Overview

Dataset statistics

Number of variables6
Number of observations215
Missing cells183
Missing cells (%)14.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory52.6 B

Variable types

Categorical1
Text2
Numeric3

Dataset

Description지방공기업 부채비율 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=RHH3LLWBYPETSJEPQHGY22824870&infSeq=1

Alerts

지방공기업부채금액(원) is highly overall correlated with 지방공기업자산금액(원)High correlation
지방공기업자산금액(원) is highly overall correlated with 지방공기업부채금액(원)High correlation
시군명 has 183 (85.1%) missing valuesMissing
지방공기업부채금액(원) has 13 (6.0%) zerosZeros
지방공기업자산금액(원) has 11 (5.1%) zerosZeros
자본대비부채비율(%) has 17 (7.9%) zerosZeros

Reproduction

Analysis started2023-12-10 22:59:25.938824
Analysis finished2023-12-10 22:59:26.995657
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2020
183 
2021
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 183
85.1%
2021 32
 
14.9%

Length

2023-12-11T07:59:27.045612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:59:27.135515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 183
85.1%
2021 32
 
14.9%

시군명
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing183
Missing (%)85.1%
Memory size1.8 KiB
2023-12-11T07:59:27.306810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.09375
Min length3

Characters and Unicode

Total characters99
Distinct characters41
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

Unique32 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row경기도
3rd row고양시
4th row과천시
5th row광명시
ValueCountFrequency (%)
경기도 1
 
3.1%
고양시 1
 
3.1%
화성시 1
 
3.1%
하남시 1
 
3.1%
포천시 1
 
3.1%
평택시 1
 
3.1%
파주시 1
 
3.1%
이천시 1
 
3.1%
의정부시 1
 
3.1%
의왕시 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T07:59:27.586770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

자치단체명
Text

Distinct183
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T07:59:27.902970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9302326
Min length4

Characters and Unicode

Total characters1060
Distinct characters123
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

Unique151 ?
Unique (%)70.2%

Sample

1st row경기가평군
2nd row경기본청
3rd row경기고양시
4th row경기과천시
5th row경기광명시
ValueCountFrequency (%)
경기가평군 2
 
0.9%
경기의왕시 2
 
0.9%
경기안성시 2
 
0.9%
경기양평군 2
 
0.9%
경기여주시 2
 
0.9%
경기연천군 2
 
0.9%
경기오산시 2
 
0.9%
경기용인시 2
 
0.9%
경기의정부시 2
 
0.9%
경기평택시 2
 
0.9%
Other values (173) 195
90.7%
2023-12-11T07:59:28.324999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 105
 
9.9%
경 103
 
9.7%
기 65
 
6.1%
군 62
 
5.8%
남 54
 
5.1%
북 46
 
4.3%
구 43
 
4.1%
천 36
 
3.4%
울 32
 
3.0%
서 30
 
2.8%
Other values (113) 484
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1060
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 105
 
9.9%
경 103
 
9.7%
기 65
 
6.1%
군 62
 
5.8%
남 54
 
5.1%
북 46
 
4.3%
구 43
 
4.1%
천 36
 
3.4%
울 32
 
3.0%
서 30
 
2.8%
Other values (113) 484
45.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1060
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 105
 
9.9%
경 103
 
9.7%
기 65
 
6.1%
군 62
 
5.8%
남 54
 
5.1%
북 46
 
4.3%
구 43
 
4.1%
천 36
 
3.4%
울 32
 
3.0%
서 30
 
2.8%
Other values (113) 484
45.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1060
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 105
 
9.9%
경 103
 
9.7%
기 65
 
6.1%
군 62
 
5.8%
남 54
 
5.1%
북 46
 
4.3%
구 43
 
4.1%
천 36
 
3.4%
울 32
 
3.0%
서 30
 
2.8%
Other values (113) 484
45.7%

지방공기업부채금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct203
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2570966 × 1011
Minimum0
Maximum2.4341135 × 1013
Zeros13
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:59:28.482577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.3964463 × 108
median3.8070361 × 109
Q36.3760492 × 1010
95-th percentile7.8128026 × 1011
Maximum2.4341135 × 1013
Range2.4341135 × 1013
Interquartile range (IQR)6.3120847 × 1010

Descriptive statistics

Standard deviation1.8770388 × 1012
Coefficient of variation (CV)5.7629203
Kurtosis127.95938
Mean3.2570966 × 1011
Median Absolute Deviation (MAD)3.8070361 × 109
Skewness10.543808
Sum7.0027577 × 1013
Variance3.5232746 × 1024
MonotonicityNot monotonic
2023-12-11T07:59:28.610533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
6.0%
1969822850 1
 
0.5%
832324579 1
 
0.5%
801864642 1
 
0.5%
609297371 1
 
0.5%
952366129 1
 
0.5%
825395176 1
 
0.5%
1070894168 1
 
0.5%
850138736 1
 
0.5%
283077206 1
 
0.5%
Other values (193) 193
89.8%
ValueCountFrequency (%)
0 13
6.0%
7100 1
 
0.5%
181430 1
 
0.5%
725180 1
 
0.5%
3532120 1
 
0.5%
18800089 1
 
0.5%
39677639 1
 
0.5%
53473928 1
 
0.5%
80706350 1
 
0.5%
86190892 1
 
0.5%
ValueCountFrequency (%)
24341135147580 1
0.5%
7026472066509 1
0.5%
6780780000000 1
0.5%
6285806664656 1
0.5%
5451246853986 1
0.5%
2757685000000 1
0.5%
2114793586949 1
0.5%
1264977952665 1
0.5%
1024356110914 1
0.5%
963037517428 1
0.5%

지방공기업자산금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct205
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2203603 × 1012
Minimum0
Maximum5.3884598 × 1013
Zeros11
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:59:28.746423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6149445 × 108
Q14.1892598 × 1010
median3.4880301 × 1011
Q38.8940429 × 1011
95-th percentile3.0204278 × 1012
Maximum5.3884598 × 1013
Range5.3884598 × 1013
Interquartile range (IQR)8.475117 × 1011

Descriptive statistics

Standard deviation4.6579773 × 1012
Coefficient of variation (CV)3.8168871
Kurtosis89.299813
Mean1.2203603 × 1012
Median Absolute Deviation (MAD)3.4689764 × 1011
Skewness8.8672314
Sum2.6237745 × 1014
Variance2.1696753 × 1025
MonotonicityNot monotonic
2023-12-11T07:59:28.897776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
5.1%
470014750774 1
 
0.5%
1101471668 1
 
0.5%
994694016 1
 
0.5%
1601864642 1
 
0.5%
859297371 1
 
0.5%
1352366129 1
 
0.5%
1085395176 1
 
0.5%
1570894168 1
 
0.5%
1300138736 1
 
0.5%
Other values (195) 195
90.7%
ValueCountFrequency (%)
0 11
5.1%
230706350 1
 
0.5%
326877220 1
 
0.5%
379361367 1
 
0.5%
658072691 1
 
0.5%
762566684 1
 
0.5%
783077206 1
 
0.5%
830861231 1
 
0.5%
859297371 1
 
0.5%
877102610 1
 
0.5%
ValueCountFrequency (%)
53884597685896 1
0.5%
34938487931328 1
0.5%
12525047058780 1
0.5%
12368411000000 1
0.5%
11378010000000 1
0.5%
11349304505581 1
0.5%
10390881626015 1
0.5%
4251115853480 1
0.5%
3997446142309 1
0.5%
3275168225136 1
0.5%

자본대비부채비율(%)
Real number (ℝ)

ZEROS 

Distinct190
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.01693
Minimum0
Maximum1355.12
Zeros17
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:59:29.014656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.595
median9.05
Q336.185
95-th percentile187.597
Maximum1355.12
Range1355.12
Interquartile range (IQR)35.59

Descriptive statistics

Standard deviation120.63148
Coefficient of variation (CV)2.6214587
Kurtosis70.869253
Mean46.01693
Median Absolute Deviation (MAD)8.96
Skewness7.3854655
Sum9893.64
Variance14551.954
MonotonicityNot monotonic
2023-12-11T07:59:29.139119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
7.9%
0.09 3
 
1.4%
0.12 2
 
0.9%
0.48 2
 
0.9%
0.63 2
 
0.9%
0.28 2
 
0.9%
0.08 2
 
0.9%
0.1 2
 
0.9%
0.6 2
 
0.9%
0.42 1
 
0.5%
Other values (180) 180
83.7%
ValueCountFrequency (%)
0.0 17
7.9%
0.01 1
 
0.5%
0.05 1
 
0.5%
0.06 1
 
0.5%
0.07 1
 
0.5%
0.08 2
 
0.9%
0.09 3
 
1.4%
0.1 2
 
0.9%
0.11 1
 
0.5%
0.12 2
 
0.9%
ValueCountFrequency (%)
1355.12 1
0.5%
781.53 1
0.5%
353.82 1
0.5%
317.46 1
0.5%
279.39 1
0.5%
279.36 1
0.5%
243.72 1
0.5%
238.09 1
0.5%
214.18 1
0.5%
192.37 1
0.5%

Interactions

2023-12-11T07:59:26.607684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.158453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.382722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.684574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.236635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.456158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.756662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.309034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:59:26.532370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:59:29.228311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명지방공기업부채금액(원)지방공기업자산금액(원)자본대비부채비율(%)
회계연도1.000NaN0.0000.0000.000
시군명NaN1.0001.0001.000NaN
지방공기업부채금액(원)0.0001.0001.0000.8480.000
지방공기업자산금액(원)0.0001.0000.8481.0000.000
자본대비부채비율(%)0.000NaN0.0000.0001.000
2023-12-11T07:59:29.309451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방공기업부채금액(원)지방공기업자산금액(원)자본대비부채비율(%)회계연도
지방공기업부채금액(원)1.0000.8120.4260.000
지방공기업자산금액(원)0.8121.000-0.0110.000
자본대비부채비율(%)0.426-0.0111.0000.000
회계연도0.0000.0000.0001.000

Missing values

2023-12-11T07:59:26.858173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:59:26.959569image/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

회계연도시군명자치단체명지방공기업부채금액(원)지방공기업자산금액(원)자본대비부채비율(%)
02021가평군경기가평군19698228504700147507740.42
12021경기도경기본청628580666465612525047058780100.75
22021고양시경기고양시8623919641713398982054096.88
32021과천시경기과천시15587694419864154380740932.1
42021광명시경기광명시38070361242304943037151.68
52021광주시경기광주시3415094907310873193617803.24
62021구리시경기구리시175592815406750846209612.67
72021군포시경기군포시19680653623164245930250.63
82021김포시경기김포시153353272744108988645716416.37
92021남양주시경기남양주시9818641177511527254831679.31
회계연도시군명자치단체명지방공기업부채금액(원)지방공기업자산금액(원)자본대비부채비율(%)
2052020<NA>경기포천시8305797656869691712130.12
2062020<NA>경기의왕시32562278523164705809231.04
2072020<NA>경기하남시588450998848591955615177.35
2082020<NA>경기여주시6953087722530645941080.28
2092020<NA>경기동두천시4105028245631022332941315.25
2102020<NA>경기과천시168469395984500471470853.89
2112020<NA>경기양평군282084661135584971125415.32
2122020<NA>경기가평군19971419504543716666160.44
2132020<NA>경기연천군4740522335339217072360.09
2142020<NA>강원본청12649779526651622494164796353.82