Overview

Dataset statistics

Number of variables4
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory36.3 B

Variable types

Numeric1
Categorical1
Text2

Dataset

Description부산광역시 수영구 대부업 등록 현황 데이터로 데이터 정보는 대부업 상호명, 대부업 소재지 주소 항목(도로명주소)을 제공합니다.
Author부산광역시 수영구
URLhttps://www.data.go.kr/data/3044035/fileData.do

Alerts

순번 has unique valuesUnique
상호 has unique valuesUnique

Reproduction

Analysis started2024-05-04 07:39:02.023157
Analysis finished2024-05-04 07:39:03.582030
Duration1.56 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2024-05-04T07:39:03.901316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.95
Q110.75
median20.5
Q330.25
95-th percentile38.05
Maximum40
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.690452
Coefficient of variation (CV)0.57026595
Kurtosis-1.2
Mean20.5
Median Absolute Deviation (MAD)10
Skewness0
Sum820
Variance136.66667
MonotonicityStrictly increasing
2024-05-04T07:39:04.461791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 1
 
2.5%
22 1
 
2.5%
24 1
 
2.5%
25 1
 
2.5%
26 1
 
2.5%
27 1
 
2.5%
28 1
 
2.5%
29 1
 
2.5%
30 1
 
2.5%
31 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
1 1
2.5%
2 1
2.5%
3 1
2.5%
4 1
2.5%
5 1
2.5%
6 1
2.5%
7 1
2.5%
8 1
2.5%
9 1
2.5%
10 1
2.5%
ValueCountFrequency (%)
40 1
2.5%
39 1
2.5%
38 1
2.5%
37 1
2.5%
36 1
2.5%
35 1
2.5%
34 1
2.5%
33 1
2.5%
32 1
2.5%
31 1
2.5%

구분
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
대부업
28 
대부중개업
12 

Length

Max length5
Median length3
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 28
70.0%
대부중개업 12
30.0%

Length

2024-05-04T07:39:05.165286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:39:05.565066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 28
70.0%
대부중개업 12
30.0%

상호
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2024-05-04T07:39:06.132315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length6.875
Min length3

Characters and Unicode

Total characters275
Distinct characters91
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row남천전당포대부
2nd row팔도전당포 대부
3rd row부광전당포대부
4th row수영전당포대부
5th row흥진전당포대부
ValueCountFrequency (%)
남천전당포대부 1
 
2.4%
우리신용파이낸스대부 1
 
2.4%
jh파이낸스대부 1
 
2.4%
24시백프로대부 1
 
2.4%
jh파이낸스대부중개 1
 
2.4%
누리대부중개 1
 
2.4%
부일대부 1
 
2.4%
다올에셋대부중개 1
 
2.4%
디넬로대부 1
 
2.4%
럭키파이낸셜대부중개 1
 
2.4%
Other values (32) 32
76.2%
2024-05-04T07:39:07.274529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 42
 
15.3%
대 40
 
14.5%
중 13
 
4.7%
개 12
 
4.4%
이 10
 
3.6%
파 9
 
3.3%
스 9
 
3.3%
낸 8
 
2.9%
포 6
 
2.2%
원 5
 
1.8%
Other values (81) 121
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
93.1%
Uppercase Letter 13
 
4.7%
Space Separator 2
 
0.7%
Decimal Number 2
 
0.7%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 42
16.4%
대 40
 
15.6%
중 13
 
5.1%
개 12
 
4.7%
이 10
 
3.9%
파 9
 
3.5%
스 9
 
3.5%
낸 8
 
3.1%
포 6
 
2.3%
원 5
 
2.0%
Other values (69) 102
39.8%
Uppercase Letter
ValueCountFrequency (%)
S 3
23.1%
C 2
15.4%
H 2
15.4%
J 2
15.4%
K 2
15.4%
B 1
 
7.7%
W 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
93.1%
Latin 13
 
4.7%
Common 6
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 42
16.4%
대 40
 
15.6%
중 13
 
5.1%
개 12
 
4.7%
이 10
 
3.9%
파 9
 
3.5%
스 9
 
3.5%
낸 8
 
3.1%
포 6
 
2.3%
원 5
 
2.0%
Other values (69) 102
39.8%
Latin
ValueCountFrequency (%)
S 3
23.1%
C 2
15.4%
H 2
15.4%
J 2
15.4%
K 2
15.4%
B 1
 
7.7%
W 1
 
7.7%
Common
ValueCountFrequency (%)
2
33.3%
2 1
16.7%
4 1
16.7%
( 1
16.7%
) 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
93.1%
ASCII 19
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 42
16.4%
대 40
 
15.6%
중 13
 
5.1%
개 12
 
4.7%
이 10
 
3.9%
파 9
 
3.5%
스 9
 
3.5%
낸 8
 
3.1%
포 6
 
2.3%
원 5
 
2.0%
Other values (69) 102
39.8%
ASCII
ValueCountFrequency (%)
S 3
15.8%
C 2
10.5%
H 2
10.5%
J 2
10.5%
K 2
10.5%
2
10.5%
2 1
 
5.3%
4 1
 
5.3%
( 1
 
5.3%
) 1
 
5.3%
Other values (2) 2
10.5%

소재지(도로명)
Text

Distinct35
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2024-05-04T07:39:08.102426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length41.5
Mean length35.3
Min length23

Characters and Unicode

Total characters1412
Distinct characters87
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

Unique30 ?
Unique (%)75.0%

Sample

1st row부산광역시 수영구 수영로394번길 22 (남천동)
2nd row부산광역시 수영구 수영로705번길 13-1 (수영동)
3rd row부산광역시 수영구 수영로642번길 36, 1층 (광안동)
4th row부산광역시 수영구 수영성로27번길 25 (수영동)
5th row부산광역시 수영구 연수로 347-6, 1층 (망미동)
ValueCountFrequency (%)
부산광역시 40
 
14.9%
수영구 40
 
14.9%
광안동 14
 
5.2%
남천동 13
 
4.8%
20 12
 
4.5%
4층 12
 
4.5%
남천동로108번길 12
 
4.5%
수영동 6
 
2.2%
수영로 6
 
2.2%
1층 5
 
1.9%
Other values (87) 109
40.5%
2024-05-04T07:39:09.336812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
229
 
16.2%
수 68
 
4.8%
영 64
 
4.5%
광 61
 
4.3%
동 56
 
4.0%
1 50
 
3.5%
구 41
 
2.9%
( 40
 
2.8%
0 40
 
2.8%
로 40
 
2.8%
Other values (77) 723
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 790
55.9%
Decimal Number 260
 
18.4%
Space Separator 229
 
16.2%
Open Punctuation 40
 
2.8%
Close Punctuation 40
 
2.8%
Other Punctuation 37
 
2.6%
Uppercase Letter 11
 
0.8%
Dash Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 68
 
8.6%
영 64
 
8.1%
광 61
 
7.7%
동 56
 
7.1%
구 41
 
5.2%
로 40
 
5.1%
부 40
 
5.1%
산 40
 
5.1%
시 40
 
5.1%
역 40
 
5.1%
Other values (59) 300
38.0%
Decimal Number
ValueCountFrequency (%)
1 50
19.2%
0 40
15.4%
2 37
14.2%
4 26
10.0%
3 26
10.0%
8 26
10.0%
6 23
8.8%
7 13
 
5.0%
5 12
 
4.6%
9 7
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
F 7
63.6%
I 2
 
18.2%
A 2
 
18.2%
Space Separator
ValueCountFrequency (%)
229
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Other Punctuation
ValueCountFrequency (%)
, 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 790
55.9%
Common 611
43.3%
Latin 11
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 68
 
8.6%
영 64
 
8.1%
광 61
 
7.7%
동 56
 
7.1%
구 41
 
5.2%
로 40
 
5.1%
부 40
 
5.1%
산 40
 
5.1%
시 40
 
5.1%
역 40
 
5.1%
Other values (59) 300
38.0%
Common
ValueCountFrequency (%)
229
37.5%
1 50
 
8.2%
( 40
 
6.5%
0 40
 
6.5%
) 40
 
6.5%
2 37
 
6.1%
, 37
 
6.1%
4 26
 
4.3%
3 26
 
4.3%
8 26
 
4.3%
Other values (5) 60
 
9.8%
Latin
ValueCountFrequency (%)
F 7
63.6%
I 2
 
18.2%
A 2
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 790
55.9%
ASCII 622
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
229
36.8%
1 50
 
8.0%
( 40
 
6.4%
0 40
 
6.4%
) 40
 
6.4%
2 37
 
5.9%
, 37
 
5.9%
4 26
 
4.2%
3 26
 
4.2%
8 26
 
4.2%
Other values (8) 71
 
11.4%
Hangul
ValueCountFrequency (%)
수 68
 
8.6%
영 64
 
8.1%
광 61
 
7.7%
동 56
 
7.1%
구 41
 
5.2%
로 40
 
5.1%
부 40
 
5.1%
산 40
 
5.1%
시 40
 
5.1%
역 40
 
5.1%
Other values (59) 300
38.0%

Interactions

2024-05-04T07:39:02.537046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:39:09.610021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분상호소재지(도로명)
순번1.0000.0001.0000.990
구분0.0001.0001.0000.000
상호1.0001.0001.0001.000
소재지(도로명)0.9900.0001.0001.000
2024-05-04T07:39:09.979868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분
순번1.0000.000
구분0.0001.000

Missing values

2024-05-04T07:39:03.030305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:39:03.458359image/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

순번구분상호소재지(도로명)
01대부업남천전당포대부부산광역시 수영구 수영로394번길 22 (남천동)
12대부업팔도전당포 대부부산광역시 수영구 수영로705번길 13-1 (수영동)
23대부업부광전당포대부부산광역시 수영구 수영로642번길 36, 1층 (광안동)
34대부업수영전당포대부부산광역시 수영구 수영성로27번길 25 (수영동)
45대부업흥진전당포대부부산광역시 수영구 연수로 347-6, 1층 (망미동)
56대부업미소대부부산광역시 수영구 수영성로 10 (수영동)
67대부업쉭중고명품대부부산광역시 수영구 수영로540번길 42, 1층 (광안동)
78대부업(주)대부홀딩스부산광역시 수영구 남천동로 103, 3층 (남천동)
89대부업용조기획대부부산광역시 수영구 구락로 72, 지하층 (망미동)
910대부업미담플러스대부부산광역시 수영구 수영로652번길 6, 201호 (광안동, 정원센텀뷰)
순번구분상호소재지(도로명)
3031대부중개업명성대부중개부산광역시 수영구 남천동로108번길 20, 4층 F8호 (남천동)
3132대부업후후캐피탈대부부산광역시 수영구 광안해변로344번길 9-11, 동남빌딩 601호 (민락동)
3233대부업경남파크대부부산광역시 수영구 남천동로108번길 20, 4층 F18호 (남천동)
3334대부중개업다누리대부중개부산광역시 수영구 수영로 668, 화목오피스텔 1015호 (광안동)
3435대부업우리신용금융대부부산광역시 수영구 남천동로108번길 20, 4층 F24호(남천동)
3536대부업원대부부산광역시 수영구 남천동로108번길 20, 4층 F25호 (남천동)
3637대부중개업원대부중개부산광역시 수영구 남천동로108번길 20, 4층 F25호 (남천동)
3738대부업원이대부부산광역시 수영구 남천동로108번길 20, 4층 I3호 (남천동)
3839대부중개업원이대부중개부산광역시 수영구 남천동로108번길 20, 4층 I3호 (남천동)
3940대부업고흥대부부산광역시 수영구 장대골로68번길 3 (광안동)