Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory52.7 B

Variable types

Text3
DateTime1
Numeric2

Dataset

Description한국가스공사의 주요사업인 천연가스의 안전하고 안정적인 공급 운영과 관련하여, 전국 26개 발전사별 대상발전소(49개소), 한국가스공사 사업소별 공급관리소 40개소 및 발전용량 세부 정보를 민간기업 및 수요처에 제공함으로써 국민편익 및 이해도를 증진함.
URLhttps://www.data.go.kr/data/15102899/fileData.do

Alerts

발전용량(MW) is highly overall correlated with 공급량(시간당톤)High correlation
공급량(시간당톤) is highly overall correlated with 발전용량(MW)High correlation
대상발전소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:28:41.913301
Analysis finished2023-12-12 09:28:43.055397
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발전사
Text

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T18:28:43.259639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.3877551
Min length4

Characters and Unicode

Total characters313
Distinct characters70
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)38.8%

Sample

1st row남동발전
2nd row중부발전
3rd row중부발전
4th row중부발전
5th row중부발전
ValueCountFrequency (%)
지역난방공사 6
 
11.5%
중부발전 5
 
9.6%
남부발전 4
 
7.7%
서부발전 3
 
5.8%
동서발전 3
 
5.8%
gs 3
 
5.8%
eps 3
 
5.8%
gs-power 2
 
3.8%
포스코에너지 2
 
3.8%
엠피씨율촌전력 2
 
3.8%
Other values (19) 19
36.5%
2023-12-12T18:28:43.647885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
전 21
 
6.7%
발 19
 
6.1%
지 18
 
5.8%
부 13
 
4.2%
( 13
 
4.2%
) 13
 
4.2%
에 12
 
3.8%
주 12
 
3.8%
너 11
 
3.5%
S 9
 
2.9%
Other values (60) 172
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 251
80.2%
Uppercase Letter 31
 
9.9%
Open Punctuation 13
 
4.2%
Close Punctuation 13
 
4.2%
Space Separator 3
 
1.0%
Dash Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 21
 
8.4%
발 19
 
7.6%
지 18
 
7.2%
부 13
 
5.2%
에 12
 
4.8%
주 12
 
4.8%
너 11
 
4.4%
남 8
 
3.2%
공 8
 
3.2%
서 8
 
3.2%
Other values (48) 121
48.2%
Uppercase Letter
ValueCountFrequency (%)
S 9
29.0%
P 5
16.1%
E 5
16.1%
G 5
16.1%
O 2
 
6.5%
W 2
 
6.5%
R 2
 
6.5%
D 1
 
3.2%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
80.2%
Common 31
 
9.9%
Latin 31
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 21
 
8.4%
발 19
 
7.6%
지 18
 
7.2%
부 13
 
5.2%
에 12
 
4.8%
주 12
 
4.8%
너 11
 
4.4%
남 8
 
3.2%
공 8
 
3.2%
서 8
 
3.2%
Other values (48) 121
48.2%
Latin
ValueCountFrequency (%)
S 9
29.0%
P 5
16.1%
E 5
16.1%
G 5
16.1%
O 2
 
6.5%
W 2
 
6.5%
R 2
 
6.5%
D 1
 
3.2%
Common
ValueCountFrequency (%)
( 13
41.9%
) 13
41.9%
3
 
9.7%
- 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 251
80.2%
ASCII 62
 
19.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
전 21
 
8.4%
발 19
 
7.6%
지 18
 
7.2%
부 13
 
5.2%
에 12
 
4.8%
주 12
 
4.8%
너 11
 
4.4%
남 8
 
3.2%
공 8
 
3.2%
서 8
 
3.2%
Other values (48) 121
48.2%
ASCII
ValueCountFrequency (%)
( 13
21.0%
) 13
21.0%
S 9
14.5%
P 5
 
8.1%
E 5
 
8.1%
G 5
 
8.1%
3
 
4.8%
- 2
 
3.2%
O 2
 
3.2%
W 2
 
3.2%
Other values (2) 3
 
4.8%

대상발전소
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T18:28:43.974805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.4693878
Min length4

Characters and Unicode

Total characters366
Distinct characters94
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row분당복합화력
2nd row인천복합화력(#1~2)
3rd row인천복합화력(#3)
4th row보령복합화력
5th row서울복합화력(#1~2)
ValueCountFrequency (%)
분당복합화력 1
 
2.0%
엠피씨율촌(2호기 1
 
2.0%
화성열병합 1
 
2.0%
판교열병합 1
 
2.0%
파주열병합 1
 
2.0%
광교열병합 1
 
2.0%
동탄열병합 1
 
2.0%
양산열병합 1
 
2.0%
인천송도발전소 1
 
2.0%
수완에너지 1
 
2.0%
Other values (39) 39
79.6%
2023-12-12T18:28:44.430537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
합 42
 
11.5%
복 24
 
6.6%
화 19
 
5.2%
열 18
 
4.9%
력 18
 
4.9%
병 18
 
4.9%
( 13
 
3.6%
) 13
 
3.6%
천 11
 
3.0%
호 10
 
2.7%
Other values (84) 180
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
83.6%
Decimal Number 22
 
6.0%
Open Punctuation 13
 
3.6%
Close Punctuation 13
 
3.6%
Other Punctuation 7
 
1.9%
Math Symbol 5
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
합 42
 
13.7%
복 24
 
7.8%
화 19
 
6.2%
열 18
 
5.9%
력 18
 
5.9%
병 18
 
5.9%
천 11
 
3.6%
호 10
 
3.3%
기 10
 
3.3%
산 9
 
2.9%
Other values (71) 127
41.5%
Decimal Number
ValueCountFrequency (%)
1 8
36.4%
2 6
27.3%
3 3
 
13.6%
9 1
 
4.5%
4 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
7 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
# 5
71.4%
, 2
 
28.6%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
83.6%
Common 60
 
16.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
합 42
 
13.7%
복 24
 
7.8%
화 19
 
6.2%
열 18
 
5.9%
력 18
 
5.9%
병 18
 
5.9%
천 11
 
3.6%
호 10
 
3.3%
기 10
 
3.3%
산 9
 
2.9%
Other values (71) 127
41.5%
Common
ValueCountFrequency (%)
( 13
21.7%
) 13
21.7%
1 8
13.3%
2 6
10.0%
# 5
 
8.3%
~ 5
 
8.3%
3 3
 
5.0%
, 2
 
3.3%
9 1
 
1.7%
4 1
 
1.7%
Other values (3) 3
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
83.6%
ASCII 60
 
16.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
합 42
 
13.7%
복 24
 
7.8%
화 19
 
6.2%
열 18
 
5.9%
력 18
 
5.9%
병 18
 
5.9%
천 11
 
3.6%
호 10
 
3.3%
기 10
 
3.3%
산 9
 
2.9%
Other values (71) 127
41.5%
ASCII
ValueCountFrequency (%)
( 13
21.7%
) 13
21.7%
1 8
13.3%
2 6
10.0%
# 5
 
8.3%
~ 5
 
8.3%
3 3
 
5.0%
, 2
 
3.3%
9 1
 
1.7%
4 1
 
1.7%
Other values (3) 3
 
5.0%

관리소
Text

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T18:28:44.669373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.122449
Min length2

Characters and Unicode

Total characters104
Distinct characters58
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

Unique33 ?
Unique (%)67.3%

Sample

1st row분당
2nd row청라
3rd row원창
4th row보령
5th row합정
ValueCountFrequency (%)
청라 3
 
6.1%
부곡 3
 
6.1%
동탄 2
 
4.1%
율촌 2
 
4.1%
서원창 2
 
4.1%
울산 2
 
4.1%
초평 2
 
4.1%
안산 1
 
2.0%
양산 1
 
2.0%
매암 1
 
2.0%
Other values (30) 30
61.2%
2023-12-12T18:28:45.032068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 8
 
7.7%
동 7
 
6.7%
청 3
 
2.9%
서 3
 
2.9%
평 3
 
2.9%
라 3
 
2.9%
창 3
 
2.9%
정 3
 
2.9%
원 3
 
2.9%
촌 3
 
2.9%
Other values (48) 65
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 8
 
7.7%
동 7
 
6.7%
청 3
 
2.9%
서 3
 
2.9%
평 3
 
2.9%
라 3
 
2.9%
창 3
 
2.9%
정 3
 
2.9%
원 3
 
2.9%
촌 3
 
2.9%
Other values (48) 65
62.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 8
 
7.7%
동 7
 
6.7%
청 3
 
2.9%
서 3
 
2.9%
평 3
 
2.9%
라 3
 
2.9%
창 3
 
2.9%
정 3
 
2.9%
원 3
 
2.9%
촌 3
 
2.9%
Other values (48) 65
62.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 8
 
7.7%
동 7
 
6.7%
청 3
 
2.9%
서 3
 
2.9%
평 3
 
2.9%
라 3
 
2.9%
창 3
 
2.9%
정 3
 
2.9%
원 3
 
2.9%
촌 3
 
2.9%
Other values (48) 65
62.5%

공급년도(월)
Date

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum1992-04-01 00:00:00
Maximum2023-01-01 00:00:00
2023-12-12T18:28:45.173091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:45.308423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

발전용량(MW)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean704.06122
Minimum100
Maximum1800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:28:45.452182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile118.4
Q1415
median533
Q3950
95-th percentile1766.4
Maximum1800
Range1700
Interquartile range (IQR)535

Descriptive statistics

Standard deviation473.91439
Coefficient of variation (CV)0.67311531
Kurtosis0.14106581
Mean704.06122
Median Absolute Deviation (MAD)315
Skewness0.85438153
Sum34499
Variance224594.85
MonotonicityNot monotonic
2023-12-12T18:28:45.598386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1800 3
 
6.1%
900 2
 
4.1%
470 2
 
4.1%
950 2
 
4.1%
834 2
 
4.1%
400 2
 
4.1%
450 2
 
4.1%
422 1
 
2.0%
415 1
 
2.0%
119 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
100 1
2.0%
102 1
2.0%
118 1
2.0%
119 1
2.0%
127 1
2.0%
138 1
2.0%
141 1
2.0%
146 1
2.0%
187 1
2.0%
380 1
2.0%
ValueCountFrequency (%)
1800 3
6.1%
1716 1
 
2.0%
1450 1
 
2.0%
1350 1
 
2.0%
1260 1
 
2.0%
1200 1
 
2.0%
1149 1
 
2.0%
1013 1
 
2.0%
960 1
 
2.0%
950 2
4.1%

공급량(시간당톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.30551
Minimum7
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T18:28:45.728938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile17.4
Q149
median74.8
Q3125
95-th percentile241.6
Maximum247
Range240
Interquartile range (IQR)76

Descriptive statistics

Standard deviation67.084422
Coefficient of variation (CV)0.69657927
Kurtosis0.12135166
Mean96.30551
Median Absolute Deviation (MAD)46.8
Skewness0.89273509
Sum4718.97
Variance4500.3197
MonotonicityNot monotonic
2023-12-12T18:28:45.877985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
125.0 3
 
6.1%
241.0 2
 
4.1%
122.0 2
 
4.1%
243.0 1
 
2.0%
28.0 1
 
2.0%
114.0 1
 
2.0%
18.0 1
 
2.0%
29.0 1
 
2.0%
15.0 1
 
2.0%
19.0 1
 
2.0%
Other values (35) 35
71.4%
ValueCountFrequency (%)
7.0 1
2.0%
15.0 1
2.0%
17.0 1
2.0%
18.0 1
2.0%
19.0 1
2.0%
19.4 1
2.0%
20.0 1
2.0%
27.0 1
2.0%
28.0 1
2.0%
29.0 1
2.0%
ValueCountFrequency (%)
247.0 1
2.0%
243.0 1
2.0%
242.0 1
2.0%
241.0 2
4.1%
187.0 1
2.0%
174.0 1
2.0%
172.8 1
2.0%
143.0 1
2.0%
139.0 1
2.0%
132.0 1
2.0%

Interactions

2023-12-12T18:28:42.577839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:42.338579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:42.682726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:42.458174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:28:45.978637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발전사대상발전소관리소공급년도(월)발전용량(MW)공급량(시간당톤)
발전사1.0001.0000.9960.9420.6370.000
대상발전소1.0001.0001.0001.0001.0001.000
관리소0.9961.0001.0000.9100.7260.000
공급년도(월)0.9421.0000.9101.0000.6430.883
발전용량(MW)0.6371.0000.7260.6431.0000.940
공급량(시간당톤)0.0001.0000.0000.8830.9401.000
2023-12-12T18:28:46.069556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발전용량(MW)공급량(시간당톤)
발전용량(MW)1.0000.965
공급량(시간당톤)0.9651.000

Missing values

2023-12-12T18:28:42.813093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:28:42.981750image/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

발전사대상발전소관리소공급년도(월)발전용량(MW)공급량(시간당톤)
0남동발전분당복합화력분당1992-04900139.0
1중부발전인천복합화력(#1~2)청라2012-011013125.0
2중부발전인천복합화력(#3)원창2012-0145062.0
3중부발전보령복합화력보령1999-101350174.0
4중부발전서울복합화력(#1~2)합정2019-01800112.0
5중부발전세종열병합세종2012-0650068.0
6서부발전서인천복합화력청라2012-101800241.0
7서부발전신평택복합포승2015-03834122.0
8서부발전군산복합화력군산2009-1171890.0
9동서발전일산복합화력일산1992-12900122.0
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