Overview

Dataset statistics

Number of variables10
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory89.9 B

Variable types

Categorical4
Numeric6

Dataset

Description한국공항공사에서 운영하고 있는 전국공항 항공기 기종별 공항사용요금 (국제선/국내선 착륙료, 조명료, 정류료 정보 제공)
URLhttps://www.data.go.kr/data/15002684/fileData.do

Alerts

비고 has constant value ""Constant
항공기(A320_74톤)(천원) is highly overall correlated with 항공기(B737_79톤)(천원) and 6 other fieldsHigh correlation
항공기(B737_79톤)(천원) is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation
항공기(B767_156톤)(천원) is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation
항공기(B787_228톤)(천원) is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation
항공기(A330_246톤)(천원) is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation
항공기(B777_299톤)(천원) is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation
국제_국내선 여부 is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation
사용료 is highly overall correlated with 항공기(A320_74톤)(천원) and 6 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 04:18:45.024905
Analysis finished2023-12-12 04:18:49.619697
Duration4.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공항
Categorical

Distinct13
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size404.0 B
김포
4 
김해
4 
제주
4 
대구
4 
청주
4 
Other values (8)
14 

Length

Max length4
Median length2
Mean length2.0588235
Min length2

Unique

Unique6 ?
Unique (%)17.6%

Sample

1st row김포
2nd row김해
3rd row제주
4th row대구
5th row청주

Common Values

ValueCountFrequency (%)
김포 4
11.8%
김해 4
11.8%
제주 4
11.8%
대구 4
11.8%
청주 4
11.8%
양양 4
11.8%
무안 4
11.8%
울산 1
 
2.9%
광주 1
 
2.9%
여수 1
 
2.9%
Other values (3) 3
8.8%

Length

2023-12-12T13:18:49.743218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김포 4
11.8%
김해 4
11.8%
제주 4
11.8%
대구 4
11.8%
청주 4
11.8%
양양 4
11.8%
무안 4
11.8%
울산 1
 
2.9%
광주 1
 
2.9%
여수 1
 
2.9%
Other values (3) 3
8.8%

국제_국내선 여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
국제선
21 
국내선
13 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국제선 21
61.8%
국내선 13
38.2%

Length

2023-12-12T13:18:49.937388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:18:50.061749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국제선 21
61.8%
국내선 13
38.2%

사용료
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
착륙료
20 
조명료
7 
정류료
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
착륙료 20
58.8%
조명료 7
 
20.6%
정류료 7
 
20.6%

Length

2023-12-12T13:18:50.205592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:18:50.339648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
착륙료 20
58.8%
조명료 7
 
20.6%
정류료 7
 
20.6%

항공기(A320_74톤)(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.08824
Minimum43
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:18:50.452773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q1128
median141.5
Q3205
95-th percentile504.3
Maximum809
Range766
Interquartile range (IQR)77

Descriptive statistics

Standard deviation173.0231
Coefficient of variation (CV)0.83955834
Kurtosis3.6943053
Mean206.08824
Median Absolute Deviation (MAD)31.5
Skewness1.8575784
Sum7007
Variance29936.992
MonotonicityNot monotonic
2023-12-12T13:18:50.591328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
128 10
29.4%
370 4
 
11.8%
43 4
 
11.8%
169 4
 
11.8%
155 3
 
8.8%
449 2
 
5.9%
52 2
 
5.9%
205 2
 
5.9%
607 1
 
2.9%
106 1
 
2.9%
ValueCountFrequency (%)
43 4
 
11.8%
52 2
 
5.9%
106 1
 
2.9%
128 10
29.4%
155 3
 
8.8%
169 4
 
11.8%
205 2
 
5.9%
370 4
 
11.8%
449 2
 
5.9%
607 1
 
2.9%
ValueCountFrequency (%)
809 1
 
2.9%
607 1
 
2.9%
449 2
 
5.9%
370 4
 
11.8%
205 2
 
5.9%
169 4
 
11.8%
155 3
 
8.8%
128 10
29.4%
106 1
 
2.9%
52 2
 
5.9%

항공기(B737_79톤)(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.67647
Minimum43
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:18:50.725938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q1138
median153
Q3205
95-th percentile540.75
Maximum809
Range766
Interquartile range (IQR)67

Descriptive statistics

Standard deviation180.55214
Coefficient of variation (CV)0.83327985
Kurtosis2.7876094
Mean216.67647
Median Absolute Deviation (MAD)31.5
Skewness1.7110383
Sum7367
Variance32599.074
MonotonicityNot monotonic
2023-12-12T13:18:51.139583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
138 10
29.4%
398 4
 
11.8%
43 4
 
11.8%
169 4
 
11.8%
168 3
 
8.8%
483 2
 
5.9%
52 2
 
5.9%
205 2
 
5.9%
648 1
 
2.9%
106 1
 
2.9%
ValueCountFrequency (%)
43 4
 
11.8%
52 2
 
5.9%
106 1
 
2.9%
138 10
29.4%
168 3
 
8.8%
169 4
 
11.8%
205 2
 
5.9%
398 4
 
11.8%
483 2
 
5.9%
648 1
 
2.9%
ValueCountFrequency (%)
809 1
 
2.9%
648 1
 
2.9%
483 2
 
5.9%
398 4
 
11.8%
205 2
 
5.9%
169 4
 
11.8%
168 3
 
8.8%
138 10
29.4%
106 1
 
2.9%
52 2
 
5.9%

항공기(B767_156톤)(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.61765
Minimum43
Maximum1268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:18:51.297292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q186
median317
Q3384
95-th percentile1086
Maximum1268
Range1225
Interquartile range (IQR)298

Descriptive statistics

Standard deviation353.58922
Coefficient of variation (CV)0.94893309
Kurtosis0.35622922
Mean372.61765
Median Absolute Deviation (MAD)221.5
Skewness1.2030218
Sum12669
Variance125025.33
MonotonicityNot monotonic
2023-12-12T13:18:51.431197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
317 10
29.4%
895 4
 
11.8%
43 4
 
11.8%
86 4
 
11.8%
384 3
 
8.8%
1086 2
 
5.9%
52 2
 
5.9%
105 2
 
5.9%
1268 1
 
2.9%
106 1
 
2.9%
ValueCountFrequency (%)
43 4
 
11.8%
52 2
 
5.9%
86 4
 
11.8%
105 2
 
5.9%
106 1
 
2.9%
317 10
29.4%
384 3
 
8.8%
391 1
 
2.9%
895 4
 
11.8%
1086 2
 
5.9%
ValueCountFrequency (%)
1268 1
 
2.9%
1086 2
 
5.9%
895 4
 
11.8%
391 1
 
2.9%
384 3
 
8.8%
317 10
29.4%
106 1
 
2.9%
105 2
 
5.9%
86 4
 
11.8%
52 2
 
5.9%

항공기(B787_228톤)(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean629.32353
Minimum43
Maximum1838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:18:51.561885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q1327
median493
Q3598
95-th percentile1677
Maximum1838
Range1795
Interquartile range (IQR)271

Descriptive statistics

Standard deviation539.62651
Coefficient of variation (CV)0.85747074
Kurtosis-0.23154547
Mean629.32353
Median Absolute Deviation (MAD)166
Skewness0.99821138
Sum21397
Variance291196.77
MonotonicityNot monotonic
2023-12-12T13:18:51.698935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
493 10
29.4%
1382 4
 
11.8%
43 4
 
11.8%
327 4
 
11.8%
598 3
 
8.8%
1677 2
 
5.9%
52 2
 
5.9%
396 2
 
5.9%
1838 1
 
2.9%
106 1
 
2.9%
ValueCountFrequency (%)
43 4
 
11.8%
52 2
 
5.9%
106 1
 
2.9%
327 4
 
11.8%
396 2
 
5.9%
493 10
29.4%
598 3
 
8.8%
1382 4
 
11.8%
1471 1
 
2.9%
1677 2
 
5.9%
ValueCountFrequency (%)
1838 1
 
2.9%
1677 2
 
5.9%
1471 1
 
2.9%
1382 4
 
11.8%
598 3
 
8.8%
493 10
29.4%
396 2
 
5.9%
327 4
 
11.8%
106 1
 
2.9%
52 2
 
5.9%

항공기(A330_246톤)(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean646.82353
Minimum43
Maximum1979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:18:51.849306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q1238
median537
Q3651
95-th percentile1824
Maximum1979
Range1936
Interquartile range (IQR)413

Descriptive statistics

Standard deviation582.68347
Coefficient of variation (CV)0.9008384
Kurtosis-0.076029328
Mean646.82353
Median Absolute Deviation (MAD)299
Skewness1.0422192
Sum21992
Variance339520.03
MonotonicityNot monotonic
2023-12-12T13:18:52.005833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
537 10
29.4%
1503 4
 
11.8%
43 4
 
11.8%
238 4
 
11.8%
651 3
 
8.8%
1824 2
 
5.9%
52 2
 
5.9%
289 2
 
5.9%
1979 1
 
2.9%
106 1
 
2.9%
ValueCountFrequency (%)
43 4
 
11.8%
52 2
 
5.9%
106 1
 
2.9%
238 4
 
11.8%
289 2
 
5.9%
537 10
29.4%
651 3
 
8.8%
1118 1
 
2.9%
1503 4
 
11.8%
1824 2
 
5.9%
ValueCountFrequency (%)
1979 1
 
2.9%
1824 2
 
5.9%
1503 4
 
11.8%
1118 1
 
2.9%
651 3
 
8.8%
537 10
29.4%
289 2
 
5.9%
238 4
 
11.8%
106 1
 
2.9%
52 2
 
5.9%

항공기(B777_299톤)(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean754.32353
Minimum43
Maximum2392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:18:52.155704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile43
Q1162
median667
Q3809
95-th percentile2259
Maximum2392
Range2349
Interquartile range (IQR)647

Descriptive statistics

Standard deviation731.37959
Coefficient of variation (CV)0.96958342
Kurtosis-0.01317155
Mean754.32353
Median Absolute Deviation (MAD)505
Skewness1.0728752
Sum25647
Variance534916.1
MonotonicityNot monotonic
2023-12-12T13:18:52.309348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
667 10
29.4%
1861 4
 
11.8%
43 4
 
11.8%
162 4
 
11.8%
809 3
 
8.8%
2259 2
 
5.9%
52 2
 
5.9%
196 2
 
5.9%
2392 1
 
2.9%
106 1
 
2.9%
ValueCountFrequency (%)
43 4
 
11.8%
52 2
 
5.9%
106 1
 
2.9%
162 4
 
11.8%
196 2
 
5.9%
667 10
29.4%
774 1
 
2.9%
809 3
 
8.8%
1861 4
 
11.8%
2259 2
 
5.9%
ValueCountFrequency (%)
2392 1
 
2.9%
2259 2
 
5.9%
1861 4
 
11.8%
809 3
 
8.8%
774 1
 
2.9%
667 10
29.4%
196 2
 
5.9%
162 4
 
11.8%
106 1
 
2.9%
52 2
 
5.9%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
부가세 포함
34 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부가세 포함
2nd row부가세 포함
3rd row부가세 포함
4th row부가세 포함
5th row부가세 포함

Common Values

ValueCountFrequency (%)
부가세 포함 34
100.0%

Length

2023-12-12T13:18:52.478156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:18:52.616843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부가세 34
50.0%
포함 34
50.0%

Interactions

2023-12-12T13:18:48.698427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:45.407629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.090495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.789737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.411933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.075604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.785848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:45.511519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.207890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.884553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.516109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.189009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.882801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:45.634027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.315248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.978633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.645489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.305979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.965141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:45.748599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.433309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.071954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.741757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.424736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:49.067924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:45.866831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.560169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.175619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.847745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.522367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:49.188245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:45.975137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:46.688036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.296483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:47.980490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:48.614324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:18:52.715832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항국제_국내선 여부사용료항공기(A320_74톤)(천원)항공기(B737_79톤)(천원)항공기(B767_156톤)(천원)항공기(B787_228톤)(천원)항공기(A330_246톤)(천원)항공기(B777_299톤)(천원)
공항1.0000.0000.0000.0000.0000.0000.0000.0000.000
국제_국내선 여부0.0001.0000.4000.6790.6790.8031.0001.0000.820
사용료0.0000.4001.0000.8330.8330.6470.9991.0000.659
항공기(A320_74톤)(천원)0.0000.6790.8331.0001.0000.9450.8800.9850.865
항공기(B737_79톤)(천원)0.0000.6790.8331.0001.0000.9450.8800.9850.865
항공기(B767_156톤)(천원)0.0000.8030.6470.9450.9451.0000.8720.8940.989
항공기(B787_228톤)(천원)0.0001.0000.9990.8800.8800.8721.0001.0000.952
항공기(A330_246톤)(천원)0.0001.0001.0000.9850.9850.8941.0001.0001.000
항공기(B777_299톤)(천원)0.0000.8200.6590.8650.8650.9890.9521.0001.000
2023-12-12T13:18:52.885272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국제_국내선 여부사용료공항
국제_국내선 여부1.0000.6200.000
사용료0.6201.0000.000
공항0.0000.0001.000
2023-12-12T13:18:52.995453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항공기(A320_74톤)(천원)항공기(B737_79톤)(천원)항공기(B767_156톤)(천원)항공기(B787_228톤)(천원)항공기(A330_246톤)(천원)항공기(B777_299톤)(천원)공항국제_국내선 여부사용료
항공기(A320_74톤)(천원)1.0001.0000.7260.7640.7580.7430.0000.6710.746
항공기(B737_79톤)(천원)1.0001.0000.7260.7640.7580.7430.0000.6710.746
항공기(B767_156톤)(천원)0.7260.7261.0000.9900.9930.9910.0000.8870.591
항공기(B787_228톤)(천원)0.7640.7640.9901.0000.9970.9910.0000.9350.906
항공기(A330_246톤)(천원)0.7580.7580.9930.9971.0000.9980.0000.9190.933
항공기(B777_299톤)(천원)0.7430.7430.9910.9910.9981.0000.0000.9000.606
공항0.0000.0000.0000.0000.0000.0001.0000.0000.000
국제_국내선 여부0.6710.6710.8870.9350.9190.9000.0001.0000.620
사용료0.7460.7460.5910.9060.9330.6060.0000.6201.000

Missing values

2023-12-12T13:18:49.339790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:18:49.535543image/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

공항국제_국내선 여부사용료항공기(A320_74톤)(천원)항공기(B737_79톤)(천원)항공기(B767_156톤)(천원)항공기(B787_228톤)(천원)항공기(A330_246톤)(천원)항공기(B777_299톤)(천원)비고
0김포국제선착륙료6076481268183819792392부가세 포함
1김해국제선착륙료4494831086167718242259부가세 포함
2제주국제선착륙료4494831086167718242259부가세 포함
3대구국제선착륙료370398895138215031861부가세 포함
4청주국제선착륙료370398895138215031861부가세 포함
5양양국제선착륙료370398895138215031861부가세 포함
6무안국제선착륙료370398895138215031861부가세 포함
7김포국내선착륙료155168384598651809부가세 포함
8김해국내선착륙료155168384598651809부가세 포함
9제주국내선착륙료155168384598651809부가세 포함
공항국제_국내선 여부사용료항공기(A320_74톤)(천원)항공기(B737_79톤)(천원)항공기(B767_156톤)(천원)항공기(B787_228톤)(천원)항공기(A330_246톤)(천원)항공기(B777_299톤)(천원)비고
24청주국제선조명료434343434343부가세 포함
25양양국제선조명료434343434343부가세 포함
26무안국제선조명료434343434343부가세 포함
27김포국제선정류료80980939114711118774부가세 포함
28김해국제선정류료205205105396289196부가세 포함
29제주국제선정류료205205105396289196부가세 포함
30대구국제선정류료16916986327238162부가세 포함
31청주국제선정류료16916986327238162부가세 포함
32양양국제선정류료16916986327238162부가세 포함
33무안국제선정류료16916986327238162부가세 포함