Overview

Dataset statistics

Number of variables8
Number of observations39
Missing cells88
Missing cells (%)28.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory73.4 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description세계 각국의 원전 운영 현황. IAEA 발표기준 국가별 원전의 운영, 건설 및 폐쇄 현황. 각 국가별 설비용량 및 원자로 개수 등. 2022년 12월 31일 기준.(단위: 개수, MW)
URLhttps://www.data.go.kr/data/15100940/fileData.do

Alerts

운영 중(원자로) is highly overall correlated with 운영 중(설비용량) and 4 other fieldsHigh correlation
운영 중(설비용량) is highly overall correlated with 운영 중(원자로) and 5 other fieldsHigh correlation
건설 중(원자로) is highly overall correlated with 운영 중(원자로) and 2 other fieldsHigh correlation
건설 중(설비용량) is highly overall correlated with 운영 중(원자로) and 2 other fieldsHigh correlation
폐쇄(원자로) is highly overall correlated with 운영 중(원자로) and 2 other fieldsHigh correlation
폐쇄(설비용량) is highly overall correlated with 운영 중(설비용량) and 1 other fieldsHigh correlation
구분 is highly overall correlated with 운영 중(원자로) and 1 other fieldsHigh correlation
구분 is highly imbalanced (51.2%)Imbalance
운영 중(원자로) has 6 (15.4%) missing valuesMissing
운영 중(설비용량) has 6 (15.4%) missing valuesMissing
건설 중(원자로) has 21 (53.8%) missing valuesMissing
건설 중(설비용량) has 21 (53.8%) missing valuesMissing
폐쇄(원자로) has 17 (43.6%) missing valuesMissing
폐쇄(설비용량) has 17 (43.6%) missing valuesMissing
국가 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:17:26.922231
Analysis finished2023-12-12 04:17:32.094512
Duration5.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
원전운영
33 
신규 건설
 
3
원전 폐쇄
 
3

Length

Max length5
Median length4
Mean length4.1538462
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원전운영 33
84.6%
신규 건설 3
 
7.7%
원전 폐쇄 3
 
7.7%

Length

2023-12-12T13:17:32.167327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:17:32.313329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원전운영 33
73.3%
신규 3
 
6.7%
건설 3
 
6.7%
원전 3
 
6.7%
폐쇄 3
 
6.7%

국가
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T13:17:32.558045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.3333333
Min length2

Characters and Unicode

Total characters130
Distinct characters77
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

Unique39 ?
Unique (%)100.0%

Sample

1st row미국
2nd row프랑스
3rd row중국
4th row러시아
5th row일본
ValueCountFrequency (%)
미국 1
 
2.6%
슬로바키아 1
 
2.6%
불가리아 1
 
2.6%
브라질 1
 
2.6%
남아공 1
 
2.6%
멕시코 1
 
2.6%
루마니아 1
 
2.6%
이란 1
 
2.6%
슬로베니아 1
 
2.6%
아르헨티나 1
 
2.6%
Other values (29) 29
74.4%
2023-12-12T13:17:33.027686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 12
 
9.2%
스 8
 
6.2%
니 4
 
3.1%
리 4
 
3.1%
이 4
 
3.1%
라 4
 
3.1%
국 4
 
3.1%
키 3
 
2.3%
란 3
 
2.3%
나 3
 
2.3%
Other values (67) 81
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
97.7%
Uppercase Letter 3
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 12
 
9.4%
스 8
 
6.3%
니 4
 
3.1%
리 4
 
3.1%
이 4
 
3.1%
라 4
 
3.1%
국 4
 
3.1%
키 3
 
2.4%
란 3
 
2.4%
나 3
 
2.4%
Other values (64) 78
61.4%
Uppercase Letter
ValueCountFrequency (%)
E 1
33.3%
U 1
33.3%
A 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127
97.7%
Latin 3
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 12
 
9.4%
스 8
 
6.3%
니 4
 
3.1%
리 4
 
3.1%
이 4
 
3.1%
라 4
 
3.1%
국 4
 
3.1%
키 3
 
2.4%
란 3
 
2.4%
나 3
 
2.4%
Other values (64) 78
61.4%
Latin
ValueCountFrequency (%)
E 1
33.3%
U 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127
97.7%
ASCII 3
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 12
 
9.4%
스 8
 
6.3%
니 4
 
3.1%
리 4
 
3.1%
이 4
 
3.1%
라 4
 
3.1%
국 4
 
3.1%
키 3
 
2.4%
란 3
 
2.4%
나 3
 
2.4%
Other values (64) 78
61.4%
ASCII
ValueCountFrequency (%)
E 1
33.3%
U 1
33.3%
A 1
33.3%

운영 중(원자로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)51.5%
Missing6
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean13.30303
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:17:33.151799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q315
95-th percentile55.4
Maximum92
Range91
Interquartile range (IQR)13

Descriptive statistics

Standard deviation20.432029
Coefficient of variation (CV)1.5358928
Kurtosis6.5341111
Mean13.30303
Median Absolute Deviation (MAD)3
Skewness2.478369
Sum439
Variance417.4678
MonotonicityNot monotonic
2023-12-12T13:17:33.276298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 5
12.8%
2 5
12.8%
3 4
10.3%
4 3
7.7%
6 3
7.7%
7 2
 
5.1%
56 1
 
2.6%
5 1
 
2.6%
92 1
 
2.6%
15 1
 
2.6%
Other values (7) 7
17.9%
(Missing) 6
15.4%
ValueCountFrequency (%)
1 5
12.8%
2 5
12.8%
3 4
10.3%
4 3
7.7%
5 1
 
2.6%
6 3
7.7%
7 2
 
5.1%
9 1
 
2.6%
15 1
 
2.6%
19 1
 
2.6%
ValueCountFrequency (%)
92 1
2.6%
56 1
2.6%
55 1
2.6%
37 1
2.6%
33 1
2.6%
25 1
2.6%
22 1
2.6%
19 1
2.6%
15 1
2.6%
9 1
2.6%

운영 중(설비용량)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing6
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean11959.939
Minimum448
Maximum94718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:17:33.412661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum448
5-th percentile605.6
Q11854
median3934
Q37121
95-th percentile55850
Maximum94718
Range94270
Interquartile range (IQR)5267

Descriptive statistics

Standard deviation20769.205
Coefficient of variation (CV)1.7365644
Kurtosis8.0282045
Mean11959.939
Median Absolute Deviation (MAD)2634
Skewness2.7680282
Sum394678
Variance4.3135987 × 108
MonotonicityNot monotonic
2023-12-12T13:17:33.550506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1552 1
 
2.6%
1916 1
 
2.6%
1868 1
 
2.6%
1641 1
 
2.6%
2006 1
 
2.6%
1884 1
 
2.6%
1854 1
 
2.6%
1300 1
 
2.6%
61370 1
 
2.6%
915 1
 
2.6%
Other values (23) 23
59.0%
(Missing) 6
 
15.4%
ValueCountFrequency (%)
448 1
2.6%
482 1
2.6%
688 1
2.6%
915 1
2.6%
1110 1
2.6%
1300 1
2.6%
1552 1
2.6%
1641 1
2.6%
1854 1
2.6%
1868 1
2.6%
ValueCountFrequency (%)
94718 1
2.6%
61370 1
2.6%
52170 1
2.6%
31679 1
2.6%
27727 1
2.6%
24431 1
2.6%
13624 1
2.6%
13107 1
2.6%
7121 1
2.6%
6882 1
2.6%

건설 중(원자로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)33.3%
Missing21
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean3.2222222
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:17:33.650184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9.65
Maximum19
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3053394
Coefficient of variation (CV)1.3361398
Kurtosis11.678059
Mean3.2222222
Median Absolute Deviation (MAD)1
Skewness3.27694
Sum58
Variance18.535948
MonotonicityNot monotonic
2023-12-12T13:17:33.765876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 7
 
17.9%
2 5
 
12.8%
4 2
 
5.1%
3 2
 
5.1%
19 1
 
2.6%
8 1
 
2.6%
(Missing) 21
53.8%
ValueCountFrequency (%)
1 7
17.9%
2 5
12.8%
3 2
 
5.1%
4 2
 
5.1%
8 1
 
2.6%
19 1
 
2.6%
ValueCountFrequency (%)
19 1
 
2.6%
8 1
 
2.6%
4 2
 
5.1%
3 2
 
5.1%
2 5
12.8%
1 7
17.9%

건설 중(설비용량)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing21
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean2901.0556
Minimum25
Maximum13875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:17:33.897676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile751.75
Q11230.5
median2115
Q33409.25
95-th percentile6637.25
Maximum13875
Range13850
Interquartile range (IQR)2178.75

Descriptive statistics

Standard deviation3081.4004
Coefficient of variation (CV)1.0621653
Kurtosis10.037531
Mean2901.0556
Median Absolute Deviation (MAD)1073
Skewness2.8990578
Sum52219
Variance9495028.5
MonotonicityNot monotonic
2023-12-12T13:17:34.050817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
25 1
 
2.6%
1194 1
 
2.6%
2160 1
 
2.6%
4456 1
 
2.6%
1110 1
 
2.6%
1345 1
 
2.6%
974 1
 
2.6%
1340 1
 
2.6%
2234 1
 
2.6%
1630 1
 
2.6%
Other values (8) 8
 
20.5%
(Missing) 21
53.8%
ValueCountFrequency (%)
25 1
2.6%
880 1
2.6%
974 1
2.6%
1110 1
2.6%
1194 1
2.6%
1340 1
2.6%
1345 1
2.6%
1630 1
2.6%
2070 1
2.6%
2160 1
2.6%
ValueCountFrequency (%)
13875 1
2.6%
5360 1
2.6%
4456 1
2.6%
4194 1
2.6%
3459 1
2.6%
3260 1
2.6%
2653 1
2.6%
2234 1
2.6%
2160 1
2.6%
2070 1
2.6%

폐쇄(원자로)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)54.5%
Missing17
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean9.2272727
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:17:34.265947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.5
Q39.25
95-th percentile35.7
Maximum41
Range40
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation12.355573
Coefficient of variation (CV)1.3390276
Kurtosis1.5718744
Mean9.2272727
Median Absolute Deviation (MAD)2.5
Skewness1.689476
Sum203
Variance152.66017
MonotonicityNot monotonic
2023-12-12T13:17:34.501327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 5
 
12.8%
2 3
 
7.7%
4 3
 
7.7%
3 3
 
7.7%
41 1
 
2.6%
14 1
 
2.6%
10 1
 
2.6%
27 1
 
2.6%
6 1
 
2.6%
36 1
 
2.6%
Other values (2) 2
 
5.1%
(Missing) 17
43.6%
ValueCountFrequency (%)
1 5
12.8%
2 3
7.7%
3 3
7.7%
4 3
7.7%
6 1
 
2.6%
7 1
 
2.6%
10 1
 
2.6%
14 1
 
2.6%
27 1
 
2.6%
30 1
 
2.6%
ValueCountFrequency (%)
41 1
 
2.6%
36 1
 
2.6%
30 1
 
2.6%
27 1
 
2.6%
14 1
 
2.6%
10 1
 
2.6%
7 1
 
2.6%
6 1
 
2.6%
4 3
7.7%
3 3
7.7%

폐쇄(설비용량)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing17
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean4456.4091
Minimum10
Maximum22180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T13:17:34.690016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile52.15
Q1511.5
median1887.5
Q34029.75
95-th percentile19833.15
Maximum22180
Range22170
Interquartile range (IQR)3518.25

Descriptive statistics

Standard deviation6568.4041
Coefficient of variation (CV)1.4739231
Kurtosis2.8271486
Mean4456.4091
Median Absolute Deviation (MAD)1712.5
Skewness1.9766539
Sum98041
Variance43143933
MonotonicityNot monotonic
2023-12-12T13:17:34.844574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
90 1
 
2.6%
52 1
 
2.6%
2370 1
 
2.6%
1423 1
 
2.6%
376 1
 
2.6%
55 1
 
2.6%
1632 1
 
2.6%
909 1
 
2.6%
379 1
 
2.6%
2193 1
 
2.6%
Other values (12) 12
30.8%
(Missing) 17
43.6%
ValueCountFrequency (%)
10 1
2.6%
52 1
2.6%
55 1
2.6%
90 1
2.6%
376 1
2.6%
379 1
2.6%
909 1
2.6%
1067 1
2.6%
1237 1
2.6%
1423 1
2.6%
ValueCountFrequency (%)
22180 1
2.6%
19976 1
2.6%
17119 1
2.6%
7755 1
2.6%
5549 1
2.6%
4054 1
2.6%
3957 1
2.6%
3515 1
2.6%
2370 1
2.6%
2193 1
2.6%

Interactions

2023-12-12T13:17:30.697676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.234731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.814850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.495960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.220721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.946520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.811690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.313534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.942861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.604376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.331940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.063993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.922548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.393846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.045036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.721691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.435058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.168716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:31.035902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.489672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.154280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.855556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.569495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.289308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:31.438292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.588481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.279893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.959052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.706596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.420937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:31.546380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:27.711673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:28.396880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.078872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:29.825913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:17:30.560297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:17:34.981726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분국가운영 중(원자로)운영 중(설비용량)건설 중(원자로)건설 중(설비용량)폐쇄(원자로)폐쇄(설비용량)
구분1.0001.000NaNNaN0.0000.0000.0000.000
국가1.0001.0001.0001.0001.0001.0001.0001.000
운영 중(원자로)NaN1.0001.0000.9950.8620.8610.8320.870
운영 중(설비용량)NaN1.0000.9951.0000.7890.7160.8860.922
건설 중(원자로)0.0001.0000.8620.7891.0000.7340.3160.000
건설 중(설비용량)0.0001.0000.8610.7160.7341.0000.0000.000
폐쇄(원자로)0.0001.0000.8320.8860.3160.0001.0000.955
폐쇄(설비용량)0.0001.0000.8700.9220.0000.0000.9551.000
2023-12-12T13:17:35.238671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영 중(원자로)운영 중(설비용량)건설 중(원자로)건설 중(설비용량)폐쇄(원자로)폐쇄(설비용량)구분
운영 중(원자로)1.0000.9530.6200.6760.5480.4991.000
운영 중(설비용량)0.9531.0000.5550.7000.6010.5911.000
건설 중(원자로)0.6200.5551.0000.855-0.243-0.0890.000
건설 중(설비용량)0.6760.7000.8551.000-0.0240.1190.000
폐쇄(원자로)0.5480.601-0.243-0.0241.0000.9290.000
폐쇄(설비용량)0.4990.591-0.0890.1190.9291.0000.000
구분1.0001.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T13:17:31.681709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:17:31.832636image/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.
2023-12-12T13:17:31.987173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분국가운영 중(원자로)운영 중(설비용량)건설 중(원자로)건설 중(설비용량)폐쇄(원자로)폐쇄(설비용량)
0원전운영미국9294718222344119976
1원전운영프랑스566137011630145549
2원전운영중국55521701913875<NA><NA>
3원전운영러시아372772743459103957
4원전운영일본3331679326532717119
5원전운영한국25244313536021237
6원전운영인도22679584194<NA><NA>
7원전운영캐나다1913624<NA><NA>62143
8원전운영우크라이나15131072207043515
9원전운영영국9588323260367755
구분국가운영 중(원자로)운영 중(설비용량)건설 중(원자로)건설 중(설비용량)폐쇄(원자로)폐쇄(설비용량)
29원전운영네덜란드1482<NA><NA>155
30원전운영아르메니아1448<NA><NA>1376
31원전운영UAE3410711345<NA><NA>
32원전운영벨라루스1111011110<NA><NA>
33신규 건설튀르키예<NA><NA>44456<NA><NA>
34신규 건설방글라데시<NA><NA>22160<NA><NA>
35신규 건설이집트<NA><NA>11194<NA><NA>
36원전 폐쇄이탈리아<NA><NA><NA><NA>41423
37원전 폐쇄리투아니아<NA><NA><NA><NA>22370
38원전 폐쇄카자흐스탄<NA><NA><NA><NA>152