Overview

Dataset statistics

Number of variables12
Number of observations290
Missing cells378
Missing cells (%)10.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.9 KiB
Average record size in memory105.5 B

Variable types

Numeric4
Categorical8

Dataset

Description2021년 12월 기준 _ 한국지역난방공사에서 운영하는 전기/열생산/냉수생산 시설에 대한 정보를 제공합니다. 현재 API를 통해 실시간으로 데이터를 제공하고 있습니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15069252/fileData.do

Alerts

단위 is highly overall correlated with 열병합(CHP) and 8 other fieldsHigh correlation
히트펌프(HP) is highly overall correlated with 열병합(CHP) and 6 other fieldsHigh correlation
지사 is highly overall correlated with 열병합(CHP) and 7 other fieldsHigh correlation
터보식 is highly overall correlated with 시설구분 and 4 other fieldsHigh correlation
구역형집단에너지(CES) is highly overall correlated with 열병합(CHP) and 6 other fieldsHigh correlation
시설구분 is highly overall correlated with 열병합(CHP) and 8 other fieldsHigh correlation
빙축열 is highly overall correlated with 시설구분 and 4 other fieldsHigh correlation
흡수식 is highly overall correlated with 시설구분 and 4 other fieldsHigh correlation
열병합(CHP) is highly overall correlated with 열전용(PLB) and 5 other fieldsHigh correlation
열전용(PLB) is highly overall correlated with 열병합(CHP) and 5 other fieldsHigh correlation
소각열(INC) is highly overall correlated with 시설구분 and 4 other fieldsHigh correlation
구역형집단에너지(CES) is highly imbalanced (66.0%)Imbalance
히트펌프(HP) is highly imbalanced (54.6%)Imbalance
흡수식 is highly imbalanced (74.9%)Imbalance
터보식 is highly imbalanced (74.9%)Imbalance
빙축열 is highly imbalanced (78.9%)Imbalance
열병합(CHP) has 66 (22.8%) missing valuesMissing
열전용(PLB) has 130 (44.8%) missing valuesMissing
소각열(INC) has 182 (62.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:21:29.833207
Analysis finished2023-12-12 17:21:32.500747
Duration2.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct9
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.1414
Minimum2013
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T02:21:32.564215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12015
median2017
Q32019
95-th percentile2021
Maximum2021
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5877028
Coefficient of variation (CV)0.0012828564
Kurtosis-1.2268205
Mean2017.1414
Median Absolute Deviation (MAD)2
Skewness-0.073717737
Sum584971
Variance6.6962057
MonotonicityIncreasing
2023-12-13T02:21:32.703861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 35
12.1%
2018 34
11.7%
2019 34
11.7%
2020 34
11.7%
2015 31
10.7%
2016 31
10.7%
2017 31
10.7%
2013 30
10.3%
2014 30
10.3%
ValueCountFrequency (%)
2013 30
10.3%
2014 30
10.3%
2015 31
10.7%
2016 31
10.7%
2017 31
10.7%
2018 34
11.7%
2019 34
11.7%
2020 34
11.7%
2021 35
12.1%
ValueCountFrequency (%)
2021 35
12.1%
2020 34
11.7%
2019 34
11.7%
2018 34
11.7%
2017 31
10.7%
2016 31
10.7%
2015 31
10.7%
2014 30
10.3%
2013 30
10.3%

시설구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
열생산시설
165 
전기생산시설
98 
냉수생산시설
27 

Length

Max length6
Median length5
Mean length5.4310345
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
열생산시설 165
56.9%
전기생산시설 98
33.8%
냉수생산시설 27
 
9.3%

Length

2023-12-13T02:21:32.846927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:32.973232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
열생산시설 165
56.9%
전기생산시설 98
33.8%
냉수생산시설 27
 
9.3%

지사
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
강남
27 
광교
 
18
판교
 
18
중앙
 
18
화성
 
18
Other values (15)
191 

Length

Max length6
Median length2
Mean length2.3241379
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row중앙
2nd row중앙(상암)
3rd row분당
4th row고양
5th row강남

Common Values

ValueCountFrequency (%)
강남 27
 
9.3%
광교 18
 
6.2%
판교 18
 
6.2%
중앙 18
 
6.2%
화성 18
 
6.2%
중앙(상암) 18
 
6.2%
파주 18
 
6.2%
청주 18
 
6.2%
수원 18
 
6.2%
대구 18
 
6.2%
Other values (10) 101
34.8%

Length

2023-12-13T02:21:33.137767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남 27
 
9.3%
청주 18
 
6.2%
삼송 18
 
6.2%
광교 18
 
6.2%
대구 18
 
6.2%
수원 18
 
6.2%
고양 18
 
6.2%
파주 18
 
6.2%
중앙(상암 18
 
6.2%
화성 18
 
6.2%
Other values (10) 101
34.8%

단위
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Gcal/h
165 
MW
98 
RT
27 

Length

Max length6
Median length6
Mean length4.2758621
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGcal/h
2nd rowGcal/h
3rd rowGcal/h
4th rowGcal/h
5th rowGcal/h

Common Values

ValueCountFrequency (%)
Gcal/h 165
56.9%
MW 98
33.8%
RT 27
 
9.3%

Length

2023-12-13T02:21:33.318692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:33.452016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gcal/h 165
56.9%
mw 98
33.8%
rt 27
 
9.3%

열병합(CHP)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)17.9%
Missing66
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean234.08482
Minimum3
Maximum768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T02:21:33.575496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q158
median133
Q3396.25
95-th percentile697
Maximum768
Range765
Interquartile range (IQR)338.25

Descriptive statistics

Standard deviation224.13172
Coefficient of variation (CV)0.9574808
Kurtosis-0.62877466
Mean234.08482
Median Absolute Deviation (MAD)90
Skewness0.86561519
Sum52435
Variance50235.029
MonotonicityNot monotonic
2023-12-13T02:21:33.745782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
512 12
 
4.1%
43 9
 
3.1%
86 9
 
3.1%
71 9
 
3.1%
105 9
 
3.1%
145 9
 
3.1%
393 9
 
3.1%
172 9
 
3.1%
98 9
 
3.1%
516 9
 
3.1%
Other values (30) 131
45.2%
(Missing) 66
22.8%
ValueCountFrequency (%)
3 9
3.1%
10 6
2.1%
18 9
3.1%
22 4
1.4%
25 3
 
1.0%
43 9
3.1%
45 4
1.4%
47 9
3.1%
58 4
1.4%
61 5
1.7%
ValueCountFrequency (%)
768 4
 
1.4%
708 3
 
1.0%
697 6
2.1%
648 3
 
1.0%
642 6
2.1%
533 4
 
1.4%
516 9
3.1%
512 12
4.1%
431 3
 
1.0%
416 1
 
0.3%

열전용(PLB)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)10.6%
Missing130
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean234.45
Minimum40
Maximum783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T02:21:33.891255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile40
Q1103
median204
Q3222
95-th percentile783
Maximum783
Range743
Interquartile range (IQR)119

Descriptive statistics

Standard deviation192.75725
Coefficient of variation (CV)0.82216782
Kurtosis2.2454108
Mean234.45
Median Absolute Deviation (MAD)68
Skewness1.7878446
Sum37512
Variance37155.356
MonotonicityNot monotonic
2023-12-13T02:21:34.030040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
206 27
 
9.3%
102 21
 
7.2%
136 13
 
4.5%
272 9
 
3.1%
428 9
 
3.1%
222 9
 
3.1%
649 9
 
3.1%
783 9
 
3.1%
171 9
 
3.1%
103 9
 
3.1%
Other values (7) 36
 
12.4%
(Missing) 130
44.8%
ValueCountFrequency (%)
40 9
3.1%
68 3
 
1.0%
102 21
7.2%
103 9
3.1%
119 1
 
0.3%
125 8
 
2.8%
136 13
4.5%
139 6
 
2.1%
171 9
3.1%
204 2
 
0.7%
ValueCountFrequency (%)
783 9
 
3.1%
649 9
 
3.1%
428 9
 
3.1%
272 9
 
3.1%
222 9
 
3.1%
209 7
 
2.4%
206 27
9.3%
204 2
 
0.7%
171 9
 
3.1%
139 6
 
2.1%

소각열(INC)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)9.3%
Missing182
Missing (%)62.8%
Infinite0
Infinite (%)0.0%
Mean19.203704
Minimum4
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T02:21:34.183286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q17
median15
Q327
95-th percentile57
Maximum57
Range53
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.055511
Coefficient of variation (CV)0.78398994
Kurtosis0.85246883
Mean19.203704
Median Absolute Deviation (MAD)11
Skewness1.160085
Sum2074
Variance226.6684
MonotonicityNot monotonic
2023-12-13T02:21:34.307647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 27
 
9.3%
27 23
 
7.9%
34 9
 
3.1%
16 9
 
3.1%
57 9
 
3.1%
14 9
 
3.1%
15 9
 
3.1%
9 9
 
3.1%
12 2
 
0.7%
8 2
 
0.7%
(Missing) 182
62.8%
ValueCountFrequency (%)
4 27
9.3%
8 2
 
0.7%
9 9
 
3.1%
12 2
 
0.7%
14 9
 
3.1%
15 9
 
3.1%
16 9
 
3.1%
27 23
7.9%
34 9
 
3.1%
57 9
 
3.1%
ValueCountFrequency (%)
57 9
 
3.1%
34 9
 
3.1%
27 23
7.9%
16 9
 
3.1%
15 9
 
3.1%
14 9
 
3.1%
12 2
 
0.7%
9 9
 
3.1%
8 2
 
0.7%
4 27
9.3%

구역형집단에너지(CES)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
254 
9
 
9
46
 
9
6
 
9
35
 
9

Length

Max length4
Median length4
Mean length3.6896552
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row9
3rd row<NA>
4th row<NA>
5th row46

Common Values

ValueCountFrequency (%)
<NA> 254
87.6%
9 9
 
3.1%
46 9
 
3.1%
6 9
 
3.1%
35 9
 
3.1%

Length

2023-12-13T02:21:34.485624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:34.651634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 254
87.6%
9 9
 
3.1%
46 9
 
3.1%
6 9
 
3.1%
35 9
 
3.1%

히트펌프(HP)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
231 
11
 
18
5
 
18
6
 
9
63
 
8

Length

Max length4
Median length4
Mean length3.4793103
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 231
79.7%
11 18
 
6.2%
5 18
 
6.2%
6 9
 
3.1%
63 8
 
2.8%
2 6
 
2.1%

Length

2023-12-13T02:21:34.784536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:34.952844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 231
79.7%
11 18
 
6.2%
5 18
 
6.2%
6 9
 
3.1%
63 8
 
2.8%
2 6
 
2.1%

흡수식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
263 
13500
 
9
16100
 
7
4000
 
5
13600
 
4

Length

Max length5
Median length4
Mean length4.0758621
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 263
90.7%
13500 9
 
3.1%
16100 7
 
2.4%
4000 5
 
1.7%
13600 4
 
1.4%
10500 2
 
0.7%

Length

2023-12-13T02:21:35.099931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:35.274258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 263
90.7%
13500 9
 
3.1%
16100 7
 
2.4%
4000 5
 
1.7%
13600 4
 
1.4%
10500 2
 
0.7%

터보식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
263 
9000
 
9
24240
 
7
4000
 
5
18400
 
4

Length

Max length5
Median length4
Mean length4.0448276
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 263
90.7%
9000 9
 
3.1%
24240 7
 
2.4%
4000 5
 
1.7%
18400 4
 
1.4%
13740 2
 
0.7%

Length

2023-12-13T02:21:35.818568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:35.953809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 263
90.7%
9000 9
 
3.1%
24240 7
 
2.4%
4000 5
 
1.7%
18400 4
 
1.4%
13740 2
 
0.7%

빙축열
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
272 
12600
 
9
9201
 
7
9180
 
2

Length

Max length5
Median length4
Mean length4.0310345
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 272
93.8%
12600 9
 
3.1%
9201 7
 
2.4%
9180 2
 
0.7%

Length

2023-12-13T02:21:36.138613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:36.286360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 272
93.8%
12600 9
 
3.1%
9201 7
 
2.4%
9180 2
 
0.7%

Interactions

2023-12-13T02:21:31.601682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.396877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.771237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.167958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.707284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.494247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.864374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.270913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.792047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.588608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.970864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.395208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.876932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:30.682649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.062260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:21:31.507957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:21:36.386010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도시설구분지사단위열병합(CHP)열전용(PLB)소각열(INC)구역형집단에너지(CES)히트펌프(HP)흡수식터보식빙축열
기준년도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
시설구분0.0001.0000.6851.0000.510NaNNaN1.000NaNNaNNaNNaN
지사0.0000.6851.0000.6850.9240.9960.9931.0001.0001.0001.0001.000
단위0.0001.0000.6851.0000.510NaNNaN1.000NaNNaNNaNNaN
열병합(CHP)0.0000.5100.9240.5101.0000.8310.937NaN1.000NaNNaNNaN
열전용(PLB)0.000NaN0.996NaN0.8311.0000.8600.9831.000NaNNaNNaN
소각열(INC)0.000NaN0.993NaN0.9370.8601.0000.9830.811NaNNaNNaN
구역형집단에너지(CES)0.0001.0001.0001.000NaN0.9830.9831.000NaNNaNNaNNaN
히트펌프(HP)0.000NaN1.000NaN1.0001.0000.811NaN1.000NaNNaNNaN
흡수식0.000NaN1.000NaNNaNNaNNaNNaNNaN1.0001.0000.697
터보식0.000NaN1.000NaNNaNNaNNaNNaNNaN1.0001.0000.697
빙축열0.000NaN1.000NaNNaNNaNNaNNaNNaN0.6970.6971.000
2023-12-13T02:21:36.542594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단위히트펌프(HP)지사터보식구역형집단에너지(CES)시설구분빙축열흡수식
단위1.0001.0000.4641.0000.9701.0001.0001.000
히트펌프(HP)1.0001.0000.981NaN1.0001.000NaNNaN
지사0.4640.9811.0000.9570.9850.4640.9680.957
터보식1.000NaN0.9571.000NaN1.0000.7041.000
구역형집단에너지(CES)0.9701.0000.985NaN1.0000.970NaNNaN
시설구분1.0001.0000.4641.0000.9701.0001.0001.000
빙축열1.000NaN0.9680.704NaN1.0001.0000.704
흡수식1.000NaN0.9571.000NaN1.0000.7041.000
2023-12-13T02:21:36.679461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도열병합(CHP)열전용(PLB)소각열(INC)시설구분지사단위구역형집단에너지(CES)히트펌프(HP)흡수식터보식빙축열
기준년도1.000-0.0270.031-0.0550.0000.0000.0000.0000.0000.0000.0000.000
열병합(CHP)-0.0271.000-0.638-0.3400.5040.6930.5041.0001.0000.0000.0000.000
열전용(PLB)0.031-0.6381.000-0.0421.0000.9541.0000.8810.9910.0000.0000.000
소각열(INC)-0.055-0.340-0.0421.0001.0000.9521.0000.8810.8090.0000.0000.000
시설구분0.0000.5041.0001.0001.0000.4641.0000.9701.0001.0001.0001.000
지사0.0000.6930.9540.9520.4641.0000.4640.9850.9810.9570.9570.968
단위0.0000.5041.0001.0001.0000.4641.0000.9701.0001.0001.0001.000
구역형집단에너지(CES)0.0001.0000.8810.8810.9700.9850.9701.0001.0000.0000.0000.000
히트펌프(HP)0.0001.0000.9910.8091.0000.9811.0001.0001.0000.0000.0000.000
흡수식0.0000.0000.0000.0001.0000.9571.0000.0000.0001.0001.0000.704
터보식0.0000.0000.0000.0001.0000.9571.0000.0000.0001.0001.0000.704
빙축열0.0000.0000.0000.0001.0000.9681.0000.0000.0000.7040.7041.000

Missing values

2023-12-13T02:21:32.020873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:21:32.225211image/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.
2023-12-13T02:21:32.384136image/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

기준년도시설구분지사단위열병합(CHP)열전용(PLB)소각열(INC)구역형집단에너지(CES)히트펌프(HP)흡수식터보식빙축열
02013열생산시설중앙Gcal/h36940<NA><NA><NA><NA><NA><NA>
12013열생산시설중앙(상암)Gcal/h<NA>119349<NA><NA><NA><NA>
22013열생산시설분당Gcal/h69710227<NA><NA><NA><NA><NA>
32013열생산시설고양Gcal/h64210216<NA><NA><NA><NA><NA>
42013열생산시설강남Gcal/h<NA>7835746<NA><NA><NA><NA>
52013열생산시설대구Gcal/h8627227<NA><NA><NA><NA><NA>
62013열생산시설수원Gcal/h7142827<NA><NA><NA><NA><NA>
72013열생산시설청주Gcal/h10522214<NA><NA><NA><NA><NA>
82013열생산시설용인Gcal/h<NA>6494<NA>6<NA><NA><NA>
92013열생산시설양산Gcal/h<NA>6815<NA><NA><NA><NA><NA>
기준년도시설구분지사단위열병합(CHP)열전용(PLB)소각열(INC)구역형집단에너지(CES)히트펌프(HP)흡수식터보식빙축열
2802021전기생산시설화성MW512<NA><NA><NA><NA><NA><NA><NA>
2812021전기생산시설파주MW516<NA><NA><NA><NA><NA><NA><NA>
2822021전기생산시설판교MW146<NA><NA><NA><NA><NA><NA><NA>
2832021전기생산시설삼송MW100<NA><NA><NA><NA><NA><NA><NA>
2842021전기생산시설광교MW145<NA><NA><NA><NA><NA><NA><NA>
2852021전기생산시설광주전남MW22<NA><NA><NA><NA><NA><NA><NA>
2862021전기생산시설동탄MW768<NA><NA><NA><NA><NA><NA><NA>
2872021냉수생산시설중앙(상암)RT<NA><NA><NA><NA><NA>161002424012600
2882021냉수생산시설고양RT<NA><NA><NA><NA><NA>13600184009201
2892021냉수생산시설강남RT<NA><NA><NA><NA><NA>135009000<NA>