Overview

Dataset statistics

Number of variables6
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory55.3 B

Variable types

Text2
Numeric2
Categorical2

Dataset

Description한국남부발전(주)_자회사_관계회사 정보에 대한 데이터로 관계회사명, 지분율, 국가명(시도명), 취득가액, 사업내용 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15073866/fileData.do

Alerts

지분율(퍼센트) is highly overall correlated with 기재부 기준 분류High correlation
기재부 기준 분류 is highly overall correlated with 지분율(퍼센트)High correlation
관계회사명 has unique valuesUnique
취득가액(백만원) has unique valuesUnique
취득가액(백만원) has 1 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-12 18:09:31.352207
Analysis finished2023-12-12 18:09:32.387557
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관계회사명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T03:09:32.543388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length12.16
Min length7

Characters and Unicode

Total characters304
Distinct characters93
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st rowKOSPO Australia Pty., Ltd.
2nd rowKOSPO JORDAN L.L.C.
3rd rowKOSPO Chile SpA
4th rowKOSPO Bylong Pty., Ltd.
5th row코스포영남파워(주)
ValueCountFrequency (%)
kospo 5
 
12.5%
pty 2
 
5.0%
ltd 2
 
5.0%
대구그린파워(주 1
 
2.5%
한국해상풍력(주 1
 
2.5%
내포그린에너지(주 1
 
2.5%
켑코솔라(주 1
 
2.5%
켑코이에스(주 1
 
2.5%
대정해상풍력발전(주 1
 
2.5%
솔라시도태양광발전(주 1
 
2.5%
Other values (24) 24
60.0%
2023-12-13T03:09:33.075604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 19
 
6.2%
주 19
 
6.2%
) 19
 
6.2%
15
 
4.9%
O 11
 
3.6%
전 10
 
3.3%
발 10
 
3.3%
P 9
 
3.0%
풍 9
 
3.0%
력 9
 
3.0%
Other values (83) 174
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
48.7%
Uppercase Letter 53
 
17.4%
Lowercase Letter 40
 
13.2%
Open Punctuation 19
 
6.2%
Close Punctuation 19
 
6.2%
Space Separator 15
 
4.9%
Other Punctuation 10
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 19
 
12.8%
전 10
 
6.8%
발 10
 
6.8%
풍 9
 
6.1%
력 9
 
6.1%
스 5
 
3.4%
코 4
 
2.7%
태 3
 
2.0%
대 3
 
2.0%
이 3
 
2.0%
Other values (46) 73
49.3%
Lowercase Letter
ValueCountFrequency (%)
t 5
12.5%
n 4
10.0%
a 4
10.0%
d 3
 
7.5%
l 3
 
7.5%
y 3
 
7.5%
i 3
 
7.5%
e 3
 
7.5%
r 2
 
5.0%
o 2
 
5.0%
Other values (7) 8
20.0%
Uppercase Letter
ValueCountFrequency (%)
O 11
20.8%
P 9
17.0%
S 8
15.1%
K 5
9.4%
L 4
 
7.5%
A 4
 
7.5%
C 3
 
5.7%
D 2
 
3.8%
W 1
 
1.9%
J 1
 
1.9%
Other values (5) 5
9.4%
Other Punctuation
ValueCountFrequency (%)
. 8
80.0%
, 2
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
48.7%
Latin 93
30.6%
Common 63
20.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 19
 
12.8%
전 10
 
6.8%
발 10
 
6.8%
풍 9
 
6.1%
력 9
 
6.1%
스 5
 
3.4%
코 4
 
2.7%
태 3
 
2.0%
대 3
 
2.0%
이 3
 
2.0%
Other values (46) 73
49.3%
Latin
ValueCountFrequency (%)
O 11
 
11.8%
P 9
 
9.7%
S 8
 
8.6%
K 5
 
5.4%
t 5
 
5.4%
n 4
 
4.3%
L 4
 
4.3%
a 4
 
4.3%
A 4
 
4.3%
d 3
 
3.2%
Other values (22) 36
38.7%
Common
ValueCountFrequency (%)
( 19
30.2%
) 19
30.2%
15
23.8%
. 8
12.7%
, 2
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156
51.3%
Hangul 148
48.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 19
 
12.2%
) 19
 
12.2%
15
 
9.6%
O 11
 
7.1%
P 9
 
5.8%
. 8
 
5.1%
S 8
 
5.1%
K 5
 
3.2%
t 5
 
3.2%
n 4
 
2.6%
Other values (27) 53
34.0%
Hangul
ValueCountFrequency (%)
주 19
 
12.8%
전 10
 
6.8%
발 10
 
6.8%
풍 9
 
6.1%
력 9
 
6.1%
스 5
 
3.4%
코 4
 
2.7%
태 3
 
2.0%
대 3
 
2.0%
이 3
 
2.0%
Other values (46) 73
49.3%

지분율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.416
Minimum6.85
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T03:09:33.260713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.85
5-th percentile8.3
Q129
median50
Q3100
95-th percentile100
Maximum100
Range93.15
Interquartile range (IQR)71

Descriptive statistics

Standard deviation34.279384
Coefficient of variation (CV)0.6185828
Kurtosis-1.4745189
Mean55.416
Median Absolute Deviation (MAD)23
Skewness0.13691028
Sum1385.4
Variance1175.0762
MonotonicityNot monotonic
2023-12-13T03:09:33.398648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100.0 7
28.0%
29.0 4
16.0%
8.3 2
 
8.0%
80.0 1
 
4.0%
60.0 1
 
4.0%
67.25 1
 
4.0%
58.0 1
 
4.0%
6.85 1
 
4.0%
73.0 1
 
4.0%
27.0 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
6.85 1
 
4.0%
8.3 2
8.0%
12.5 1
 
4.0%
27.0 1
 
4.0%
29.0 4
16.0%
29.2 1
 
4.0%
42.0 1
 
4.0%
47.0 1
 
4.0%
50.0 1
 
4.0%
58.0 1
 
4.0%
ValueCountFrequency (%)
100.0 7
28.0%
80.0 1
 
4.0%
73.0 1
 
4.0%
67.25 1
 
4.0%
60.0 1
 
4.0%
58.0 1
 
4.0%
50.0 1
 
4.0%
47.0 1
 
4.0%
42.0 1
 
4.0%
29.2 1
 
4.0%

국가명(시도명)
Text

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T03:09:33.616197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.08
Min length2

Characters and Unicode

Total characters52
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)36.0%

Sample

1st row호주
2nd row요르단
3rd row칠레
4th row호주
5th row울산
ValueCountFrequency (%)
강원 4
16.0%
경기 3
12.0%
서울 3
12.0%
호주 2
 
8.0%
요르단 2
 
8.0%
전남 2
 
8.0%
칠레 1
 
4.0%
울산 1
 
4.0%
부산 1
 
4.0%
미국 1
 
4.0%
Other values (5) 5
20.0%
2023-12-13T03:09:34.010150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 4
 
7.7%
경 4
 
7.7%
원 4
 
7.7%
울 4
 
7.7%
주 3
 
5.8%
전 3
 
5.8%
남 3
 
5.8%
기 3
 
5.8%
서 3
 
5.8%
호 2
 
3.8%
Other values (14) 19
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 4
 
7.7%
경 4
 
7.7%
원 4
 
7.7%
울 4
 
7.7%
주 3
 
5.8%
전 3
 
5.8%
남 3
 
5.8%
기 3
 
5.8%
서 3
 
5.8%
호 2
 
3.8%
Other values (14) 19
36.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 4
 
7.7%
경 4
 
7.7%
원 4
 
7.7%
울 4
 
7.7%
주 3
 
5.8%
전 3
 
5.8%
남 3
 
5.8%
기 3
 
5.8%
서 3
 
5.8%
호 2
 
3.8%
Other values (14) 19
36.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 4
 
7.7%
경 4
 
7.7%
원 4
 
7.7%
울 4
 
7.7%
주 3
 
5.8%
전 3
 
5.8%
남 3
 
5.8%
기 3
 
5.8%
서 3
 
5.8%
호 2
 
3.8%
Other values (14) 19
36.5%

취득가액(백만원)
Real number (ℝ)

UNIQUE  ZEROS 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24353.32
Minimum0
Maximum130630
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T03:09:34.192835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile311.4
Q16000
median11494
Q326600
95-th percentile85279.2
Maximum130630
Range130630
Interquartile range (IQR)20600

Descriptive statistics

Standard deviation31630.958
Coefficient of variation (CV)1.2988356
Kurtosis4.8463232
Mean24353.32
Median Absolute Deviation (MAD)10198
Skewness2.1908923
Sum608833
Variance1.0005175 × 109
MonotonicityNot monotonic
2023-12-13T03:09:34.352265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
23393 1
 
4.0%
397 1
 
4.0%
6531 1
 
4.0%
10752 1
 
4.0%
7493 1
 
4.0%
290 1
 
4.0%
46226 1
 
4.0%
5220 1
 
4.0%
5190 1
 
4.0%
25000 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
0 1
4.0%
290 1
4.0%
397 1
4.0%
1296 1
4.0%
5190 1
4.0%
5220 1
4.0%
6000 1
4.0%
6531 1
4.0%
7493 1
4.0%
9329 1
4.0%
ValueCountFrequency (%)
130630 1
4.0%
86599 1
4.0%
80000 1
4.0%
46226 1
4.0%
40854 1
4.0%
29200 1
4.0%
26600 1
4.0%
25000 1
4.0%
23393 1
4.0%
16650 1
4.0%

사업내용
Categorical

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
풍력발전
9 
태양광발전
3 
광산개발 및 발전연료확보
2 
해외발전소개발,투자
2 
집단에너지사업
2 
Other values (7)
7 

Length

Max length14
Median length13
Mean length6.96
Min length4

Unique

Unique7 ?
Unique (%)28.0%

Sample

1st row광산개발 및 발전연료확보
2nd row해외LNG복합발전소운영
3rd row해외발전소개발,투자
4th row광산개발 및 발전연료확보
5th row화력발전업

Common Values

ValueCountFrequency (%)
풍력발전 9
36.0%
태양광발전 3
 
12.0%
광산개발 및 발전연료확보 2
 
8.0%
해외발전소개발,투자 2
 
8.0%
집단에너지사업 2
 
8.0%
해외LNG복합발전소운영 1
 
4.0%
화력발전업 1
 
4.0%
사옥 미화,경비 사업 1
 
4.0%
집단에너지 사업 1
 
4.0%
해상풍력발전 1
 
4.0%
Other values (2) 2
 
8.0%

Length

2023-12-13T03:09:34.505846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
풍력발전 9
25.0%
및 3
 
8.3%
태양광발전 3
 
8.3%
사업 3
 
8.3%
광산개발 2
 
5.6%
발전연료확보 2
 
5.6%
해외발전소개발,투자 2
 
5.6%
집단에너지사업 2
 
5.6%
해상풍력발전 1
 
2.8%
운전 1
 
2.8%
Other values (8) 8
22.2%

기재부 기준 분류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
자회사
13 
출자회사
12 

Length

Max length4
Median length3
Mean length3.48
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자회사 13
52.0%
출자회사 12
48.0%

Length

2023-12-13T03:09:34.657238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:09:34.766630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자회사 13
52.0%
출자회사 12
48.0%

Interactions

2023-12-13T03:09:31.913202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:31.689697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:32.024832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:31.797063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:09:34.854790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관계회사명지분율(퍼센트)국가명(시도명)취득가액(백만원)사업내용기재부 기준 분류
관계회사명1.0001.0001.0001.0001.0001.000
지분율(퍼센트)1.0001.0000.7200.0000.0000.996
국가명(시도명)1.0000.7201.0000.8880.8210.894
취득가액(백만원)1.0000.0000.8881.0000.8900.466
사업내용1.0000.0000.8210.8901.0000.812
기재부 기준 분류1.0000.9960.8940.4660.8121.000
2023-12-13T03:09:34.988629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업내용기재부 기준 분류
사업내용1.0000.480
기재부 기준 분류0.4801.000
2023-12-13T03:09:35.084121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지분율(퍼센트)취득가액(백만원)사업내용기재부 기준 분류
지분율(퍼센트)1.000-0.0080.0000.810
취득가액(백만원)-0.0081.0000.4490.292
사업내용0.0000.4491.0000.480
기재부 기준 분류0.8100.2920.4801.000

Missing values

2023-12-13T03:09:32.197882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:09:32.335333image/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

관계회사명지분율(퍼센트)국가명(시도명)취득가액(백만원)사업내용기재부 기준 분류
0KOSPO Australia Pty., Ltd.100.0호주23393광산개발 및 발전연료확보자회사
1KOSPO JORDAN L.L.C.100.0요르단397해외LNG복합발전소운영자회사
2KOSPO Chile SpA100.0칠레86599해외발전소개발,투자자회사
3KOSPO Bylong Pty., Ltd.100.0호주0광산개발 및 발전연료확보자회사
4코스포영남파워(주)100.0울산80000화력발전업자회사
5코스포서비스(주)100.0부산6000사옥 미화,경비 사업자회사
6KOSPO USA Inc.100.0미국130630해외발전소개발,투자자회사
7정암풍력발전(주)80.0강원11388풍력발전자회사
8태백풍력발전(주)60.0강원11494풍력발전자회사
9태백귀네미풍력발전(주)67.25강원12637풍력발전자회사
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