Overview

Dataset statistics

Number of variables8
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory75.7 B

Variable types

Categorical1
Numeric7

Dataset

Description한국광해광업공단은 석탄산업 사양화로 인해 침체된 폐광지역 경제 활성화를 위해 ‘폐광지역 개발지원에 관한 특별법(1995.12.)’ 과 ‘2단계 폐광지역 투자계획(2002.11., 산업통상자원부)’에 근거하여 대체법인을 설립·관리하고 있습니다. 공단은 강원랜드 이익금 일부를 출자하여 해당지자체 및 ㈜강원랜드와 공동으로 대체법인을 설립하여 지역발전과 함께 지역주민의 삶의 질 향상에 기여하고 있습니다. 공단 출자회사의 자산, 자본, 부채, 매출액, 영업손익, 당기손익액 등 실적 현황 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15050002/fileData.do

Alerts

자산(백만원) is highly overall correlated with 자본(백만원) and 5 other fieldsHigh correlation
자본(백만원) is highly overall correlated with 자산(백만원) and 5 other fieldsHigh correlation
부채(백만원) is highly overall correlated with 자산(백만원) and 4 other fieldsHigh correlation
매출액(백만원) is highly overall correlated with 자산(백만원) and 4 other fieldsHigh correlation
영업손익(백만원) is highly overall correlated with 자산(백만원) and 4 other fieldsHigh correlation
당기손익(백만원) is highly overall correlated with 자산(백만원) and 4 other fieldsHigh correlation
법인명 is highly overall correlated with 자산(백만원) and 1 other fieldsHigh correlation
자산(백만원) has unique valuesUnique
자본(백만원) has unique valuesUnique
부채(백만원) has unique valuesUnique
영업손익(백만원) has unique valuesUnique
당기손익(백만원) has unique valuesUnique
매출액(백만원) has 3 (10.7%) zerosZeros

Reproduction

Analysis started2023-12-12 13:39:32.085257
Analysis finished2023-12-12 13:39:37.917774
Duration5.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법인명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
㈜강원랜드
7 
㈜문경레저타운
7 
블랙밸리CC㈜
7 
㈜키즈라라
7 

Length

Max length7
Median length6
Mean length6
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row㈜강원랜드
2nd row㈜강원랜드
3rd row㈜강원랜드
4th row㈜강원랜드
5th row㈜강원랜드

Common Values

ValueCountFrequency (%)
㈜강원랜드 7
25.0%
㈜문경레저타운 7
25.0%
블랙밸리CC㈜ 7
25.0%
㈜키즈라라 7
25.0%

Length

2023-12-12T22:39:38.029122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:39:38.178908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
㈜강원랜드 7
25.0%
㈜문경레저타운 7
25.0%
블랙밸리cc㈜ 7
25.0%
㈜키즈라라 7
25.0%

연도
Real number (ℝ)

Distinct7
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T22:39:38.300642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0367003
Coefficient of variation (CV)0.0010087669
Kurtosis-1.2565385
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum56532
Variance4.1481481
MonotonicityNot monotonic
2023-12-12T22:39:38.433730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 4
14.3%
2017 4
14.3%
2018 4
14.3%
2019 4
14.3%
2020 4
14.3%
2021 4
14.3%
2022 4
14.3%
ValueCountFrequency (%)
2016 4
14.3%
2017 4
14.3%
2018 4
14.3%
2019 4
14.3%
2020 4
14.3%
2021 4
14.3%
2022 4
14.3%
ValueCountFrequency (%)
2022 4
14.3%
2021 4
14.3%
2020 4
14.3%
2019 4
14.3%
2018 4
14.3%
2017 4
14.3%
2016 4
14.3%

자산(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1075118.5
Minimum56607
Maximum4438119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T22:39:38.595301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56607
5-th percentile59465.6
Q173481.75
median85419
Q3986449.75
95-th percentile4215286.6
Maximum4438119
Range4381512
Interquartile range (IQR)912968

Descriptive statistics

Standard deviation1763817.8
Coefficient of variation (CV)1.6405799
Kurtosis-0.47296808
Mean1075118.5
Median Absolute Deviation (MAD)18830.5
Skewness1.2396069
Sum30103318
Variance3.1110533 × 1012
MonotonicityNot monotonic
2023-12-12T22:39:38.766207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3978988 1
 
3.6%
91483 1
 
3.6%
56607 1
 
3.6%
58837 1
 
3.6%
60633 1
 
3.6%
61118 1
 
3.6%
61190 1
 
3.6%
62202 1
 
3.6%
63728 1
 
3.6%
101389 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
56607 1
3.6%
58837 1
3.6%
60633 1
3.6%
61118 1
3.6%
61190 1
3.6%
62202 1
3.6%
63728 1
3.6%
76733 1
3.6%
76958 1
3.6%
77451 1
3.6%
ValueCountFrequency (%)
4438119 1
3.6%
4231241 1
3.6%
4185657 1
3.6%
4167753 1
3.6%
3978988 1
3.6%
3827273 1
3.6%
3641632 1
3.6%
101389 1
3.6%
97542 1
3.6%
94626 1
3.6%

자본(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean912006.39
Minimum55226
Maximum3717759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T22:39:38.912420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum55226
5-th percentile59265.05
Q165518.5
median82553.5
Q3886525.5
95-th percentile3556837.6
Maximum3717759
Range3662533
Interquartile range (IQR)821007

Descriptive statistics

Standard deviation1480060.8
Coefficient of variation (CV)1.6228624
Kurtosis-0.49615825
Mean912006.39
Median Absolute Deviation (MAD)18023
Skewness1.2339251
Sum25536179
Variance2.1905801 × 1012
MonotonicityNot monotonic
2023-12-12T22:39:39.048607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3252727 1
 
3.6%
90976 1
 
3.6%
55226 1
 
3.6%
58753 1
 
3.6%
60216 1
 
3.6%
60732 1
 
3.6%
61151 1
 
3.6%
62079 1
 
3.6%
63645 1
 
3.6%
99691 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
55226 1
3.6%
58753 1
3.6%
60216 1
3.6%
60732 1
3.6%
61151 1
3.6%
62079 1
3.6%
63645 1
3.6%
66143 1
3.6%
67058 1
3.6%
67541 1
3.6%
ValueCountFrequency (%)
3717759 1
3.6%
3586777 1
3.6%
3501236 1
3.6%
3415378 1
3.6%
3252793 1
3.6%
3252727 1
3.6%
3247029 1
3.6%
99691 1
3.6%
96507 1
3.6%
93840 1
3.6%

부채(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163112.14
Minimum39
Maximum752375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T22:39:39.183271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile83.35
Q1409.25
median2778
Q3106593.5
95-th percentile724195.65
Maximum752375
Range752336
Interquartile range (IQR)106184.25

Descriptive statistics

Standard deviation287878.06
Coefficient of variation (CV)1.7649088
Kurtosis-0.061240479
Mean163112.14
Median Absolute Deviation (MAD)2717
Skewness1.3469112
Sum4567140
Variance8.2873777 × 1010
MonotonicityNot monotonic
2023-12-12T22:39:39.292670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
726261 1
 
3.6%
507 1
 
3.6%
1381 1
 
3.6%
84 1
 
3.6%
417 1
 
3.6%
386 1
 
3.6%
39 1
 
3.6%
123 1
 
3.6%
83 1
 
3.6%
1698 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
39 1
3.6%
83 1
3.6%
84 1
3.6%
123 1
3.6%
246 1
3.6%
285 1
3.6%
386 1
3.6%
417 1
3.6%
507 1
3.6%
553 1
3.6%
ValueCountFrequency (%)
752375 1
3.6%
726261 1
3.6%
720360 1
3.6%
684421 1
3.6%
644464 1
3.6%
574481 1
3.6%
394604 1
3.6%
10590 1
3.6%
10080 1
3.6%
9920 1
3.6%

매출액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314768.11
Minimum0
Maximum1643596
Zeros3
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T22:39:39.412655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13154.75
median9668.5
Q3131034.25
95-th percentile1538079.8
Maximum1643596
Range1643596
Interquartile range (IQR)127879.5

Descriptive statistics

Standard deviation582716.05
Coefficient of variation (CV)1.851255
Kurtosis0.73214727
Mean314768.11
Median Absolute Deviation (MAD)7519
Skewness1.5632452
Sum8813507
Variance3.3955799 × 1011
MonotonicityNot monotonic
2023-12-12T22:39:39.549464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3
 
10.7%
1643596 1
 
3.6%
4157 1
 
3.6%
35 1
 
3.6%
4 1
 
3.6%
17 1
 
3.6%
148 1
 
3.6%
9411 1
 
3.6%
7829 1
 
3.6%
6318 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
0 3
10.7%
4 1
 
3.6%
17 1
 
3.6%
35 1
 
3.6%
148 1
 
3.6%
4157 1
 
3.6%
4468 1
 
3.6%
4571 1
 
3.6%
5434 1
 
3.6%
6318 1
 
3.6%
ValueCountFrequency (%)
1643596 1
3.6%
1547772 1
3.6%
1520080 1
3.6%
1438059 1
3.6%
1270686 1
3.6%
788433 1
3.6%
478579 1
3.6%
15186 1
3.6%
14175 1
3.6%
11898 1
3.6%

영업손익(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65290.607
Minimum-431584
Maximum618616
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)32.1%
Memory size384.0 B
2023-12-12T22:39:39.670202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-431584
5-th percentile-35578.25
Q1-781.5
median890.5
Q33280.25
95-th percentile520486.6
Maximum618616
Range1050200
Interquartile range (IQR)4061.75

Descriptive statistics

Standard deviation212419.12
Coefficient of variation (CV)3.2534407
Kurtosis2.469095
Mean65290.607
Median Absolute Deviation (MAD)2001
Skewness1.1463579
Sum1828137
Variance4.5121883 × 1010
MonotonicityNot monotonic
2023-12-12T22:39:39.787315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
618616 1
 
3.6%
935 1
 
3.6%
-3790 1
 
3.6%
-1650 1
 
3.6%
-1008 1
 
3.6%
-1213 1
 
3.6%
-864 1
 
3.6%
-754 1
 
3.6%
-717 1
 
3.6%
3888 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
-431584 1
3.6%
-52695 1
3.6%
-3790 1
3.6%
-1650 1
3.6%
-1213 1
3.6%
-1008 1
3.6%
-864 1
3.6%
-754 1
3.6%
-717 1
3.6%
449 1
3.6%
ValueCountFrequency (%)
618616 1
3.6%
530897 1
3.6%
501153 1
3.6%
430700 1
3.6%
217565 1
3.6%
3888 1
3.6%
3536 1
3.6%
3195 1
3.6%
3122 1
3.6%
2091 1
3.6%

당기손익(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48703.179
Minimum-275879
Maximum454534
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)32.1%
Memory size384.0 B
2023-12-12T22:39:39.914501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-275879
5-th percentile-8112.75
Q1-594.25
median801
Q32703.25
95-th percentile401530.55
Maximum454534
Range730413
Interquartile range (IQR)3297.5

Descriptive statistics

Standard deviation151732.89
Coefficient of variation (CV)3.1154617
Kurtosis2.8968776
Mean48703.179
Median Absolute Deviation (MAD)1777
Skewness1.4603255
Sum1363689
Variance2.3022869 × 1010
MonotonicityNot monotonic
2023-12-12T22:39:40.059006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
454534 1
 
3.6%
897 1
 
3.6%
-3527 1
 
3.6%
-1463 1
 
3.6%
-516 1
 
3.6%
-418 1
 
3.6%
-928 1
 
3.6%
-1566 1
 
3.6%
-829 1
 
3.6%
3183 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
-275879 1
3.6%
-10582 1
3.6%
-3527 1
3.6%
-1566 1
3.6%
-1463 1
3.6%
-928 1
3.6%
-829 1
3.6%
-516 1
3.6%
-418 1
3.6%
454 1
3.6%
ValueCountFrequency (%)
454534 1
3.6%
437541 1
3.6%
334654 1
3.6%
297237 1
3.6%
115613 1
3.6%
3183 1
3.6%
2809 1
3.6%
2668 1
3.6%
2626 1
3.6%
1612 1
3.6%

Interactions

2023-12-12T22:39:36.864160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.336294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.017199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.773763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.533584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.362044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.047442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.950965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.429028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.117861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.878221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.657627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.448749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.400908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:37.046186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.520902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.202854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.967312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.765069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.550431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.464435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:37.140669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.623200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.337806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.069168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.904860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.645730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.543681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:37.259650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.703244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.459701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.196008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.063006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.741323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.619964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:37.404643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.793464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.570347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.323660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.171535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.838370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.711261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:37.554527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:32.900329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:33.666259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:34.425683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.275233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:35.955962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:39:36.784110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:39:40.166712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인명연도자산(백만원)자본(백만원)부채(백만원)매출액(백만원)영업손익(백만원)당기손익(백만원)
법인명1.0000.0000.6390.6390.5440.6030.5390.438
연도0.0001.0000.0000.0000.0000.0000.0000.000
자산(백만원)0.6390.0001.0001.0000.8310.9890.8460.702
자본(백만원)0.6390.0001.0001.0000.8310.9890.8460.702
부채(백만원)0.5440.0000.8310.8311.0001.0000.8780.815
매출액(백만원)0.6030.0000.9890.9891.0001.0000.9950.988
영업손익(백만원)0.5390.0000.8460.8460.8780.9951.0000.988
당기손익(백만원)0.4380.0000.7020.7020.8150.9880.9881.000
2023-12-12T22:39:40.286418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도자산(백만원)자본(백만원)부채(백만원)매출액(백만원)영업손익(백만원)당기손익(백만원)법인명
연도1.0000.0380.0550.1190.113-0.033-0.0040.000
자산(백만원)0.0381.0000.9980.6730.7780.6740.6420.648
자본(백만원)0.0550.9981.0000.6680.7770.6600.6310.648
부채(백만원)0.1190.6730.6681.0000.9450.5180.5470.456
매출액(백만원)0.1130.7780.7770.9451.0000.6220.6520.408
영업손익(백만원)-0.0330.6740.6600.5180.6221.0000.9800.408
당기손익(백만원)-0.0040.6420.6310.5470.6520.9801.0000.314
법인명0.0000.6480.6480.4560.4080.4080.3141.000

Missing values

2023-12-12T22:39:37.699791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:39:37.858823image/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

법인명연도자산(백만원)자본(백만원)부채(백만원)매출액(백만원)영업손익(백만원)당기손익(백만원)
0㈜강원랜드2016397898832527277262611643596618616454534
1㈜강원랜드2017418565735012366844211547772530897437541
2㈜강원랜드2018423124135867776444641438059430700297237
3㈜강원랜드2019443811937177597203601520080501153334654
4㈜강원랜드202036416323247029394604478579-431584-275879
5㈜강원랜드202138272733252793574481788433-52695-10582
6㈜강원랜드2022416775334153787523751270686217565115613
7㈜문경레저타운20167673366143105909926585585
8㈜문경레저타운20177695867058990010546944915
9㈜문경레저타운20187745167541991010817449482
법인명연도자산(백만원)자본(백만원)부채(백만원)매출액(백만원)영업손익(백만원)당기손익(백만원)
18블랙밸리CC㈜20209462693840786631820911612
19블랙밸리CC㈜202197542965071035782935362668
20블랙밸리CC㈜2022101389996911698941138883183
21㈜키즈라라20166372863645830-717-829
22㈜키즈라라201762202620791230-754-1566
23㈜키즈라라20186119061151390-864-928
24㈜키즈라라20196111860732386148-1213-418
25㈜키즈라라2020606336021641717-1008-516
26㈜키즈라라20215883758753844-1650-1463
27㈜키즈라라20225660755226138135-3790-3527