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

Numeric6
Categorical4

Dataset

Description공항별 화물실적 통계 서비스에 대한 데이터로 연도, 공항, 노선, 화물, 수하물, 메일 등
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2630

Alerts

연도 has constant value ""Constant
출발 화물 is highly overall correlated with 도착 화물 and 4 other fieldsHigh correlation
도착 화물 is highly overall correlated with 출발 화물 and 4 other fieldsHigh correlation
출발 수하물 is highly overall correlated with 출발 화물 and 4 other fieldsHigh correlation
도착 수하물 is highly overall correlated with 출발 화물 and 4 other fieldsHigh correlation
출발 메일 is highly overall correlated with 출발 화물 and 4 other fieldsHigh correlation
도착 메일 is highly overall correlated with 출발 화물 and 4 other fieldsHigh correlation
출발 화물 has 6 (13.0%) zerosZeros
도착 화물 has 27 (58.7%) zerosZeros
출발 수하물 has 31 (67.4%) zerosZeros
도착 수하물 has 5 (10.9%) zerosZeros
출발 메일 has 38 (82.6%) zerosZeros
도착 메일 has 39 (84.8%) zerosZeros

Reproduction

Analysis started2024-01-09 23:09:33.504092
Analysis finished2024-01-09 23:09:37.721752
Duration4.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

출발 화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4287.0565
Minimum0
Maximum80731.2
Zeros6
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T08:09:37.784427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.375
median66.5
Q3933.8
95-th percentile23716.5
Maximum80731.2
Range80731.2
Interquartile range (IQR)922.425

Descriptive statistics

Standard deviation14150.889
Coefficient of variation (CV)3.3008404
Kurtosis21.016806
Mean4287.0565
Median Absolute Deviation (MAD)66.5
Skewness4.459205
Sum197204.6
Variance2.0024767 × 108
MonotonicityNot monotonic
2024-01-10T08:09:37.906397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 6
 
13.0%
17.0 1
 
2.2%
340.5 1
 
2.2%
640.6 1
 
2.2%
939.7 1
 
2.2%
3364.5 1
 
2.2%
90.8 1
 
2.2%
52.0 1
 
2.2%
67.2 1
 
2.2%
27.1 1
 
2.2%
Other values (31) 31
67.4%
ValueCountFrequency (%)
0.0 6
13.0%
1.7 1
 
2.2%
1.9 1
 
2.2%
2.5 1
 
2.2%
5.9 1
 
2.2%
7.2 1
 
2.2%
11.0 1
 
2.2%
12.5 1
 
2.2%
13.8 1
 
2.2%
17.0 1
 
2.2%
ValueCountFrequency (%)
80731.2 1
2.2%
48288.0 1
2.2%
28609.6 1
2.2%
9037.2 1
2.2%
6175.8 1
2.2%
5255.6 1
2.2%
3364.5 1
2.2%
3214.4 1
2.2%
2923.0 1
2.2%
2496.7 1
2.2%

도착 화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22744.557
Minimum0
Maximum775488.4
Zeros27
Zeros (%)58.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T08:09:38.014665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.15
95-th percentile21338
Maximum775488.4
Range775488.4
Interquartile range (IQR)14.15

Descriptive statistics

Standard deviation118273.34
Coefficient of variation (CV)5.2000723
Kurtosis38.676325
Mean22744.557
Median Absolute Deviation (MAD)0
Skewness6.1003581
Sum1046249.6
Variance1.3988583 × 1010
MonotonicityNot monotonic
2024-01-10T08:09:38.119485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.0 27
58.7%
0.1 2
 
4.3%
0.3 2
 
4.3%
38.6 1
 
2.2%
739.8 1
 
2.2%
2.0 1
 
2.2%
236.2 1
 
2.2%
19.2 1
 
2.2%
396.5 1
 
2.2%
226012.8 1
 
2.2%
Other values (8) 8
 
17.4%
ValueCountFrequency (%)
0.0 27
58.7%
0.1 2
 
4.3%
0.3 2
 
4.3%
0.5 1
 
2.2%
1.3 1
 
2.2%
2.0 1
 
2.2%
18.2 1
 
2.2%
19.2 1
 
2.2%
38.6 1
 
2.2%
141.0 1
 
2.2%
ValueCountFrequency (%)
775488.4 1
2.2%
226012.8 1
2.2%
22821.6 1
2.2%
16887.2 1
2.2%
3445.5 1
2.2%
739.8 1
2.2%
396.5 1
2.2%
236.2 1
2.2%
141.0 1
2.2%
38.6 1
2.2%

출발 수하물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22447.759
Minimum0
Maximum734301.9
Zeros31
Zeros (%)67.4%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T08:09:38.218657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.325
95-th percentile21777.8
Maximum734301.9
Range734301.9
Interquartile range (IQR)0.325

Descriptive statistics

Standard deviation113634.61
Coefficient of variation (CV)5.0621805
Kurtosis36.330377
Mean22447.759
Median Absolute Deviation (MAD)0
Skewness5.899044
Sum1032596.9
Variance1.2912824 × 1010
MonotonicityNot monotonic
2024-01-10T08:09:38.316782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 31
67.4%
0.1 3
 
6.5%
734301.9 1
 
2.2%
15155.0 1
 
2.2%
3879.6 1
 
2.2%
23985.4 1
 
2.2%
333.3 1
 
2.2%
0.4 1
 
2.2%
253449.1 1
 
2.2%
436.7 1
 
2.2%
Other values (4) 4
 
8.7%
ValueCountFrequency (%)
0.0 31
67.4%
0.1 3
 
6.5%
0.4 1
 
2.2%
0.5 1
 
2.2%
119.0 1
 
2.2%
320.1 1
 
2.2%
333.3 1
 
2.2%
436.7 1
 
2.2%
615.6 1
 
2.2%
3879.6 1
 
2.2%
ValueCountFrequency (%)
734301.9 1
2.2%
253449.1 1
2.2%
23985.4 1
2.2%
15155.0 1
2.2%
3879.6 1
2.2%
615.6 1
2.2%
436.7 1
2.2%
333.3 1
2.2%
320.1 1
2.2%
119.0 1
2.2%

도착 수하물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4179.8783
Minimum0
Maximum77327.1
Zeros5
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T08:09:38.428759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.1
median73.35
Q3848.15
95-th percentile26403
Maximum77327.1
Range77327.1
Interquartile range (IQR)838.05

Descriptive statistics

Standard deviation13540.886
Coefficient of variation (CV)3.2395408
Kurtosis20.608774
Mean4179.8783
Median Absolute Deviation (MAD)73.35
Skewness4.3965032
Sum192274.4
Variance1.833556 × 108
MonotonicityNot monotonic
2024-01-10T08:09:38.555625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.0 5
 
10.9%
16.8 1
 
2.2%
284.0 1
 
2.2%
653.2 1
 
2.2%
823.4 1
 
2.2%
3046.7 1
 
2.2%
72.8 1
 
2.2%
59.1 1
 
2.2%
56.7 1
 
2.2%
35.5 1
 
2.2%
Other values (32) 32
69.6%
ValueCountFrequency (%)
0.0 5
10.9%
0.8 1
 
2.2%
2.3 1
 
2.2%
2.8 1
 
2.2%
4.4 1
 
2.2%
5.8 1
 
2.2%
7.9 1
 
2.2%
10.0 1
 
2.2%
10.4 1
 
2.2%
10.9 1
 
2.2%
ValueCountFrequency (%)
77327.1 1
2.2%
43071.2 1
2.2%
32125.3 1
2.2%
9236.1 1
2.2%
5996.7 1
2.2%
4660.8 1
2.2%
3602.5 1
2.2%
3191.7 1
2.2%
3046.7 1
2.2%
2756.1 1
2.2%

출발 메일
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.51522
Minimum0
Maximum11747.7
Zeros38
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T08:09:38.659427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.95
Maximum11747.7
Range11747.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1787.4683
Coefficient of variation (CV)5.4743799
Kurtosis39.302886
Mean326.51522
Median Absolute Deviation (MAD)0
Skewness6.1577285
Sum15019.7
Variance3195043.1
MonotonicityNot monotonic
2024-01-10T08:09:38.755559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 38
82.6%
0.6 2
 
4.3%
11747.7 1
 
2.2%
2.2 1
 
2.2%
1.2 1
 
2.2%
0.3 1
 
2.2%
0.2 1
 
2.2%
3266.9 1
 
2.2%
ValueCountFrequency (%)
0.0 38
82.6%
0.2 1
 
2.2%
0.3 1
 
2.2%
0.6 2
 
4.3%
1.2 1
 
2.2%
2.2 1
 
2.2%
3266.9 1
 
2.2%
11747.7 1
 
2.2%
ValueCountFrequency (%)
11747.7 1
 
2.2%
3266.9 1
 
2.2%
2.2 1
 
2.2%
1.2 1
 
2.2%
0.6 2
 
4.3%
0.3 1
 
2.2%
0.2 1
 
2.2%
0.0 38
82.6%

도착 메일
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.09565
Minimum0
Maximum7046.9
Zeros39
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size546.0 B
2024-01-10T08:09:38.842625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60.35
Maximum7046.9
Range7046.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1161.2446
Coefficient of variation (CV)4.9394558
Kurtosis28.828916
Mean235.09565
Median Absolute Deviation (MAD)0
Skewness5.2887159
Sum10814.4
Variance1348489
MonotonicityNot monotonic
2024-01-10T08:09:38.932835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 39
84.8%
7046.9 1
 
2.2%
1.3 1
 
2.2%
3.8 1
 
2.2%
0.6 1
 
2.2%
3682.3 1
 
2.2%
79.2 1
 
2.2%
0.3 1
 
2.2%
ValueCountFrequency (%)
0.0 39
84.8%
0.3 1
 
2.2%
0.6 1
 
2.2%
1.3 1
 
2.2%
3.8 1
 
2.2%
79.2 1
 
2.2%
3682.3 1
 
2.2%
7046.9 1
 
2.2%
ValueCountFrequency (%)
7046.9 1
 
2.2%
3682.3 1
 
2.2%
79.2 1
 
2.2%
3.8 1
 
2.2%
1.3 1
 
2.2%
0.6 1
 
2.2%
0.3 1
 
2.2%
0.0 39
84.8%

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

2024-01-10T08:09:39.034908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:39.118927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내선 30
65.2%
국제선 16
34.8%

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2022
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 46
100.0%

Length

2024-01-10T08:09:39.202119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:39.276412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 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 length11
Median length10
Mean length8.7826087
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

2024-01-10T08:09:39.397073image/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 (20) 52
56.5%

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

2024-01-10T08:09:39.552611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:09:39.661661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 23
50.0%
부정기 23
50.0%

Interactions

2024-01-10T08:09:36.998605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:33.834849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.460078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.062131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.672127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.507140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:37.086004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:33.923967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.574091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.163515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.759091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.594125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:37.169980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.031248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.662336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.269914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.852696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.686952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:37.255884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.139775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.751978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.370683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.957511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.766512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:37.353359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.247474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.858429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.475448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.064158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.845438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:37.436716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.355192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:34.959467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:35.573054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.427108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:09:36.919845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:09:39.720118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출발 화물도착 화물출발 수하물도착 수하물출발 메일도착 메일노선공항정기_부정기여부
출발 화물1.0000.6930.6931.0000.6930.6930.0000.0000.078
도착 화물0.6931.0001.0000.6931.0001.0000.1260.0000.000
출발 수하물0.6931.0001.0000.6931.0001.0000.1260.0000.000
도착 수하물1.0000.6930.6931.0000.6930.6930.0000.0000.078
출발 메일0.6931.0001.0000.6931.0001.0000.1260.0000.000
도착 메일0.6931.0001.0000.6931.0001.0000.1260.0000.000
노선0.0000.1260.1260.0000.1260.1261.0000.0000.000
공항0.0000.0000.0000.0000.0000.0000.0001.0000.000
정기_부정기여부0.0780.0000.0000.0780.0000.0000.0000.0001.000
2024-01-10T08:09:39.825647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선공항정기_부정기여부
노선1.0000.0000.000
공항0.0001.0000.000
정기_부정기여부0.0000.0001.000
2024-01-10T08:09:39.930974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출발 화물도착 화물출발 수하물도착 수하물출발 메일도착 메일노선공항정기_부정기여부
출발 화물1.0000.8240.7470.9870.5870.5330.0000.0000.081
도착 화물0.8241.0000.8630.8180.5400.5990.2040.0000.000
출발 수하물0.7470.8631.0000.7480.5190.6720.2040.0000.000
도착 수하물0.9870.8180.7481.0000.5900.5230.0000.0000.081
출발 메일0.5870.5400.5190.5901.0000.5030.2040.0000.000
도착 메일0.5330.5990.6720.5230.5031.0000.2040.0000.000
노선0.0000.2040.2040.0000.2040.2041.0000.0000.000
공항0.0000.0000.0000.0000.0000.0000.0001.0000.000
정기_부정기여부0.0810.0000.0000.0810.0000.0000.0000.0001.000

Missing values

2024-01-10T08:09:37.547732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:09:37.672431image/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

출발 화물도착 화물출발 수하물도착 수하물출발 메일도착 메일노선연도공항정기_부정기여부
00.00.00.00.00.00.0국내선2022인천 INCHEON정기
180731.2775488.4734301.977327.111747.77046.9국제선2022인천 INCHEON정기
228609.622821.615155.032125.32.21.3국내선2022김포 GIMPO정기
31.70.00.00.80.00.0국제선2022김포 GIMPO정기
49037.23445.53879.69236.11.20.0국내선2022김해 GIMHAE정기
5660.30.10.0797.20.00.0국제선2022김해 GIMHAE정기
648288.016887.223985.443071.20.03.8국내선2022제주 JEJU정기
712.50.00.04.40.00.0국제선2022제주 JEJU정기
83214.4141.0333.33602.50.30.0국내선2022대구 DAEGU정기
931.50.00.073.90.00.0국제선2022대구 DAEGU정기
출발 화물도착 화물출발 수하물도착 수하물출발 메일도착 메일노선연도공항정기_부정기여부
3642.90.00.057.40.00.0국제선2022무안 MUAN부정기
3728.10.00.035.80.00.0국내선2022양양 YANGYANG부정기
3811.00.00.010.00.00.0국제선2022양양 YANGYANG부정기
3943.60.00.047.40.00.0국내선2022광주 GWANGJU부정기
405.90.00.05.80.00.0국내선2022여수 YEOSU부정기
4113.80.00.010.40.00.0국내선2022울산 ULSAN부정기
420.00.00.00.00.00.0국내선2022사천 SACHEON부정기
432.50.00.02.30.00.0국내선2022포항 POHANG부정기
4465.80.00.176.70.00.0국내선2022군산 GUNSAN부정기
451.90.00.02.80.00.0국내선2022원주 WONJU부정기