Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory918.0 KiB
Average record size in memory94.0 B

Variable types

DateTime1
Categorical2
Text1
Numeric6

Dataset

Description항공, 선박, 육로 포함 국가별, 일자별 검역정보 (검역일, 검역소, 구분, 출발국가, 운송수단 검역수, 내국인 승무원수, 외국인 승무원수, 내국인 승객수, 외국인 승객수, 환승객수)
Author질병관리청
URLhttps://www.data.go.kr/data/3074708/fileData.do

Alerts

검역소 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 검역소High correlation
운송수단 검역수 is highly overall correlated with 내국인 승무원수 and 1 other fieldsHigh correlation
내국인 승무원수 is highly overall correlated with 운송수단 검역수 and 3 other fieldsHigh correlation
외국인 승무원수 is highly overall correlated with 운송수단 검역수High correlation
내국인 승객수 is highly overall correlated with 내국인 승무원수 and 2 other fieldsHigh correlation
외국인 승객수 is highly overall correlated with 내국인 승무원수 and 2 other fieldsHigh correlation
환승객수 is highly overall correlated with 내국인 승무원수 and 2 other fieldsHigh correlation
내국인 승무원수 has 3926 (39.3%) zerosZeros
외국인 승무원수 has 1199 (12.0%) zerosZeros
내국인 승객수 has 6126 (61.3%) zerosZeros
외국인 승객수 has 6157 (61.6%) zerosZeros
환승객수 has 6959 (69.6%) zerosZeros

Reproduction

Analysis started2023-12-12 09:46:36.138632
Analysis finished2023-12-12 09:46:41.626846
Duration5.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검역일
Date

Distinct365
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-12-31 00:00:00
2023-12-12T18:46:41.713911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:41.869142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검역소
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
인천공항
3863 
여수
1001 
부산
981 
평택
755 
울산
750 
Other values (8)
2650 

Length

Max length4
Median length2
Mean length2.8518
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row울산
2nd row울산
3rd row마산
4th row부산
5th row평택

Common Values

ValueCountFrequency (%)
인천공항 3863
38.6%
여수 1001
 
10.0%
부산 981
 
9.8%
평택 755
 
7.5%
울산 750
 
7.5%
인천 589
 
5.9%
김해공항 396
 
4.0%
마산 388
 
3.9%
포항 384
 
3.8%
군산 308
 
3.1%
Other values (3) 585
 
5.9%

Length

2023-12-12T18:46:42.081247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천공항 3863
38.6%
여수 1001
 
10.0%
부산 981
 
9.8%
평택 755
 
7.5%
울산 750
 
7.5%
인천 589
 
5.9%
김해공항 396
 
4.0%
마산 388
 
3.9%
포항 384
 
3.8%
군산 308
 
3.1%
Other values (3) 585
 
5.9%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
선박
5463 
항공
4537 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
선박 5463
54.6%
항공 4537
45.4%

Length

2023-12-12T18:46:42.237551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:46:42.352761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선박 5463
54.6%
항공 4537
45.4%

출발국가
Text

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T18:46:42.632701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length3.1414
Min length1

Characters and Unicode

Total characters31414
Distinct characters153
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st row프랑스(레위니옹섬)
2nd row일본
3rd row중국
4th row러시아연방
5th row인도
ValueCountFrequency (%)
일본 1053
 
10.3%
중국 1040
 
10.2%
러시아연방 478
 
4.7%
대만 455
 
4.4%
베트남 441
 
4.3%
미국 436
 
4.3%
싱가폴 430
 
4.2%
호주 417
 
4.1%
인도네시아 348
 
3.4%
필리핀 345
 
3.4%
Other values (104) 4784
46.8%
2023-12-12T18:46:43.086704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 2181
 
6.9%
아 1929
 
6.1%
일 1155
 
3.7%
본 1053
 
3.4%
중 1040
 
3.3%
시 948
 
3.0%
이 746
 
2.4%
트 690
 
2.2%
리 678
 
2.2%
미 638
 
2.0%
Other values (143) 20356
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31177
99.2%
Space Separator 227
 
0.7%
Close Punctuation 5
 
< 0.1%
Open Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 2181
 
7.0%
아 1929
 
6.2%
일 1155
 
3.7%
본 1053
 
3.4%
중 1040
 
3.3%
시 948
 
3.0%
이 746
 
2.4%
트 690
 
2.2%
리 678
 
2.2%
미 638
 
2.0%
Other values (140) 20119
64.5%
Space Separator
ValueCountFrequency (%)
227
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31177
99.2%
Common 237
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 2181
 
7.0%
아 1929
 
6.2%
일 1155
 
3.7%
본 1053
 
3.4%
중 1040
 
3.3%
시 948
 
3.0%
이 746
 
2.4%
트 690
 
2.2%
리 678
 
2.2%
미 638
 
2.0%
Other values (140) 20119
64.5%
Common
ValueCountFrequency (%)
227
95.8%
) 5
 
2.1%
( 5
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31177
99.2%
ASCII 237
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 2181
 
7.0%
아 1929
 
6.2%
일 1155
 
3.7%
본 1053
 
3.4%
중 1040
 
3.3%
시 948
 
3.0%
이 746
 
2.4%
트 690
 
2.2%
리 678
 
2.2%
미 638
 
2.0%
Other values (140) 20119
64.5%
ASCII
ValueCountFrequency (%)
227
95.8%
) 5
 
2.1%
( 5
 
2.1%

운송수단 검역수
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0001
Minimum1
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:46:43.591802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile20
Maximum111
Range110
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.8004237
Coefficient of variation (CV)1.9600455
Kurtosis23.855344
Mean5.0001
Median Absolute Deviation (MAD)1
Skewness4.4386582
Sum50001
Variance96.048305
MonotonicityNot monotonic
2023-12-12T18:46:43.772519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4888
48.9%
2 1655
 
16.6%
3 813
 
8.1%
4 372
 
3.7%
5 278
 
2.8%
6 194
 
1.9%
7 180
 
1.8%
8 178
 
1.8%
10 133
 
1.3%
9 132
 
1.3%
Other values (74) 1177
 
11.8%
ValueCountFrequency (%)
1 4888
48.9%
2 1655
 
16.6%
3 813
 
8.1%
4 372
 
3.7%
5 278
 
2.8%
6 194
 
1.9%
7 180
 
1.8%
8 178
 
1.8%
9 132
 
1.3%
10 133
 
1.3%
ValueCountFrequency (%)
111 1
 
< 0.1%
100 1
 
< 0.1%
99 3
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
< 0.1%
80 2
< 0.1%

내국인 승무원수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct308
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.9174
Minimum0
Maximum1072
Zeros3926
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:46:43.946193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q318
95-th percentile80
Maximum1072
Range1072
Interquartile range (IQR)18

Descriptive statistics

Standard deviation53.476632
Coefficient of variation (CV)2.6849203
Kurtosis63.959063
Mean19.9174
Median Absolute Deviation (MAD)4
Skewness6.8450875
Sum199174
Variance2859.7502
MonotonicityNot monotonic
2023-12-12T18:46:44.114388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3926
39.3%
3 384
 
3.8%
4 370
 
3.7%
2 346
 
3.5%
6 316
 
3.2%
5 237
 
2.4%
8 218
 
2.2%
7 193
 
1.9%
10 191
 
1.9%
12 180
 
1.8%
Other values (298) 3639
36.4%
ValueCountFrequency (%)
0 3926
39.3%
1 70
 
0.7%
2 346
 
3.5%
3 384
 
3.8%
4 370
 
3.7%
5 237
 
2.4%
6 316
 
3.2%
7 193
 
1.9%
8 218
 
2.2%
9 134
 
1.3%
ValueCountFrequency (%)
1072 1
< 0.1%
782 1
< 0.1%
708 1
< 0.1%
698 1
< 0.1%
656 1
< 0.1%
647 1
< 0.1%
629 1
< 0.1%
613 1
< 0.1%
608 1
< 0.1%
600 1
< 0.1%

외국인 승무원수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct394
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.1207
Minimum0
Maximum726
Zeros1199
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:46:44.279286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median20
Q336
95-th percentile189.05
Maximum726
Range726
Interquartile range (IQR)26

Descriptive statistics

Standard deviation65.956998
Coefficient of variation (CV)1.6439643
Kurtosis15.819564
Mean40.1207
Median Absolute Deviation (MAD)12
Skewness3.5426356
Sum401207
Variance4350.3256
MonotonicityNot monotonic
2023-12-12T18:46:44.497083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1199
 
12.0%
21 397
 
4.0%
22 378
 
3.8%
20 339
 
3.4%
19 310
 
3.1%
23 291
 
2.9%
18 277
 
2.8%
17 259
 
2.6%
16 255
 
2.5%
13 240
 
2.4%
Other values (384) 6055
60.6%
ValueCountFrequency (%)
0 1199
12.0%
1 205
 
2.1%
2 68
 
0.7%
3 156
 
1.6%
4 118
 
1.2%
5 106
 
1.1%
6 183
 
1.8%
7 108
 
1.1%
8 131
 
1.3%
9 176
 
1.8%
ValueCountFrequency (%)
726 1
< 0.1%
667 1
< 0.1%
653 1
< 0.1%
647 1
< 0.1%
604 1
< 0.1%
598 1
< 0.1%
554 1
< 0.1%
549 1
< 0.1%
525 1
< 0.1%
520 1
< 0.1%

내국인 승객수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1089
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.0584
Minimum0
Maximum13601
Zeros6126
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:46:44.689550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3104
95-th percentile741
Maximum13601
Range13601
Interquartile range (IQR)104

Descriptive statistics

Standard deviation697.99126
Coefficient of variation (CV)3.8550615
Kurtosis127.52126
Mean181.0584
Median Absolute Deviation (MAD)0
Skewness9.6039352
Sum1810584
Variance487191.8
MonotonicityNot monotonic
2023-12-12T18:46:44.862801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6126
61.3%
1 40
 
0.4%
2 33
 
0.3%
3 25
 
0.2%
13 20
 
0.2%
104 20
 
0.2%
123 19
 
0.2%
85 19
 
0.2%
6 18
 
0.2%
90 18
 
0.2%
Other values (1079) 3662
36.6%
ValueCountFrequency (%)
0 6126
61.3%
1 40
 
0.4%
2 33
 
0.3%
3 25
 
0.2%
4 18
 
0.2%
5 17
 
0.2%
6 18
 
0.2%
7 13
 
0.1%
8 15
 
0.1%
9 10
 
0.1%
ValueCountFrequency (%)
13601 1
< 0.1%
13401 1
< 0.1%
12206 1
< 0.1%
12135 1
< 0.1%
12128 1
< 0.1%
12083 1
< 0.1%
11718 1
< 0.1%
11498 1
< 0.1%
11492 1
< 0.1%
10944 1
< 0.1%

외국인 승객수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct832
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.1988
Minimum0
Maximum4724
Zeros6157
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:46:45.043436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360
95-th percentile451
Maximum4724
Range4724
Interquartile range (IQR)60

Descriptive statistics

Standard deviation287.05936
Coefficient of variation (CV)3.0800757
Kurtosis53.054666
Mean93.1988
Median Absolute Deviation (MAD)0
Skewness6.245912
Sum931988
Variance82403.075
MonotonicityNot monotonic
2023-12-12T18:46:45.224521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6157
61.6%
1 110
 
1.1%
2 58
 
0.6%
3 56
 
0.6%
4 41
 
0.4%
6 41
 
0.4%
5 39
 
0.4%
8 34
 
0.3%
20 32
 
0.3%
12 30
 
0.3%
Other values (822) 3402
34.0%
ValueCountFrequency (%)
0 6157
61.6%
1 110
 
1.1%
2 58
 
0.6%
3 56
 
0.6%
4 41
 
0.4%
5 39
 
0.4%
6 41
 
0.4%
7 26
 
0.3%
8 34
 
0.3%
9 18
 
0.2%
ValueCountFrequency (%)
4724 1
< 0.1%
4574 1
< 0.1%
4161 1
< 0.1%
3869 1
< 0.1%
3661 1
< 0.1%
3489 1
< 0.1%
3402 1
< 0.1%
3150 1
< 0.1%
3017 1
< 0.1%
3010 1
< 0.1%

환승객수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct678
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.5625
Minimum0
Maximum7832
Zeros6959
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:46:45.397863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile301
Maximum7832
Range7832
Interquartile range (IQR)6

Descriptive statistics

Standard deviation246.22353
Coefficient of variation (CV)4.2044573
Kurtosis173.6828
Mean58.5625
Median Absolute Deviation (MAD)0
Skewness10.09449
Sum585625
Variance60626.028
MonotonicityNot monotonic
2023-12-12T18:46:45.601687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6959
69.6%
1 132
 
1.3%
2 119
 
1.2%
3 112
 
1.1%
4 93
 
0.9%
6 77
 
0.8%
5 68
 
0.7%
11 54
 
0.5%
7 54
 
0.5%
8 50
 
0.5%
Other values (668) 2282
 
22.8%
ValueCountFrequency (%)
0 6959
69.6%
1 132
 
1.3%
2 119
 
1.2%
3 112
 
1.1%
4 93
 
0.9%
5 68
 
0.7%
6 77
 
0.8%
7 54
 
0.5%
8 50
 
0.5%
9 49
 
0.5%
ValueCountFrequency (%)
7832 1
< 0.1%
4697 1
< 0.1%
4334 1
< 0.1%
4241 1
< 0.1%
3797 1
< 0.1%
3567 1
< 0.1%
3424 1
< 0.1%
3041 1
< 0.1%
2939 1
< 0.1%
2828 1
< 0.1%

Interactions

2023-12-12T18:46:40.702222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:37.408623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.007354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.658340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.318894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.012383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.814305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:37.498092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.119260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.754840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.433346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.112615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.917625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:37.598294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.212607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.852746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.539404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.223466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:41.035692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:37.692536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.326802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.974443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.659935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.341331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:41.149329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:37.779794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.441108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.092343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.795947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.484503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:41.240722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:37.885103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:38.548959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.191334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:39.894413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:46:40.601370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:46:45.710190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검역소구분운송수단 검역수내국인 승무원수외국인 승무원수내국인 승객수외국인 승객수환승객수
검역소1.0000.9550.2450.1820.3170.1420.2050.135
구분0.9551.0000.2530.2050.1960.2420.3160.141
운송수단 검역수0.2450.2531.0000.8060.7460.8800.8360.624
내국인 승무원수0.1820.2050.8061.0000.3390.7820.7160.794
외국인 승무원수0.3170.1960.7460.3391.0000.3500.3770.348
내국인 승객수0.1420.2420.8800.7820.3501.0000.8400.538
외국인 승객수0.2050.3160.8360.7160.3770.8401.0000.577
환승객수0.1350.1410.6240.7940.3480.5380.5771.000
2023-12-12T18:46:45.835573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검역소구분
검역소1.0000.968
구분0.9681.000
2023-12-12T18:46:45.927030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운송수단 검역수내국인 승무원수외국인 승무원수내국인 승객수외국인 승객수환승객수검역소구분
운송수단 검역수1.0000.7020.5840.4080.4170.4610.1060.193
내국인 승무원수0.7021.0000.0990.5630.5400.5690.0790.204
외국인 승무원수0.5840.0991.000-0.137-0.087-0.0290.1360.150
내국인 승객수0.4080.563-0.1371.0000.9330.8130.0590.185
외국인 승객수0.4170.540-0.0870.9331.0000.8320.0860.242
환승객수0.4610.569-0.0290.8130.8321.0000.0630.151
검역소0.1060.0790.1360.0590.0860.0631.0000.968
구분0.1930.2040.1500.1850.2420.1510.9681.000

Missing values

2023-12-12T18:46:41.404678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:46:41.560710image/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

검역일검역소구분출발국가운송수단 검역수내국인 승무원수외국인 승무원수내국인 승객수외국인 승객수환승객수
312942022-12-20울산선박프랑스(레위니옹섬)1024000
97342022-04-29울산선박일본52643000
270982022-11-10마산선박중국4061000
43392022-02-23부산선박러시아연방3066000
131112022-06-08평택선박인도1028000
46082022-02-26여수선박중국715111000
223592022-09-21인천공항항공호주112213810622
28762022-02-04포항선박러시아연방1022000
154982022-07-07부산선박통가1020000
217182022-09-13인천공항항공카자흐스탄202012224250
검역일검역소구분출발국가운송수단 검역수내국인 승무원수외국인 승무원수내국인 승객수외국인 승객수환승객수
213372022-09-09부산선박베트남1022000
251872022-10-21인천공항항공베트남4623512940861272770
95412022-04-27마산선박싱가폴1219000
257502022-10-27울산선박우르과이1023000
158722022-07-11인천선박중국1014205000
14712022-01-18인천선박중국1028190000
264362022-11-03인천공항항공라오스21572081041
154992022-07-07부산선박러시아연방5586000
168112022-07-21인천공항항공네팔1220131633
60492022-03-15인천공항항공미얀마30304818015