Overview

Dataset statistics

Number of variables19
Number of observations365
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory61.1 KiB
Average record size in memory171.4 B

Variable types

Categorical9
Numeric10

Dataset

Description2019년의 일간 발전설비를 연료원에 따라 분류한 정보(원자력,유연탄,무연탄,유류,LNG,양수,연료전지, 석탄가스화, 태양, 풍력, 수력, 해양, 바이오, 폐기물, 기타)
Author한국전력거래소
URLhttps://www.data.go.kr/data/15099892/fileData.do

Alerts

has constant value ""Constant
무연탄 has constant value ""Constant
양수 has constant value ""Constant
석탄가스화 has constant value ""Constant
Dataset has 1 (0.3%) duplicate rowsDuplicates
해양 is highly overall correlated with and 11 other fieldsHigh correlation
폐기물 is highly overall correlated with and 12 other fieldsHigh correlation
원자력 is highly overall correlated with and 11 other fieldsHigh correlation
유연탄 is highly overall correlated with and 2 other fieldsHigh correlation
is highly overall correlated with 유류 and 11 other fieldsHigh correlation
유류 is highly overall correlated with and 10 other fieldsHigh correlation
액화천연가스(LNG) is highly overall correlated with and 11 other fieldsHigh correlation
연료전지 is highly overall correlated with and 10 other fieldsHigh correlation
태양 is highly overall correlated with and 11 other fieldsHigh correlation
풍력 is highly overall correlated with 액화천연가스(LNG) and 7 other fieldsHigh correlation
수력 is highly overall correlated with and 12 other fieldsHigh correlation
바이오 is highly overall correlated with and 10 other fieldsHigh correlation
합계 is highly overall correlated with and 11 other fieldsHigh correlation
기타 is highly overall correlated with and 11 other fieldsHigh correlation
유연탄 is highly imbalanced (66.1%)Imbalance

Reproduction

Analysis started2023-12-12 11:16:10.153223
Analysis finished2023-12-12 11:16:30.210769
Duration20.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables


Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2019
365 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 365
100.0%

Length

2023-12-12T20:16:30.348855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:30.536601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 365
100.0%


Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5260274
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:30.734449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4525841
Coefficient of variation (CV)0.52904837
Kurtosis-1.2071313
Mean6.5260274
Median Absolute Deviation (MAD)3
Skewness-0.010499603
Sum2382
Variance11.920337
MonotonicityIncreasing
2023-12-12T20:16:30.960220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 31
8.5%
3 31
8.5%
5 31
8.5%
7 31
8.5%
8 31
8.5%
10 31
8.5%
12 31
8.5%
4 30
8.2%
6 30
8.2%
9 30
8.2%
Other values (2) 58
15.9%
ValueCountFrequency (%)
1 31
8.5%
2 28
7.7%
3 31
8.5%
4 30
8.2%
5 31
8.5%
6 30
8.2%
7 31
8.5%
8 31
8.5%
9 30
8.2%
10 31
8.5%
ValueCountFrequency (%)
12 31
8.5%
11 30
8.2%
10 31
8.5%
9 30
8.2%
8 31
8.5%
7 31
8.5%
6 30
8.2%
5 31
8.5%
4 30
8.2%
3 31
8.5%


Real number (ℝ)

Distinct31
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.506849
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:31.176338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6474339
Coefficient of variation (CV)0.55765254
Kurtosis-1.1233492
Mean15.506849
Median Absolute Deviation (MAD)7
Skewness0.032369729
Sum5660
Variance74.778112
MonotonicityNot monotonic
2023-12-12T20:16:31.431714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 24
 
6.6%
1 12
 
3.3%
11 12
 
3.3%
2 12
 
3.3%
16 12
 
3.3%
15 12
 
3.3%
14 12
 
3.3%
13 12
 
3.3%
12 12
 
3.3%
17 12
 
3.3%
Other values (21) 233
63.8%
ValueCountFrequency (%)
1 12
3.3%
2 12
3.3%
3 12
3.3%
4 12
3.3%
5 12
3.3%
6 12
3.3%
7 12
3.3%
8 12
3.3%
9 12
3.3%
10 12
3.3%
ValueCountFrequency (%)
31 7
1.9%
30 10
2.7%
29 10
2.7%
28 11
3.0%
27 11
3.0%
26 11
3.0%
25 11
3.0%
24 11
3.0%
23 11
3.0%
22 11
3.0%

원자력
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
21850
241 
23250
124 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21850
2nd row21850
3rd row21850
4th row21850
5th row21850

Common Values

ValueCountFrequency (%)
21850 241
66.0%
23250 124
34.0%

Length

2023-12-12T20:16:31.710680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:31.923393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
21850 241
66.0%
23250 124
34.0%

유연탄
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
36392
342 
36299
 
23

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36299
2nd row36299
3rd row36299
4th row36299
5th row36299

Common Values

ValueCountFrequency (%)
36392 342
93.7%
36299 23
 
6.3%

Length

2023-12-12T20:16:32.126500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:32.306335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36392 342
93.7%
36299 23
 
6.3%

무연탄
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
600
365 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row600
2nd row600
3rd row600
4th row600
5th row600

Common Values

ValueCountFrequency (%)
600 365
100.0%

Length

2023-12-12T20:16:32.502107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:32.681047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
600 365
100.0%

유류
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4092.9178
Minimum3771
Maximum4319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:32.840921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3771
5-th percentile3875
Q13969
median4069
Q34144
95-th percentile4319
Maximum4319
Range548
Interquartile range (IQR)175

Descriptive statistics

Standard deviation153.82995
Coefficient of variation (CV)0.037584421
Kurtosis-1.0817218
Mean4092.9178
Median Absolute Deviation (MAD)100
Skewness0.14598739
Sum1493915
Variance23663.653
MonotonicityDecreasing
2023-12-12T20:16:33.033636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4319 85
23.3%
4144 80
21.9%
4069 76
20.8%
3875 63
17.3%
3969 60
16.4%
3771 1
 
0.3%
ValueCountFrequency (%)
3771 1
 
0.3%
3875 63
17.3%
3969 60
16.4%
4069 76
20.8%
4144 80
21.9%
4319 85
23.3%
ValueCountFrequency (%)
4319 85
23.3%
4144 80
21.9%
4069 76
20.8%
3969 60
16.4%
3875 63
17.3%
3771 1
 
0.3%

액화천연가스(LNG)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38240.942
Minimum37834
Maximum39655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:33.230283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37834
5-th percentile37834
Q137834
median38204
Q338204
95-th percentile39530
Maximum39655
Range1821
Interquartile range (IQR)370

Descriptive statistics

Standard deviation585.87324
Coefficient of variation (CV)0.015320575
Kurtosis0.63687282
Mean38240.942
Median Absolute Deviation (MAD)370
Skewness1.4639278
Sum13957944
Variance343247.45
MonotonicityIncreasing
2023-12-12T20:16:33.456762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
37834 179
49.0%
38204 122
33.4%
39530 35
 
9.6%
39161 15
 
4.1%
39550 13
 
3.6%
39655 1
 
0.3%
ValueCountFrequency (%)
37834 179
49.0%
38204 122
33.4%
39161 15
 
4.1%
39530 35
 
9.6%
39550 13
 
3.6%
39655 1
 
0.3%
ValueCountFrequency (%)
39655 1
 
0.3%
39550 13
 
3.6%
39530 35
 
9.6%
39161 15
 
4.1%
38204 122
33.4%
37834 179
49.0%

양수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
4700
365 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4700
2nd row4700
3rd row4700
4th row4700
5th row4700

Common Values

ValueCountFrequency (%)
4700 365
100.0%

Length

2023-12-12T20:16:33.720355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:33.933160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4700 365
100.0%

연료전지
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.06849
Minimum344
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:34.104677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum344
5-th percentile344
Q1345
median376
Q3426
95-th percentile453
Maximum464
Range120
Interquartile range (IQR)81

Descriptive statistics

Standard deviation41.833897
Coefficient of variation (CV)0.10920736
Kurtosis-1.3679313
Mean383.06849
Median Absolute Deviation (MAD)32
Skewness0.53349188
Sum139820
Variance1750.075
MonotonicityIncreasing
2023-12-12T20:16:34.330362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
344 85
23.3%
376 79
21.6%
345 77
21.1%
426 60
16.4%
446 36
9.9%
453 27
 
7.4%
464 1
 
0.3%
ValueCountFrequency (%)
344 85
23.3%
345 77
21.1%
376 79
21.6%
426 60
16.4%
446 36
9.9%
453 27
 
7.4%
464 1
 
0.3%
ValueCountFrequency (%)
464 1
 
0.3%
453 27
 
7.4%
446 36
9.9%
426 60
16.4%
376 79
21.6%
345 77
21.1%
344 85
23.3%

석탄가스화
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
346
365 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row346
2nd row346
3rd row346
4th row346
5th row346

Common Values

ValueCountFrequency (%)
346 365
100.0%

Length

2023-12-12T20:16:34.591444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:34.788511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
346 365
100.0%

태양
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8396.9616
Minimum7130
Maximum10505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:34.971263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7130
5-th percentile7130
Q17756
median8630
Q39090
95-th percentile10058
Maximum10505
Range3375
Interquartile range (IQR)1334

Descriptive statistics

Standard deviation930.08325
Coefficient of variation (CV)0.11076426
Kurtosis-1.1485308
Mean8396.9616
Median Absolute Deviation (MAD)874
Skewness0.26305145
Sum3064891
Variance865054.85
MonotonicityIncreasing
2023-12-12T20:16:35.189887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7756 77
21.1%
7285 62
17.0%
8630 62
17.0%
9090 49
13.4%
9678 36
9.9%
10058 27
 
7.4%
7130 23
 
6.3%
8326 17
 
4.7%
9048 11
 
3.0%
10505 1
 
0.3%
ValueCountFrequency (%)
7130 23
 
6.3%
7285 62
17.0%
7756 77
21.1%
8326 17
 
4.7%
8630 62
17.0%
9048 11
 
3.0%
9090 49
13.4%
9678 36
9.9%
10058 27
 
7.4%
10505 1
 
0.3%
ValueCountFrequency (%)
10505 1
 
0.3%
10058 27
 
7.4%
9678 36
9.9%
9090 49
13.4%
9048 11
 
3.0%
8630 62
17.0%
8326 17
 
4.7%
7756 77
21.1%
7285 62
17.0%
7130 23
 
6.3%

풍력
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1444.989
Minimum1420
Maximum1512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:35.402539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1420
5-th percentile1420
Q11425
median1425
Q31462
95-th percentile1512
Maximum1512
Range92
Interquartile range (IQR)37

Descriptive statistics

Standard deviation29.358618
Coefficient of variation (CV)0.020317537
Kurtosis-0.27576259
Mean1444.989
Median Absolute Deviation (MAD)3
Skewness1.0136476
Sum527421
Variance861.92845
MonotonicityNot monotonic
2023-12-12T20:16:35.596844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1425 139
38.1%
1462 79
21.6%
1422 60
16.4%
1487 36
 
9.9%
1512 28
 
7.7%
1420 23
 
6.3%
ValueCountFrequency (%)
1420 23
 
6.3%
1422 60
16.4%
1425 139
38.1%
1462 79
21.6%
1487 36
 
9.9%
1512 28
 
7.7%
ValueCountFrequency (%)
1512 28
 
7.7%
1487 36
 
9.9%
1462 79
21.6%
1425 139
38.1%
1422 60
16.4%
1420 23
 
6.3%

수력
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1795.0849
Minimum1790
Maximum1808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:35.796874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1790
5-th percentile1790
Q11793
median1796
Q31796
95-th percentile1805
Maximum1808
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3856384
Coefficient of variation (CV)0.0018860603
Kurtosis3.4988544
Mean1795.0849
Median Absolute Deviation (MAD)3
Skewness1.7288177
Sum655206
Variance11.462547
MonotonicityIncreasing
2023-12-12T20:16:36.017744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1796 158
43.3%
1793 139
38.1%
1805 27
 
7.4%
1790 23
 
6.3%
1794 17
 
4.7%
1808 1
 
0.3%
ValueCountFrequency (%)
1790 23
 
6.3%
1793 139
38.1%
1794 17
 
4.7%
1796 158
43.3%
1805 27
 
7.4%
1808 1
 
0.3%
ValueCountFrequency (%)
1808 1
 
0.3%
1805 27
 
7.4%
1796 158
43.3%
1794 17
 
4.7%
1793 139
38.1%
1790 23
 
6.3%

해양
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
255
241 
256
124 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row255
2nd row255
3rd row255
4th row255
5th row255

Common Values

ValueCountFrequency (%)
255 241
66.0%
256 124
34.0%

Length

2023-12-12T20:16:36.293693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:36.498813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
255 241
66.0%
256 124
34.0%

바이오
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.07123
Minimum538
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:36.678127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum538
5-th percentile538
Q1714
median791
Q3888
95-th percentile893
Maximum900
Range362
Interquartile range (IQR)174

Descriptive statistics

Standard deviation133.67182
Coefficient of variation (CV)0.17845007
Kurtosis-1.1097692
Mean749.07123
Median Absolute Deviation (MAD)97
Skewness-0.51479125
Sum273411
Variance17868.154
MonotonicityNot monotonic
2023-12-12T20:16:36.871563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
538 85
23.3%
714 77
21.1%
791 76
20.8%
891 60
16.4%
888 36
9.9%
893 27
 
7.4%
716 3
 
0.8%
900 1
 
0.3%
ValueCountFrequency (%)
538 85
23.3%
714 77
21.1%
716 3
 
0.8%
791 76
20.8%
888 36
9.9%
891 60
16.4%
893 27
 
7.4%
900 1
 
0.3%
ValueCountFrequency (%)
900 1
 
0.3%
893 27
 
7.4%
891 60
16.4%
888 36
9.9%
791 76
20.8%
716 3
 
0.8%
714 77
21.1%
538 85
23.3%

폐기물
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
1595
156 
1593
73 
0
64 
1068
49 
1590
23 

Length

Max length4
Median length4
Mean length3.4739726
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1590
2nd row1590
3rd row1590
4th row1590
5th row1590

Common Values

ValueCountFrequency (%)
1595 156
42.7%
1593 73
20.0%
0 64
17.5%
1068 49
 
13.4%
1590 23
 
6.3%

Length

2023-12-12T20:16:37.098288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:37.305174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1595 156
42.7%
1593 73
20.0%
0 64
17.5%
1068 49
 
13.4%
1590 23
 
6.3%

기타
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
77
162 
81
139 
1149
36 
1163
27 
1178
 
1

Length

Max length4
Median length2
Mean length2.3506849
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row77
2nd row77
3rd row77
4th row77
5th row77

Common Values

ValueCountFrequency (%)
77 162
44.4%
81 139
38.1%
1149 36
 
9.9%
1163 27
 
7.4%
1178 1
 
0.3%

Length

2023-12-12T20:16:37.560259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:16:37.793045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
77 162
44.4%
81 139
38.1%
1149 36
 
9.9%
1163 27
 
7.4%
1178 1
 
0.3%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121227.21
Minimum119092
Maximum125338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T20:16:37.972830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum119092
5-th percentile119092
Q1119826
median121147
Q3122490
95-th percentile124833
Maximum125338
Range6246
Interquartile range (IQR)2664

Descriptive statistics

Standard deviation1885.3156
Coefficient of variation (CV)0.015551918
Kurtosis-0.88016994
Mean121227.21
Median Absolute Deviation (MAD)1343
Skewness0.67744272
Sum44247931
Variance3554414.8
MonotonicityNot monotonic
2023-12-12T20:16:38.153841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
119826 77
21.1%
119350 62
17.0%
121147 62
17.0%
122490 49
13.4%
119092 23
 
6.3%
124392 21
 
5.8%
120471 17
 
4.7%
124023 15
 
4.1%
124833 14
 
3.8%
124854 13
 
3.6%
Other values (2) 12
 
3.3%
ValueCountFrequency (%)
119092 23
 
6.3%
119350 62
17.0%
119826 77
21.1%
120471 17
 
4.7%
121147 62
17.0%
122490 49
13.4%
122973 11
 
3.0%
124023 15
 
4.1%
124392 21
 
5.8%
124833 14
 
3.8%
ValueCountFrequency (%)
125338 1
 
0.3%
124854 13
 
3.6%
124833 14
 
3.8%
124392 21
 
5.8%
124023 15
 
4.1%
122973 11
 
3.0%
122490 49
13.4%
121147 62
17.0%
120471 17
 
4.7%
119826 77
21.1%

Interactions

2023-12-12T20:16:27.768199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:11.609138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:13.151146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:14.774416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:16.861141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:18.764509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:20.495526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.168709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:23.749721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:25.550407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:27.916194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:11.768841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:13.300827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:14.929472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:17.039659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:18.941872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:20.672800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.308154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:23.915839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:25.720317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:28.059831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:11.920713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:13.445999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:15.093411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:17.228737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:19.114583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:20.844248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.463785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:24.088012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:25.901421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:28.226019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.071710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:13.594830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:15.229703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:17.430391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:19.300017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:21.003065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.624033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:24.241141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:26.064006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:28.395367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.231626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:13.754566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:15.407420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:17.633501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:19.473140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:21.188414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.787998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:24.427156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:26.249776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:28.591424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.371291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:13.901952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:15.570574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:17.823897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:19.652841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:21.370664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.957053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:24.604270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:26.431128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:28.772657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.526996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:14.064414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:15.744107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:18.020226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:19.836562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:21.522153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:23.109535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:24.799976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:26.611893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:28.933780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.652504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:14.245528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:15.892621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:18.195422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:20.004536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:21.681736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:23.259798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:24.954492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:26.783733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:29.120293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.820642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:14.421209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:16.057550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:18.387785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:20.155831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:21.877376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:23.431625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:25.151233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:26.985783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:29.312433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:12.998397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:14.600630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:16.695208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:18.581995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:20.326474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:22.018358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:23.594393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:25.353215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:16:27.624712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:16:38.340605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원자력유연탄유류액화천연가스(LNG)연료전지태양풍력수력해양바이오폐기물기타합계
1.0000.0001.0000.7350.9980.9300.9100.9180.8910.9421.0000.9970.9850.9400.913
0.0001.0000.0000.0000.0000.3550.3130.1810.2830.3870.0000.0000.0000.3450.261
원자력1.0000.0001.0000.2581.0000.3131.0001.0000.8770.5971.0001.0000.8130.6331.000
유연탄0.7350.0000.2581.0000.378NaNNaNNaNNaNNaN0.258NaN1.0000.223NaN
유류0.9980.0001.0000.3781.0000.9410.9630.9830.8320.7351.0001.0000.9850.8340.990
액화천연가스(LNG)0.9300.3550.313NaN0.9411.0000.7370.7720.7370.8190.3130.3130.6720.7371.000
연료전지0.9100.3131.000NaN0.9630.7371.0001.0001.0000.9631.0000.9330.8390.9911.000
태양0.9180.1811.000NaN0.9830.7721.0001.0001.0001.0001.0000.9600.9981.0000.975
풍력0.8910.2830.877NaN0.8320.7371.0001.0001.0000.7680.8770.7360.9330.8941.000
수력0.9420.3870.597NaN0.7350.8190.9631.0000.7681.0000.5970.7490.5820.9920.934
해양1.0000.0001.0000.2581.0000.3131.0001.0000.8770.5971.0001.0000.8130.6331.000
바이오0.9970.0001.000NaN1.0000.3130.9330.9600.7360.7491.0001.0000.6730.7730.966
폐기물0.9850.0000.8131.0000.9850.6720.8390.9980.9330.5820.8130.6731.0000.9041.000
기타0.9400.3450.6330.2230.8340.7370.9911.0000.8940.9920.6330.7730.9041.0000.934
합계0.9130.2611.000NaN0.9901.0001.0000.9751.0000.9341.0000.9661.0000.9341.000
2023-12-12T20:16:39.170245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
해양폐기물원자력기타유연탄
해양1.0000.9370.9940.7580.166
폐기물0.9371.0000.9370.5750.996
원자력0.9940.9371.0000.7580.166
기타0.7580.5750.7581.0000.271
유연탄0.1660.9960.1660.2711.000
2023-12-12T20:16:39.389000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유류액화천연가스(LNG)연료전지태양풍력수력바이오합계원자력유연탄해양폐기물기타
1.000-0.027-0.9770.9180.9790.9860.4920.9260.9540.9830.9770.5700.9770.8210.665
-0.0271.000-0.0330.0080.0280.0360.0580.0400.0300.0290.0000.0000.0000.0000.142
유류-0.977-0.0331.000-0.916-0.996-0.990-0.475-0.908-0.963-0.9900.9940.4560.9940.8230.857
액화천연가스(LNG)0.9180.008-0.9161.0000.9130.9280.5370.9340.8670.9290.7880.2490.7880.6670.820
연료전지0.9790.028-0.9960.9131.0000.9920.4810.9190.9730.9920.9960.2710.9960.7130.864
태양0.9860.036-0.9900.9280.9921.0000.5040.9400.9660.9950.9920.4500.9920.8200.996
풍력0.4920.058-0.4750.5370.4810.5041.0000.5460.3310.5040.6770.1880.6770.6600.895
수력0.9260.040-0.9080.9340.9190.9400.5461.0000.9210.9390.7260.9940.7260.6650.867
바이오0.9540.030-0.9630.8670.9730.9660.3310.9211.0000.9660.9970.4620.9970.7740.641
합계0.9830.029-0.9900.9290.9920.9950.5040.9390.9661.0000.9900.4470.9900.8640.858
원자력0.9770.0000.9940.7880.9960.9920.6770.7260.9970.9901.0000.1660.9940.9370.758
유연탄0.5700.0000.4560.2490.2710.4500.1880.9940.4620.4470.1661.0000.1660.9960.271
해양0.9770.0000.9940.7880.9960.9920.6770.7260.9970.9900.9940.1661.0000.9370.758
폐기물0.8210.0000.8230.6670.7130.8200.6600.6650.7740.8640.9370.9960.9371.0000.575
기타0.6650.1420.8570.8200.8640.9960.8950.8670.6410.8580.7580.2710.7580.5751.000

Missing values

2023-12-12T20:16:29.606511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:16:30.049821image/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

원자력유연탄무연탄유류액화천연가스(LNG)양수연료전지석탄가스화태양풍력수력해양바이오폐기물기타합계
020191121850362996004319378344700344346713014201790255538159077119092
120191221850362996004319378344700344346713014201790255538159077119092
220191321850362996004319378344700344346713014201790255538159077119092
320191421850362996004319378344700344346713014201790255538159077119092
420191521850362996004319378344700344346713014201790255538159077119092
520191621850362996004319378344700344346713014201790255538159077119092
620191721850362996004319378344700344346713014201790255538159077119092
720191821850362996004319378344700344346713014201790255538159077119092
820191921850362996004319378344700344346713014201790255538159077119092
9201911021850362996004319378344700344346713014201790255538159077119092
원자력유연탄무연탄유류액화천연가스(LNG)양수연료전지석탄가스화태양풍력수력해양바이오폐기물기타합계
3552019121823250363926003875395504700453346100581512180525689301163124854
3562019121823250363926003875395504700453346100581512180525689301163124854
3572019121823250363926003875395504700453346100581512180525689301163124854
3582019121823250363926003875395504700453346100581512180525689301163124854
3592019121823250363926003875395504700453346100581512180525689301163124854
3602019121823250363926003875395504700453346100581512180525689301163124854
3612019121823250363926003875395504700453346100581512180525689301163124854
3622019121823250363926003875395504700453346100581512180525689301163124854
3632019121823250363926003875395504700453346100581512180525689301163124854
3642019123123250363926003771396554700464346105051512180825690001178125338

Duplicate rows

Most frequently occurring

원자력유연탄무연탄유류액화천연가스(LNG)양수연료전지석탄가스화태양풍력수력해양바이오폐기물기타합계# duplicates
0201912182325036392600387539550470045334610058151218052568930116312485413