Overview

Dataset statistics

Number of variables7
Number of observations207
Missing cells74
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory57.6 B

Variable types

Numeric1
Categorical3
Text3

Dataset

Description인천광역시 남동구에 위치하는 특색 음식거리 현황에 대한 데이터로 연번, 거리명, 점포명, 유형, 위치, 연락처, 데이터기준일자를 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15090599&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 거리명High correlation
거리명 is highly overall correlated with 연번High correlation
연락처 has 74 (35.7%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 11:34:57.857839
Analysis finished2024-01-28 11:34:58.374556
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104
Minimum1
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-01-28T20:34:58.429463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.3
Q152.5
median104
Q3155.5
95-th percentile196.7
Maximum207
Range206
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.899917
Coefficient of variation (CV)0.57596074
Kurtosis-1.2
Mean104
Median Absolute Deviation (MAD)52
Skewness0
Sum21528
Variance3588
MonotonicityStrictly increasing
2024-01-28T20:34:58.529719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
2 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
140 1
 
0.5%
Other values (197) 197
95.2%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
207 1
0.5%
206 1
0.5%
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%

거리명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
구월로데오음식문화거리
107 
구월문예길음식문화1번가
77 
소래포구신도로횟집거리
12 
운연동 추어마을
11 

Length

Max length12
Median length11
Mean length11.21256
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구월문예길음식문화1번가
2nd row구월문예길음식문화1번가
3rd row구월문예길음식문화1번가
4th row구월문예길음식문화1번가
5th row구월문예길음식문화1번가

Common Values

ValueCountFrequency (%)
구월로데오음식문화거리 107
51.7%
구월문예길음식문화1번가 77
37.2%
소래포구신도로횟집거리 12
 
5.8%
운연동 추어마을 11
 
5.3%

Length

2024-01-28T20:34:58.629592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:34:58.713608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구월로데오음식문화거리 107
49.1%
구월문예길음식문화1번가 77
35.3%
소래포구신도로횟집거리 12
 
5.5%
운연동 11
 
5.0%
추어마을 11
 
5.0%
Distinct204
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T20:34:58.895243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length6.9855072
Min length2

Characters and Unicode

Total characters1446
Distinct characters369
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

Unique201 ?
Unique (%)97.1%

Sample

1st row주촌
2nd row감자탕형제들
3rd row홍두깨 칼국수
4th row용궁정
5th row송원식당
ValueCountFrequency (%)
구월점 10
 
4.2%
인천구월점 5
 
2.1%
구월로데오점 3
 
1.3%
김밥천국 2
 
0.8%
본점 2
 
0.8%
육시리 2
 
0.8%
타코비 2
 
0.8%
베스킨라빈스구월점 1
 
0.4%
천유향 1
 
0.4%
황해수육구월로데오점 1
 
0.4%
Other values (209) 209
87.8%
2024-01-28T20:34:59.208743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
 
4.8%
68
 
4.7%
59
 
4.1%
31
 
2.1%
25
 
1.7%
) 22
 
1.5%
21
 
1.5%
( 21
 
1.5%
21
 
1.5%
20
 
1.4%
Other values (359) 1088
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1297
89.7%
Space Separator 31
 
2.1%
Lowercase Letter 24
 
1.7%
Decimal Number 23
 
1.6%
Uppercase Letter 23
 
1.6%
Close Punctuation 22
 
1.5%
Open Punctuation 21
 
1.5%
Other Punctuation 4
 
0.3%
Modifier Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
70
 
5.4%
68
 
5.2%
59
 
4.5%
25
 
1.9%
21
 
1.6%
21
 
1.6%
20
 
1.5%
18
 
1.4%
17
 
1.3%
17
 
1.3%
Other values (316) 961
74.1%
Uppercase Letter
ValueCountFrequency (%)
O 4
17.4%
K 2
 
8.7%
I 2
 
8.7%
N 2
 
8.7%
R 2
 
8.7%
P 2
 
8.7%
E 1
 
4.3%
B 1
 
4.3%
J 1
 
4.3%
L 1
 
4.3%
Other values (5) 5
21.7%
Lowercase Letter
ValueCountFrequency (%)
e 5
20.8%
o 3
12.5%
t 3
12.5%
y 2
 
8.3%
b 2
 
8.3%
s 2
 
8.3%
a 1
 
4.2%
r 1
 
4.2%
m 1
 
4.2%
p 1
 
4.2%
Other values (3) 3
12.5%
Decimal Number
ValueCountFrequency (%)
0 6
26.1%
9 4
17.4%
4 3
13.0%
1 2
 
8.7%
3 2
 
8.7%
8 2
 
8.7%
2 2
 
8.7%
7 1
 
4.3%
5 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
& 2
50.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1297
89.7%
Common 102
 
7.1%
Latin 47
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
70
 
5.4%
68
 
5.2%
59
 
4.5%
25
 
1.9%
21
 
1.6%
21
 
1.6%
20
 
1.5%
18
 
1.4%
17
 
1.3%
17
 
1.3%
Other values (316) 961
74.1%
Latin
ValueCountFrequency (%)
e 5
 
10.6%
O 4
 
8.5%
o 3
 
6.4%
t 3
 
6.4%
y 2
 
4.3%
K 2
 
4.3%
b 2
 
4.3%
s 2
 
4.3%
I 2
 
4.3%
N 2
 
4.3%
Other values (18) 20
42.6%
Common
ValueCountFrequency (%)
31
30.4%
) 22
21.6%
( 21
20.6%
0 6
 
5.9%
9 4
 
3.9%
4 3
 
2.9%
1 2
 
2.0%
3 2
 
2.0%
8 2
 
2.0%
. 2
 
2.0%
Other values (5) 7
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1297
89.7%
ASCII 149
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
70
 
5.4%
68
 
5.2%
59
 
4.5%
25
 
1.9%
21
 
1.6%
21
 
1.6%
20
 
1.5%
18
 
1.4%
17
 
1.3%
17
 
1.3%
Other values (316) 961
74.1%
ASCII
ValueCountFrequency (%)
31
20.8%
) 22
14.8%
( 21
14.1%
0 6
 
4.0%
e 5
 
3.4%
9 4
 
2.7%
O 4
 
2.7%
o 3
 
2.0%
4 3
 
2.0%
t 3
 
2.0%
Other values (33) 47
31.5%

유형
Categorical

Distinct19
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
한식
69 
기타
25 
호프/통닭
24 
정종/대포집/소주방
19 
식육(숯불구이)
18 
Other values (14)
52 

Length

Max length10
Median length2
Mean length3.9323671
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row식육(숯불구이)
2nd row한식
3rd row한식
4th row한식
5th row한식

Common Values

ValueCountFrequency (%)
한식 69
33.3%
기타 25
 
12.1%
호프/통닭 24
 
11.6%
정종/대포집/소주방 19
 
9.2%
식육(숯불구이) 18
 
8.7%
횟집 15
 
7.2%
커피숍 8
 
3.9%
분식 5
 
2.4%
<NA> 3
 
1.4%
일식 3
 
1.4%
Other values (9) 18
 
8.7%

Length

2024-01-28T20:34:59.330791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 69
33.0%
기타 27
 
12.9%
호프/통닭 24
 
11.5%
정종/대포집/소주방 19
 
9.1%
식육(숯불구이 18
 
8.6%
횟집 15
 
7.2%
커피숍 8
 
3.8%
분식 5
 
2.4%
중국식 3
 
1.4%
일식 3
 
1.4%
Other values (9) 18
 
8.6%

위치
Text

Distinct204
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T20:34:59.529640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length47
Mean length36.898551
Min length22

Characters and Unicode

Total characters7638
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

Unique201 ?
Unique (%)97.1%

Sample

1st row인천광역시 남동구 문화서로4번길 41-42 (구월동,(문화서로4번길 41-42) 1층일부)
2nd row인천광역시 남동구 문화서로4번길 65 (구월동, 1층)
3rd row인천광역시 남동구 문화서로4번길 61-34 (구월동,(문화서로4번길61-34))
4th row인천광역시 남동구 문화서로4번길 61-36, 1층 (구월동)
5th row인천광역시 남동구 문화서로4번길 61-14 (구월동)
ValueCountFrequency (%)
남동구 207
 
14.7%
인천광역시 201
 
14.3%
구월동 149
 
10.6%
1층 88
 
6.3%
문화서로4번길 53
 
3.8%
인하로 34
 
2.4%
성말로 32
 
2.3%
10 29
 
2.1%
인하로489번길 28
 
2.0%
2층 16
 
1.1%
Other values (237) 569
40.5%
2024-01-28T20:34:59.830840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1206
 
15.8%
1 423
 
5.5%
419
 
5.5%
400
 
5.2%
289
 
3.8%
, 268
 
3.5%
237
 
3.1%
) 228
 
3.0%
( 228
 
3.0%
209
 
2.7%
Other values (93) 3731
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4109
53.8%
Decimal Number 1491
 
19.5%
Space Separator 1206
 
15.8%
Other Punctuation 268
 
3.5%
Close Punctuation 228
 
3.0%
Open Punctuation 228
 
3.0%
Dash Punctuation 99
 
1.3%
Math Symbol 5
 
0.1%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
419
 
10.2%
400
 
9.7%
289
 
7.0%
237
 
5.8%
209
 
5.1%
205
 
5.0%
201
 
4.9%
201
 
4.9%
201
 
4.9%
183
 
4.5%
Other values (75) 1564
38.1%
Decimal Number
ValueCountFrequency (%)
1 423
28.4%
4 209
14.0%
2 182
12.2%
0 177
11.9%
3 100
 
6.7%
8 87
 
5.8%
9 85
 
5.7%
6 84
 
5.6%
7 79
 
5.3%
5 65
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%
Space Separator
ValueCountFrequency (%)
1206
100.0%
Other Punctuation
ValueCountFrequency (%)
, 268
100.0%
Close Punctuation
ValueCountFrequency (%)
) 228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4109
53.8%
Common 3525
46.2%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
419
 
10.2%
400
 
9.7%
289
 
7.0%
237
 
5.8%
209
 
5.1%
205
 
5.0%
201
 
4.9%
201
 
4.9%
201
 
4.9%
183
 
4.5%
Other values (75) 1564
38.1%
Common
ValueCountFrequency (%)
1206
34.2%
1 423
 
12.0%
, 268
 
7.6%
) 228
 
6.5%
( 228
 
6.5%
4 209
 
5.9%
2 182
 
5.2%
0 177
 
5.0%
3 100
 
2.8%
- 99
 
2.8%
Other values (6) 405
 
11.5%
Latin
ValueCountFrequency (%)
B 3
75.0%
A 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4109
53.8%
ASCII 3529
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1206
34.2%
1 423
 
12.0%
, 268
 
7.6%
) 228
 
6.5%
( 228
 
6.5%
4 209
 
5.9%
2 182
 
5.2%
0 177
 
5.0%
3 100
 
2.8%
- 99
 
2.8%
Other values (8) 409
 
11.6%
Hangul
ValueCountFrequency (%)
419
 
10.2%
400
 
9.7%
289
 
7.0%
237
 
5.8%
209
 
5.1%
205
 
5.0%
201
 
4.9%
201
 
4.9%
201
 
4.9%
183
 
4.5%
Other values (75) 1564
38.1%

연락처
Text

MISSING 

Distinct132
Distinct (%)99.2%
Missing74
Missing (%)35.7%
Memory size1.7 KiB
2024-01-28T20:35:00.017704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1596
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

Unique131 ?
Unique (%)98.5%

Sample

1st row032-427-9279
2nd row032-438-4400
3rd row032-424-8256
4th row032-438-0500
5th row032-432-6948
ValueCountFrequency (%)
032-422-6446 2
 
1.5%
032-423-8839 1
 
0.8%
032-427-9279 1
 
0.8%
032-424-2202 1
 
0.8%
032-427-2217 1
 
0.8%
032-442-3550 1
 
0.8%
032-421-3558 1
 
0.8%
032-433-4984 1
 
0.8%
032-439-4392 1
 
0.8%
032-421-4351 1
 
0.8%
Other values (122) 122
91.7%
2024-01-28T20:35:00.335277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/