Overview

Dataset statistics

Number of variables9
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory78.6 B

Variable types

Numeric4
Categorical1
Text4

Alerts

RSTRNT_ID has unique valuesUnique
RSTRNT_NM has unique valuesUnique
RSTRNT_ROAD_NM_ADDR has unique valuesUnique
RSTRNT_LNM_ADDR has unique valuesUnique
RSTRNT_TEL_NO has unique valuesUnique
RSTRNT_LA has unique valuesUnique
RSTRNT_LO has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:07:02.310858
Analysis finished2023-12-10 10:07:07.565092
Duration5.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483906.44
Minimum759
Maximum805559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:07:08.358400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum759
5-th percentile4850.05
Q1286897
median541138
Q3717932.5
95-th percentile792483.25
Maximum805559
Range804800
Interquartile range (IQR)431035.5

Descriptive statistics

Standard deviation265836.54
Coefficient of variation (CV)0.54935523
Kurtosis-0.94569803
Mean483906.44
Median Absolute Deviation (MAD)191493.5
Skewness-0.62181117
Sum24195322
Variance7.0669063 × 1010
MonotonicityNot monotonic
2023-12-10T19:07:08.722526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
528908 1
 
2.0%
775477 1
 
2.0%
541208 1
 
2.0%
541068 1
 
2.0%
246804 1
 
2.0%
141628 1
 
2.0%
7758 1
 
2.0%
109665 1
 
2.0%
336838 1
 
2.0%
793471 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
759 1
2.0%
1499 1
2.0%
3586 1
2.0%
6395 1
2.0%
7758 1
2.0%
27606 1
2.0%
82120 1
2.0%
109665 1
2.0%
139848 1
2.0%
141628 1
2.0%
ValueCountFrequency (%)
805559 1
2.0%
803138 1
2.0%
793471 1
2.0%
791276 1
2.0%
775477 1
2.0%
775135 1
2.0%
764269 1
2.0%
746718 1
2.0%
739810 1
2.0%
738926 1
2.0%

RSTRNT_CTGRY_NM
Categorical

Distinct21
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
디저트
13 
양식
7 
카페
4 
호프
3 
요리주점
3 
Other values (16)
20 

Length

Max length7
Median length6
Mean length3.02
Min length1

Unique

Unique12 ?
Unique (%)24.0%

Sample

1st row디저트
2nd row디저트
3rd row떡볶이
4th row디저트
5th row고기요리

Common Values

ValueCountFrequency (%)
디저트 13
26.0%
양식 7
14.0%
카페 4
 
8.0%
호프 3
 
6.0%
요리주점 3
 
6.0%
고기요리 2
 
4.0%
와인 2
 
4.0%
떡볶이 2
 
4.0%
돼지고기구이 2
 
4.0%
소고기구이 1
 
2.0%
Other values (11) 11
22.0%

Length

2023-12-10T19:07:09.155880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
디저트 13
26.0%
양식 7
14.0%
카페 4
 
8.0%
호프 3
 
6.0%
요리주점 3
 
6.0%
고기요리 2
 
4.0%
와인 2
 
4.0%
떡볶이 2
 
4.0%
돼지고기구이 2
 
4.0%
이자카야 1
 
2.0%
Other values (11) 11
22.0%

RSTRNT_NM
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:07:09.856950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.74
Min length2

Characters and Unicode

Total characters237
Distinct characters145
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

Unique50 ?
Unique (%)100.0%

Sample

1st row터치카페
2nd row플리즈웨잇
3rd row볶떡빵
4th row카페산
5th row느린하루
ValueCountFrequency (%)
터치카페 1
 
1.8%
장화신은고양이 1
 
1.8%
네이버후드 1
 
1.8%
커피 1
 
1.8%
묘한술책 1
 
1.8%
오바도즈 1
 
1.8%
마우디 1
 
1.8%
커피냅로스터스 1
 
1.8%
연남동 1
 
1.8%
gongrot 1
 
1.8%
Other values (45) 45
81.8%
2023-12-10T19:07:10.614204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 10
 
4.2%
카 6
 
2.5%
이 6
 
2.5%
페 5
 
2.1%
5
 
2.1%
하 5
 
2.1%
피 5
 
2.1%
드 4
 
1.7%
t 4
 
1.7%
커 4
 
1.7%
Other values (135) 183
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 204
86.1%
Lowercase Letter 16
 
6.8%
Decimal Number 6
 
2.5%
Space Separator 5
 
2.1%
Uppercase Letter 5
 
2.1%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 10
 
4.9%
카 6
 
2.9%
이 6
 
2.9%
페 5
 
2.5%
하 5
 
2.5%
피 5
 
2.5%
드 4
 
2.0%
커 4
 
2.0%
리 3
 
1.5%
레 3
 
1.5%
Other values (115) 153
75.0%
Lowercase Letter
ValueCountFrequency (%)
t 4
25.0%
n 2
12.5%
r 2
12.5%
e 2
12.5%
o 2
12.5%
g 2
12.5%
h 1
 
6.2%
d 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
5 2
33.3%
6 1
16.7%
1 1
16.7%
4 1
16.7%
2 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
20.0%
Y 1
20.0%
U 1
20.0%
J 1
20.0%
S 1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 204
86.1%
Latin 21
 
8.9%
Common 12
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 10
 
4.9%
카 6
 
2.9%
이 6
 
2.9%
페 5
 
2.5%
하 5
 
2.5%
피 5
 
2.5%
드 4
 
2.0%
커 4
 
2.0%
리 3
 
1.5%
레 3
 
1.5%
Other values (115) 153
75.0%
Latin
ValueCountFrequency (%)
t 4
19.0%
n 2
9.5%
r 2
9.5%
e 2
9.5%
o 2
9.5%
g 2
9.5%
L 1
 
4.8%
Y 1
 
4.8%
U 1
 
4.8%
J 1
 
4.8%
Other values (3) 3
14.3%
Common
ValueCountFrequency (%)
5
41.7%
5 2
 
16.7%
6 1
 
8.3%
1 1
 
8.3%
. 1
 
8.3%
4 1
 
8.3%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 204
86.1%
ASCII 33
 
13.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 10
 
4.9%
카 6
 
2.9%
이 6
 
2.9%
페 5
 
2.5%
하 5
 
2.5%
피 5
 
2.5%
드 4
 
2.0%
커 4
 
2.0%
리 3
 
1.5%
레 3
 
1.5%
Other values (115) 153
75.0%
ASCII
ValueCountFrequency (%)
5
15.2%
t 4
 
12.1%
5 2
 
6.1%
n 2
 
6.1%
r 2
 
6.1%
e 2
 
6.1%
o 2
 
6.1%
g 2
 
6.1%
6 1
 
3.0%
L 1
 
3.0%
Other values (10) 10
30.3%

RSTRNT_ROAD_NM_ADDR
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:07:11.083127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22.5
Mean length19.26
Min length15

Characters and Unicode

Total characters963
Distinct characters91
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row서울특별시 서대문구 이화여대7길 35
2nd row강원도 양양군 현남면 인구길 28-23
3rd row경기도 고양시 일산동구 백석로86번길 64
4th row충청북도 단양군 가곡면 두산길 196-86
5th row대전광역시 서구 도산로369번길 72
ValueCountFrequency (%)
서울특별시 46
22.7%
마포구 35
 
17.2%
서대문구 7
 
3.4%
72 3
 
1.5%
희우정로 3
 
1.5%
포은로 3
 
1.5%
37 2
 
1.0%
35 2
 
1.0%
57 2
 
1.0%
영등포구 2
 
1.0%
Other values (97) 98
48.3%
2023-12-10T19:07:11.859222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
 
15.9%
서 54
 
5.6%
구 49
 
5.1%
시 48
 
5.0%
로 47
 
4.9%
특 46
 
4.8%
별 46
 
4.8%
울 46
 
4.8%
포 41
 
4.3%
길 36
 
3.7%
Other values (81) 397
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 623
64.7%
Decimal Number 175
 
18.2%
Space Separator 153
 
15.9%
Dash Punctuation 12
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 54
 
8.7%
구 49
 
7.9%
시 48
 
7.7%
로 47
 
7.5%
특 46
 
7.4%
별 46
 
7.4%
울 46
 
7.4%
포 41
 
6.6%
길 36
 
5.8%
마 35
 
5.6%
Other values (69) 175
28.1%
Decimal Number
ValueCountFrequency (%)
2 29
16.6%
1 29
16.6%
3 20
11.4%
7 18
10.3%
9 17
9.7%
6 17
9.7%
5 16
9.1%
8 12
6.9%
4 10
 
5.7%
0 7
 
4.0%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 623
64.7%
Common 340
35.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 54
 
8.7%
구 49
 
7.9%
시 48
 
7.7%
로 47
 
7.5%
특 46
 
7.4%
별 46
 
7.4%
울 46
 
7.4%
포 41
 
6.6%
길 36
 
5.8%
마 35
 
5.6%
Other values (69) 175
28.1%
Common
ValueCountFrequency (%)
153
45.0%
2 29
 
8.5%
1 29
 
8.5%
3 20
 
5.9%
7 18
 
5.3%
9 17
 
5.0%
6 17
 
5.0%
5 16
 
4.7%
8 12
 
3.5%
- 12
 
3.5%
Other values (2) 17
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 623
64.7%
ASCII 340
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
45.0%
2 29
 
8.5%
1 29
 
8.5%
3 20
 
5.9%
7 18
 
5.3%
9 17
 
5.0%
6 17
 
5.0%
5 16
 
4.7%
8 12
 
3.5%
- 12
 
3.5%
Other values (2) 17
 
5.0%
Hangul
ValueCountFrequency (%)
서 54
 
8.7%
구 49
 
7.9%
시 48
 
7.7%
로 47
 
7.5%
특 46
 
7.4%
별 46
 
7.4%
울 46
 
7.4%
포 41
 
6.6%
길 36
 
5.8%
마 35
 
5.6%
Other values (69) 175
28.1%

RSTRNT_LNM_ADDR
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:07:12.308370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length19.86
Min length17

Characters and Unicode

Total characters993
Distinct characters74
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row서울특별시 서대문구 대현동 37-9
2nd row강원도 양양군 현남면 인구리 636-5
3rd row경기도 고양시 일산동구 백석동 1260-9
4th row충청북도 단양군 가곡면 사평리 246-33
5th row대전광역시 서구 괴정동 423-18
ValueCountFrequency (%)
서울특별시 46
22.7%
마포구 35
17.2%
서교동 11
 
5.4%
망원동 10
 
4.9%
연남동 8
 
3.9%
서대문구 7
 
3.4%
연희동 3
 
1.5%
합정동 2
 
1.0%
영등포구 2
 
1.0%
330-18 1
 
0.5%
Other values (78) 78
38.4%
2023-12-10T19:07:13.148022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
 
15.4%
서 65
 
6.5%
동 50
 
5.0%
구 49
 
4.9%
시 48
 
4.8%
3 47
 
4.7%
특 46
 
4.6%
별 46
 
4.6%
울 46
 
4.6%
- 46
 
4.6%
Other values (64) 397
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 567
57.1%
Decimal Number 227
22.9%
Space Separator 153
 
15.4%
Dash Punctuation 46
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 65
11.5%
동 50
 
8.8%
구 49
 
8.6%
시 48
 
8.5%
특 46
 
8.1%
별 46
 
8.1%
울 46
 
8.1%
포 37
 
6.5%
마 35
 
6.2%
교 12
 
2.1%
Other values (52) 133
23.5%
Decimal Number
ValueCountFrequency (%)
3 47
20.7%
1 30
13.2%
5 30
13.2%
4 27
11.9%
2 25
11.0%
9 17
 
7.5%
0 16
 
7.0%
8 13
 
5.7%
6 11
 
4.8%
7 11
 
4.8%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 567
57.1%
Common 426
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 65
11.5%
동 50
 
8.8%
구 49
 
8.6%
시 48
 
8.5%
특 46
 
8.1%
별 46
 
8.1%
울 46
 
8.1%
포 37
 
6.5%
마 35
 
6.2%
교 12
 
2.1%
Other values (52) 133
23.5%
Common
ValueCountFrequency (%)
153
35.9%
3 47
 
11.0%
- 46
 
10.8%
1 30
 
7.0%
5 30
 
7.0%
4 27
 
6.3%
2 25
 
5.9%
9 17
 
4.0%
0 16
 
3.8%
8 13
 
3.1%
Other values (2) 22
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 567
57.1%
ASCII 426
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
35.9%
3 47
 
11.0%
- 46
 
10.8%
1 30
 
7.0%
5 30
 
7.0%
4 27
 
6.3%
2 25
 
5.9%
9 17
 
4.0%
0 16
 
3.8%
8 13
 
3.1%
Other values (2) 22
 
5.2%
Hangul
ValueCountFrequency (%)
서 65
11.5%
동 50
 
8.8%
구 49
 
8.6%
시 48
 
8.5%
특 46
 
8.1%
별 46
 
8.1%
울 46
 
8.1%
포 37
 
6.5%
마 35
 
6.2%
교 12
 
2.1%
Other values (52) 133
23.5%

RSTRNT_TEL_NO
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22457183
Minimum15225647
Maximum23367559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:07:13.426132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15225647
5-th percentile16200970
Q123220116
median23237620
Q323331917
95-th percentile23363445
Maximum23367559
Range8141912
Interquartile range (IQR)111800.75

Descriptive statistics

Standard deviation2259021.6
Coefficient of variation (CV)0.10059238
Kurtosis4.8644286
Mean22457183
Median Absolute Deviation (MAD)90585.5
Skewness-2.5432898
Sum1.1228592 × 109
Variance5.1031786 × 1012
MonotonicityStrictly increasing
2023-12-10T19:07:13.755811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15225647 1
 
2.0%
23332825 1
 
2.0%
23258899 1
 
2.0%
23260845 1
 
2.0%
23263366 1
 
2.0%
23323933 1
 
2.0%
23324131 1
 
2.0%
23327133 1
 
2.0%
23329279 1
 
2.0%
23330250 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
15225647 1
2.0%
15778647 1
2.0%
16001575 1
2.0%
16444674 1
2.0%
16615389 1
2.0%
18994666 1
2.0%
23029952 1
2.0%
23075533 1
2.0%
23087984 1
2.0%
23174001 1
2.0%
ValueCountFrequency (%)
23367559 1
2.0%
23365253 1
2.0%
23364729 1
2.0%
23361875 1
2.0%
23360716 1
2.0%
23357772 1
2.0%
23352046 1
2.0%
23351110 1
2.0%
23349902 1
2.0%
23337575 1
2.0%

SLE_MENU_INFO_DC
Text

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-10T19:07:14.204347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length404
Median length60.5
Mean length62.1
Min length5

Characters and Unicode

Total characters3105
Distinct characters419
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

Unique48 ?
Unique (%)96.0%

Sample

1st row그린티 말차 라떼,밀크티,아이스 그린티 말차 라떼,아이스 밀크티,아이스 초코릿,아이스 카페라떼,에스프레소,연한 아메리카노,연한 아이스 아메리카노,진한 아메리카노,진한 아이스 아메리카노,카페라떼,카푸치노,핫 초콜릿
2nd row달고나라떼,밀크티,바다우유,블루하와이안에이드,아메리카노,아이스바라떼
3rd row아메리카노(롱블랙),인절미 눈꽃빙수,퐁듀 치즈떡볶이
4th row망고크러쉬,메이플라떼,바닐라라떼,아메리카노,아이스티,얼그레이,유자차,초콜릿라떼,카페라떼,카푸치노,크림카푸치노,패러글라이딩예약권,헤이즐넛라떼
5th row생연어3종셋트,생연어사시미,생연어타다끼
ValueCountFrequency (%)
6
 
2.1%
칵테일 4
 
1.4%
hot 3
 
1.1%
삼겹살 3
 
1.1%
아메리카노 2
 
0.7%
burger 2
 
0.7%
1 2
 
0.7%
및 2
 
0.7%
그 2
 
0.7%
그린티 2
 
0.7%
Other values (244) 253
90.0%
2023-12-10T19:07:14.940293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 333
 
10.7%
231
 
7.4%
이 70
 
2.3%
라 62
 
2.0%
스 62
 
2.0%
e 57
 
1.8%
리 51
 
1.6%
카 45
 
1.4%
a 44
 
1.4%
아 41
 
1.3%
Other values (409) 2109
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1910
61.5%
Lowercase Letter 394
 
12.7%
Other Punctuation 356
 
11.5%
Space Separator 231
 
7.4%
Uppercase Letter 97
 
3.1%
Decimal Number 73
 
2.4%
Open Punctuation 15
 
0.5%
Close Punctuation 15
 
0.5%
Math Symbol 14
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 70
 
3.7%
라 62
 
3.2%
스 62
 
3.2%
리 51
 
2.7%
카 45
 
2.4%
아 41
 
2.1%
크 37
 
1.9%
치 28
 
1.5%
떼 27
 
1.4%
메 26
 
1.4%
Other values (344) 1461
76.5%
Lowercase Letter
ValueCountFrequency (%)
e 57
14.5%
a 44
11.2%
o 38
 
9.6%
t 33
 
8.4%
c 23
 
5.8%
n 22
 
5.6%
i 22
 
5.6%
g 19
 
4.8%
r 19
 
4.8%
h 18
 
4.6%
Other values (14) 99
25.1%
Uppercase Letter
ValueCountFrequency (%)
A 13
13.4%
L 12
12.4%
E 11
11.3%
P 9
9.3%
S 8
8.2%
W 6
 
6.2%
C 6
 
6.2%
T 6
 
6.2%
B 5
 
5.2%
H 3
 
3.1%
Other values (12) 18
18.6%
Decimal Number
ValueCountFrequency (%)
0 25
34.2%
1 14
19.2%
3 11
15.1%
2 9
 
12.3%
8 5
 
6.8%
5 5
 
6.8%
6 2
 
2.7%
7 1
 
1.4%
9 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 333
93.5%
/ 14
 
3.9%
& 4
 
1.1%
. 3
 
0.8%
' 2
 
0.6%
Math Symbol
ValueCountFrequency (%)
+ 12
85.7%
~ 2
 
14.3%
Space Separator
ValueCountFrequency (%)
231
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1910
61.5%
Common 704
 
22.7%
Latin 491
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 70
 
3.7%
라 62
 
3.2%
스 62
 
3.2%
리 51
 
2.7%
카 45
 
2.4%
아 41
 
2.1%
크 37
 
1.9%
치 28
 
1.5%
떼 27
 
1.4%
메 26
 
1.4%
Other values (344) 1461
76.5%
Latin
ValueCountFrequency (%)
e 57
 
11.6%
a 44
 
9.0%
o 38
 
7.7%
t 33
 
6.7%
c 23
 
4.7%
n 22
 
4.5%
i 22
 
4.5%
g 19
 
3.9%
r 19
 
3.9%
h 18
 
3.7%
Other values (36) 196
39.9%
Common
ValueCountFrequency (%)
, 333
47.3%
231
32.8%
0 25
 
3.6%
( 15
 
2.1%
) 15
 
2.1%
/ 14
 
2.0%
1 14
 
2.0%
+ 12
 
1.7%
3 11
 
1.6%
2 9
 
1.3%
Other values (9) 25
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1910
61.5%
ASCII 1195
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 333
27.9%
231
19.3%
e 57
 
4.8%
a 44
 
3.7%
o 38
 
3.2%
t 33
 
2.8%
0 25
 
2.1%
c 23
 
1.9%
n 22
 
1.8%
i 22
 
1.8%
Other values (55) 367
30.7%
Hangul
ValueCountFrequency (%)
이 70
 
3.7%
라 62
 
3.2%
스 62
 
3.2%
리 51
 
2.7%
카 45
 
2.4%
아 41
 
2.1%
크 37
 
1.9%
치 28
 
1.5%
떼 27
 
1.4%
메 26
 
1.4%
Other values (344) 1461
76.5%

RSTRNT_LA
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.53135
Minimum36.339818
Maximum37.968639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:07:15.233478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.339818
5-th percentile37.522406
Q137.551102
median37.556592
Q337.562099
95-th percentile37.581998
Maximum37.968639
Range1.6288208
Interquartile range (IQR)0.010997275

Descriptive statistics

Standard deviation0.19937127
Coefficient of variation (CV)0.0053121262
Kurtosis27.984482
Mean37.53135
Median Absolute Deviation (MAD)0.0057008
Skewness-4.7212477
Sum1876.5675
Variance0.039748903
MonotonicityNot monotonic
2023-12-10T19:07:15.555248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5586078 1
 
2.0%
37.5567556 1
 
2.0%
37.549115 1
 
2.0%
37.567387 1
 
2.0%
37.5487553 1
 
2.0%
37.5674911 1
 
2.0%
37.5664219 1
 
2.0%
37.5662741 1
 
2.0%
37.5561006 1
 
2.0%
37.559026 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
36.3398185 1
2.0%
36.9946676 1
2.0%
37.5142662 1
2.0%
37.5323543 1
2.0%
37.5455076 1
2.0%
37.5476002 1
2.0%
37.5487553 1
2.0%
37.549115 1
2.0%
37.5500419 1
2.0%
37.5503966 1
2.0%
ValueCountFrequency (%)
37.9686393 1
2.0%
37.6457264 1
2.0%
37.5820926 1
2.0%
37.5818829 1
2.0%
37.5748528 1
2.0%
37.5727513 1
2.0%
37.5674911 1
2.0%
37.567387 1
2.0%
37.5667114 1
2.0%
37.5664219 1
2.0%

RSTRNT_LO
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99253
Minimum126.78983
Maximum128.76186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-10T19:07:15.831597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.78983
5-th percentile126.89866
Q1126.90706
median126.91989
Q3126.92593
95-th percentile127.20737
Maximum128.76186
Range1.9720224
Interquartile range (IQR)0.018868925

Descriptive statistics

Standard deviation0.3366775
Coefficient of variation (CV)0.0026511599
Kurtosis21.386082
Mean126.99253
Median Absolute Deviation (MAD)0.01056775
Skewness4.638777
Sum6349.6263
Variance0.11335174
MonotonicityNot monotonic
2023-12-10T19:07:16.109504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9437149 1
 
2.0%
126.9081659 1
 
2.0%
126.9162305 1
 
2.0%
126.9289156 1
 
2.0%
126.9193894 1
 
2.0%
126.9301499 1
 
2.0%
126.9202225 1
 
2.0%
126.9200451 1
 
2.0%
126.9090144 1
 
2.0%
126.9077441 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
126.7898336 1
2.0%
126.8889847 1
2.0%
126.8976372 1
2.0%
126.8999007 1
2.0%
126.9023322 1
2.0%
126.9030614 1
2.0%
126.9037427 1
2.0%
126.9045157 1
2.0%
126.9055227 1
2.0%
126.9057993 1
2.0%
ValueCountFrequency (%)
128.761856 1
2.0%
128.3943776 1
2.0%
127.3894494 1
2.0%
126.9848309 1
2.0%
126.9795418 1
2.0%
126.9437149 1
2.0%
126.9346957 1
2.0%
126.9346012 1
2.0%
126.9301499 1
2.0%
126.9289156 1
2.0%

Interactions

2023-12-10T19:07:06.033745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.399669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.276268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.035958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:06.275031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.629573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.448250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.267115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:06.527509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.921289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.641202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.526468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:06.759379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.100974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.835640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.815690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:07:16.329500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_CTGRY_NMRSTRNT_NMRSTRNT_ROAD_NM_ADDRRSTRNT_LNM_ADDRRSTRNT_TEL_NOSLE_MENU_INFO_DCRSTRNT_LARSTRNT_LO
RSTRNT_ID1.0000.2911.0001.0001.0000.7360.9270.0000.159
RSTRNT_CTGRY_NM0.2911.0001.0001.0001.0000.0001.0000.0000.000
RSTRNT_NM1.0001.0001.0001.0001.0001.0001.0001.0001.000
RSTRNT_ROAD_NM_ADDR1.0001.0001.0001.0001.0001.0001.0001.0001.000
RSTRNT_LNM_ADDR1.0001.0001.0001.0001.0001.0001.0001.0001.000
RSTRNT_TEL_NO0.7360.0001.0001.0001.0001.0001.0000.8280.943
SLE_MENU_INFO_DC0.9271.0001.0001.0001.0001.0001.0001.0001.000
RSTRNT_LA0.0000.0001.0001.0001.0000.8281.0001.0001.000
RSTRNT_LO0.1590.0001.0001.0001.0000.9431.0001.0001.000
2023-12-10T19:07:16.648167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LORSTRNT_CTGRY_NM
RSTRNT_ID1.0000.005-0.018-0.2450.000
RSTRNT_TEL_NO0.0051.000-0.073-0.1960.000
RSTRNT_LA-0.018-0.0731.0000.1900.000
RSTRNT_LO-0.245-0.1960.1901.0000.000
RSTRNT_CTGRY_NM0.0000.0000.0000.0001.000

Missing values

2023-12-10T19:07:07.063482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:07.404912image/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.

Sample

RSTRNT_IDRSTRNT_CTGRY_NMRSTRNT_NMRSTRNT_ROAD_NM_ADDRRSTRNT_LNM_ADDRRSTRNT_TEL_NOSLE_MENU_INFO_DCRSTRNT_LARSTRNT_LO
0528908디저트터치카페서울특별시 서대문구 이화여대7길 35서울특별시 서대문구 대현동 37-915225647그린티 말차 라떼,밀크티,아이스 그린티 말차 라떼,아이스 밀크티,아이스 초코릿,아이스 카페라떼,에스프레소,연한 아메리카노,연한 아이스 아메리카노,진한 아메리카노,진한 아이스 아메리카노,카페라떼,카푸치노,핫 초콜릿37.558608126.943715
182120디저트플리즈웨잇강원도 양양군 현남면 인구길 28-23강원도 양양군 현남면 인구리 636-515778647달고나라떼,밀크티,바다우유,블루하와이안에이드,아메리카노,아이스바라떼37.968639128.761856
2775135떡볶이볶떡빵경기도 고양시 일산동구 백석로86번길 64경기도 고양시 일산동구 백석동 1260-916001575아메리카노(롱블랙),인절미 눈꽃빙수,퐁듀 치즈떡볶이37.645726126.789834
3722052디저트카페산충청북도 단양군 가곡면 두산길 196-86충청북도 단양군 가곡면 사평리 246-3316444674망고크러쉬,메이플라떼,바닐라라떼,아메리카노,아이스티,얼그레이,유자차,초콜릿라떼,카페라떼,카푸치노,크림카푸치노,패러글라이딩예약권,헤이즐넛라떼36.994668128.394378
4582795고기요리느린하루대전광역시 서구 도산로369번길 72대전광역시 서구 괴정동 423-1816615389생연어3종셋트,생연어사시미,생연어타다끼36.339818127.389449
5224693양식잇웰서울특별시 영등포구 당산로 203서울특별시 영등포구 당산동5가 818994666스테이크,파스타,흰살생선구이37.532354126.899901
6698212디저트모과나무카페서울특별시 서대문구 모래내로 231서울특별시 서대문구 남가좌동 330-4323029952라떼,수제요거트,에이드,쥬스,커피,티37.574853126.924517
7139848디저트카페소미서울특별시 서대문구 거북골로 8-20서울특별시 서대문구 홍은동 400-4523075533달고나라떼,더치베이비 팬케잌,아메리카노,크로플37.581883126.925538
8460458카페행복커피서울특별시 마포구 월드컵북로54길 25서울특별시 마포구 상암동 159623087984아메리카노,에스프레소,카페라떼,플레인라씨,행복커피37.582093126.888985
927606중식당팔레드신서울특별시 중구 퇴계로 67서울특별시 중구 회현동1가 21023174001꿀소스 이베리코 차슈,라탕면,메추리알 트러플 샤오마이 (3pcs),바삭한 삼겹살 구이,북경오리,북경오리 To Go Kit '럭키덕키',사천식 라즈지,산라 소룡포 (3pcs),소흥주 칠리 새우,홍콩식 소갈비 구이,흑식초 광동식 탕수육37.559666126.979542
RSTRNT_IDRSTRNT_CTGRY_NMRSTRNT_NMRSTRNT_ROAD_NM_ADDRRSTRNT_LNM_ADDRRSTRNT_TEL_NOSLE_MENU_INFO_DCRSTRNT_LARSTRNT_LO
40726337디저트비바쌀롱서울특별시 마포구 포은로 84서울특별시 마포구 망원동 400-823337575몬스터라떼/찰리브라운/커피스누피,아메리카노/카페라떼,얼그레이,유자차,포키스토리37.554485126.905523
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