Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory127.4 B

Variable types

DateTime2
Categorical6
Text5
Numeric2

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/024b7da5-fc18-4452-a10f-b288f14c6d88

Alerts

기준년월 has constant value ""Constant
등록일 has constant value ""Constant
작업자명 has constant value ""Constant
시군구명 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
가공기업구분코드 is highly overall correlated with 가공기업구분High correlation
가공기업구분 is highly overall correlated with 가공기업구분코드High correlation
시도명 is highly imbalanced (64.7%)Imbalance
시군구명 is highly imbalanced (64.7%)Imbalance
사건가능성 is highly imbalanced (53.1%)Imbalance
업력구간코드 has 3 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:46:14.182157
Analysis finished2023-12-10 13:46:18.807991
Duration4.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-12-01 00:00:00
Maximum2023-12-01 00:00:00
2023-12-10T22:46:18.876437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:19.024266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
강원
28 
충북
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원
2nd row충북
3rd row강원
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
강원 28
93.3%
충북 2
 
6.7%

Length

2023-12-10T22:46:19.223244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:19.373572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원 28
93.3%
충북 2
 
6.7%

시군구명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
강릉시
28 
충주시
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강릉시
2nd row충주시
3rd row강릉시
4th row강릉시
5th row강릉시

Common Values

ValueCountFrequency (%)
강릉시 28
93.3%
충주시 2
 
6.7%

Length

2023-12-10T22:46:19.541468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:19.695778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강릉시 28
93.3%
충주시 2
 
6.7%

행정동명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
강남동
10 
강동면
10 
강문동
호암.직동

Length

Max length5
Median length3
Mean length3.1333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강남동
2nd row호암.직동
3rd row강남동
4th row강남동
5th row강남동

Common Values

ValueCountFrequency (%)
강남동 10
33.3%
강동면 10
33.3%
강문동 8
26.7%
호암.직동 2
 
6.7%

Length

2023-12-10T22:46:19.870126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:46:20.043623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강남동 10
33.3%
강동면 10
33.3%
강문동 8
26.7%
호암.직동 2
 
6.7%
Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:20.194763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30
Distinct characters15
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

Unique7 ?
Unique (%)23.3%

Sample

1st rowA
2nd rowN
3rd rowB
4th rowC
5th rowC
ValueCountFrequency (%)
c 6
20.0%
b 3
10.0%
s 3
10.0%
m 3
10.0%
e 2
 
6.7%
g 2
 
6.7%
u 2
 
6.7%
h 2
 
6.7%
a 1
 
3.3%
n 1
 
3.3%
Other values (5) 5
16.7%
2023-12-10T22:46:20.572632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 6
20.0%
B 3
10.0%
S 3
10.0%
M 3
10.0%
E 2
 
6.7%
G 2
 
6.7%
U 2
 
6.7%
H 2
 
6.7%
A 1
 
3.3%
N 1
 
3.3%
Other values (5) 5
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 30
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 6
20.0%
B 3
10.0%
S 3
10.0%
M 3
10.0%
E 2
 
6.7%
G 2
 
6.7%
U 2
 
6.7%
H 2
 
6.7%
A 1
 
3.3%
N 1
 
3.3%
Other values (5) 5
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 30
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 6
20.0%
B 3
10.0%
S 3
10.0%
M 3
10.0%
E 2
 
6.7%
G 2
 
6.7%
U 2
 
6.7%
H 2
 
6.7%
A 1
 
3.3%
N 1
 
3.3%
Other values (5) 5
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 6
20.0%
B 3
10.0%
S 3
10.0%
M 3
10.0%
E 2
 
6.7%
G 2
 
6.7%
U 2
 
6.7%
H 2
 
6.7%
A 1
 
3.3%
N 1
 
3.3%
Other values (5) 5
16.7%
Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:20.888519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters25
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

Unique20 ?
Unique (%)66.7%

Sample

1st rowA02
2nd rowN76
3rd rowB08
4th rowC15
5th rowC24
ValueCountFrequency (%)
b08 2
 
6.7%
u99 2
 
6.7%
h50 2
 
6.7%
s96 2
 
6.7%
g46 2
 
6.7%
b05 1
 
3.3%
a02 1
 
3.3%
k66 1
 
3.3%
f42 1
 
3.3%
e37 1
 
3.3%
Other values (15) 15
50.0%
2023-12-10T22:46:21.507160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8
 
8.9%
6 8
 
8.9%
7 7
 
7.8%
9 7
 
7.8%
3 7
 
7.8%
5 7
 
7.8%
C 6
 
6.7%
4 5
 
5.6%
2 5
 
5.6%
8 4
 
4.4%
Other values (15) 26
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Uppercase Letter 30
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 6
20.0%
M 3
10.0%
B 3
10.0%
S 3
10.0%
H 2
 
6.7%
E 2
 
6.7%
U 2
 
6.7%
G 2
 
6.7%
J 1
 
3.3%
N 1
 
3.3%
Other values (5) 5
16.7%
Decimal Number
ValueCountFrequency (%)
0 8
13.3%
6 8
13.3%
7 7
11.7%
9 7
11.7%
3 7
11.7%
5 7
11.7%
4 5
8.3%
2 5
8.3%
8 4
6.7%
1 2
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 60
66.7%
Latin 30
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 6
20.0%
M 3
10.0%
B 3
10.0%
S 3
10.0%
H 2
 
6.7%
E 2
 
6.7%
U 2
 
6.7%
G 2
 
6.7%
J 1
 
3.3%
N 1
 
3.3%
Other values (5) 5
16.7%
Common
ValueCountFrequency (%)
0 8
13.3%
6 8
13.3%
7 7
11.7%
9 7
11.7%
3 7
11.7%
5 7
11.7%
4 5
8.3%
2 5
8.3%
8 4
6.7%
1 2
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
 
8.9%
6 8
 
8.9%
7 7
 
7.8%
9 7
 
7.8%
3 7
 
7.8%
5 7
 
7.8%
C 6
 
6.7%
4 5
 
5.6%
2 5
 
5.6%
8 4
 
4.4%
Other values (15) 26
28.9%
Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:21.846020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length16
Mean length10.5
Min length2

Characters and Unicode

Total characters315
Distinct characters64
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row농업; 임업 및 어업
2nd row사업시설 관리; 사업 지원 및 임대 서비스업
3rd row광업
4th row제조업
5th row제조업
ValueCountFrequency (%)
22
21.4%
서비스업 8
 
7.8%
제조업 6
 
5.8%
협회 3
 
2.9%
단체 3
 
2.9%
수리 3
 
2.9%
기타 3
 
2.9%
개인 3
 
2.9%
전문 3
 
2.9%
과학 3
 
2.9%
Other values (30) 46
44.7%
2023-12-10T22:46:22.408695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
23.2%
33
 
10.5%
22
 
7.0%
; 12
 
3.8%
10
 
3.2%
9
 
2.9%
8
 
2.5%
8
 
2.5%
8
 
2.5%
8
 
2.5%
Other values (54) 124
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 230
73.0%
Space Separator 73
 
23.2%
Other Punctuation 12
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
33
 
14.3%
22
 
9.6%
10
 
4.3%
9
 
3.9%
8
 
3.5%
8
 
3.5%
8
 
3.5%
8
 
3.5%
6
 
2.6%
6
 
2.6%
Other values (52) 112
48.7%
Space Separator
ValueCountFrequency (%)
73
100.0%
Other Punctuation
ValueCountFrequency (%)
; 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 230
73.0%
Common 85
 
27.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
33
 
14.3%
22
 
9.6%
10
 
4.3%
9
 
3.9%
8
 
3.5%
8
 
3.5%
8
 
3.5%
8
 
3.5%
6
 
2.6%
6
 
2.6%
Other values (52) 112
48.7%
Common
ValueCountFrequency (%)
73
85.9%
; 12
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 230
73.0%
ASCII 85
 
27.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
85.9%
; 12
 
14.1%
Hangul
ValueCountFrequency (%)
33
 
14.3%
22
 
9.6%
10
 
4.3%
9
 
3.9%
8
 
3.5%
8
 
3.5%
8
 
3.5%
8
 
3.5%
6
 
2.6%
6
 
2.6%
Other values (52) 112
48.7%
Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:46:22.786002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length10.433333
Min length2

Characters and Unicode

Total characters313
Distinct characters93
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)66.7%

Sample

1st row임업
2nd row임대업; 부동산 제외
3rd row광업 지원 서비스업
4th row가죽; 가방 및 신발 제조업
5th row1차 금속 제조업
ValueCountFrequency (%)
13
 
13.5%
서비스업 8
 
8.3%
제조업 6
 
6.2%
기타 6
 
6.2%
광업 3
 
3.1%
상품 2
 
2.1%
지원 2
 
2.1%
국제 2
 
2.1%
외국기관 2
 
2.1%
수상 2
 
2.1%
Other values (46) 50
52.1%
2023-12-10T22:46:23.403967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/