Overview

Dataset statistics

Number of variables14
Number of observations41
Missing cells40
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory118.2 B

Variable types

Categorical5
Text6
Numeric1
Boolean2

Dataset

Description행정안전부시책 기준에 의거 경사북도와 영주시가 지정한 물가안정모범업소
Author경상북도 영주시
URLhttps://www.data.go.kr/data/3079443/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
가격2 is highly overall correlated with 가격1 and 4 other fieldsHigh correlation
가격3 is highly overall correlated with 가격1 and 3 other fieldsHigh correlation
품목2 is highly overall correlated with 가격1 and 4 other fieldsHigh correlation
주차가능여부 is highly overall correlated with 업종 and 3 other fieldsHigh correlation
업종 is highly overall correlated with 가격1 and 4 other fieldsHigh correlation
가격1 is highly overall correlated with 업종 and 3 other fieldsHigh correlation
배달가능여부 is highly overall correlated with 품목2High correlation
가격3 is highly imbalanced (64.7%)Imbalance
주차가능여부 is highly imbalanced (53.9%)Imbalance
전화번호 has 3 (7.3%) missing valuesMissing
품목3 has 37 (90.2%) missing valuesMissing
업소명 has unique valuesUnique
대표자 has unique valuesUnique
도로명 주소 has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:58:28.293367
Analysis finished2023-12-11 22:58:29.393607
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size460.0 B
한식
22 
세탁업
11 
이미용업
4 
중식
 
2
기타양식
 
1

Length

Max length4
Median length2
Mean length2.5365854
Min length2

Unique

Unique2 ?
Unique (%)4.9%

Sample

1st row한식
2nd row한식
3rd row중식
4th row한식
5th row한식

Common Values

ValueCountFrequency (%)
한식 22
53.7%
세탁업 11
26.8%
이미용업 4
 
9.8%
중식 2
 
4.9%
기타양식 1
 
2.4%
숙박업 1
 
2.4%

Length

2023-12-12T07:58:29.465997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:29.600775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 22
53.7%
세탁업 11
26.8%
이미용업 4
 
9.8%
중식 2
 
4.9%
기타양식 1
 
2.4%
숙박업 1
 
2.4%

업소명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T07:58:29.823971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.2926829
Min length3

Characters and Unicode

Total characters217
Distinct characters117
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

Unique41 ?
Unique (%)100.0%

Sample

1st row갈비촌
2nd row개나리식당
3rd row경해성
4th row김가네식당
5th row단지식당
ValueCountFrequency (%)
갈비촌 1
 
2.4%
태흥냉면 1
 
2.4%
함밭숯불생고기 1
 
2.4%
횡재먹거리 1
 
2.4%
강민서헤어삽 1
 
2.4%
시대이발관 1
 
2.4%
유미용실 1
 
2.4%
성원이발관 1
 
2.4%
금성세탁소 1
 
2.4%
기능사세탁소 1
 
2.4%
Other values (31) 31
75.6%
2023-12-12T07:58:30.213885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 14
 
6.5%
당 11
 
5.1%
탁 8
 
3.7%
리 8
 
3.7%
세 8
 
3.7%
라 5
 
2.3%
대 5
 
2.3%
소 5
 
2.3%
자 4
 
1.8%
비 4
 
1.8%
Other values (107) 145
66.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 217
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 14
 
6.5%
당 11
 
5.1%
탁 8
 
3.7%
리 8
 
3.7%
세 8
 
3.7%
라 5
 
2.3%
대 5
 
2.3%
소 5
 
2.3%
자 4
 
1.8%
비 4
 
1.8%
Other values (107) 145
66.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 217
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 14
 
6.5%
당 11
 
5.1%
탁 8
 
3.7%
리 8
 
3.7%
세 8
 
3.7%
라 5
 
2.3%
대 5
 
2.3%
소 5
 
2.3%
자 4
 
1.8%
비 4
 
1.8%
Other values (107) 145
66.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 217
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 14
 
6.5%
당 11
 
5.1%
탁 8
 
3.7%
리 8
 
3.7%
세 8
 
3.7%
라 5
 
2.3%
대 5
 
2.3%
소 5
 
2.3%
자 4
 
1.8%
비 4
 
1.8%
Other values (107) 145
66.8%

대표자
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T07:58:30.455483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123
Distinct characters65
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

Unique41 ?
Unique (%)100.0%

Sample

1st row신부자
2nd row이옥현
3rd row박화숙
4th row김성숙
5th row윤순녀
ValueCountFrequency (%)
신부자 1
 
2.4%
이정미 1
 
2.4%
김대호 1
 
2.4%
조정희 1
 
2.4%
강민서 1
 
2.4%
정연학 1
 
2.4%
김순득 1
 
2.4%
김오규 1
 
2.4%
여재철 1
 
2.4%
송홍준 1
 
2.4%
Other values (31) 31
75.6%
2023-12-12T07:58:30.830059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 8
 
6.5%
이 7
 
5.7%
박 7
 
5.7%
정 7
 
5.7%
순 6
 
4.9%
희 4
 
3.3%
성 4
 
3.3%
용 3
 
2.4%
숙 3
 
2.4%
손 2
 
1.6%
Other values (55) 72
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 8
 
6.5%
이 7
 
5.7%
박 7
 
5.7%
정 7
 
5.7%
순 6
 
4.9%
희 4
 
3.3%
성 4
 
3.3%
용 3
 
2.4%
숙 3
 
2.4%
손 2
 
1.6%
Other values (55) 72
58.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 8
 
6.5%
이 7
 
5.7%
박 7
 
5.7%
정 7
 
5.7%
순 6
 
4.9%
희 4
 
3.3%
성 4
 
3.3%
용 3
 
2.4%
숙 3
 
2.4%
손 2
 
1.6%
Other values (55) 72
58.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 8
 
6.5%
이 7
 
5.7%
박 7
 
5.7%
정 7
 
5.7%
순 6
 
4.9%
희 4
 
3.3%
성 4
 
3.3%
용 3
 
2.4%
숙 3
 
2.4%
손 2
 
1.6%
Other values (55) 72
58.5%

도로명 주소
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T07:58:31.068647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length23.390244
Min length18

Characters and Unicode

Total characters959
Distinct characters63
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row경상북도 영주시 번영로12번길 2 (휴천동)
2nd row경상북도 영주시 안정면 신재로 520-1
3rd row경상북도 영주시 광복로 122 (하망동)
4th row경상북도 영주시 장수면 옥계로 9
5th row경상북도 영주시 중앙로126번길 18 (하망동)
ValueCountFrequency (%)
경상북도 41
20.0%
영주시 41
20.0%
휴천동 9
 
4.4%
가흥동 9
 
4.4%
하망동 8
 
3.9%
영주동 8
 
3.9%
선비로 4
 
2.0%
원당로 3
 
1.5%
신재로 3
 
1.5%
안정면 3
 
1.5%
Other values (70) 76
37.1%
2023-12-12T07:58:31.392679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
167
17.4%
영 55
 
5.7%
주 50
 
5.2%
상 42
 
4.4%
시 42
 
4.4%
로 41
 
4.3%
경 41
 
4.3%
도 41
 
4.3%
북 41
 
4.3%
동 39
 
4.1%
Other values (53) 400
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 566
59.0%
Space Separator 167
 
17.4%
Decimal Number 146
 
15.2%
Close Punctuation 35
 
3.6%
Open Punctuation 35
 
3.6%
Dash Punctuation 10
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 55
 
9.7%
주 50
 
8.8%
상 42
 
7.4%
시 42
 
7.4%
로 41
 
7.2%
경 41
 
7.2%
도 41
 
7.2%
북 41
 
7.2%
동 39
 
6.9%
번 21
 
3.7%
Other values (39) 153
27.0%
Decimal Number
ValueCountFrequency (%)
1 34
23.3%
2 25
17.1%
3 21
14.4%
5 15
10.3%
6 14
9.6%
0 10
 
6.8%
7 8
 
5.5%
4 7
 
4.8%
9 7
 
4.8%
8 5
 
3.4%
Space Separator
ValueCountFrequency (%)
167
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 566
59.0%
Common 393
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 55
 
9.7%
주 50
 
8.8%
상 42
 
7.4%
시 42
 
7.4%
로 41
 
7.2%
경 41
 
7.2%
도 41
 
7.2%
북 41
 
7.2%
동 39
 
6.9%
번 21
 
3.7%
Other values (39) 153
27.0%
Common
ValueCountFrequency (%)
167
42.5%
) 35
 
8.9%
( 35
 
8.9%
1 34
 
8.7%
2 25
 
6.4%
3 21
 
5.3%
5 15
 
3.8%
6 14
 
3.6%
0 10
 
2.5%
- 10
 
2.5%
Other values (4) 27
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 566
59.0%
ASCII 393
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
167
42.5%
) 35
 
8.9%
( 35
 
8.9%
1 34
 
8.7%
2 25
 
6.4%
3 21
 
5.3%
5 15
 
3.8%
6 14
 
3.6%
0 10
 
2.5%
- 10
 
2.5%
Other values (4) 27
 
6.9%
Hangul
ValueCountFrequency (%)
영 55
 
9.7%
주 50
 
8.8%
상 42
 
7.4%
시 42
 
7.4%
로 41
 
7.2%
경 41
 
7.2%
도 41
 
7.2%
북 41
 
7.2%
동 39
 
6.9%
번 21
 
3.7%
Other values (39) 153
27.0%

전화번호
Text

MISSING 

Distinct38
Distinct (%)100.0%
Missing3
Missing (%)7.3%
Memory size460.0 B
2023-12-12T07:58:31.605416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters456
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row054-631-1702
2nd row054-635-2199
3rd row054-631-4682
4th row054-637-3312
5th row054-634-1985
ValueCountFrequency (%)
054-636-6998 1
 
2.6%
054-632-1390 1
 
2.6%
054-633-7237 1
 
2.6%
054-631-2850 1
 
2.6%
054-635-5579 1
 
2.6%
054-638-0094 1
 
2.6%
054-632-7704 1
 
2.6%
054-635-5378 1
 
2.6%
054-633-5050 1
 
2.6%
054-634-7007 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T07:58:31.939965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 76
16.7%
5 62
13.6%
3 59
12.9%
0 56
12.3%
6 50
11.0%
4 48
10.5%
2 27
 
5.9%
9 25
 
5.5%
7 20
 
4.4%
8 19
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 380
83.3%
Dash Punctuation 76
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 62
16.3%
3 59
15.5%
0 56
14.7%
6 50
13.2%
4 48
12.6%
2 27
7.1%
9 25
6.6%
7 20
 
5.3%
8 19
 
5.0%
1 14
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 76
16.7%
5 62
13.6%
3 59
12.9%
0 56
12.3%
6 50
11.0%
4 48
10.5%
2 27
 
5.9%
9 25
 
5.5%
7 20
 
4.4%
8 19
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 76
16.7%
5 62
13.6%
3 59
12.9%
0 56
12.3%
6 50
11.0%
4 48
10.5%
2 27
 
5.9%
9 25
 
5.5%
7 20
 
4.4%
8 19
 
4.2%

품목1
Text

Distinct21
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T07:58:32.145229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.9756098
Min length2

Characters and Unicode

Total characters204
Distinct characters57
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)34.1%

Sample

1st row돼지갈비(200g)
2nd row비빔밥(보리밥)
3rd row자장면
4th row된장찌개
5th row된장찌개
ValueCountFrequency (%)
된장찌개 8
17.8%
신사복 8
17.8%
신사복(정장 3
 
6.7%
1벌 3
 
6.7%
이발 2
 
4.4%
컷트 2
 
4.4%
불고기(200g 2
 
4.4%
김치찌개 2
 
4.4%
물냉면(홀방문 1
 
2.2%
돼지갈비(200g 1
 
2.2%
Other values (13) 13
28.9%
2023-12-12T07:58:32.451891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 14
 
6.9%
) 11
 
5.4%
신 11
 
5.4%
사 11
 
5.4%
복 11
 
5.4%
( 11
 
5.4%
찌 10
 
4.9%
개 10
 
4.9%
0 8
 
3.9%
된 8
 
3.9%
Other values (47) 99
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 159
77.9%
Decimal Number 15
 
7.4%
Close Punctuation 11
 
5.4%
Open Punctuation 11
 
5.4%
Space Separator 4
 
2.0%
Lowercase Letter 4
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 14
 
8.8%
신 11
 
6.9%
사 11
 
6.9%
복 11
 
6.9%
찌 10
 
6.3%
개 10
 
6.3%
된 8
 
5.0%
밥 7
 
4.4%
비 4
 
2.5%
정 4
 
2.5%
Other values (40) 69
43.4%
Decimal Number
ValueCountFrequency (%)
0 8
53.3%
2 4
26.7%
1 3
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 159
77.9%
Common 41
 
20.1%
Latin 4
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 14
 
8.8%
신 11
 
6.9%
사 11
 
6.9%
복 11
 
6.9%
찌 10
 
6.3%
개 10
 
6.3%
된 8
 
5.0%
밥 7
 
4.4%
비 4
 
2.5%
정 4
 
2.5%
Other values (40) 69
43.4%
Common
ValueCountFrequency (%)
) 11
26.8%
( 11
26.8%
0 8
19.5%
2 4
 
9.8%
4
 
9.8%
1 3
 
7.3%
Latin
ValueCountFrequency (%)
g 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 159
77.9%
ASCII 45
 
22.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 14
 
8.8%
신 11
 
6.9%
사 11
 
6.9%
복 11
 
6.9%
찌 10
 
6.3%
개 10
 
6.3%
된 8
 
5.0%
밥 7
 
4.4%
비 4
 
2.5%
정 4
 
2.5%
Other values (40) 69
43.4%
ASCII
ValueCountFrequency (%)
) 11
24.4%
( 11
24.4%
0 8
17.8%
2 4
 
8.9%
4
 
8.9%
g 4
 
8.9%
1 3
 
6.7%

가격1
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6753.6585
Minimum2500
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T07:58:32.584153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile4500
Q15000
median6000
Q37000
95-th percentile10900
Maximum25000
Range22500
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation3479.3029
Coefficient of variation (CV)0.51517306
Kurtosis19.029286
Mean6753.6585
Median Absolute Deviation (MAD)1000
Skewness3.7801952
Sum276900
Variance12105549
MonotonicityNot monotonic
2023-12-12T07:58:32.692923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5000 16
39.0%
7000 8
19.5%
6000 4
 
9.8%
8000 4
 
9.8%
9000 2
 
4.9%
2500 1
 
2.4%
10900 1
 
2.4%
10000 1
 
2.4%
3000 1
 
2.4%
4500 1
 
2.4%
Other values (2) 2
 
4.9%
ValueCountFrequency (%)
2500 1
 
2.4%
3000 1
 
2.4%
4500 1
 
2.4%
5000 16
39.0%
6000 4
 
9.8%
7000 8
19.5%
8000 4
 
9.8%
9000 2
 
4.9%
10000 1
 
2.4%
10900 1
 
2.4%
ValueCountFrequency (%)
25000 1
 
2.4%
11000 1
 
2.4%
10900 1
 
2.4%
10000 1
 
2.4%
9000 2
 
4.9%
8000 4
 
9.8%
7000 8
19.5%
6000 4
 
9.8%
5000 16
39.0%
4500 1
 
2.4%

품목2
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size460.0 B
<NA>
26 
김치찌개
8 
냉면
 
2
순대국밥
 
1
된장찌개
 
1
Other values (3)
3 

Length

Max length5
Median length4
Mean length3.902439
Min length2

Unique

Unique5 ?
Unique (%)12.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row김치찌개
5th row김치찌개

Common Values

ValueCountFrequency (%)
<NA> 26
63.4%
김치찌개 8
 
19.5%
냉면 2
 
4.9%
순대국밥 1
 
2.4%
된장찌개 1
 
2.4%
콩나물국밥 1
 
2.4%
잔치국수 1
 
2.4%
갈비탕 1
 
2.4%

Length

2023-12-12T07:58:32.857393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:33.039448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
63.4%
김치찌개 8
 
19.5%
냉면 2
 
4.9%
순대국밥 1
 
2.4%
된장찌개 1
 
2.4%
콩나물국밥 1
 
2.4%
잔치국수 1
 
2.4%
갈비탕 1
 
2.4%

가격2
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
<NA>
26 
5000
13 
3500
 
1
6000
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)4.9%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row5000
5th row5000

Common Values

ValueCountFrequency (%)
<NA> 26
63.4%
5000 13
31.7%
3500 1
 
2.4%
6000 1
 
2.4%

Length

2023-12-12T07:58:33.500807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:33.602284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
63.4%
5000 13
31.7%
3500 1
 
2.4%
6000 1
 
2.4%

품목3
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing37
Missing (%)90.2%
Memory size460.0 B
2023-12-12T07:58:33.729457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length4.25
Min length2

Characters and Unicode

Total characters17
Distinct characters16
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row삼계탕
2nd row비빕밥
3rd row불고기(200g)
4th row냉면
ValueCountFrequency (%)
삼계탕 1
25.0%
비빕밥 1
25.0%
불고기(200g 1
25.0%
냉면 1
25.0%
2023-12-12T07:58:34.038868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2
 
11.8%
삼 1
 
5.9%
계 1
 
5.9%
탕 1
 
5.9%
비 1
 
5.9%
빕 1
 
5.9%
밥 1
 
5.9%
불 1
 
5.9%
고 1
 
5.9%
기 1
 
5.9%
Other values (6) 6
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11
64.7%
Decimal Number 3
 
17.6%
Open Punctuation 1
 
5.9%
Lowercase Letter 1
 
5.9%
Close Punctuation 1
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
삼 1
9.1%
계 1
9.1%
탕 1
9.1%
비 1
9.1%
빕 1
9.1%
밥 1
9.1%
불 1
9.1%
고 1
9.1%
기 1
9.1%
냉 1
9.1%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
2 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11
64.7%
Common 5
29.4%
Latin 1
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
삼 1
9.1%
계 1
9.1%
탕 1
9.1%
비 1
9.1%
빕 1
9.1%
밥 1
9.1%
불 1
9.1%
고 1
9.1%
기 1
9.1%
냉 1
9.1%
Common
ValueCountFrequency (%)
0 2
40.0%
( 1
20.0%
2 1
20.0%
) 1
20.0%
Latin
ValueCountFrequency (%)
g 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11
64.7%
ASCII 6
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
33.3%
( 1
16.7%
2 1
16.7%
g 1
16.7%
) 1
16.7%
Hangul
ValueCountFrequency (%)
삼 1
9.1%
계 1
9.1%
탕 1
9.1%
비 1
9.1%
빕 1
9.1%
밥 1
9.1%
불 1
9.1%
고 1
9.1%
기 1
9.1%
냉 1
9.1%

가격3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
<NA>
37 
10000
 
2
5000
 
2

Length

Max length5
Median length4
Mean length4.0487805
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 37
90.2%
10000 2
 
4.9%
5000 2
 
4.9%

Length

2023-12-12T07:58:34.210431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:34.318852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 37
90.2%
10000 2
 
4.9%
5000 2
 
4.9%

배달가능여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size173.0 B
True
27 
False
14 
ValueCountFrequency (%)
True 27
65.9%
False 14
34.1%
2023-12-12T07:58:34.411835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

주차가능여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size173.0 B
True
37 
False
4 
ValueCountFrequency (%)
True 37
90.2%
False 4
 
9.8%
2023-12-12T07:58:34.490752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
2017-04-30
41 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-04-30
2nd row2017-04-30
3rd row2017-04-30
4th row2017-04-30
5th row2017-04-30

Common Values

ValueCountFrequency (%)
2017-04-30 41
100.0%

Length

2023-12-12T07:58:34.583560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:58:34.680511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-04-30 41
100.0%

Interactions

2023-12-12T07:58:28.906754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:58:34.764876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종업소명대표자도로명 주소전화번호품목1가격1품목2가격2품목3가격3배달가능여부주차가능여부
업종1.0001.0001.0001.0001.0001.0000.797NaNNaNNaNNaN0.6260.750
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대표자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명 주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
품목11.0001.0001.0001.0001.0001.0000.9900.9951.0001.0000.0000.5930.000
가격10.7971.0001.0001.0001.0000.9901.0000.8871.000NaNNaN0.0000.000
품목2NaN1.0001.0001.0001.0000.9950.8871.0001.0001.0000.0000.657NaN
가격2NaN1.0001.0001.0001.0001.0001.0001.0001.000NaNNaN0.185NaN
품목3NaN1.0001.0001.0001.0001.000NaN1.000NaN1.0001.0001.000NaN
가격3NaN1.0001.0001.0001.0000.000NaN0.000NaN1.0001.0000.000NaN
배달가능여부0.6261.0001.0001.0001.0000.5930.0000.6570.1851.0000.0001.0000.000
주차가능여부0.7501.0001.0001.0001.0000.0000.000NaNNaNNaNNaN0.0001.000
2023-12-12T07:58:34.917384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가격2가격3배달가능여부품목2주차가능여부업종
가격21.0001.0000.2750.8161.0001.000
가격31.0001.0000.0000.0001.0001.000
배달가능여부0.2750.0001.0000.5420.0000.429
품목20.8160.0000.5421.0001.0001.000
주차가능여부1.0001.0000.0001.0001.0000.527
업종1.0001.0000.4291.0000.5271.000
2023-12-12T07:58:35.035934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가격1업종품목2가격2가격3배달가능여부주차가능여부
가격11.0000.7070.7050.9571.0000.0000.000
업종0.7071.0001.0001.0001.0000.4290.527
품목20.7051.0001.0000.8160.0000.5421.000
가격20.9571.0000.8161.0001.0000.2751.000
가격31.0001.0000.0001.0001.0000.0001.000
배달가능여부0.0000.4290.5420.2750.0001.0000.000
주차가능여부0.0000.5271.0001.0001.0000.0001.000

Missing values

2023-12-12T07:58:29.038518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:58:29.209700image/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.
2023-12-12T07:58:29.332970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업종업소명대표자도로명 주소전화번호품목1가격1품목2가격2품목3가격3배달가능여부주차가능여부데이터기준일자
0한식갈비촌신부자경상북도 영주시 번영로12번길 2 (휴천동)054-631-1702돼지갈비(200g)7000<NA><NA><NA><NA>YY2017-04-30
1한식개나리식당이옥현경상북도 영주시 안정면 신재로 520-1054-635-2199비빔밥(보리밥)5000<NA><NA><NA><NA>YY2017-04-30
2중식경해성박화숙경상북도 영주시 광복로 122 (하망동)054-631-4682자장면2500<NA><NA><NA><NA>YY2017-04-30
3한식김가네식당김성숙경상북도 영주시 장수면 옥계로 9054-637-3312된장찌개5000김치찌개5000<NA><NA>YY2017-04-30
4한식단지식당윤순녀경상북도 영주시 중앙로126번길 18 (하망동)054-634-1985된장찌개5000김치찌개5000<NA><NA>YY2017-04-30
5한식대박식당박상용경상북도 영주시 구성로330번길 34-1 (하망동)054-637-1888곤드레나물밥6000<NA><NA><NA><NA>YY2017-04-30
6한식동해숯불뷔페갈비박애금경상북도 영주시 구성로235번길 11 (휴천동)054-633-0575뷔페10900<NA><NA><NA><NA>NY2017-04-30
7한식밀양돼지국밥홍동주경상북도 영주시 대동로 155-1 (가흥동)054-632-9049돼지국밥5000순대국밥5000<NA><NA>NY2017-04-30
8한식분수대숯불갈비회관권오직경상북도 영주시 번영로173번길 15 (영주동)054-632-5255불고기(200g)10000냉면5000<NA><NA>YY2017-04-30
9한식비행장기사식당손춘희경상북도 영주시 안정면 신재로 532054-632-9213된장찌개5000김치찌개5000<NA><NA>YY2017-04-30
업종업소명대표자도로명 주소전화번호품목1가격1품목2가격2품목3가격3배달가능여부주차가능여부데이터기준일자
31세탁업대원세탁프라자권순만경상북도 영주시 원당로 15 (가흥동)054-636-0918신사복7000<NA><NA><NA><NA>YY2017-04-30
32세탁업소망세탁소이정선경상북도 영주시 서원로 221-10 (하망동)054-632-5889신사복7000<NA><NA><NA><NA>NY2017-04-30
33세탁업스피드세탁소김규창경상북도 영주시 번영로 61-1 (휴천동)054-636-8669신사복(정장 1벌)6000<NA><NA><NA><NA>YY2017-04-30
34세탁업오토크리닝강충구경상북도 영주시 신재로12번길 56 (가흥동)<NA>신사복6000<NA><NA><NA><NA>YY2017-04-30
35세탁업우리세탁플라자현점식경상북도 영주시 대동로 160 (가흥동)054-638-9700신사복(정장 1벌)7000<NA><NA><NA><NA>YY2017-04-30
36세탁업우신세탁프라자최외식경상북도 영주시 선비로 226 (영주동)054-635-2024신사복8000<NA><NA><NA><NA>YN2017-04-30
37세탁업코아루세탁프라자손태환경상북도 영주시 원당로225번길 67 (상망동)<NA>신사복6000<NA><NA><NA><NA>NN2017-04-30
38세탁업크리스탈크리닝박상국경상북도 영주시 선비로 193 (영주동)054-635-3213신사복(정장 1벌)7000<NA><NA><NA><NA>NY2017-04-30
39세탁업팡팡빨래방신정섭경상북도 영주시 번영로110번길 29 (하망동)<NA>신사복7000<NA><NA><NA><NA>YY2017-04-30
40숙박업제우스모텔김성용경상북도 영주시 영주로176번길 3 (영주동)054-635-5822숙박료25000<NA><NA><NA><NA>NY2017-04-30