Overview

Dataset statistics

Number of variables5
Number of observations103
Missing cells49
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory42.3 B

Variable types

Numeric1
Text4

Dataset

Description부산광역시_해운대구_옥외광고업현황_20190828
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3075793

Alerts

영업장전화번호 has 49 (47.6%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:32:53.146579
Analysis finished2023-12-10 16:32:53.880416
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52
Minimum1
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:32:54.014361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q126.5
median52
Q377.5
95-th percentile97.9
Maximum103
Range102
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.877528
Coefficient of variation (CV)0.57456784
Kurtosis-1.2
Mean52
Median Absolute Deviation (MAD)26
Skewness0
Sum5356
Variance892.66667
MonotonicityStrictly increasing
2023-12-11T01:32:54.272715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
Other values (93) 93
90.3%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
103 1
1.0%
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
Distinct102
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:32:54.645849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length10
Mean length6.2621359
Min length2

Characters and Unicode

Total characters645
Distinct characters173
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)98.1%

Sample

1st row디자인 혼
2nd row주식회사 피엠컴퍼니
3rd row(주)피알컴
4th row조이멤버스 주식회사
5th row(주)디자인잇다
ValueCountFrequency (%)
주식회사 8
 
6.7%
부산광고사 2
 
1.7%
디자인 2
 
1.7%
광고기획 2
 
1.7%
대광광고사 1
 
0.8%
사인포유 1
 
0.8%
하람디자인 1
 
0.8%
터전애드 1
 
0.8%
토마코 1
 
0.8%
주)나노디자인그룹 1
 
0.8%
Other values (99) 99
83.2%
2023-12-11T01:32:55.265781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
4.8%
24
 
3.7%
23
 
3.6%
( 23
 
3.6%
) 23
 
3.6%
22
 
3.4%
21
 
3.3%
20
 
3.1%
19
 
2.9%
19
 
2.9%
Other values (163) 420
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 567
87.9%
Open Punctuation 23
 
3.6%
Close Punctuation 23
 
3.6%
Space Separator 16
 
2.5%
Uppercase Letter 16
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
 
5.5%
24
 
4.2%
23
 
4.1%
22
 
3.9%
21
 
3.7%
20
 
3.5%
19
 
3.4%
19
 
3.4%
18
 
3.2%
17
 
3.0%
Other values (148) 353
62.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
18.8%
S 3
18.8%
T 1
 
6.2%
N 1
 
6.2%
I 1
 
6.2%
D 1
 
6.2%
R 1
 
6.2%
M 1
 
6.2%
C 1
 
6.2%
E 1
 
6.2%
Other values (2) 2
12.5%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 567
87.9%
Common 62
 
9.6%
Latin 16
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
 
5.5%
24
 
4.2%
23
 
4.1%
22
 
3.9%
21
 
3.7%
20
 
3.5%
19
 
3.4%
19
 
3.4%
18
 
3.2%
17
 
3.0%
Other values (148) 353
62.3%
Latin
ValueCountFrequency (%)
A 3
18.8%
S 3
18.8%
T 1
 
6.2%
N 1
 
6.2%
I 1
 
6.2%
D 1
 
6.2%
R 1
 
6.2%
M 1
 
6.2%
C 1
 
6.2%
E 1
 
6.2%
Other values (2) 2
12.5%
Common
ValueCountFrequency (%)
( 23
37.1%
) 23
37.1%
16
25.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 567
87.9%
ASCII 78
 
12.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
31
 
5.5%
24
 
4.2%
23
 
4.1%
22
 
3.9%
21
 
3.7%
20
 
3.5%
19
 
3.4%
19
 
3.4%
18
 
3.2%
17
 
3.0%
Other values (148) 353
62.3%
ASCII
ValueCountFrequency (%)
( 23
29.5%
) 23
29.5%
16
20.5%
A 3
 
3.8%
S 3
 
3.8%
T 1
 
1.3%
N 1
 
1.3%
I 1
 
1.3%
D 1
 
1.3%
R 1
 
1.3%
Other values (5) 5
 
6.4%

영업장전화번호
Text

MISSING 

Distinct54
Distinct (%)100.0%
Missing49
Missing (%)47.6%
Memory size956.0 B
2023-12-11T01:32:55.655435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters648
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

Unique54 ?
Unique (%)100.0%

Sample

1st row051-741-6113
2nd row051-747-9066
3rd row051-753-5234
4th row051-740-7760
5th row051-746-6524
ValueCountFrequency (%)
051-704-1238 1
 
1.9%
051-704-4006 1
 
1.9%
051-504-7001 1
 
1.9%
051-744-8500 1
 
1.9%
051-741-4433 1
 
1.9%
051-759-5221 1
 
1.9%
051-747-6105 1
 
1.9%
051-746-0906 1
 
1.9%
051-747-4481 1
 
1.9%
051-731-4152 1
 
1.9%
Other values (44) 44
81.5%
2023-12-11T01:32:56.227064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 108
16.7%
0 94
14.5%
5 90
13.9%
1 85
13.1%
7 77
11.9%
4 69
10.6%
2 33
 
5.1%
8 33
 
5.1%
3 26
 
4.0%
6 21
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 540
83.3%
Dash Punctuation 108
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94
17.4%
5 90
16.7%
1 85
15.7%
7 77
14.3%
4 69
12.8%
2 33
 
6.1%
8 33
 
6.1%
3 26
 
4.8%
6 21
 
3.9%
9 12
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 108
16.7%
0 94
14.5%
5 90
13.9%
1 85
13.1%
7 77
11.9%
4 69
10.6%
2 33
 
5.1%
8 33
 
5.1%
3 26
 
4.0%
6 21
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 108
16.7%
0 94
14.5%
5 90
13.9%
1 85
13.1%
7 77
11.9%
4 69
10.6%
2 33
 
5.1%
8 33
 
5.1%
3 26
 
4.0%
6 21
 
3.2%
Distinct101
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:32:56.596350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length41
Mean length33.883495
Min length22

Characters and Unicode

Total characters3490
Distinct characters143
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

Unique99 ?
Unique (%)96.1%

Sample

1st row부산광역시 해운대구 센텀북대로 60, 센텀아이에스타워 905-906호 (재송동)
2nd row부산광역시 해운대구 센텀중앙로 78, 센텀그린타워 1601호 (우동)
3rd row부산광역시 해운대구 구남로29번길 21, 세이브존 16층 (중동)
4th row부산광역시 해운대구 해운대해변로 310, 호텔마리안느 102호 (중동)
5th row부산광역시 해운대구 센텀북대로 60, 센텀아이에스타워 807호 (재송동)
ValueCountFrequency (%)
부산광역시 103
 
16.0%
해운대구 103
 
16.0%
우동 32
 
5.0%
재송동 25
 
3.9%
중동 13
 
2.0%
센텀중앙로 11
 
1.7%
좌동 9
 
1.4%
apec로 8
 
1.2%
60 7
 
1.1%
센텀동로 7
 
1.1%
Other values (213) 326
50.6%
2023-12-11T01:32:57.197174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
561
 
16.1%
142
 
4.1%
126
 
3.6%
1 121
 
3.5%
118
 
3.4%
115
 
3.3%
114
 
3.3%
109
 
3.1%
105
 
3.0%
104
 
3.0%
Other values (133) 1875
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2063
59.1%
Space Separator 561
 
16.1%
Decimal Number 529
 
15.2%
Open Punctuation 95
 
2.7%
Close Punctuation 95
 
2.7%
Other Punctuation 86
 
2.5%
Uppercase Letter 43
 
1.2%
Dash Punctuation 16
 
0.5%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
142
 
6.9%
126
 
6.1%
118
 
5.7%
115
 
5.6%
114
 
5.5%
109
 
5.3%
105
 
5.1%
104
 
5.0%
103
 
5.0%
103
 
5.0%
Other values (109) 924
44.8%
Decimal Number
ValueCountFrequency (%)
1 121
22.9%
0 71
13.4%
2 62
11.7%
5 59
11.2%
7 44
 
8.3%
3 40
 
7.6%
6 37
 
7.0%
4 34
 
6.4%
8 31
 
5.9%
9 30
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 13
30.2%
C 8
18.6%
E 8
18.6%
P 8
18.6%
B 2
 
4.7%
T 2
 
4.7%
I 1
 
2.3%
S 1
 
2.3%
Space Separator
ValueCountFrequency (%)
561
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Other Punctuation
ValueCountFrequency (%)
, 86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2063
59.1%
Common 1382
39.6%
Latin 45
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
142
 
6.9%
126
 
6.1%
118
 
5.7%
115
 
5.6%
114
 
5.5%
109
 
5.3%
105
 
5.1%
104
 
5.0%
103
 
5.0%
103
 
5.0%
Other values (109) 924
44.8%
Common
ValueCountFrequency (%)
561
40.6%
1 121
 
8.8%
( 95
 
6.9%
) 95
 
6.9%
, 86
 
6.2%
0 71
 
5.1%
2 62
 
4.5%
5 59
 
4.3%
7 44
 
3.2%
3 40
 
2.9%
Other values (5) 148
 
10.7%
Latin
ValueCountFrequency (%)
A 13
28.9%
C 8
17.8%
E 8
17.8%
P 8
17.8%
B 2
 
4.4%
e 2
 
4.4%
T 2
 
4.4%
I 1
 
2.2%
S 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2063
59.1%
ASCII 1427
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
561
39.3%
1 121
 
8.5%
( 95
 
6.7%
) 95
 
6.7%
, 86
 
6.0%
0 71
 
5.0%
2 62
 
4.3%
5 59
 
4.1%
7 44
 
3.1%
3 40
 
2.8%
Other values (14) 193
 
13.5%
Hangul
ValueCountFrequency (%)
142
 
6.9%
126
 
6.1%
118
 
5.7%
115
 
5.6%
114
 
5.5%
109
 
5.3%
105
 
5.1%
104
 
5.0%
103
 
5.0%
103
 
5.0%
Other values (109) 924
44.8%
Distinct71
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-11T01:32:57.590288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length9.6116505
Min length4

Characters and Unicode

Total characters990
Distinct characters74
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)59.2%

Sample

1st row광고물 제작
2nd row광고물 설치,대행
3rd row옥외광고물 제작 설치
4th row옥외광고물(전광판 등) 광고
5th row옥외광고물 제작
ValueCountFrequency (%)
제작 30
 
13.3%
간판제작등 20
 
8.8%
옥외광고물 17
 
7.5%
설치 16
 
7.1%
15
 
6.6%
옥외광고 8
 
3.5%
시공 7
 
3.1%
광고물 7
 
3.1%
광고대행 6
 
2.7%
간판제작 6
 
2.7%
Other values (56) 94
41.6%
2023-12-11T01:32:58.335347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
12.4%
85
 
8.6%
76
 
7.7%
72
 
7.3%
71
 
7.2%
, 46
 
4.6%
42
 
4.2%
39
 
3.9%
39
 
3.9%
39
 
3.9%
Other values (64) 358
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 815
82.3%
Space Separator 123
 
12.4%
Other Punctuation 46
 
4.6%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
85
 
10.4%
76
 
9.3%
72
 
8.8%
71
 
8.7%
42
 
5.2%
39
 
4.8%
39
 
4.8%
39
 
4.8%
39
 
4.8%
31
 
3.8%
Other values (60) 282
34.6%
Space Separator
ValueCountFrequency (%)
123
100.0%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 815
82.3%
Common 175
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
85
 
10.4%
76
 
9.3%
72
 
8.8%
71
 
8.7%
42
 
5.2%
39
 
4.8%
39
 
4.8%
39
 
4.8%
39
 
4.8%
31
 
3.8%
Other values (60) 282
34.6%
Common
ValueCountFrequency (%)
123
70.3%
, 46
 
26.3%
) 3
 
1.7%
( 3
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 815
82.3%
ASCII 175
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
70.3%
, 46
 
26.3%
) 3
 
1.7%
( 3
 
1.7%
Hangul
ValueCountFrequency (%)
85
 
10.4%
76
 
9.3%
72
 
8.8%
71
 
8.7%
42
 
5.2%
39
 
4.8%
39
 
4.8%
39
 
4.8%
39
 
4.8%
31
 
3.8%
Other values (60) 282
34.6%

Interactions

2023-12-11T01:32:53.521103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:32:58.498813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번영업장전화번호영업내용
순번1.0001.0000.693
영업장전화번호1.0001.0001.000
영업내용0.6931.0001.000

Missing values

2023-12-11T01:32:53.659624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:32:53.818707image/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

순번업소명영업장전화번호영업장도로명주소영업내용
01디자인 혼<NA>부산광역시 해운대구 센텀북대로 60, 센텀아이에스타워 905-906호 (재송동)광고물 제작
12주식회사 피엠컴퍼니<NA>부산광역시 해운대구 센텀중앙로 78, 센텀그린타워 1601호 (우동)광고물 설치,대행
23(주)피알컴<NA>부산광역시 해운대구 구남로29번길 21, 세이브존 16층 (중동)옥외광고물 제작 설치
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