Overview

Dataset statistics

Number of variables18
Number of observations320
Missing cells407
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory150.4 B

Variable types

Numeric6
Categorical5
Text7

Dataset

Description평균가격 이하로 서비스하는 착한가격업소 데이터 입니다. (업소명, 업종, 대표자, 시도, 시군구, 새 주소, 전화번호, 품목, 가격, 영업시간, 배달 여부, 주차 여부, 업소자랑거리 등, 홍보 아이템, 데이터 기준일)
URLhttps://www.data.go.kr/data/15083301/fileData.do

Alerts

시도 has constant value ""Constant
업종1(대분류) is highly overall correlated with 가격3 and 2 other fieldsHigh correlation
업종2(세분류) is highly overall correlated with 구분 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 시군구High correlation
가격1 is highly overall correlated with 가격2 and 1 other fieldsHigh correlation
가격2 is highly overall correlated with 가격1 and 1 other fieldsHigh correlation
가격3 is highly overall correlated with 가격1 and 2 other fieldsHigh correlation
시군구 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 시군구 and 2 other fieldsHigh correlation
업종1(대분류) is highly imbalanced (51.7%)Imbalance
연락처 has 25 (7.8%) missing valuesMissing
품목2 has 46 (14.4%) missing valuesMissing
가격2 has 46 (14.4%) missing valuesMissing
품목3 has 145 (45.3%) missing valuesMissing
가격3 has 145 (45.3%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:14:40.466760
Analysis finished2023-12-12 07:14:46.527004
Duration6.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.5
Minimum1
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T16:14:46.614897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.95
Q180.75
median160.5
Q3240.25
95-th percentile304.05
Maximum320
Range319
Interquartile range (IQR)159.5

Descriptive statistics

Standard deviation92.520268
Coefficient of variation (CV)0.57645027
Kurtosis-1.2
Mean160.5
Median Absolute Deviation (MAD)80
Skewness0
Sum51360
Variance8560
MonotonicityStrictly increasing
2023-12-12T16:14:46.807204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
162 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
214 1
 
0.3%
213 1
 
0.3%
Other values (310) 310
96.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
320 1
0.3%
319 1
0.3%
318 1
0.3%
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%
313 1
0.3%
312 1
0.3%
311 1
0.3%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
충청북도
320 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 320
100.0%

Length

2023-12-12T16:14:46.984212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:14:47.100424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 320
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
청주시
83 
제천시
47 
충주시
46 
옥천군
25 
음성군
23 
Other values (6)
96 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청주시 83
25.9%
제천시 47
14.7%
충주시 46
14.4%
옥천군 25
 
7.8%
음성군 23
 
7.2%
진천군 20
 
6.2%
보은군 19
 
5.9%
단양군 18
 
5.6%
괴산군 16
 
5.0%
증평군 12
 
3.8%

Length

2023-12-12T16:14:47.217054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청주시 83
25.9%
제천시 47
14.7%
충주시 46
14.4%
옥천군 25
 
7.8%
음성군 23
 
7.2%
진천군 20
 
6.2%
보은군 19
 
5.9%
단양군 18
 
5.6%
괴산군 16
 
5.0%
증평군 12
 
3.8%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
외식업
245 
기타개인서비스업
57 
요식업
 
14
비요식업
 
4

Length

Max length8
Median length3
Mean length3.903125
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
외식업 245
76.6%
기타개인서비스업 57
 
17.8%
요식업 14
 
4.4%
비요식업 4
 
1.2%

Length

2023-12-12T16:14:47.367414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:14:47.477443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
외식업 245
76.6%
기타개인서비스업 57
 
17.8%
요식업 14
 
4.4%
비요식업 4
 
1.2%

업종1(대분류)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
한식
225 
미용업
35 
중식
 
18
기타요식업
 
13
기타비요식업
 
7
Other values (6)
 
22

Length

Max length6
Median length2
Mean length2.378125
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
한식 225
70.3%
미용업 35
 
10.9%
중식 18
 
5.6%
기타요식업 13
 
4.1%
기타비요식업 7
 
2.2%
세탁업 7
 
2.2%
이용업 7
 
2.2%
숙박업 3
 
0.9%
양식 2
 
0.6%
목욕업 2
 
0.6%

Length

2023-12-12T16:14:47.601664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 225
70.3%
미용업 35
 
10.9%
중식 18
 
5.6%
기타요식업 13
 
4.1%
기타비요식업 7
 
2.2%
세탁업 7
 
2.2%
이용업 7
 
2.2%
숙박업 3
 
0.9%
양식 2
 
0.6%
목욕업 2
 
0.6%

업종2(세분류)
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
한식_일반
132 
한식_육류
60 
미용업
31 
한식_면류
27 
중식
18 
Other values (10)
52 

Length

Max length6
Median length5
Mean length4.50625
Min length2

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row한식_면류
2nd row한식_육류
3rd row한식_육류
4th row한식_육류
5th row한식_육류

Common Values

ValueCountFrequency (%)
한식_일반 132
41.2%
한식_육류 60
18.8%
미용업 31
 
9.7%
한식_면류 27
 
8.4%
중식 18
 
5.6%
기타요식업 12
 
3.8%
기타비요식업 11
 
3.4%
세탁업 7
 
2.2%
이용업 7
 
2.2%
한식_기타 5
 
1.6%
Other values (5) 10
 
3.1%

Length

2023-12-12T16:14:47.769362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식_일반 132
41.2%
한식_육류 60
18.8%
미용업 31
 
9.7%
한식_면류 27
 
8.4%
중식 18
 
5.6%
기타요식업 12
 
3.8%
기타비요식업 11
 
3.4%
세탁업 7
 
2.2%
이용업 7
 
2.2%
한식_기타 5
 
1.6%
Other values (5) 10
 
3.1%

업소명
Text

Distinct318
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T16:14:48.053200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length5.26875
Min length2

Characters and Unicode

Total characters1686
Distinct characters367
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)99.1%

Sample

1st row나누리장터(산더미해물칼국수)
2nd row대박삼겹살
3rd row야간비행
4th row충주돌구이
5th row한우일번지
ValueCountFrequency (%)
제일미용실 3
 
0.9%
용천막국수 2
 
0.6%
중앙식당 1
 
0.3%
금강한우농장 1
 
0.3%
중식당향수 1
 
0.3%
은경이네 1
 
0.3%
군서보리밥 1
 
0.3%
민들레 1
 
0.3%
카페보노롱 1
 
0.3%
진실미용실 1
 
0.3%
Other values (321) 321
96.1%
2023-12-12T16:14:48.472757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 61
 
3.6%
당 60
 
3.6%
미 39
 
2.3%
수 36
 
2.1%
이 33
 
2.0%
용 30
 
1.8%
국 29
 
1.7%
가 28
 
1.7%
실 26
 
1.5%
한 25
 
1.5%
Other values (357) 1319
78.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1632
96.8%
Space Separator 14
 
0.8%
Close Punctuation 9
 
0.5%
Open Punctuation 9
 
0.5%
Other Punctuation 7
 
0.4%
Lowercase Letter 6
 
0.4%
Decimal Number 6
 
0.4%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 61
 
3.7%
당 60
 
3.7%
미 39
 
2.4%
수 36
 
2.2%
이 33
 
2.0%
용 30
 
1.8%
국 29
 
1.8%
가 28
 
1.7%
실 26
 
1.6%
한 25
 
1.5%
Other values (338) 1265
77.5%
Lowercase Letter
ValueCountFrequency (%)
f 2
33.3%
e 2
33.3%
o 1
16.7%
c 1
16.7%
Decimal Number
ValueCountFrequency (%)
5 2
33.3%
3 2
33.3%
6 1
16.7%
7 1
16.7%
Other Punctuation
ValueCountFrequency (%)
& 5
71.4%
! 1
 
14.3%
· 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
Y 1
33.3%
E 1
33.3%
S 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 8
88.9%
] 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 8
88.9%
[ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1632
96.8%
Common 45
 
2.7%
Latin 9
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 61
 
3.7%
당 60
 
3.7%
미 39
 
2.4%
수 36
 
2.2%
이 33
 
2.0%
용 30
 
1.8%
국 29
 
1.8%
가 28
 
1.7%
실 26
 
1.6%
한 25
 
1.5%
Other values (338) 1265
77.5%
Common
ValueCountFrequency (%)
14
31.1%
) 8
17.8%
( 8
17.8%
& 5
 
11.1%
5 2
 
4.4%
3 2
 
4.4%
] 1
 
2.2%
[ 1
 
2.2%
! 1
 
2.2%
· 1
 
2.2%
Other values (2) 2
 
4.4%
Latin
ValueCountFrequency (%)
f 2
22.2%
e 2
22.2%
Y 1
11.1%
E 1
11.1%
S 1
11.1%
o 1
11.1%
c 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1632
96.8%
ASCII 53
 
3.1%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 61
 
3.7%
당 60
 
3.7%
미 39
 
2.4%
수 36
 
2.2%
이 33
 
2.0%
용 30
 
1.8%
국 29
 
1.8%
가 28
 
1.7%
실 26
 
1.6%
한 25
 
1.5%
Other values (338) 1265
77.5%
ASCII
ValueCountFrequency (%)
14
26.4%
) 8
15.1%
( 8
15.1%
& 5
 
9.4%
f 2
 
3.8%
e 2
 
3.8%
5 2
 
3.8%
3 2
 
3.8%
] 1
 
1.9%
[ 1
 
1.9%
Other values (8) 8
15.1%
None
ValueCountFrequency (%)
· 1
100.0%

대표자명
Text

Distinct316
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T16:14:48.793516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.0125
Min length2

Characters and Unicode

Total characters964
Distinct characters159
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)97.5%

Sample

1st row박경희
2nd row김초
3rd row정광옥
4th row김윤희
5th row김길운
ValueCountFrequency (%)
김순옥 2
 
0.6%
이명순 2
 
0.6%
김경자 2
 
0.6%
이혜진 2
 
0.6%
윤현숙 1
 
0.3%
강태관 1
 
0.3%
박은경 1
 
0.3%
신은정 1
 
0.3%
전현미 1
 
0.3%
박경희 1
 
0.3%
Other values (306) 306
95.6%
2023-12-12T16:14:49.276587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 76
 
7.9%
이 58
 
6.0%
순 45
 
4.7%
정 44
 
4.6%
자 36
 
3.7%
숙 31
 
3.2%
영 29
 
3.0%
희 25
 
2.6%
박 24
 
2.5%
최 23
 
2.4%
Other values (149) 573
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 954
99.0%
Uppercase Letter 10
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 76
 
8.0%
이 58
 
6.1%
순 45
 
4.7%
정 44
 
4.6%
자 36
 
3.8%
숙 31
 
3.2%
영 29
 
3.0%
희 25
 
2.6%
박 24
 
2.5%
최 23
 
2.4%
Other values (142) 563
59.0%
Uppercase Letter
ValueCountFrequency (%)
N 2
20.0%
E 2
20.0%
A 2
20.0%
L 1
10.0%
J 1
10.0%
I 1
10.0%
T 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 954
99.0%
Latin 10
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 76
 
8.0%
이 58
 
6.1%
순 45
 
4.7%
정 44
 
4.6%
자 36
 
3.8%
숙 31
 
3.2%
영 29
 
3.0%
희 25
 
2.6%
박 24
 
2.5%
최 23
 
2.4%
Other values (142) 563
59.0%
Latin
ValueCountFrequency (%)
N 2
20.0%
E 2
20.0%
A 2
20.0%
L 1
10.0%
J 1
10.0%
I 1
10.0%
T 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 954
99.0%
ASCII 10
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 76
 
8.0%
이 58
 
6.1%
순 45
 
4.7%
정 44
 
4.6%
자 36
 
3.8%
숙 31
 
3.2%
영 29
 
3.0%
희 25
 
2.6%
박 24
 
2.5%
최 23
 
2.4%
Other values (142) 563
59.0%
ASCII
ValueCountFrequency (%)
N 2
20.0%
E 2
20.0%
A 2
20.0%
L 1
10.0%
J 1
10.0%
I 1
10.0%
T 1
10.0%

주소(도로명 주소)
Text

Distinct314
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T16:14:49.591720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length29
Mean length19.95
Min length9

Characters and Unicode

Total characters6384
Distinct characters196
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

Unique309 ?
Unique (%)96.6%

Sample

1st row충청북도 청주시 상당구 쇠내로 69-1(금천동)
2nd row충청북도 청주시 상당구 남사로89번길 56(서문동)
3rd row충청북도 청주시 상당구 남사로89번길 63(서문동)
4th row충청북도 청주시 상당구 남사로89번길 37(서문동)
5th row충청북도 청주시 상당구 남일면 고은두산로 29
ValueCountFrequency (%)
충청북도 125
 
8.9%
충북 72
 
5.1%
청주시 71
 
5.1%
제천시 47
 
3.4%
충주시 46
 
3.3%
상당구 39
 
2.8%
옥천군 25
 
1.8%
음성군 23
 
1.6%
진천군 20
 
1.4%
옥천읍 19
 
1.4%
Other values (560) 915
65.3%
2023-12-12T16:14:50.014549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1087
 
17.0%
1 288
 
4.5%
로 260
 
4.1%
충 251
 
3.9%
청 238
 
3.7%
북 210
 
3.3%
길 172
 
2.7%
시 169
 
2.6%
동 158
 
2.5%
천 149
 
2.3%
Other values (186) 3402
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3881
60.8%
Space Separator 1087
 
17.0%
Decimal Number 1071
 
16.8%
Close Punctuation 118
 
1.8%
Open Punctuation 117
 
1.8%
Dash Punctuation 87
 
1.4%
Other Punctuation 20
 
0.3%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 260
 
6.7%
충 251
 
6.5%
청 238
 
6.1%
북 210
 
5.4%
길 172
 
4.4%
시 169
 
4.4%
동 158
 
4.1%
천 149
 
3.8%
군 134
 
3.5%
도 133
 
3.4%
Other values (168) 2007
51.7%
Decimal Number
ValueCountFrequency (%)
1 288
26.9%
2 142
13.3%
3 114
 
10.6%
4 104
 
9.7%
5 95
 
8.9%
6 84
 
7.8%
7 78
 
7.3%
0 59
 
5.5%
8 54
 
5.0%
9 53
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
B 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
1087
100.0%
Close Punctuation
ValueCountFrequency (%)
) 118
100.0%
Open Punctuation
ValueCountFrequency (%)
( 117
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 87
100.0%
Other Punctuation
ValueCountFrequency (%)
, 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3881
60.8%
Common 2500
39.2%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 260
 
6.7%
충 251
 
6.5%
청 238
 
6.1%
북 210
 
5.4%
길 172
 
4.4%
시 169
 
4.4%
동 158
 
4.1%
천 149
 
3.8%
군 134
 
3.5%
도 133
 
3.4%
Other values (168) 2007
51.7%
Common
ValueCountFrequency (%)
1087
43.5%
1 288
 
11.5%
2 142
 
5.7%
) 118
 
4.7%
( 117
 
4.7%
3 114
 
4.6%
4 104
 
4.2%
5 95
 
3.8%
- 87
 
3.5%
6 84
 
3.4%
Other values (5) 264
 
10.6%
Latin
ValueCountFrequency (%)
C 1
33.3%
B 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3881
60.8%
ASCII 2503
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1087
43.4%
1 288
 
11.5%
2 142
 
5.7%
) 118
 
4.7%
( 117
 
4.7%
3 114
 
4.6%
4 104
 
4.2%
5 95
 
3.8%
- 87
 
3.5%
6 84
 
3.4%
Other values (8) 267
 
10.7%
Hangul
ValueCountFrequency (%)
로 260
 
6.7%
충 251
 
6.5%
청 238
 
6.1%
북 210
 
5.4%
길 172
 
4.4%
시 169
 
4.4%
동 158
 
4.1%
천 149
 
3.8%
군 134
 
3.5%
도 133
 
3.4%
Other values (168) 2007
51.7%

연락처
Text

MISSING 

Distinct294
Distinct (%)99.7%
Missing25
Missing (%)7.8%
Memory size2.6 KiB
2023-12-12T16:14:50.219923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.030508
Min length12

Characters and Unicode

Total characters3549
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

Unique293 ?
Unique (%)99.3%

Sample

1st row043-224-3992
2nd row043-257-8222
3rd row043-253-0531
4th row043-292-1238
5th row043-297-5162
ValueCountFrequency (%)
043-532-9889 2
 
0.7%
043-732-2339 1
 
0.3%
043-743-4165 1
 
0.3%
043-743-5685 1
 
0.3%
043-742-1531 1
 
0.3%
043-744-1185 1
 
0.3%
043-731-1850 1
 
0.3%
043-731-7342 1
 
0.3%
043-731-5777 1
 
0.3%
043-733-9237 1
 
0.3%
Other values (284) 284
96.3%
2023-12-12T16:14:50.551446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 590
16.6%
3 555
15.6%
4 513
14.5%
0 435
12.3%
2 309
8.7%
8 248
7.0%
5 236
 
6.6%
7 197
 
5.6%
6 165
 
4.6%
1 158
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2959
83.4%
Dash Punctuation 590
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 555
18.8%
4 513
17.3%
0 435
14.7%
2 309
10.4%
8 248
8.4%
5 236
8.0%
7 197
 
6.7%
6 165
 
5.6%
1 158
 
5.3%
9 143
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 590
16.6%
3 555
15.6%
4 513
14.5%
0 435
12.3%
2 309
8.7%
8 248
7.0%
5 236
 
6.6%
7 197
 
5.6%
6 165
 
4.6%
1 158
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 590
16.6%
3 555
15.6%
4 513
14.5%
0 435
12.3%
2 309
8.7%
8 248
7.0%
5 236
 
6.6%
7 197
 
5.6%
6 165
 
4.6%
1 158
 
4.5%

전체 메뉴 수
Real number (ℝ)

Distinct37
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.696875
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T16:14:50.689485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7.5
Q311
95-th percentile28.05
Maximum52
Range51
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.2863329
Coefficient of variation (CV)0.85453643
Kurtosis8.0348269
Mean9.696875
Median Absolute Deviation (MAD)2.5
Skewness2.557268
Sum3103
Variance68.663313
MonotonicityNot monotonic
2023-12-12T16:14:50.809494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
7 33
 
10.3%
5 32
 
10.0%
10 30
 
9.4%
6 27
 
8.4%
3 26
 
8.1%
8 21
 
6.6%
9 19
 
5.9%
4 18
 
5.6%
2 14
 
4.4%
11 14
 
4.4%
Other values (27) 86
26.9%
ValueCountFrequency (%)
1 10
 
3.1%
2 14
4.4%
3 26
8.1%
4 18
5.6%
5 32
10.0%
6 27
8.4%
7 33
10.3%
8 21
6.6%
9 19
5.9%
10 30
9.4%
ValueCountFrequency (%)
52 2
0.6%
48 1
0.3%
45 1
0.3%
42 1
0.3%
40 1
0.3%
38 1
0.3%
37 2
0.6%
36 1
0.3%
35 1
0.3%
33 1
0.3%

착한가격 메뉴 수
Real number (ℝ)

Distinct13
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.234375
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T16:14:51.001322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile6
Maximum52
Range51
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.264537
Coefficient of variation (CV)1.3185042
Kurtosis106.63605
Mean3.234375
Median Absolute Deviation (MAD)1
Skewness9.5950601
Sum1035
Variance18.186275
MonotonicityNot monotonic
2023-12-12T16:14:51.123457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 105
32.8%
2 98
30.6%
1 46
14.4%
4 33
 
10.3%
5 14
 
4.4%
6 10
 
3.1%
8 3
 
0.9%
10 3
 
0.9%
7 3
 
0.9%
52 2
 
0.6%
Other values (3) 3
 
0.9%
ValueCountFrequency (%)
1 46
14.4%
2 98
30.6%
3 105
32.8%
4 33
 
10.3%
5 14
 
4.4%
6 10
 
3.1%
7 3
 
0.9%
8 3
 
0.9%
9 1
 
0.3%
10 3
 
0.9%
ValueCountFrequency (%)
52 2
 
0.6%
16 1
 
0.3%
12 1
 
0.3%
10 3
 
0.9%
9 1
 
0.3%
8 3
 
0.9%
7 3
 
0.9%
6 10
 
3.1%
5 14
4.4%
4 33
10.3%

품목1
Text

Distinct176
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T16:14:51.366248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length4.471875
Min length1

Characters and Unicode

Total characters1431
Distinct characters222
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)43.4%

Sample

1st row장터칼국수
2nd row삼겹살(200g)
3rd row삼겹살(200g)
4th row삼겹살(200g)
5th row한우등심(500g)
ValueCountFrequency (%)
커트 29
 
9.0%
삼겹살(200g 14
 
4.3%
칼국수 12
 
3.7%
짜장면 12
 
3.7%
청국장 9
 
2.8%
백반 8
 
2.5%
삼겹살 7
 
2.2%
된장찌개 7
 
2.2%
보리밥 5
 
1.6%
손칼국수 5
 
1.6%
Other values (168) 214
66.5%
2023-12-12T16:14:51.804332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 62
 
4.3%
( 59
 
4.1%
) 59
 
4.1%
0 56
 
3.9%
장 46
 
3.2%
수 46
 
3.2%
트 38
 
2.7%
커 38
 
2.7%
삼 35
 
2.4%
g 33
 
2.3%
Other values (212) 959
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1171
81.8%
Decimal Number 105
 
7.3%
Open Punctuation 59
 
4.1%
Close Punctuation 59
 
4.1%
Lowercase Letter 33
 
2.3%
Space Separator 2
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 62
 
5.3%
장 46
 
3.9%
수 46
 
3.9%
트 38
 
3.2%
커 38
 
3.2%
삼 35
 
3.0%
살 33
 
2.8%
겹 32
 
2.7%
밥 32
 
2.7%
칼 28
 
2.4%
Other values (199) 781
66.7%
Decimal Number
ValueCountFrequency (%)
0 56
53.3%
2 25
23.8%
1 8
 
7.6%
5 5
 
4.8%
8 4
 
3.8%
6 3
 
2.9%
4 3
 
2.9%
3 1
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 33
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1170
81.8%
Common 227
 
15.9%
Latin 33
 
2.3%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 62
 
5.3%
장 46
 
3.9%
수 46
 
3.9%
트 38
 
3.2%
커 38
 
3.2%
삼 35
 
3.0%
살 33
 
2.8%
겹 32
 
2.7%
밥 32
 
2.7%
칼 28
 
2.4%
Other values (198) 780
66.7%
Common
ValueCountFrequency (%)
( 59
26.0%
) 59
26.0%
0 56
24.7%
2 25
11.0%
1 8
 
3.5%
5 5
 
2.2%
8 4
 
1.8%
6 3
 
1.3%
4 3
 
1.3%
2
 
0.9%
Other values (2) 3
 
1.3%
Latin
ValueCountFrequency (%)
g 33
100.0%
Han
ValueCountFrequency (%)
大 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1170
81.8%
ASCII 260
 
18.2%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 62
 
5.3%
장 46
 
3.9%
수 46
 
3.9%
트 38
 
3.2%
커 38
 
3.2%
삼 35
 
3.0%
살 33
 
2.8%
겹 32
 
2.7%
밥 32
 
2.7%
칼 28
 
2.4%
Other values (198) 780
66.7%
ASCII
ValueCountFrequency (%)
( 59
22.7%
) 59
22.7%
0 56
21.5%
g 33
12.7%
2 25
9.6%
1 8
 
3.1%
5 5
 
1.9%
8 4
 
1.5%
6 3
 
1.2%
4 3
 
1.2%
Other values (3) 5
 
1.9%
CJK
ValueCountFrequency (%)
大 1
100.0%

가격1
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10735.312
Minimum700
Maximum75000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T16:14:51.951353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile3000
Q16000
median7950
Q311000
95-th percentile35000
Maximum75000
Range74300
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation11206.538
Coefficient of variation (CV)1.0438949
Kurtosis13.821502
Mean10735.312
Median Absolute Deviation (MAD)2500
Skewness3.509627
Sum3435300
Variance1.2558649 × 108
MonotonicityNot monotonic
2023-12-12T16:14:52.096901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7000 47
14.7%
8000 41
12.8%
5000 36
11.2%
6000 32
10.0%
10000 24
 
7.5%
13000 22
 
6.9%
12000 16
 
5.0%
4000 14
 
4.4%
9000 11
 
3.4%
2500 9
 
2.8%
Other values (32) 68
21.2%
ValueCountFrequency (%)
700 1
 
0.3%
1000 3
 
0.9%
2000 2
 
0.6%
2500 9
2.8%
3000 5
 
1.6%
3500 1
 
0.3%
4000 14
4.4%
4100 1
 
0.3%
4200 1
 
0.3%
4500 4
 
1.2%
ValueCountFrequency (%)
75000 2
0.6%
70000 1
 
0.3%
65000 2
0.6%
60000 1
 
0.3%
50000 2
0.6%
45000 3
0.9%
40000 4
1.2%
35000 2
0.6%
30000 2
0.6%
28000 1
 
0.3%

품목2
Text

MISSING 

Distinct177
Distinct (%)64.6%
Missing46
Missing (%)14.4%
Memory size2.6 KiB
2023-12-12T16:14:52.395770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length4.6861314
Min length2

Characters and Unicode

Total characters1284
Distinct characters234
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

Unique145 ?
Unique (%)52.9%

Sample

1st row바지락칼국수
2nd row목살(200g)
3rd row꽃목살(200g)
4th row목살(200g)
5th row특수모듬(500g)
ValueCountFrequency (%)
파마 18
 
6.5%
짬뽕 13
 
4.7%
청국장 10
 
3.6%
목살(200g 9
 
3.2%
김치찌개 8
 
2.9%
삼겹살 7
 
2.5%
염색 6
 
2.2%
칼국수 5
 
1.8%
두부찌개 5
 
1.8%
냉면 4
 
1.4%
Other values (160) 192
69.3%
2023-12-12T16:14:52.921431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 51
 
4.0%
( 51
 
4.0%
0 47
 
3.7%
국 44
 
3.4%
35
 
2.7%
살 32
 
2.5%
수 31
 
2.4%
장 29
 
2.3%
개 29
 
2.3%
g 28
 
2.2%
Other values (224) 907
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1024
79.8%
Decimal Number 90
 
7.0%
Close Punctuation 51
 
4.0%
Open Punctuation 51
 
4.0%
Space Separator 35
 
2.7%
Lowercase Letter 28
 
2.2%
Other Punctuation 3
 
0.2%
Math Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 44
 
4.3%
살 32
 
3.1%
수 31
 
3.0%
장 29
 
2.8%
개 29
 
2.8%
두 24
 
2.3%
비 23
 
2.2%
찌 23
 
2.2%
치 22
 
2.1%
마 20
 
2.0%
Other values (208) 747
72.9%
Decimal Number
ValueCountFrequency (%)
0 47
52.2%
2 25
27.8%
1 6
 
6.7%
5 6
 
6.7%
6 2
 
2.2%
3 2
 
2.2%
7 1
 
1.1%
8 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
/ 1
33.3%
Math Symbol
ValueCountFrequency (%)
~ 1
50.0%
+ 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1023
79.7%
Common 232
 
18.1%
Latin 28
 
2.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 44
 
4.3%
살 32
 
3.1%
수 31
 
3.0%
장 29
 
2.8%
개 29
 
2.8%
두 24
 
2.3%
비 23
 
2.2%
찌 23
 
2.2%
치 22
 
2.2%
마 20
 
2.0%
Other values (207) 746
72.9%
Common
ValueCountFrequency (%)
) 51
22.0%
( 51
22.0%
0 47
20.3%
35
15.1%
2 25
10.8%
1 6
 
2.6%
5 6
 
2.6%
6 2
 
0.9%
, 2
 
0.9%
3 2
 
0.9%
Other values (5) 5
 
2.2%
Latin
ValueCountFrequency (%)
g 28
100.0%
Han
ValueCountFrequency (%)
大 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1023
79.7%
ASCII 260
 
20.2%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 51
19.6%
( 51
19.6%
0 47
18.1%
35
13.5%
g 28
10.8%
2 25
9.6%
1 6
 
2.3%
5 6
 
2.3%
6 2
 
0.8%
, 2
 
0.8%
Other values (6) 7
 
2.7%
Hangul
ValueCountFrequency (%)
국 44
 
4.3%
살 32
 
3.1%
수 31
 
3.0%
장 29
 
2.8%
개 29
 
2.8%
두 24
 
2.3%
비 23
 
2.2%
찌 23
 
2.2%
치 22
 
2.2%
마 20
 
2.0%
Other values (207) 746
72.9%
CJK
ValueCountFrequency (%)
大 1
100.0%

가격2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)15.0%
Missing46
Missing (%)14.4%
Infinite0
Infinite (%)0.0%
Mean12311.679
Minimum700
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T16:14:53.055384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile3430
Q16000
median8000
Q313000
95-th percentile35000
Maximum150000
Range149300
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation13965.2
Coefficient of variation (CV)1.1343051
Kurtosis39.804486
Mean12311.679
Median Absolute Deviation (MAD)2000
Skewness5.2414919
Sum3373400
Variance1.9502682 × 108
MonotonicityNot monotonic
2023-12-12T16:14:53.198507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7000 46
14.4%
6000 28
 
8.8%
8000 24
 
7.5%
9000 22
 
6.9%
10000 18
 
5.6%
5000 17
 
5.3%
13000 14
 
4.4%
12000 13
 
4.1%
20000 12
 
3.8%
4000 9
 
2.8%
Other values (31) 71
22.2%
(Missing) 46
14.4%
ValueCountFrequency (%)
700 1
 
0.3%
1500 1
 
0.3%
2500 2
 
0.6%
3000 9
 
2.8%
3300 1
 
0.3%
3500 4
 
1.2%
4000 9
 
2.8%
5000 17
5.3%
5500 1
 
0.3%
6000 28
8.8%
ValueCountFrequency (%)
150000 1
 
0.3%
90000 1
 
0.3%
75000 1
 
0.3%
70000 1
 
0.3%
55000 1
 
0.3%
50000 2
0.6%
45000 1
 
0.3%
42000 1
 
0.3%
40000 2
0.6%
35000 4
1.2%

품목3
Text

MISSING 

Distinct130
Distinct (%)74.3%
Missing145
Missing (%)45.3%
Memory size2.6 KiB
2023-12-12T16:14:53.471520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length11
Mean length5.04
Min length2

Characters and Unicode

Total characters882
Distinct characters212
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

Unique108 ?
Unique (%)61.7%

Sample

1st row비빔국수
2nd row항정살(200g)
3rd row뽈대기살(200g)
4th row항정살(200g)
5th row항정살(200g)
ValueCountFrequency (%)
염색 18
 
9.9%
항정살(200g 5
 
2.7%
탕수육(소 4
 
2.2%
김치찌개 3
 
1.6%
비지장 3
 
1.6%
잔치국수 3
 
1.6%
육개장 3
 
1.6%
순두부 3
 
1.6%
청국장 3
 
1.6%
된장찌개 3
 
1.6%
Other values (123) 134
73.6%
2023-12-12T16:14:53.897397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 39
 
4.4%
( 39
 
4.4%
0 38
 
4.3%
31
 
3.5%
국 26
 
2.9%
장 23
 
2.6%
수 22
 
2.5%
g 21
 
2.4%
개 19
 
2.2%
염 19
 
2.2%
Other values (202) 605
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 678
76.9%
Decimal Number 70
 
7.9%
Close Punctuation 39
 
4.4%
Open Punctuation 39
 
4.4%
Space Separator 31
 
3.5%
Lowercase Letter 23
 
2.6%
Math Symbol 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 26
 
3.8%
장 23
 
3.4%
수 22
 
3.2%
개 19
 
2.8%
염 19
 
2.8%
색 19
 
2.8%
육 18
 
2.7%
비 17
 
2.5%
살 17
 
2.5%
치 16
 
2.4%
Other values (185) 482
71.1%
Decimal Number
ValueCountFrequency (%)
0 38
54.3%
2 18
25.7%
1 5
 
7.1%
8 3
 
4.3%
5 2
 
2.9%
3 1
 
1.4%
7 1
 
1.4%
4 1
 
1.4%
9 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
g 21
91.3%
l 1
 
4.3%
m 1
 
4.3%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 677
76.8%
Common 181
 
20.5%
Latin 23
 
2.6%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 26
 
3.8%
장 23
 
3.4%
수 22
 
3.2%
개 19
 
2.8%
염 19
 
2.8%
색 19
 
2.8%
육 18
 
2.7%
비 17
 
2.5%
살 17
 
2.5%
치 16
 
2.4%
Other values (184) 481
71.0%
Common
ValueCountFrequency (%)
) 39
21.5%
( 39
21.5%
0 38
21.0%
31
17.1%
2 18
9.9%
1 5
 
2.8%
8 3
 
1.7%
5 2
 
1.1%
3 1
 
0.6%
~ 1
 
0.6%
Other values (4) 4
 
2.2%
Latin
ValueCountFrequency (%)
g 21
91.3%
l 1
 
4.3%
m 1
 
4.3%
Han
ValueCountFrequency (%)
中 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 677
76.8%
ASCII 204
 
23.1%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 39
19.1%
( 39
19.1%
0 38
18.6%
31
15.2%
g 21
10.3%
2 18
8.8%
1 5
 
2.5%
8 3
 
1.5%
5 2
 
1.0%
3 1
 
0.5%
Other values (7) 7
 
3.4%
Hangul
ValueCountFrequency (%)
국 26
 
3.8%
장 23
 
3.4%
수 22
 
3.2%
개 19
 
2.8%
염 19
 
2.8%
색 19
 
2.8%
육 18
 
2.7%
비 17
 
2.5%
살 17
 
2.5%
치 16
 
2.4%
Other values (184) 481
71.0%
CJK
ValueCountFrequency (%)
中 1
100.0%

가격3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)20.0%
Missing145
Missing (%)45.3%
Infinite0
Infinite (%)0.0%
Mean14414.286
Minimum1000
Maximum350000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T16:14:54.032166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q16000
median8000
Q313000
95-th percentile30000
Maximum350000
Range349000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation30952.895
Coefficient of variation (CV)2.1473762
Kurtosis86.982661
Mean14414.286
Median Absolute Deviation (MAD)3000
Skewness8.7449063
Sum2522500
Variance9.5808169 × 108
MonotonicityNot monotonic
2023-12-12T16:14:54.171766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7000 27
 
8.4%
8000 16
 
5.0%
6000 14
 
4.4%
10000 11
 
3.4%
13000 11
 
3.4%
9000 10
 
3.1%
5000 10
 
3.1%
15000 9
 
2.8%
20000 9
 
2.8%
3000 8
 
2.5%
Other values (25) 50
 
15.6%
(Missing) 145
45.3%
ValueCountFrequency (%)
1000 2
 
0.6%
1500 1
 
0.3%
2000 3
 
0.9%
3000 8
2.5%
3500 2
 
0.6%
4000 4
 
1.2%
4500 2
 
0.6%
5000 10
3.1%
5500 2
 
0.6%
6000 14
4.4%
ValueCountFrequency (%)
350000 1
 
0.3%
200000 1
 
0.3%
70000 1
 
0.3%
65000 1
 
0.3%
60000 1
 
0.3%
50000 1
 
0.3%
40000 2
0.6%
30000 2
0.6%
27000 2
0.6%
25000 4
1.2%

Interactions

2023-12-12T16:14:44.948663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:41.837753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.513052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.129712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.720704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.294092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:45.050850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:41.957896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.641253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.245198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.810169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.412425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:45.153359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.056473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.739246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.337552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.899318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.501110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:45.285768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.174763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.833978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.423191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.993974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.607707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:45.414737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.296803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.933047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.528869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.085100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.712650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:45.539472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:42.405514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.043547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:43.630738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.185280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:44.830282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:14:54.323220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구구분업종1(대분류)업종2(세분류)전체 메뉴 수착한가격 메뉴 수가격1가격2가격3
연번1.0000.9280.6030.0000.2050.3090.2040.2370.0000.000
시군구0.9281.0000.7480.2340.3510.1520.3200.2500.1620.000
구분0.6030.7481.0000.7580.7810.1870.0000.1960.3740.262
업종1(대분류)0.0000.2340.7581.0000.9900.3210.0000.4640.6510.679
업종2(세분류)0.2050.3510.7810.9901.0000.3370.0000.5940.7570.592
전체 메뉴 수0.3090.1520.1870.3210.3371.0000.3750.0000.0000.000
착한가격 메뉴 수0.2040.3200.0000.0000.0000.3751.0000.0000.0000.000
가격10.2370.2500.1960.4640.5940.0000.0001.0000.8190.997
가격20.0000.1620.3740.6510.7570.0000.0000.8191.0000.989
가격30.0000.0000.2620.6790.5920.0000.0000.9970.9891.000
2023-12-12T16:14:54.450095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구업종1(대분류)업종2(세분류)구분
시군구1.0000.0700.1410.558
업종1(대분류)0.0701.0000.9490.570
업종2(세분류)0.1410.9491.0000.566
구분0.5580.5700.5661.000
2023-12-12T16:14:54.552864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번전체 메뉴 수착한가격 메뉴 수가격1가격2가격3시군구구분업종1(대분류)업종2(세분류)
연번1.000-0.115-0.027-0.044-0.110-0.1620.7410.4030.0000.076
전체 메뉴 수-0.1151.0000.405-0.201-0.280-0.2140.0640.1110.1420.130
착한가격 메뉴 수-0.0270.4051.000-0.070-0.089-0.1490.1960.0000.0000.000
가격1-0.044-0.201-0.0701.0000.6810.7130.1080.1170.2180.265
가격2-0.110-0.280-0.0890.6811.0000.8430.0790.2640.4010.381
가격3-0.162-0.214-0.1490.7130.8431.0000.0000.1070.5030.379
시군구0.7410.0640.1960.1080.0790.0001.0000.5580.0700.141
구분0.4030.1110.0000.1170.2640.1070.5581.0000.5700.566
업종1(대분류)0.0000.1420.0000.2180.4010.5030.0700.5701.0000.949
업종2(세분류)0.0760.1300.0000.2650.3810.3790.1410.5660.9491.000

Missing values

2023-12-12T16:14:45.717695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:14:46.292271image/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.
2023-12-12T16:14:46.437301image/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

연번시도시군구구분업종1(대분류)업종2(세분류)업소명대표자명주소(도로명 주소)연락처전체 메뉴 수착한가격 메뉴 수품목1가격1품목2가격2품목3가격3
01충청북도청주시외식업한식한식_면류나누리장터(산더미해물칼국수)박경희충청북도 청주시 상당구 쇠내로 69-1(금천동)<NA>98장터칼국수4000바지락칼국수5000비빔국수5000
12충청북도청주시외식업한식한식_육류대박삼겹살김초충청북도 청주시 상당구 남사로89번길 56(서문동)043-224-399233삼겹살(200g)13000목살(200g)13000항정살(200g)17000
23충청북도청주시외식업한식한식_육류야간비행정광옥충청북도 청주시 상당구 남사로89번길 63(서문동)043-257-822233삼겹살(200g)13000꽃목살(200g)13000뽈대기살(200g)13000
34충청북도청주시외식업한식한식_육류충주돌구이김윤희충청북도 청주시 상당구 남사로89번길 37(서문동)043-253-053164삼겹살(200g)12000목살(200g)12000항정살(200g)17000
45충청북도청주시외식업한식한식_육류한우일번지김길운충청북도 청주시 상당구 남일면 고은두산로 29043-292-123862한우등심(500g)75000특수모듬(500g)75000<NA><NA>
56충청북도청주시외식업한식한식_육류삼흥집우영철충청북도 청주시 상당구 남일면 단재로 748043-297-516233삼겹살(200g)13000목살(200g)13000항정살(200g)13000
67충청북도청주시외식업한식한식_일반점심시간엔도미충청북도 청주시 상당구 대성로 164(수동)043-223-722111한식뷔페4500<NA><NA><NA><NA>
78충청북도청주시외식업중식중식청해루이상범충청북도 청주시 상당구 대성로 38(서운동)043-221-6101402짬뽕8000미니탕수육9000<NA><NA>
89충청북도청주시외식업한식한식_육류순신토불이식당김진순충청북도 청주시 상당구 무농정로 116-6(용암동)043-293-2423113삼겹살(250g)13000목살(250g)13000돈육갈비(450g)13000
910충청북도청주시외식업한식한식_육류한식더고기김영일충청북도 청주시 상당구 무심동로372번길 21-14(서문동)043-223-8259103삼겹살(200g)13000목살(200g)15000짜글이8000
연번시도시군구구분업종1(대분류)업종2(세분류)업소명대표자명주소(도로명 주소)연락처전체 메뉴 수착한가격 메뉴 수품목1가격1품목2가격2품목3가격3
310311충청북도단양군요식업한식한식_일반놀부순대배명숙충북 단양군 매포읍 평동24길 3-7043-421-195072순대국밥7000돼지곱창전골(소)20000<NA><NA>
311312충청북도단양군요식업한식한식_일반오뎅과튀김곽희숙충북 단양군 매포읍 평동3로 6043-421-5355124우동3000라면3000튀김(2~3개)1000
312313충청북도단양군요식업한식한식_육류시골왕족김경자충북 단양군 매포읍 평동24길 3-8043-422-708072족발(소)25000보쌈(소)25000<NA><NA>
313314충청북도단양군요식업한식한식_일반감골묵촌석미향충북 단양군 매포읍 평동23길 13043-422-497652도토리묵밥7000오삼불고기10000<NA><NA>
314315충청북도단양군요식업한식한식_일반할매주막김봉희충북 단양군 대강면 두음1길 1043-422-5008132손칼국수6000김치찌개7000<NA><NA>
315316충청북도단양군요식업한식한식_일반준이네조경자충북 단양군 영춘면 온달평강로 76043-423-1053122뼈해장국7000백반7000<NA><NA>
316317충청북도단양군비요식업미용업미용업한별미용실김경희충북 단양군 단양읍 상진로 66043-422-4247205커트(학생)10000커트(성인여성)12000커트(성인남성)12000
317318충청북도단양군비요식업미용업미용업보람머리방강영미충북 단양군 단양읍 상진7길 5043-423-3978205커트(학생)7000커트(성인여성)8000커트(성인남성)9000
318319충청북도단양군비요식업미용업미용업준헤어샵윤창옥충북 단양군 단양읍 도전7길 13-3043-423-1655114커트(학생)8000커트(성인)10000염색(기본)20000
319320충청북도단양군비요식업미용업미용업서현미용실오미자충북 단양군 매포읍 평동24길 3-4043-421-8189155커트(학생)8000커트(성인남성)12000커트(성인여성)10000