Overview

Dataset statistics

Number of variables4
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory957.0 B
Average record size in memory38.3 B

Variable types

Numeric1
Categorical1
Text2

Dataset

Description부산광역시_수영구_대부업등록현황_20230721
Author부산광역시 수영구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3044035

Alerts

순번 has unique valuesUnique
상호 has unique valuesUnique
소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:03:45.450472
Analysis finished2023-12-10 16:03:46.173634
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T01:03:46.259893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2023-12-11T01:03:46.421955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

구분
Categorical

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
대부업
21 
대부중개업
4 

Length

Max length5
Median length3
Mean length3.32
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대부업 21
84.0%
대부중개업 4
 
16.0%

Length

2023-12-11T01:03:46.684770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:03:46.849689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대부업 21
84.0%
대부중개업 4
 
16.0%

상호
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T01:03:47.103774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length10
Mean length7.12
Min length4

Characters and Unicode

Total characters178
Distinct characters80
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

Unique25 ?
Unique (%)100.0%

Sample

1st row바로머니대부
2nd row엠에스대부
3rd row엔틱대부
4th row드림대부중개
5th row소원파이낸셜대부
ValueCountFrequency (%)
바로머니대부 1
 
3.6%
엠에스대부 1
 
3.6%
대부 1
 
3.6%
팔도전당포 1
 
3.6%
부광전당포대부 1
 
3.6%
수영전당포대부 1
 
3.6%
흥진전당포대부 1
 
3.6%
미소대부 1
 
3.6%
쉭중고명품대부 1
 
3.6%
주)대부홀딩스 1
 
3.6%
Other values (18) 18
64.3%
2023-12-11T01:03:47.538827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 26
 
14.6%
대 25
 
14.0%
포 7
 
3.9%
당 6
 
3.4%
전 6
 
3.4%
이 5
 
2.8%
스 5
 
2.8%
중 5
 
2.8%
개 4
 
2.2%
3
 
1.7%
Other values (70) 86
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
93.3%
Uppercase Letter 7
 
3.9%
Space Separator 3
 
1.7%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 26
 
15.7%
대 25
 
15.1%
포 7
 
4.2%
당 6
 
3.6%
전 6
 
3.6%
이 5
 
3.0%
스 5
 
3.0%
중 5
 
3.0%
개 4
 
2.4%
낸 3
 
1.8%
Other values (62) 74
44.6%
Uppercase Letter
ValueCountFrequency (%)
K 2
28.6%
S 2
28.6%
W 1
14.3%
C 1
14.3%
B 1
14.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
93.3%
Latin 7
 
3.9%
Common 5
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 26
 
15.7%
대 25
 
15.1%
포 7
 
4.2%
당 6
 
3.6%
전 6
 
3.6%
이 5
 
3.0%
스 5
 
3.0%
중 5
 
3.0%
개 4
 
2.4%
낸 3
 
1.8%
Other values (62) 74
44.6%
Latin
ValueCountFrequency (%)
K 2
28.6%
S 2
28.6%
W 1
14.3%
C 1
14.3%
B 1
14.3%
Common
ValueCountFrequency (%)
3
60.0%
( 1
 
20.0%
) 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
93.3%
ASCII 12
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 26
 
15.7%
대 25
 
15.1%
포 7
 
4.2%
당 6
 
3.6%
전 6
 
3.6%
이 5
 
3.0%
스 5
 
3.0%
중 5
 
3.0%
개 4
 
2.4%
낸 3
 
1.8%
Other values (62) 74
44.6%
ASCII
ValueCountFrequency (%)
3
25.0%
K 2
16.7%
S 2
16.7%
( 1
 
8.3%
) 1
 
8.3%
W 1
 
8.3%
C 1
 
8.3%
B 1
 
8.3%

소재지(도로명)
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T01:03:47.881520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length37
Mean length33.44
Min length23

Characters and Unicode

Total characters836
Distinct characters73
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row부산광역시 수영구 광안로16번길 58, 원 비치텔 3층 307호 (광안동)
2nd row부산광역시 수영구 남천동로108번길 20, 4층 다1호 (남천동)
3rd row부산광역시 수영구 남천동로108번길 20, 4층 다23호 (남천동)
4th row부산광역시 수영구 남천동로108번길 20, 4층 가14호 (남천동)
5th row부산광역시 수영구 수영로 691-8, 수영 엔스타 오피스텔 1102호 (수영동)
ValueCountFrequency (%)
부산광역시 25
 
15.2%
수영구 25
 
15.2%
광안동 9
 
5.5%
남천동 7
 
4.3%
수영동 5
 
3.0%
수영로 4
 
2.4%
남천동로108번길 4
 
2.4%
20 4
 
2.4%
4층 4
 
2.4%
망미동 4
 
2.4%
Other values (67) 73
44.5%
2023-12-11T01:03:48.423052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
139
 
16.6%
수 46
 
5.5%
영 43
 
5.1%
광 36
 
4.3%
동 30
 
3.6%
1 26
 
3.1%
구 26
 
3.1%
부 25
 
3.0%
( 25
 
3.0%
산 25
 
3.0%
Other values (63) 415
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 472
56.5%
Decimal Number 148
 
17.7%
Space Separator 139
 
16.6%
Open Punctuation 25
 
3.0%
Close Punctuation 25
 
3.0%
Other Punctuation 23
 
2.8%
Dash Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 46
 
9.7%
영 43
 
9.1%
광 36
 
7.6%
동 30
 
6.4%
구 26
 
5.5%
부 25
 
5.3%
산 25
 
5.3%
로 25
 
5.3%
역 25
 
5.3%
시 25
 
5.3%
Other values (48) 166
35.2%
Decimal Number
ValueCountFrequency (%)
1 26
17.6%
2 23
15.5%
0 21
14.2%
4 15
10.1%
3 14
9.5%
8 13
8.8%
6 13
8.8%
7 9
 
6.1%
5 8
 
5.4%
9 6
 
4.1%
Space Separator
ValueCountFrequency (%)
139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 472
56.5%
Common 364
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 46
 
9.7%
영 43
 
9.1%
광 36
 
7.6%
동 30
 
6.4%
구 26
 
5.5%
부 25
 
5.3%
산 25
 
5.3%
로 25
 
5.3%
역 25
 
5.3%
시 25
 
5.3%
Other values (48) 166
35.2%
Common
ValueCountFrequency (%)
139
38.2%
1 26
 
7.1%
( 25
 
6.9%
) 25
 
6.9%
, 23
 
6.3%
2 23
 
6.3%
0 21
 
5.8%
4 15
 
4.1%
3 14
 
3.8%
8 13
 
3.6%
Other values (5) 40
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 472
56.5%
ASCII 364
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139
38.2%
1 26
 
7.1%
( 25
 
6.9%
) 25
 
6.9%
, 23
 
6.3%
2 23
 
6.3%
0 21
 
5.8%
4 15
 
4.1%
3 14
 
3.8%
8 13
 
3.6%
Other values (5) 40
 
11.0%
Hangul
ValueCountFrequency (%)
수 46
 
9.7%
영 43
 
9.1%
광 36
 
7.6%
동 30
 
6.4%
구 26
 
5.5%
부 25
 
5.3%
산 25
 
5.3%
로 25
 
5.3%
역 25
 
5.3%
시 25
 
5.3%
Other values (48) 166
35.2%

Interactions

2023-12-11T01:03:45.835660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:03:48.575044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분상호소재지(도로명)
순번1.0000.4991.0001.000
구분0.4991.0001.0001.000
상호1.0001.0001.0001.000
소재지(도로명)1.0001.0001.0001.000
2023-12-11T01:03:48.707340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분
순번1.0000.367
구분0.3671.000

Missing values

2023-12-11T01:03:46.012415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:03:46.129183image/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)
01대부업바로머니대부부산광역시 수영구 광안로16번길 58, 원 비치텔 3층 307호 (광안동)
12대부업엠에스대부부산광역시 수영구 남천동로108번길 20, 4층 다1호 (남천동)
23대부업엔틱대부부산광역시 수영구 남천동로108번길 20, 4층 다23호 (남천동)
34대부중개업드림대부중개부산광역시 수영구 남천동로108번길 20, 4층 가14호 (남천동)
45대부업소원파이낸셜대부부산광역시 수영구 수영로 691-8, 수영 엔스타 오피스텔 1102호 (수영동)
56대부중개업에스티론대부중개부산광역시 수영구 광서로36번길 27-3, 덕운오피스텔 204호 (광안동)
67대부업WS대부부산광역시 수영구 수영로 759, 알파오피스텔 301호 (수영동)
78대부중개업더블K 파이낸셜대부중개부산광역시 수영구 남천동로108번길 20, 4층 상28호 (남천동)
89대부업국민금융대부부산광역시 수영구 황령대로473번길 25, 목연 마이텔 801호 (남천동)
910대부업한율대부부산광역시 수영구 연수로 269, 10호 (망미동)
순번구분상호소재지(도로명)
1516대부업미담플러스대부부산광역시 수영구 수영로652번길 6, 201호 (광안동, 정원센텀뷰)
1617대부업용조기획대부부산광역시 수영구 구락로 72, 지하층 (망미동)
1718대부업(주)대부홀딩스부산광역시 수영구 남천동로 103, 3층 (남천동)
1819대부업쉭중고명품대부부산광역시 수영구 수영로540번길 42, 1층 (광안동)
1920대부업미소대부부산광역시 수영구 수영성로 10 (수영동)
2021대부업흥진전당포대부부산광역시 수영구 연수로 347-6, 1층 (망미동)
2122대부업수영전당포대부부산광역시 수영구 수영성로27번길 25 (수영동)
2223대부업부광전당포대부부산광역시 수영구 수영로642번길 36, 1층 (광안동)
2324대부업팔도전당포 대부부산광역시 수영구 수영로705번길 13-1 (수영동)
2425대부업남천전당포대부부산광역시 수영구 수영로394번길 22 (남천동)