Overview

Dataset statistics

Number of variables4
Number of observations5249
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory174.4 KiB
Average record size in memory34.0 B

Variable types

Categorical1
Numeric1
Text2

Dataset

Description월별 외국인 입국자의 수 및 국적별 외국인 입국자의 수에 대한 데이터로 월별로 업데이트하여 제공 (항목은 연도, 월, 국적별, 입국자수)
Author법무부
URLhttps://www.data.go.kr/data/15099989/fileData.do

Reproduction

Analysis started2024-04-29 22:57:28.293681
Analysis finished2024-04-29 22:57:29.910143
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023
2392 
2022
2265 
2024
592 

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 (%)
2023 2392
45.6%
2022 2265
43.2%
2024 592
 
11.3%

Length

2024-04-30T07:57:29.971710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:57:30.065255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 2392
45.6%
2022 2265
43.2%
2024 592
 
11.3%

월
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0720137
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-30T07:57:30.153876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5496473
Coefficient of variation (CV)0.58459145
Kurtosis-1.2855891
Mean6.0720137
Median Absolute Deviation (MAD)3
Skewness0.14682835
Sum31872
Variance12.599996
MonotonicityNot monotonic
2024-04-30T07:57:30.257036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 579
11.0%
2 574
10.9%
1 554
10.6%
9 405
7.7%
10 403
7.7%
11 401
7.6%
8 397
7.6%
7 391
7.4%
5 390
7.4%
6 388
7.4%
Other values (2) 767
14.6%
ValueCountFrequency (%)
1 554
10.6%
2 574
10.9%
3 579
11.0%
4 381
7.3%
5 390
7.4%
6 388
7.4%
7 391
7.4%
8 397
7.6%
9 405
7.7%
10 403
7.7%
ValueCountFrequency (%)
12 386
7.4%
11 401
7.6%
10 403
7.7%
9 405
7.7%
8 397
7.6%
7 391
7.4%
6 388
7.4%
5 390
7.4%
4 381
7.3%
3 579
11.0%

국적지역
Text

Distinct223
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2024-04-30T07:57:30.577482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.0140979
Min length2

Characters and Unicode

Total characters21070
Distinct characters208
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st row미국
2nd row필리핀
3rd row중국
4th row미얀마
5th row인도네시아
ValueCountFrequency (%)
미국 27
 
0.5%
이스라엘 27
 
0.5%
짐바브웨 27
 
0.5%
파라과이 27
 
0.5%
에콰도르 27
 
0.5%
자메이카 27
 
0.5%
쿠웨이트 27
 
0.5%
세네갈 27
 
0.5%
코스타리카 27
 
0.5%
르완다 27
 
0.5%
Other values (213) 4980
94.9%
2024-04-30T07:57:30.986754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 1442
 
6.8%
리 812
 
3.9%
스 787
 
3.7%
이 611
 
2.9%
르 599
 
2.8%
니 587
 
2.8%
라 537
 
2.5%
나 401
 
1.9%
국 399
 
1.9%
바 394
 
1.9%
Other values (198) 14501
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20997
99.7%
Dash Punctuation 26
 
0.1%
Open Punctuation 23
 
0.1%
Close Punctuation 23
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 1442
 
6.9%
리 812
 
3.9%
스 787
 
3.7%
이 611
 
2.9%
르 599
 
2.9%
니 587
 
2.8%
라 537
 
2.6%
나 401
 
1.9%
국 399
 
1.9%
바 394
 
1.9%
Other values (194) 14428
68.7%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20997
99.7%
Common 73
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 1442
 
6.9%
리 812
 
3.9%
스 787
 
3.7%
이 611
 
2.9%
르 599
 
2.9%
니 587
 
2.8%
라 537
 
2.6%
나 401
 
1.9%
국 399
 
1.9%
바 394
 
1.9%
Other values (194) 14428
68.7%
Common
ValueCountFrequency (%)
- 26
35.6%
( 23
31.5%
) 23
31.5%
1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20997
99.7%
ASCII 73
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 1442
 
6.9%
리 812
 
3.9%
스 787
 
3.7%
이 611
 
2.9%
르 599
 
2.9%
니 587
 
2.8%
라 537
 
2.6%
나 401
 
1.9%
국 399
 
1.9%
바 394
 
1.9%
Other values (194) 14428
68.7%
ASCII
ValueCountFrequency (%)
- 26
35.6%
( 23
31.5%
) 23
31.5%
1
 
1.4%

입국자수
Text

Distinct1686
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2024-04-30T07:57:31.308259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.5252429
Min length1

Characters and Unicode

Total characters13255
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1221 ?
Unique (%)23.3%

Sample

1st row20123
2nd row9478
3rd row8408
4th row5228
5th row4591
ValueCountFrequency (%)
1 198
 
3.8%
2 182
 
3.5%
3 167
 
3.2%
4 126
 
2.4%
5 125
 
2.4%
7 106
 
2.0%
6 106
 
2.0%
8 99
 
1.9%
11 74
 
1.4%
9 74
 
1.4%
Other values (1676) 3992
76.1%
2024-04-30T07:57:31.774958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2590
19.5%
2 1780
13.4%
3 1465
11.1%
4 1288
9.7%
5 1146
8.6%
6 1089
8.2%
7 1022
 
7.7%
8 962
 
7.3%
9 932
 
7.0%
0 929
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13203
99.6%
Other Punctuation 52
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2590
19.6%
2 1780
13.5%
3 1465
11.1%
4 1288
9.8%
5 1146
8.7%
6 1089
8.2%
7 1022
 
7.7%
8 962
 
7.3%
9 932
 
7.1%
0 929
 
7.0%
Other Punctuation
ValueCountFrequency (%)
, 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13255
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2590
19.5%
2 1780
13.4%
3 1465
11.1%
4 1288
9.7%
5 1146
8.6%
6 1089
8.2%
7 1022
 
7.7%
8 962
 
7.3%
9 932
 
7.0%
0 929
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2590
19.5%
2 1780
13.4%
3 1465
11.1%
4 1288
9.7%
5 1146
8.6%
6 1089
8.2%
7 1022
 
7.7%
8 962
 
7.3%
9 932
 
7.0%
0 929
 
7.0%

Interactions

2024-04-30T07:57:29.623044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:57:31.873176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월
년1.0000.516
월0.5161.000
2024-04-30T07:57:31.952020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월년
월1.0000.361
년0.3611.000

Missing values

2024-04-30T07:57:29.789609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:57:29.870082image/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

년월국적지역입국자수
020221미국20123
120221필리핀9478
220221중국8408
320221미얀마5228
420221인도네시아4591
520221캐나다3524
620221러시아연방3250
720221인도2921
820221베트남2660
920221한국계중국인2322
년월국적지역입국자수
523920243에스와티니3
524020243중앙아프리카공화국3
524120243남수단공화국2
524220243에리트레아2
524320243적도기니2
524420243콩고2
524520243니제르1
524620243소말리아1
524720243차드1
524820243기타89