Overview

Dataset statistics

Number of variables12
Number of observations750
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.3 KiB
Average record size in memory104.2 B

Variable types

Numeric7
Categorical4
Text1

Dataset

Description부산광역시_해운대구_재정정보공개시스템_부서정보_20191230
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15050176

Alerts

부서구분명 is highly overall correlated with 관서코드 and 7 other fieldsHigh correlation
실국명 is highly overall correlated with 실국코드 and 4 other fieldsHigh correlation
부서구분 is highly overall correlated with 관서코드 and 7 other fieldsHigh correlation
관서명 is highly overall correlated with 관서코드 and 5 other fieldsHigh correlation
관서코드 is highly overall correlated with 관서정렬값 and 7 other fieldsHigh correlation
관서정렬값 is highly overall correlated with 관서코드 and 7 other fieldsHigh correlation
실국코드 is highly overall correlated with 관서코드 and 7 other fieldsHigh correlation
실국정렬값 is highly overall correlated with 관서코드 and 6 other fieldsHigh correlation
부서코드 is highly overall correlated with 관서코드 and 7 other fieldsHigh correlation
부서정렬값 is highly overall correlated with 관서코드 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 16:40:04.516581
Analysis finished2023-12-10 16:40:14.555456
Duration10.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.3613
Minimum2008
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:14.653088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12010
median2013
Q32016
95-th percentile2019
Maximum2019
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.413841
Coefficient of variation (CV)0.0016955928
Kurtosis-1.1932749
Mean2013.3613
Median Absolute Deviation (MAD)3
Skewness0.035978753
Sum1510021
Variance11.654311
MonotonicityNot monotonic
2023-12-11T01:40:14.836908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2009 65
8.7%
2008 65
8.7%
2015 64
8.5%
2014 64
8.5%
2013 64
8.5%
2012 64
8.5%
2011 64
8.5%
2010 64
8.5%
2016 63
8.4%
2017 60
8.0%
Other values (2) 113
15.1%
ValueCountFrequency (%)
2008 65
8.7%
2009 65
8.7%
2010 64
8.5%
2011 64
8.5%
2012 64
8.5%
2013 64
8.5%
2014 64
8.5%
2015 64
8.5%
2016 63
8.4%
2017 60
8.0%
ValueCountFrequency (%)
2019 54
7.2%
2018 59
7.9%
2017 60
8.0%
2016 63
8.4%
2015 64
8.5%
2014 64
8.5%
2013 64
8.5%
2012 64
8.5%
2011 64
8.5%
2010 64
8.5%

관서코드
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.890667
Minimum10
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:15.018309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q3117.5
95-th percentile240
Maximum253
Range243
Interquartile range (IQR)107.5

Descriptive statistics

Standard deviation80.361508
Coefficient of variation (CV)1.238414
Kurtosis-0.11086722
Mean64.890667
Median Absolute Deviation (MAD)0
Skewness1.1790859
Sum48668
Variance6457.972
MonotonicityNot monotonic
2023-12-11T01:40:15.256253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10 430
57.3%
30 24
 
3.2%
20 12
 
1.6%
253 12
 
1.6%
251 12
 
1.6%
240 12
 
1.6%
250 12
 
1.6%
140 12
 
1.6%
150 12
 
1.6%
160 12
 
1.6%
Other values (17) 200
26.7%
ValueCountFrequency (%)
10 430
57.3%
20 12
 
1.6%
30 24
 
3.2%
40 12
 
1.6%
50 12
 
1.6%
60 12
 
1.6%
70 12
 
1.6%
80 12
 
1.6%
90 12
 
1.6%
100 12
 
1.6%
ValueCountFrequency (%)
253 12
1.6%
251 12
1.6%
250 12
1.6%
240 12
1.6%
230 8
1.1%
220 12
1.6%
210 12
1.6%
200 12
1.6%
190 12
1.6%
180 12
1.6%

관서명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
본청
430 
보건소
 
24
의회사무국
 
12
관광시설관리사업소
 
12
좌2동
 
12
Other values (22)
260 

Length

Max length9
Median length2
Mean length2.9173333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
본청 430
57.3%
보건소 24
 
3.2%
의회사무국 12
 
1.6%
관광시설관리사업소 12
 
1.6%
좌2동 12
 
1.6%
좌1동 12
 
1.6%
중2동 12
 
1.6%
중1동 12
 
1.6%
우2동 12
 
1.6%
우1동 12
 
1.6%
Other values (17) 200
26.7%

Length

2023-12-11T01:40:15.528971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
본청 430
57.3%
보건소 24
 
3.2%
반송1동 12
 
1.6%
반여4동 12
 
1.6%
인문학도서관 12
 
1.6%
우3동 12
 
1.6%
재송1동 12
 
1.6%
재송2동 12
 
1.6%
좌3동 12
 
1.6%
좌4동 12
 
1.6%
Other values (17) 200
26.7%

관서정렬값
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5093333
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:15.781415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q311.75
95-th percentile24
Maximum27
Range26
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation8.0875908
Coefficient of variation (CV)1.2424607
Kurtosis-0.035655057
Mean6.5093333
Median Absolute Deviation (MAD)0
Skewness1.1971068
Sum4882
Variance65.409125
MonotonicityNot monotonic
2023-12-11T01:40:15.967397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 430
57.3%
3 24
 
3.2%
2 12
 
1.6%
6 12
 
1.6%
11 12
 
1.6%
25 12
 
1.6%
26 12
 
1.6%
16 12
 
1.6%
17 12
 
1.6%
18 12
 
1.6%
Other values (17) 200
26.7%
ValueCountFrequency (%)
1 430
57.3%
2 12
 
1.6%
3 24
 
3.2%
4 12
 
1.6%
5 12
 
1.6%
6 12
 
1.6%
7 12
 
1.6%
8 12
 
1.6%
9 12
 
1.6%
10 12
 
1.6%
ValueCountFrequency (%)
27 8
1.1%
26 12
1.6%
25 12
1.6%
24 12
1.6%
23 12
1.6%
22 12
1.6%
21 12
1.6%
20 12
1.6%
19 12
1.6%
18 12
1.6%

부서구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
10
430 
30
224 
21
60 
11
 
24
20
 
12

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 430
57.3%
30 224
29.9%
21 60
 
8.0%
11 24
 
3.2%
20 12
 
1.6%

Length

2023-12-11T01:40:16.121162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:40:16.272598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 430
57.3%
30 224
29.9%
21 60
 
8.0%
11 24
 
3.2%
20 12
 
1.6%

부서구분명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
본청
430 
읍면동
224 
사업소
60 
직속기관
 
24
외청
 
12

Length

Max length4
Median length2
Mean length2.4426667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
본청 430
57.3%
읍면동 224
29.9%
사업소 60
 
8.0%
직속기관 24
 
3.2%
외청 12
 
1.6%

Length

2023-12-11T01:40:16.482384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:40:16.668859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본청 430
57.3%
읍면동 224
29.9%
사업소 60
 
8.0%
직속기관 24
 
3.2%
외청 12
 
1.6%

실국코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1715.716
Minimum1002
Maximum3005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:16.830142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1004
Q11011
median1031
Q33005
95-th percentile3005
Maximum3005
Range2003
Interquartile range (IQR)1994

Descriptive statistics

Standard deviation896.58407
Coefficient of variation (CV)0.52257138
Kurtosis-1.4977917
Mean1715.716
Median Absolute Deviation (MAD)27
Skewness0.6145897
Sum1286787
Variance803862.99
MonotonicityNot monotonic
2023-12-11T01:40:16.994722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3005 224
29.9%
1020 71
 
9.5%
1004 67
 
8.9%
2105 60
 
8.0%
1025 56
 
7.5%
1031 53
 
7.1%
1008 39
 
5.2%
1015 37
 
4.9%
1105 24
 
3.2%
1009 17
 
2.3%
Other values (10) 102
13.6%
ValueCountFrequency (%)
1002 8
 
1.1%
1003 5
 
0.7%
1004 67
8.9%
1005 12
 
1.6%
1006 12
 
1.6%
1007 15
 
2.0%
1008 39
5.2%
1009 17
 
2.3%
1010 4
 
0.5%
1011 16
 
2.1%
ValueCountFrequency (%)
3005 224
29.9%
2105 60
 
8.0%
2005 12
 
1.6%
1105 24
 
3.2%
1032 6
 
0.8%
1031 53
 
7.1%
1030 12
 
1.6%
1025 56
 
7.5%
1020 71
 
9.5%
1015 37
 
4.9%

실국명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
동사무소
224 
주민생활지원국
71 
일자리산업국
67 
행정관리국
65 
사업소
60 
Other values (13)
263 

Length

Max length8
Median length7
Mean length4.8773333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교통안전도시국
2nd row교통안전도시국
3rd row교통안전도시국
4th row교통안전도시국
5th row행정지원국

Common Values

ValueCountFrequency (%)
동사무소 224
29.9%
주민생활지원국 71
 
9.5%
일자리산업국 67
 
8.9%
행정관리국 65
 
8.7%
사업소 60
 
8.0%
안전도시국 56
 
7.5%
관광경제국 52
 
6.9%
주민복지국 39
 
5.2%
보건소 24
 
3.2%
교통안전도시국 17
 
2.3%
Other values (8) 75
 
10.0%

Length

2023-12-11T01:40:17.200622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동사무소 224
29.9%
주민생활지원국 71
 
9.5%
일자리산업국 67
 
8.9%
행정관리국 65
 
8.7%
사업소 60
 
8.0%
안전도시국 56
 
7.5%
관광경제국 52
 
6.9%
주민복지국 39
 
5.2%
보건소 24
 
3.2%
교통안전도시국 17
 
2.3%
Other values (8) 75
 
10.0%

실국정렬값
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.958667
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:17.351562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median17
Q321
95-th percentile21
Maximum21
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.0692179
Coefficient of variation (CV)0.40573254
Kurtosis-0.96209544
Mean14.958667
Median Absolute Deviation (MAD)4
Skewness-0.62808916
Sum11219
Variance36.835405
MonotonicityNot monotonic
2023-12-11T01:40:17.520275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
21 224
29.9%
16 71
 
9.5%
10 67
 
8.9%
20 60
 
8.0%
17 56
 
7.5%
9 53
 
7.1%
6 39
 
5.2%
15 37
 
4.9%
19 24
 
3.2%
7 17
 
2.3%
Other values (10) 102
13.6%
ValueCountFrequency (%)
1 8
 
1.1%
2 12
 
1.6%
3 12
 
1.6%
4 16
 
2.1%
5 15
 
2.0%
6 39
5.2%
7 17
 
2.3%
8 12
 
1.6%
9 53
7.1%
10 67
8.9%
ValueCountFrequency (%)
21 224
29.9%
20 60
 
8.0%
19 24
 
3.2%
18 12
 
1.6%
17 56
 
7.5%
16 71
 
9.5%
15 37
 
4.9%
14 6
 
0.8%
13 5
 
0.7%
12 4
 
0.5%

부서코드
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1716239.9
Minimum1004001
Maximum3005182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:17.739731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1004001
5-th percentile1004005
Q11015020
median1030030
Q33005037.5
95-th percentile3005160
Maximum3005182
Range2001181
Interquartile range (IQR)1990017.5

Descriptive statistics

Standard deviation896242.42
Coefficient of variation (CV)0.52221277
Kurtosis-1.4975909
Mean1716239.9
Median Absolute Deviation (MAD)26024
Skewness0.61475009
Sum1.28718 × 109
Variance8.0325047 × 1011
MonotonicityNot monotonic
2023-12-11T01:40:17.968052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025030 12
 
1.6%
1004003 12
 
1.6%
1015020 12
 
1.6%
1030020 12
 
1.6%
1030010 12
 
1.6%
1025040 12
 
1.6%
1005012 12
 
1.6%
1010010 12
 
1.6%
1020051 12
 
1.6%
1020050 12
 
1.6%
Other values (55) 630
84.0%
ValueCountFrequency (%)
1004001 11
1.5%
1004003 12
1.6%
1004004 12
1.6%
1004005 12
1.6%
1004006 12
1.6%
1004007 11
1.5%
1005012 12
1.6%
1006001 12
1.6%
1007001 12
1.6%
1008001 12
1.6%
ValueCountFrequency (%)
3005182 12
1.6%
3005180 12
1.6%
3005170 12
1.6%
3005160 8
1.1%
3005150 12
1.6%
3005140 12
1.6%
3005130 12
1.6%
3005120 12
1.6%
3005110 12
1.6%
3005100 12
1.6%

부서명
Text

Distinct65
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2023-12-11T01:40:18.307492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.716
Min length3

Characters and Unicode

Total characters3537
Distinct characters103
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row안전총괄과
2nd row건설과
3rd row건축과
4th row토지정보과
5th row세무2과
ValueCountFrequency (%)
관광문화과 32
 
4.3%
청소행정과 22
 
2.9%
교통행정과 22
 
2.9%
경제진흥과 21
 
2.8%
늘푸른과 21
 
2.8%
재무과 14
 
1.9%
행정지원과 12
 
1.6%
우3동 12
 
1.6%
민원여권과 12
 
1.6%
재송2동 12
 
1.6%
Other values (55) 570
76.0%
2023-12-11T01:40:18.831980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 399
 
11.3%
동 224
 
6.3%
관 116
 
3.3%
정 104
 
2.9%
반 92
 
2.6%
1 84
 
2.4%
2 84
 
2.4%
지 83
 
2.3%
송 80
 
2.3%
여 72
 
2.0%
Other values (93) 2199
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3301
93.3%
Decimal Number 236
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 399
 
12.1%
동 224
 
6.8%
관 116
 
3.5%
정 104
 
3.2%
반 92
 
2.8%
지 83
 
2.5%
송 80
 
2.4%
여 72
 
2.2%
도 72
 
2.2%
복 68
 
2.1%
Other values (89) 1991
60.3%
Decimal Number
ValueCountFrequency (%)
1 84
35.6%
2 84
35.6%
3 44
18.6%
4 24
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3301
93.3%
Common 236
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 399
 
12.1%
동 224
 
6.8%
관 116
 
3.5%
정 104
 
3.2%
반 92
 
2.8%
지 83
 
2.5%
송 80
 
2.4%
여 72
 
2.2%
도 72
 
2.2%
복 68
 
2.1%
Other values (89) 1991
60.3%
Common
ValueCountFrequency (%)
1 84
35.6%
2 84
35.6%
3 44
18.6%
4 24
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3301
93.3%
ASCII 236
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 399
 
12.1%
동 224
 
6.8%
관 116
 
3.5%
정 104
 
3.2%
반 92
 
2.8%
지 83
 
2.5%
송 80
 
2.4%
여 72
 
2.2%
도 72
 
2.2%
복 68
 
2.1%
Other values (89) 1991
60.3%
ASCII
ValueCountFrequency (%)
1 84
35.6%
2 84
35.6%
3 44
18.6%
4 24
 
10.2%

부서정렬값
Real number (ℝ)

HIGH CORRELATION 

Distinct207
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.54533
Minimum4
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-11T01:40:19.039554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q184
median164
Q3212
95-th percentile246
Maximum253
Range249
Interquartile range (IQR)128

Descriptive statistics

Standard deviation76.340971
Coefficient of variation (CV)0.52451679
Kurtosis-1.1116055
Mean145.54533
Median Absolute Deviation (MAD)56
Skewness-0.40542483
Sum109159
Variance5827.9439
MonotonicityNot monotonic
2023-12-11T01:40:19.258561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 16
 
2.1%
195 16
 
2.1%
114 15
 
2.0%
150 14
 
1.9%
15 13
 
1.7%
183 13
 
1.7%
4 12
 
1.6%
245 12
 
1.6%
246 12
 
1.6%
253 11
 
1.5%
Other values (197) 616
82.1%
ValueCountFrequency (%)
4 12
1.6%
11 4
 
0.5%
12 6
0.8%
13 8
1.1%
15 13
1.7%
16 4
 
0.5%
17 4
 
0.5%
18 7
0.9%
19 6
0.8%
20 4
 
0.5%
ValueCountFrequency (%)
253 11
1.5%
252 11
1.5%
247 10
1.3%
246 12
1.6%
245 12
1.6%
243 9
1.2%
241 2
 
0.3%
240 10
1.3%
239 3
 
0.4%
238 3
 
0.4%

Interactions

2023-12-11T01:40:12.828869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:05.679390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:07.004982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:08.256214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:09.333145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.454079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:11.638795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:12.952728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:05.820764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:07.205388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:08.408952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:09.541712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.624503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:11.803976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:13.078736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:05.978904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:07.377379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:08.541760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:09.698211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.780170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:11.973818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:13.207101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:06.128030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:07.516130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:08.703374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:09.842978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.903690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:12.164353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:13.346467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:06.333969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:07.719717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:08.865206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.009371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:11.045235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:12.349661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:13.471887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:06.622869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:07.873656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:09.030404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.170057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:11.295552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:12.522643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:13.641513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:06.851886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:08.050560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:09.211064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:10.311876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:11.478632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:40:12.693375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:40:19.424205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도관서코드관서명관서정렬값부서구분부서구분명실국코드실국명실국정렬값부서코드부서명부서정렬값
회계연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.411
관서코드0.0001.0001.0000.9810.9240.9240.9050.7480.7160.9051.0000.662
관서명0.0001.0001.0001.0001.0001.0001.0000.8840.7171.0001.0000.611
관서정렬값0.0000.9811.0001.0000.9530.9530.9550.7900.7160.9551.0000.661
부서구분0.0000.9241.0000.9531.0001.0001.0001.0000.8971.0001.0000.731
부서구분명0.0000.9241.0000.9531.0001.0001.0001.0000.8971.0001.0000.731
실국코드0.0000.9051.0000.9551.0001.0001.0001.0000.7811.0001.0000.655
실국명0.0000.7480.8840.7901.0001.0001.0001.0000.9941.0000.9970.802
실국정렬값0.0000.7160.7170.7160.8970.8970.7810.9941.0000.7810.9950.844
부서코드0.0000.9051.0000.9551.0001.0001.0001.0000.7811.0001.0000.663
부서명0.0001.0001.0001.0001.0001.0001.0000.9970.9951.0001.0000.893
부서정렬값0.4110.6620.6110.6610.7310.7310.6550.8020.8440.6630.8931.000
2023-12-11T01:40:19.633652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서구분명실국명부서구분관서명
부서구분명1.0000.9911.0000.985
실국명0.9911.0000.9910.453
부서구분1.0000.9911.0000.985
관서명0.9850.4530.9851.000
2023-12-11T01:40:19.812607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도관서코드관서정렬값실국코드실국정렬값부서코드부서정렬값관서명부서구분부서구분명실국명
회계연도1.0000.0180.016-0.013-0.0600.0110.0990.0000.0000.0000.000
관서코드0.0181.0000.9780.8720.8450.8910.5150.9880.6310.6310.401
관서정렬값0.0160.9781.0000.8840.8580.8930.5020.9880.7000.7000.449
실국코드-0.0130.8720.8841.0000.8760.9600.3630.9840.9990.9990.990
실국정렬값-0.0600.8450.8580.8761.0000.8540.4390.4100.6650.6650.935
부서코드0.0110.8910.8930.9600.8541.0000.3570.9840.9990.9990.990
부서정렬값0.0990.5150.5020.3630.4390.3571.0000.2680.3880.3880.465
관서명0.0000.9880.9880.9840.4100.9840.2681.0000.9850.9850.453
부서구분0.0000.6310.7000.9990.6650.9990.3880.9851.0001.0000.991
부서구분명0.0000.6310.7000.9990.6650.9990.3880.9851.0001.0000.991
실국명0.0000.4010.4490.9900.9350.9900.4650.4530.9910.9911.000

Missing values

2023-12-11T01:40:13.840166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:40:14.453578image/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

회계연도관서코드관서명관서정렬값부서구분부서구분명실국코드실국명실국정렬값부서코드부서명부서정렬값
0201910본청110본청1009교통안전도시국71025030안전총괄과145
1201910본청110본청1009교통안전도시국71025040건설과151
2201910본청110본청1009교통안전도시국71025050건축과157
3201910본청110본청1009교통안전도시국71030040토지정보과164
4201910본청110본청1011행정지원국41030030세무2과21
5201910본청110본청1025안전도시국171025010도시디자인과140
6201910본청110본청1020주민생활지원국161020010주민복지과122
7201920의회사무국220외청2005의회사무국82005010의회사무국171
8201940관광시설관리사업소421사업소2105사업소202105010관광시설관리사업소208
92019130좌2동1530읍면동3005동사무소213005060좌2동230
회계연도관서코드관서명관서정렬값부서구분부서구분명실국코드실국명실국정렬값부서코드부서명부서정렬값
7402015251우3동1130읍면동3005동사무소213005182우3동183
7412014251우3동1130읍면동3005동사무소213005182우3동183
7422013251우3동1130읍면동3005동사무소213005182우3동183
7432012251우3동1130읍면동3005동사무소213005182우3동183
7442011251우3동1130읍면동3005동사무소213005182우3동183
7452010251우3동1130읍면동3005동사무소213005182우3동183
7462009251우3동1130읍면동3005동사무소213005182우3동183
7472008251우3동1130읍면동3005동사무소213005182우3동183
7482017251우3동1130읍면동3005동사무소213005182우3동218
7492018251우3동1130읍면동3005동사무소213005182우3동225