Overview

Dataset statistics

Number of variables6
Number of observations1130
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.5 KiB
Average record size in memory52.1 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 재정자립도(%), 자체수입(원), 자치단체 예산규모(원)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110189

Alerts

재정자립도(퍼센트) is highly overall correlated with 자체수입(원)High correlation
자체수입(원) is highly overall correlated with 재정자립도(퍼센트) and 1 other fieldsHigh correlation
자치단체 예산규모(원) is highly overall correlated with 자체수입(원)High correlation
자체수입(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:25:22.733909
Analysis finished2023-12-10 23:25:24.504135
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2017
226 
2018
226 
2019
226 
2020
226 
2021
226 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 226
20.0%
2018 226
20.0%
2019 226
20.0%
2020 226
20.0%
2021 226
20.0%

Length

2023-12-11T08:25:24.608967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:25:24.725796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 226
20.0%
2018 226
20.0%
2019 226
20.0%
2020 226
20.0%
2021 226
20.0%

시도명
Categorical

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
155 
서울특별시
125 
경상북도
115 
전라남도
110 
강원도
90 
Other values (10)
535 

Length

Max length5
Median length4
Mean length4.1106195
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.7%
서울특별시 125
11.1%
경상북도 115
10.2%
전라남도 110
9.7%
강원도 90
8.0%
경상남도 90
8.0%
부산광역시 80
7.1%
충청남도 75
6.6%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (5) 165
14.6%

Length

2023-12-11T08:25:24.883679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.7%
서울특별시 125
11.1%
경상북도 115
10.2%
전라남도 110
9.7%
강원도 90
8.0%
경상남도 90
8.0%
부산광역시 80
7.1%
충청남도 75
6.6%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (5) 165
14.6%

시군구명
Text

Distinct204
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-11T08:25:25.289059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9274336
Min length2

Characters and Unicode

Total characters3308
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 30
 
2.7%
중구 30
 
2.7%
서구 25
 
2.2%
남구 21
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
김제시 5
 
0.4%
진안군 5
 
0.4%
완주군 5
 
0.4%
Other values (194) 969
85.8%
2023-12-11T08:25:25.896737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 425
 
12.8%
시 380
 
11.5%
구 370
 
11.2%
천 110
 
3.3%
주 95
 
2.9%
양 90
 
2.7%
성 90
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
남 61
 
1.8%
Other values (121) 1522
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3308
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 425
 
12.8%
시 380
 
11.5%
구 370
 
11.2%
천 110
 
3.3%
주 95
 
2.9%
양 90
 
2.7%
성 90
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
남 61
 
1.8%
Other values (121) 1522
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3308
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 425
 
12.8%
시 380
 
11.5%
구 370
 
11.2%
천 110
 
3.3%
주 95
 
2.9%
양 90
 
2.7%
성 90
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
남 61
 
1.8%
Other values (121) 1522
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3308
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 425
 
12.8%
시 380
 
11.5%
구 370
 
11.2%
천 110
 
3.3%
주 95
 
2.9%
양 90
 
2.7%
성 90
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
남 61
 
1.8%
Other values (121) 1522
46.0%

재정자립도(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct951
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.440389
Minimum4.02
Maximum68.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:25:26.094275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.02
5-th percentile6.7445
Q110.6425
median17.79
Q327.1675
95-th percentile44.812
Maximum68.86
Range64.84
Interquartile range (IQR)16.525

Descriptive statistics

Standard deviation12.324048
Coefficient of variation (CV)0.60292628
Kurtosis0.76645904
Mean20.440389
Median Absolute Deviation (MAD)8.03
Skewness1.0877116
Sum23097.64
Variance151.88216
MonotonicityNot monotonic
2023-12-11T08:25:26.267067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.78 4
 
0.4%
7.44 3
 
0.3%
14.17 3
 
0.3%
11.9 3
 
0.3%
6.88 3
 
0.3%
8.45 3
 
0.3%
18.16 3
 
0.3%
7.52 3
 
0.3%
6.18 3
 
0.3%
18.59 3
 
0.3%
Other values (941) 1099
97.3%
ValueCountFrequency (%)
4.02 1
0.1%
4.07 1
0.1%
4.22 1
0.1%
5.01 1
0.1%
5.06 1
0.1%
5.1 1
0.1%
5.15 1
0.1%
5.17 1
0.1%
5.31 1
0.1%
5.5 1
0.1%
ValueCountFrequency (%)
68.86 1
0.1%
66.26 1
0.1%
64.34 1
0.1%
60.49 1
0.1%
60.15 1
0.1%
59.15 1
0.1%
58.65 1
0.1%
58.54 1
0.1%
58.45 1
0.1%
58.4 1
0.1%

자체수입(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4608271 × 1011
Minimum9.198123 × 109
Maximum1.3760116 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:25:26.414423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.198123 × 109
5-th percentile2.2411544 × 1010
Q14.0939951 × 1010
median8.4944063 × 1010
Q31.6761437 × 1011
95-th percentile4.7036622 × 1011
Maximum1.3760116 × 1012
Range1.3668135 × 1012
Interquartile range (IQR)1.2667441 × 1011

Descriptive statistics

Standard deviation1.865653 × 1011
Coefficient of variation (CV)1.277121
Kurtosis13.605545
Mean1.4608271 × 1011
Median Absolute Deviation (MAD)5.1937756 × 1010
Skewness3.3286475
Sum1.6507346 × 1014
Variance3.4806612 × 1022
MonotonicityNot monotonic
2023-12-11T08:25:26.572459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151987860000 1
 
0.1%
311349000000 1
 
0.1%
559895097000 1
 
0.1%
506475152000 1
 
0.1%
1065294264000 1
 
0.1%
714715643000 1
 
0.1%
1307778064000 1
 
0.1%
1095665873000 1
 
0.1%
98927199000 1
 
0.1%
453834175000 1
 
0.1%
Other values (1120) 1120
99.1%
ValueCountFrequency (%)
9198123000 1
0.1%
9881103000 1
0.1%
10376228000 1
0.1%
14825050000 1
0.1%
15753005000 1
0.1%
15836987000 1
0.1%
16019000000 1
0.1%
16075761000 1
0.1%
16218411000 1
0.1%
16241000000 1
0.1%
ValueCountFrequency (%)
1376011621000 1
0.1%
1336495395000 1
0.1%
1307778064000 1
0.1%
1252306491000 1
0.1%
1226773005000 1
0.1%
1214172938000 1
0.1%
1213375267000 1
0.1%
1096086160000 1
0.1%
1095665873000 1
0.1%
1072743296000 1
0.1%

자치단체 예산규모(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct1121
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9883856 × 1011
Minimum1.18 × 1011
Maximum2.6865658 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:25:26.736396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.18 × 1011
5-th percentile2.5506561 × 1011
Q13.6003222 × 1011
median4.8784546 × 1011
Q36.9991715 × 1011
95-th percentile1.3677331 × 1012
Maximum2.6865658 × 1012
Range2.5685658 × 1012
Interquartile range (IQR)3.3988494 × 1011

Descriptive statistics

Standard deviation3.8118732 × 1011
Coefficient of variation (CV)0.63654437
Kurtosis6.3886913
Mean5.9883856 × 1011
Median Absolute Deviation (MAD)1.5415427 × 1011
Skewness2.287746
Sum6.7668758 × 1014
Variance1.4530377 × 1023
MonotonicityNot monotonic
2023-12-11T08:25:26.879005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
710000000000 3
 
0.3%
700000000000 2
 
0.2%
482700000000 2
 
0.2%
442000000000 2
 
0.2%
291600000000 2
 
0.2%
660000000000 2
 
0.2%
378000000000 2
 
0.2%
650000000000 2
 
0.2%
1636639119000 1
 
0.1%
1509463068000 1
 
0.1%
Other values (1111) 1111
98.3%
ValueCountFrequency (%)
118000000000 1
0.1%
121375987000 1
0.1%
133000000000 1
0.1%
135434464000 1
0.1%
138808907000 1
0.1%
150204388000 1
0.1%
150500000000 1
0.1%
150862890000 1
0.1%
152610204000 1
0.1%
154300000000 1
0.1%
ValueCountFrequency (%)
2686565754000 1
0.1%
2641693620000 1
0.1%
2384931433000 1
0.1%
2371464092000 1
0.1%
2355322421000 1
0.1%
2350732644000 1
0.1%
2279469083000 1
0.1%
2269637308000 1
0.1%
2203637600000 1
0.1%
2171900000000 1
0.1%

Interactions

2023-12-11T08:25:23.908316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:23.021381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:23.332663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:24.003720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:23.120461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:23.687458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:24.131911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:23.225003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:23.798556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:25:26.972887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명재정자립도(퍼센트)자체수입(원)자치단체 예산규모(원)
통계연도1.0000.0000.0000.0000.298
시도명0.0001.0000.6160.4690.420
재정자립도(퍼센트)0.0000.6161.0000.8390.630
자체수입(원)0.0000.4690.8391.0000.888
자치단체 예산규모(원)0.2980.4200.6300.8881.000
2023-12-11T08:25:27.105830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T08:25:27.197930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재정자립도(퍼센트)자체수입(원)자치단체 예산규모(원)통계연도시도명
재정자립도(퍼센트)1.0000.8890.4640.0000.280
자체수입(원)0.8891.0000.8050.0000.194
자치단체 예산규모(원)0.4640.8051.0000.1290.169
통계연도0.0000.0000.1291.0000.000
시도명0.2800.1940.1690.0001.000

Missing values

2023-12-11T08:25:24.263687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:25:24.435744image/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

통계연도시도명시군구명재정자립도(퍼센트)자체수입(원)자치단체 예산규모(원)
02017서울특별시종로구50.77151987860000299384354000
12017서울특별시중구58.4197389342000337993213000
22017서울특별시용산구42.41135511015000319539944000
32017서울특별시성동구34.5136109124000394523209000
42017서울특별시광진구28.06109243647000389306703000
52017서울특별시동대문구27.22120328535000442000000000
62017서울특별시중랑구20.5899273196000482400000000
72017서울특별시성북구21.77117512230000539829678000
82017서울특별시강북구18.7888026489000468787116000
92017서울특별시도봉구19.7685839256000434417166000
통계연도시도명시군구명재정자립도(퍼센트)자체수입(원)자치단체 예산규모(원)
11202021경상남도의령군8.5530455727000356035547000
11212021경상남도함안군16.1683610323000517492368000
11222021경상남도창녕군11.9761865375000517017406000
11232021경상남도고성군10.2153275740000521555202000
11242021경상남도남해군7.0730083629000425516222000
11252021경상남도하동군10.865947305000610750898000
11262021경상남도산청군9.3542890298000458842494000
11272021경상남도함양군9.6247652242000495283010000
11282021경상남도거창군7.7745441524000584501948000
11292021경상남도합천군8.1346861703000576234100000