Overview

Dataset statistics

Number of variables5
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory45.4 B

Variable types

Categorical4
Text1

Dataset

Description샘플 데이터
Author한국신용데이터
URLhttps://bigdata-region.kr/#/dataset/e3fdd23e-0b9a-4a49-8efc-509c51145655

Alerts

년월 has constant value ""Constant
매출평균액 has constant value ""Constant
유형명 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 유형명High correlation

Reproduction

Analysis started2023-12-10 14:14:22.508540
Analysis finished2023-12-10 14:14:23.067495
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유형명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
지역
17 
업종
9 
지역x업종
4 

Length

Max length5
Median length2
Mean length2.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역 17
56.7%
업종 9
30.0%
지역x업종 4
 
13.3%

Length

2023-12-10T23:14:23.207338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:23.461024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역 17
56.7%
업종 9
30.0%
지역x업종 4
 
13.3%

년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Sep-21
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSep-21
2nd rowSep-21
3rd rowSep-21
4th rowSep-21
5th rowSep-21

Common Values

ValueCountFrequency (%)
Sep-21 30
100.0%

Length

2023-12-10T23:14:23.662986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:23.874380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sep-21 30
100.0%

업종명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
전체
17 
유통업
4 
서비스업
2 
정보통신업
 
1
제조업
 
1
Other values (5)
5 

Length

Max length8
Median length2
Mean length2.7333333
Min length2

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row정보통신업
2nd row제조업
3rd row외식업
4th row농업/임업/어업
5th row서비스업

Common Values

ValueCountFrequency (%)
전체 17
56.7%
유통업 4
 
13.3%
서비스업 2
 
6.7%
정보통신업 1
 
3.3%
제조업 1
 
3.3%
외식업 1
 
3.3%
농업/임업/어업 1
 
3.3%
부동산업 1
 
3.3%
건설업 1
 
3.3%
기타 1
 
3.3%

Length

2023-12-10T23:14:24.106846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:24.325610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 17
56.7%
유통업 4
 
13.3%
서비스업 2
 
6.7%
정보통신업 1
 
3.3%
제조업 1
 
3.3%
외식업 1
 
3.3%
농업/임업/어업 1
 
3.3%
부동산업 1
 
3.3%
건설업 1
 
3.3%
기타 1
 
3.3%

시도명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:14:24.591226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.8333333
Min length2

Characters and Unicode

Total characters115
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row전국
2nd row전국
3rd row전국
4th row전국
5th row전국
ValueCountFrequency (%)
전국 9
30.0%
대구광역시 2
 
6.7%
충청북도 2
 
6.7%
전라북도 2
 
6.7%
부산광역시 2
 
6.7%
경상남도 1
 
3.3%
제주특별자치도 1
 
3.3%
울산광역시 1
 
3.3%
전라남도 1
 
3.3%
충청남도 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T23:14:25.059744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
전 13
 
11.3%
도 11
 
9.6%
시 10
 
8.7%
광 9
 
7.8%
국 9
 
7.8%
역 8
 
7.0%
북 5
 
4.3%
남 3
 
2.6%
별 3
 
2.6%
특 3
 
2.6%
Other values (22) 41
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 13
 
11.3%
도 11
 
9.6%
시 10
 
8.7%
광 9
 
7.8%
국 9
 
7.8%
역 8
 
7.0%
북 5
 
4.3%
남 3
 
2.6%
별 3
 
2.6%
특 3
 
2.6%
Other values (22) 41
35.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 13
 
11.3%
도 11
 
9.6%
시 10
 
8.7%
광 9
 
7.8%
국 9
 
7.8%
역 8
 
7.0%
북 5
 
4.3%
남 3
 
2.6%
별 3
 
2.6%
특 3
 
2.6%
Other values (22) 41
35.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
전 13
 
11.3%
도 11
 
9.6%
시 10
 
8.7%
광 9
 
7.8%
국 9
 
7.8%
역 8
 
7.0%
북 5
 
4.3%
남 3
 
2.6%
별 3
 
2.6%
특 3
 
2.6%
Other values (22) 41
35.7%

매출평균액
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
10000000
30 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10000000
2nd row10000000
3rd row10000000
4th row10000000
5th row10000000

Common Values

ValueCountFrequency (%)
10000000 30
100.0%

Length

2023-12-10T23:14:25.328811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:25.509825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000000 30
100.0%

Correlations

2023-12-10T23:14:25.620875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형명업종명시도명
유형명1.0000.8830.799
업종명0.8831.0000.000
시도명0.7990.0001.000
2023-12-10T23:14:25.860132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형명업종명
유형명1.0000.706
업종명0.7061.000
2023-12-10T23:14:26.002460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형명업종명
유형명1.0000.706
업종명0.7061.000

Missing values

2023-12-10T23:14:22.820143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:22.999888image/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

유형명년월업종명시도명매출평균액
0업종Sep-21정보통신업전국10000000
1업종Sep-21제조업전국10000000
2업종Sep-21외식업전국10000000
3업종Sep-21농업/임업/어업전국10000000
4업종Sep-21서비스업전국10000000
5업종Sep-21부동산업전국10000000
6업종Sep-21유통업전국10000000
7업종Sep-21건설업전국10000000
8업종Sep-21기타전국10000000
9지역Sep-21전체경상북도10000000
유형명년월업종명시도명매출평균액
20지역Sep-21전체대전광역시10000000
21지역Sep-21전체부산광역시10000000
22지역Sep-21전체인천광역시10000000
23지역Sep-21전체세종특별자치시10000000
24지역Sep-21전체경기도10000000
25지역Sep-21전체대구광역시10000000
26지역x업종Sep-21유통업충청북도10000000
27지역x업종Sep-21서비스업부산광역시10000000
28지역x업종Sep-21유통업전라북도10000000
29지역x업종Sep-21유통업대구광역시10000000