Overview

Dataset statistics

Number of variables13
Number of observations40
Missing cells35
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory111.3 B

Variable types

Numeric4
Categorical2
Text7

Alerts

MOP_LANG_NM has constant value ""Constant
RSTRNT_TEL_NO is highly overall correlated with RSTRNT_CTGRY_NMHigh correlation
RSTRNT_CTGRY_NM is highly overall correlated with RSTRNT_TEL_NOHigh correlation
RSTRNT_LNM_ADDR has 13 (32.5%) missing valuesMissing
RSTRNT_ENG_LNM_ADDR has 13 (32.5%) missing valuesMissing
HMPG_URL has 9 (22.5%) missing valuesMissing
RSTRNT_ID has unique valuesUnique
RSTRNT_NM has unique valuesUnique
RSTRNT_ENG_NM has unique valuesUnique
RSTRNT_ROAD_NM_ADDR has unique valuesUnique
RSTRNT_ENG_RDNMADR_NM has unique valuesUnique
RSTRNT_TEL_NO has unique valuesUnique
RSTRNT_LO has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:02:26.332211
Analysis finished2023-12-10 10:02:31.005986
Duration4.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RSTRNT_ID
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.075
Minimum3
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-10T19:02:31.124810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9.8
Q124.25
median41.5
Q353.25
95-th percentile68.15
Maximum73
Range70
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.049716
Coefficient of variation (CV)0.48751672
Kurtosis-0.92393766
Mean39.075
Median Absolute Deviation (MAD)14.5
Skewness-0.14063152
Sum1563
Variance362.89167
MonotonicityStrictly increasing
2023-12-10T19:02:31.720130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3 1
 
2.5%
43 1
 
2.5%
46 1
 
2.5%
47 1
 
2.5%
48 1
 
2.5%
49 1
 
2.5%
50 1
 
2.5%
52 1
 
2.5%
53 1
 
2.5%
54 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
3 1
2.5%
6 1
2.5%
10 1
2.5%
12 1
2.5%
14 1
2.5%
15 1
2.5%
16 1
2.5%
17 1
2.5%
20 1
2.5%
22 1
2.5%
ValueCountFrequency (%)
73 1
2.5%
71 1
2.5%
68 1
2.5%
67 1
2.5%
62 1
2.5%
59 1
2.5%
57 1
2.5%
56 1
2.5%
55 1
2.5%
54 1
2.5%

MOP_LANG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
ENG
40 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ENG 40
100.0%

Length

2023-12-10T19:02:31.955437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:32.126354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eng 40
100.0%

RSTRNT_CTGRY_NM
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
한국
22 
미국 요리
바베큐
멕시코 요리
 
2
인도 요리
 
2
Other values (5)

Length

Max length7
Median length2
Mean length3.15
Min length2

Unique

Unique2 ?
Unique (%)5.0%

Sample

1st row멕시코 요리
2nd row스웨덴
3rd row한국
4th row아프리카 요리
5th row미국 요리

Common Values

ValueCountFrequency (%)
한국 22
55.0%
미국 요리 3
 
7.5%
바베큐 3
 
7.5%
멕시코 요리 2
 
5.0%
인도 요리 2
 
5.0%
인터내셔널 2
 
5.0%
아시아 요리 2
 
5.0%
퓨전 2
 
5.0%
스웨덴 1
 
2.5%
아프리카 요리 1
 
2.5%

Length

2023-12-10T19:02:32.332143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:02:32.569578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국 22
44.0%
요리 10
20.0%
미국 3
 
6.0%
바베큐 3
 
6.0%
멕시코 2
 
4.0%
인도 2
 
4.0%
인터내셔널 2
 
4.0%
아시아 2
 
4.0%
퓨전 2
 
4.0%
스웨덴 1
 
2.0%

RSTRNT_NM
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-10T19:02:33.005095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.85
Min length3

Characters and Unicode

Total characters314
Distinct characters143
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row구스토 타코
2nd row헴라갓
3rd row정식당
4th row브라이 리퍼블릭
5th row타볼로 24
ValueCountFrequency (%)
4
 
4.9%
본점 4
 
4.9%
명동 3
 
3.7%
닭한마리 2
 
2.5%
홍대 2
 
2.5%
홍대점 2
 
2.5%
할랄 2
 
2.5%
코리안 2
 
2.5%
이태원점 2
 
2.5%
원조 2
 
2.5%
Other values (55) 56
69.1%
2023-12-10T19:02:33.843333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
 
13.1%
10
 
3.2%
9
 
2.9%
8
 
2.5%
6
 
1.9%
6
 
1.9%
6
 
1.9%
5
 
1.6%
5
 
1.6%
5
 
1.6%
Other values (133) 213
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
84.1%
Space Separator 41
 
13.1%
Dash Punctuation 4
 
1.3%
Uppercase Letter 3
 
1.0%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10
 
3.8%
9
 
3.4%
8
 
3.0%
6
 
2.3%
6
 
2.3%
6
 
2.3%
5
 
1.9%
5
 
1.9%
5
 
1.9%
5
 
1.9%
Other values (126) 199
75.4%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
B 1
33.3%
L 1
33.3%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
84.1%
Common 47
 
15.0%
Latin 3
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10
 
3.8%
9
 
3.4%
8
 
3.0%
6
 
2.3%
6
 
2.3%
6
 
2.3%
5
 
1.9%
5
 
1.9%
5
 
1.9%
5
 
1.9%
Other values (126) 199
75.4%
Common
ValueCountFrequency (%)
41
87.2%
- 4
 
8.5%
4 1
 
2.1%
2 1
 
2.1%
Latin
ValueCountFrequency (%)
T 1
33.3%
B 1
33.3%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 260
82.8%
ASCII 50
 
15.9%
Jamo 4
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
82.0%
- 4
 
8.0%
T 1
 
2.0%
B 1
 
2.0%
L 1
 
2.0%
4 1
 
2.0%
2 1
 
2.0%
Hangul
ValueCountFrequency (%)
10
 
3.8%
9
 
3.5%
8
 
3.1%
6
 
2.3%
6
 
2.3%
6
 
2.3%
5
 
1.9%
5
 
1.9%
5
 
1.9%
5
 
1.9%
Other values (122) 195
75.0%
Jamo
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

RSTRNT_ENG_NM
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-10T19:02:34.337063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length25.5
Mean length19.475
Min length4

Characters and Unicode

Total characters779
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st rowGusto Taco
2nd rowHemlagat
3rd rowJungsik
4th rowBraai Republic
5th rowTavolo 24
ValueCountFrequency (%)
restaurant 4
 
3.5%
main 4
 
3.5%
myeongdong 4
 
3.5%
hongdae 4
 
3.5%
3
 
2.6%
dakhanmari 3
 
2.6%
store 3
 
2.6%
house 2
 
1.8%
itaewon 2
 
1.8%
halal 2
 
1.8%
Other values (77) 83
72.8%
2023-12-10T19:02:35.069275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 86
 
11.0%
74
 
9.5%
n 73
 
9.4%
o 62
 
8.0%
e 54
 
6.9%
i 39
 
5.0%
g 37
 
4.7%
r 25
 
3.2%
t 25
 
3.2%
l 23
 
3.0%
Other values (41) 281
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 582
74.7%
Uppercase Letter 117
 
15.0%
Space Separator 74
 
9.5%
Dash Punctuation 3
 
0.4%
Decimal Number 2
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 86
14.8%
n 73
12.5%
o 62
10.7%
e 54
 
9.3%
i 39
 
6.7%
g 37
 
6.4%
r 25
 
4.3%
t 25
 
4.3%
l 23
 
4.0%
k 22
 
3.8%
Other values (13) 136
23.4%
Uppercase Letter
ValueCountFrequency (%)
M 13
11.1%
S 13
11.1%
H 13
11.1%
B 9
 
7.7%
J 9
 
7.7%
T 8
 
6.8%
R 6
 
5.1%
D 5
 
4.3%
K 5
 
4.3%
I 5
 
4.3%
Other values (13) 31
26.5%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 699
89.7%
Common 80
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 86
 
12.3%
n 73
 
10.4%
o 62
 
8.9%
e 54
 
7.7%
i 39
 
5.6%
g 37
 
5.3%
r 25
 
3.6%
t 25
 
3.6%
l 23
 
3.3%
k 22
 
3.1%
Other values (36) 253
36.2%
Common
ValueCountFrequency (%)
74
92.5%
- 3
 
3.8%
' 1
 
1.2%
4 1
 
1.2%
2 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 86
 
11.0%
74
 
9.5%
n 73
 
9.4%
o 62
 
8.0%
e 54
 
6.9%
i 39
 
5.0%
g 37
 
4.7%
r 25
 
3.2%
t 25
 
3.2%
l 23
 
3.0%
Other values (41) 281
36.1%

RSTRNT_ROAD_NM_ADDR
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-10T19:02:35.563036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length30
Mean length20.55
Min length13

Characters and Unicode

Total characters822
Distinct characters134
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row서울 마포구 와우산로 41
2nd row서울 중구 소공로 35 남산 롯데캐슬 아이리스 123호
3rd row서울 강남구 선릉로158길 11
4th row서울 용산구 이태원로14길 19
5th row서울 종로구 청계천로 279
ValueCountFrequency (%)
서울 42
 
21.0%
마포구 10
 
5.0%
종로구 10
 
5.0%
중구 9
 
4.5%
용산구 7
 
3.5%
1층 5
 
2.5%
종로40가길 3
 
1.5%
강남구 3
 
1.5%
우사단로10길 2
 
1.0%
32 2
 
1.0%
Other values (102) 107
53.5%
2023-12-10T19:02:36.304144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/