Overview

Dataset statistics

Number of variables14
Number of observations82
Missing cells112
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory116.6 B

Variable types

Text6
Categorical3
DateTime1
Boolean1
Numeric3

Dataset

Description대구광역시 북구 관광지주변 쇼핑시설 현황에 대한 데이터로 점포명, 서비스명, 전화번호, 주소, 영업시간 등의 데이터를 제공합니다.
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15095879/fileData.do

Alerts

업종분류 has constant value ""Constant
서비스명 is highly overall correlated with 서비스IDHigh correlation
서비스ID is highly overall correlated with 서비스명High correlation
도로명우편번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 도로명우편번호High correlation
영업상태 is highly imbalanced (83.5%)Imbalance
인허가일자 has 1 (1.2%) missing valuesMissing
소재지전화 has 20 (24.4%) missing valuesMissing
구우편번호 has 26 (31.7%) missing valuesMissing
도로명우편번호 has 1 (1.2%) missing valuesMissing
도로명주소 has 5 (6.1%) missing valuesMissing
경도 has 1 (1.2%) missing valuesMissing
위도 has 1 (1.2%) missing valuesMissing
영업시간 has 57 (69.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 09:30:03.360143
Analysis finished2023-12-12 09:30:06.791815
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct79
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T18:30:07.035046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14.5
Mean length8.1829268
Min length3

Characters and Unicode

Total characters671
Distinct characters155
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

Unique76 ?
Unique (%)92.7%

Sample

1st row노원휴먼프라자
2nd row세븐밸리
3rd row스펙트럼시티
4th row삼성테스코(주)홈프러스 칠곡점
5th row한국까르푸(주) 강북점
ValueCountFrequency (%)
대구종합유통단지 5
 
5.0%
이코노마트 2
 
2.0%
칠성점 2
 
2.0%
강북점 2
 
2.0%
대구점 2
 
2.0%
나이스마트 2
 
2.0%
칠곡점 2
 
2.0%
스펙트럼시티 2
 
2.0%
ok마트 1
 
1.0%
대백마트(국우점 1
 
1.0%
Other values (79) 79
79.0%
2023-12-12T18:30:07.609717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 41
 
6.1%
마 36
 
5.4%
점 34
 
5.1%
대 25
 
3.7%
) 20
 
3.0%
( 20
 
3.0%
스 18
 
2.7%
18
 
2.7%
시 16
 
2.4%
구 16
 
2.4%
Other values (145) 427
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 600
89.4%
Close Punctuation 20
 
3.0%
Open Punctuation 20
 
3.0%
Space Separator 18
 
2.7%
Uppercase Letter 8
 
1.2%
Lowercase Letter 4
 
0.6%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 41
 
6.8%
마 36
 
6.0%
점 34
 
5.7%
대 25
 
4.2%
스 18
 
3.0%
시 16
 
2.7%
구 16
 
2.7%
이 13
 
2.2%
주 13
 
2.2%
장 12
 
2.0%
Other values (132) 376
62.7%
Uppercase Letter
ValueCountFrequency (%)
O 3
37.5%
K 2
25.0%
E 1
 
12.5%
X 1
 
12.5%
C 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
p 1
25.0%
o 1
25.0%
h 1
25.0%
s 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 600
89.4%
Common 59
 
8.8%
Latin 12
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 41
 
6.8%
마 36
 
6.0%
점 34
 
5.7%
대 25
 
4.2%
스 18
 
3.0%
시 16
 
2.7%
구 16
 
2.7%
이 13
 
2.2%
주 13
 
2.2%
장 12
 
2.0%
Other values (132) 376
62.7%
Latin
ValueCountFrequency (%)
O 3
25.0%
K 2
16.7%
E 1
 
8.3%
X 1
 
8.3%
p 1
 
8.3%
o 1
 
8.3%
h 1
 
8.3%
s 1
 
8.3%
C 1
 
8.3%
Common
ValueCountFrequency (%)
) 20
33.9%
( 20
33.9%
18
30.5%
2 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 600
89.4%
ASCII 71
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 41
 
6.8%
마 36
 
6.0%
점 34
 
5.7%
대 25
 
4.2%
스 18
 
3.0%
시 16
 
2.7%
구 16
 
2.7%
이 13
 
2.2%
주 13
 
2.2%
장 12
 
2.0%
Other values (132) 376
62.7%
ASCII
ValueCountFrequency (%)
) 20
28.2%
( 20
28.2%
18
25.4%
O 3
 
4.2%
K 2
 
2.8%
E 1
 
1.4%
2 1
 
1.4%
X 1
 
1.4%
p 1
 
1.4%
o 1
 
1.4%
Other values (3) 3
 
4.2%

업종분류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
쇼핑시설
82 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
쇼핑시설 82
100.0%

Length

2023-12-12T18:30:07.772725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:07.920630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쇼핑시설 82
100.0%

서비스명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
식품판매업(기타)
43 
대규모점포
39 

Length

Max length9
Median length9
Mean length7.097561
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품판매업(기타) 43
52.4%
대규모점포 39
47.6%

Length

2023-12-12T18:30:08.076931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:08.206848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품판매업(기타 43
52.4%
대규모점포 39
47.6%

서비스ID
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
07_22_13_P
43 
08_25_01_P
39 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08_25_01_P
2nd row08_25_01_P
3rd row08_25_01_P
4th row08_25_01_P
5th row08_25_01_P

Common Values

ValueCountFrequency (%)
07_22_13_P 43
52.4%
08_25_01_P 39
47.6%

Length

2023-12-12T18:30:08.351958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:08.462242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
07_22_13_p 43
52.4%
08_25_01_p 39
47.6%

인허가일자
Date

MISSING 

Distinct77
Distinct (%)95.1%
Missing1
Missing (%)1.2%
Memory size788.0 B
Minimum1960-10-27 00:00:00
Maximum2021-05-03 00:00:00
2023-12-12T18:30:08.585074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:08.780330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태
Boolean

IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size214.0 B
True
80 
False
 
2
ValueCountFrequency (%)
True 80
97.6%
False 2
 
2.4%
2023-12-12T18:30:08.936272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

소재지전화
Text

MISSING 

Distinct60
Distinct (%)96.8%
Missing20
Missing (%)24.4%
Memory size788.0 B
2023-12-12T18:30:09.191039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.854839
Min length9

Characters and Unicode

Total characters735
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

Unique58 ?
Unique (%)93.5%

Sample

1st row053-327-3110
2nd row053-607-8000
3rd row1899-9900
4th row053-350-8000
5th row053-359-3000
ValueCountFrequency (%)
053-609-1234 2
 
3.2%
053-607-8000 2
 
3.2%
053-311-2171 1
 
1.6%
053-719-4230 1
 
1.6%
053-352-4992 1
 
1.6%
053-327-2900 1
 
1.6%
053-313-5999 1
 
1.6%
053-381-0556 1
 
1.6%
053-956-8002 1
 
1.6%
053-359-3300 1
 
1.6%
Other values (50) 50
80.6%
2023-12-12T18:30:09.565097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 134
18.2%
3 122
16.6%
- 121
16.5%
5 102
13.9%
9 56
7.6%
1 49
 
6.7%
2 46
 
6.3%
7 31
 
4.2%
8 29
 
3.9%
4 24
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 614
83.5%
Dash Punctuation 121
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134
21.8%
3 122
19.9%
5 102
16.6%
9 56
9.1%
1 49
 
8.0%
2 46
 
7.5%
7 31
 
5.0%
8 29
 
4.7%
4 24
 
3.9%
6 21
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134
18.2%
3 122
16.6%
- 121
16.5%
5 102
13.9%
9 56
7.6%
1 49
 
6.7%
2 46
 
6.3%
7 31
 
4.2%
8 29
 
3.9%
4 24
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134
18.2%
3 122
16.6%
- 121
16.5%
5 102
13.9%
9 56
7.6%
1 49
 
6.7%
2 46
 
6.3%
7 31
 
4.2%
8 29
 
3.9%
4 24
 
3.3%

구우편번호
Text

MISSING 

Distinct43
Distinct (%)76.8%
Missing26
Missing (%)31.7%
Memory size788.0 B
2023-12-12T18:30:09.773838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters392
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

Unique32 ?
Unique (%)57.1%

Sample

1st row702-250
2nd row702-845
3rd row702-200
4th row702-851
5th row702-062
ValueCountFrequency (%)
702-865 3
 
5.4%
702-250 3
 
5.4%
702-866 2
 
3.6%
702-062 2
 
3.6%
702-863 2
 
3.6%
702-886 2
 
3.6%
702-851 2
 
3.6%
702-881 2
 
3.6%
702-847 2
 
3.6%
702-130 2
 
3.6%
Other values (33) 34
60.7%
2023-12-12T18:30:10.110594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84
21.4%
2 74
18.9%
7 67
17.1%
- 56
14.3%
8 43
11.0%
6 18
 
4.6%
1 15
 
3.8%
5 12
 
3.1%
3 10
 
2.6%
4 9
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336
85.7%
Dash Punctuation 56
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84
25.0%
2 74
22.0%
7 67
19.9%
8 43
12.8%
6 18
 
5.4%
1 15
 
4.5%
5 12
 
3.6%
3 10
 
3.0%
4 9
 
2.7%
9 4
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84
21.4%
2 74
18.9%
7 67
17.1%
- 56
14.3%
8 43
11.0%
6 18
 
4.6%
1 15
 
3.8%
5 12
 
3.1%
3 10
 
2.6%
4 9
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84
21.4%
2 74
18.9%
7 67
17.1%
- 56
14.3%
8 43
11.0%
6 18
 
4.6%
1 15
 
3.8%
5 12
 
3.1%
3 10
 
2.6%
4 9
 
2.3%

주소
Text

Distinct77
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-12T18:30:10.412196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length21.658537
Min length17

Characters and Unicode

Total characters1776
Distinct characters84
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

Unique74 ?
Unique (%)90.2%

Sample

1st row대구광역시 북구 노원동3가 1022호
2nd row대구광역시 북구 동천동 894번지 1호
3rd row대구광역시 북구 칠성동2가 20-1호
4th row대구광역시 북구 동천동 968호
5th row대구광역시 북구 관음동 1369번지 1호
ValueCountFrequency (%)
대구광역시 82
23.3%
북구 82
23.3%
산격동 11
 
3.1%
태전동 10
 
2.8%
1호 9
 
2.6%
칠성동2가 8
 
2.3%
동천동 7
 
2.0%
20번지 4
 
1.1%
관음동 4
 
1.1%
읍내동 4
 
1.1%
Other values (107) 131
37.2%
2023-12-12T18:30:10.834831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
364
20.5%
구 168
 
9.5%
동 92
 
5.2%
대 84
 
4.7%
광 82
 
4.6%
역 82
 
4.6%
시 82
 
4.6%
북 82
 
4.6%
1 81
 
4.6%
2 63
 
3.5%
Other values (74) 596
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1016
57.2%
Space Separator 364
 
20.5%
Decimal Number 355
 
20.0%
Dash Punctuation 34
 
1.9%
Uppercase Letter 3
 
0.2%
Other Punctuation 2
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 168
16.5%
동 92
9.1%
대 84
8.3%
광 82
8.1%
역 82
8.1%
시 82
8.1%
북 82
8.1%
지 57
 
5.6%
번 55
 
5.4%
호 34
 
3.3%
Other values (56) 198
19.5%
Decimal Number
ValueCountFrequency (%)
1 81
22.8%
2 63
17.7%
6 35
9.9%
9 32
 
9.0%
3 32
 
9.0%
8 26
 
7.3%
7 24
 
6.8%
5 22
 
6.2%
0 21
 
5.9%
4 19
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
A 1
33.3%
P 1
33.3%
Space Separator
ValueCountFrequency (%)
364
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1016
57.2%
Common 757
42.6%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 168
16.5%
동 92
9.1%
대 84
8.3%
광 82
8.1%
역 82
8.1%
시 82
8.1%
북 82
8.1%
지 57
 
5.6%
번 55
 
5.4%
호 34
 
3.3%
Other values (56) 198
19.5%
Common
ValueCountFrequency (%)
364
48.1%
1 81
 
10.7%
2 63
 
8.3%
6 35
 
4.6%
- 34
 
4.5%
9 32
 
4.2%
3 32
 
4.2%
8 26
 
3.4%
7 24
 
3.2%
5 22
 
2.9%
Other values (5) 44
 
5.8%
Latin
ValueCountFrequency (%)
T 1
33.3%
A 1
33.3%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1016
57.2%
ASCII 760
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
364
47.9%
1 81
 
10.7%
2 63
 
8.3%
6 35
 
4.6%
- 34
 
4.5%
9 32
 
4.2%
3 32
 
4.2%
8 26
 
3.4%
7 24
 
3.2%
5 22
 
2.9%
Other values (8) 47
 
6.2%
Hangul
ValueCountFrequency (%)
구 168
16.5%
동 92
9.1%
대 84
8.3%
광 82
8.1%
역 82
8.1%
시 82
8.1%
북 82
8.1%
지 57
 
5.6%
번 55
 
5.4%
호 34
 
3.3%
Other values (56) 198
19.5%

도로명우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)65.4%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean41497.284
Minimum41401
Maximum41599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T18:30:11.012393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41401
5-th percentile41412
Q141438
median41504
Q341558
95-th percentile41593
Maximum41599
Range198
Interquartile range (IQR)120

Descriptive statistics

Standard deviation62.971072
Coefficient of variation (CV)0.0015174745
Kurtosis-1.4174624
Mean41497.284
Median Absolute Deviation (MAD)56
Skewness0.11651954
Sum3361280
Variance3965.3559
MonotonicityNot monotonic
2023-12-12T18:30:11.202420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41593 5
 
6.1%
41518 5
 
6.1%
41557 3
 
3.7%
41558 3
 
3.7%
41570 2
 
2.4%
41467 2
 
2.4%
41427 2
 
2.4%
41423 2
 
2.4%
41409 2
 
2.4%
41524 2
 
2.4%
Other values (43) 53
64.6%
ValueCountFrequency (%)
41401 1
1.2%
41405 1
1.2%
41409 2
2.4%
41412 2
2.4%
41416 1
1.2%
41418 1
1.2%
41419 1
1.2%
41422 2
2.4%
41423 2
2.4%
41424 2
2.4%
ValueCountFrequency (%)
41599 1
 
1.2%
41593 5
6.1%
41591 1
 
1.2%
41586 2
 
2.4%
41583 2
 
2.4%
41582 1
 
1.2%
41581 1
 
1.2%
41570 2
 
2.4%
41564 1
 
1.2%
41560 1
 
1.2%

도로명주소
Text

MISSING 

Distinct71
Distinct (%)92.2%
Missing5
Missing (%)6.1%
Memory size788.0 B
2023-12-12T18:30:11.515363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length34
Mean length25.480519
Min length20

Characters and Unicode

Total characters1962
Distinct characters99
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

Unique67 ?
Unique (%)87.0%

Sample

1st row대구광역시 북구 팔달로35길 69 (노원동3가)
2nd row대구광역시 북구 동암로 90 (동천동)
3rd row대구광역시 북구 동암로12길 8 (동천동)
4th row대구광역시 북구 관음로 43 (관음동)
5th row대구광역시 북구 관음로 43 (관음동)
ValueCountFrequency (%)
대구광역시 77
18.7%
북구 77
18.7%
1층 15
 
3.6%
산격동 12
 
2.9%
태전동 9
 
2.2%
칠성동2가 7
 
1.7%
동천동 7
 
1.7%
유통단지로 7
 
1.7%
8 5
 
1.2%
구암동 4
 
1.0%
Other values (139) 192
46.6%
2023-12-12T18:30:12.004722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
335
17.1%
구 162
 
8.3%
동 98
 
5.0%
대 95
 
4.8%
북 79
 
4.0%
시 78
 
4.0%
( 78
 
4.0%
) 78
 
4.0%
로 77
 
3.9%
광 77
 
3.9%
Other values (89) 805
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1173
59.8%
Space Separator 335
 
17.1%
Decimal Number 260
 
13.3%
Open Punctuation 78
 
4.0%
Close Punctuation 78
 
4.0%
Other Punctuation 27
 
1.4%
Dash Punctuation 8
 
0.4%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 162
13.8%
동 98
 
8.4%
대 95
 
8.1%
북 79
 
6.7%
시 78
 
6.6%
로 77
 
6.6%
광 77
 
6.6%
역 77
 
6.6%
길 30
 
2.6%
산 22
 
1.9%
Other values (71) 378
32.2%
Decimal Number
ValueCountFrequency (%)
1 59
22.7%
2 41
15.8%
3 38
14.6%
4 22
 
8.5%
5 21
 
8.1%
6 19
 
7.3%
0 17
 
6.5%
9 15
 
5.8%
8 14
 
5.4%
7 14
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
T 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
335
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Other Punctuation
ValueCountFrequency (%)
, 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1173
59.8%
Common 786
40.1%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 162
13.8%
동 98
 
8.4%
대 95
 
8.1%
북 79
 
6.7%
시 78
 
6.6%
로 77
 
6.6%
광 77
 
6.6%
역 77
 
6.6%
길 30
 
2.6%
산 22
 
1.9%
Other values (71) 378
32.2%
Common
ValueCountFrequency (%)
335
42.6%
( 78
 
9.9%
) 78
 
9.9%
1 59
 
7.5%
2 41
 
5.2%
3 38
 
4.8%
, 27
 
3.4%
4 22
 
2.8%
5 21
 
2.7%
6 19
 
2.4%
Other values (5) 68
 
8.7%
Latin
ValueCountFrequency (%)
P 1
33.3%
T 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1173
59.8%
ASCII 789
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
335
42.5%
( 78
 
9.9%
) 78
 
9.9%
1 59
 
7.5%
2 41
 
5.2%
3 38
 
4.8%
, 27
 
3.4%
4 22
 
2.8%
5 21
 
2.7%
6 19
 
2.4%
Other values (8) 71
 
9.0%
Hangul
ValueCountFrequency (%)
구 162
13.8%
동 98
 
8.4%
대 95
 
8.1%
북 79
 
6.7%
시 78
 
6.6%
로 77
 
6.6%
광 77
 
6.6%
역 77
 
6.6%
길 30
 
2.6%
산 22
 
1.9%
Other values (71) 378
32.2%

경도
Real number (ℝ)

MISSING 

Distinct72
Distinct (%)88.9%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean128.57938
Minimum128.5103
Maximum128.62957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T18:30:12.212522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.5103
5-th percentile128.54346
Q1128.55541
median128.57625
Q3128.6045
95-th percentile128.61808
Maximum128.62957
Range0.1192652
Interquartile range (IQR)0.0490923

Descriptive statistics

Standard deviation0.027380898
Coefficient of variation (CV)0.00021294937
Kurtosis-1.133888
Mean128.57938
Median Absolute Deviation (MAD)0.0260257
Skewness-0.084795758
Sum10414.93
Variance0.00074971357
MonotonicityNot monotonic
2023-12-12T18:30:12.439296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.5897899 5
 
6.1%
128.5701732 2
 
2.4%
128.5404661 2
 
2.4%
128.5603226 2
 
2.4%
128.5488914 2
 
2.4%
128.5559113 2
 
2.4%
128.6098656 1
 
1.2%
128.5446659 1
 
1.2%
128.5463794 1
 
1.2%
128.5952519 1
 
1.2%
Other values (62) 62
75.6%
ValueCountFrequency (%)
128.5103045 1
1.2%
128.5404661 2
2.4%
128.5428898 1
1.2%
128.5434596 1
1.2%
128.5435351 1
1.2%
128.5440655 1
1.2%
128.544167 1
1.2%
128.5446659 1
1.2%
128.5463794 1
1.2%
128.5474444 1
1.2%
ValueCountFrequency (%)
128.6295697 1
1.2%
128.6246794 1
1.2%
128.619881 1
1.2%
128.6188776 1
1.2%
128.6180812 1
1.2%
128.6174304 1
1.2%
128.6154356 1
1.2%
128.6143104 1
1.2%
128.6130195 1
1.2%
128.6105175 1
1.2%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)88.9%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean35.912952
Minimum35.875653
Maximum35.952422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T18:30:12.719144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.875653
5-th percentile35.879966
Q135.892085
median35.907963
Q335.934668
95-th percentile35.946657
Maximum35.952422
Range0.07676852
Interquartile range (IQR)0.04258325

Descriptive statistics

Standard deviation0.02314615
Coefficient of variation (CV)0.00064450702
Kurtosis-1.3417435
Mean35.912952
Median Absolute Deviation (MAD)0.01961503
Skewness0.074702765
Sum2908.9491
Variance0.00053574425
MonotonicityNot monotonic
2023-12-12T18:30:12.916405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.88522557 5
 
6.1%
35.89208491 2
 
2.4%
35.9353392 2
 
2.4%
35.94413787 2
 
2.4%
35.93227079 2
 
2.4%
35.94482828 2
 
2.4%
35.88053874 1
 
1.2%
35.92212549 1
 
1.2%
35.92414019 1
 
1.2%
35.87996589 1
 
1.2%
Other values (62) 62
75.6%
ValueCountFrequency (%)
35.87565299 1
 
1.2%
35.87588495 1
 
1.2%
35.87697957 1
 
1.2%
35.8778191 1
 
1.2%
35.87996589 1
 
1.2%
35.88053874 1
 
1.2%
35.88111887 1
 
1.2%
35.88148155 1
 
1.2%
35.88522557 5
6.1%
35.88834783 1
 
1.2%
ValueCountFrequency (%)
35.95242151 1
1.2%
35.95146588 1
1.2%
35.94818766 1
1.2%
35.94804896 1
1.2%
35.94665729 1
1.2%
35.94482828 2
2.4%
35.9444037 1
1.2%
35.94413787 2
2.4%
35.94328718 1
1.2%
35.94252496 1
1.2%

영업시간
Text

MISSING 

Distinct17
Distinct (%)68.0%
Missing57
Missing (%)69.5%
Memory size788.0 B
2023-12-12T18:30:13.124698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length13
Mean length15.6
Min length11

Characters and Unicode

Total characters390
Distinct characters26
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)44.0%

Sample

1st row11:00 - 21:00
2nd row10:00 - 24:00
3rd row09:00 - 22:00/둘째, 넷째 일요일 휴무
4th row10:00 - 24:00
5th row 09:30 - 20:30/셋째 수요일 휴무
ValueCountFrequency (%)
22
26.8%
10:00 6
 
7.3%
23:00 6
 
7.3%
09:00 5
 
6.1%
휴무 4
 
4.9%
24:00 4
 
4.9%
23:30 3
 
3.7%
22:00 3
 
3.7%
08:00 3
 
3.7%
일요일 3
 
3.7%
Other values (15) 23
28.0%
2023-12-12T18:30:13.471057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
30.8%
57
14.6%
: 52
13.3%
2 30
 
7.7%
- 27
 
6.9%
1 19
 
4.9%
3 19
 
4.9%
9 8
 
2.1%
째 7
 
1.8%
일 7
 
1.8%
Other values (16) 44
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 208
53.3%
Other Punctuation 60
 
15.4%
Space Separator 58
 
14.9%
Other Letter 37
 
9.5%
Dash Punctuation 27
 
6.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
째 7
18.9%
일 7
18.9%
무 4
10.8%
요 4
10.8%
휴 4
10.8%
넷 3
8.1%
둘 3
8.1%
셋 1
 
2.7%
수 1
 
2.7%
월 1
 
2.7%
Other values (2) 2
 
5.4%
Decimal Number
ValueCountFrequency (%)
0 120
57.7%
2 30
 
14.4%
1 19
 
9.1%
3 19
 
9.1%
9 8
 
3.8%
8 4
 
1.9%
7 4
 
1.9%
4 4
 
1.9%
Other Punctuation
ValueCountFrequency (%)
: 52
86.7%
/ 5
 
8.3%
, 3
 
5.0%
Space Separator
ValueCountFrequency (%)
57
98.3%
  1
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 353
90.5%
Hangul 37
 
9.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
34.0%
57
16.1%
: 52
14.7%
2 30
 
8.5%
- 27
 
7.6%
1 19
 
5.4%
3 19
 
5.4%
9 8
 
2.3%
/ 5
 
1.4%
8 4
 
1.1%
Other values (4) 12
 
3.4%
Hangul
ValueCountFrequency (%)
째 7
18.9%
일 7
18.9%
무 4
10.8%
요 4
10.8%
휴 4
10.8%
넷 3
8.1%
둘 3
8.1%
셋 1
 
2.7%
수 1
 
2.7%
월 1
 
2.7%
Other values (2) 2
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 352
90.3%
Hangul 37
 
9.5%
None 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
34.1%
57
16.2%
: 52
14.8%
2 30
 
8.5%
- 27
 
7.7%
1 19
 
5.4%
3 19
 
5.4%
9 8
 
2.3%
/ 5
 
1.4%
8 4
 
1.1%
Other values (3) 11
 
3.1%
Hangul
ValueCountFrequency (%)
째 7
18.9%
일 7
18.9%
무 4
10.8%
요 4
10.8%
휴 4
10.8%
넷 3
8.1%
둘 3
8.1%
셋 1
 
2.7%
수 1
 
2.7%
월 1
 
2.7%
Other values (2) 2
 
5.4%
None
ValueCountFrequency (%)
  1
100.0%

Interactions

2023-12-12T18:30:05.763805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:04.669191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:05.044459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:05.905979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:04.798621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:05.477176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:06.012372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:04.918566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:05.607583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:30:13.615901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명서비스명서비스ID인허가일자영업상태소재지전화구우편번호주소도로명우편번호도로명주소경도위도영업시간
사업장명1.0001.0001.0000.9901.0000.9920.9770.9870.9380.9830.9330.7281.000
서비스명1.0001.0000.9991.0000.0001.0000.9551.0000.6331.0000.0000.5270.877
서비스ID1.0000.9991.0001.0000.0001.0000.9551.0000.6331.0000.0000.5270.877
인허가일자0.9901.0001.0001.0001.0000.9990.9960.9970.9690.9980.9430.9691.000
영업상태1.0000.0000.0001.0001.000NaNNaN1.0000.3581.0000.5260.418NaN
소재지전화0.9921.0001.0000.999NaN1.0001.0000.9991.0001.0001.0001.0001.000
구우편번호0.9770.9550.9550.996NaN1.0001.0000.9970.9910.9970.9610.9640.867
주소0.9871.0001.0000.9971.0000.9990.9971.0001.0001.0001.0001.0001.000
도로명우편번호0.9380.6330.6330.9690.3581.0000.9911.0001.0001.0000.8110.9000.745
도로명주소0.9831.0001.0000.9981.0001.0000.9971.0001.0001.0001.0001.0001.000
경도0.9330.0000.0000.9430.5261.0000.9611.0000.8111.0001.0000.7550.904
위도0.7280.5270.5270.9690.4181.0000.9641.0000.9001.0000.7551.0000.861
영업시간1.0000.8770.8771.000NaN1.0000.8671.0000.7451.0000.9040.8611.000
2023-12-12T18:30:14.160577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서비스명영업상태서비스ID
서비스명1.0000.0000.975
영업상태0.0001.0000.000
서비스ID0.9750.0001.000
2023-12-12T18:30:14.291728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명우편번호경도위도서비스명서비스ID영업상태
도로명우편번호1.0000.375-0.9530.4740.4740.261
경도0.3751.000-0.4660.0000.0000.364
위도-0.953-0.4661.0000.3830.3830.302
서비스명0.4740.0000.3831.0000.9750.000
서비스ID0.4740.0000.3830.9751.0000.000
영업상태0.2610.3640.3020.0000.0001.000

Missing values

2023-12-12T18:30:06.194929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:30:06.428722image/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.
2023-12-12T18:30:06.658713image/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

사업장명업종분류서비스명서비스ID인허가일자영업상태소재지전화구우편번호주소도로명우편번호도로명주소경도위도영업시간
0노원휴먼프라자쇼핑시설대규모점포08_25_01_P2002-09-06Y<NA><NA>대구광역시 북구 노원동3가 1022호41557대구광역시 북구 팔달로35길 69 (노원동3가)128.57017335.892085<NA>
1세븐밸리쇼핑시설대규모점포08_25_01_P2007-10-22Y053-327-3110702-250대구광역시 북구 동천동 894번지 1호41423대구광역시 북구 동암로 90 (동천동)128.56032335.94413811:00 - 21:00
2스펙트럼시티쇼핑시설대규모점포08_25_01_P2002-09-06Y<NA><NA>대구광역시 북구 칠성동2가 20-1호41593<NA>128.5897935.885226<NA>
3삼성테스코(주)홈프러스 칠곡점쇼핑시설대규모점포08_25_01_P2006-04-17Y053-607-8000<NA>대구광역시 북구 동천동 968호41422대구광역시 북구 동암로12길 8 (동천동)128.55591135.94482810:00 - 24:00
4한국까르푸(주) 강북점쇼핑시설대규모점포08_25_01_P2005-06-10N<NA><NA>대구광역시 북구 관음동 1369번지 1호41448대구광역시 북구 관음로 43 (관음동)128.54046635.935339<NA>
5한국까르푸칠곡점쇼핑시설대규모점포08_25_01_P2005-06-27N<NA><NA>대구광역시 북구 관음동 1369번지 1호41448대구광역시 북구 관음로 43 (관음동)128.54046635.935339<NA>
6코스트코홀세일쇼핑시설대규모점포08_25_01_P2005-11-17Y1899-9900<NA>대구광역시 북구 산격동 1817호41516대구광역시 북구 검단로 97 (산격동)128.61808135.9062809:00 - 22:00/둘째, 넷째 일요일 휴무
7홈플러스 대구점쇼핑시설대규모점포08_25_01_P1997-08-07Y053-350-8000<NA>대구광역시 북구 칠성동2가 378번지 23호41586대구광역시 북구 중앙대로 543 (칠성동2가)128.59576635.88148210:00 - 24:00
8아이미즈쇼핑몰쇼핑시설대규모점포08_25_01_P1999-02-11Y053-359-3000<NA>대구광역시 북구 노원동2가 381호41558대구광역시 북구 팔달로 183 (노원동2가)128.57011835.888921<NA>
9대구기계공구상협동조합쇼핑시설대규모점포08_25_01_P1999-06-12Y053-604-0700<NA>대구광역시 북구 산격동 1629호41518대구광역시 북구 유통단지로 16 (산격동)128.60571435.904108<NA>
사업장명업종분류서비스명서비스ID인허가일자영업상태소재지전화구우편번호주소도로명우편번호도로명주소경도위도영업시간
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73대백마트동천점쇼핑시설식품판매업(기타)07_22_13_P2018-07-31Y053-216-5353702-886대구광역시 북구 동천동 927번지41427대구광역시 북구 대천로17길 5, 1층 (동천동)128.55832735.93773<NA>
74빅마트쇼핑시설식품판매업(기타)07_22_13_P2021-04-26Y053-325-1009702-807대구광역시 북구 구암동 771-441424대구광역시 북구 구암로65길 50-6, 1층 (구암동)128.56490535.941691<NA>
75대백마트(영진점)쇼핑시설식품판매업(기타)07_22_13_P2021-03-03Y053-957-9075702-830대구광역시 북구 복현동 249-141528대구광역시 북구 복현로 40, 1층 (복현동)128.62467935.895392<NA>
76나이스마트(연경점)쇼핑시설식품판매업(기타)07_22_13_P2021-04-12Y053-986-9980702-130대구광역시 북구 연경동 103941409대구광역시 북구 연경중앙로2길 1, 1층 (연경동)128.61887835.94328707:30 - 23:30
77나이스식자재마트쇼핑시설식품판매업(기타)07_22_13_P2006-02-14Y053-353-5002702-857대구광역시 북구 침산동 375-1번지41591대구광역시 북구 침산남로 122-6 (침산동)128.58857235.888348<NA>
78나이스마트(도청점)쇼핑시설식품판매업(기타)07_22_13_P2005-10-21Y053-951-7400702-838대구광역시 북구 산격동 1101-5번지41541대구광역시 북구 연암공원로 30 (산격동)128.59787935.893396<NA>
79대백마트원대점쇼핑시설식품판매업(기타)07_22_13_P2005-07-27Y053-354-9097702-812대구광역시 북구 노원동2가 343-1번지41558대구광역시 북구 팔달로41길 17 (노원동2가)128.57294835.889014<NA>
80퍼펙트마켓쇼핑시설식품판매업(기타)07_22_13_P2005-04-01Y053-321-8696702-804대구광역시 북구 관음동 605-33번지41445대구광역시 북구 관음중앙로 43 (관음동)128.54406535.937668<NA>
81효성파머스마켓(칠곡점)쇼핑시설식품판매업(기타)07_22_13_P2003-11-24Y053-324-0722702-807대구광역시 북구 구암동 781번지41424대구광역시 북구 동암로38길 27 (구암동)128.56935635.941377<NA>