Overview

Dataset statistics

Number of variables16
Number of observations223
Missing cells1047
Missing cells (%)29.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.3 KiB
Average record size in memory134.6 B

Variable types

Numeric6
Categorical2
Text8

Dataset

Description광주광역시 착한가격업소로 선정된 업소 현황입니다. 업소명, 전화번호, 소재지도로명주소, 영업시간, 대표품목, 가격 등의 내용을 제공합니다.매해 저렴한 가격으로 양질의 서비스를 제공하는 착한가격업소를 각 자치구에서 선정함※선정 기준 :「착한가격업소 점검표」에 의한 점검 결과 70점 이상 및 가격기준 평점이 29점 이상- 가격(45점),위생‧청결(30점),품질･서비스(20점),공공성(5점),가점부여(10점)
Author광주광역시
URLhttps://www.data.go.kr/data/3041840/fileData.do

Alerts

연번 is highly overall correlated with 시군구High correlation
가격1 is highly overall correlated with 가격2 and 3 other fieldsHigh correlation
가격2 is highly overall correlated with 가격1 and 3 other fieldsHigh correlation
가격3 is highly overall correlated with 가격1 and 3 other fieldsHigh correlation
가격4 is highly overall correlated with 가격1 and 3 other fieldsHigh correlation
가격5 is highly overall correlated with 가격1 and 3 other fieldsHigh correlation
시군구 is highly overall correlated with 연번High correlation
연락처 has 15 (6.7%) missing valuesMissing
메뉴2 has 25 (11.2%) missing valuesMissing
가격2 has 25 (11.2%) missing valuesMissing
메뉴3 has 105 (47.1%) missing valuesMissing
가격3 has 105 (47.1%) missing valuesMissing
메뉴4 has 177 (79.4%) missing valuesMissing
가격4 has 177 (79.4%) missing valuesMissing
메뉴5 has 209 (93.7%) missing valuesMissing
가격5 has 209 (93.7%) missing valuesMissing
연번 has unique valuesUnique
업소명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 13:41:02.546222
Analysis finished2024-03-14 13:41:14.137785
Duration11.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct223
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T22:41:14.567818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.1
Q156.5
median112
Q3167.5
95-th percentile211.9
Maximum223
Range222
Interquartile range (IQR)111

Descriptive statistics

Standard deviation64.518731
Coefficient of variation (CV)0.5760601
Kurtosis-1.2
Mean112
Median Absolute Deviation (MAD)56
Skewness0
Sum24976
Variance4162.6667
MonotonicityStrictly increasing
2024-03-14T22:41:14.991649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
143 1
 
0.4%
144 1
 
0.4%
145 1
 
0.4%
146 1
 
0.4%
147 1
 
0.4%
148 1
 
0.4%
149 1
 
0.4%
150 1
 
0.4%
Other values (213) 213
95.5%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
223 1
0.4%
222 1
0.4%
221 1
0.4%
220 1
0.4%
219 1
0.4%
218 1
0.4%
217 1
0.4%
216 1
0.4%
215 1
0.4%
214 1
0.4%

시군구
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
북구
55 
광산구
50 
남구
41 
서구
40 
동구
37 

Length

Max length3
Median length2
Mean length2.2242152
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북구 55
24.7%
광산구 50
22.4%
남구 41
18.4%
서구 40
17.9%
동구 37
16.6%

Length

2024-03-14T22:41:15.410783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:41:15.763093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북구 55
24.7%
광산구 50
22.4%
남구 41
18.4%
서구 40
17.9%
동구 37
16.6%

업종
Categorical

Distinct12
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
한식
119 
미용업
48 
중식
18 
세탁업
 
10
목욕업
 
9
Other values (7)
19 

Length

Max length7
Median length2
Mean length2.529148
Min length2

Unique

Unique4 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 119
53.4%
미용업 48
21.5%
중식 18
 
8.1%
세탁업 10
 
4.5%
목욕업 9
 
4.0%
기타요식업 9
 
4.0%
기타비요식업 4
 
1.8%
이용업 2
 
0.9%
숙박업 1
 
0.4%
양식 1
 
0.4%
Other values (2) 2
 
0.9%

Length

2024-03-14T22:41:16.171601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 119
53.1%
미용업 48
21.4%
중식 18
 
8.0%
세탁업 10
 
4.5%
목욕업 9
 
4.0%
기타요식업 9
 
4.0%
기타비요식업 4
 
1.8%
이용업 2
 
0.9%
숙박업 1
 
0.4%
양식 1
 
0.4%
Other values (3) 3
 
1.3%

업소명
Text

UNIQUE 

Distinct223
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-14T22:41:17.242042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.1076233
Min length1

Characters and Unicode

Total characters1139
Distinct characters325
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

Unique223 ?
Unique (%)100.0%

Sample

1st row반디식당
2nd row명지원
3rd row산수골
4th row한우물
5th row진 숯불갈비
ValueCountFrequency (%)
반디식당 1
 
0.4%
우리뷔페(북동점 1
 
0.4%
우리뷔페(중흥점 1
 
0.4%
일곡녹차사우나 1
 
0.4%
일곡치킨 1
 
0.4%
전라도홍어 1
 
0.4%
정헤어 1
 
0.4%
주공세탁 1
 
0.4%
주공이용원 1
 
0.4%
청송반점 1
 
0.4%
Other values (228) 228
95.8%
2024-03-14T22:41:18.730680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 30
 
2.6%
식 27
 
2.4%
헤 24
 
2.1%
리 23
 
2.0%
당 19
 
1.7%
스 18
 
1.6%
이 17
 
1.5%
대 17
 
1.5%
우 16
 
1.4%
미 16
 
1.4%
Other values (315) 932
81.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1100
96.6%
Space Separator 15
 
1.3%
Decimal Number 8
 
0.7%
Close Punctuation 7
 
0.6%
Open Punctuation 7
 
0.6%
Lowercase Letter 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 30
 
2.7%
식 27
 
2.5%
헤 24
 
2.2%
리 23
 
2.1%
당 19
 
1.7%
스 18
 
1.6%
이 17
 
1.5%
대 17
 
1.5%
우 16
 
1.5%
미 16
 
1.5%
Other values (304) 893
81.2%
Decimal Number
ValueCountFrequency (%)
0 2
25.0%
1 2
25.0%
9 1
12.5%
2 1
12.5%
4 1
12.5%
6 1
12.5%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1100
96.6%
Common 38
 
3.3%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 30
 
2.7%
식 27
 
2.5%
헤 24
 
2.2%
리 23
 
2.1%
당 19
 
1.7%
스 18
 
1.6%
이 17
 
1.5%
대 17
 
1.5%
우 16
 
1.5%
미 16
 
1.5%
Other values (304) 893
81.2%
Common
ValueCountFrequency (%)
15
39.5%
) 7
18.4%
( 7
18.4%
0 2
 
5.3%
1 2
 
5.3%
9 1
 
2.6%
2 1
 
2.6%
4 1
 
2.6%
6 1
 
2.6%
' 1
 
2.6%
Latin
ValueCountFrequency (%)
s 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1100
96.6%
ASCII 39
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 30
 
2.7%
식 27
 
2.5%
헤 24
 
2.2%
리 23
 
2.1%
당 19
 
1.7%
스 18
 
1.6%
이 17
 
1.5%
대 17
 
1.5%
우 16
 
1.5%
미 16
 
1.5%
Other values (304) 893
81.2%
ASCII
ValueCountFrequency (%)
15
38.5%
) 7
17.9%
( 7
17.9%
0 2
 
5.1%
1 2
 
5.1%
9 1
 
2.6%
2 1
 
2.6%
4 1
 
2.6%
s 1
 
2.6%
6 1
 
2.6%

연락처
Text

MISSING 

Distinct207
Distinct (%)99.5%
Missing15
Missing (%)6.7%
Memory size1.9 KiB
2024-03-14T22:41:19.492203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.076923
Min length12

Characters and Unicode

Total characters2512
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

Unique206 ?
Unique (%)99.0%

Sample

1st row062-222-0809
2nd row062-227-3565
3rd row062-233-3318
4th row062-675-9944
5th row062-234-2002
ValueCountFrequency (%)
062-673-9928 2
 
1.0%
062-266-3989 1
 
0.5%
062-222-0809 1
 
0.5%
062-268-6551 1
 
0.5%
062-261-4389 1
 
0.5%
062-529-3047 1
 
0.5%
062-262-9885 1
 
0.5%
062-228-2002 1
 
0.5%
062-526-6688 1
 
0.5%
062-576-7801 1
 
0.5%
Other values (197) 197
94.7%
2024-03-14T22:41:20.499292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 421
16.8%
- 416
16.6%
6 380
15.1%
0 318
12.7%
5 189
7.5%
3 181
7.2%
1 133
 
5.3%
9 126
 
5.0%
4 120
 
4.8%
7 119
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2096
83.4%
Dash Punctuation 416
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 421
20.1%
6 380
18.1%
0 318
15.2%
5 189
9.0%
3 181
8.6%
1 133
 
6.3%
9 126
 
6.0%
4 120
 
5.7%
7 119
 
5.7%
8 109
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 421
16.8%
- 416
16.6%
6 380
15.1%
0 318
12.7%
5 189
7.5%
3 181
7.2%
1 133
 
5.3%
9 126
 
5.0%
4 120
 
4.8%
7 119
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 421
16.8%
- 416
16.6%
6 380
15.1%
0 318
12.7%
5 189
7.5%
3 181
7.2%
1 133
 
5.3%
9 126
 
5.0%
4 120
 
4.8%
7 119
 
4.7%

주소
Text

Distinct219
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-14T22:41:21.746031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length33
Mean length24.515695
Min length15

Characters and Unicode

Total characters5467
Distinct characters156
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)96.4%

Sample

1st row광주광역시 동구 구성로 258(계림동)
2nd row광주광역시 동구 구성로 252(계림동)
3rd row광주광역시 동구 경양로 340(산수동)
4th row광주광역시 동구 필문대로191번길 15-2(산수동)
5th row광주광역시 동구 무등로 529(산수동)
ValueCountFrequency (%)
광주광역시 221
 
21.6%
광산구 50
 
4.9%
북구 48
 
4.7%
서구 40
 
3.9%
남구 40
 
3.9%
동구 37
 
3.6%
용봉동 11
 
1.1%
1층 9
 
0.9%
설죽로 7
 
0.7%
오치동 6
 
0.6%
Other values (441) 553
54.1%
2024-03-14T22:41:23.247753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
801
 
14.7%
광 507
 
9.3%
동 262
 
4.8%
주 231
 
4.2%
시 225
 
4.1%
역 222
 
4.1%
구 221
 
4.0%
1 219
 
4.0%
로 216
 
4.0%
( 205
 
3.7%
Other values (146) 2358
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3259
59.6%
Decimal Number 876
 
16.0%
Space Separator 801
 
14.7%
Open Punctuation 205
 
3.7%
Close Punctuation 205
 
3.7%
Dash Punctuation 61
 
1.1%
Other Punctuation 59
 
1.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 507
15.6%
동 262
 
8.0%
주 231
 
7.1%
시 225
 
6.9%
역 222
 
6.8%
구 221
 
6.8%
로 216
 
6.6%
길 114
 
3.5%
번 106
 
3.3%
산 105
 
3.2%
Other values (129) 1050
32.2%
Decimal Number
ValueCountFrequency (%)
1 219
25.0%
2 142
16.2%
3 100
11.4%
0 68
 
7.8%
5 65
 
7.4%
7 62
 
7.1%
4 62
 
7.1%
6 54
 
6.2%
9 52
 
5.9%
8 52
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 58
98.3%
@ 1
 
1.7%
Space Separator
ValueCountFrequency (%)
801
100.0%
Open Punctuation
ValueCountFrequency (%)
( 205
100.0%
Close Punctuation
ValueCountFrequency (%)
) 205
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3259
59.6%
Common 2207
40.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 507
15.6%
동 262
 
8.0%
주 231
 
7.1%
시 225
 
6.9%
역 222
 
6.8%
구 221
 
6.8%
로 216
 
6.6%
길 114
 
3.5%
번 106
 
3.3%
산 105
 
3.2%
Other values (129) 1050
32.2%
Common
ValueCountFrequency (%)
801
36.3%
1 219
 
9.9%
( 205
 
9.3%
) 205
 
9.3%
2 142
 
6.4%
3 100
 
4.5%
0 68
 
3.1%
5 65
 
2.9%
7 62
 
2.8%
4 62
 
2.8%
Other values (6) 278
 
12.6%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3259
59.6%
ASCII 2208
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
801
36.3%
1 219
 
9.9%
( 205
 
9.3%
) 205
 
9.3%
2 142
 
6.4%
3 100
 
4.5%
0 68
 
3.1%
5 65
 
2.9%
7 62
 
2.8%
4 62
 
2.8%
Other values (7) 279
 
12.6%
Hangul
ValueCountFrequency (%)
광 507
15.6%
동 262
 
8.0%
주 231
 
7.1%
시 225
 
6.9%
역 222
 
6.8%
구 221
 
6.8%
로 216
 
6.6%
길 114
 
3.5%
번 106
 
3.3%
산 105
 
3.2%
Other values (129) 1050
32.2%

메뉴1
Text

Distinct106
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-14T22:41:24.170739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.0493274
Min length2

Characters and Unicode

Total characters1126
Distinct characters166
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)31.8%

Sample

1st row백반
2nd row생삼겹(200g)
3rd row병어조림(2인)
4th row생선구이(모듬)
5th row짜장면
ValueCountFrequency (%)
컷트 14
 
6.3%
커트 12
 
5.4%
짜장면 10
 
4.5%
김치찌개 8
 
3.6%
남자커트 8
 
3.6%
목욕료 7
 
3.1%
아메리카노 7
 
3.1%
자장면 7
 
3.1%
삼겹살(200g 6
 
2.7%
김밥 6
 
2.7%
Other values (96) 138
61.9%
2024-03-14T22:41:25.607462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 53
 
4.7%
) 53
 
4.7%
0 48
 
4.3%
트 46
 
4.1%
장 29
 
2.6%
g 28
 
2.5%
비 27
 
2.4%
커 26
 
2.3%
밥 26
 
2.3%
2 25
 
2.2%
Other values (156) 765
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 874
77.6%
Decimal Number 99
 
8.8%
Open Punctuation 53
 
4.7%
Close Punctuation 53
 
4.7%
Lowercase Letter 30
 
2.7%
Other Punctuation 15
 
1.3%
Dash Punctuation 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 46
 
5.3%
장 29
 
3.3%
비 27
 
3.1%
커 26
 
3.0%
밥 26
 
3.0%
삼 21
 
2.4%
면 21
 
2.4%
자 21
 
2.4%
김 21
 
2.4%
겹 21
 
2.4%
Other values (142) 615
70.4%
Decimal Number
ValueCountFrequency (%)
0 48
48.5%
2 25
25.3%
1 12
 
12.1%
5 6
 
6.1%
3 4
 
4.0%
8 3
 
3.0%
4 1
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
g 28
93.3%
p 2
 
6.7%
Open Punctuation
ValueCountFrequency (%)
( 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 53
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 873
77.5%
Common 221
 
19.6%
Latin 31
 
2.8%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 46
 
5.3%
장 29
 
3.3%
비 27
 
3.1%
커 26
 
3.0%
밥 26
 
3.0%
삼 21
 
2.4%
면 21
 
2.4%
자 21
 
2.4%
김 21
 
2.4%
겹 21
 
2.4%
Other values (141) 614
70.3%
Common
ValueCountFrequency (%)
( 53
24.0%
) 53
24.0%
0 48
21.7%
2 25
11.3%
, 15
 
6.8%
1 12
 
5.4%
5 6
 
2.7%
3 4
 
1.8%
8 3
 
1.4%
- 1
 
0.5%
Latin
ValueCountFrequency (%)
g 28
90.3%
p 2
 
6.5%
L 1
 
3.2%
Han
ValueCountFrequency (%)
中 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 873
77.5%
ASCII 252
 
22.4%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 53
21.0%
) 53
21.0%
0 48
19.0%
g 28
11.1%
2 25
9.9%
, 15
 
6.0%
1 12
 
4.8%
5 6
 
2.4%
3 4
 
1.6%
8 3
 
1.2%
Other values (4) 5
 
2.0%
Hangul
ValueCountFrequency (%)
트 46
 
5.3%
장 29
 
3.3%
비 27
 
3.1%
커 26
 
3.0%
밥 26
 
3.0%
삼 21
 
2.4%
면 21
 
2.4%
자 21
 
2.4%
김 21
 
2.4%
겹 21
 
2.4%
Other values (141) 614
70.3%
CJK
ValueCountFrequency (%)
中 1
100.0%

가격1
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10160.987
Minimum1000
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T22:41:25.939871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2500
Q16000
median8000
Q312000
95-th percentile26800
Maximum100000
Range99000
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation9548.0507
Coefficient of variation (CV)0.93967753
Kurtosis40.112754
Mean10160.987
Median Absolute Deviation (MAD)2500
Skewness5.2592176
Sum2265900
Variance91165273
MonotonicityNot monotonic
2024-03-14T22:41:26.358940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
6000 28
12.6%
7000 27
12.1%
8000 26
11.7%
5000 24
10.8%
10000 19
 
8.5%
12000 13
 
5.8%
13000 12
 
5.4%
2500 8
 
3.6%
15000 8
 
3.6%
14000 7
 
3.1%
Other values (27) 51
22.9%
ValueCountFrequency (%)
1000 1
 
0.4%
1200 1
 
0.4%
2000 3
 
1.3%
2500 8
 
3.6%
3000 4
 
1.8%
4000 1
 
0.4%
4500 1
 
0.4%
4900 1
 
0.4%
5000 24
10.8%
5500 6
 
2.7%
ValueCountFrequency (%)
100000 1
 
0.4%
65000 1
 
0.4%
43000 1
 
0.4%
38000 1
 
0.4%
35000 1
 
0.4%
30000 4
1.8%
29000 1
 
0.4%
28000 1
 
0.4%
27000 1
 
0.4%
25000 2
0.9%

메뉴2
Text

MISSING 

Distinct98
Distinct (%)49.5%
Missing25
Missing (%)11.2%
Memory size1.9 KiB
2024-03-14T22:41:27.415005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length9
Mean length4.0505051
Min length1

Characters and Unicode

Total characters802
Distinct characters158
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)36.4%

Sample

1st row비빔밥
2nd row갈치조림(2인가능)
3rd row짬뽕
4th row돌솥(된장,순두부,청국장,김치)
5th row비빔밥
ValueCountFrequency (%)
김치찌개 16
 
8.1%
짬뽕 16
 
8.1%
파마 13
 
6.6%
비빔밥 9
 
4.5%
여자커트 8
 
4.0%
라면 8
 
4.0%
펌 7
 
3.5%
된장찌개 5
 
2.5%
일반펌 4
 
2.0%
갈비탕 4
 
2.0%
Other values (88) 108
54.5%
2024-03-14T22:41:28.796397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 32
 
4.0%
) 29
 
3.6%
( 29
 
3.6%
개 26
 
3.2%
밥 26
 
3.2%
찌 26
 
3.2%
0 23
 
2.9%
치 20
 
2.5%
빔 19
 
2.4%
트 17
 
2.1%
Other values (148) 555
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 682
85.0%
Decimal Number 45
 
5.6%
Close Punctuation 29
 
3.6%
Open Punctuation 29
 
3.6%
Lowercase Letter 12
 
1.5%
Other Punctuation 3
 
0.4%
Uppercase Letter 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 32
 
4.7%
개 26
 
3.8%
밥 26
 
3.8%
찌 26
 
3.8%
치 20
 
2.9%
빔 19
 
2.8%
트 17
 
2.5%
김 17
 
2.5%
짬 16
 
2.3%
뽕 16
 
2.3%
Other values (135) 467
68.5%
Decimal Number
ValueCountFrequency (%)
0 23
51.1%
2 9
 
20.0%
1 6
 
13.3%
3 5
 
11.1%
5 1
 
2.2%
7 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
g 10
83.3%
p 2
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 682
85.0%
Common 107
 
13.3%
Latin 13
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 32
 
4.7%
개 26
 
3.8%
밥 26
 
3.8%
찌 26
 
3.8%
치 20
 
2.9%
빔 19
 
2.8%
트 17
 
2.5%
김 17
 
2.5%
짬 16
 
2.3%
뽕 16
 
2.3%
Other values (135) 467
68.5%
Common
ValueCountFrequency (%)
) 29
27.1%
( 29
27.1%
0 23
21.5%
2 9
 
8.4%
1 6
 
5.6%
3 5
 
4.7%
, 3
 
2.8%
5 1
 
0.9%
+ 1
 
0.9%
7 1
 
0.9%
Latin
ValueCountFrequency (%)
g 10
76.9%
p 2
 
15.4%
S 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 682
85.0%
ASCII 120
 
15.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 32
 
4.7%
개 26
 
3.8%
밥 26
 
3.8%
찌 26
 
3.8%
치 20
 
2.9%
빔 19
 
2.8%
트 17
 
2.5%
김 17
 
2.5%
짬 16
 
2.3%
뽕 16
 
2.3%
Other values (135) 467
68.5%
ASCII
ValueCountFrequency (%)
) 29
24.2%
( 29
24.2%
0 23
19.2%
g 10
 
8.3%
2 9
 
7.5%
1 6
 
5.0%
3 5
 
4.2%
, 3
 
2.5%
p 2
 
1.7%
5 1
 
0.8%
Other values (3) 3
 
2.5%

가격2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)17.7%
Missing25
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean11504.545
Minimum1200
Maximum45000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T22:41:29.194671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile3000
Q16000
median8000
Q313000
95-th percentile35000
Maximum45000
Range43800
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation9568.7377
Coefficient of variation (CV)0.8317354
Kurtosis2.442186
Mean11504.545
Median Absolute Deviation (MAD)2500
Skewness1.7736951
Sum2277900
Variance91560741
MonotonicityNot monotonic
2024-03-14T22:41:29.588927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7000 34
15.2%
8000 22
 
9.9%
6000 16
 
7.2%
10000 15
 
6.7%
3000 12
 
5.4%
30000 9
 
4.0%
5000 8
 
3.6%
3500 8
 
3.6%
4000 6
 
2.7%
25000 6
 
2.7%
Other values (25) 62
27.8%
(Missing) 25
11.2%
ValueCountFrequency (%)
1200 1
 
0.4%
2000 1
 
0.4%
2500 2
 
0.9%
3000 12
5.4%
3500 8
3.6%
4000 6
 
2.7%
5000 8
3.6%
5500 3
 
1.3%
6000 16
7.2%
6500 2
 
0.9%
ValueCountFrequency (%)
45000 2
 
0.9%
40000 5
2.2%
38000 1
 
0.4%
35000 3
 
1.3%
30000 9
4.0%
27000 1
 
0.4%
25000 6
2.7%
23000 1
 
0.4%
22000 1
 
0.4%
20000 5
2.2%

메뉴3
Text

MISSING 

Distinct66
Distinct (%)55.9%
Missing105
Missing (%)47.1%
Memory size1.9 KiB
2024-03-14T22:41:30.446724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length4.2118644
Min length2

Characters and Unicode

Total characters497
Distinct characters142
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)43.2%

Sample

1st row곰탕
2nd row탕수육
3rd row덮밥(낙지,새우,제육)
4th row알탕+알밥
5th row목살(180g)
ValueCountFrequency (%)
염색 18
 
15.3%
파마 6
 
5.1%
비빔밥 6
 
5.1%
순두부찌개 6
 
5.1%
탕수육 5
 
4.2%
냉면 4
 
3.4%
김치찌개 4
 
3.4%
된장찌개 3
 
2.5%
전체염색 3
 
2.5%
곰탕 2
 
1.7%
Other values (56) 61
51.7%
2024-03-14T22:41:31.469434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
밥 22
 
4.4%
염 21
 
4.2%
색 21
 
4.2%
찌 21
 
4.2%
개 20
 
4.0%
) 19
 
3.8%
( 19
 
3.8%
비 16
 
3.2%
빔 14
 
2.8%
탕 13
 
2.6%
Other values (132) 311
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 429
86.3%
Close Punctuation 19
 
3.8%
Open Punctuation 19
 
3.8%
Decimal Number 13
 
2.6%
Other Punctuation 7
 
1.4%
Lowercase Letter 4
 
0.8%
Math Symbol 3
 
0.6%
Uppercase Letter 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
밥 22
 
5.1%
염 21
 
4.9%
색 21
 
4.9%
찌 21
 
4.9%
개 20
 
4.7%
비 16
 
3.7%
빔 14
 
3.3%
탕 13
 
3.0%
수 12
 
2.8%
육 12
 
2.8%
Other values (118) 257
59.9%
Decimal Number
ValueCountFrequency (%)
0 5
38.5%
1 5
38.5%
2 1
 
7.7%
5 1
 
7.7%
8 1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
R 1
33.3%
X 1
33.3%
T 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
g 3
75.0%
p 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 426
85.7%
Common 61
 
12.3%
Latin 7
 
1.4%
Han 3
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
밥 22
 
5.2%
염 21
 
4.9%
색 21
 
4.9%
찌 21
 
4.9%
개 20
 
4.7%
비 16
 
3.8%
빔 14
 
3.3%
탕 13
 
3.1%
수 12
 
2.8%
육 12
 
2.8%
Other values (117) 254
59.6%
Common
ValueCountFrequency (%)
) 19
31.1%
( 19
31.1%
, 7
 
11.5%
0 5
 
8.2%
1 5
 
8.2%
+ 3
 
4.9%
2 1
 
1.6%
5 1
 
1.6%
8 1
 
1.6%
Latin
ValueCountFrequency (%)
g 3
42.9%
R 1
 
14.3%
X 1
 
14.3%
T 1
 
14.3%
p 1
 
14.3%
Han
ValueCountFrequency (%)
中 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 426
85.7%
ASCII 68
 
13.7%
CJK 3
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
밥 22
 
5.2%
염 21
 
4.9%
색 21
 
4.9%
찌 21
 
4.9%
개 20
 
4.7%
비 16
 
3.8%
빔 14
 
3.3%
탕 13
 
3.1%
수 12
 
2.8%
육 12
 
2.8%
Other values (117) 254
59.6%
ASCII
ValueCountFrequency (%)
) 19
27.9%
( 19
27.9%
, 7
 
10.3%
0 5
 
7.4%
1 5
 
7.4%
+ 3
 
4.4%
g 3
 
4.4%
2 1
 
1.5%
R 1
 
1.5%
X 1
 
1.5%
Other values (4) 4
 
5.9%
CJK
ValueCountFrequency (%)
中 3
100.0%

가격3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)30.5%
Missing105
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean14083.11
Minimum7
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T22:41:31.853871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile3000
Q16000
median8000
Q320000
95-th percentile35000
Maximum60000
Range59993
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation10990.248
Coefficient of variation (CV)0.78038499
Kurtosis1.614058
Mean14083.11
Median Absolute Deviation (MAD)3750
Skewness1.2874718
Sum1661807
Variance1.2078555 × 108
MonotonicityNot monotonic
2024-03-14T22:41:32.256097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8000 16
 
7.2%
7000 12
 
5.4%
6000 11
 
4.9%
20000 9
 
4.0%
5000 7
 
3.1%
25000 7
 
3.1%
30000 6
 
2.7%
5500 5
 
2.2%
35000 5
 
2.2%
15000 5
 
2.2%
Other values (26) 35
 
15.7%
(Missing) 105
47.1%
ValueCountFrequency (%)
7 1
 
0.4%
1500 1
 
0.4%
2500 1
 
0.4%
3000 4
 
1.8%
3500 2
 
0.9%
4000 1
 
0.4%
4500 1
 
0.4%
5000 7
3.1%
5500 5
2.2%
6000 11
4.9%
ValueCountFrequency (%)
60000 1
 
0.4%
40000 2
 
0.9%
38000 1
 
0.4%
35000 5
2.2%
30000 6
2.7%
29000 1
 
0.4%
28000 1
 
0.4%
25000 7
3.1%
24000 1
 
0.4%
23000 2
 
0.9%

메뉴4
Text

MISSING 

Distinct31
Distinct (%)67.4%
Missing177
Missing (%)79.4%
Memory size1.9 KiB
2024-03-14T22:41:33.026983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length14
Mean length4.8913043
Min length2

Characters and Unicode

Total characters225
Distinct characters98
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

Unique25 ?
Unique (%)54.3%

Sample

1st row돼지갈비(200g)
2nd row돼지숯불갈비(220g)
3rd row뼈다귀탕
4th row갈매기살(180g)
5th row돈가스
ValueCountFrequency (%)
볶음밥 5
 
10.9%
염색 4
 
8.7%
돈가스 4
 
8.7%
파마 4
 
8.7%
족발(중 2
 
4.3%
돌솥비빔밥 2
 
4.3%
아구찜(중 1
 
2.2%
김치찌개 1
 
2.2%
자몽차 1
 
2.2%
암뽕순대국밥 1
 
2.2%
Other values (21) 21
45.7%
2024-03-14T22:41:33.950845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
밥 14
 
6.2%
( 10
 
4.4%
) 10
 
4.4%
비 7
 
3.1%
스 7
 
3.1%
가 6
 
2.7%
음 6
 
2.7%
볶 6
 
2.7%
돈 6
 
2.7%
마 5
 
2.2%
Other values (88) 148
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
82.7%
Open Punctuation 10
 
4.4%
Close Punctuation 10
 
4.4%
Decimal Number 10
 
4.4%
Other Punctuation 5
 
2.2%
Lowercase Letter 4
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
밥 14
 
7.5%
비 7
 
3.8%
스 7
 
3.8%
가 6
 
3.2%
음 6
 
3.2%
볶 6
 
3.2%
돈 6
 
3.2%
마 5
 
2.7%
치 5
 
2.7%
빔 5
 
2.7%
Other values (78) 119
64.0%
Decimal Number
ValueCountFrequency (%)
0 4
40.0%
2 3
30.0%
6 1
 
10.0%
1 1
 
10.0%
8 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
g 3
75.0%
p 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 186
82.7%
Common 35
 
15.6%
Latin 4
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
밥 14
 
7.5%
비 7
 
3.8%
스 7
 
3.8%
가 6
 
3.2%
음 6
 
3.2%
볶 6
 
3.2%
돈 6
 
3.2%
마 5
 
2.7%
치 5
 
2.7%
빔 5
 
2.7%
Other values (78) 119
64.0%
Common
ValueCountFrequency (%)
( 10
28.6%
) 10
28.6%
, 5
14.3%
0 4
 
11.4%
2 3
 
8.6%
6 1
 
2.9%
1 1
 
2.9%
8 1
 
2.9%
Latin
ValueCountFrequency (%)
g 3
75.0%
p 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 186
82.7%
ASCII 39
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
밥 14
 
7.5%
비 7
 
3.8%
스 7
 
3.8%
가 6
 
3.2%
음 6
 
3.2%
볶 6
 
3.2%
돈 6
 
3.2%
마 5
 
2.7%
치 5
 
2.7%
빔 5
 
2.7%
Other values (78) 119
64.0%
ASCII
ValueCountFrequency (%)
( 10
25.6%
) 10
25.6%
, 5
12.8%
0 4
 
10.3%
2 3
 
7.7%
g 3
 
7.7%
p 1
 
2.6%
6 1
 
2.6%
1 1
 
2.6%
8 1
 
2.6%

가격4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)52.2%
Missing177
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean13650
Minimum1500
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T22:41:34.159131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile4125
Q16125
median8000
Q315675
95-th percentile38750
Maximum50000
Range48500
Interquartile range (IQR)9550

Descriptive statistics

Standard deviation11770.467
Coefficient of variation (CV)0.86230526
Kurtosis1.4803555
Mean13650
Median Absolute Deviation (MAD)3000
Skewness1.5573225
Sum627900
Variance1.3854389 × 108
MonotonicityNot monotonic
2024-03-14T22:41:34.376167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7000 7
 
3.1%
6000 5
 
2.2%
8000 5
 
2.2%
35000 3
 
1.3%
5000 3
 
1.3%
40000 2
 
0.9%
20000 2
 
0.9%
9500 2
 
0.9%
25000 2
 
0.9%
1500 1
 
0.4%
Other values (14) 14
 
6.3%
(Missing) 177
79.4%
ValueCountFrequency (%)
1500 1
 
0.4%
3500 1
 
0.4%
4000 1
 
0.4%
4500 1
 
0.4%
5000 3
1.3%
6000 5
2.2%
6500 1
 
0.4%
7000 7
3.1%
8000 5
2.2%
9000 1
 
0.4%
ValueCountFrequency (%)
50000 1
 
0.4%
40000 2
0.9%
35000 3
1.3%
30000 1
 
0.4%
25000 2
0.9%
20000 2
0.9%
15900 1
 
0.4%
15000 1
 
0.4%
14000 1
 
0.4%
13000 1
 
0.4%

메뉴5
Text

MISSING 

Distinct13
Distinct (%)92.9%
Missing209
Missing (%)93.7%
Memory size1.9 KiB
2024-03-14T22:41:34.905595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length9
Mean length6.5
Min length2

Characters and Unicode

Total characters91
Distinct characters51
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

Unique12 ?
Unique (%)85.7%

Sample

1st row냉면(물,비빔)
2nd row오므라이스(카레,야채)
3rd row덮밥(참치마요,치킨마요,제육덮밥,돈가스덮밥)
4th row육회비빔밥
5th row대왕돈가스
ValueCountFrequency (%)
냉면 2
14.3%
냉면(물,비빔 1
 
7.1%
오므라이스(카레,야채 1
 
7.1%
덮밥(참치마요,치킨마요,제육덮밥,돈가스덮밥 1
 
7.1%
육회비빔밥 1
 
7.1%
대왕돈가스 1
 
7.1%
탕수육 1
 
7.1%
돈가스 1
 
7.1%
삼겹살(200g 1
 
7.1%
회덮밥 1
 
7.1%
Other values (3) 3
21.4%
2024-03-14T22:41:35.645141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 5
 
5.5%
, 5
 
5.5%
) 5
 
5.5%
밥 5
 
5.5%
스 4
 
4.4%
덮 4
 
4.4%
냉 3
 
3.3%
가 3
 
3.3%
돈 3
 
3.3%
면 3
 
3.3%
Other values (41) 51
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
79.1%
Open Punctuation 5
 
5.5%
Other Punctuation 5
 
5.5%
Close Punctuation 5
 
5.5%
Decimal Number 3
 
3.3%
Lowercase Letter 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
밥 5
 
6.9%
스 4
 
5.6%
덮 4
 
5.6%
냉 3
 
4.2%
가 3
 
4.2%
돈 3
 
4.2%
면 3
 
4.2%
육 3
 
4.2%
요 2
 
2.8%
대 2
 
2.8%
Other values (35) 40
55.6%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
2 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
79.1%
Common 18
 
19.8%
Latin 1
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
밥 5
 
6.9%
스 4
 
5.6%
덮 4
 
5.6%
냉 3
 
4.2%
가 3
 
4.2%
돈 3
 
4.2%
면 3
 
4.2%
육 3
 
4.2%
요 2
 
2.8%
대 2
 
2.8%
Other values (35) 40
55.6%
Common
ValueCountFrequency (%)
( 5
27.8%
, 5
27.8%
) 5
27.8%
0 2
 
11.1%
2 1
 
5.6%
Latin
ValueCountFrequency (%)
g 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
79.1%
ASCII 19
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 5
26.3%
, 5
26.3%
) 5
26.3%
0 2
 
10.5%
g 1
 
5.3%
2 1
 
5.3%
Hangul
ValueCountFrequency (%)
밥 5
 
6.9%
스 4
 
5.6%
덮 4
 
5.6%
냉 3
 
4.2%
가 3
 
4.2%
돈 3
 
4.2%
면 3
 
4.2%
육 3
 
4.2%
요 2
 
2.8%
대 2
 
2.8%
Other values (35) 40
55.6%

가격5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)78.6%
Missing209
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean10892.857
Minimum1500
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T22:41:35.920422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile3125
Q16125
median7000
Q39500
95-th percentile30500
Maximum50000
Range48500
Interquartile range (IQR)3375

Descriptive statistics

Standard deviation12072.184
Coefficient of variation (CV)1.1082661
Kurtosis9.7493965
Mean10892.857
Median Absolute Deviation (MAD)1250
Skewness2.9992265
Sum152500
Variance1.4573764 × 108
MonotonicityNot monotonic
2024-03-14T22:41:36.278047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7000 4
 
1.8%
5500 1
 
0.4%
4000 1
 
0.4%
6500 1
 
0.4%
6000 1
 
0.4%
20000 1
 
0.4%
13000 1
 
0.4%
8000 1
 
0.4%
10000 1
 
0.4%
50000 1
 
0.4%
(Missing) 209
93.7%
ValueCountFrequency (%)
1500 1
 
0.4%
4000 1
 
0.4%
5500 1
 
0.4%
6000 1
 
0.4%
6500 1
 
0.4%
7000 4
1.8%
8000 1
 
0.4%
10000 1
 
0.4%
13000 1
 
0.4%
20000 1
 
0.4%
ValueCountFrequency (%)
50000 1
 
0.4%
20000 1
 
0.4%
13000 1
 
0.4%
10000 1
 
0.4%
8000 1
 
0.4%
7000 4
1.8%
6500 1
 
0.4%
6000 1
 
0.4%
5500 1
 
0.4%
4000 1
 
0.4%

Interactions

2024-03-14T22:41:11.440406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:04.234110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:05.667670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:07.134123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:08.600582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:09.959604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:11.694546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:04.464916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:05.904490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:07.367241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:08.845748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:10.191136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:11.937371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:04.705071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:06.148391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:07.609993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:09.089067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:10.442557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:12.197788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:04.936255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:06.387510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:07.842978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:09.354552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:10.702833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:12.407845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:05.182982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:06.646128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:08.100780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:09.603142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:10.960814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:12.546513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:05.421004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:06.891200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:08.352587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:09.752776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:41:11.204452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:41:36.522318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구업종가격1메뉴2가격2메뉴3가격3메뉴4가격4메뉴5가격5
연번1.0000.9910.3410.1020.8420.4810.5820.3750.9010.0611.0000.818
시군구0.9911.0000.2640.1690.8770.3910.7590.3350.9290.3771.0000.437
업종0.3410.2641.0000.6670.9990.6870.9990.6970.9500.4771.0000.000
가격10.1020.1690.6671.0000.9590.2040.9250.4830.9620.5361.0000.000
메뉴20.8420.8770.9990.9591.0000.9020.9910.7260.9830.0000.9350.942
가격20.4810.3910.6870.2040.9021.0000.0000.7500.9760.7570.0000.000
메뉴30.5820.7590.9990.9250.9910.0001.0000.5480.9820.0001.0001.000
가격30.3750.3350.6970.4830.7260.7500.5481.0000.8580.8311.0000.586
메뉴40.9010.9290.9500.9620.9830.9760.9820.8581.0000.0000.9650.449
가격40.0610.3770.4770.5360.0000.7570.0000.8310.0001.0000.0000.764
메뉴51.0001.0001.0001.0000.9350.0001.0001.0000.9650.0001.0001.000
가격50.8180.4370.0000.0000.9420.0001.0000.5860.4490.7641.0001.000
2024-03-14T22:41:36.830853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구업종
시군구1.0000.146
업종0.1461.000
2024-03-14T22:41:37.078642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번가격1가격2가격3가격4가격5시군구업종
연번1.000-0.0080.0650.2630.3430.4110.8610.157
가격1-0.0081.0000.5910.5110.7210.6500.1070.399
가격20.0650.5911.0000.7620.7720.6490.1660.373
가격30.2630.5110.7621.0000.8570.8000.2090.297
가격40.3430.7210.7720.8571.0000.7470.1950.324
가격50.4110.6500.6490.8000.7471.0000.0830.000
시군구0.8610.1070.1660.2090.1950.0831.0000.146
업종0.1570.3990.3730.2970.3240.0000.1461.000

Missing values

2024-03-14T22:41:12.836646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:41:13.460008image/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.
2024-03-14T22:41:13.885852image/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

연번시군구업종업소명연락처주소메뉴1가격1메뉴2가격2메뉴3가격3메뉴4가격4메뉴5가격5
01동구한식반디식당062-222-0809광주광역시 동구 구성로 258(계림동)백반6000<NA><NA><NA><NA><NA><NA><NA><NA>
12동구한식명지원062-227-3565광주광역시 동구 구성로 252(계림동)생삼겹(200g)14000비빔밥8000곰탕8000돼지갈비(200g)13000<NA><NA>
23동구한식산수골062-233-3318광주광역시 동구 경양로 340(산수동)병어조림(2인)30000<NA><NA><NA><NA><NA><NA><NA><NA>
34동구한식한우물062-675-9944광주광역시 동구 필문대로191번길 15-2(산수동)생선구이(모듬)8000갈치조림(2인가능)11000<NA><NA><NA><NA><NA><NA>
45동구중식진 숯불갈비<NA>광주광역시 동구 무등로 529(산수동)짜장면5000짬뽕7000탕수육19000돼지숯불갈비(220g)14000<NA><NA>
56동구한식수라상062-234-2002광주광역시 동구 제봉로82번길 13-9(동명동)찌개(된장,순두부,청국장,김치)6000돌솥(된장,순두부,청국장,김치)8000덮밥(낙지,새우,제육)7000뼈다귀탕7000<NA><NA>
67동구한식화롯불062-224-6119광주광역시 동구 백서로 168(서석동)돼지갈비(250g)14000비빔밥9000알탕+알밥10000<NA><NA><NA><NA>
78동구한식장독대062-223-5630광주광역시 동구 문화전당로 43, 3층(광산동)주물럭쌈밥(2인이상)12000<NA><NA><NA><NA><NA><NA><NA><NA>
89동구한식미미상회062-236-2879광주광역시 동구 백서로 182, 1층(서석동)생삼겹(180g)11000돼지갈비(250g)11000목살(180g)11000갈매기살(180g)11000<NA><NA>
910동구한식돈까스틱062-376-2278광주광역시 동구 지산로 7, 2층(서석동)왕돈가스7500순두부찌개7500돈가스+치즈돈가스8000<NA><NA><NA><NA>
연번시군구업종업소명연락처주소메뉴1가격1메뉴2가격2메뉴3가격3메뉴4가격4메뉴5가격5
213214광산구중식현대062-944-5471광주광역시 광산구 광산로67번길 24(송정동)짜장면5000짬뽕6000탕수육(中)23000볶음밥7000<NA><NA>
214215광산구한식백복발본점062-951-1033광주광역시 광산구 왕버들로252번길 33(신창동, 1층)족발(소)30000보쌈(소)30000보쌈(중)35000족발(중)35000<NA><NA>
215216광산구한식븟<NA>광주광역시 광산구 수완로74번길 11-28, 1층(수완동)광어(3인)27000연어(3인)27000광어+연어29000<NA><NA><NA><NA>
216217광산구한식자리봉국밥 수완직영점062-952-0555광주광역시 광산구 장신로20번길 10, 101호(수완동)모듬국밥9000순대국밥9000머리국밥9000암뽕순대국밥9500<NA><NA>
217218광산구한식한국수산062-962-3663광주광역시 광산구 장신로 199 (수완동)광어(3인)38000우럭(3인)38000모듬회38000<NA><NA><NA><NA>
218219광산구기타요식업플라워카페홀릭스062-953-0953광주광역시 광산구 수완로11번길 17, 1층(수완동)아메리카노3000레몬차5000수제패션후르츠5000자몽차5000<NA><NA>
219220광산구미용업뷰티스토리<NA>광주광역시 광산구 수등로 273-7, 1층(신가동)커트7000일반커트10000파마20000염색20000<NA><NA>
220221서구한식청림정062-363-1739광주광역시 서구 화정로293번길 25(농성동)삼겹살(200g)12000김치찌개7000불고기정식10000백반7000<NA><NA>
221222서구한식맛집장어구이 광주점0507-1396-3235광주광역시 서구 금화로73번길 18-2(금호동)장어구이(특)20000장어구이(대)10000<NA><NA><NA><NA><NA><NA>
222223서구미용업깻잎머리062-361-1781광주광역시 서구 경열로76번길 6(농성동)성인커트10000학생커트8000<NA><NA><NA><NA><NA><NA>