Overview

Dataset statistics

Number of variables11
Number of observations1150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.1 KiB
Average record size in memory97.1 B

Variable types

Categorical3
Text1
Numeric7

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 용량(kwh), 태양광(kwh), 매립지가스(LFG)(kwh), 바이오(kwh), 소수력(kwh), 연료전지(kwh), 폐기물(kwh), 풍력(kwh)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110107

Alerts

용량(kwh) is highly overall correlated with 태양광(kwh)High correlation
태양광(kwh) is highly overall correlated with 용량(kwh)High correlation
폐기물(kwh) is highly overall correlated with 매립지가스(LFG)(kwh)High correlation
매립지가스(LFG)(kwh) is highly overall correlated with 폐기물(kwh)High correlation
매립지가스(LFG)(kwh) is highly imbalanced (95.8%)Imbalance
바이오(kwh) has 1090 (94.8%) zerosZeros
소수력(kwh) has 1036 (90.1%) zerosZeros
연료전지(kwh) has 1130 (98.3%) zerosZeros
폐기물(kwh) has 1099 (95.6%) zerosZeros
풍력(kwh) has 1126 (97.9%) zerosZeros

Reproduction

Analysis started2023-12-11 00:31:10.229982
Analysis finished2023-12-11 00:31:16.550648
Duration6.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
2020
239 
2019
229 
2018
228 
2016
227 
2017
227 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2019
3rd row2016
4th row2017
5th row2019

Common Values

ValueCountFrequency (%)
2020 239
20.8%
2019 229
19.9%
2018 228
19.8%
2016 227
19.7%
2017 227
19.7%

Length

2023-12-11T09:31:16.611184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:16.715573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 239
20.8%
2019 229
19.9%
2018 228
19.8%
2016 227
19.7%
2017 227
19.7%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
경기도
163 
서울특별시
120 
경상북도
111 
전라남도
110 
경상남도
94 
Other values (11)
552 

Length

Max length7
Median length5
Mean length4.1173913
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row서울특별시
3rd row강원도
4th row대구광역시
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 163
14.2%
서울특별시 120
10.4%
경상북도 111
9.7%
전라남도 110
9.6%
경상남도 94
8.2%
강원도 90
7.8%
충청남도 81
7.0%
부산광역시 78
6.8%
전라북도 71
6.2%
충청북도 62
 
5.4%
Other values (6) 170
14.8%

Length

2023-12-11T09:31:16.828438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 163
14.2%
서울특별시 120
10.4%
경상북도 111
9.7%
전라남도 110
9.6%
경상남도 94
8.2%
강원도 90
7.8%
충청남도 81
7.0%
부산광역시 78
6.8%
전라북도 71
6.2%
충청북도 62
 
5.4%
Other values (6) 170
14.8%

시군구명
Text

Distinct243
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
2023-12-11T09:31:17.134282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9243478
Min length2

Characters and Unicode

Total characters3363
Distinct characters139
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

Unique37 ?
Unique (%)3.2%

Sample

1st row횡성군
2nd row구로구
3rd row횡성군
4th row수성구
5th row평택시
ValueCountFrequency (%)
동구 28
 
2.4%
중구 26
 
2.3%
서구 24
 
2.1%
남구 24
 
2.1%
북구 21
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
김해시 5
 
0.4%
달서구 5
 
0.4%
횡성군 5
 
0.4%
Other values (233) 992
86.3%
2023-12-11T09:31:17.640118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 429
 
12.8%
구 389
 
11.6%
시 377
 
11.2%
천 108
 
3.2%
주 97
 
2.9%
성 89
 
2.6%
양 88
 
2.6%
동 83
 
2.5%
산 83
 
2.5%
서 65
 
1.9%
Other values (129) 1555
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3363
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 429
 
12.8%
구 389
 
11.6%
시 377
 
11.2%
천 108
 
3.2%
주 97
 
2.9%
성 89
 
2.6%
양 88
 
2.6%
동 83
 
2.5%
산 83
 
2.5%
서 65
 
1.9%
Other values (129) 1555
46.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3363
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 429
 
12.8%
구 389
 
11.6%
시 377
 
11.2%
천 108
 
3.2%
주 97
 
2.9%
성 89
 
2.6%
양 88
 
2.6%
동 83
 
2.5%
산 83
 
2.5%
서 65
 
1.9%
Other values (129) 1555
46.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3363
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 429
 
12.8%
구 389
 
11.6%
시 377
 
11.2%
천 108
 
3.2%
주 97
 
2.9%
성 89
 
2.6%
양 88
 
2.6%
동 83
 
2.5%
산 83
 
2.5%
서 65
 
1.9%
Other values (129) 1555
46.2%

용량(kwh)
Real number (ℝ)

HIGH CORRELATION 

Distinct1088
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18539.337
Minimum9
Maximum325852.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:17.795778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile91.8
Q1991.7125
median7910.17
Q324609.4
95-th percentile69402.424
Maximum325852.03
Range325843.03
Interquartile range (IQR)23617.688

Descriptive statistics

Standard deviation29258.792
Coefficient of variation (CV)1.5782005
Kurtosis21.974562
Mean18539.337
Median Absolute Deviation (MAD)7540.105
Skewness3.723742
Sum21320238
Variance8.5607688 × 108
MonotonicityNot monotonic
2023-12-11T09:31:17.968124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 4
 
0.3%
94.0 4
 
0.3%
49.55 4
 
0.3%
112.85 4
 
0.3%
1775.79 4
 
0.3%
61.0 3
 
0.3%
588.54 3
 
0.3%
317.38 3
 
0.3%
243.06 3
 
0.3%
54.0 3
 
0.3%
Other values (1078) 1115
97.0%
ValueCountFrequency (%)
9.0 2
0.2%
17.0 1
0.1%
18.0 1
0.1%
19.0 2
0.2%
19.52 1
0.1%
20.0 2
0.2%
21.0 2
0.2%
25.0 1
0.1%
28.0 2
0.2%
29.0 1
0.1%
ValueCountFrequency (%)
325852.03 1
0.1%
261871.25 1
0.1%
219620.98 1
0.1%
187765.62 1
0.1%
176099.83 1
0.1%
173813.48 1
0.1%
173742.34 1
0.1%
173300.05 1
0.1%
170150.68 1
0.1%
153672.23 1
0.1%

태양광(kwh)
Real number (ℝ)

HIGH CORRELATION 

Distinct1088
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18456.487
Minimum9
Maximum325802.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:18.146078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile91.8
Q1978.445
median7765.385
Q324323.748
95-th percentile69402.424
Maximum325802.03
Range325793.03
Interquartile range (IQR)23345.303

Descriptive statistics

Standard deviation29215.238
Coefficient of variation (CV)1.5829252
Kurtosis22.086519
Mean18456.487
Median Absolute Deviation (MAD)7410.58
Skewness3.7337959
Sum21224960
Variance8.5353014 × 108
MonotonicityNot monotonic
2023-12-11T09:31:18.597578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1775.79 4
 
0.3%
94.0 4
 
0.3%
49.55 4
 
0.3%
40.0 4
 
0.3%
112.85 4
 
0.3%
588.54 3
 
0.3%
243.06 3
 
0.3%
61.0 3
 
0.3%
54.0 3
 
0.3%
317.38 3
 
0.3%
Other values (1078) 1115
97.0%
ValueCountFrequency (%)
9.0 2
0.2%
17.0 1
0.1%
18.0 1
0.1%
19.0 2
0.2%
19.52 1
0.1%
20.0 2
0.2%
21.0 2
0.2%
25.0 1
0.1%
28.0 2
0.2%
29.0 1
0.1%
ValueCountFrequency (%)
325802.03 1
0.1%
261871.25 1
0.1%
219100.98 1
0.1%
187765.62 1
0.1%
176049.83 1
0.1%
173624.48 1
0.1%
173285.05 1
0.1%
173222.34 1
0.1%
170150.68 1
0.1%
152752.23 1
0.1%

매립지가스(LFG)(kwh)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
0
1142 
95
 
4
99
 
4

Length

Max length2
Median length1
Mean length1.0069565
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1142
99.3%
95 4
 
0.3%
99 4
 
0.3%

Length

2023-12-11T09:31:18.780174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:31:18.898855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1142
99.3%
95 4
 
0.3%
99 4
 
0.3%

바이오(kwh)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.949565
Minimum0
Maximum2360
Zeros1090
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:19.003066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum2360
Range2360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation182.13802
Coefficient of variation (CV)5.7007981
Kurtosis72.285417
Mean31.949565
Median Absolute Deviation (MAD)0
Skewness7.751449
Sum36742
Variance33174.259
MonotonicityNot monotonic
2023-12-11T09:31:19.117566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1090
94.8%
450 7
 
0.6%
800 4
 
0.3%
20 4
 
0.3%
80 4
 
0.3%
520 4
 
0.3%
50 4
 
0.3%
750 4
 
0.3%
600 4
 
0.3%
640 4
 
0.3%
Other values (13) 21
 
1.8%
ValueCountFrequency (%)
0 1090
94.8%
20 4
 
0.3%
50 4
 
0.3%
60 1
 
0.1%
80 4
 
0.3%
99 2
 
0.2%
250 2
 
0.2%
450 7
 
0.6%
498 2
 
0.2%
500 3
 
0.3%
ValueCountFrequency (%)
2360 1
 
0.1%
2240 1
 
0.1%
1870 2
0.2%
1460 1
 
0.1%
1420 2
0.2%
998 1
 
0.1%
980 2
0.2%
900 1
 
0.1%
800 4
0.3%
750 4
0.3%

소수력(kwh)
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.44087
Minimum0
Maximum870
Zeros1036
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:19.256202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180
Maximum870
Range870
Interquartile range (IQR)0

Descriptive statistics

Standard deviation96.440133
Coefficient of variation (CV)4.1141875
Kurtosis31.073429
Mean23.44087
Median Absolute Deviation (MAD)0
Skewness5.2340188
Sum26957
Variance9300.6993
MonotonicityNot monotonic
2023-12-11T09:31:19.377452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 1036
90.1%
180 9
 
0.8%
45 7
 
0.6%
99 6
 
0.5%
55 5
 
0.4%
870 4
 
0.3%
530 4
 
0.3%
220 4
 
0.3%
430 4
 
0.3%
100 4
 
0.3%
Other values (25) 67
 
5.8%
ValueCountFrequency (%)
0 1036
90.1%
6 1
 
0.1%
9 2
 
0.2%
10 2
 
0.2%
15 1
 
0.1%
16 3
 
0.3%
25 2
 
0.2%
30 1
 
0.1%
35 4
 
0.3%
45 7
 
0.6%
ValueCountFrequency (%)
870 4
0.3%
600 2
0.2%
530 4
0.3%
490 4
0.3%
480 4
0.3%
465 4
0.3%
430 4
0.3%
410 4
0.3%
360 3
0.3%
320 4
0.3%

연료전지(kwh)
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4191304
Minimum0
Maximum1365
Zeros1130
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:19.477747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1365
Range1365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation72.317945
Coefficient of variation (CV)9.747496
Kurtosis196.57143
Mean7.4191304
Median Absolute Deviation (MAD)0
Skewness13.104031
Sum8532
Variance5229.8851
MonotonicityNot monotonic
2023-12-11T09:31:19.598020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1130
98.3%
440 3
 
0.3%
297 2
 
0.2%
300 2
 
0.2%
210 2
 
0.2%
96 2
 
0.2%
880 1
 
0.1%
420 1
 
0.1%
100 1
 
0.1%
945 1
 
0.1%
Other values (5) 5
 
0.4%
ValueCountFrequency (%)
0 1130
98.3%
96 2
 
0.2%
100 1
 
0.1%
105 1
 
0.1%
198 1
 
0.1%
210 2
 
0.2%
297 2
 
0.2%
300 2
 
0.2%
315 1
 
0.1%
420 1
 
0.1%
ValueCountFrequency (%)
1365 1
 
0.1%
1078 1
 
0.1%
945 1
 
0.1%
880 1
 
0.1%
440 3
0.3%
420 1
 
0.1%
315 1
 
0.1%
300 2
0.2%
297 2
0.2%
210 2
0.2%

폐기물(kwh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum0
Maximum909
Zeros1099
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:19.738108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum909
Range909
Interquartile range (IQR)0

Descriptive statistics

Standard deviation98.260302
Coefficient of variation (CV)5.4771629
Kurtosis44.82898
Mean17.94
Median Absolute Deviation (MAD)0
Skewness6.4520986
Sum20631
Variance9655.0869
MonotonicityNot monotonic
2023-12-11T09:31:19.866478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1099
95.6%
160 8
 
0.7%
140 4
 
0.3%
440 4
 
0.3%
133 4
 
0.3%
500 4
 
0.3%
760 4
 
0.3%
498 4
 
0.3%
170 4
 
0.3%
400 4
 
0.3%
Other values (5) 11
 
1.0%
ValueCountFrequency (%)
0 1099
95.6%
133 4
 
0.3%
140 4
 
0.3%
160 8
 
0.7%
170 4
 
0.3%
280 1
 
0.1%
400 4
 
0.3%
440 4
 
0.3%
480 3
 
0.3%
490 2
 
0.2%
ValueCountFrequency (%)
909 3
0.3%
880 2
0.2%
760 4
0.3%
500 4
0.3%
498 4
0.3%
490 2
0.2%
480 3
0.3%
440 4
0.3%
400 4
0.3%
280 1
 
0.1%

풍력(kwh)
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1387826
Minimum0
Maximum99.4
Zeros1126
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-11T09:31:19.974752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum99.4
Range99.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.7028116
Coefficient of variation (CV)8.520337
Kurtosis86.18113
Mean1.1387826
Median Absolute Deviation (MAD)0
Skewness9.254037
Sum1309.6
Variance94.144552
MonotonicityNot monotonic
2023-12-11T09:31:20.071538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 1126
97.9%
6.0 4
 
0.3%
99.0 4
 
0.3%
99.4 4
 
0.3%
80.0 4
 
0.3%
20.0 4
 
0.3%
8.0 2
 
0.2%
21.0 1
 
0.1%
55.0 1
 
0.1%
ValueCountFrequency (%)
0.0 1126
97.9%
6.0 4
 
0.3%
8.0 2
 
0.2%
20.0 4
 
0.3%
21.0 1
 
0.1%
55.0 1
 
0.1%
80.0 4
 
0.3%
99.0 4
 
0.3%
99.4 4
 
0.3%
ValueCountFrequency (%)
99.4 4
 
0.3%
99.0 4
 
0.3%
80.0 4
 
0.3%
55.0 1
 
0.1%
21.0 1
 
0.1%
20.0 4
 
0.3%
8.0 2
 
0.2%
6.0 4
 
0.3%
0.0 1126
97.9%

Interactions

2023-12-11T09:31:15.612435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.869279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.919711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.631180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.269509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.072794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.904537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.721757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:10.982574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.030825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.737542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.379237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.184015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.023524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.822652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.089511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.136571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.837199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.480003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.295699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.125194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.909378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.197210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.235365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.918435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.592543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.430621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.225070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:16.001975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.301264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.338772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.998660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.729037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.551518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.326632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:16.094949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.735189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.440777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.092374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.850992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.676660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.421193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:16.171987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:11.823131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:12.537239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.183119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:13.950509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:14.779639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:31:15.510822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:31:20.181332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명용량(kwh)태양광(kwh)매립지가스(LFG)(kwh)바이오(kwh)소수력(kwh)연료전지(kwh)폐기물(kwh)풍력(kwh)
통계연도1.0000.0000.2280.2240.0000.0000.0000.0000.0000.000
시도명0.0001.0000.4300.4310.4950.3220.4570.0970.3370.441
용량(kwh)0.2280.4301.0001.0000.0000.2680.1450.1940.0000.000
태양광(kwh)0.2240.4311.0001.0000.0000.2680.1460.1730.0000.000
매립지가스(LFG)(kwh)0.0000.4950.0000.0001.0000.0000.0000.0000.7670.000
바이오(kwh)0.0000.3220.2680.2680.0001.0000.2960.0000.0000.000
소수력(kwh)0.0000.4570.1450.1460.0000.2961.0000.0000.2690.726
연료전지(kwh)0.0000.0970.1940.1730.0000.0000.0001.0000.2360.217
폐기물(kwh)0.0000.3370.0000.0000.7670.0000.2690.2361.0000.000
풍력(kwh)0.0000.4410.0000.0000.0000.0000.7260.2170.0001.000
2023-12-11T09:31:20.307599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명매립지가스(LFG)(kwh)통계연도
시도명1.0000.3100.000
매립지가스(LFG)(kwh)0.3101.0000.000
통계연도0.0000.0001.000
2023-12-11T09:31:20.416668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용량(kwh)태양광(kwh)바이오(kwh)소수력(kwh)연료전지(kwh)폐기물(kwh)풍력(kwh)통계연도시도명매립지가스(LFG)(kwh)
용량(kwh)1.0001.0000.2260.1960.0870.1240.0720.1330.1920.000
태양광(kwh)1.0001.0000.2220.1900.0850.1200.0700.1300.1920.000
바이오(kwh)0.2260.2221.0000.063-0.031-0.0510.0200.0000.1170.000
소수력(kwh)0.1960.1900.0631.000-0.024-0.0100.2250.0000.1760.000
연료전지(kwh)0.0870.085-0.031-0.0241.0000.0960.0280.0000.0440.000
폐기물(kwh)0.1240.120-0.051-0.0100.0961.000-0.0310.0000.1600.704
풍력(kwh)0.0720.0700.0200.2250.028-0.0311.0000.0000.2410.000
통계연도0.1330.1300.0000.0000.0000.0000.0001.0000.0000.000
시도명0.1920.1920.1170.1760.0440.1600.2410.0001.0000.310
매립지가스(LFG)(kwh)0.0000.0000.0000.0000.0000.7040.0000.0000.3101.000

Missing values

2023-12-11T09:31:16.299953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:31:16.482146image/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

통계연도시도명시군구명용량(kwh)태양광(kwh)매립지가스(LFG)(kwh)바이오(kwh)소수력(kwh)연료전지(kwh)폐기물(kwh)풍력(kwh)
02020강원도횡성군6295.396295.39000000.0
12019서울특별시구로구1067.781067.78000000.0
22016강원도횡성군10267.9510267.95000000.0
32017대구광역시수성구394.16394.16000000.0
42019경기도평택시33379.8433379.84000000.0
52018전라남도영광군25021.8425021.84000000.0
62018전라남도보성군28391.9928391.99000000.0
72020경상북도영주시32921.7532921.75000000.0
82020경상남도통영시1941.591941.59000000.0
92016경상북도경주시8633.418633.41000000.0
통계연도시도명시군구명용량(kwh)태양광(kwh)매립지가스(LFG)(kwh)바이오(kwh)소수력(kwh)연료전지(kwh)폐기물(kwh)풍력(kwh)
11402016강원도화천군1807.771709.770098000.0
11412016대전광역시서구578.44578.44000000.0
11422020강원도영월군12568.9812568.98000000.0
11432020충청남도보령시27959.4427959.44000000.0
11442019인천광역시부평구860.36860.36000000.0
11452018전라남도곡성군31621.631621.6000000.0
11462018대전광역시대덕구3738.943639.949900000.0
11472016충청북도옥천군15482.5715482.57000000.0
11482017대구광역시북구1237.31237.3000000.0
11492017경기도양주시2965.742965.74000000.0