Overview

Dataset statistics

Number of variables5
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory43.6 B

Variable types

Numeric1
Text2
Categorical1
DateTime1

Dataset

Description인천광역시 남동구에 위치한 중고가전판매업 현황에 대한 데이터로 연번, 업체명, 구분, 주소지, 데이터기준일자 항목을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15091364&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
주소지 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:55:39.271969
Analysis finished2024-03-18 04:55:41.064266
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T13:55:41.164676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)0.57166195
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2024-03-18T13:55:41.307082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

업체명
Text

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-03-18T13:55:41.540390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length15
Mean length8
Min length2

Characters and Unicode

Total characters400
Distinct characters128
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)94.0%

Sample

1st row중고가전
2nd row인천중고가전재활용센터
3rd row중고가전매입판매
4th row인천주방중고매장
5th row중고가전냉장고세탁기에어컨매입가구폐기가정페기물
ValueCountFrequency (%)
중고가전재활용센터알뜰매장 3
 
5.4%
중고가전 2
 
3.6%
제이더블유중고몰 1
 
1.8%
인천시청점 1
 
1.8%
마루컴 1
 
1.8%
우성냉동 1
 
1.8%
대산전자 1
 
1.8%
용현길전자 1
 
1.8%
중고가전매장 1
 
1.8%
원앤원 1
 
1.8%
Other values (43) 43
76.8%
2024-03-18T13:55:41.870369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 26
 
6.5%
중 25
 
6.2%
가 21
 
5.2%
전 20
 
5.0%
매 16
 
4.0%
장 14
 
3.5%
인 9
 
2.2%
이 9
 
2.2%
터 9
 
2.2%
기 8
 
2.0%
Other values (118) 243
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 387
96.8%
Space Separator 6
 
1.5%
Uppercase Letter 4
 
1.0%
Decimal Number 3
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 26
 
6.7%
중 25
 
6.5%
가 21
 
5.4%
전 20
 
5.2%
매 16
 
4.1%
장 14
 
3.6%
인 9
 
2.3%
이 9
 
2.3%
터 9
 
2.3%
기 8
 
2.1%
Other values (110) 230
59.4%
Uppercase Letter
ValueCountFrequency (%)
S 1
25.0%
J 1
25.0%
P 1
25.0%
C 1
25.0%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
1 1
33.3%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 387
96.8%
Common 9
 
2.2%
Latin 4
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 26
 
6.7%
중 25
 
6.5%
가 21
 
5.4%
전 20
 
5.2%
매 16
 
4.1%
장 14
 
3.6%
인 9
 
2.3%
이 9
 
2.3%
터 9
 
2.3%
기 8
 
2.1%
Other values (110) 230
59.4%
Common
ValueCountFrequency (%)
6
66.7%
3 1
 
11.1%
1 1
 
11.1%
5 1
 
11.1%
Latin
ValueCountFrequency (%)
S 1
25.0%
J 1
25.0%
P 1
25.0%
C 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 387
96.8%
ASCII 13
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 26
 
6.7%
중 25
 
6.5%
가 21
 
5.4%
전 20
 
5.2%
매 16
 
4.1%
장 14
 
3.6%
인 9
 
2.3%
이 9
 
2.3%
터 9
 
2.3%
기 8
 
2.1%
Other values (110) 230
59.4%
ASCII
ValueCountFrequency (%)
6
46.2%
3 1
 
7.7%
S 1
 
7.7%
J 1
 
7.7%
1 1
 
7.7%
P 1
 
7.7%
C 1
 
7.7%
5 1
 
7.7%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
중고가전
37 
컴퓨터,모니터
11 
에어컨
 
2

Length

Max length7
Median length4
Mean length4.62
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중고가전 37
74.0%
컴퓨터,모니터 11
 
22.0%
에어컨 2
 
4.0%

Length

2024-03-18T13:55:41.999628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:55:42.087989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중고가전 37
74.0%
컴퓨터,모니터 11
 
22.0%
에어컨 2
 
4.0%

주소지
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-03-18T13:55:42.301521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length25
Mean length18.3
Min length10

Characters and Unicode

Total characters915
Distinct characters98
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

Unique50 ?
Unique (%)100.0%

Sample

1st row인천 남동구 호구포로810번길 66-15
2nd row인천 남동구 인주대로612번길 15
3rd row인천 남동구 청능대로 596 푸르지오시티
4th row인천 남동구 백범로 443
5th row인천 남동구 논현고잔로88번길 54
ValueCountFrequency (%)
인천 50
23.3%
남동구 50
23.3%
인주대로 4
 
1.9%
1층 4
 
1.9%
백범로 3
 
1.4%
532 2
 
0.9%
석정로 2
 
0.9%
27 2
 
0.9%
구월로 2
 
0.9%
15 2
 
0.9%
Other values (93) 94
43.7%
2024-03-18T13:55:42.673817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
165
18.0%
인 61
 
6.7%
구 59
 
6.4%
동 57
 
6.2%
남 54
 
5.9%
천 52
 
5.7%
로 46
 
5.0%
1 30
 
3.3%
3 27
 
3.0%
2 26
 
2.8%
Other values (88) 338
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 545
59.6%
Decimal Number 196
 
21.4%
Space Separator 165
 
18.0%
Dash Punctuation 9
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 61
11.2%
구 59
10.8%
동 57
 
10.5%
남 54
 
9.9%
천 52
 
9.5%
로 46
 
8.4%
번 24
 
4.4%
길 24
 
4.4%
대 14
 
2.6%
주 11
 
2.0%
Other values (76) 143
26.2%
Decimal Number
ValueCountFrequency (%)
1 30
15.3%
3 27
13.8%
2 26
13.3%
4 25
12.8%
7 23
11.7%
6 20
10.2%
5 16
8.2%
8 13
6.6%
9 8
 
4.1%
0 8
 
4.1%
Space Separator
ValueCountFrequency (%)
165
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 545
59.6%
Common 370
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 61
11.2%
구 59
10.8%
동 57
 
10.5%
남 54
 
9.9%
천 52
 
9.5%
로 46
 
8.4%
번 24
 
4.4%
길 24
 
4.4%
대 14
 
2.6%
주 11
 
2.0%
Other values (76) 143
26.2%
Common
ValueCountFrequency (%)
165
44.6%
1 30
 
8.1%
3 27
 
7.3%
2 26
 
7.0%
4 25
 
6.8%
7 23
 
6.2%
6 20
 
5.4%
5 16
 
4.3%
8 13
 
3.5%
- 9
 
2.4%
Other values (2) 16
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 545
59.6%
ASCII 370
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
44.6%
1 30
 
8.1%
3 27
 
7.3%
2 26
 
7.0%
4 25
 
6.8%
7 23
 
6.2%
6 20
 
5.4%
5 16
 
4.3%
8 13
 
3.5%
- 9
 
2.4%
Other values (2) 16
 
4.3%
Hangul
ValueCountFrequency (%)
인 61
11.2%
구 59
10.8%
동 57
 
10.5%
남 54
 
9.9%
천 52
 
9.5%
로 46
 
8.4%
번 24
 
4.4%
길 24
 
4.4%
대 14
 
2.6%
주 11
 
2.0%
Other values (76) 143
26.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Minimum2023-09-11 00:00:00
Maximum2023-09-11 00:00:00
2024-03-18T13:55:42.769739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:55:42.840704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T13:55:40.718044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:55:42.902883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업체명구분주소지
연번1.0000.8610.8021.000
업체명0.8611.0001.0001.000
구분0.8021.0001.0001.000
주소지1.0001.0001.0001.000
2024-03-18T13:55:42.995187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분
연번1.0000.638
구분0.6381.000

Missing values

2024-03-18T13:55:40.915666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:55:41.010743image/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

연번업체명구분주소지데이터기준일자
01중고가전중고가전인천 남동구 호구포로810번길 66-152023-09-11
12인천중고가전재활용센터중고가전인천 남동구 인주대로612번길 152023-09-11
23중고가전매입판매중고가전인천 남동구 청능대로 596 푸르지오시티2023-09-11
34인천주방중고매장중고가전인천 남동구 백범로 4432023-09-11
45중고가전냉장고세탁기에어컨매입가구폐기가정페기물중고가전인천 남동구 논현고잔로88번길 542023-09-11
56중고알뜰매장중고가전인천 남동구 구월로 3462023-09-11
67토탈앤리싸이클중고가전인천 남동구 남동대로 9282023-09-11
78제일가전중고종합할인매장중고가전인천 남동구 인주대로 5302023-09-11
89리싸이클153구월점중고가전인천 남동구 인주대로763번길 58-12023-09-11
910플러스중고주방중고가전인천 남동구 문화서로4번길 41-172023-09-11
연번업체명구분주소지데이터기준일자
4041산타컴컴퓨터,모니터인천 남동구 인주대로751번길 42-13 1층2023-09-11
4142컴사랑컴퓨터,모니터인천 남동구 구월로372번길 73 1층2023-09-11
4243제이에스케이무역컴퓨터,모니터인천 남동구 운연동 343-62023-09-11
4344JS냉난방에어컨인천 남동구 문화로89번길 8-422023-09-11
4445팀공조시스템에어컨인천 남동구 찬우물로 28 1층2023-09-11
4546싼피시컴퓨터,모니터인천 남동구 백범로477번길 152023-09-11
4647네오컴퓨터,모니터인천 남동구 인주대로681번길 162023-09-11
4748제이더블유중고몰컴퓨터,모니터인천 남동구 문화서로46번길 22 1층2023-09-11
4849구월동 미래컴퓨터컴퓨터,모니터인천 남동구 남동대로712번길 5-16 예승파크빌7차2023-09-11
4950아이온테크컴퓨터,모니터인천 남동구 논고개로123번길 172023-09-11