Overview

Dataset statistics

Number of variables4
Number of observations386
Missing cells302
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 KiB
Average record size in memory32.3 B

Variable types

Categorical1
Text3

Dataset

Description부산광역시 수영구 내의 개인이 운영하는 카페 및 프랜차이즈 카페 현황에 대한 데이터로서 업소명, 주소, 전화번호를 포함하고 있습니다.
Author부산광역시 수영구
URLhttps://www.data.go.kr/data/15094378/fileData.do

Alerts

업종명 has constant value ""Constant
소재지전화 has 302 (78.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 06:55:22.646528
Analysis finished2023-12-12 06:55:23.140234
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
휴게음식점
386 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
휴게음식점 386
100.0%

Length

2023-12-12T15:55:23.205462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:23.310815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
휴게음식점 386
100.0%

업소명
Text

Distinct383
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T15:55:23.549814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length9.3419689
Min length2

Characters and Unicode

Total characters3606
Distinct characters413
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

Unique381 ?
Unique (%)98.7%

Sample

1st row바다커피숍
2nd row경진커피
3rd row미화커피숍
4th row누구나
5th row동남
ValueCountFrequency (%)
컴포즈커피 14
 
2.2%
coffee 14
 
2.2%
수영점 13
 
2.0%
남천점 13
 
2.0%
광안점 10
 
1.5%
커피 8
 
1.2%
블루샥 8
 
1.2%
스타벅스 8
 
1.2%
텐퍼센트커피 7
 
1.1%
카페 7
 
1.1%
Other values (463) 544
84.2%
2023-12-12T15:55:24.007240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
 
7.2%
점 149
 
4.1%
커 125
 
3.5%
피 124
 
3.4%
스 92
 
2.6%
( 78
 
2.2%
) 78
 
2.2%
수 67
 
1.9%
e 63
 
1.7%
리 55
 
1.5%
Other values (403) 2515
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2501
69.4%
Lowercase Letter 360
 
10.0%
Space Separator 260
 
7.2%
Uppercase Letter 248
 
6.9%
Open Punctuation 78
 
2.2%
Close Punctuation 78
 
2.2%
Decimal Number 68
 
1.9%
Other Punctuation 10
 
0.3%
Math Symbol 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 149
 
6.0%
커 125
 
5.0%
피 124
 
5.0%
스 92
 
3.7%
수 67
 
2.7%
리 55
 
2.2%
이 52
 
2.1%
광 52
 
2.1%
안 50
 
2.0%
영 46
 
1.8%
Other values (339) 1689
67.5%
Uppercase Letter
ValueCountFrequency (%)
E 34
13.7%
O 23
 
9.3%
A 19
 
7.7%
C 18
 
7.3%
T 17
 
6.9%
S 16
 
6.5%
F 15
 
6.0%
D 12
 
4.8%
R 11
 
4.4%
L 10
 
4.0%
Other values (14) 73
29.4%
Lowercase Letter
ValueCountFrequency (%)
e 63
17.5%
o 42
11.7%
a 30
 
8.3%
f 30
 
8.3%
r 20
 
5.6%
c 18
 
5.0%
s 17
 
4.7%
l 16
 
4.4%
t 15
 
4.2%
m 15
 
4.2%
Other values (11) 94
26.1%
Decimal Number
ValueCountFrequency (%)
1 16
23.5%
0 11
16.2%
5 9
13.2%
2 7
10.3%
4 7
10.3%
3 6
 
8.8%
6 5
 
7.4%
9 4
 
5.9%
8 3
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 5
50.0%
' 3
30.0%
: 1
 
10.0%
· 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Space Separator
ValueCountFrequency (%)
260
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2501
69.4%
Latin 608
 
16.9%
Common 497
 
13.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 149
 
6.0%
커 125
 
5.0%
피 124
 
5.0%
스 92
 
3.7%
수 67
 
2.7%
리 55
 
2.2%
이 52
 
2.1%
광 52
 
2.1%
안 50
 
2.0%
영 46
 
1.8%
Other values (339) 1689
67.5%
Latin
ValueCountFrequency (%)
e 63
 
10.4%
o 42
 
6.9%
E 34
 
5.6%
a 30
 
4.9%
f 30
 
4.9%
O 23
 
3.8%
r 20
 
3.3%
A 19
 
3.1%
c 18
 
3.0%
C 18
 
3.0%
Other values (35) 311
51.2%
Common
ValueCountFrequency (%)
260
52.3%
( 78
 
15.7%
) 78
 
15.7%
1 16
 
3.2%
0 11
 
2.2%
5 9
 
1.8%
2 7
 
1.4%
4 7
 
1.4%
3 6
 
1.2%
. 5
 
1.0%
Other values (9) 20
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2501
69.4%
ASCII 1104
30.6%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
23.6%
( 78
 
7.1%
) 78
 
7.1%
e 63
 
5.7%
o 42
 
3.8%
E 34
 
3.1%
a 30
 
2.7%
f 30
 
2.7%
O 23
 
2.1%
r 20
 
1.8%
Other values (53) 446
40.4%
Hangul
ValueCountFrequency (%)
점 149
 
6.0%
커 125
 
5.0%
피 124
 
5.0%
스 92
 
3.7%
수 67
 
2.7%
리 55
 
2.2%
이 52
 
2.1%
광 52
 
2.1%
안 50
 
2.0%
영 46
 
1.8%
Other values (339) 1689
67.5%
None
ValueCountFrequency (%)
· 1
100.0%

소재지(도로명)
Text

Distinct384
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T15:55:24.343068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length55
Mean length34.067358
Min length21

Characters and Unicode

Total characters13150
Distinct characters207
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)99.0%

Sample

1st row부산광역시 수영구 남천동로 103 (남천동)
2nd row부산광역시 수영구 수영로 606, 지하1층 (광안동)
3rd row부산광역시 수영구 수영로725번길 10 (수영동)
4th row부산광역시 수영구 광안로7번길 34 (광안동)
5th row부산광역시 수영구 과정로 40, 2층 (망미동)
ValueCountFrequency (%)
부산광역시 386
 
14.9%
수영구 386
 
14.9%
1층 275
 
10.6%
광안동 165
 
6.4%
남천동 83
 
3.2%
민락동 61
 
2.4%
망미동 49
 
1.9%
수영로 43
 
1.7%
수영동 35
 
1.4%
광안해변로 20
 
0.8%
Other values (541) 1089
42.0%
2023-12-12T15:55:24.855855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2206
 
16.8%
1 693
 
5.3%
광 679
 
5.2%
수 581
 
4.4%
영 544
 
4.1%
동 463
 
3.5%
, 447
 
3.4%
부 406
 
3.1%
산 404
 
3.1%
구 396
 
3.0%
Other values (197) 6331
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7386
56.2%
Decimal Number 2229
 
17.0%
Space Separator 2206
 
16.8%
Other Punctuation 448
 
3.4%
Close Punctuation 390
 
3.0%
Open Punctuation 390
 
3.0%
Dash Punctuation 49
 
0.4%
Uppercase Letter 23
 
0.2%
Math Symbol 19
 
0.1%
Lowercase Letter 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 679
 
9.2%
수 581
 
7.9%
영 544
 
7.4%
동 463
 
6.3%
부 406
 
5.5%
산 404
 
5.5%
구 396
 
5.4%
시 394
 
5.3%
역 390
 
5.3%
로 386
 
5.2%
Other values (166) 2743
37.1%
Uppercase Letter
ValueCountFrequency (%)
B 5
21.7%
S 3
13.0%
A 3
13.0%
K 3
13.0%
C 2
 
8.7%
D 2
 
8.7%
H 1
 
4.3%
W 1
 
4.3%
E 1
 
4.3%
I 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 693
31.1%
2 283
12.7%
0 231
 
10.4%
3 208
 
9.3%
4 178
 
8.0%
6 166
 
7.4%
5 150
 
6.7%
7 124
 
5.6%
9 102
 
4.6%
8 94
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
e 8
80.0%
s 1
 
10.0%
y 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 447
99.8%
/ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 390
100.0%
Open Punctuation
ValueCountFrequency (%)
( 390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7386
56.2%
Common 5731
43.6%
Latin 33
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 679
 
9.2%
수 581
 
7.9%
영 544
 
7.4%
동 463
 
6.3%
부 406
 
5.5%
산 404
 
5.5%
구 396
 
5.4%
시 394
 
5.3%
역 390
 
5.3%
로 386
 
5.2%
Other values (166) 2743
37.1%
Common
ValueCountFrequency (%)
2206
38.5%
1 693
 
12.1%
, 447
 
7.8%
) 390
 
6.8%
( 390
 
6.8%
2 283
 
4.9%
0 231
 
4.0%
3 208
 
3.6%
4 178
 
3.1%
6 166
 
2.9%
Other values (7) 539
 
9.4%
Latin
ValueCountFrequency (%)
e 8
24.2%
B 5
15.2%
S 3
 
9.1%
A 3
 
9.1%
K 3
 
9.1%
C 2
 
6.1%
D 2
 
6.1%
H 1
 
3.0%
W 1
 
3.0%
E 1
 
3.0%
Other values (4) 4
12.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7386
56.2%
ASCII 5764
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2206
38.3%
1 693
 
12.0%
, 447
 
7.8%
) 390
 
6.8%
( 390
 
6.8%
2 283
 
4.9%
0 231
 
4.0%
3 208
 
3.6%
4 178
 
3.1%
6 166
 
2.9%
Other values (21) 572
 
9.9%
Hangul
ValueCountFrequency (%)
광 679
 
9.2%
수 581
 
7.9%
영 544
 
7.4%
동 463
 
6.3%
부 406
 
5.5%
산 404
 
5.5%
구 396
 
5.4%
시 394
 
5.3%
역 390
 
5.3%
로 386
 
5.2%
Other values (166) 2743
37.1%

소재지전화
Text

MISSING 

Distinct83
Distinct (%)98.8%
Missing302
Missing (%)78.2%
Memory size3.1 KiB
2023-12-12T15:55:25.093708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.083333
Min length12

Characters and Unicode

Total characters1015
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

Unique82 ?
Unique (%)97.6%

Sample

1st row051-625-5955
2nd row051-753-0342
3rd row051-752-0825
4th row051-751-6549
5th row051-761-3300
ValueCountFrequency (%)
051-757-4034 2
 
2.4%
051-751-2528 1
 
1.2%
051-625-5955 1
 
1.2%
051-755-7009 1
 
1.2%
051-753-7676 1
 
1.2%
051-623-5333 1
 
1.2%
051-751-7373 1
 
1.2%
051-624-0510 1
 
1.2%
051-755-1312 1
 
1.2%
051-754-3590 1
 
1.2%
Other values (73) 73
86.9%
2023-12-12T15:55:25.470375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 168
16.6%
0 157
15.5%
5 150
14.8%
1 144
14.2%
7 92
9.1%
6 63
 
6.2%
2 59
 
5.8%
9 49
 
4.8%
4 48
 
4.7%
3 43
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 847
83.4%
Dash Punctuation 168
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157
18.5%
5 150
17.7%
1 144
17.0%
7 92
10.9%
6 63
7.4%
2 59
 
7.0%
9 49
 
5.8%
4 48
 
5.7%
3 43
 
5.1%
8 42
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1015
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 168
16.6%
0 157
15.5%
5 150
14.8%
1 144
14.2%
7 92
9.1%
6 63
 
6.2%
2 59
 
5.8%
9 49
 
4.8%
4 48
 
4.7%
3 43
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 168
16.6%
0 157
15.5%
5 150
14.8%
1 144
14.2%
7 92
9.1%
6 63
 
6.2%
2 59
 
5.8%
9 49
 
4.8%
4 48
 
4.7%
3 43
 
4.2%

Missing values

2023-12-12T15:55:23.002321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:55:23.101640image/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

업종명업소명소재지(도로명)소재지전화
0휴게음식점바다커피숍부산광역시 수영구 남천동로 103 (남천동)051-625-5955
1휴게음식점경진커피부산광역시 수영구 수영로 606, 지하1층 (광안동)051-753-0342
2휴게음식점미화커피숍부산광역시 수영구 수영로725번길 10 (수영동)051-752-0825
3휴게음식점누구나부산광역시 수영구 광안로7번길 34 (광안동)051-751-6549
4휴게음식점동남부산광역시 수영구 과정로 40, 2층 (망미동)051-761-3300
5휴게음식점공차 광안리점부산광역시 수영구 광안해변로 233, 1~3층 (민락동)<NA>
6휴게음식점스타벅스광안리점부산광역시 수영구 광안해변로 247, 1~3층 (민락동)051-761-1844
7휴게음식점CAFFE MISO(까페미소)부산광역시 수영구 수영로 493 (남천동)051-612-0009
8휴게음식점하나커피숖부산광역시 수영구 과정로 3 (망미동)051-751-2199
9휴게음식점파스쿠찌 센트로광안리점부산광역시 수영구 광안해변로 241, 1~4층 (민락동)051-752-8497
업종명업소명소재지(도로명)소재지전화
376휴게음식점빽다방 부산수영구청점부산광역시 수영구 광남로 31, 비케이빌딩 1층 (남천동)<NA>
377휴게음식점텐퍼센트커피 남천하늘채점부산광역시 수영구 남천동로 20, 1층 (남천동)<NA>
378휴게음식점고더커피 남천점부산광역시 수영구 남천동로 23, 1층 5호 (남천동)<NA>
379휴게음식점스타벅스 남천더샵점부산광역시 수영구 수영로 389, 1층 110호 (남천동, 더샵 남천프레스티지)<NA>
380휴게음식점노비커피 수변공원점부산광역시 수영구 광안해변로358번길 14, 씨플렉스 1층 101호 (민락동)<NA>
381휴게음식점베들레헴카페부산광역시 수영구 수영로594번길 67-5, 1층 (광안동)<NA>
382휴게음식점해그리다부산광역시 수영구 민락수변로239번길 14, 3층 (민락동)<NA>
383휴게음식점카페 마일로 수영센텀병원점부산광역시 수영구 광서로10번길 54, 1층 (광안동)<NA>
384휴게음식점엑스오엑스오 케이크(XOXO cake)부산광역시 수영구 연수로416번길 69-1, 1층 (광안동)<NA>
385휴게음식점하삼동커피 망미삼성아파트점부산광역시 수영구 망미로22번길 51, 1층 (망미동)<NA>