Overview

Dataset statistics

Number of variables5
Number of observations1038
Missing cells0
Missing cells (%)0.0%
Duplicate rows22
Duplicate rows (%)2.1%
Total size in memory41.7 KiB
Average record size in memory41.1 B

Variable types

Numeric1
Categorical3
Text1

Dataset

Description부산광역시 해운대구의 재정정보시스템에 대한 데이터로 부서정보를 제공합니다.(2023년 12월 13일, 기획조정실 예산팀)
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15050176/fileData.do

Alerts

Dataset has 22 (2.1%) duplicate rowsDuplicates
부서구분명(dept_fg_nm) is highly overall correlated with 관서명(gov_office_nm) and 1 other fieldsHigh correlation
실국명(office_nm) is highly overall correlated with 부서구분명(dept_fg_nm)High correlation
관서명(gov_office_nm) is highly overall correlated with 부서구분명(dept_fg_nm)High correlation

Reproduction

Analysis started2023-12-23 07:11:29.164294
Analysis finished2023-12-23 07:11:33.215126
Duration4.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도(fis_year)
Real number (ℝ)

Distinct16
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.1484
Minimum2008
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-23T07:11:33.767362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12011
median2015
Q32019
95-th percentile2023
Maximum2023
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.5411599
Coefficient of variation (CV)0.0022535114
Kurtosis-1.156857
Mean2015.1484
Median Absolute Deviation (MAD)4
Skewness0.09469427
Sum2091724
Variance20.622133
MonotonicityNot monotonic
2023-12-23T07:11:34.676322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2009 70
 
6.7%
2008 70
 
6.7%
2015 69
 
6.6%
2013 69
 
6.6%
2012 69
 
6.6%
2011 69
 
6.6%
2010 69
 
6.6%
2014 69
 
6.6%
2016 68
 
6.6%
2017 65
 
6.3%
Other values (6) 351
33.8%
ValueCountFrequency (%)
2008 70
6.7%
2009 70
6.7%
2010 69
6.6%
2011 69
6.6%
2012 69
6.6%
2013 69
6.6%
2014 69
6.6%
2015 69
6.6%
2016 68
6.6%
2017 65
6.3%
ValueCountFrequency (%)
2023 56
5.4%
2022 56
5.4%
2021 55
5.3%
2020 59
5.7%
2019 61
5.9%
2018 64
6.2%
2017 65
6.3%
2016 68
6.6%
2015 69
6.6%
2014 69
6.6%

관서명(gov_office_nm)
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
본청
614 
보건소
 
32
반여도서관
 
16
좌1동
 
16
우1동
 
16
Other values (22)
344 

Length

Max length9
Median length2
Mean length2.8786127
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송정동
2nd row송정동
3rd row좌3동
4th row보건소
5th row좌1동

Common Values

ValueCountFrequency (%)
본청 614
59.2%
보건소 32
 
3.1%
반여도서관 16
 
1.5%
좌1동 16
 
1.5%
우1동 16
 
1.5%
재송어린이도서관 16
 
1.5%
의회사무국 16
 
1.5%
우3동 16
 
1.5%
반송1동 16
 
1.5%
해운대문화회관 16
 
1.5%
Other values (17) 264
25.4%

Length

2023-12-23T07:11:36.432877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
본청 614
59.2%
보건소 32
 
3.1%
재송1동 16
 
1.5%
송정동 16
 
1.5%
재송2동 16
 
1.5%
인문학도서관 16
 
1.5%
중2동 16
 
1.5%
반여1동 16
 
1.5%
반여3동 16
 
1.5%
반송2동 16
 
1.5%
Other values (17) 264
25.4%

부서구분명(dept_fg_nm)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
본청
614 
읍면동
296 
사업소
80 
직속기관
 
32
외청
 
16

Length

Max length4
Median length2
Mean length2.4238921
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row읍면동
2nd row읍면동
3rd row읍면동
4th row직속기관
5th row읍면동

Common Values

ValueCountFrequency (%)
본청 614
59.2%
읍면동 296
28.5%
사업소 80
 
7.7%
직속기관 32
 
3.1%
외청 16
 
1.5%

Length

2023-12-23T07:11:37.282704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:11:38.137693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본청 614
59.2%
읍면동 296
28.5%
사업소 80
 
7.7%
직속기관 32
 
3.1%
외청 16
 
1.5%

실국명(office_nm)
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
동 행정복지센터
296 
사업소
80 
주민생활지원국
73 
일자리산업국
70 
행정국
68 
Other values (17)
451 

Length

Max length8
Median length7
Mean length5.8969171
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동 행정복지센터
2nd row동 행정복지센터
3rd row동 행정복지센터
4th row보건소
5th row동 행정복지센터

Common Values

ValueCountFrequency (%)
동 행정복지센터 296
28.5%
사업소 80
 
7.7%
주민생활지원국 73
 
7.0%
일자리산업국 70
 
6.7%
행정국 68
 
6.6%
행정관리국 65
 
6.3%
생활복지국 59
 
5.7%
안전도시국 57
 
5.5%
문화관광경제국 45
 
4.3%
관광경제국 37
 
3.6%
Other values (12) 188
18.1%

Length

2023-12-23T07:11:39.023397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동 296
22.2%
행정복지센터 296
22.2%
사업소 80
 
6.0%
주민생활지원국 73
 
5.5%
일자리산업국 70
 
5.2%
행정국 68
 
5.1%
행정관리국 65
 
4.9%
생활복지국 59
 
4.4%
안전도시국 57
 
4.3%
문화관광경제국 45
 
3.4%
Other values (13) 225
16.9%

부서명(dept_nm)
Text

Distinct82
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-23T07:11:40.328811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.7928709
Min length3

Characters and Unicode

Total characters4975
Distinct characters117
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row송정동
2nd row송정동
3rd row좌3동
4th row보건소
5th row좌1동
ValueCountFrequency (%)
관광문화과 34
 
3.3%
늘푸른과 25
 
2.4%
교통행정과 25
 
2.4%
청소행정과 22
 
2.1%
경제진흥과 22
 
2.1%
재무과 18
 
1.7%
반여4동 16
 
1.5%
감사담당관 16
 
1.5%
건설과 16
 
1.5%
재송1동 16
 
1.5%
Other values (71) 828
79.8%
2023-12-23T07:11:42.896326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 581
 
11.7%
동 312
 
6.3%
관 186
 
3.7%
정 146
 
2.9%
반 120
 
2.4%
지 117
 
2.4%
2 109
 
2.2%
도 109
 
2.2%
1 109
 
2.2%
송 104
 
2.1%
Other values (107) 3082
61.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4655
93.6%
Decimal Number 306
 
6.2%
Space Separator 14
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 581
 
12.5%
동 312
 
6.7%
관 186
 
4.0%
정 146
 
3.1%
반 120
 
2.6%
지 117
 
2.5%
도 109
 
2.3%
송 104
 
2.2%
여 96
 
2.1%
재 90
 
1.9%
Other values (102) 2794
60.0%
Decimal Number
ValueCountFrequency (%)
2 109
35.6%
1 109
35.6%
3 56
18.3%
4 32
 
10.5%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4655
93.6%
Common 320
 
6.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 581
 
12.5%
동 312
 
6.7%
관 186
 
4.0%
정 146
 
3.1%
반 120
 
2.6%
지 117
 
2.5%
도 109
 
2.3%
송 104
 
2.2%
여 96
 
2.1%
재 90
 
1.9%
Other values (102) 2794
60.0%
Common
ValueCountFrequency (%)
2 109
34.1%
1 109
34.1%
3 56
17.5%
4 32
 
10.0%
14
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4655
93.6%
ASCII 320
 
6.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 581
 
12.5%
동 312
 
6.7%
관 186
 
4.0%
정 146
 
3.1%
반 120
 
2.6%
지 117
 
2.5%
도 109
 
2.3%
송 104
 
2.2%
여 96
 
2.1%
재 90
 
1.9%
Other values (102) 2794
60.0%
ASCII
ValueCountFrequency (%)
2 109
34.1%
1 109
34.1%
3 56
17.5%
4 32
 
10.0%
14
 
4.4%

Interactions

2023-12-23T07:11:30.794278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:11:43.573370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도(fis_year)관서명(gov_office_nm)부서구분명(dept_fg_nm)실국명(office_nm)부서명(dept_nm)
회계연도(fis_year)1.0000.0000.0000.2320.000
관서명(gov_office_nm)0.0001.0001.0000.8621.000
부서구분명(dept_fg_nm)0.0001.0001.0001.0001.000
실국명(office_nm)0.2320.8621.0001.0000.997
부서명(dept_nm)0.0001.0001.0000.9971.000
2023-12-23T07:11:44.448424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서구분명(dept_fg_nm)실국명(office_nm)관서명(gov_office_nm)
부서구분명(dept_fg_nm)1.0000.9920.989
실국명(office_nm)0.9921.0000.411
관서명(gov_office_nm)0.9890.4111.000
2023-12-23T07:11:44.964865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도(fis_year)관서명(gov_office_nm)부서구분명(dept_fg_nm)실국명(office_nm)
회계연도(fis_year)1.0000.0000.0000.091
관서명(gov_office_nm)0.0001.0000.9890.411
부서구분명(dept_fg_nm)0.0000.9891.0000.992
실국명(office_nm)0.0910.4110.9921.000

Missing values

2023-12-23T07:11:31.698157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:11:32.616004image/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

회계연도(fis_year)관서명(gov_office_nm)부서구분명(dept_fg_nm)실국명(office_nm)부서명(dept_nm)
02015송정동읍면동동 행정복지센터송정동
12014송정동읍면동동 행정복지센터송정동
22015좌3동읍면동동 행정복지센터좌3동
32015보건소직속기관보건소보건소
42015좌1동읍면동동 행정복지센터좌1동
52015우1동읍면동동 행정복지센터우1동
62015재송어린이도서관사업소사업소재송어린이도서관
72014재송어린이도서관사업소사업소재송어린이도서관
82015본청본청안전도시국토지정보과
92015본청본청주민생활지원국행복나눔과
회계연도(fis_year)관서명(gov_office_nm)부서구분명(dept_fg_nm)실국명(office_nm)부서명(dept_nm)
10282008본청본청기획감사실기획감사실
10292008본청본청관광경제국늘푸른과
10302008본청본청행정관리국세무1과
10312008반여1동읍면동동 행정복지센터반여1동
10322008반여도서관사업소사업소반여도서관
10332008본청본청안전도시국재난안전과
10342008재송어린이도서관사업소사업소재송어린이도서관
10352008반여3동읍면동동 행정복지센터반여3동
10362008재송2동읍면동동 행정복지센터재송2동
10372008본청본청관광경제국청소행정과

Duplicate rows

Most frequently occurring

회계연도(fis_year)관서명(gov_office_nm)부서구분명(dept_fg_nm)실국명(office_nm)부서명(dept_nm)# duplicates
02008본청본청일자리산업국관광문화과2
12008본청본청행정관리국재무과2
22009본청본청일자리산업국관광문화과2
32009본청본청행정관리국재무과2
42010본청본청일자리산업국관광문화과2
52010본청본청주민생활지원국청소행정과2
62011본청본청일자리산업국관광문화과2
72011본청본청주민생활지원국청소행정과2
82012본청본청일자리산업국관광문화과2
92012본청본청주민생활지원국청소행정과2