Overview

Dataset statistics

Number of variables15
Number of observations45
Missing cells86
Missing cells (%)12.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory129.9 B

Variable types

Numeric7
Categorical1
Text7

Dataset

Description부산광역시_수영구_착한가격업소현황_20230718
Author부산광역시 수영구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3080440

Alerts

전체메뉴 is highly overall correlated with 착한가격메뉴 and 1 other fieldsHigh correlation
착한가격메뉴 is highly overall correlated with 전체메뉴 and 1 other fieldsHigh correlation
가격1 is highly overall correlated with 가격2 and 3 other fieldsHigh correlation
가격2 is highly overall correlated with 가격1 and 2 other fieldsHigh correlation
가격3 is highly overall correlated with 가격1 and 2 other fieldsHigh correlation
가격4 is highly overall correlated with 가격1 and 2 other fieldsHigh correlation
업종 is highly overall correlated with 전체메뉴 and 2 other fieldsHigh correlation
연락처 has 10 (22.2%) missing valuesMissing
품목2 has 1 (2.2%) missing valuesMissing
가격2 has 1 (2.2%) missing valuesMissing
품목3 has 6 (13.3%) missing valuesMissing
가격3 has 6 (13.3%) missing valuesMissing
품목4 has 31 (68.9%) missing valuesMissing
가격4 has 31 (68.9%) missing valuesMissing
번호 has unique valuesUnique
업소명 has unique valuesUnique
주소(도로명 새주소) has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:40:58.101626
Analysis finished2023-12-10 17:41:11.715580
Duration13.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:11.983012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.2
Q112
median23
Q334
95-th percentile42.8
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.133926
Coefficient of variation (CV)0.57104024
Kurtosis-1.2
Mean23
Median Absolute Deviation (MAD)11
Skewness0
Sum1035
Variance172.5
MonotonicityStrictly increasing
2023-12-11T02:41:13.004554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 1
 
2.2%
35 1
 
2.2%
26 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%
36 1
2.2%

업종
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size492.0 B
한식
19 
이미용업
14 
중식
4 
목욕업
2 
일식
2 
Other values (3)
4 

Length

Max length5
Median length2
Mean length2.8
Min length2

Unique

Unique2 ?
Unique (%)4.4%

Sample

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

Common Values

ValueCountFrequency (%)
한식 19
42.2%
이미용업 14
31.1%
중식 4
 
8.9%
목욕업 2
 
4.4%
일식 2
 
4.4%
수리업 2
 
4.4%
경양식 1
 
2.2%
기타음식점 1
 
2.2%

Length

2023-12-11T02:41:13.328965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:41:13.659593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 19
42.2%
이미용업 14
31.1%
중식 4
 
8.9%
목욕업 2
 
4.4%
일식 2
 
4.4%
수리업 2
 
4.4%
경양식 1
 
2.2%
기타음식점 1
 
2.2%

업소명
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-11T02:41:14.227397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4222222
Min length3

Characters and Unicode

Total characters244
Distinct characters129
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row오곡흑미쌀짜장
2nd row루비헤어샵
3rd row서면손칼국수
4th row자연분식소문난칼국수
5th row돈까스&오색비빔밥
ValueCountFrequency (%)
손칼국수 2
 
3.8%
오곡흑미쌀짜장 1
 
1.9%
㈜송현 1
 
1.9%
하진식당 1
 
1.9%
진수성 1
 
1.9%
아케스시 1
 
1.9%
남자머리 1
 
1.9%
김밥마을 1
 
1.9%
김도형남성컷 1
 
1.9%
순영헤어샵 1
 
1.9%
Other values (41) 41
78.8%
2023-12-11T02:41:15.319609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 8
 
3.3%
국 8
 
3.3%
7
 
2.9%
식 7
 
2.9%
어 6
 
2.5%
칼 6
 
2.5%
당 6
 
2.5%
미 6
 
2.5%
리 5
 
2.0%
남 5
 
2.0%
Other values (119) 180
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 233
95.5%
Space Separator 7
 
2.9%
Uppercase Letter 2
 
0.8%
Other Symbol 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 8
 
3.4%
국 8
 
3.4%
식 7
 
3.0%
어 6
 
2.6%
칼 6
 
2.6%
당 6
 
2.6%
미 6
 
2.6%
리 5
 
2.1%
남 5
 
2.1%
실 4
 
1.7%
Other values (115) 172
73.8%
Space Separator
ValueCountFrequency (%)
7
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 234
95.9%
Common 8
 
3.3%
Latin 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 8
 
3.4%
국 8
 
3.4%
식 7
 
3.0%
어 6
 
2.6%
칼 6
 
2.6%
당 6
 
2.6%
미 6
 
2.6%
리 5
 
2.1%
남 5
 
2.1%
실 4
 
1.7%
Other values (116) 173
73.9%
Common
ValueCountFrequency (%)
7
87.5%
& 1
 
12.5%
Latin
ValueCountFrequency (%)
J 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 233
95.5%
ASCII 10
 
4.1%
None 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 8
 
3.4%
국 8
 
3.4%
식 7
 
3.0%
어 6
 
2.6%
칼 6
 
2.6%
당 6
 
2.6%
미 6
 
2.6%
리 5
 
2.1%
남 5
 
2.1%
실 4
 
1.7%
Other values (115) 172
73.8%
ASCII
ValueCountFrequency (%)
7
70.0%
J 2
 
20.0%
& 1
 
10.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

주소(도로명 새주소)
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-11T02:41:15.938416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length26.377778
Min length20

Characters and Unicode

Total characters1187
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row부산광역시 수영구 연수로415번길 30-13(수영동)
2nd row부산광역시 수영구 좌수영로101번길 50-5(망미2동)
3rd row부산광역시 수영구 과정로 55(망미1동)
4th row부산광역시 수영구 연수로415번길 26(수영동)
5th row부산광역시 수영구 연수로416번길 9(광안3동)
ValueCountFrequency (%)
부산광역시 45
24.6%
수영구 45
24.6%
연수로415번길 2
 
1.1%
남천바다로10번길 2
 
1.1%
망미번영로38번길 2
 
1.1%
수영로606번길 2
 
1.1%
수영로 2
 
1.1%
1층(광안동 2
 
1.1%
호암로29번길 2
 
1.1%
42(남천동 1
 
0.5%
Other values (78) 78
42.6%
2023-12-11T02:41:16.931687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
 
11.6%
수 72
 
6.1%
영 72
 
6.1%
광 68
 
5.7%
구 46
 
3.9%
부 45
 
3.8%
( 45
 
3.8%
역 45
 
3.8%
시 45
 
3.8%
산 45
 
3.8%
Other values (40) 566
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 729
61.4%
Decimal Number 213
 
17.9%
Space Separator 138
 
11.6%
Open Punctuation 45
 
3.8%
Close Punctuation 45
 
3.8%
Dash Punctuation 11
 
0.9%
Other Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 72
 
9.9%
영 72
 
9.9%
광 68
 
9.3%
구 46
 
6.3%
부 45
 
6.2%
역 45
 
6.2%
시 45
 
6.2%
산 45
 
6.2%
로 45
 
6.2%
동 45
 
6.2%
Other values (25) 201
27.6%
Decimal Number
ValueCountFrequency (%)
1 43
20.2%
2 34
16.0%
3 26
12.2%
6 22
10.3%
5 20
9.4%
4 19
8.9%
0 17
 
8.0%
8 16
 
7.5%
9 11
 
5.2%
7 5
 
2.3%
Space Separator
ValueCountFrequency (%)
138
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 729
61.4%
Common 458
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 72
 
9.9%
영 72
 
9.9%
광 68
 
9.3%
구 46
 
6.3%
부 45
 
6.2%
역 45
 
6.2%
시 45
 
6.2%
산 45
 
6.2%
로 45
 
6.2%
동 45
 
6.2%
Other values (25) 201
27.6%
Common
ValueCountFrequency (%)
138
30.1%
( 45
 
9.8%
) 45
 
9.8%
1 43
 
9.4%
2 34
 
7.4%
3 26
 
5.7%
6 22
 
4.8%
5 20
 
4.4%
4 19
 
4.1%
0 17
 
3.7%
Other values (5) 49
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 729
61.4%
ASCII 458
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
30.1%
( 45
 
9.8%
) 45
 
9.8%
1 43
 
9.4%
2 34
 
7.4%
3 26
 
5.7%
6 22
 
4.8%
5 20
 
4.4%
4 19
 
4.1%
0 17
 
3.7%
Other values (5) 49
 
10.7%
Hangul
ValueCountFrequency (%)
수 72
 
9.9%
영 72
 
9.9%
광 68
 
9.3%
구 46
 
6.3%
부 45
 
6.2%
역 45
 
6.2%
시 45
 
6.2%
산 45
 
6.2%
로 45
 
6.2%
동 45
 
6.2%
Other values (25) 201
27.6%

연락처
Text

MISSING 

Distinct35
Distinct (%)100.0%
Missing10
Missing (%)22.2%
Memory size492.0 B
2023-12-11T02:41:17.366932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.057143
Min length12

Characters and Unicode

Total characters422
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

Unique35 ?
Unique (%)100.0%

Sample

1st row051-752-4947
2nd row051-751-5079
3rd row051-753-5696
4th row051-752-1279
5th row051-753-3716
ValueCountFrequency (%)
051-752-4947 1
 
2.9%
051-752-3948 1
 
2.9%
051-751-5079 1
 
2.9%
051-623-6271 1
 
2.9%
051-628-2824 1
 
2.9%
051-753-5534 1
 
2.9%
051-751-0077 1
 
2.9%
051-628-5577 1
 
2.9%
051-758-8060 1
 
2.9%
051-623-2446 1
 
2.9%
Other values (25) 25
71.4%
2023-12-11T02:41:18.072594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 85
20.1%
- 70
16.6%
1 55
13.0%
0 50
11.8%
7 50
11.8%
6 28
 
6.6%
2 20
 
4.7%
4 18
 
4.3%
8 16
 
3.8%
3 16
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 352
83.4%
Dash Punctuation 70
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 85
24.1%
1 55
15.6%
0 50
14.2%
7 50
14.2%
6 28
 
8.0%
2 20
 
5.7%
4 18
 
5.1%
8 16
 
4.5%
3 16
 
4.5%
9 14
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 85
20.1%
- 70
16.6%
1 55
13.0%
0 50
11.8%
7 50
11.8%
6 28
 
6.6%
2 20
 
4.7%
4 18
 
4.3%
8 16
 
3.8%
3 16
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 85
20.1%
- 70
16.6%
1 55
13.0%
0 50
11.8%
7 50
11.8%
6 28
 
6.6%
2 20
 
4.7%
4 18
 
4.3%
8 16
 
3.8%
3 16
 
3.8%

전체메뉴
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5555556
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:18.343313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median8
Q310
95-th percentile19
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.0791712
Coefficient of variation (CV)0.59366936
Kurtosis6.8070076
Mean8.5555556
Median Absolute Deviation (MAD)2
Skewness2.1668246
Sum385
Variance25.79798
MonotonicityNot monotonic
2023-12-11T02:41:18.555496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8 7
15.6%
7 6
13.3%
5 6
13.3%
10 6
13.3%
9 3
6.7%
6 3
6.7%
20 2
 
4.4%
13 2
 
4.4%
2 2
 
4.4%
4 2
 
4.4%
Other values (5) 6
13.3%
ValueCountFrequency (%)
2 2
 
4.4%
3 2
 
4.4%
4 2
 
4.4%
5 6
13.3%
6 3
6.7%
7 6
13.3%
8 7
15.6%
9 3
6.7%
10 6
13.3%
11 1
 
2.2%
ValueCountFrequency (%)
30 1
 
2.2%
20 2
 
4.4%
15 1
 
2.2%
13 2
 
4.4%
12 1
 
2.2%
11 1
 
2.2%
10 6
13.3%
9 3
6.7%
8 7
15.6%
7 6
13.3%

착한가격메뉴
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9111111
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:18.775459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile8
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.9296464
Coefficient of variation (CV)0.74905733
Kurtosis20.998882
Mean3.9111111
Median Absolute Deviation (MAD)1
Skewness4.0983406
Sum176
Variance8.5828283
MonotonicityNot monotonic
2023-12-11T02:41:19.006880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 19
42.2%
4 10
22.2%
2 6
 
13.3%
5 4
 
8.9%
1 2
 
4.4%
8 2
 
4.4%
9 1
 
2.2%
20 1
 
2.2%
ValueCountFrequency (%)
1 2
 
4.4%
2 6
 
13.3%
3 19
42.2%
4 10
22.2%
5 4
 
8.9%
8 2
 
4.4%
9 1
 
2.2%
20 1
 
2.2%
ValueCountFrequency (%)
20 1
 
2.2%
9 1
 
2.2%
8 2
 
4.4%
5 4
 
8.9%
4 10
22.2%
3 19
42.2%
2 6
 
13.3%
1 2
 
4.4%

품목1
Text

Distinct25
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-11T02:41:19.302507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.9555556
Min length2

Characters and Unicode

Total characters178
Distinct characters74
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)42.2%

Sample

1st row자장면
2nd row커트
3rd row칼국수
4th row칼국수
5th row비빔밥
ValueCountFrequency (%)
커트 13
27.7%
자장면 4
 
8.5%
칼국수 4
 
8.5%
돼지국밥 2
 
4.3%
김밥 2
 
4.3%
엔진오일(국산중형 2
 
4.3%
된장찌개 2
 
4.3%
소고기국밥(점심특선 1
 
2.1%
돈까스 1
 
2.1%
커트(남 1
 
2.1%
Other values (15) 15
31.9%
2023-12-11T02:41:19.839835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
커 14
 
7.9%
트 14
 
7.9%
국 10
 
5.6%
밥 9
 
5.1%
장 7
 
3.9%
( 7
 
3.9%
) 7
 
3.9%
면 5
 
2.8%
5
 
2.8%
자 4
 
2.2%
Other values (64) 96
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 153
86.0%
Open Punctuation 7
 
3.9%
Close Punctuation 7
 
3.9%
Space Separator 5
 
2.8%
Decimal Number 3
 
1.7%
Math Symbol 2
 
1.1%
Lowercase Letter 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
커 14
 
9.2%
트 14
 
9.2%
국 10
 
6.5%
밥 9
 
5.9%
장 7
 
4.6%
면 5
 
3.3%
자 4
 
2.6%
칼 4
 
2.6%
수 4
 
2.6%
모 3
 
2.0%
Other values (56) 79
51.6%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
2 1
33.3%
8 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 153
86.0%
Common 24
 
13.5%
Latin 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
커 14
 
9.2%
트 14
 
9.2%
국 10
 
6.5%
밥 9
 
5.9%
장 7
 
4.6%
면 5
 
3.3%
자 4
 
2.6%
칼 4
 
2.6%
수 4
 
2.6%
모 3
 
2.0%
Other values (56) 79
51.6%
Common
ValueCountFrequency (%)
( 7
29.2%
) 7
29.2%
5
20.8%
+ 2
 
8.3%
1 1
 
4.2%
2 1
 
4.2%
8 1
 
4.2%
Latin
ValueCountFrequency (%)
p 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 153
86.0%
ASCII 25
 
14.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
커 14
 
9.2%
트 14
 
9.2%
국 10
 
6.5%
밥 9
 
5.9%
장 7
 
4.6%
면 5
 
3.3%
자 4
 
2.6%
칼 4
 
2.6%
수 4
 
2.6%
모 3
 
2.0%
Other values (56) 79
51.6%
ASCII
ValueCountFrequency (%)
( 7
28.0%
) 7
28.0%
5
20.0%
+ 2
 
8.0%
1 1
 
4.0%
2 1
 
4.0%
8 1
 
4.0%
p 1
 
4.0%

가격1
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9582.2222
Minimum2200
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:20.088778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile4100
Q15000
median7000
Q39000
95-th percentile26600
Maximum55000
Range52800
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation10726.969
Coefficient of variation (CV)1.1194657
Kurtosis13.89401
Mean9582.2222
Median Absolute Deviation (MAD)2000
Skewness3.7291798
Sum431200
Variance1.1506786 × 108
MonotonicityNot monotonic
2023-12-11T02:41:20.346771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5000 7
15.6%
6000 5
11.1%
10000 5
11.1%
8000 5
11.1%
7000 5
11.1%
9000 3
6.7%
5500 2
 
4.4%
4500 2
 
4.4%
6500 2
 
4.4%
55000 2
 
4.4%
Other values (7) 7
15.6%
ValueCountFrequency (%)
2200 1
 
2.2%
2500 1
 
2.2%
4000 1
 
2.2%
4500 2
 
4.4%
5000 7
15.6%
5500 2
 
4.4%
6000 5
11.1%
6500 2
 
4.4%
7000 5
11.1%
7500 1
 
2.2%
ValueCountFrequency (%)
55000 2
 
4.4%
30000 1
 
2.2%
13000 1
 
2.2%
12000 1
 
2.2%
10000 5
11.1%
9000 3
6.7%
8000 5
11.1%
7500 1
 
2.2%
7000 5
11.1%
6500 2
 
4.4%

품목2
Text

MISSING 

Distinct36
Distinct (%)81.8%
Missing1
Missing (%)2.2%
Memory size492.0 B
2023-12-11T02:41:20.703060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length4.1136364
Min length1

Characters and Unicode

Total characters181
Distinct characters85
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)70.5%

Sample

1st row짬뽕
2nd row펌(일반)
3rd row비빔국수
4th row국수
5th row돌솥비빔밥
ValueCountFrequency (%)
염색 3
 
6.7%
펌 3
 
6.7%
일반펌 3
 
6.7%
엔진오일(국산대형 2
 
4.4%
짬뽕 2
 
4.4%
커트 1
 
2.2%
소고기국밥 1
 
2.2%
김치찌개+돈까스 1
 
2.2%
설렁탕 1
 
2.2%
냉모밀 1
 
2.2%
Other values (27) 27
60.0%
2023-12-11T02:41:21.313028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 9
 
5.0%
펌 8
 
4.4%
( 8
 
4.4%
밥 8
 
4.4%
) 8
 
4.4%
일 6
 
3.3%
반 5
 
2.8%
수 4
 
2.2%
빔 4
 
2.2%
비 4
 
2.2%
Other values (75) 117
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
87.3%
Open Punctuation 8
 
4.4%
Close Punctuation 8
 
4.4%
Decimal Number 3
 
1.7%
Lowercase Letter 2
 
1.1%
Space Separator 1
 
0.6%
Math Symbol 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 9
 
5.7%
펌 8
 
5.1%
밥 8
 
5.1%
일 6
 
3.8%
반 5
 
3.2%
수 4
 
2.5%
빔 4
 
2.5%
비 4
 
2.5%
대 4
 
2.5%
기 4
 
2.5%
Other values (67) 102
64.6%
Decimal Number
ValueCountFrequency (%)
8 1
33.3%
1 1
33.3%
0 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
87.3%
Common 21
 
11.6%
Latin 2
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 9
 
5.7%
펌 8
 
5.1%
밥 8
 
5.1%
일 6
 
3.8%
반 5
 
3.2%
수 4
 
2.5%
빔 4
 
2.5%
비 4
 
2.5%
대 4
 
2.5%
기 4
 
2.5%
Other values (67) 102
64.6%
Common
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
1
 
4.8%
8 1
 
4.8%
1 1
 
4.8%
+ 1
 
4.8%
0 1
 
4.8%
Latin
ValueCountFrequency (%)
p 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
87.3%
ASCII 23
 
12.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 9
 
5.7%
펌 8
 
5.1%
밥 8
 
5.1%
일 6
 
3.8%
반 5
 
3.2%
수 4
 
2.5%
빔 4
 
2.5%
비 4
 
2.5%
대 4
 
2.5%
기 4
 
2.5%
Other values (67) 102
64.6%
ASCII
ValueCountFrequency (%)
( 8
34.8%
) 8
34.8%
p 2
 
8.7%
1
 
4.3%
8 1
 
4.3%
1 1
 
4.3%
+ 1
 
4.3%
0 1
 
4.3%

가격2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)47.7%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean13602.273
Minimum2000
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:21.558164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile4075
Q16000
median8000
Q315000
95-th percentile44250
Maximum65000
Range63000
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation14619.03
Coefficient of variation (CV)1.0747491
Kurtosis5.9255608
Mean13602.273
Median Absolute Deviation (MAD)2750
Skewness2.4524055
Sum598500
Variance2.1371604 × 108
MonotonicityNot monotonic
2023-12-11T02:41:21.790421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6000 7
15.6%
8000 6
13.3%
5000 4
 
8.9%
7000 4
 
8.9%
20000 4
 
8.9%
15000 2
 
4.4%
9000 2
 
4.4%
65000 2
 
4.4%
10000 1
 
2.2%
14000 1
 
2.2%
Other values (11) 11
24.4%
ValueCountFrequency (%)
2000 1
 
2.2%
3000 1
 
2.2%
4000 1
 
2.2%
4500 1
 
2.2%
5000 4
8.9%
5500 1
 
2.2%
6000 7
15.6%
6500 1
 
2.2%
7000 4
8.9%
8000 6
13.3%
ValueCountFrequency (%)
65000 2
4.4%
45000 1
 
2.2%
40000 1
 
2.2%
30000 1
 
2.2%
25000 1
 
2.2%
20000 4
8.9%
15000 2
4.4%
14000 1
 
2.2%
13000 1
 
2.2%
10000 1
 
2.2%

품목3
Text

MISSING 

Distinct32
Distinct (%)82.1%
Missing6
Missing (%)13.3%
Memory size492.0 B
2023-12-11T02:41:22.146760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length4.5128205
Min length1

Characters and Unicode

Total characters176
Distinct characters92
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)71.8%

Sample

1st row쟁반짜장
2nd row염색
3rd row김밥
4th row비빔(칼)국수
5th row불고기비빔밥
ValueCountFrequency (%)
염색 6
 
15.0%
떡국 2
 
5.0%
에어컨필터(국산중형 2
 
5.0%
우동 2
 
5.0%
펌 1
 
2.5%
도가니탕 1
 
2.5%
새우튀김우동 1
 
2.5%
남성 1
 
2.5%
청국장 1
 
2.5%
쟁반짜장 1
 
2.5%
Other values (22) 22
55.0%
2023-12-11T02:41:22.775862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 12
 
6.8%
( 9
 
5.1%
) 9
 
5.1%
염 8
 
4.5%
색 8
 
4.5%
밥 7
 
4.0%
장 5
 
2.8%
우 4
 
2.3%
수 4
 
2.3%
김 3
 
1.7%
Other values (82) 107
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
85.2%
Open Punctuation 9
 
5.1%
Close Punctuation 9
 
5.1%
Math Symbol 3
 
1.7%
Lowercase Letter 2
 
1.1%
Decimal Number 2
 
1.1%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 12
 
8.0%
염 8
 
5.3%
색 8
 
5.3%
밥 7
 
4.7%
장 5
 
3.3%
우 4
 
2.7%
수 4
 
2.7%
김 3
 
2.0%
반 3
 
2.0%
칼 3
 
2.0%
Other values (75) 93
62.0%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
2 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
85.2%
Common 24
 
13.6%
Latin 2
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 12
 
8.0%
염 8
 
5.3%
색 8
 
5.3%
밥 7
 
4.7%
장 5
 
3.3%
우 4
 
2.7%
수 4
 
2.7%
김 3
 
2.0%
반 3
 
2.0%
칼 3
 
2.0%
Other values (75) 93
62.0%
Common
ValueCountFrequency (%)
( 9
37.5%
) 9
37.5%
+ 3
 
12.5%
3 1
 
4.2%
2 1
 
4.2%
1
 
4.2%
Latin
ValueCountFrequency (%)
p 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
85.2%
ASCII 26
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 12
 
8.0%
염 8
 
5.3%
색 8
 
5.3%
밥 7
 
4.7%
장 5
 
3.3%
우 4
 
2.7%
수 4
 
2.7%
김 3
 
2.0%
반 3
 
2.0%
칼 3
 
2.0%
Other values (75) 93
62.0%
ASCII
ValueCountFrequency (%)
( 9
34.6%
) 9
34.6%
+ 3
 
11.5%
p 2
 
7.7%
3 1
 
3.8%
2 1
 
3.8%
1
 
3.8%

가격3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)46.2%
Missing6
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean13397.436
Minimum2500
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:23.013621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile4450
Q16000
median8000
Q320000
95-th percentile31000
Maximum50000
Range47500
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation11037.722
Coefficient of variation (CV)0.82386825
Kurtosis2.2249317
Mean13397.436
Median Absolute Deviation (MAD)2000
Skewness1.5751372
Sum522500
Variance1.2183131 × 108
MonotonicityNot monotonic
2023-12-11T02:41:23.223560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6000 6
13.3%
20000 5
11.1%
30000 3
 
6.7%
8000 3
 
6.7%
7000 3
 
6.7%
22000 2
 
4.4%
10000 2
 
4.4%
9000 2
 
4.4%
5000 2
 
4.4%
7500 2
 
4.4%
Other values (8) 9
20.0%
(Missing) 6
13.3%
ValueCountFrequency (%)
2500 1
 
2.2%
4000 1
 
2.2%
4500 1
 
2.2%
5000 2
 
4.4%
5500 1
 
2.2%
6000 6
13.3%
6500 2
 
4.4%
7000 3
6.7%
7500 2
 
4.4%
8000 3
6.7%
ValueCountFrequency (%)
50000 1
 
2.2%
40000 1
 
2.2%
30000 3
6.7%
25000 1
 
2.2%
22000 2
 
4.4%
20000 5
11.1%
10000 2
 
4.4%
9000 2
 
4.4%
8000 3
6.7%
7500 2
 
4.4%

품목4
Text

MISSING 

Distinct12
Distinct (%)85.7%
Missing31
Missing (%)68.9%
Memory size492.0 B
2023-12-11T02:41:23.528676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.6428571
Min length2

Characters and Unicode

Total characters65
Distinct characters41
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

Unique10 ?
Unique (%)71.4%

Sample

1st row어묵칼국수
2nd row볶음밥
3rd row우동
4th row에어컨필터(수입중형)
5th row에어컨필터(수입중형)
ValueCountFrequency (%)
에어컨필터(수입중형 2
14.3%
매직 2
14.3%
어묵칼국수 1
7.1%
볶음밥 1
7.1%
우동 1
7.1%
컬러염색 1
7.1%
잡채밥 1
7.1%
육개장 1
7.1%
판모밀+수제돈가스 1
7.1%
염색 1
7.1%
Other values (2) 2
14.3%
2023-12-11T02:41:24.093252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 4
 
6.2%
) 3
 
4.6%
어 3
 
4.6%
밥 3
 
4.6%
염 3
 
4.6%
색 3
 
4.6%
( 3
 
4.6%
직 2
 
3.1%
매 2
 
3.1%
에 2
 
3.1%
Other values (31) 37
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58
89.2%
Close Punctuation 3
 
4.6%
Open Punctuation 3
 
4.6%
Math Symbol 1
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 4
 
6.9%
어 3
 
5.2%
밥 3
 
5.2%
염 3
 
5.2%
색 3
 
5.2%
직 2
 
3.4%
매 2
 
3.4%
에 2
 
3.4%
형 2
 
3.4%
중 2
 
3.4%
Other values (28) 32
55.2%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58
89.2%
Common 7
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 4
 
6.9%
어 3
 
5.2%
밥 3
 
5.2%
염 3
 
5.2%
색 3
 
5.2%
직 2
 
3.4%
매 2
 
3.4%
에 2
 
3.4%
형 2
 
3.4%
중 2
 
3.4%
Other values (28) 32
55.2%
Common
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
+ 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58
89.2%
ASCII 7
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 4
 
6.9%
어 3
 
5.2%
밥 3
 
5.2%
염 3
 
5.2%
색 3
 
5.2%
직 2
 
3.4%
매 2
 
3.4%
에 2
 
3.4%
형 2
 
3.4%
중 2
 
3.4%
Other values (28) 32
55.2%
ASCII
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
+ 1
 
14.3%

가격4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing31
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean22821.429
Minimum3500
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T02:41:24.359350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3500
5-th percentile3825
Q16125
median17500
Q333750
95-th percentile55000
Maximum55000
Range51500
Interquartile range (IQR)27625

Descriptive statistics

Standard deviation19515.878
Coefficient of variation (CV)0.85515586
Kurtosis-1.070344
Mean22821.429
Median Absolute Deviation (MAD)12250
Skewness0.67587399
Sum319500
Variance3.8086951 × 108
MonotonicityNot monotonic
2023-12-11T02:41:24.635081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
55000 2
 
4.4%
25000 2
 
4.4%
5500 1
 
2.2%
6500 1
 
2.2%
3500 1
 
2.2%
35000 1
 
2.2%
50000 1
 
2.2%
6000 1
 
2.2%
4000 1
 
2.2%
30000 1
 
2.2%
Other values (2) 2
 
4.4%
(Missing) 31
68.9%
ValueCountFrequency (%)
3500 1
2.2%
4000 1
2.2%
5500 1
2.2%
6000 1
2.2%
6500 1
2.2%
9000 1
2.2%
10000 1
2.2%
25000 2
4.4%
30000 1
2.2%
35000 1
2.2%
ValueCountFrequency (%)
55000 2
4.4%
50000 1
2.2%
35000 1
2.2%
30000 1
2.2%
25000 2
4.4%
10000 1
2.2%
9000 1
2.2%
6500 1
2.2%
6000 1
2.2%
5500 1
2.2%

Interactions

2023-12-11T02:41:08.481472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:59.450958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:00.948440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:02.317639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:03.851139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:05.316886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:06.856220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:08.870811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:59.650010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:01.156477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:02.495005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:04.023499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:05.575945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:07.031651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:09.087302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:59.844764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:01.363608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:02.710396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:04.204348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:05.792319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:07.212318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:09.278987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:00.058602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:01.584477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:03.011902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:04.417105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:06.027356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:07.445677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:09.471418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:00.250803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:01.761608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:03.240277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:04.633912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:06.240236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:07.708857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:09.751638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:00.454935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:01.954748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:03.467366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:04.882559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:06.431786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:08.061071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:10.029348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:00.715714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:02.136738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:03.655065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:05.089846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:06.658847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:41:08.272037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:41:24.887362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호업종업소명주소(도로명 새주소)연락처전체메뉴착한가격메뉴품목1가격1품목2가격2품목3가격3품목4가격4
번호1.0000.3911.0001.0001.0000.0000.2710.6150.6710.8900.3250.8430.5250.9920.385
업종0.3911.0001.0001.0001.0000.7670.8060.9860.7371.0000.6110.9780.7511.0000.588
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소(도로명 새주소)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전체메뉴0.0000.7671.0001.0001.0001.0000.8830.4040.0590.0000.0000.9300.0000.9750.580
착한가격메뉴0.2710.8061.0001.0001.0000.8831.0000.8090.5360.9290.0000.9870.0001.0000.736
품목10.6150.9861.0001.0001.0000.4040.8091.0000.9951.0000.0000.9830.4711.0000.466
가격10.6710.7371.0001.0001.0000.0590.5360.9951.0000.9870.7500.7810.7471.0000.780
품목20.8901.0001.0001.0001.0000.0000.9291.0000.9871.0000.0000.9810.0000.9740.486
가격20.3250.6111.0001.0001.0000.0000.0000.0000.7500.0001.0000.0000.9461.0000.771
품목30.8430.9781.0001.0001.0000.9300.9870.9830.7810.9810.0001.0000.0001.0000.787
가격30.5250.7511.0001.0001.0000.0000.0000.4710.7470.0000.9460.0001.0001.0000.689
품목40.9921.0001.0001.0001.0000.9751.0001.0001.0000.9741.0001.0001.0001.0000.849
가격40.3850.5881.0001.0001.0000.5800.7360.4660.7800.4860.7710.7870.6890.8491.000
2023-12-11T02:41:25.237228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호전체메뉴착한가격메뉴가격1가격2가격3가격4업종
번호1.000-0.0900.3190.4750.4120.3330.1040.190
전체메뉴-0.0901.0000.609-0.160-0.209-0.320-0.3300.539
착한가격메뉴0.3190.6091.0000.014-0.029-0.180-0.1930.600
가격10.475-0.1600.0141.0000.7430.6660.8740.560
가격20.412-0.209-0.0290.7431.0000.8630.9160.381
가격30.333-0.320-0.1800.6660.8631.0000.8690.349
가격40.104-0.330-0.1930.8740.9160.8691.0000.412
업종0.1900.5390.6000.5600.3810.3490.4121.000

Missing values

2023-12-11T02:41:10.559832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:41:11.101968image/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.
2023-12-11T02:41:11.502847image/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

번호업종업소명주소(도로명 새주소)연락처전체메뉴착한가격메뉴품목1가격1품목2가격2품목3가격3품목4가격4
01중식오곡흑미쌀짜장부산광역시 수영구 연수로415번길 30-13(수영동)051-752-4947152자장면5000짬뽕6000쟁반짜장9000<NA><NA>
12이미용업루비헤어샵부산광역시 수영구 좌수영로101번길 50-5(망미2동)051-751-507973커트5000펌(일반)15000염색10000<NA><NA>
23한식서면손칼국수부산광역시 수영구 과정로 55(망미1동)051-753-569653칼국수4500비빔국수4500김밥2500<NA><NA>
34한식자연분식소문난칼국수부산광역시 수영구 연수로415번길 26(수영동)051-752-127991칼국수5000국수5000비빔(칼)국수6000<NA><NA>
45경양식돈까스&오색비빔밥부산광역시 수영구 연수로416번길 9(광안3동)051-753-371683비빔밥6500돌솥비빔밥7000불고기비빔밥7500<NA><NA>
56한식연자방아칼국수부산광역시 수영구 망미번영로85번길 52(망미2동)051-751-988194칼국수4500김밥2000잔치국수4500어묵칼국수5500
67중식남천홍반장부산광역시 수영구 수영로408번길 9, 1층(남천동)051-611-1552103자장면5000짜장밥6000볶음밥7500<NA><NA>
78한식겐조식당부산광역시 수영구 좌수영로101번길52 (망미2동)051-757-1514125동태찌개6500만두백반6000떡국5000<NA><NA>
89한식장터국밥부산광역시 수영구 수영성로 21-4(수영동)051-758-666964돼지국밥7000순대국밥7000내장국밥7000<NA><NA>
910한식옛맛 손칼국수부산광역시 수영구 과정로67번길 8(망미동)051-754-754283칼국수6000비빔칼국수6000만두칼국수7000<NA><NA>
번호업종업소명주소(도로명 새주소)연락처전체메뉴착한가격메뉴품목1가격1품목2가격2품목3가격3품목4가격4
3536이미용업JJ남성커트전문점부산광역시 수영구 수영로394번길 10, 3층(남천1동)<NA>32커트10000염색13000<NA><NA><NA><NA>
3637이미용업박수정미용실부산광역시 수영구 광서로10번길 65(광안3동)051-752-394874커트10000일반펌20000염색20000매직50000
3738한식우리막썰어횟집부산광역시 수영구 연수로264번길 4(망미1동)051-758-999683모듬회(소)30000모듬회(중)40000모듬회(대)50000<NA><NA>
3839중식중국관부산광역시 수영구 호암로29번길 83(광안2동)051-757-0411208자장면5000간짜장6000짬뽕6000잡채밥6000
3940한식다다생모밀부산광역시 수영구 수영로652번길 68-1(광안1동)0507-1349-016384생모밀4000온모밀4000우동4000육개장4000
4041이미용업핑크헤어부산광역시 수영구 연수로369번길 20(수영동)051-757-266394커트9000펌20000염색20000매직30000
4142한식두보완당부산광역시 수영구 수영로679번길 26(광안3동)051-752-665584완당7000유부초밥(8p)5000완당+유부(3p)+김밥(2p)9000판모밀+수제돈가스10000
4243이미용업양지미용실부산광역시 수영구 망미번영로38번길 31(광안3동)051-756-193854커트6000펌20000열펌30000염색25000
4344이미용업가위스토리부산광역시 수영구 망미번영로38번길 40(광안3동)051-758-806074커트(남)7000커트(여)9000염색(남)20000염색(여)25000
4445일식킹콩초밥부산광역시 수영구 구락로8-4(수영동)051-751-5036114모듬초밥(12p)13000연어초밥(10p)14000우동6500회덮밥9000