Overview

Dataset statistics

Number of variables10
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory89.9 B

Variable types

Categorical4
Numeric6

Dataset

Description한국공항공사의 공항별 화물실적 통계 서비스에 대한 데이터로 연도, 공항, 노선, 화물, 수하물, 메일 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15002612/fileData.do

Alerts

연도 has constant value ""Constant
도착 화물 is highly overall correlated with 출발 화물 and 4 other fieldsHigh correlation
출발 화물 is highly overall correlated with 도착 화물 and 4 other fieldsHigh correlation
도착 수하물 is highly overall correlated with 도착 화물 and 4 other fieldsHigh correlation
출발 수하물 is highly overall correlated with 도착 화물 and 4 other fieldsHigh correlation
도착 메일 is highly overall correlated with 도착 화물 and 4 other fieldsHigh correlation
출발 메일 is highly overall correlated with 도착 화물 and 4 other fieldsHigh correlation
도착 화물 has 25 (54.3%) zerosZeros
출발 화물 has 29 (63.0%) zerosZeros
도착 수하물 has 6 (13.0%) zerosZeros
출발 수하물 has 7 (15.2%) zerosZeros
도착 메일 has 39 (84.8%) zerosZeros
출발 메일 has 37 (80.4%) zerosZeros

Reproduction

Analysis started2023-12-12 10:58:34.696230
Analysis finished2023-12-12 10:58:41.870058
Duration7.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 46
100.0%

Length

2023-12-12T19:58:41.949643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:58:42.081202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 46
100.0%

공항
Categorical

Distinct15
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size500.0 B
인천 INCHEON
4 
김포 GIMPO
4 
김해 GIMHAE
4 
제주 JEJU
4 
대구 DAEGU
4 
Other values (10)
26 

Length

Max length20
Median length11
Mean length9.2608696
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천 INCHEON
2nd row인천 INCHEON
3rd row김포 GIMPO
4th row김포 GIMPO
5th row김해 GIMHAE

Common Values

ValueCountFrequency (%)
인천 INCHEON 4
 
8.7%
김포 GIMPO 4
 
8.7%
김해 GIMHAE 4
 
8.7%
제주 JEJU 4
 
8.7%
대구 DAEGU 4
 
8.7%
청주 CHENGJU 4
 
8.7%
무안 MUAN 4
 
8.7%
양양 YANGYANG 4
 
8.7%
광주 GWANGJU 2
 
4.3%
여수 YEOSU 2
 
4.3%
Other values (5) 10
21.7%

Length

2023-12-12T19:58:42.241728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천 4
 
4.3%
daegu 4
 
4.3%
incheon 4
 
4.3%
양양 4
 
4.3%
muan 4
 
4.3%
무안 4
 
4.3%
chengju 4
 
4.3%
청주 4
 
4.3%
yangyang 4
 
4.3%
대구 4
 
4.3%
Other values (21) 54
57.4%

노선
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
국내선
30 
국제선
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국내선 30
65.2%
국제선 16
34.8%

Length

2023-12-12T19:58:42.449864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:58:42.600987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내선 30
65.2%
국제선 16
34.8%

정기_부정기여부
Categorical

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
정기
23 
부정기
23 

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 23
50.0%
부정기 23
50.0%

Length

2023-12-12T19:58:42.761048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:58:42.915189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 23
50.0%
부정기 23
50.0%

도착 화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17870.007
Minimum0
Maximum716354.9
Zeros25
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T19:58:43.067390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q332.5
95-th percentile16622
Maximum716354.9
Range716354.9
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation105836.09
Coefficient of variation (CV)5.9225545
Kurtosis44.965873
Mean17870.007
Median Absolute Deviation (MAD)0
Skewness6.6765977
Sum822020.3
Variance1.1201277 × 1010
MonotonicityNot monotonic
2023-12-12T19:58:43.263953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 25
54.3%
0.1 2
 
4.3%
7.1 1
 
2.2%
13.7 1
 
2.2%
92.3 1
 
2.2%
27.1 1
 
2.2%
67.2 1
 
2.2%
70.9 1
 
2.2%
134.5 1
 
2.2%
71326.8 1
 
2.2%
Other values (11) 11
23.9%
ValueCountFrequency (%)
0.0 25
54.3%
0.1 2
 
4.3%
0.3 1
 
2.2%
1.4 1
 
2.2%
7.1 1
 
2.2%
11.3 1
 
2.2%
13.7 1
 
2.2%
26.5 1
 
2.2%
27.1 1
 
2.2%
34.3 1
 
2.2%
ValueCountFrequency (%)
716354.9 1
2.2%
71326.8 1
2.2%
17783.6 1
2.2%
13137.2 1
2.2%
2469.0 1
2.2%
397.3 1
2.2%
134.5 1
2.2%
92.3 1
2.2%
70.9 1
2.2%
67.2 1
2.2%

출발 화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16969.674
Minimum0
Maximum688547.6
Zeros29
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T19:58:43.488189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q371.1
95-th percentile16824.85
Maximum688547.6
Range688547.6
Interquartile range (IQR)71.1

Descriptive statistics

Standard deviation101570.65
Coefficient of variation (CV)5.9854211
Kurtosis45.323096
Mean16969.674
Median Absolute Deviation (MAD)0
Skewness6.7121635
Sum780605
Variance1.0316596 × 1010
MonotonicityNot monotonic
2023-12-12T19:58:43.703295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 29
63.0%
0.1 2
 
4.3%
221.1 1
 
2.2%
7.2 1
 
2.2%
201.2 1
 
2.2%
85.9 1
 
2.2%
558.8 1
 
2.2%
26.7 1
 
2.2%
53771.5 1
 
2.2%
0.3 1
 
2.2%
Other values (7) 7
 
15.2%
ValueCountFrequency (%)
0.0 29
63.0%
0.1 2
 
4.3%
0.3 1
 
2.2%
7.2 1
 
2.2%
26.7 1
 
2.2%
85.9 1
 
2.2%
155.3 1
 
2.2%
201.2 1
 
2.2%
219.8 1
 
2.2%
221.1 1
 
2.2%
ValueCountFrequency (%)
688547.6 1
2.2%
53771.5 1
2.2%
18457.9 1
2.2%
11925.7 1
2.2%
3539.8 1
2.2%
2886.0 1
2.2%
558.8 1
2.2%
221.1 1
2.2%
219.8 1
2.2%
201.2 1
2.2%

도착 수하물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7693.9783
Minimum0
Maximum223403.8
Zeros6
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T19:58:43.945930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147.2
median333.6
Q32036.05
95-th percentile23818.65
Maximum223403.8
Range223403.8
Interquartile range (IQR)1988.85

Descriptive statistics

Standard deviation33257.337
Coefficient of variation (CV)4.322515
Kurtosis41.750312
Mean7693.9783
Median Absolute Deviation (MAD)333.6
Skewness6.3500888
Sum353923
Variance1.1060505 × 109
MonotonicityNot monotonic
2023-12-12T19:58:44.178426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 6
 
13.0%
1727.2 1
 
2.2%
49.0 1
 
2.2%
6281.1 1
 
2.2%
781.9 1
 
2.2%
1675.8 1
 
2.2%
380.3 1
 
2.2%
1177.3 1
 
2.2%
740.7 1
 
2.2%
236.7 1
 
2.2%
Other values (31) 31
67.4%
ValueCountFrequency (%)
0.0 6
13.0%
0.9 1
 
2.2%
1.0 1
 
2.2%
1.1 1
 
2.2%
4.0 1
 
2.2%
12.1 1
 
2.2%
46.6 1
 
2.2%
49.0 1
 
2.2%
55.8 1
 
2.2%
59.4 1
 
2.2%
ValueCountFrequency (%)
223403.8 1
2.2%
36687.2 1
2.2%
25602.5 1
2.2%
18467.1 1
2.2%
9339.1 1
2.2%
7313.8 1
2.2%
6281.1 1
2.2%
4780.5 1
2.2%
3291.6 1
2.2%
2823.1 1
2.2%

출발 수하물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7668.0283
Minimum0
Maximum224795.8
Zeros7
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T19:58:44.385700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.075
median311.05
Q32117.25
95-th percentile21881.3
Maximum224795.8
Range224795.8
Interquartile range (IQR)2099.175

Descriptive statistics

Standard deviation33497.277
Coefficient of variation (CV)4.3684342
Kurtosis41.642446
Mean7668.0283
Median Absolute Deviation (MAD)311.05
Skewness6.3423385
Sum352729.3
Variance1.1220675 × 109
MonotonicityNot monotonic
2023-12-12T19:58:44.595801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 7
 
15.2%
1676.1 1
 
2.2%
35.7 1
 
2.2%
5700.8 1
 
2.2%
604.4 1
 
2.2%
1572.4 1
 
2.2%
353.3 1
 
2.2%
1059.5 1
 
2.2%
1016.1 1
 
2.2%
286.6 1
 
2.2%
Other values (30) 30
65.2%
ValueCountFrequency (%)
0.0 7
15.2%
0.2 1
 
2.2%
1.2 1
 
2.2%
3.4 1
 
2.2%
4.5 1
 
2.2%
12.2 1
 
2.2%
35.7 1
 
2.2%
41.6 1
 
2.2%
45.1 1
 
2.2%
109.0 1
 
2.2%
ValueCountFrequency (%)
224795.8 1
2.2%
40051.2 1
2.2%
23671.5 1
2.2%
16510.7 1
2.2%
9083.5 1
2.2%
7566.2 1
2.2%
5700.8 1
2.2%
4280.4 1
2.2%
2976.9 1
2.2%
2603.9 1
2.2%

도착 메일
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.22609
Minimum0
Maximum6485.2
Zeros39
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T19:58:44.772764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile169.525
Maximum6485.2
Range6485.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation959.61579
Coefficient of variation (CV)5.9152989
Kurtosis44.65825
Mean162.22609
Median Absolute Deviation (MAD)0
Skewness6.6462591
Sum7462.4
Variance920862.46
MonotonicityNot monotonic
2023-12-12T19:58:44.926116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 39
84.8%
6485.2 1
 
2.2%
1.1 1
 
2.2%
221.6 1
 
2.2%
0.6 1
 
2.2%
4.2 1
 
2.2%
736.4 1
 
2.2%
13.3 1
 
2.2%
ValueCountFrequency (%)
0.0 39
84.8%
0.6 1
 
2.2%
1.1 1
 
2.2%
4.2 1
 
2.2%
13.3 1
 
2.2%
221.6 1
 
2.2%
736.4 1
 
2.2%
6485.2 1
 
2.2%
ValueCountFrequency (%)
6485.2 1
 
2.2%
736.4 1
 
2.2%
221.6 1
 
2.2%
13.3 1
 
2.2%
4.2 1
 
2.2%
1.1 1
 
2.2%
0.6 1
 
2.2%
0.0 39
84.8%

출발 메일
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.25
Minimum0
Maximum12632.8
Zeros37
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T19:58:45.213209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.925
Maximum12632.8
Range12632.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1862.5731
Coefficient of variation (CV)6.4841537
Kurtosis45.802662
Mean287.25
Median Absolute Deviation (MAD)0
Skewness6.7615929
Sum13213.5
Variance3469178.7
MonotonicityNot monotonic
2023-12-12T19:58:45.555266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 37
80.4%
0.3 2
 
4.3%
1.2 2
 
4.3%
12632.8 1
 
2.2%
3.6 1
 
2.2%
6.7 1
 
2.2%
0.9 1
 
2.2%
566.5 1
 
2.2%
ValueCountFrequency (%)
0.0 37
80.4%
0.3 2
 
4.3%
0.9 1
 
2.2%
1.2 2
 
4.3%
3.6 1
 
2.2%
6.7 1
 
2.2%
566.5 1
 
2.2%
12632.8 1
 
2.2%
ValueCountFrequency (%)
12632.8 1
 
2.2%
566.5 1
 
2.2%
6.7 1
 
2.2%
3.6 1
 
2.2%
1.2 2
 
4.3%
0.9 1
 
2.2%
0.3 2
 
4.3%
0.0 37
80.4%

Interactions

2023-12-12T19:58:40.544338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:35.232785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:36.142926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.031638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.001052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.920309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:40.736403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:35.401250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:36.287977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.206167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.163789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:39.578944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:40.898308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:35.550788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:36.432008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.376485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.320641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:39.744630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:41.056948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:35.674003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:36.586660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.517079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.458092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:39.953481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:41.197340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:35.841140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:36.737376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.684681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.618283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:40.148691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:41.353549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:35.984908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:36.893726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:37.850322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:38.771452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:58:40.345567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:58:45.836131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항노선정기_부정기여부도착 화물출발 화물도착 수하물출발 수하물도착 메일출발 메일
공항1.0000.0000.0000.0000.0000.0000.0000.0000.000
노선0.0001.0000.0000.0000.0000.0880.0880.1260.000
정기_부정기여부0.0000.0001.0000.0000.0000.1000.1000.0000.000
도착 화물0.0000.0000.0001.0000.6741.0001.0001.0000.674
출발 화물0.0000.0000.0000.6741.0001.0001.0001.0000.674
도착 수하물0.0000.0880.1001.0001.0001.0001.0000.9361.000
출발 수하물0.0000.0880.1001.0001.0001.0001.0000.9361.000
도착 메일0.0000.1260.0001.0001.0000.9360.9361.0001.000
출발 메일0.0000.0000.0000.6740.6741.0001.0001.0001.000
2023-12-12T19:58:46.045747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정기_부정기여부공항노선
정기_부정기여부1.0000.0000.000
공항0.0001.0000.000
노선0.0000.0001.000
2023-12-12T19:58:46.248051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도착 화물출발 화물도착 수하물출발 수하물도착 메일출발 메일공항노선정기_부정기여부
도착 화물1.0000.7830.7580.7570.6690.6620.0000.0000.000
출발 화물0.7831.0000.6810.6860.7190.5620.0000.0000.000
도착 수하물0.7580.6811.0000.9920.5750.6230.0000.1410.161
출발 수하물0.7570.6860.9921.0000.5750.6200.0000.1410.161
도착 메일0.6690.7190.5750.5751.0000.7360.0000.2040.000
출발 메일0.6620.5620.6230.6200.7361.0000.0000.0000.000
공항0.0000.0000.0000.0000.0000.0001.0000.0000.000
노선0.0000.0000.1410.1410.2040.0000.0001.0000.000
정기_부정기여부0.0000.0000.1610.1610.0000.0000.0000.0001.000

Missing values

2023-12-12T19:58:41.566830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:58:41.798584image/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

연도공항노선정기_부정기여부도착 화물출발 화물도착 수하물출발 수하물도착 메일출발 메일
02023인천 INCHEON국내선정기0.00.01727.21676.10.00.0
12023인천 INCHEON국제선정기716354.9688547.6223403.8224795.86485.212632.8
22023김포 GIMPO국내선정기17783.611925.725602.523671.51.13.6
32023김포 GIMPO국제선정기397.33539.87313.87566.2221.66.7
42023김해 GIMHAE국내선정기2469.02886.09339.19083.50.60.3
52023김해 GIMHAE국제선정기34.3155.318467.116510.70.00.3
62023제주 JEJU국내선정기13137.218457.936687.240051.24.20.9
72023제주 JEJU국제선정기0.00.12139.02558.10.00.0
82023대구 DAEGU국내선정기64.7219.83291.62976.90.00.0
92023대구 DAEGU국제선정기0.30.02595.92264.30.01.2
연도공항노선정기_부정기여부도착 화물출발 화물도착 수하물출발 수하물도착 메일출발 메일
362023무안 MUAN국제선부정기0.00.0643.7571.20.00.0
372023양양 YANGYANG국내선부정기0.00.00.90.20.00.0
382023양양 YANGYANG국제선부정기0.00.012.112.20.00.0
392023광주 GWANGJU국내선부정기0.00.01.13.40.00.0
402023여수 YEOSU국내선부정기0.00.04.04.50.00.0
412023울산 ULSAN국내선부정기0.00.01.01.20.00.0
422023사천 SACHEON국내선부정기0.00.00.00.00.00.0
432023포항경주 POHANG GYEONGJU국내선부정기0.00.00.00.00.00.0
442023군산 GUNSAN국내선부정기0.00.00.00.00.00.0
452023원주 WONJU국내선부정기0.00.00.00.00.00.0