Overview

Dataset statistics

Number of variables5
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory48.7 B

Variable types

Text1
Numeric3
Categorical1

Dataset

Description연간 선박 관련 국적 및 항구별 상륙허가자 현황( 제공항목은 항구별, 승무원상륙, 긴급상륙, 재난상륙, 상륙허가자출국)
Author법무부
URLhttps://www.data.go.kr/data/3074959/fileData.do

Alerts

재난상륙 has constant value ""Constant
승무원상륙 is highly overall correlated with 긴급상륙 and 1 other fieldsHigh correlation
긴급상륙 is highly overall correlated with 승무원상륙 and 1 other fieldsHigh correlation
상륙허가자출국 is highly overall correlated with 승무원상륙 and 1 other fieldsHigh correlation
항구별 has unique valuesUnique
승무원상륙 has unique valuesUnique
상륙허가자출국 has unique valuesUnique
긴급상륙 has 10 (35.7%) zerosZeros
상륙허가자출국 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2024-01-14 13:38:20.607097
Analysis finished2024-01-14 13:38:22.695873
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

항구별
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-01-14T22:38:22.808855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6785714
Min length4

Characters and Unicode

Total characters131
Distinct characters41
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row인천공항
2nd row부 산 항
3rd row인 천 항
4th row김해공항
5th row제주공항
ValueCountFrequency (%)
항 20
30.3%
산 4
 
6.1%
천 3
 
4.5%
제 2
 
3.0%
포 2
 
3.0%
인천공항 1
 
1.5%
초 1
 
1.5%
거 1
 
1.5%
군 1
 
1.5%
동 1
 
1.5%
Other values (30) 30
45.5%
2024-01-14T22:38:23.316085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
29.0%
항 29
22.1%
공 9
 
6.9%
천 4
 
3.1%
산 4
 
3.1%
포 3
 
2.3%
제 3
 
2.3%
양 3
 
2.3%
주 3
 
2.3%
인 2
 
1.5%
Other values (31) 33
25.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
71.0%
Space Separator 38
29.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 29
31.2%
공 9
 
9.7%
천 4
 
4.3%
산 4
 
4.3%
포 3
 
3.2%
제 3
 
3.2%
양 3
 
3.2%
주 3
 
3.2%
인 2
 
2.2%
김 2
 
2.2%
Other values (30) 31
33.3%
Space Separator
ValueCountFrequency (%)
38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
71.0%
Common 38
29.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 29
31.2%
공 9
 
9.7%
천 4
 
4.3%
산 4
 
4.3%
포 3
 
3.2%
제 3
 
3.2%
양 3
 
3.2%
주 3
 
3.2%
인 2
 
2.2%
김 2
 
2.2%
Other values (30) 31
33.3%
Common
ValueCountFrequency (%)
38
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
71.0%
ASCII 38
29.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
100.0%
Hangul
ValueCountFrequency (%)
항 29
31.2%
공 9
 
9.7%
천 4
 
4.3%
산 4
 
4.3%
포 3
 
3.2%
제 3
 
3.2%
양 3
 
3.2%
주 3
 
3.2%
인 2
 
2.2%
김 2
 
2.2%
Other values (30) 31
33.3%

승무원상륙
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18028.5
Minimum1
Maximum247014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-14T22:38:23.468883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.45
Q1353.25
median2233
Q313026.5
95-th percentile63731.4
Maximum247014
Range247013
Interquartile range (IQR)12673.25

Descriptive statistics

Standard deviation47940.525
Coefficient of variation (CV)2.6591522
Kurtosis20.87566
Mean18028.5
Median Absolute Deviation (MAD)2223
Skewness4.4108169
Sum504798
Variance2.298294 × 109
MonotonicityNot monotonic
2024-01-14T22:38:23.633906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
247014 1
 
3.6%
928 1
 
3.6%
6147 1
 
3.6%
19 1
 
3.6%
9232 1
 
3.6%
2655 1
 
3.6%
351 1
 
3.6%
162 1
 
3.6%
26 1
 
3.6%
105 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
19 1
3.6%
26 1
3.6%
32 1
3.6%
105 1
3.6%
162 1
3.6%
351 1
3.6%
354 1
3.6%
698 1
3.6%
718 1
3.6%
ValueCountFrequency (%)
247014 1
3.6%
81790 1
3.6%
30194 1
3.6%
29475 1
3.6%
27796 1
3.6%
16798 1
3.6%
13337 1
3.6%
12923 1
3.6%
9232 1
3.6%
8151 1
3.6%

긴급상륙
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.821429
Minimum0
Maximum368
Zeros10
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-14T22:38:23.800818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.5
Q331.25
95-th percentile224.7
Maximum368
Range368
Interquartile range (IQR)31.25

Descriptive statistics

Standard deviation87.156757
Coefficient of variation (CV)1.902096
Kurtosis7.7967767
Mean45.821429
Median Absolute Deviation (MAD)15.5
Skewness2.7789256
Sum1283
Variance7596.3003
MonotonicityNot monotonic
2024-01-14T22:38:24.031997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 10
35.7%
6 2
 
7.1%
15 1
 
3.6%
1 1
 
3.6%
16 1
 
3.6%
52 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
31 1
 
3.6%
27 1
 
3.6%
Other values (8) 8
28.6%
ValueCountFrequency (%)
0 10
35.7%
1 1
 
3.6%
6 2
 
7.1%
15 1
 
3.6%
16 1
 
3.6%
20 1
 
3.6%
23 1
 
3.6%
24 1
 
3.6%
27 1
 
3.6%
28 1
 
3.6%
ValueCountFrequency (%)
368 1
3.6%
280 1
3.6%
122 1
3.6%
118 1
3.6%
114 1
3.6%
52 1
3.6%
32 1
3.6%
31 1
3.6%
28 1
3.6%
27 1
3.6%

재난상륙
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
0
28 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
100.0%

Length

2024-01-14T22:38:24.263355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:38:24.427847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
100.0%

상륙허가자출국
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17750.143
Minimum0
Maximum240216
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-14T22:38:24.611948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.7
Q1378.75
median1952
Q312999.5
95-th percentile63627.35
Maximum240216
Range240216
Interquartile range (IQR)12620.75

Descriptive statistics

Standard deviation46723.893
Coefficient of variation (CV)2.6323108
Kurtosis20.587623
Mean17750.143
Median Absolute Deviation (MAD)1942.5
Skewness4.3764971
Sum497004
Variance2.1831222 × 109
MonotonicityNot monotonic
2024-01-14T22:38:24.828392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
240216 1
 
3.6%
1007 1
 
3.6%
6255 1
 
3.6%
19 1
 
3.6%
9736 1
 
3.6%
2205 1
 
3.6%
309 1
 
3.6%
166 1
 
3.6%
21 1
 
3.6%
139 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
19 1
3.6%
21 1
3.6%
29 1
3.6%
139 1
3.6%
166 1
3.6%
309 1
3.6%
402 1
3.6%
728 1
3.6%
851 1
3.6%
ValueCountFrequency (%)
240216 1
3.6%
81519 1
3.6%
30400 1
3.6%
28708 1
3.6%
28120 1
3.6%
16032 1
3.6%
13415 1
3.6%
12861 1
3.6%
9736 1
3.6%
7883 1
3.6%

Interactions

2024-01-14T22:38:21.743015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:20.861684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:21.246956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:21.867868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:20.966223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:21.408445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:21.985179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:21.111080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:21.582503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:38:24.954352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항구별승무원상륙긴급상륙상륙허가자출국
항구별1.0001.0001.0001.000
승무원상륙1.0001.0000.6801.000
긴급상륙1.0000.6801.0000.680
상륙허가자출국1.0001.0000.6801.000
2024-01-14T22:38:25.074630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승무원상륙긴급상륙상륙허가자출국
승무원상륙1.0000.6500.999
긴급상륙0.6501.0000.654
상륙허가자출국0.9990.6541.000

Missing values

2024-01-14T22:38:22.158992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:38:22.640074image/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

항구별승무원상륙긴급상륙재난상륙상륙허가자출국
0인천공항24701400240216
1부 산 항81790280081519
2인 천 항12923114012861
3김해공항5883006129
4제주공항71800728
5대구공항320029
6여 수 항27796122028120
7청주공항1000
8김포공항1326001307
9울 산 항30194118030400
항구별승무원상륙긴급상륙재난상륙상륙허가자출국
18군 산 항81513107883
19동 해 항53242305238
20속 초 항10500139
21양양공항260021
22무안공항16200166
23성남공항35100309
24서 산 항26552402205
25창 원 항92325209736
26제 주 항1916019
27당 진 항6147106255