Overview

Dataset statistics

Number of variables15
Number of observations122
Missing cells153
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.1 KiB
Average record size in memory127.1 B

Variable types

Categorical6
Numeric3
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분,소재지전화번호
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-11526/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정번호 and 6 other fieldsHigh correlation
지정년도 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
지정일자 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High correlation
지정번호 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정번호 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
업태명 is highly imbalanced (54.5%)Imbalance
주된음식 has 112 (91.8%) missing valuesMissing
소재지전화번호 has 41 (33.6%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명 has unique valuesUnique
허가(신고)번호 has unique valuesUnique

Reproduction

Analysis started2024-05-10 23:44:01.132086
Analysis finished2024-05-10 23:44:05.914185
Duration4.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3200000
122 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 122
100.0%

Length

2024-05-10T23:44:06.126163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:44:06.455062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 122
100.0%

지정년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2018
70 
2023
21 
2019
12 
2021
11 
2020
8 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2019
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 70
57.4%
2023 21
 
17.2%
2019 12
 
9.8%
2021 11
 
9.0%
2020 8
 
6.6%

Length

2024-05-10T23:44:06.800990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:44:07.140559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 70
57.4%
2023 21
 
17.2%
2019 12
 
9.8%
2021 11
 
9.0%
2020 8
 
6.6%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.762295
Minimum1
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-10T23:44:07.541395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median20.5
Q369.75
95-th percentile114.95
Maximum129
Range128
Interquartile range (IQR)61.75

Descriptive statistics

Standard deviation39.316485
Coefficient of variation (CV)0.98878812
Kurtosis-0.72295866
Mean39.762295
Median Absolute Deviation (MAD)17.5
Skewness0.84410669
Sum4851
Variance1545.786
MonotonicityNot monotonic
2024-05-10T23:44:07.990778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
4.1%
7 5
 
4.1%
10 5
 
4.1%
9 5
 
4.1%
2 5
 
4.1%
4 5
 
4.1%
6 4
 
3.3%
8 4
 
3.3%
11 3
 
2.5%
1 3
 
2.5%
Other values (70) 78
63.9%
ValueCountFrequency (%)
1 3
2.5%
2 5
4.1%
3 5
4.1%
4 5
4.1%
5 3
2.5%
6 4
3.3%
7 5
4.1%
8 4
3.3%
9 5
4.1%
10 5
4.1%
ValueCountFrequency (%)
129 1
0.8%
125 1
0.8%
122 1
0.8%
120 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
111 1
0.8%
109 1
0.8%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20157446
Minimum20010630
Maximum20231204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-10T23:44:08.403846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010630
5-th percentile20050614
Q120090510
median20180831
Q320211031
95-th percentile20230831
Maximum20231204
Range220574
Interquartile range (IQR)120521

Descriptive statistics

Standard deviation62751.175
Coefficient of variation (CV)0.0031130519
Kurtosis-0.81241967
Mean20157446
Median Absolute Deviation (MAD)49900.5
Skewness-0.63834014
Sum2.4592084 × 109
Variance3.9377099 × 109
MonotonicityNot monotonic
2024-05-10T23:44:08.771688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20230831 20
16.4%
20190830 12
9.8%
20211031 11
9.0%
20170920 9
 
7.4%
20180831 9
 
7.4%
20080710 8
 
6.6%
20200831 8
 
6.6%
20050614 7
 
5.7%
20160920 7
 
5.7%
20070720 5
 
4.1%
Other values (16) 26
21.3%
ValueCountFrequency (%)
20010630 2
 
1.6%
20011230 1
 
0.8%
20050614 7
5.7%
20051205 1
 
0.8%
20060510 1
 
0.8%
20060703 1
 
0.8%
20070720 5
4.1%
20080710 8
6.6%
20090313 4
3.3%
20090510 2
 
1.6%
ValueCountFrequency (%)
20231204 1
 
0.8%
20230831 20
16.4%
20211031 11
9.0%
20200831 8
 
6.6%
20190830 12
9.8%
20181109 1
 
0.8%
20180831 9
7.4%
20170920 9
7.4%
20160920 7
 
5.7%
20150616 2
 
1.6%

지정일자
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
20181109
70 
20231204
21 
20191111
12 
20211115
11 
20201111
8 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20181109
2nd row20191111
3rd row20181109
4th row20181109
5th row20181109

Common Values

ValueCountFrequency (%)
20181109 70
57.4%
20231204 21
 
17.2%
20191111 12
 
9.8%
20211115 11
 
9.0%
20201111 8
 
6.6%

Length

2024-05-10T23:44:09.086548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:44:09.280308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20181109 70
57.4%
20231204 21
 
17.2%
20191111 12
 
9.8%
20211115 11
 
9.0%
20201111 8
 
6.6%

업소명
Text

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-10T23:44:09.733469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length6.6311475
Min length2

Characters and Unicode

Total characters809
Distinct characters283
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

Unique122 ?
Unique (%)100.0%

Sample

1st row신호등장작구이&원주골돌솥추어탕
2nd row정성
3rd row짬뽕지존 봉천점
4th row양평해장국
5th row탐나종합어시장(난곡사거리점)
ValueCountFrequency (%)
신림점 4
 
2.6%
명태어장 2
 
1.3%
신호등장작구이&원주골돌솥추어탕 1
 
0.7%
광시생고기 1
 
0.7%
미가할매집 1
 
0.7%
상도늘보리 1
 
0.7%
봉천역점 1
 
0.7%
남원추어탕 1
 
0.7%
청진동해장국 1
 
0.7%
정수사(鄭壽司 1
 
0.7%
Other values (138) 138
90.8%
2024-05-10T23:44:10.515833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
3.7%
장 20
 
2.5%
점 19
 
2.3%
구 15
 
1.9%
국 13
 
1.6%
가 12
 
1.5%
이 12
 
1.5%
탕 11
 
1.4%
수 11
 
1.4%
지 11
 
1.4%
Other values (273) 655
81.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 741
91.6%
Space Separator 30
 
3.7%
Close Punctuation 8
 
1.0%
Open Punctuation 8
 
1.0%
Decimal Number 7
 
0.9%
Lowercase Letter 7
 
0.9%
Other Punctuation 6
 
0.7%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 20
 
2.7%
점 19
 
2.6%
구 15
 
2.0%
국 13
 
1.8%
가 12
 
1.6%
이 12
 
1.6%
탕 11
 
1.5%
수 11
 
1.5%
지 11
 
1.5%
리 10
 
1.3%
Other values (254) 607
81.9%
Lowercase Letter
ValueCountFrequency (%)
u 2
28.6%
a 1
14.3%
b 1
14.3%
n 1
14.3%
e 1
14.3%
v 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 2
28.6%
0 2
28.6%
4 1
14.3%
8 1
14.3%
5 1
14.3%
Other Punctuation
ValueCountFrequency (%)
& 3
50.0%
. 2
33.3%
/ 1
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
J 1
50.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 736
91.0%
Common 59
 
7.3%
Latin 9
 
1.1%
Han 5
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 20
 
2.7%
점 19
 
2.6%
구 15
 
2.0%
국 13
 
1.8%
가 12
 
1.6%
이 12
 
1.6%
탕 11
 
1.5%
수 11
 
1.5%
지 11
 
1.5%
리 10
 
1.4%
Other values (249) 602
81.8%
Common
ValueCountFrequency (%)
30
50.8%
) 8
 
13.6%
( 8
 
13.6%
& 3
 
5.1%
1 2
 
3.4%
. 2
 
3.4%
0 2
 
3.4%
4 1
 
1.7%
8 1
 
1.7%
5 1
 
1.7%
Latin
ValueCountFrequency (%)
u 2
22.2%
C 1
11.1%
a 1
11.1%
b 1
11.1%
n 1
11.1%
e 1
11.1%
J 1
11.1%
v 1
11.1%
Han
ValueCountFrequency (%)
鄭 1
20.0%
壽 1
20.0%
司 1
20.0%
東 1
20.0%
海 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 736
91.0%
ASCII 68
 
8.4%
CJK 5
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
44.1%
) 8
 
11.8%
( 8
 
11.8%
& 3
 
4.4%
1 2
 
2.9%
. 2
 
2.9%
u 2
 
2.9%
0 2
 
2.9%
C 1
 
1.5%
a 1
 
1.5%
Other values (9) 9
 
13.2%
Hangul
ValueCountFrequency (%)
장 20
 
2.7%
점 19
 
2.6%
구 15
 
2.0%
국 13
 
1.8%
가 12
 
1.6%
이 12
 
1.6%
탕 11
 
1.5%
수 11
 
1.5%
지 11
 
1.5%
리 10
 
1.4%
Other values (249) 602
81.8%
CJK
ValueCountFrequency (%)
鄭 1
20.0%
壽 1
20.0%
司 1
20.0%
東 1
20.0%
海 1
20.0%

소재지도로명
Text

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-10T23:44:11.202454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length43
Mean length29.401639
Min length22

Characters and Unicode

Total characters3587
Distinct characters103
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)100.0%

Sample

1st row서울특별시 관악구 봉천로 190, 2층 (신림동)
2nd row서울특별시 관악구 관악로 154-5, 2층 (봉천동)
3rd row서울특별시 관악구 남부순환로 1770, (봉천동, 지상1층)
4th row서울특별시 관악구 봉천로 229, (봉천동,(972번지 3호))
5th row서울특별시 관악구 남부순환로 1481, 1층 (신림동)
ValueCountFrequency (%)
서울특별시 122
17.1%
관악구 122
17.1%
1층 53
 
7.4%
신림동 52
 
7.3%
봉천동 46
 
6.5%
봉천로 19
 
2.7%
2층 16
 
2.2%
남부순환로 16
 
2.2%
관악로 11
 
1.5%
신림로 9
 
1.3%
Other values (189) 247
34.6%
2024-05-10T23:44:12.141528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
592
 
16.5%
1 158
 
4.4%
, 154
 
4.3%
관 142
 
4.0%
악 142
 
4.0%
동 125
 
3.5%
) 123
 
3.4%
( 123
 
3.4%
서 123
 
3.4%
시 123
 
3.4%
Other values (93) 1782
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1996
55.6%
Space Separator 592
 
16.5%
Decimal Number 556
 
15.5%
Other Punctuation 154
 
4.3%
Close Punctuation 123
 
3.4%
Open Punctuation 123
 
3.4%
Uppercase Letter 19
 
0.5%
Dash Punctuation 18
 
0.5%
Math Symbol 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 142
 
7.1%
악 142
 
7.1%
동 125
 
6.3%
서 123
 
6.2%
시 123
 
6.2%
구 122
 
6.1%
울 122
 
6.1%
특 122
 
6.1%
별 122
 
6.1%
로 105
 
5.3%
Other values (66) 748
37.5%
Uppercase Letter
ValueCountFrequency (%)
B 5
26.3%
E 3
15.8%
O 2
 
10.5%
C 2
 
10.5%
A 1
 
5.3%
P 1
 
5.3%
R 1
 
5.3%
S 1
 
5.3%
I 1
 
5.3%
D 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 158
28.4%
2 104
18.7%
3 54
 
9.7%
4 44
 
7.9%
6 43
 
7.7%
0 42
 
7.6%
5 42
 
7.6%
8 27
 
4.9%
7 22
 
4.0%
9 20
 
3.6%
Space Separator
ValueCountFrequency (%)
592
100.0%
Other Punctuation
ValueCountFrequency (%)
, 154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1996
55.6%
Common 1572
43.8%
Latin 19
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 142
 
7.1%
악 142
 
7.1%
동 125
 
6.3%
서 123
 
6.2%
시 123
 
6.2%
구 122
 
6.1%
울 122
 
6.1%
특 122
 
6.1%
별 122
 
6.1%
로 105
 
5.3%
Other values (66) 748
37.5%
Common
ValueCountFrequency (%)
592
37.7%
1 158
 
10.1%
, 154
 
9.8%
) 123
 
7.8%
( 123
 
7.8%
2 104
 
6.6%
3 54
 
3.4%
4 44
 
2.8%
6 43
 
2.7%
0 42
 
2.7%
Other values (6) 135
 
8.6%
Latin
ValueCountFrequency (%)
B 5
26.3%
E 3
15.8%
O 2
 
10.5%
C 2
 
10.5%
A 1
 
5.3%
P 1
 
5.3%
R 1
 
5.3%
S 1
 
5.3%
I 1
 
5.3%
D 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1996
55.6%
ASCII 1591
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
592
37.2%
1 158
 
9.9%
, 154
 
9.7%
) 123
 
7.7%
( 123
 
7.7%
2 104
 
6.5%
3 54
 
3.4%
4 44
 
2.8%
6 43
 
2.7%
0 42
 
2.6%
Other values (17) 154
 
9.7%
Hangul
ValueCountFrequency (%)
관 142
 
7.1%
악 142
 
7.1%
동 125
 
6.3%
서 123
 
6.2%
시 123
 
6.2%
구 122
 
6.1%
울 122
 
6.1%
특 122
 
6.1%
별 122
 
6.1%
로 105
 
5.3%
Other values (66) 748
37.5%

소재지지번
Text

Distinct119
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-10T23:44:12.649441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length40
Mean length27.655738
Min length23

Characters and Unicode

Total characters3374
Distinct characters65
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)95.1%

Sample

1st row서울특별시 관악구 신림동 1445번지 14호
2nd row서울특별시 관악구 봉천동 1598번지 2호
3rd row서울특별시 관악구 봉천동 894번지 20호
4th row서울특별시 관악구 봉천동 972번지 4호 (972번지 3호)
5th row서울특별시 관악구 신림동 527번지 9호
ValueCountFrequency (%)
서울특별시 122
18.9%
관악구 122
18.9%
신림동 60
 
9.3%
봉천동 53
 
8.2%
1호 12
 
1.9%
지상1층 12
 
1.9%
남현동 9
 
1.4%
2호 7
 
1.1%
5호 7
 
1.1%
1598번지 7
 
1.1%
Other values (155) 233
36.2%
2024-05-10T23:44:13.489264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
859
25.5%
1 170
 
5.0%
지 147
 
4.4%
번 123
 
3.6%
호 123
 
3.6%
서 122
 
3.6%
구 122
 
3.6%
동 122
 
3.6%
울 122
 
3.6%
악 122
 
3.6%
Other values (55) 1342
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1808
53.6%
Space Separator 859
25.5%
Decimal Number 675
 
20.0%
Uppercase Letter 18
 
0.5%
Other Punctuation 9
 
0.3%
Dash Punctuation 2
 
0.1%
Math Symbol 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 147
 
8.1%
번 123
 
6.8%
호 123
 
6.8%
서 122
 
6.7%
구 122
 
6.7%
동 122
 
6.7%
울 122
 
6.7%
악 122
 
6.7%
관 122
 
6.7%
시 122
 
6.7%
Other values (29) 561
31.0%
Decimal Number
ValueCountFrequency (%)
1 170
25.2%
2 99
14.7%
6 81
12.0%
5 60
 
8.9%
4 57
 
8.4%
3 49
 
7.3%
0 46
 
6.8%
8 41
 
6.1%
9 40
 
5.9%
7 32
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 5
27.8%
E 3
16.7%
C 2
 
11.1%
O 2
 
11.1%
N 1
 
5.6%
D 1
 
5.6%
I 1
 
5.6%
S 1
 
5.6%
R 1
 
5.6%
P 1
 
5.6%
Space Separator
ValueCountFrequency (%)
859
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1808
53.6%
Common 1548
45.9%
Latin 18
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 147
 
8.1%
번 123
 
6.8%
호 123
 
6.8%
서 122
 
6.7%
구 122
 
6.7%
동 122
 
6.7%
울 122
 
6.7%
악 122
 
6.7%
관 122
 
6.7%
시 122
 
6.7%
Other values (29) 561
31.0%
Common
ValueCountFrequency (%)
859
55.5%
1 170
 
11.0%
2 99
 
6.4%
6 81
 
5.2%
5 60
 
3.9%
4 57
 
3.7%
3 49
 
3.2%
0 46
 
3.0%
8 41
 
2.6%
9 40
 
2.6%
Other values (6) 46
 
3.0%
Latin
ValueCountFrequency (%)
B 5
27.8%
E 3
16.7%
C 2
 
11.1%
O 2
 
11.1%
N 1
 
5.6%
D 1
 
5.6%
I 1
 
5.6%
S 1
 
5.6%
R 1
 
5.6%
P 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1808
53.6%
ASCII 1566
46.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
859
54.9%
1 170
 
10.9%
2 99
 
6.3%
6 81
 
5.2%
5 60
 
3.8%
4 57
 
3.6%
3 49
 
3.1%
0 46
 
2.9%
8 41
 
2.6%
9 40
 
2.6%
Other values (16) 64
 
4.1%
Hangul
ValueCountFrequency (%)
지 147
 
8.1%
번 123
 
6.8%
호 123
 
6.8%
서 122
 
6.7%
구 122
 
6.7%
동 122
 
6.7%
울 122
 
6.7%
악 122
 
6.7%
관 122
 
6.7%
시 122
 
6.7%
Other values (29) 561
31.0%

허가(신고)번호
Text

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-10T23:44:13.926836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2684
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

Unique122 ?
Unique (%)100.0%

Sample

1st row3200000-101-2016-00216
2nd row3200000-101-2016-00035
3rd row3200000-101-2007-00052
4th row3200000-101-1995-02581
5th row3200000-101-2014-00365
ValueCountFrequency (%)
3200000-101-2016-00216 1
 
0.8%
3200000-101-1996-06099 1
 
0.8%
3200000-101-2003-00386 1
 
0.8%
3200000-101-2003-00216 1
 
0.8%
3200000-101-1997-06015 1
 
0.8%
3200000-101-2007-00080 1
 
0.8%
3200000-101-2002-00083 1
 
0.8%
3200000-101-1997-05900 1
 
0.8%
3200000-101-2005-00118 1
 
0.8%
3200000-101-2009-00003 1
 
0.8%
Other values (112) 112
91.8%
2024-05-10T23:44:14.828508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1137
42.4%
1 384
 
14.3%
- 366
 
13.6%
2 276
 
10.3%
3 184
 
6.9%
9 89
 
3.3%
5 58
 
2.2%
6 57
 
2.1%
4 49
 
1.8%
7 48
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2318
86.4%
Dash Punctuation 366
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1137
49.1%
1 384
 
16.6%
2 276
 
11.9%
3 184
 
7.9%
9 89
 
3.8%
5 58
 
2.5%
6 57
 
2.5%
4 49
 
2.1%
7 48
 
2.1%
8 36
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1137
42.4%
1 384
 
14.3%
- 366
 
13.6%
2 276
 
10.3%
3 184
 
6.9%
9 89
 
3.3%
5 58
 
2.2%
6 57
 
2.1%
4 49
 
1.8%
7 48
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1137
42.4%
1 384
 
14.3%
- 366
 
13.6%
2 276
 
10.3%
3 184
 
6.9%
9 89
 
3.3%
5 58
 
2.2%
6 57
 
2.1%
4 49
 
1.8%
7 48
 
1.8%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
한식
91 
일식
 
7
중국식
 
5
경양식
 
3
회집
 
3
Other values (7)
13 

Length

Max length15
Median length2
Mean length2.4180328
Min length2

Unique

Unique3 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
한식 91
74.6%
일식 7
 
5.7%
중국식 5
 
4.1%
경양식 3
 
2.5%
회집 3
 
2.5%
호프/통닭 3
 
2.5%
분식 3
 
2.5%
기타 2
 
1.6%
식육(숯불구이) 2
 
1.6%
뷔페식 1
 
0.8%
Other values (2) 2
 
1.6%

Length

2024-05-10T23:44:15.265569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 91
74.6%
일식 7
 
5.7%
중국식 5
 
4.1%
경양식 3
 
2.5%
회집 3
 
2.5%
호프/통닭 3
 
2.5%
분식 3
 
2.5%
기타 2
 
1.6%
식육(숯불구이 2
 
1.6%
뷔페식 1
 
0.8%
Other values (2) 2
 
1.6%

주된음식
Text

MISSING 

Distinct7
Distinct (%)70.0%
Missing112
Missing (%)91.8%
Memory size1.1 KiB
2024-05-10T23:44:15.588818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.1
Min length2

Characters and Unicode

Total characters31
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)50.0%

Sample

1st row초밥
2nd row초밥
3rd row삼계탕
4th row돈가스
5th row순대국
ValueCountFrequency (%)
초밥 3
30.0%
순대국 2
20.0%
삼계탕 1
 
10.0%
돈가스 1
 
10.0%
찜류 1
 
10.0%
명태조림김치찜 1
 
10.0%
꿔바로우 1
 
10.0%
2024-05-10T23:44:16.094133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
초 3
 
9.7%
밥 3
 
9.7%
국 2
 
6.5%
찜 2
 
6.5%
대 2
 
6.5%
순 2
 
6.5%
태 1
 
3.2%
로 1
 
3.2%
바 1
 
3.2%
꿔 1
 
3.2%
Other values (13) 13
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
초 3
 
9.7%
밥 3
 
9.7%
국 2
 
6.5%
찜 2
 
6.5%
대 2
 
6.5%
순 2
 
6.5%
태 1
 
3.2%
로 1
 
3.2%
바 1
 
3.2%
꿔 1
 
3.2%
Other values (13) 13
41.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
초 3
 
9.7%
밥 3
 
9.7%
국 2
 
6.5%
찜 2
 
6.5%
대 2
 
6.5%
순 2
 
6.5%
태 1
 
3.2%
로 1
 
3.2%
바 1
 
3.2%
꿔 1
 
3.2%
Other values (13) 13
41.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
초 3
 
9.7%
밥 3
 
9.7%
국 2
 
6.5%
찜 2
 
6.5%
대 2
 
6.5%
순 2
 
6.5%
태 1
 
3.2%
로 1
 
3.2%
바 1
 
3.2%
꿔 1
 
3.2%
Other values (13) 13
41.9%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.89754
Minimum0
Maximum1924.11
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-10T23:44:16.346402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.1545
Q172.6375
median99.72
Q3155.6825
95-th percentile480.6685
Maximum1924.11
Range1924.11
Interquartile range (IQR)83.045

Descriptive statistics

Standard deviation228.59353
Coefficient of variation (CV)1.3862761
Kurtosis31.447504
Mean164.89754
Median Absolute Deviation (MAD)43.455
Skewness4.9971824
Sum20117.5
Variance52255.001
MonotonicityNot monotonic
2024-05-10T23:44:16.809941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0 2
 
1.6%
49.5 2
 
1.6%
186.6 1
 
0.8%
198.0 1
 
0.8%
128.7 1
 
0.8%
61.38 1
 
0.8%
0.0 1
 
0.8%
98.67 1
 
0.8%
155.8 1
 
0.8%
220.0 1
 
0.8%
Other values (110) 110
90.2%
ValueCountFrequency (%)
0.0 1
0.8%
24.42 1
0.8%
30.84 1
0.8%
33.56 1
0.8%
34.0 1
0.8%
40.0 1
0.8%
40.01 1
0.8%
42.9 1
0.8%
42.92 1
0.8%
44.71 1
0.8%
ValueCountFrequency (%)
1924.11 1
0.8%
996.01 1
0.8%
955.35 1
0.8%
900.36 1
0.8%
690.03 1
0.8%
494.97 1
0.8%
481.63 1
0.8%
462.4 1
0.8%
363.4 1
0.8%
322.0 1
0.8%

행정동명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
낙성대동
20 
신림동
13 
청룡동
9 
남현동
9 
서원동
9 
Other values (15)
62 

Length

Max length4
Median length3
Mean length3.1967213
Min length3

Unique

Unique4 ?
Unique (%)3.3%

Sample

1st row신림동
2nd row낙성대동
3rd row청룡동
4th row보라매동
5th row신사동

Common Values

ValueCountFrequency (%)
낙성대동 20
16.4%
신림동 13
10.7%
청룡동 9
 
7.4%
남현동 9
 
7.4%
서원동 9
 
7.4%
행운동 7
 
5.7%
조원동 7
 
5.7%
미성동 6
 
4.9%
대학동 6
 
4.9%
난곡동 6
 
4.9%
Other values (10) 30
24.6%

Length

2024-05-10T23:44:17.109365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙성대동 20
16.4%
신림동 13
10.7%
청룡동 9
 
7.4%
남현동 9
 
7.4%
서원동 9
 
7.4%
행운동 7
 
5.7%
조원동 7
 
5.7%
난곡동 6
 
4.9%
은천동 6
 
4.9%
대학동 6
 
4.9%
Other values (10) 30
24.6%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
상수도전용
86 
<NA>
36 

Length

Max length5
Median length5
Mean length4.704918
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 86
70.5%
<NA> 36
29.5%

Length

2024-05-10T23:44:17.339871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:44:17.517535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 86
70.5%
na 36
29.5%

소재지전화번호
Text

MISSING 

Distinct81
Distinct (%)100.0%
Missing41
Missing (%)33.6%
Memory size1.1 KiB
2024-05-10T23:44:17.879930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.901235
Min length10

Characters and Unicode

Total characters883
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

Unique81 ?
Unique (%)100.0%

Sample

1st row02 8754500
2nd row02 8850090
3rd row02 888 2235
4th row02 875 2366
5th row02 62070056
ValueCountFrequency (%)
02 78
39.8%
877 4
 
2.0%
888 4
 
2.0%
875 3
 
1.5%
883 3
 
1.5%
882 2
 
1.0%
887 2
 
1.0%
873 2
 
1.0%
865 2
 
1.0%
070 2
 
1.0%
Other values (94) 94
48.0%
2024-05-10T23:44:18.862764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 154
17.4%
147
16.6%
2 128
14.5%
0 125
14.2%
7 65
7.4%
5 60
 
6.8%
3 56
 
6.3%
6 41
 
4.6%
1 40
 
4.5%
9 39
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 736
83.4%
Space Separator 147
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 154
20.9%
2 128
17.4%
0 125
17.0%
7 65
8.8%
5 60
 
8.2%
3 56
 
7.6%
6 41
 
5.6%
1 40
 
5.4%
9 39
 
5.3%
4 28
 
3.8%
Space Separator
ValueCountFrequency (%)
147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 154
17.4%
147
16.6%
2 128
14.5%
0 125
14.2%
7 65
7.4%
5 60
 
6.8%
3 56
 
6.3%
6 41
 
4.6%
1 40
 
4.5%
9 39
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 154
17.4%
147
16.6%
2 128
14.5%
0 125
14.2%
7 65
7.4%
5 60
 
6.8%
3 56
 
6.3%
6 41
 
4.6%
1 40
 
4.5%
9 39
 
4.4%

Interactions

2024-05-10T23:44:03.794475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:02.486224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:03.056266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:04.048870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:02.669722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:03.301509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:04.369843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:02.862334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:44:03.533108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:44:19.182900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자업태명주된음식영업장면적(㎡)행정동명소재지전화번호
지정년도1.0000.7010.9441.0000.3180.0000.0000.5071.000
지정번호0.7011.0000.9130.7010.000NaN0.5270.5841.000
신청일자0.9440.9131.0000.9440.000NaN0.4380.5871.000
지정일자1.0000.7010.9441.0000.3180.0000.0000.5071.000
업태명0.3180.0000.0000.3181.0001.0000.5730.0191.000
주된음식0.000NaNNaN0.0001.0001.0001.0000.5541.000
영업장면적(㎡)0.0000.5270.4380.0000.5731.0001.0000.0001.000
행정동명0.5070.5840.5870.5070.0190.5540.0001.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-10T23:44:19.555324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분지정년도행정동명지정일자업태명
급수시설구분1.0001.0001.0001.0001.000
지정년도1.0001.0000.2241.0000.174
행정동명1.0000.2241.0000.2240.000
지정일자1.0001.0000.2241.0000.174
업태명1.0000.1740.0000.1741.000
2024-05-10T23:44:19.946967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호신청일자영업장면적(㎡)지정년도지정일자업태명행정동명급수시설구분
지정번호1.000-0.7010.4420.3560.3560.0000.2011.000
신청일자-0.7011.000-0.4670.6630.6630.0000.1991.000
영업장면적(㎡)0.442-0.4671.0000.0000.0000.3160.0001.000
지정년도0.3560.6630.0001.0001.0000.1740.2241.000
지정일자0.3560.6630.0001.0001.0000.1740.2241.000
업태명0.0000.0000.3160.1740.1741.0000.0001.000
행정동명0.2010.1990.0000.2240.2240.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-10T23:44:04.700847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:44:05.320296image/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.
2024-05-10T23:44:05.733839image/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

시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
032000002018712016092020181109신호등장작구이&원주골돌솥추어탕서울특별시 관악구 봉천로 190, 2층 (신림동)서울특별시 관악구 신림동 1445번지 14호3200000-101-2016-00216한식<NA>275.0신림동상수도전용02 8754500
13200000201932019083020191111정성서울특별시 관악구 관악로 154-5, 2층 (봉천동)서울특별시 관악구 봉천동 1598번지 2호3200000-101-2016-00035일식초밥66.0낙성대동상수도전용02 8850090
232000002018812013070120181109짬뽕지존 봉천점서울특별시 관악구 남부순환로 1770, (봉천동, 지상1층)서울특별시 관악구 봉천동 894번지 20호3200000-101-2007-00052한식<NA>152.4청룡동<NA>02 888 2235
3320000020181142008071020181109양평해장국서울특별시 관악구 봉천로 229, (봉천동,(972번지 3호))서울특별시 관악구 봉천동 972번지 4호 (972번지 3호)3200000-101-1995-02581한식<NA>133.47보라매동상수도전용02 875 2366
432000002018742015061620181109탐나종합어시장(난곡사거리점)서울특별시 관악구 남부순환로 1481, 1층 (신림동)서울특별시 관악구 신림동 527번지 9호3200000-101-2014-00365한식<NA>188.0신사동상수도전용02 62070056
532000002023102023120420231204완산정서울특별시 관악구 봉천로 484, 2층 (봉천동)서울특별시 관악구 봉천동 858번지 2호 지상2층3200000-101-1979-00193한식<NA>56.29행운동상수도전용02 8783400
632000002023112023083120231204이태리파파서울특별시 관악구 관악로12길 3-8, (봉천동, 지상1층)서울특별시 관악구 봉천동 1598번지 26호3200000-101-2010-00051경양식<NA>75.02낙성대동<NA><NA>
73200000201912019083020191111스시건서울특별시 관악구 호암로24길 35, 1층 (신림동)서울특별시 관악구 신림동 1522번지 6호3200000-101-2014-00049일식초밥50.6대학동상수도전용070 88733541
83200000202092020083120201111이선생 신림점서울특별시 관악구 대학6길 22, 지하1층 (신림동)서울특별시 관악구 신림동 1537번지 5호3200000-101-2003-00290한식삼계탕57.9대학동<NA>02 8747455
932000002019162019083020191111라공방 신림점서울특별시 관악구 신림로 340, 르네상스복합쇼핑몰 1층 53~88호 (신림동)서울특별시 관악구 신림동 1422번지 5호 르네상스복합쇼핑몰3200000-101-2019-00305중국식<NA>124.82신림동<NA><NA>
시군구코드지정년도지정번호신청일자지정일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분소재지전화번호
1123200000202122021103120211115일구칠구 매운탕서울특별시 관악구 난곡로66가길 6, 1층 (신림동)서울특별시 관악구 신림동 528번지 19호3200000-101-2021-00242한식<NA>100.77신사동상수도전용<NA>
11332000002018892012062920181109굿맘할매순대국 & 양선지해장국서울특별시 관악구 봉천로 594, (봉천동)서울특별시 관악구 봉천동 1632번지 1호3200000-101-2001-09538한식<NA>78.2인헌동<NA><NA>
11432000002018562017092020181109풍무 양꼬치서울특별시 관악구 시흥대로 572, 2층 (신림동)서울특별시 관악구 신림동 1643번지 6호3200000-101-2011-00363중국식<NA>363.4조원동<NA>02 809 9292
11532000002018282005061420181109신안산낙지철판서울특별시 관악구 봉천로 213-20, 1층 (봉천동)서울특별시 관악구 봉천동 972번지 29호3200000-101-2003-00211한식<NA>78.89보라매동<NA>02 8861265
11632000002018522017092020181109한고을식당서울특별시 관악구 난곡로 100, A동 1층 101호 (신림동, 파로스프라자)서울특별시 관악구 신림동 1738번지3200000-101-2014-00393한식<NA>143.2난향동상수도전용<NA>
1173200000202132021103120211115서천아구대구뽈찜탕서울특별시 관악구 남현1길 40, 유일빌딩 1층 (남현동)서울특별시 관악구 남현동 1062번지 1호 유일빌딩3200000-101-2018-00279한식<NA>91.6남현동<NA><NA>
11832000002023162023083120231204키라키라윤 샤로수길점서울특별시 관악구 관악로 154-5, 1층 (봉천동)서울특별시 관악구 봉천동 1598번지 2호3200000-101-2022-00234한식<NA>90.91낙성대동상수도전용<NA>
11932000002020102020083120201111만성찬팅서울특별시 관악구 신림로 322-4, 지하1층 (신림동)서울특별시 관악구 신림동 75번지 41호3200000-101-2013-00369호프/통닭꿔바로우320.73서원동상수도전용02 8831978
1203200000202192021103120211115정숙성서울특별시 관악구 남부순환로226길 31, 1층 (봉천동)서울특별시 관악구 봉천동 1603번지 3호3200000-101-2021-00062한식<NA>189.81낙성대동상수도전용<NA>
12132000002018232008071020181109대호아구집서울특별시 관악구 남부순환로 1829-9, (봉천동)서울특별시 관악구 봉천동 858번지 4호3200000-101-1983-00400한식<NA>110.34행운동상수도전용02 8891700