Overview

Dataset statistics

Number of variables8
Number of observations155
Missing cells155
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory69.9 B

Variable types

Categorical2
Unsupported1
Text2
Numeric3

Dataset

Description지방공기업별 부채비율 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=70BR85BY7SFW967HQ81P22814977&infSeq=1

Alerts

회계연도 has constant value ""Constant
부채금액(원) is highly overall correlated with 자산금액(원)High correlation
자산금액(원) is highly overall correlated with 부채금액(원)High correlation
시군명 has 155 (100.0%) missing valuesMissing
시군명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부채금액(원) has 5 (3.2%) zerosZeros
자산금액(원) has 5 (3.2%) zerosZeros
자본대비부채비율(%) has 7 (4.5%) zerosZeros

Reproduction

Analysis started2023-12-10 21:41:43.316018
Analysis finished2023-12-10 21:41:44.399333
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2019
155 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 155
100.0%

Length

2023-12-11T06:41:44.447360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:41:44.549404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 155
100.0%

시군명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing155
Missing (%)100.0%
Memory size1.5 KiB

자치단체명
Text

Distinct121
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:41:44.796005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6903226
Min length4

Characters and Unicode

Total characters727
Distinct characters95
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

Unique106 ?
Unique (%)68.4%

Sample

1st row부산본청
2nd row부산본청
3rd row부산기장군
4th row대구본청
5th row대구본청
ValueCountFrequency (%)
부산본청 6
 
3.9%
인천본청 5
 
3.2%
서울본청 5
 
3.2%
광주본청 4
 
2.6%
대전본청 4
 
2.6%
대구본청 4
 
2.6%
울산본청 3
 
1.9%
경기본청 3
 
1.9%
제주본청 3
 
1.9%
경남창원시 2
 
1.3%
Other values (111) 116
74.8%
2023-12-11T06:41:45.164253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 58
 
8.0%
시 55
 
7.6%
청 50
 
6.9%
본 47
 
6.5%
구 45
 
6.2%
울 37
 
5.1%
기 37
 
5.1%
서 33
 
4.5%
남 24
 
3.3%
천 24
 
3.3%
Other values (85) 317
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 727
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 58
 
8.0%
시 55
 
7.6%
청 50
 
6.9%
본 47
 
6.5%
구 45
 
6.2%
울 37
 
5.1%
기 37
 
5.1%
서 33
 
4.5%
남 24
 
3.3%
천 24
 
3.3%
Other values (85) 317
43.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 727
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 58
 
8.0%
시 55
 
7.6%
청 50
 
6.9%
본 47
 
6.5%
구 45
 
6.2%
울 37
 
5.1%
기 37
 
5.1%
서 33
 
4.5%
남 24
 
3.3%
천 24
 
3.3%
Other values (85) 317
43.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 727
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 58
 
8.0%
시 55
 
7.6%
청 50
 
6.9%
본 47
 
6.5%
구 45
 
6.2%
울 37
 
5.1%
기 37
 
5.1%
서 33
 
4.5%
남 24
 
3.3%
천 24
 
3.3%
Other values (85) 317
43.6%

공기업명
Text

Distinct154
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:41:45.664741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length8.2774194
Min length1

Characters and Unicode

Total characters1283
Distinct characters132
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

Unique153 ?
Unique (%)98.7%

Sample

1st row부산교통공사
2nd row부산도시공사
3rd row기장군도시관리공단
4th row대구시설공단
5th row대구환경공단
ValueCountFrequency (%)
울산광역시중구도시관리공단 2
 
1.3%
문경관광진흥공단 1
 
0.6%
경상북도관광공사 1
 
0.6%
밀양시시설관리공단 1
 
0.6%
청도공영사업공사 1
 
0.6%
포항시설관리공단 1
 
0.6%
경주시시설관리공단 1
 
0.6%
안동시시설관리공단 1
 
0.6%
구미시설공단 1
 
0.6%
청송사과유통공사 1
 
0.6%
Other values (145) 145
92.9%
2023-12-11T06:41:46.029310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 155
 
12.1%
공 153
 
11.9%
단 89
 
6.9%
관 86
 
6.7%
리 77
 
6.0%
사 67
 
5.2%
설 66
 
5.1%
도 57
 
4.4%
구 45
 
3.5%
광 35
 
2.7%
Other values (122) 453
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1282
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 155
 
12.1%
공 153
 
11.9%
단 89
 
6.9%
관 86
 
6.7%
리 77
 
6.0%
사 67
 
5.2%
설 66
 
5.1%
도 57
 
4.4%
구 45
 
3.5%
광 35
 
2.7%
Other values (121) 452
35.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1282
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 155
 
12.1%
공 153
 
11.9%
단 89
 
6.9%
관 86
 
6.7%
리 77
 
6.0%
사 67
 
5.2%
설 66
 
5.1%
도 57
 
4.4%
구 45
 
3.5%
광 35
 
2.7%
Other values (121) 452
35.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1282
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 155
 
12.1%
공 153
 
11.9%
단 89
 
6.9%
관 86
 
6.7%
리 77
 
6.0%
사 67
 
5.2%
설 66
 
5.1%
도 57
 
4.4%
구 45
 
3.5%
광 35
 
2.7%
Other values (121) 452
35.3%
ASCII
ValueCountFrequency (%)
1
100.0%

구분명
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
공단
88 
공사
67 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공사
2nd row공사
3rd row공단
4th row공단
5th row공단

Common Values

ValueCountFrequency (%)
공단 88
56.8%
공사 67
43.2%

Length

2023-12-11T06:41:46.152469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:41:46.238728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공단 88
56.8%
공사 67
43.2%

부채금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8833487 × 1011
Minimum0
Maximum1.6248155 × 1013
Zeros5
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T06:41:46.334603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75594633
Q14.8480714 × 108
median1.71764 × 109
Q32.5440034 × 1010
95-th percentile8.588355 × 1011
Maximum1.6248155 × 1013
Range1.6248155 × 1013
Interquartile range (IQR)2.4955227 × 1010

Descriptive statistics

Standard deviation1.5096852 × 1012
Coefficient of variation (CV)5.2358745
Kurtosis84.386592
Mean2.8833487 × 1011
Median Absolute Deviation (MAD)1.5058394 × 109
Skewness8.6153104
Sum4.4691905 × 1013
Variance2.2791495 × 1024
MonotonicityNot monotonic
2023-12-11T06:41:46.483254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
3.2%
955062000000 1
 
0.6%
2355562627 1
 
0.6%
444662411 1
 
0.6%
1465457017 1
 
0.6%
392386005 1
 
0.6%
1717639950 1
 
0.6%
417174227 1
 
0.6%
13052470000 1
 
0.6%
506934000000 1
 
0.6%
Other values (141) 141
91.0%
ValueCountFrequency (%)
0 5
3.2%
8158250 1
 
0.6%
59384754 1
 
0.6%
59614879 1
 
0.6%
82443099 1
 
0.6%
89134845 1
 
0.6%
126124408 1
 
0.6%
142063783 1
 
0.6%
196349575 1
 
0.6%
200683740 1
 
0.6%
ValueCountFrequency (%)
16248155000000 1
0.6%
6280517337427 1
0.6%
5514611070077 1
0.6%
4645470000000 1
0.6%
1417794000000 1
0.6%
1240627991617 1
0.6%
1054103722014 1
0.6%
955062000000 1
0.6%
817595567056 1
0.6%
518105714444 1
0.6%

자산금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1058581 × 1011
Minimum0
Maximum2.4755565 × 1013
Zeros5
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T06:41:46.616242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6980767 × 108
Q11.0294489 × 109
median4.1008954 × 109
Q31.2882916 × 1011
95-th percentile2.1905053 × 1012
Maximum2.4755565 × 1013
Range2.4755565 × 1013
Interquartile range (IQR)1.2779971 × 1011

Descriptive statistics

Standard deviation2.5265746 × 1012
Coefficient of variation (CV)4.1379518
Kurtosis59.270936
Mean6.1058581 × 1011
Median Absolute Deviation (MAD)3.7605704 × 109
Skewness7.1207709
Sum9.46408 × 1013
Variance6.3835793 × 1024
MonotonicityNot monotonic
2023-12-11T06:41:46.752862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
3.2%
4178854000000 1
 
0.6%
4710641760 1
 
0.6%
644662411 1
 
0.6%
1565457017 1
 
0.6%
592386005 1
 
0.6%
4100895434 1
 
0.6%
517174227 1
 
0.6%
29571743000 1
 
0.6%
965660000000 1
 
0.6%
Other values (141) 141
91.0%
ValueCountFrequency (%)
0 5
3.2%
108158250 1
 
0.6%
159384754 1
 
0.6%
340324986 1
 
0.6%
382443099 1
 
0.6%
396349575 1
 
0.6%
408843137 1
 
0.6%
428815168 1
 
0.6%
435141009 1
 
0.6%
441000000 1
 
0.6%
ValueCountFrequency (%)
24755565000000 1
0.6%
13199045000000 1
0.6%
9429288302688 1
0.6%
8828012031302 1
0.6%
4541901179664 1
0.6%
4178854000000 1
0.6%
4083505471231 1
0.6%
3338224000000 1
0.6%
1698625843814 1
0.6%
1674554903358 1
0.6%

자본대비부채비율(%)
Real number (ℝ)

ZEROS 

Distinct149
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.90942
Minimum0
Maximum6533.09
Zeros7
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T06:41:46.873867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.563
Q125.375
median92.07
Q3191.425
95-th percentile585.13
Maximum6533.09
Range6533.09
Interquartile range (IQR)166.05

Descriptive statistics

Standard deviation701.11918
Coefficient of variation (CV)3.1173402
Kurtosis63.820953
Mean224.90942
Median Absolute Deviation (MAD)77.38
Skewness7.7396243
Sum34860.96
Variance491568.11
MonotonicityNot monotonic
2023-12-11T06:41:47.002275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
4.5%
29.63 1
 
0.6%
79.01 1
 
0.6%
73.96 1
 
0.6%
222.33 1
 
0.6%
1465.46 1
 
0.6%
196.19 1
 
0.6%
72.07 1
 
0.6%
417.17 1
 
0.6%
100.02 1
 
0.6%
Other values (139) 139
89.7%
ValueCountFrequency (%)
0.0 7
4.5%
1.5 1
 
0.6%
1.59 1
 
0.6%
2.08 1
 
0.6%
2.16 1
 
0.6%
2.83 1
 
0.6%
2.99 1
 
0.6%
3.04 1
 
0.6%
4.15 1
 
0.6%
4.73 1
 
0.6%
ValueCountFrequency (%)
6533.09 1
0.6%
5553.18 1
0.6%
1465.46 1
0.6%
1290.49 1
0.6%
916.32 1
0.6%
743.39 1
0.6%
638.93 1
0.6%
597.38 1
0.6%
579.88 1
0.6%
493.04 1
0.6%

Interactions

2023-12-11T06:41:44.032331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:43.562435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:43.823740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:44.108434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:43.666233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:43.903336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:44.175803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:43.738892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:41:43.966703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:41:47.084025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명부채금액(원)자산금액(원)자본대비부채비율(%)
구분명1.0000.1890.1770.000
부채금액(원)0.1891.0001.0000.000
자산금액(원)0.1771.0001.0000.000
자본대비부채비율(%)0.0000.0000.0001.000
2023-12-11T06:41:47.169268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부채금액(원)자산금액(원)자본대비부채비율(%)구분명
부채금액(원)1.0000.9460.0970.124
자산금액(원)0.9461.000-0.1400.214
자본대비부채비율(%)0.097-0.1401.0000.000
구분명0.1240.2140.0001.000

Missing values

2023-12-11T06:41:44.269462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:41:44.361614image/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

회계연도시군명자치단체명공기업명구분명부채금액(원)자산금액(원)자본대비부채비율(%)
02019<NA>부산본청부산교통공사공사955062000000417885400000029.63
12019<NA>부산본청부산도시공사공사1417794000000333822400000073.83
22019<NA>부산기장군기장군도시관리공단공단33623700083623700067.25
32019<NA>대구본청대구시설공단공단81353500362122155374262.17
42019<NA>대구본청대구환경공단공단92994815395938184829961.59
52019<NA>대구본청대구도시공사공사10541037220141698625843814163.55
62019<NA>대구본청대구도시철도공사공사472457861460454190117966411.61
72019<NA>대구달성군대구광역시달성군시설관리공단공단575583057875583057191.86
82019<NA>인천본청인천시설공단공단887197766515783339075128.37
92019<NA>인천본청인천환경공단공단73956464258895646425493.04
회계연도시군명자치단체명공기업명구분명부채금액(원)자산금액(원)자본대비부채비율(%)
1452019<NA>서울영등포구영등포구시설관리공단공단1168210475312147819559.81
1462019<NA>서울동작구동작구시설관리공단공단7717826921471782692110.25
1472019<NA>서울관악구관악구시설관리공단공단370807950107080795052.97
1482019<NA>서울강남구강남구도시관리공단공단834511781203451178169.54
1492019<NA>서울송파구송파구시설관리공단공단2615422204523212040499.95
1502019<NA>서울강동구강동구도시관리공단공단19395850002539585000323.26
1512019<NA>부산본청부산시설공단공단4557500000052708000000638.93
1522019<NA>부산본청부산지방공단스포원공단12516000000125160000000.0
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1542019<NA>부산본청부산관광공사공사33650000002547200000015.22