Overview

Dataset statistics

Number of variables11
Number of observations27
Missing cells64
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory103.9 B

Variable types

Numeric11

Dataset

Description"95년부터 "21년도의 연도별 유형별 집단에너지 공급기준 공급세대수 및 업체수 정보로, 공급세대수 및 업체수 등의 내용을 제공합니다.-연도,지역난방_주택수(천세대),지역난방_난방빌딩수,지역난방_냉방빌딩수,산업단지_업체수,병행_주택수(천세대),병행_난방빌딩수,병행_냉방빌딩수,병행_업체수,국내_총_주택_수(천호),보급율(%)
Author산업통상자원부
URLhttps://www.data.go.kr/data/15054430/fileData.do

Alerts

연도 is highly overall correlated with 지역난방_주택수(천세대) and 8 other fieldsHigh correlation
지역난방_주택수(천세대) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
지역난방_난방빌딩수 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
지역난방_냉방빌딩수 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
산업단지_업체수 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
병행_주택수(천세대) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
병행_난방빌딩수 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
병행_냉방빌딩수 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
국내_총_주택_수(천호) is highly overall correlated with 연도 and 8 other fieldsHigh correlation
보급율 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
병행_주택수(천세대) has 16 (59.3%) missing valuesMissing
병행_난방빌딩수 has 16 (59.3%) missing valuesMissing
병행_냉방빌딩수 has 16 (59.3%) missing valuesMissing
병행_업체수 has 16 (59.3%) missing valuesMissing
연도 has unique valuesUnique
지역난방_주택수(천세대) has unique valuesUnique
지역난방_난방빌딩수 has unique valuesUnique
지역난방_냉방빌딩수 has unique valuesUnique
산업단지_업체수 has unique valuesUnique
국내_총_주택_수(천호) has unique valuesUnique
보급율 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:14:32.961557
Analysis finished2023-12-12 05:14:48.624667
Duration15.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008
Minimum1995
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:48.708853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1996.3
Q12001.5
median2008
Q32014.5
95-th percentile2019.7
Maximum2021
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.0039528157
Kurtosis-1.2
Mean2008
Median Absolute Deviation (MAD)7
Skewness0
Sum54216
Variance63
MonotonicityStrictly increasing
2023-12-12T14:14:48.886765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1995 1
 
3.7%
1996 1
 
3.7%
2021 1
 
3.7%
2020 1
 
3.7%
2019 1
 
3.7%
2018 1
 
3.7%
2017 1
 
3.7%
2016 1
 
3.7%
2015 1
 
3.7%
2014 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1995 1
3.7%
1996 1
3.7%
1997 1
3.7%
1998 1
3.7%
1999 1
3.7%
2000 1
3.7%
2001 1
3.7%
2002 1
3.7%
2003 1
3.7%
2004 1
3.7%
ValueCountFrequency (%)
2021 1
3.7%
2020 1
3.7%
2019 1
3.7%
2018 1
3.7%
2017 1
3.7%
2016 1
3.7%
2015 1
3.7%
2014 1
3.7%
2013 1
3.7%
2012 1
3.7%

지역난방_주택수(천세대)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1820.4444
Minimum525
Maximum3427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:49.054070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile656
Q11130
median1736
Q32377
95-th percentile3259.5
Maximum3427
Range2902
Interquartile range (IQR)1247

Descriptive statistics

Standard deviation861.71664
Coefficient of variation (CV)0.47335509
Kurtosis-0.9716286
Mean1820.4444
Median Absolute Deviation (MAD)653
Skewness0.32026943
Sum49152
Variance742555.56
MonotonicityStrictly increasing
2023-12-12T14:14:49.194108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
525 1
 
3.7%
620 1
 
3.7%
3427 1
 
3.7%
3303 1
 
3.7%
3158 1
 
3.7%
3017 1
 
3.7%
2812 1
 
3.7%
2618 1
 
3.7%
2410 1
 
3.7%
2344 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
525 1
3.7%
620 1
3.7%
740 1
3.7%
839 1
3.7%
912 1
3.7%
980 1
3.7%
1083 1
3.7%
1177 1
3.7%
1251 1
3.7%
1337 1
3.7%
ValueCountFrequency (%)
3427 1
3.7%
3303 1
3.7%
3158 1
3.7%
3017 1
3.7%
2812 1
3.7%
2618 1
3.7%
2410 1
3.7%
2344 1
3.7%
2238 1
3.7%
2153 1
3.7%

지역난방_난방빌딩수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3166.7037
Minimum1253
Maximum6145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:49.392151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1253
5-th percentile1543.9
Q12387
median3148
Q33536
95-th percentile4998.1
Maximum6145
Range4892
Interquartile range (IQR)1149

Descriptive statistics

Standard deviation1142.7458
Coefficient of variation (CV)0.36086288
Kurtosis0.5844773
Mean3166.7037
Median Absolute Deviation (MAD)636
Skewness0.59395329
Sum85501
Variance1305868
MonotonicityNot monotonic
2023-12-12T14:14:49.558058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1253 1
 
3.7%
1477 1
 
3.7%
6145 1
 
3.7%
5098 1
 
3.7%
4765 1
 
3.7%
4708 1
 
3.7%
4156 1
 
3.7%
3784 1
 
3.7%
3526 1
 
3.7%
3400 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1253 1
3.7%
1477 1
3.7%
1700 1
3.7%
1824 1
3.7%
1977 1
3.7%
2102 1
3.7%
2281 1
3.7%
2493 1
3.7%
2786 1
3.7%
2871 1
3.7%
ValueCountFrequency (%)
6145 1
3.7%
5098 1
3.7%
4765 1
3.7%
4708 1
3.7%
4156 1
3.7%
3784 1
3.7%
3546 1
3.7%
3526 1
3.7%
3509 1
3.7%
3400 1
3.7%

지역난방_냉방빌딩수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773
Minimum60
Maximum2653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:49.703830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile119.5
Q1314.5
median492
Q3982.5
95-th percentile2320.2
Maximum2653
Range2593
Interquartile range (IQR)668

Descriptive statistics

Standard deviation714.68993
Coefficient of variation (CV)0.92456653
Kurtosis1.2973586
Mean773
Median Absolute Deviation (MAD)281
Skewness1.4645561
Sum20871
Variance510781.69
MonotonicityStrictly increasing
2023-12-12T14:14:49.826314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
60 1
 
3.7%
106 1
 
3.7%
2653 1
 
3.7%
2418 1
 
3.7%
2092 1
 
3.7%
1756 1
 
3.7%
1498 1
 
3.7%
1139 1
 
3.7%
1049 1
 
3.7%
916 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
60 1
3.7%
106 1
3.7%
151 1
3.7%
175 1
3.7%
211 1
3.7%
231 1
3.7%
278 1
3.7%
351 1
3.7%
391 1
3.7%
403 1
3.7%
ValueCountFrequency (%)
2653 1
3.7%
2418 1
3.7%
2092 1
3.7%
1756 1
3.7%
1498 1
3.7%
1139 1
3.7%
1049 1
3.7%
916 1
3.7%
802 1
3.7%
692 1
3.7%

산업단지_업체수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean667.55556
Minimum463
Maximum908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:49.971280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum463
5-th percentile470.5
Q1543.5
median672
Q3774
95-th percentile869.1
Maximum908
Range445
Interquartile range (IQR)230.5

Descriptive statistics

Standard deviation145.66144
Coefficient of variation (CV)0.21820123
Kurtosis-1.3007297
Mean667.55556
Median Absolute Deviation (MAD)113
Skewness0.088971559
Sum18024
Variance21217.256
MonotonicityNot monotonic
2023-12-12T14:14:50.132087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
463 1
 
3.7%
474 1
 
3.7%
908 1
 
3.7%
867 1
 
3.7%
870 1
 
3.7%
862 1
 
3.7%
866 1
 
3.7%
835 1
 
3.7%
779 1
 
3.7%
769 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
463 1
3.7%
469 1
3.7%
474 1
3.7%
480 1
3.7%
484 1
3.7%
500 1
3.7%
528 1
3.7%
559 1
3.7%
563 1
3.7%
582 1
3.7%
ValueCountFrequency (%)
908 1
3.7%
870 1
3.7%
867 1
3.7%
866 1
3.7%
862 1
3.7%
835 1
3.7%
779 1
3.7%
769 1
3.7%
768 1
3.7%
718 1
3.7%

병행_주택수(천세대)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing16
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean80.545455
Minimum65
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:50.256981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile65.5
Q170.5
median76
Q391.5
95-th percentile97.5
Maximum98
Range33
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.4849
Coefficient of variation (CV)0.1550044
Kurtosis-1.6612311
Mean80.545455
Median Absolute Deviation (MAD)10
Skewness0.22858361
Sum886
Variance155.87273
MonotonicityStrictly increasing
2023-12-12T14:14:50.380679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
65 1
 
3.7%
66 1
 
3.7%
69 1
 
3.7%
72 1
 
3.7%
75 1
 
3.7%
76 1
 
3.7%
85 1
 
3.7%
89 1
 
3.7%
94 1
 
3.7%
97 1
 
3.7%
(Missing) 16
59.3%
ValueCountFrequency (%)
65 1
3.7%
66 1
3.7%
69 1
3.7%
72 1
3.7%
75 1
3.7%
76 1
3.7%
85 1
3.7%
89 1
3.7%
94 1
3.7%
97 1
3.7%
ValueCountFrequency (%)
98 1
3.7%
97 1
3.7%
94 1
3.7%
89 1
3.7%
85 1
3.7%
76 1
3.7%
75 1
3.7%
72 1
3.7%
69 1
3.7%
66 1
3.7%

병행_난방빌딩수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)72.7%
Missing16
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean59.909091
Minimum26
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:50.501388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile26
Q139.5
median53
Q383
95-th percentile106
Maximum106
Range80
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation30.088053
Coefficient of variation (CV)0.5022285
Kurtosis-1.1729918
Mean59.909091
Median Absolute Deviation (MAD)20
Skewness0.59395137
Sum659
Variance905.29091
MonotonicityNot monotonic
2023-12-12T14:14:50.643146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
26 2
 
7.4%
42 2
 
7.4%
106 2
 
7.4%
37 1
 
3.7%
55 1
 
3.7%
53 1
 
3.7%
73 1
 
3.7%
93 1
 
3.7%
(Missing) 16
59.3%
ValueCountFrequency (%)
26 2
7.4%
37 1
3.7%
42 2
7.4%
53 1
3.7%
55 1
3.7%
73 1
3.7%
93 1
3.7%
106 2
7.4%
ValueCountFrequency (%)
106 2
7.4%
93 1
3.7%
73 1
3.7%
55 1
3.7%
53 1
3.7%
42 2
7.4%
37 1
3.7%
26 2
7.4%

병행_냉방빌딩수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing16
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean21.818182
Minimum4
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:50.776511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.5
Q18.5
median12
Q334.5
95-th percentile47
Maximum49
Range45
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.785817
Coefficient of variation (CV)0.76934992
Kurtosis-1.4711305
Mean21.818182
Median Absolute Deviation (MAD)8
Skewness0.5158271
Sum240
Variance281.76364
MonotonicityStrictly increasing
2023-12-12T14:14:50.931779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 1
 
3.7%
5 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%
11 1
 
3.7%
12 1
 
3.7%
28 1
 
3.7%
32 1
 
3.7%
37 1
 
3.7%
45 1
 
3.7%
(Missing) 16
59.3%
ValueCountFrequency (%)
4 1
3.7%
5 1
3.7%
8 1
3.7%
9 1
3.7%
11 1
3.7%
12 1
3.7%
28 1
3.7%
32 1
3.7%
37 1
3.7%
45 1
3.7%
ValueCountFrequency (%)
49 1
3.7%
45 1
3.7%
37 1
3.7%
32 1
3.7%
28 1
3.7%
12 1
3.7%
11 1
3.7%
9 1
3.7%
8 1
3.7%
5 1
3.7%

병행_업체수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)63.6%
Missing16
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean70.545455
Minimum66
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:51.071944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile66
Q169
median71
Q372.5
95-th percentile74
Maximum75
Range9
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.8762349
Coefficient of variation (CV)0.040771371
Kurtosis-0.57543775
Mean70.545455
Median Absolute Deviation (MAD)2
Skewness-0.36550632
Sum776
Variance8.2727273
MonotonicityNot monotonic
2023-12-12T14:14:51.189340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
66 2
 
7.4%
72 2
 
7.4%
73 2
 
7.4%
69 2
 
7.4%
75 1
 
3.7%
71 1
 
3.7%
70 1
 
3.7%
(Missing) 16
59.3%
ValueCountFrequency (%)
66 2
7.4%
69 2
7.4%
70 1
3.7%
71 1
3.7%
72 2
7.4%
73 2
7.4%
75 1
3.7%
ValueCountFrequency (%)
75 1
3.7%
73 2
7.4%
72 2
7.4%
71 1
3.7%
70 1
3.7%
69 2
7.4%
66 2
7.4%

국내_총_주택_수(천호)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14176.148
Minimum9570
Maximum18812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:51.337828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9570
5-th percentile10267.2
Q112125
median14168
Q316178
95-th percentile18406.3
Maximum18812
Range9242
Interquartile range (IQR)4053

Descriptive statistics

Standard deviation2681.6725
Coefficient of variation (CV)0.18916793
Kurtosis-0.99072358
Mean14176.148
Median Absolute Deviation (MAD)2199
Skewness0.065005402
Sum382756
Variance7191367.6
MonotonicityStrictly increasing
2023-12-12T14:14:51.542700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
9570 1
 
3.7%
10113 1
 
3.7%
18812 1
 
3.7%
18526 1
 
3.7%
18127 1
 
3.7%
17633 1
 
3.7%
17123 1
 
3.7%
16692 1
 
3.7%
16367 1
 
3.7%
15989 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
9570 1
3.7%
10113 1
3.7%
10627 1
3.7%
10827 1
3.7%
11181 1
3.7%
11472 1
3.7%
11892 1
3.7%
12358 1
3.7%
12669 1
3.7%
12987 1
3.7%
ValueCountFrequency (%)
18812 1
3.7%
18526 1
3.7%
18127 1
3.7%
17633 1
3.7%
17123 1
3.7%
16692 1
3.7%
16367 1
3.7%
15989 1
3.7%
15628 1
3.7%
15306 1
3.7%

보급율
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.354074
Minimum5.49
Maximum18.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T14:14:52.007517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.49
5-th percentile6.379
Q19.315
median12.25
Q315.14
95-th percentile18.227
Maximum18.74
Range13.25
Interquartile range (IQR)5.825

Descriptive statistics

Standard deviation3.9558615
Coefficient of variation (CV)0.32020704
Kurtosis-1.140351
Mean12.354074
Median Absolute Deviation (MAD)2.93
Skewness-0.0095154055
Sum333.56
Variance15.64884
MonotonicityNot monotonic
2023-12-12T14:14:52.221089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5.49 1
 
3.7%
6.13 1
 
3.7%
18.74 1
 
3.7%
18.35 1
 
3.7%
17.94 1
 
3.7%
17.61 1
 
3.7%
16.92 1
 
3.7%
16.14 1
 
3.7%
15.18 1
 
3.7%
15.1 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
5.49 1
3.7%
6.13 1
3.7%
6.96 1
3.7%
7.75 1
3.7%
8.16 1
3.7%
8.54 1
3.7%
9.11 1
3.7%
9.52 1
3.7%
9.87 1
3.7%
10.29 1
3.7%
ValueCountFrequency (%)
18.74 1
3.7%
18.35 1
3.7%
17.94 1
3.7%
17.61 1
3.7%
16.92 1
3.7%
16.14 1
3.7%
15.18 1
3.7%
15.1 1
3.7%
14.76 1
3.7%
14.51 1
3.7%

Interactions

2023-12-12T14:14:46.986016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:33.311431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.590105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.789765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.625894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.918305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.626293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.790993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.947699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:44.232084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.826620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.080883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:33.424466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.702595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.924522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.725322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:39.067480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.747003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.882539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.068820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:44.321063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.949009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.180028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:33.547320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.809026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:36.441579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.842847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:39.224831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.836084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.985358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.189945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:44.765393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.065065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.272714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:33.648615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.916377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:36.564468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.929682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:39.458774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.930135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.092448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.330050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:44.869486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.157785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.422010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:33.807110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.009525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:36.698769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.044405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:39.602579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.044271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.221649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.466969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.025588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.250864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.549123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:33.923356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.099460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:36.818882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.148417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:39.714961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.133919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.328909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.565238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.163611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.340771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.644087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.041014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.208997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:36.986235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.289504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:39.879004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.252294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.445427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.658431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.308574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.442260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.782971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.141433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.321152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.120184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.439688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.088154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.381525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.550637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.782074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.419580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.571511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.890893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.244560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.450031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.233159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.578429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.265847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.467441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.637815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.892962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.509186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.668422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:47.972753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.339283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.566625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.355065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.703656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.374470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.570268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.729042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:43.998452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.630176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.779959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:48.079366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:34.446568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:35.672384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:37.485170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:38.795591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:40.493623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:41.709510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:42.855808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:44.133925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:45.735880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:14:46.873843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:14:52.361342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역난방_주택수(천세대)지역난방_난방빌딩수지역난방_냉방빌딩수산업단지_업체수병행_주택수(천세대)병행_난방빌딩수병행_냉방빌딩수병행_업체수국내_총_주택_수(천호)보급율
연도1.0000.9850.9320.8660.9220.9750.7460.9220.9060.9670.966
지역난방_주택수(천세대)0.9851.0000.8840.9190.8940.8910.7930.8950.8720.9570.995
지역난방_난방빌딩수0.9320.8841.0000.7850.7430.0000.0000.4050.8150.7840.843
지역난방_냉방빌딩수0.8660.9190.7851.0000.7390.9240.9541.0000.7360.8200.854
산업단지_업체수0.9220.8940.7430.7391.0000.3360.4050.0000.6070.8720.822
병행_주택수(천세대)0.9750.8910.0000.9240.3361.0001.0001.0000.7750.8490.960
병행_난방빌딩수0.7460.7930.0000.9540.4051.0001.0000.9220.8190.7680.655
병행_냉방빌딩수0.9220.8950.4051.0000.0001.0000.9221.0000.4510.7150.891
병행_업체수0.9060.8720.8150.7360.6070.7750.8190.4511.0000.5110.916
국내_총_주택_수(천호)0.9670.9570.7840.8200.8720.8490.7680.7150.5111.0000.967
보급율0.9660.9950.8430.8540.8220.9600.6550.8910.9160.9671.000
2023-12-12T14:14:52.561564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역난방_주택수(천세대)지역난방_난방빌딩수지역난방_냉방빌딩수산업단지_업체수병행_주택수(천세대)병행_난방빌딩수병행_냉방빌딩수병행_업체수국내_총_주택_수(천호)보급율
연도1.0001.0000.9881.0000.9861.0000.9381.0000.0921.0000.999
지역난방_주택수(천세대)1.0001.0000.9881.0000.9861.0000.9381.0000.0921.0000.999
지역난방_난방빌딩수0.9880.9881.0000.9880.9830.9730.8740.973-0.0500.9880.985
지역난방_냉방빌딩수1.0001.0000.9881.0000.9861.0000.9381.0000.0921.0000.999
산업단지_업체수0.9860.9860.9830.9861.0000.9820.9060.9820.1100.9860.985
병행_주택수(천세대)1.0001.0000.9731.0000.9821.0000.9381.0000.0921.0000.991
병행_난방빌딩수0.9380.9380.8740.9380.9060.9381.0000.9380.1430.9380.938
병행_냉방빌딩수1.0001.0000.9731.0000.9821.0000.9381.0000.0921.0000.991
병행_업체수0.0920.092-0.0500.0920.1100.0920.1430.0921.0000.0920.092
국내_총_주택_수(천호)1.0001.0000.9881.0000.9861.0000.9381.0000.0921.0000.999
보급율0.9990.9990.9850.9990.9850.9910.9380.9910.0920.9991.000

Missing values

2023-12-12T14:14:48.230751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:14:48.411731image/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.
2023-12-12T14:14:48.547523image/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

연도지역난방_주택수(천세대)지역난방_난방빌딩수지역난방_냉방빌딩수산업단지_업체수병행_주택수(천세대)병행_난방빌딩수병행_냉방빌딩수병행_업체수국내_총_주택_수(천호)보급율
01995525125360463<NA><NA><NA><NA>95705.49
119966201477106474<NA><NA><NA><NA>101136.13
219977401700151480<NA><NA><NA><NA>106276.96
319988391824175469<NA><NA><NA><NA>108277.75
419999121977211484<NA><NA><NA><NA>111818.16
520009802102231500<NA><NA><NA><NA>114728.54
6200110832281278559<NA><NA><NA><NA>118929.11
7200211772493351563<NA><NA><NA><NA>123589.52
8200312512786391528<NA><NA><NA><NA>126699.87
9200413372871403582<NA><NA><NA><NA>1298710.29
연도지역난방_주택수(천세대)지역난방_난방빌딩수지역난방_냉방빌딩수산업단지_업체수병행_주택수(천세대)병행_난방빌딩수병행_냉방빌딩수병행_업체수국내_총_주택_수(천호)보급율
1720122153350969268966265661530614.5
1820132238335780276869378721562814.76
1920142344340091676972559751598915.1
202015241035261049779754211711636715.18
212016261837841139835764212731669216.14
222017281241561498866855328731712316.92
232018301747081756862897332721763317.61
242019315847652092870949337691812717.94
2520203303509824188679710645691852618.35
2620213427614526539089810649701881218.74