Overview

Dataset statistics

Number of variables15
Number of observations42
Missing cells86
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory130.1 B

Variable types

Numeric7
Categorical1
Text7

Dataset

Description물가안정 모범업소 및 가격 저렴한 업소 현황에 대한 데이터로 부산광역시 수영구 내 착한가격업소의 업소명, 주소, 연락처, 품목가격 관련 정보입니다.
Author부산광역시 수영구
URLhttps://www.data.go.kr/data/3080440/fileData.do

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 3 other fieldsHigh correlation
업종 is highly overall correlated with 전체메뉴 and 3 other fieldsHigh correlation
연락처 has 10 (23.8%) missing valuesMissing
품목2 has 1 (2.4%) missing valuesMissing
가격2 has 1 (2.4%) missing valuesMissing
품목3 has 7 (16.7%) missing valuesMissing
가격3 has 7 (16.7%) missing valuesMissing
품목4 has 30 (71.4%) missing valuesMissing
가격4 has 30 (71.4%) missing valuesMissing
번호 has unique valuesUnique
업소명 has unique valuesUnique
주소(도로명 새주소) has unique valuesUnique

Reproduction

Analysis started2023-12-23 07:53:31.046069
Analysis finished2023-12-23 07:54:00.322741
Duration29.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:00.643665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.05
Q111.25
median21.5
Q331.75
95-th percentile39.95
Maximum42
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.267844
Coefficient of variation (CV)0.5705974
Kurtosis-1.2
Mean21.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum903
Variance150.5
MonotonicityStrictly increasing
2023-12-23T07:54:01.358387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 1
 
2.4%
33 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
32 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
42 1
2.4%
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%

업종
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
한식
18 
이미용업
12 
중식
4 
목욕업
2 
일식
2 
Other values (2)
4 

Length

Max length5
Median length2
Mean length2.8095238
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 18
42.9%
이미용업 12
28.6%
중식 4
 
9.5%
목욕업 2
 
4.8%
일식 2
 
4.8%
기타음식점 2
 
4.8%
수리업 2
 
4.8%

Length

2023-12-23T07:54:02.058868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:54:02.709266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 18
42.9%
이미용업 12
28.6%
중식 4
 
9.5%
목욕업 2
 
4.8%
일식 2
 
4.8%
기타음식점 2
 
4.8%
수리업 2
 
4.8%

업소명
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-23T07:54:03.413673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.3809524
Min length3

Characters and Unicode

Total characters226
Distinct characters122
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row오곡흑미쌀짜장
2nd row루비헤어샵
3rd row서면손칼국수
4th row자연분식소문난칼국수
5th row연자방아칼국수
ValueCountFrequency (%)
손칼국수 2
 
4.1%
오곡흑미쌀짜장 1
 
2.0%
하오르모터스 1
 
2.0%
하진식당 1
 
2.0%
킹콩초밥 1
 
2.0%
진수성 1
 
2.0%
아케스시 1
 
2.0%
남자머리 1
 
2.0%
김밥마을 1
 
2.0%
김도형남성컷 1
 
2.0%
Other values (38) 38
77.6%
2023-12-23T07:54:05.233622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 8
 
3.5%
7
 
3.1%
수 7
 
3.1%
식 7
 
3.1%
당 6
 
2.7%
칼 6
 
2.7%
리 6
 
2.7%
어 5
 
2.2%
미 5
 
2.2%
남 5
 
2.2%
Other values (112) 164
72.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 213
94.2%
Space Separator 7
 
3.1%
Uppercase Letter 2
 
0.9%
Decimal Number 2
 
0.9%
Dash Punctuation 1
 
0.4%
Other Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 8
 
3.8%
수 7
 
3.3%
식 7
 
3.3%
당 6
 
2.8%
칼 6
 
2.8%
리 6
 
2.8%
어 5
 
2.3%
미 5
 
2.3%
남 5
 
2.3%
장 4
 
1.9%
Other values (106) 154
72.3%
Decimal Number
ValueCountFrequency (%)
6 1
50.0%
7 1
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
94.7%
Common 10
 
4.4%
Latin 2
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 8
 
3.7%
수 7
 
3.3%
식 7
 
3.3%
당 6
 
2.8%
칼 6
 
2.8%
리 6
 
2.8%
어 5
 
2.3%
미 5
 
2.3%
남 5
 
2.3%
장 4
 
1.9%
Other values (107) 155
72.4%
Common
ValueCountFrequency (%)
7
70.0%
6 1
 
10.0%
- 1
 
10.0%
7 1
 
10.0%
Latin
ValueCountFrequency (%)
J 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 213
94.2%
ASCII 12
 
5.3%
None 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 8
 
3.8%
수 7
 
3.3%
식 7
 
3.3%
당 6
 
2.8%
칼 6
 
2.8%
리 6
 
2.8%
어 5
 
2.3%
미 5
 
2.3%
남 5
 
2.3%
장 4
 
1.9%
Other values (106) 154
72.3%
ASCII
ValueCountFrequency (%)
7
58.3%
J 2
 
16.7%
6 1
 
8.3%
- 1
 
8.3%
7 1
 
8.3%
None
ValueCountFrequency (%)
㈜ 1
100.0%

주소(도로명 새주소)
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-23T07:54:06.489782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length26.380952
Min length20

Characters and Unicode

Total characters1108
Distinct characters48
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

Unique42 ?
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 망미번영로85번길 52(망미2동)
ValueCountFrequency (%)
수영구 42
24.4%
부산광역시 41
23.8%
과정로 2
 
1.2%
1층(광안동 2
 
1.2%
호암로29번길 2
 
1.2%
수영로606번길 2
 
1.2%
수영로 2
 
1.2%
남천바다로10번길 2
 
1.2%
연수로415번길 2
 
1.2%
망미번영로38번길 2
 
1.2%
Other values (73) 73
42.4%
2023-12-23T07:54:08.731046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
 
11.7%
영 69
 
6.2%
수 67
 
6.0%
광 60
 
5.4%
구 43
 
3.9%
부 42
 
3.8%
( 42
 
3.8%
산 42
 
3.8%
로 42
 
3.8%
) 42
 
3.8%
Other values (38) 529
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 680
61.4%
Decimal Number 198
 
17.9%
Space Separator 130
 
11.7%
Open Punctuation 42
 
3.8%
Close Punctuation 42
 
3.8%
Dash Punctuation 10
 
0.9%
Other Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 69
 
10.1%
수 67
 
9.9%
광 60
 
8.8%
구 43
 
6.3%
부 42
 
6.2%
산 42
 
6.2%
로 42
 
6.2%
동 42
 
6.2%
역 41
 
6.0%
시 41
 
6.0%
Other values (23) 191
28.1%
Decimal Number
ValueCountFrequency (%)
1 40
20.2%
2 31
15.7%
3 23
11.6%
6 21
10.6%
5 19
9.6%
4 18
9.1%
0 16
 
8.1%
8 16
 
8.1%
9 8
 
4.0%
7 6
 
3.0%
Space Separator
ValueCountFrequency (%)
130
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 680
61.4%
Common 428
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 69
 
10.1%
수 67
 
9.9%
광 60
 
8.8%
구 43
 
6.3%
부 42
 
6.2%
산 42
 
6.2%
로 42
 
6.2%
동 42
 
6.2%
역 41
 
6.0%
시 41
 
6.0%
Other values (23) 191
28.1%
Common
ValueCountFrequency (%)
130
30.4%
( 42
 
9.8%
) 42
 
9.8%
1 40
 
9.3%
2 31
 
7.2%
3 23
 
5.4%
6 21
 
4.9%
5 19
 
4.4%
4 18
 
4.2%
0 16
 
3.7%
Other values (5) 46
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 680
61.4%
ASCII 428
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
30.4%
( 42
 
9.8%
) 42
 
9.8%
1 40
 
9.3%
2 31
 
7.2%
3 23
 
5.4%
6 21
 
4.9%
5 19
 
4.4%
4 18
 
4.2%
0 16
 
3.7%
Other values (5) 46
 
10.7%
Hangul
ValueCountFrequency (%)
영 69
 
10.1%
수 67
 
9.9%
광 60
 
8.8%
구 43
 
6.3%
부 42
 
6.2%
산 42
 
6.2%
로 42
 
6.2%
동 42
 
6.2%
역 41
 
6.0%
시 41
 
6.0%
Other values (23) 191
28.1%

연락처
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing10
Missing (%)23.8%
Memory size468.0 B
2023-12-23T07:54:09.712832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.0625
Min length12

Characters and Unicode

Total characters386
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

Unique32 ?
Unique (%)100.0%

Sample

1st row051-752-4947
2nd row051-751-5079
3rd row051-753-5696
4th row051-752-1279
5th row051-751-9881
ValueCountFrequency (%)
051-751-5079 1
 
3.1%
051-753-5696 1
 
3.1%
051-623-2446 1
 
3.1%
051-758-8060 1
 
3.1%
051-756-1938 1
 
3.1%
051-752-6655 1
 
3.1%
051-757-2663 1
 
3.1%
0507-1349-0163 1
 
3.1%
051-757-0411 1
 
3.1%
051-758-9996 1
 
3.1%
Other values (22) 22
68.8%
2023-12-23T07:54:11.955138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 78
20.2%
- 64
16.6%
1 49
12.7%
0 47
12.2%
7 46
11.9%
6 26
 
6.7%
2 19
 
4.9%
4 17
 
4.4%
8 15
 
3.9%
9 13
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 322
83.4%
Dash Punctuation 64
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 78
24.2%
1 49
15.2%
0 47
14.6%
7 46
14.3%
6 26
 
8.1%
2 19
 
5.9%
4 17
 
5.3%
8 15
 
4.7%
9 13
 
4.0%
3 12
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 386
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 78
20.2%
- 64
16.6%
1 49
12.7%
0 47
12.2%
7 46
11.9%
6 26
 
6.7%
2 19
 
4.9%
4 17
 
4.4%
8 15
 
3.9%
9 13
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 78
20.2%
- 64
16.6%
1 49
12.7%
0 47
12.2%
7 46
11.9%
6 26
 
6.7%
2 19
 
4.9%
4 17
 
4.4%
8 15
 
3.9%
9 13
 
3.4%

전체메뉴
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3333333
Minimum2
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:12.581209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15.25
median8
Q310
95-th percentile20
Maximum35
Range33
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation6.61361
Coefficient of variation (CV)0.70860107
Kurtosis6.4590923
Mean9.3333333
Median Absolute Deviation (MAD)2
Skewness2.3261315
Sum392
Variance43.739837
MonotonicityNot monotonic
2023-12-23T07:54:13.416904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10 6
14.3%
5 5
11.9%
8 5
11.9%
7 5
11.9%
9 3
7.1%
6 3
7.1%
20 2
 
4.8%
3 2
 
4.8%
13 2
 
4.8%
2 2
 
4.8%
Other values (6) 7
16.7%
ValueCountFrequency (%)
2 2
 
4.8%
3 2
 
4.8%
4 2
 
4.8%
5 5
11.9%
6 3
7.1%
7 5
11.9%
8 5
11.9%
9 3
7.1%
10 6
14.3%
11 1
 
2.4%
ValueCountFrequency (%)
35 1
 
2.4%
30 1
 
2.4%
20 2
 
4.8%
15 1
 
2.4%
13 2
 
4.8%
12 1
 
2.4%
11 1
 
2.4%
10 6
14.3%
9 3
7.1%
8 5
11.9%

착한가격메뉴
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9285714
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:14.147241image/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 deviation3.031484
Coefficient of variation (CV)0.77165048
Kurtosis19.560582
Mean3.9285714
Median Absolute Deviation (MAD)1
Skewness3.9654737
Sum165
Variance9.1898955
MonotonicityNot monotonic
2023-12-23T07:54:14.713202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 18
42.9%
4 8
19.0%
2 6
 
14.3%
5 4
 
9.5%
1 2
 
4.8%
8 2
 
4.8%
9 1
 
2.4%
20 1
 
2.4%
ValueCountFrequency (%)
1 2
 
4.8%
2 6
 
14.3%
3 18
42.9%
4 8
19.0%
5 4
 
9.5%
8 2
 
4.8%
9 1
 
2.4%
20 1
 
2.4%
ValueCountFrequency (%)
20 1
 
2.4%
9 1
 
2.4%
8 2
 
4.8%
5 4
 
9.5%
4 8
19.0%
3 18
42.9%
2 6
 
14.3%
1 2
 
4.8%

품목1
Text

Distinct24
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-23T07:54:15.425412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.1190476
Min length2

Characters and Unicode

Total characters173
Distinct characters75
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

Unique18 ?
Unique (%)42.9%

Sample

1st row자장면
2nd row커트
3rd row칼국수
4th row칼국수
5th row칼국수
ValueCountFrequency (%)
커트 11
25.0%
자장면 4
 
9.1%
칼국수 4
 
9.1%
돼지국밥 2
 
4.5%
김밥 2
 
4.5%
엔진오일(국산중형 2
 
4.5%
된장찌개 2
 
4.5%
뼈다귀해장국 1
 
2.3%
곰탕 1
 
2.3%
모듬초밥(12p 1
 
2.3%
Other values (14) 14
31.8%
2023-12-23T07:54:16.921578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
커 12
 
6.9%
트 12
 
6.9%
국 10
 
5.8%
밥 8
 
4.6%
장 7
 
4.0%
) 7
 
4.0%
( 7
 
4.0%
면 5
 
2.9%
5
 
2.9%
칼 4
 
2.3%
Other values (65) 96
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
85.5%
Close Punctuation 7
 
4.0%
Open Punctuation 7
 
4.0%
Space Separator 5
 
2.9%
Decimal Number 3
 
1.7%
Math Symbol 2
 
1.2%
Lowercase Letter 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
커 12
 
8.1%
트 12
 
8.1%
국 10
 
6.8%
밥 8
 
5.4%
장 7
 
4.7%
면 5
 
3.4%
칼 4
 
2.7%
수 4
 
2.7%
자 4
 
2.7%
개 3
 
2.0%
Other values (57) 79
53.4%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
8 1
33.3%
1 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open 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 148
85.5%
Common 24
 
13.9%
Latin 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
커 12
 
8.1%
트 12
 
8.1%
국 10
 
6.8%
밥 8
 
5.4%
장 7
 
4.7%
면 5
 
3.4%
칼 4
 
2.7%
수 4
 
2.7%
자 4
 
2.7%
개 3
 
2.0%
Other values (57) 79
53.4%
Common
ValueCountFrequency (%)
) 7
29.2%
( 7
29.2%
5
20.8%
+ 2
 
8.3%
2 1
 
4.2%
8 1
 
4.2%
1 1
 
4.2%
Latin
ValueCountFrequency (%)
p 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
85.5%
ASCII 25
 
14.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
커 12
 
8.1%
트 12
 
8.1%
국 10
 
6.8%
밥 8
 
5.4%
장 7
 
4.7%
면 5
 
3.4%
칼 4
 
2.7%
수 4
 
2.7%
자 4
 
2.7%
개 3
 
2.0%
Other values (57) 79
53.4%
ASCII
ValueCountFrequency (%)
) 7
28.0%
( 7
28.0%
5
20.0%
+ 2
 
8.0%
2 1
 
4.0%
8 1
 
4.0%
1 1
 
4.0%
p 1
 
4.0%

가격1
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9716.6667
Minimum1900
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:17.616879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile3050
Q15125
median7000
Q39750
95-th percentile29150
Maximum55000
Range53100
Interquartile range (IQR)4625

Descriptive statistics

Standard deviation11120.82
Coefficient of variation (CV)1.1445098
Kurtosis12.635441
Mean9716.6667
Median Absolute Deviation (MAD)2000
Skewness3.5618979
Sum408100
Variance1.2367264 × 108
MonotonicityNot monotonic
2023-12-23T07:54:18.543417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
10000 6
14.3%
6000 6
14.3%
5000 5
11.9%
7000 5
11.9%
8000 3
 
7.1%
55000 2
 
4.8%
9000 2
 
4.8%
5500 2
 
4.8%
4500 2
 
4.8%
7500 1
 
2.4%
Other values (8) 8
19.0%
ValueCountFrequency (%)
1900 1
 
2.4%
2200 1
 
2.4%
3000 1
 
2.4%
4000 1
 
2.4%
4500 2
 
4.8%
5000 5
11.9%
5500 2
 
4.8%
6000 6
14.3%
6500 1
 
2.4%
7000 5
11.9%
ValueCountFrequency (%)
55000 2
 
4.8%
30000 1
 
2.4%
13000 1
 
2.4%
12000 1
 
2.4%
10000 6
14.3%
9000 2
 
4.8%
8000 3
7.1%
7500 1
 
2.4%
7000 5
11.9%
6500 1
 
2.4%

품목2
Text

MISSING 

Distinct34
Distinct (%)82.9%
Missing1
Missing (%)2.4%
Memory size468.0 B
2023-12-23T07:54:19.124818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.195122
Min length1

Characters and Unicode

Total characters172
Distinct characters84
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

Unique30 ?
Unique (%)73.2%

Sample

1st row짬뽕
2nd row펌(일반)
3rd row비빔국수
4th row국수
5th row김밥
ValueCountFrequency (%)
펌 4
 
9.3%
염색 3
 
7.0%
짬뽕 2
 
4.7%
엔진오일(국산대형 2
 
4.7%
김치찌개 2
 
4.7%
경로우대 1
 
2.3%
유부초밥(8p 1
 
2.3%
온모밀 1
 
2.3%
간짜장 1
 
2.3%
모듬회(중 1
 
2.3%
Other values (25) 25
58.1%
2023-12-23T07:54:20.823794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 9
 
5.2%
( 8
 
4.7%
) 8
 
4.7%
밥 7
 
4.1%
펌 6
 
3.5%
수 4
 
2.3%
일 4
 
2.3%
대 4
 
2.3%
4
 
2.3%
장 3
 
1.7%
Other values (74) 115
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 146
84.9%
Open Punctuation 8
 
4.7%
Close Punctuation 8
 
4.7%
Space Separator 4
 
2.3%
Decimal Number 3
 
1.7%
Lowercase Letter 2
 
1.2%
Math Symbol 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 9
 
6.2%
밥 7
 
4.8%
펌 6
 
4.1%
수 4
 
2.7%
일 4
 
2.7%
대 4
 
2.7%
장 3
 
2.1%
반 3
 
2.1%
김 3
 
2.1%
기 3
 
2.1%
Other values (66) 100
68.5%
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%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 146
84.9%
Common 24
 
14.0%
Latin 2
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 9
 
6.2%
밥 7
 
4.8%
펌 6
 
4.1%
수 4
 
2.7%
일 4
 
2.7%
대 4
 
2.7%
장 3
 
2.1%
반 3
 
2.1%
김 3
 
2.1%
기 3
 
2.1%
Other values (66) 100
68.5%
Common
ValueCountFrequency (%)
( 8
33.3%
) 8
33.3%
4
16.7%
+ 1
 
4.2%
8 1
 
4.2%
1 1
 
4.2%
0 1
 
4.2%
Latin
ValueCountFrequency (%)
p 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 146
84.9%
ASCII 26
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 9
 
6.2%
밥 7
 
4.8%
펌 6
 
4.1%
수 4
 
2.7%
일 4
 
2.7%
대 4
 
2.7%
장 3
 
2.1%
반 3
 
2.1%
김 3
 
2.1%
기 3
 
2.1%
Other values (66) 100
68.5%
ASCII
ValueCountFrequency (%)
( 8
30.8%
) 8
30.8%
4
15.4%
p 2
 
7.7%
+ 1
 
3.8%
8 1
 
3.8%
1 1
 
3.8%
0 1
 
3.8%

가격2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)46.3%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean13241.463
Minimum2000
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:21.361208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile4000
Q16000
median7000
Q314000
95-th percentile40000
Maximum65000
Range63000
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation14744.168
Coefficient of variation (CV)1.1134848
Kurtosis6.3265786
Mean13241.463
Median Absolute Deviation (MAD)2000
Skewness2.5380548
Sum542900
Variance2.1739049 × 108
MonotonicityNot monotonic
2023-12-23T07:54:22.063935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6000 5
11.9%
8000 5
11.9%
7000 4
 
9.5%
5000 4
 
9.5%
20000 3
 
7.1%
65000 2
 
4.8%
10000 2
 
4.8%
4000 2
 
4.8%
15000 2
 
4.8%
40000 2
 
4.8%
Other values (9) 10
23.8%
ValueCountFrequency (%)
2000 1
 
2.4%
2900 1
 
2.4%
4000 2
 
4.8%
4500 1
 
2.4%
5000 4
9.5%
5500 1
 
2.4%
6000 5
11.9%
6500 2
 
4.8%
7000 4
9.5%
8000 5
11.9%
ValueCountFrequency (%)
65000 2
 
4.8%
40000 2
 
4.8%
30000 1
 
2.4%
20000 3
7.1%
15000 2
 
4.8%
14000 1
 
2.4%
13000 1
 
2.4%
12000 1
 
2.4%
10000 2
 
4.8%
8000 5
11.9%

품목3
Text

MISSING 

Distinct29
Distinct (%)82.9%
Missing7
Missing (%)16.7%
Memory size468.0 B
2023-12-23T07:54:23.029162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length4.8
Min length1

Characters and Unicode

Total characters168
Distinct characters91
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

Unique25 ?
Unique (%)71.4%

Sample

1st row해물쟁반짜장
2nd row염색
3rd row김밥
4th row비빔(칼)국수
5th row잔치국수
ValueCountFrequency (%)
염색 5
 
13.9%
떡국 2
 
5.6%
에어컨필터(국산중형 2
 
5.6%
우동 2
 
5.6%
새우튀김우동 1
 
2.8%
해물쟁반짜장 1
 
2.8%
도가니탕 1
 
2.8%
염색(남 1
 
2.8%
열펌 1
 
2.8%
완당+유부(3p)+김밥(2p 1
 
2.8%
Other values (19) 19
52.8%
2023-12-23T07:54:24.687856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 10
 
6.0%
) 9
 
5.4%
( 9
 
5.4%
염 6
 
3.6%
색 6
 
3.6%
밥 5
 
3.0%
우 4
 
2.4%
+ 4
 
2.4%
장 4
 
2.4%
수 4
 
2.4%
Other values (81) 107
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
83.9%
Close Punctuation 9
 
5.4%
Open Punctuation 9
 
5.4%
Math Symbol 4
 
2.4%
Lowercase Letter 2
 
1.2%
Decimal Number 2
 
1.2%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 10
 
7.1%
염 6
 
4.3%
색 6
 
4.3%
밥 5
 
3.5%
우 4
 
2.8%
장 4
 
2.8%
수 4
 
2.8%
니 3
 
2.1%
칼 3
 
2.1%
김 3
 
2.1%
Other values (74) 93
66.0%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
3 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
83.9%
Common 25
 
14.9%
Latin 2
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 10
 
7.1%
염 6
 
4.3%
색 6
 
4.3%
밥 5
 
3.5%
우 4
 
2.8%
장 4
 
2.8%
수 4
 
2.8%
니 3
 
2.1%
칼 3
 
2.1%
김 3
 
2.1%
Other values (74) 93
66.0%
Common
ValueCountFrequency (%)
) 9
36.0%
( 9
36.0%
+ 4
16.0%
1
 
4.0%
2 1
 
4.0%
3 1
 
4.0%
Latin
ValueCountFrequency (%)
p 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
83.9%
ASCII 27
 
16.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 10
 
7.1%
염 6
 
4.3%
색 6
 
4.3%
밥 5
 
3.5%
우 4
 
2.8%
장 4
 
2.8%
수 4
 
2.8%
니 3
 
2.1%
칼 3
 
2.1%
김 3
 
2.1%
Other values (74) 93
66.0%
ASCII
ValueCountFrequency (%)
) 9
33.3%
( 9
33.3%
+ 4
14.8%
p 2
 
7.4%
1
 
3.7%
2 1
 
3.7%
3 1
 
3.7%

가격3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)54.3%
Missing7
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean13671.429
Minimum2500
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:25.222152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile4350
Q16000
median7000
Q320000
95-th percentile40000
Maximum50000
Range47500
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation12040.338
Coefficient of variation (CV)0.88069351
Kurtosis1.6635828
Mean13671.429
Median Absolute Deviation (MAD)2000
Skewness1.5381249
Sum478500
Variance1.4496975 × 108
MonotonicityNot monotonic
2023-12-23T07:54:25.921158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6000 6
14.3%
20000 3
 
7.1%
7000 3
 
7.1%
30000 2
 
4.8%
22000 2
 
4.8%
5500 2
 
4.8%
6500 2
 
4.8%
40000 2
 
4.8%
5000 2
 
4.8%
8000 2
 
4.8%
Other values (9) 9
21.4%
(Missing) 7
16.7%
ValueCountFrequency (%)
2500 1
 
2.4%
4000 1
 
2.4%
4500 1
 
2.4%
5000 2
 
4.8%
5500 2
 
4.8%
6000 6
14.3%
6500 2
 
4.8%
7000 3
7.1%
8000 2
 
4.8%
8500 1
 
2.4%
ValueCountFrequency (%)
50000 1
 
2.4%
40000 2
4.8%
30000 2
4.8%
25000 1
 
2.4%
22000 2
4.8%
20000 3
7.1%
14000 1
 
2.4%
10000 1
 
2.4%
9000 1
 
2.4%
8500 1
 
2.4%

품목4
Text

MISSING 

Distinct11
Distinct (%)91.7%
Missing30
Missing (%)71.4%
Memory size468.0 B
2023-12-23T07:54:26.707870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.9166667
Min length2

Characters and Unicode

Total characters59
Distinct characters39
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 (%)83.3%

Sample

1st row어묵칼국수
2nd row볶음밥
3rd row우동
4th row에어컨필터(수입중형)
5th row에어컨필터(수입중형)
ValueCountFrequency (%)
에어컨필터(수입중형 2
16.7%
어묵칼국수 1
8.3%
볶음밥 1
8.3%
우동 1
8.3%
잡채밥 1
8.3%
육개장 1
8.3%
매직 1
8.3%
판모밀+수제돈가스 1
8.3%
염색 1
8.3%
염색(여 1
8.3%
2023-12-23T07:54:28.907815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 4
 
6.8%
어 3
 
5.1%
밥 3
 
5.1%
( 3
 
5.1%
) 3
 
5.1%
에 2
 
3.4%
염 2
 
3.4%
색 2
 
3.4%
형 2
 
3.4%
중 2
 
3.4%
Other values (29) 33
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
88.1%
Open Punctuation 3
 
5.1%
Close Punctuation 3
 
5.1%
Math Symbol 1
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 4
 
7.7%
어 3
 
5.8%
밥 3
 
5.8%
에 2
 
3.8%
염 2
 
3.8%
색 2
 
3.8%
형 2
 
3.8%
중 2
 
3.8%
입 2
 
3.8%
터 2
 
3.8%
Other values (26) 28
53.8%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52
88.1%
Common 7
 
11.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 4
 
7.7%
어 3
 
5.8%
밥 3
 
5.8%
에 2
 
3.8%
염 2
 
3.8%
색 2
 
3.8%
형 2
 
3.8%
중 2
 
3.8%
입 2
 
3.8%
터 2
 
3.8%
Other values (26) 28
53.8%
Common
ValueCountFrequency (%)
( 3
42.9%
) 3
42.9%
+ 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52
88.1%
ASCII 7
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 4
 
7.7%
어 3
 
5.8%
밥 3
 
5.8%
에 2
 
3.8%
염 2
 
3.8%
색 2
 
3.8%
형 2
 
3.8%
중 2
 
3.8%
입 2
 
3.8%
터 2
 
3.8%
Other values (26) 28
53.8%
ASCII
ValueCountFrequency (%)
( 3
42.9%
) 3
42.9%
+ 1
 
14.3%

가격4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)83.3%
Missing30
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean19958.333
Minimum3500
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-23T07:54:29.836189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3500
5-th percentile3775
Q15875
median9500
Q330000
95-th percentile55000
Maximum55000
Range51500
Interquartile range (IQR)24125

Descriptive statistics

Standard deviation19099.57
Coefficient of variation (CV)0.95697217
Kurtosis-0.10975694
Mean19958.333
Median Absolute Deviation (MAD)5750
Skewness1.0835575
Sum239500
Variance3.6479356 × 108
MonotonicityNot monotonic
2023-12-23T07:54:31.162895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
55000 2
 
4.8%
30000 2
 
4.8%
5500 1
 
2.4%
6500 1
 
2.4%
3500 1
 
2.4%
6000 1
 
2.4%
4000 1
 
2.4%
10000 1
 
2.4%
25000 1
 
2.4%
9000 1
 
2.4%
(Missing) 30
71.4%
ValueCountFrequency (%)
3500 1
2.4%
4000 1
2.4%
5500 1
2.4%
6000 1
2.4%
6500 1
2.4%
9000 1
2.4%
10000 1
2.4%
25000 1
2.4%
30000 2
4.8%
55000 2
4.8%
ValueCountFrequency (%)
55000 2
4.8%
30000 2
4.8%
25000 1
2.4%
10000 1
2.4%
9000 1
2.4%
6500 1
2.4%
6000 1
2.4%
5500 1
2.4%
4000 1
2.4%
3500 1
2.4%

Interactions

2023-12-23T07:53:53.307209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:33.571029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:36.622956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:39.430299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:43.146706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:46.681609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:49.681897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:54.371923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:33.974581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:36.999259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:39.979028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:43.819614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:47.141666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:50.136763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:55.340201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:34.462545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:37.519668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:40.453805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:44.275770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:47.890228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:50.595159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:56.039457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:34.827607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:38.152592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:40.852264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:44.779971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:48.324507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:51.305045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:56.666860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:35.312197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:38.501768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:41.552481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:45.178011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:48.571322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:51.871874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:57.167123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:35.796734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:38.862740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:42.241777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:45.650685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:48.885404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:52.376472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:57.643735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:36.237978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:39.124919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:42.657202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:46.242893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:49.277645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:53:53.007360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:54:32.027364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호업종업소명주소(도로명 새주소)연락처전체메뉴착한가격메뉴품목1가격1품목2가격2품목3가격3품목4가격4
번호1.0000.4181.0001.0001.0000.0410.3750.7130.7330.8630.3850.8370.2051.0000.784
업종0.4181.0001.0001.0001.0000.8910.7090.9890.7411.0000.6570.9680.5771.0000.934
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
전체메뉴0.0410.8911.0001.0001.0001.0000.8390.7970.0000.9210.0000.9540.0001.0000.572
착한가격메뉴0.3750.7091.0001.0001.0000.8391.0000.8430.2220.9320.0000.9860.0001.0000.715
품목10.7130.9891.0001.0001.0000.7970.8431.0000.9731.0000.2990.9790.8241.0000.825
가격10.7330.7411.0001.0001.0000.0000.2220.9731.0000.9750.7650.7400.7811.0000.853
품목20.8631.0001.0001.0001.0000.9210.9321.0000.9751.0000.0000.9860.0001.0000.750
가격20.3850.6571.0001.0001.0000.0000.0000.2990.7650.0001.0000.0000.8311.0000.767
품목30.8370.9681.0001.0001.0000.9540.9860.9790.7400.9860.0001.0000.8931.0000.909
가격30.2050.5771.0001.0001.0000.0000.0000.8240.7810.0000.8310.8931.0001.0000.958
품목41.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
가격40.7840.9341.0001.0001.0000.5720.7150.8250.8530.7500.7670.9090.9581.0001.000
2023-12-23T07:54:33.098059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호전체메뉴착한가격메뉴가격1가격2가격3가격4업종
번호1.000-0.0240.2560.3890.2820.2470.3230.203
전체메뉴-0.0241.0000.590-0.262-0.258-0.344-0.2810.518
착한가격메뉴0.2560.5901.000-0.034-0.082-0.197-0.1960.512
가격10.389-0.262-0.0341.0000.7380.7000.8910.585
가격20.282-0.258-0.0820.7381.0000.8580.8690.461
가격30.247-0.344-0.1970.7000.8581.0000.8770.354
가격40.323-0.281-0.1960.8910.8690.8771.0000.633
업종0.2030.5180.5120.5850.4610.3540.6331.000

Missing values

2023-12-23T07:53:58.473978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:53:59.509144image/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.
2023-12-23T07:53:59.999508image/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)051-752-4947152자장면5500짬뽕7000해물쟁반짜장8500<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>
45한식연자방아칼국수부산광역시 수영구 망미번영로85번길 52(망미2동)051-751-988194칼국수4500김밥2000잔치국수4500어묵칼국수5500
56중식남천홍반장부산광역시 수영구 수영로408번길 9, 1층(남천동)051-611-1552103자장면5000짜장밥6000볶음밥8000<NA><NA>
67한식겐조식당부산광역시 수영구 좌수영로101번길52 (망미2동)051-757-1514125동태찌개6500만두백반6000떡국5000<NA><NA>
78한식장터국밥부산광역시 수영구 수영성로 21-4(수영동)051-758-666964돼지국밥7000순대국밥7000내장국밥7000<NA><NA>
89한식옛맛 손칼국수부산광역시 수영구 과정로67번길 8(망미동)051-754-754283칼국수6000비빔칼국수6000만두칼국수7000<NA><NA>
910한식해돋이숯불갈비부산광역시 수영구 망미번영로55번길 23(망미2동)051-751-205063쌈밥정식7000생아구탕8000아구찜(소)30000<NA><NA>
번호업종업소명주소(도로명 새주소)연락처전체메뉴착한가격메뉴품목1가격1품목2가격2품목3가격3품목4가격4
3233이미용업JJ남성커트전문점부산광역시 수영구 수영로394번길 10, 3층(남천1동)<NA>32커트10000염색13000<NA><NA><NA><NA>
3334한식우리막썰어횟집부산광역시 수영구 연수로264번길 4(망미1동)051-758-999683모듬회(소)30000모듬회(중)40000모듬회(대)50000<NA><NA>
3435중식중국관부산광역시 수영구 호암로29번길 83(광안2동)051-757-0411208자장면5000간짜장6000짬뽕6000잡채밥6000
3536한식다다생모밀부산광역시 수영구 수영로652번길 68-1(광안1동)0507-1349-016384생모밀4000온모밀4000우동4000육개장4000
3637이미용업핑크헤어부산광역시 수영구 연수로369번길 20(수영동)051-757-266394커트9000펌20000염색20000매직30000
3738한식두보완당부산광역시 수영구 수영로679번길 26(광안3동)051-752-665584완당7000유부초밥(8p)5000완당+유부(3p)+김밥(2p)9000판모밀+수제돈가스10000
3839이미용업양지미용실부산광역시 수영구 망미번영로38번길 31(광안3동)051-756-193854커트6000펌20000열펌30000염색25000
3940이미용업가위스토리부산광역시 수영구 망미번영로38번길 40(광안3동)051-758-806074커트(남)10000커트(여)12000염색(남)25000염색(여)30000
4041일식킹콩초밥부산광역시 수영구 구락로8-4(수영동)051-751-5036114모듬초밥(12p)13000연어초밥(10p)14000우동6500회덮밥9000
4142기타음식점구름다리6-7부산 수영구 과정로 6-7(망미2동)<NA>353아메리카노1900카페라떼2900커피+햄치즈파니니5500<NA><NA>