Overview

Dataset statistics

Number of variables5
Number of observations342
Missing cells126
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory41.4 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description부산광역시해운대구_신고체육시설업현황_20201005
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3075744

Alerts

연번 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 연번High correlation
연락처 has 126 (36.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:04:19.155329
Analysis finished2023-12-10 17:04:20.419677
Duration1.26 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct342
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.5
Minimum1
Maximum342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T02:04:20.583430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.05
Q186.25
median171.5
Q3256.75
95-th percentile324.95
Maximum342
Range341
Interquartile range (IQR)170.5

Descriptive statistics

Standard deviation98.871128
Coefficient of variation (CV)0.57650804
Kurtosis-1.2
Mean171.5
Median Absolute Deviation (MAD)85.5
Skewness0
Sum58653
Variance9775.5
MonotonicityStrictly increasing
2023-12-11T02:04:20.990787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
227 1
 
0.3%
235 1
 
0.3%
234 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
231 1
 
0.3%
230 1
 
0.3%
229 1
 
0.3%
228 1
 
0.3%
Other values (332) 332
97.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
342 1
0.3%
341 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%
337 1
0.3%
336 1
0.3%
335 1
0.3%
334 1
0.3%
333 1
0.3%

업종
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
체육도장업
100 
체력단련장업
96 
골프연습장업
73 
당구장업
59 
수영장업
 
9
Other values (4)
 
5

Length

Max length7
Median length6
Mean length5.2953216
Min length4

Unique

Unique3 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
체육도장업 100
29.2%
체력단련장업 96
28.1%
골프연습장업 73
21.3%
당구장업 59
17.3%
수영장업 9
 
2.6%
무도학원업 2
 
0.6%
썰매장업 1
 
0.3%
빙상장업 1
 
0.3%
종합체육시설업 1
 
0.3%

Length

2023-12-11T02:04:21.330774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:04:21.612253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육도장업 100
29.2%
체력단련장업 96
28.1%
골프연습장업 73
21.3%
당구장업 59
17.3%
수영장업 9
 
2.6%
무도학원업 2
 
0.6%
썰매장업 1
 
0.3%
빙상장업 1
 
0.3%
종합체육시설업 1
 
0.3%

상호명
Text

Distinct339
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T02:04:22.528755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length8.2222222
Min length2

Characters and Unicode

Total characters2812
Distinct characters341
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

Unique336 ?
Unique (%)98.2%

Sample

1st row(주)파라다이스호텔부산신관수영장(실외)
2nd row아이 올림픽
3rd row(주)신세계조선호텔 부산 수영장
4th row트리니티스포츠클럽&스파
5th row코오롱씨클라우드호텔수영장(실외)
ValueCountFrequency (%)
태권도 18
 
3.2%
당구장 15
 
2.6%
당구클럽 12
 
2.1%
골프 10
 
1.8%
휘트니스 10
 
1.8%
피트니스 9
 
1.6%
스크린 7
 
1.2%
부산 5
 
0.9%
더 5
 
0.9%
태권도장 5
 
0.9%
Other values (421) 475
83.2%
2023-12-11T02:04:23.285110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
229
 
8.1%
스 164
 
5.8%
장 77
 
2.7%
프 71
 
2.5%
골 68
 
2.4%
도 65
 
2.3%
구 60
 
2.1%
당 57
 
2.0%
트 56
 
2.0%
태 54
 
1.9%
Other values (331) 1911
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2284
81.2%
Space Separator 229
 
8.1%
Uppercase Letter 164
 
5.8%
Lowercase Letter 45
 
1.6%
Open Punctuation 25
 
0.9%
Close Punctuation 25
 
0.9%
Other Punctuation 20
 
0.7%
Decimal Number 19
 
0.7%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 164
 
7.2%
장 77
 
3.4%
프 71
 
3.1%
골 68
 
3.0%
도 65
 
2.8%
구 60
 
2.6%
당 57
 
2.5%
트 56
 
2.5%
태 54
 
2.4%
권 52
 
2.3%
Other values (274) 1560
68.3%
Uppercase Letter
ValueCountFrequency (%)
T 19
 
11.6%
G 13
 
7.9%
M 12
 
7.3%
A 11
 
6.7%
O 10
 
6.1%
I 10
 
6.1%
S 10
 
6.1%
B 10
 
6.1%
J 8
 
4.9%
C 7
 
4.3%
Other values (14) 54
32.9%
Lowercase Letter
ValueCountFrequency (%)
o 6
13.3%
i 6
13.3%
l 4
 
8.9%
m 4
 
8.9%
s 3
 
6.7%
u 3
 
6.7%
r 3
 
6.7%
k 2
 
4.4%
d 2
 
4.4%
a 2
 
4.4%
Other values (8) 10
22.2%
Decimal Number
ValueCountFrequency (%)
2 8
42.1%
1 6
31.6%
5 1
 
5.3%
8 1
 
5.3%
9 1
 
5.3%
0 1
 
5.3%
4 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
& 10
50.0%
. 5
25.0%
, 4
 
20.0%
: 1
 
5.0%
Space Separator
ValueCountFrequency (%)
229
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2284
81.2%
Common 319
 
11.3%
Latin 209
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 164
 
7.2%
장 77
 
3.4%
프 71
 
3.1%
골 68
 
3.0%
도 65
 
2.8%
구 60
 
2.6%
당 57
 
2.5%
트 56
 
2.5%
태 54
 
2.4%
권 52
 
2.3%
Other values (274) 1560
68.3%
Latin
ValueCountFrequency (%)
T 19
 
9.1%
G 13
 
6.2%
M 12
 
5.7%
A 11
 
5.3%
O 10
 
4.8%
I 10
 
4.8%
S 10
 
4.8%
B 10
 
4.8%
J 8
 
3.8%
C 7
 
3.3%
Other values (32) 99
47.4%
Common
ValueCountFrequency (%)
229
71.8%
( 25
 
7.8%
) 25
 
7.8%
& 10
 
3.1%
2 8
 
2.5%
1 6
 
1.9%
. 5
 
1.6%
, 4
 
1.3%
+ 1
 
0.3%
5 1
 
0.3%
Other values (5) 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2284
81.2%
ASCII 528
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
229
43.4%
( 25
 
4.7%
) 25
 
4.7%
T 19
 
3.6%
G 13
 
2.5%
M 12
 
2.3%
A 11
 
2.1%
& 10
 
1.9%
O 10
 
1.9%
I 10
 
1.9%
Other values (47) 164
31.1%
Hangul
ValueCountFrequency (%)
스 164
 
7.2%
장 77
 
3.4%
프 71
 
3.1%
골 68
 
3.0%
도 65
 
2.8%
구 60
 
2.6%
당 57
 
2.5%
트 56
 
2.5%
태 54
 
2.4%
권 52
 
2.3%
Other values (274) 1560
68.3%

소재지
Text

Distinct335
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T02:04:23.867522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length45
Mean length33.400585
Min length22

Characters and Unicode

Total characters11423
Distinct characters236
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)96.2%

Sample

1st row부산광역시 해운대구 해운대해변로 296 (중동)
2nd row부산광역시 해운대구 재반로125번길 63 (재송동)
3rd row부산광역시 해운대구 동백로 67 (우동)
4th row부산광역시 해운대구 센텀남대로 35 (우동)
5th row부산광역시 해운대구 해운대해변로 287, 4층 (중동)
ValueCountFrequency (%)
부산광역시 342
 
16.2%
해운대구 342
 
16.2%
우동 77
 
3.6%
좌동 72
 
3.4%
반여동 46
 
2.2%
재송동 44
 
2.1%
중동 37
 
1.8%
3층 35
 
1.7%
재반로 28
 
1.3%
좌동순환로 24
 
1.1%
Other values (544) 1066
50.4%
2023-12-11T02:04:24.593083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1835
 
16.1%
동 452
 
4.0%
대 440
 
3.9%
운 425
 
3.7%
해 423
 
3.7%
시 385
 
3.4%
1 363
 
3.2%
산 359
 
3.1%
, 358
 
3.1%
( 346
 
3.0%
Other values (226) 6037
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6720
58.8%
Space Separator 1835
 
16.1%
Decimal Number 1714
 
15.0%
Other Punctuation 363
 
3.2%
Open Punctuation 346
 
3.0%
Close Punctuation 346
 
3.0%
Dash Punctuation 48
 
0.4%
Uppercase Letter 42
 
0.4%
Math Symbol 8
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 452
 
6.7%
대 440
 
6.5%
운 425
 
6.3%
해 423
 
6.3%
시 385
 
5.7%
산 359
 
5.3%
부 346
 
5.1%
구 345
 
5.1%
광 344
 
5.1%
역 342
 
5.1%
Other values (192) 2859
42.5%
Uppercase Letter
ValueCountFrequency (%)
B 13
31.0%
C 7
16.7%
S 4
 
9.5%
N 3
 
7.1%
A 3
 
7.1%
I 2
 
4.8%
K 2
 
4.8%
F 2
 
4.8%
E 1
 
2.4%
H 1
 
2.4%
Other values (4) 4
 
9.5%
Decimal Number
ValueCountFrequency (%)
1 363
21.2%
2 277
16.2%
3 227
13.2%
0 212
12.4%
4 149
8.7%
5 124
 
7.2%
7 107
 
6.2%
6 106
 
6.2%
8 83
 
4.8%
9 66
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 358
98.6%
@ 3
 
0.8%
？ 1
 
0.3%
? 1
 
0.3%
Space Separator
ValueCountFrequency (%)
1835
100.0%
Open Punctuation
ValueCountFrequency (%)
( 346
100.0%
Close Punctuation
ValueCountFrequency (%)
) 346
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6720
58.8%
Common 4660
40.8%
Latin 43
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 452
 
6.7%
대 440
 
6.5%
운 425
 
6.3%
해 423
 
6.3%
시 385
 
5.7%
산 359
 
5.3%
부 346
 
5.1%
구 345
 
5.1%
광 344
 
5.1%
역 342
 
5.1%
Other values (192) 2859
42.5%
Common
ValueCountFrequency (%)
1835
39.4%
1 363
 
7.8%
, 358
 
7.7%
( 346
 
7.4%
) 346
 
7.4%
2 277
 
5.9%
3 227
 
4.9%
0 212
 
4.5%
4 149
 
3.2%
5 124
 
2.7%
Other values (9) 423
 
9.1%
Latin
ValueCountFrequency (%)
B 13
30.2%
C 7
16.3%
S 4
 
9.3%
N 3
 
7.0%
A 3
 
7.0%
I 2
 
4.7%
K 2
 
4.7%
F 2
 
4.7%
e 1
 
2.3%
E 1
 
2.3%
Other values (5) 5
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6720
58.8%
ASCII 4702
41.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1835
39.0%
1 363
 
7.7%
, 358
 
7.6%
( 346
 
7.4%
) 346
 
7.4%
2 277
 
5.9%
3 227
 
4.8%
0 212
 
4.5%
4 149
 
3.2%
5 124
 
2.6%
Other values (23) 465
 
9.9%
Hangul
ValueCountFrequency (%)
동 452
 
6.7%
대 440
 
6.5%
운 425
 
6.3%
해 423
 
6.3%
시 385
 
5.7%
산 359
 
5.3%
부 346
 
5.1%
구 345
 
5.1%
광 344
 
5.1%
역 342
 
5.1%
Other values (192) 2859
42.5%
None
ValueCountFrequency (%)
？ 1
100.0%

연락처
Text

MISSING 

Distinct206
Distinct (%)95.4%
Missing126
Missing (%)36.8%
Memory size2.8 KiB
2023-12-11T02:04:25.000103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.981481
Min length9

Characters and Unicode

Total characters2588
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

Unique198 ?
Unique (%)91.7%

Sample

1st row051-749-2613
2nd row051-747-7783
3rd row051-749-7000
4th row051-745-1900
5th row051-933-1441
ValueCountFrequency (%)
051-749-2613 4
 
1.9%
051-703-2872 2
 
0.9%
051-744-0753 2
 
0.9%
051-745-1900 2
 
0.9%
051-701-8262 2
 
0.9%
051-703-1138 2
 
0.9%
051-664-6650 2
 
0.9%
051-747-1145 2
 
0.9%
051-542-3700 1
 
0.5%
051-701-5075 1
 
0.5%
Other values (196) 196
90.7%
2023-12-11T02:04:25.666339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 431
16.7%
0 399
15.4%
1 378
14.6%
5 359
13.9%
7 271
10.5%
4 185
7.1%
2 146
 
5.6%
3 130
 
5.0%
8 116
 
4.5%
6 91
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2157
83.3%
Dash Punctuation 431
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
18.5%
1 378
17.5%
5 359
16.6%
7 271
12.6%
4 185
8.6%
2 146
 
6.8%
3 130
 
6.0%
8 116
 
5.4%
6 91
 
4.2%
9 82
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 431
16.7%
0 399
15.4%
1 378
14.6%
5 359
13.9%
7 271
10.5%
4 185
7.1%
2 146
 
5.6%
3 130
 
5.0%
8 116
 
4.5%
6 91
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 431
16.7%
0 399
15.4%
1 378
14.6%
5 359
13.9%
7 271
10.5%
4 185
7.1%
2 146
 
5.6%
3 130
 
5.0%
8 116
 
4.5%
6 91
 
3.5%

Interactions

2023-12-11T02:04:19.821373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:04:25.850344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.836
업종0.8361.000
2023-12-11T02:04:26.002553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.581
업종0.5811.000

Missing values

2023-12-11T02:04:20.086204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:04:20.308920image/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

연번업종상호명소재지연락처
01수영장업(주)파라다이스호텔부산신관수영장(실외)부산광역시 해운대구 해운대해변로 296 (중동)051-749-2613
12수영장업아이 올림픽부산광역시 해운대구 재반로125번길 63 (재송동)051-747-7783
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340341빙상장업신세계센텀시티 아이스링크부산광역시 해운대구 센텀남대로 35 (우동)051-745-1400
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