Overview

Dataset statistics

Number of variables4
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory34.4 B

Variable types

Text4

Dataset

Description울산광역시 외국인 투자 현황 국가별(네덜란드, 미국, 일본 등), 업종별(석유, 화학, 금속 전기전자 등), 연도별 등 정보를 제공하고 있음.
URLhttps://www.data.go.kr/data/3081767/fileData.do

Reproduction

Analysis started2023-12-12 01:58:41.617739
Analysis finished2023-12-12 01:58:42.205172
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국가
Text

Distinct54
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T10:58:42.392201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.4363636
Min length2

Characters and Unicode

Total characters244
Distinct characters83
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

Unique53 ?
Unique (%)96.4%

Sample

1st row네덜란드
2nd row일본
3rd row독일
4th row싱가포르
5th row미국
ValueCountFrequency (%)
기타 2
 
3.6%
2008년 1
 
1.8%
2021년 1
 
1.8%
2010년 1
 
1.8%
1990년대 1
 
1.8%
2000년 1
 
1.8%
2001년 1
 
1.8%
2002년 1
 
1.8%
2003년 1
 
1.8%
2004년 1
 
1.8%
Other values (44) 44
80.0%
2023-12-12T10:58:42.831661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40
 
16.4%
2 29
 
11.9%
년 27
 
11.1%
1 17
 
7.0%
9 7
 
2.9%
기 6
 
2.5%
- 6
 
2.5%
대 5
 
2.0%
이 4
 
1.6%
8 3
 
1.2%
Other values (73) 100
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
52.5%
Decimal Number 108
44.3%
Dash Punctuation 6
 
2.5%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
년 27
 
21.1%
기 6
 
4.7%
대 5
 
3.9%
이 4
 
3.1%
전 3
 
2.3%
금 3
 
2.3%
스 3
 
2.3%
도 3
 
2.3%
국 2
 
1.6%
구 2
 
1.6%
Other values (60) 70
54.7%
Decimal Number
ValueCountFrequency (%)
0 40
37.0%
2 29
26.9%
1 17
15.7%
9 7
 
6.5%
8 3
 
2.8%
6 3
 
2.8%
7 3
 
2.8%
5 2
 
1.9%
4 2
 
1.9%
3 2
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
52.5%
Common 116
47.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
년 27
 
21.1%
기 6
 
4.7%
대 5
 
3.9%
이 4
 
3.1%
전 3
 
2.3%
금 3
 
2.3%
스 3
 
2.3%
도 3
 
2.3%
국 2
 
1.6%
구 2
 
1.6%
Other values (60) 70
54.7%
Common
ValueCountFrequency (%)
0 40
34.5%
2 29
25.0%
1 17
14.7%
9 7
 
6.0%
- 6
 
5.2%
8 3
 
2.6%
6 3
 
2.6%
7 3
 
2.6%
5 2
 
1.7%
4 2
 
1.7%
Other values (3) 4
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
52.5%
ASCII 116
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40
34.5%
2 29
25.0%
1 17
14.7%
9 7
 
6.0%
- 6
 
5.2%
8 3
 
2.6%
6 3
 
2.6%
7 3
 
2.6%
5 2
 
1.7%
4 2
 
1.7%
Other values (3) 4
 
3.4%
Hangul
ValueCountFrequency (%)
년 27
 
21.1%
기 6
 
4.7%
대 5
 
3.9%
이 4
 
3.1%
전 3
 
2.3%
금 3
 
2.3%
스 3
 
2.3%
도 3
 
2.3%
국 2
 
1.6%
구 2
 
1.6%
Other values (60) 70
54.7%

신고건수
Text

Distinct37
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T10:58:43.101750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.9454545
Min length1

Characters and Unicode

Total characters107
Distinct characters14
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

Unique27 ?
Unique (%)49.1%

Sample

1st row27
2nd row130
3rd row45
4th row39
5th row55
ValueCountFrequency (%)
27 4
 
7.3%
14 4
 
7.3%
19 4
 
7.3%
8 3
 
5.5%
13 3
 
5.5%
12 2
 
3.6%
신고건수 2
 
3.6%
21 2
 
3.6%
2 2
 
3.6%
3 2
 
3.6%
Other values (27) 27
49.1%
2023-12-12T10:58:43.492589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
20.6%
2 21
19.6%
3 11
10.3%
9 9
8.4%
7 8
 
7.5%
5 7
 
6.5%
4 6
 
5.6%
6 6
 
5.6%
8 5
 
4.7%
0 4
 
3.7%
Other values (4) 8
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99
92.5%
Other Letter 8
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
22.2%
2 21
21.2%
3 11
11.1%
9 9
9.1%
7 8
 
8.1%
5 7
 
7.1%
4 6
 
6.1%
6 6
 
6.1%
8 5
 
5.1%
0 4
 
4.0%
Other Letter
ValueCountFrequency (%)
신 2
25.0%
고 2
25.0%
건 2
25.0%
수 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99
92.5%
Hangul 8
 
7.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
22.2%
2 21
21.2%
3 11
11.1%
9 9
9.1%
7 8
 
8.1%
5 7
 
7.1%
4 6
 
6.1%
6 6
 
6.1%
8 5
 
5.1%
0 4
 
4.0%
Hangul
ValueCountFrequency (%)
신 2
25.0%
고 2
25.0%
건 2
25.0%
수 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
92.5%
Hangul 8
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
22.2%
2 21
21.2%
3 11
11.1%
9 9
9.1%
7 8
 
8.1%
5 7
 
7.1%
4 6
 
6.1%
6 6
 
6.1%
8 5
 
5.1%
0 4
 
4.0%
Hangul
ValueCountFrequency (%)
신 2
25.0%
고 2
25.0%
건 2
25.0%
수 2
25.0%

투자액
Text

Distinct54
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T10:58:43.770328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.8545455
Min length5

Characters and Unicode

Total characters377
Distinct characters15
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

Unique53 ?
Unique (%)96.4%

Sample

1st row5674.10
2nd row2074.92
3rd row1375.38
4th row1338.34
5th row847.77
ValueCountFrequency (%)
투자액 2
 
3.6%
447.02 1
 
1.8%
94.33 1
 
1.8%
1373.37 1
 
1.8%
519.80 1
 
1.8%
39.73 1
 
1.8%
60.68 1
 
1.8%
6.17 1
 
1.8%
14.92 1
 
1.8%
36.67 1
 
1.8%
Other values (44) 44
80.0%
2023-12-12T10:58:44.487557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57
15.1%
. 53
14.1%
2 35
9.3%
1 33
8.8%
5 31
8.2%
7 30
8.0%
3 25
6.6%
8 24
6.4%
0 22
 
5.8%
9 21
 
5.6%
Other values (5) 46
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 261
69.2%
Space Separator 57
 
15.1%
Other Punctuation 53
 
14.1%
Other Letter 6
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35
13.4%
1 33
12.6%
5 31
11.9%
7 30
11.5%
3 25
9.6%
8 24
9.2%
0 22
8.4%
9 21
8.0%
6 20
7.7%
4 20
7.7%
Other Letter
ValueCountFrequency (%)
투 2
33.3%
자 2
33.3%
액 2
33.3%
Space Separator
ValueCountFrequency (%)
57
100.0%
Other Punctuation
ValueCountFrequency (%)
. 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 371
98.4%
Hangul 6
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
57
15.4%
. 53
14.3%
2 35
9.4%
1 33
8.9%
5 31
8.4%
7 30
8.1%
3 25
6.7%
8 24
6.5%
0 22
 
5.9%
9 21
 
5.7%
Other values (2) 40
10.8%
Hangul
ValueCountFrequency (%)
투 2
33.3%
자 2
33.3%
액 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371
98.4%
Hangul 6
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
15.4%
. 53
14.3%
2 35
9.4%
1 33
8.9%
5 31
8.4%
7 30
8.1%
3 25
6.7%
8 24
6.5%
0 22
 
5.9%
9 21
 
5.7%
Other values (2) 40
10.8%
Hangul
ValueCountFrequency (%)
투 2
33.3%
자 2
33.3%
액 2
33.3%

백분율
Text

Distinct51
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-12T10:58:44.760880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9454545
Min length1

Characters and Unicode

Total characters217
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)85.5%

Sample

1st row38.04
2nd row13.91
3rd row9.22
4th row8.97
5th row5.68
ValueCountFrequency (%)
백분율 2
 
3.6%
0.1 2
 
3.6%
0.41 2
 
3.6%
1.82 2
 
3.6%
3 1
 
1.8%
1.65 1
 
1.8%
4.4 1
 
1.8%
2.05 1
 
1.8%
9.21 1
 
1.8%
3.48 1
 
1.8%
Other values (41) 41
74.5%
2023-12-12T10:58:45.232630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 52
24.0%
0 25
11.5%
1 23
10.6%
2 19
 
8.8%
4 18
 
8.3%
3 17
 
7.8%
8 16
 
7.4%
5 13
 
6.0%
6 11
 
5.1%
9 9
 
4.1%
Other values (4) 14
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
73.3%
Other Punctuation 52
 
24.0%
Other Letter 6
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25
15.7%
1 23
14.5%
2 19
11.9%
4 18
11.3%
3 17
10.7%
8 16
10.1%
5 13
8.2%
6 11
6.9%
9 9
 
5.7%
7 8
 
5.0%
Other Letter
ValueCountFrequency (%)
백 2
33.3%
분 2
33.3%
율 2
33.3%
Other Punctuation
ValueCountFrequency (%)
. 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 211
97.2%
Hangul 6
 
2.8%

Most frequent character per script

Common
ValueCountFrequency (%)
. 52
24.6%
0 25
11.8%
1 23
10.9%
2 19
 
9.0%
4 18
 
8.5%
3 17
 
8.1%
8 16
 
7.6%
5 13
 
6.2%
6 11
 
5.2%
9 9
 
4.3%
Hangul
ValueCountFrequency (%)
백 2
33.3%
분 2
33.3%
율 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211
97.2%
Hangul 6
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 52
24.6%
0 25
11.8%
1 23
10.9%
2 19
 
9.0%
4 18
 
8.5%
3 17
 
8.1%
8 16
 
7.6%
5 13
 
6.2%
6 11
 
5.2%
9 9
 
4.3%
Hangul
ValueCountFrequency (%)
백 2
33.3%
분 2
33.3%
율 2
33.3%

Correlations

2023-12-12T10:58:45.350498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가신고건수투자액백분율
국가1.0000.9530.9970.989
신고건수0.9531.0001.0000.976
투자액0.9971.0001.0001.000
백분율0.9890.9761.0001.000

Missing values

2023-12-12T10:58:42.045433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:58:42.161446image/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

국가신고건수투자액백분율
0네덜란드275674.1038.04
1일본1302074.9213.91
2독일451375.389.22
3싱가포르391338.348.97
4미국55847.775.68
5영국26682.894.58
6바베이도스3598.854.01
7쿠웨이트2554.963.72
8프랑스27377.052.53
9말레이시아8322.412.16
국가신고건수투자액백분율
452013년1345.870.31
462014년232422.7916.24
472015년22975.906.54
482016년9272.811.83
492017년14504.063.38
502018년191568.1710.51
512019년14806.205.4
522020년14175.681.18
532021년2094.330.63
542022년193329.0522.32