공동의여부 is highly overall correlated with 선택수집조회동의여부 and 2 other fieldsHigh correlation
선택수집조회동의여부 is highly overall correlated with 선택식별정보동의여부 and 2 other fieldsHigh correlation
선택식별동의여부 is highly overall correlated with 선택수집조회동의여부 and 2 other fieldsHigh correlation
선택수집조회동의여부 is highly imbalanced (56.8%)Imbalance
선택식별정보동의여부 is highly imbalanced (56.8%)Imbalance
선택제공동의여부 is highly imbalanced (56.8%)Imbalance
선택식별동의여부 is highly imbalanced (56.8%)Imbalance
컨설팅번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:26:18.939254
Analysis finished2023-12-12 19:26:19.367653
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

컨설팅번호
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2023-12-13T04:26:19.533504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters948
Distinct characters23
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

Unique79 ?
Unique (%)100.0%

Sample

1st rowTHT2019R0004
2nd rowTHI2019R0011
3rd rowNCN2019R0016
4th rowTAN2019R0004
5th rowNHN2019R0045
ValueCountFrequency (%)
tht2019r0004 1
 
1.3%
taa2021r0024 1
 
1.3%
tid2021r0010 1
 
1.3%
non2020r0023 1
 
1.3%
ncn2022r0026 1
 
1.3%
nhn2021r0001 1
 
1.3%
ncn2022r0001 1
 
1.3%
nhn2021r0004 1
 
1.3%
nhn2021r0018 1
 
1.3%
nhn2021r0012 1
 
1.3%
Other values (69) 69
87.3%
2023-12-13T04:26:19.908345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 288
30.4%
2 161
17.0%
N 144
15.2%
1 82
 
8.6%
R 79
 
8.3%
9 38
 
4.0%
H 28
 
3.0%
C 18
 
1.9%
O 16
 
1.7%
3 15
 
1.6%
Other values (13) 79
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 632
66.7%
Uppercase Letter 316
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 144
45.6%
R 79
25.0%
H 28
 
8.9%
C 18
 
5.7%
O 16
 
5.1%
T 9
 
2.8%
K 7
 
2.2%
A 7
 
2.2%
B 3
 
0.9%
I 2
 
0.6%
Other values (3) 3
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 288
45.6%
2 161
25.5%
1 82
 
13.0%
9 38
 
6.0%
3 15
 
2.4%
4 13
 
2.1%
6 12
 
1.9%
8 9
 
1.4%
5 8
 
1.3%
7 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 632
66.7%
Latin 316
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 144
45.6%
R 79
25.0%
H 28
 
8.9%
C 18
 
5.7%
O 16
 
5.1%
T 9
 
2.8%
K 7
 
2.2%
A 7
 
2.2%
B 3
 
0.9%
I 2
 
0.6%
Other values (3) 3
 
0.9%
Common
ValueCountFrequency (%)
0 288
45.6%
2 161
25.5%
1 82
 
13.0%
9 38
 
6.0%
3 15
 
2.4%
4 13
 
2.1%
6 12
 
1.9%
8 9
 
1.4%
5 8
 
1.3%
7 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288
30.4%
2 161
17.0%
N 144
15.2%
1 82
 
8.6%
R 79
 
8.3%
9 38
 
4.0%
H 28
 
3.0%
C 18
 
1.9%
O 16
 
1.7%
3 15
 
1.6%
Other values (13) 79
 
8.3%

선택수집조회동의여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size211.0 B
True
72 
False
 
7
ValueCountFrequency (%)
True 72
91.1%
False 7
 
8.9%
2023-12-13T04:26:20.070359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

선택식별정보동의여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size211.0 B
True
72 
False
 
7
ValueCountFrequency (%)
True 72
91.1%
False 7
 
8.9%
2023-12-13T04:26:20.172884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

선택제공동의여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size211.0 B
True
72 
False
 
7
ValueCountFrequency (%)
True 72
91.1%
False 7
 
8.9%
2023-12-13T04:26:20.287403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

선택식별동의여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size211.0 B
True
72 
False
 
7
ValueCountFrequency (%)
True 72
91.1%
False 7
 
8.9%
2023-12-13T04:26:20.415712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:26:20.499218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
컨설팅번호선택수집조회동의여부선택식별정보동의여부선택제공동의여부선택식별동의여부
컨설팅번호1.0001.0001.0001.0001.000
선택수집조회동의여부1.0001.0000.9920.9920.992
선택식별정보동의여부1.0000.9921.0000.9920.992
선택제공동의여부1.0000.9920.9921.0000.992
선택식별동의여부1.0000.9920.9920.9921.000
2023-12-13T04:26:20.626437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선택식별정보동의여부선택제공동의여부선택수집조회동의여부선택식별동의여부
선택식별정보동의여부1.0000.9210.9210.921
선택제공동의여부0.9211.0000.9210.921
선택수집조회동의여부0.9210.9211.0000.921
선택식별동의여부0.9210.9210.9211.000
2023-12-13T04:26:20.784299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선택수집조회동의여부선택식별정보동의여부선택제공동의여부선택식별동의여부
선택수집조회동의여부1.0000.9210.9210.921
선택식별정보동의여부0.9211.0000.9210.921
선택제공동의여부0.9210.9211.0000.921
선택식별동의여부0.9210.9210.9211.000

Missing values

2023-12-13T04:26:19.176638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:26:19.304608image/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

컨설팅번호선택수집조회동의여부선택식별정보동의여부선택제공동의여부선택식별동의여부
0THT2019R0004YYYY
1THI2019R0011YYYY
2NCN2019R0016YYYY
3TAN2019R0004YYYY
4NHN2019R0045YYYY
5NHN2019R0030YYYY
6NHN2019R0013YYYY
7NKN2019R0036YYYY
8NHN2019R0025YYYY
9NKN2019R0028YYYY
컨설팅번호선택수집조회동의여부선택식별정보동의여부선택제공동의여부선택식별동의여부
69NON2020R0024YYYY
70NCN2020R0012YYYY
71NHN2020R0016YYYY
72NON2020R0017YYYY
73NCN2022R0027YYYY
74NON2021R0021YYYY
75NCN2022R0031YYYY
76NCN2020R0006YYYY
77NON2021R0057YYYY
78NON2020R0012YYYY