Overview

Dataset statistics

Number of variables11
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory103.1 B

Variable types

Numeric11

Dataset

Description천연가스 수요의 용도별 TDR(Top Down Ratio) 자료입니다. TDR은 [연중 최고 판매 월의 판매량 / 연중 최저 판매 월의 판매량] 으로 정의되며, 천연가스의 소비 특성을 나타냅니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15102989/fileData.do

Alerts

연도 is highly overall correlated with 산업용 and 5 other fieldsHigh correlation
산업용 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
일반용 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
열병합용 is highly overall correlated with 수송용High correlation
수송용 is highly overall correlated with 산업용 and 2 other fieldsHigh correlation
직공급 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
업무난방용 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
냉방용 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
도시가스 통합 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
연도 has unique valuesUnique
열병합용 has 8 (25.0%) zerosZeros
수송용 has 11 (34.4%) zerosZeros
직공급 has 11 (34.4%) zerosZeros
업무난방용 has 18 (56.2%) zerosZeros
냉방용 has 22 (68.8%) zerosZeros
발전용 has 5 (15.6%) zerosZeros

Reproduction

Analysis started2023-12-23 06:54:43.134114
Analysis finished2023-12-23 06:55:27.288065
Duration44.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.5
Minimum1991
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:27.572215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1992.55
Q11998.75
median2006.5
Q32014.25
95-th percentile2020.45
Maximum2022
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.0046752213
Kurtosis-1.2
Mean2006.5
Median Absolute Deviation (MAD)8
Skewness0
Sum64208
Variance88
MonotonicityStrictly increasing
2023-12-23T06:55:28.005728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1991 1
 
3.1%
2008 1
 
3.1%
2022 1
 
3.1%
2021 1
 
3.1%
2020 1
 
3.1%
2019 1
 
3.1%
2018 1
 
3.1%
2017 1
 
3.1%
2016 1
 
3.1%
2015 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1991 1
3.1%
1992 1
3.1%
1993 1
3.1%
1994 1
3.1%
1995 1
3.1%
1996 1
3.1%
1997 1
3.1%
1998 1
3.1%
1999 1
3.1%
2000 1
3.1%
ValueCountFrequency (%)
2022 1
3.1%
2021 1
3.1%
2020 1
3.1%
2019 1
3.1%
2018 1
3.1%
2017 1
3.1%
2016 1
3.1%
2015 1
3.1%
2014 1
3.1%
2013 1
3.1%

주택용
Real number (ℝ)

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.16875
Minimum7.7
Maximum12.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:29.045047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile8.085
Q19.4
median10.1
Q310.8
95-th percentile12.545
Maximum12.9
Range5.2
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.3004807
Coefficient of variation (CV)0.12788993
Kurtosis-0.088296883
Mean10.16875
Median Absolute Deviation (MAD)0.7
Skewness0.16348532
Sum325.4
Variance1.69125
MonotonicityNot monotonic
2023-12-23T06:55:29.830509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8.8 2
 
6.2%
7.7 2
 
6.2%
10.4 2
 
6.2%
10.0 2
 
6.2%
10.1 2
 
6.2%
11.5 2
 
6.2%
10.8 2
 
6.2%
9.9 2
 
6.2%
9.8 2
 
6.2%
9.5 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
7.7 2
6.2%
8.4 1
3.1%
8.6 1
3.1%
8.8 2
6.2%
9.0 1
3.1%
9.1 1
3.1%
9.5 1
3.1%
9.8 2
6.2%
9.9 2
6.2%
10.0 2
6.2%
ValueCountFrequency (%)
12.9 1
3.1%
12.6 1
3.1%
12.5 1
3.1%
11.8 1
3.1%
11.5 2
6.2%
11.2 1
3.1%
10.8 2
6.2%
10.7 1
3.1%
10.6 1
3.1%
10.4 2
6.2%

산업용
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.078125
Minimum1.5
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:30.804170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.555
Q11.7
median1.8
Q32.025
95-th percentile3.69
Maximum3.9
Range2.4
Interquartile range (IQR)0.325

Descriptive statistics

Standard deviation0.6790574
Coefficient of variation (CV)0.32676446
Kurtosis2.0835898
Mean2.078125
Median Absolute Deviation (MAD)0.15
Skewness1.7941768
Sum66.5
Variance0.46111895
MonotonicityNot monotonic
2023-12-23T06:55:32.139716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.8 6
18.8%
1.7 6
18.8%
1.9 4
12.5%
1.6 4
12.5%
2.0 2
 
6.2%
1.5 2
 
6.2%
2.9 1
 
3.1%
3.4 1
 
3.1%
3.8 1
 
3.1%
3.9 1
 
3.1%
Other values (4) 4
12.5%
ValueCountFrequency (%)
1.5 2
 
6.2%
1.6 4
12.5%
1.7 6
18.8%
1.8 6
18.8%
1.9 4
12.5%
2.0 2
 
6.2%
2.1 1
 
3.1%
2.3 1
 
3.1%
2.5 1
 
3.1%
2.9 1
 
3.1%
ValueCountFrequency (%)
3.9 1
 
3.1%
3.8 1
 
3.1%
3.6 1
 
3.1%
3.4 1
 
3.1%
2.9 1
 
3.1%
2.5 1
 
3.1%
2.3 1
 
3.1%
2.1 1
 
3.1%
2.0 2
6.2%
1.9 4
12.5%

일반용
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:32.977337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.4
median2.75
Q33.75
95-th percentile8.94
Maximum9.7
Range7.7
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation2.5676649
Coefficient of variation (CV)0.64191623
Kurtosis0.08244571
Mean4
Median Absolute Deviation (MAD)0.45
Skewness1.3433333
Sum128
Variance6.5929032
MonotonicityNot monotonic
2023-12-23T06:55:33.732876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.3 3
 
9.4%
2.4 3
 
9.4%
3.3 3
 
9.4%
2.6 3
 
9.4%
2.8 3
 
9.4%
2.7 2
 
6.2%
2.1 2
 
6.2%
8.3 2
 
6.2%
5.1 1
 
3.1%
2.2 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
2.0 1
 
3.1%
2.1 2
6.2%
2.2 1
 
3.1%
2.3 3
9.4%
2.4 3
9.4%
2.5 1
 
3.1%
2.6 3
9.4%
2.7 2
6.2%
2.8 3
9.4%
2.9 1
 
3.1%
ValueCountFrequency (%)
9.7 1
 
3.1%
9.6 1
 
3.1%
8.4 1
 
3.1%
8.3 2
6.2%
8.2 1
 
3.1%
7.9 1
 
3.1%
5.1 1
 
3.1%
3.3 3
9.4%
3.1 1
 
3.1%
2.9 1
 
3.1%

열병합용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.846875
Minimum0
Maximum15
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:34.807303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.075
median10.1
Q311.7
95-th percentile14.5
Maximum15
Range15
Interquartile range (IQR)8.625

Descriptive statistics

Standard deviation5.375331
Coefficient of variation (CV)0.68502825
Kurtosis-1.3259183
Mean7.846875
Median Absolute Deviation (MAD)3.85
Skewness-0.43493385
Sum251.1
Variance28.894183
MonotonicityNot monotonic
2023-12-23T06:55:35.736335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 8
25.0%
14.5 2
 
6.2%
5.1 2
 
6.2%
10.8 1
 
3.1%
11.4 1
 
3.1%
12.6 1
 
3.1%
8.7 1
 
3.1%
7.7 1
 
3.1%
4.1 1
 
3.1%
5.8 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
0.0 8
25.0%
4.1 1
 
3.1%
5.1 2
 
6.2%
5.8 1
 
3.1%
6.8 1
 
3.1%
7.7 1
 
3.1%
8.7 1
 
3.1%
9.6 1
 
3.1%
10.6 1
 
3.1%
10.8 1
 
3.1%
ValueCountFrequency (%)
15.0 1
3.1%
14.5 2
6.2%
14.2 1
3.1%
13.7 1
3.1%
12.8 1
3.1%
12.6 1
3.1%
12.0 1
3.1%
11.6 1
3.1%
11.5 1
3.1%
11.4 1
3.1%

수송용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.140625
Minimum0
Maximum4.1
Zeros11
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:36.420412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.4
Q31.7
95-th percentile2.19
Maximum4.1
Range4.1
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation0.96648995
Coefficient of variation (CV)0.84733365
Kurtosis1.1396271
Mean1.140625
Median Absolute Deviation (MAD)0.35
Skewness0.53593577
Sum36.5
Variance0.93410282
MonotonicityNot monotonic
2023-12-23T06:55:37.049896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 11
34.4%
1.4 7
21.9%
1.7 4
 
12.5%
1.5 4
 
12.5%
4.1 1
 
3.1%
2.3 1
 
3.1%
2.0 1
 
3.1%
2.1 1
 
3.1%
1.6 1
 
3.1%
1.8 1
 
3.1%
ValueCountFrequency (%)
0.0 11
34.4%
1.4 7
21.9%
1.5 4
 
12.5%
1.6 1
 
3.1%
1.7 4
 
12.5%
1.8 1
 
3.1%
2.0 1
 
3.1%
2.1 1
 
3.1%
2.3 1
 
3.1%
4.1 1
 
3.1%
ValueCountFrequency (%)
4.1 1
 
3.1%
2.3 1
 
3.1%
2.1 1
 
3.1%
2.0 1
 
3.1%
1.8 1
 
3.1%
1.7 4
 
12.5%
1.6 1
 
3.1%
1.5 4
 
12.5%
1.4 7
21.9%
0.0 11
34.4%

직공급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.04375
Minimum0
Maximum3.5
Zeros11
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:37.910100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.4
Q31.5
95-th percentile1.88
Maximum3.5
Range3.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.85569723
Coefficient of variation (CV)0.81982968
Kurtosis0.44955031
Mean1.04375
Median Absolute Deviation (MAD)0.15
Skewness0.28414647
Sum33.4
Variance0.73221774
MonotonicityNot monotonic
2023-12-23T06:55:38.625109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 11
34.4%
1.5 9
28.1%
1.3 4
 
12.5%
1.4 3
 
9.4%
1.6 2
 
6.2%
3.5 1
 
3.1%
1.7 1
 
3.1%
2.1 1
 
3.1%
ValueCountFrequency (%)
0.0 11
34.4%
1.3 4
 
12.5%
1.4 3
 
9.4%
1.5 9
28.1%
1.6 2
 
6.2%
1.7 1
 
3.1%
2.1 1
 
3.1%
3.5 1
 
3.1%
ValueCountFrequency (%)
3.5 1
 
3.1%
2.1 1
 
3.1%
1.7 1
 
3.1%
1.6 2
 
6.2%
1.5 9
28.1%
1.4 3
 
9.4%
1.3 4
 
12.5%
0.0 11
34.4%

업무난방용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.43125
Minimum0
Maximum21.4
Zeros18
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:39.464930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.75
95-th percentile18.655
Maximum21.4
Range21.4
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation6.8540752
Coefficient of variation (CV)1.2619701
Kurtosis-0.46053077
Mean5.43125
Median Absolute Deviation (MAD)0
Skewness0.85894385
Sum173.8
Variance46.978347
MonotonicityNot monotonic
2023-12-23T06:55:40.309067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 18
56.2%
9.5 2
 
6.2%
17.8 1
 
3.1%
21.4 1
 
3.1%
19.7 1
 
3.1%
15.3 1
 
3.1%
11.3 1
 
3.1%
9.7 1
 
3.1%
8.7 1
 
3.1%
9.9 1
 
3.1%
Other values (4) 4
 
12.5%
ValueCountFrequency (%)
0.0 18
56.2%
8.6 1
 
3.1%
8.7 1
 
3.1%
9.5 2
 
6.2%
9.6 1
 
3.1%
9.7 1
 
3.1%
9.9 1
 
3.1%
10.6 1
 
3.1%
11.3 1
 
3.1%
12.2 1
 
3.1%
ValueCountFrequency (%)
21.4 1
3.1%
19.7 1
3.1%
17.8 1
3.1%
15.3 1
3.1%
12.2 1
3.1%
11.3 1
3.1%
10.6 1
3.1%
9.9 1
3.1%
9.7 1
3.1%
9.6 1
3.1%

냉방용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.290625
Minimum0
Maximum9.8
Zeros22
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:40.950786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.925
95-th percentile8.67
Maximum9.8
Range9.8
Interquartile range (IQR)5.925

Descriptive statistics

Standard deviation3.5392144
Coefficient of variation (CV)1.5450868
Kurtosis-0.78830411
Mean2.290625
Median Absolute Deviation (MAD)0
Skewness1.0141102
Sum73.3
Variance12.526038
MonotonicityNot monotonic
2023-12-23T06:55:41.729398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 22
68.8%
7.1 2
 
6.2%
6.3 1
 
3.1%
9.8 1
 
3.1%
5.8 1
 
3.1%
9.0 1
 
3.1%
8.4 1
 
3.1%
5.1 1
 
3.1%
7.8 1
 
3.1%
6.9 1
 
3.1%
ValueCountFrequency (%)
0.0 22
68.8%
5.1 1
 
3.1%
5.8 1
 
3.1%
6.3 1
 
3.1%
6.9 1
 
3.1%
7.1 2
 
6.2%
7.8 1
 
3.1%
8.4 1
 
3.1%
9.0 1
 
3.1%
9.8 1
 
3.1%
ValueCountFrequency (%)
9.8 1
 
3.1%
9.0 1
 
3.1%
8.4 1
 
3.1%
7.8 1
 
3.1%
7.1 2
 
6.2%
6.9 1
 
3.1%
6.3 1
 
3.1%
5.8 1
 
3.1%
5.1 1
 
3.1%
0.0 22
68.8%

도시가스 통합
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.040625
Minimum2.9
Maximum6.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:42.256502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile3.055
Q13.3
median3.55
Q34.525
95-th percentile6.28
Maximum6.5
Range3.6
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation1.0530629
Coefficient of variation (CV)0.26061882
Kurtosis0.30159377
Mean4.040625
Median Absolute Deviation (MAD)0.45
Skewness1.1614195
Sum129.3
Variance1.1089415
MonotonicityNot monotonic
2023-12-23T06:55:42.985305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3.3 6
18.8%
6.5 2
 
6.2%
3.2 2
 
6.2%
4.5 2
 
6.2%
3.1 2
 
6.2%
4.0 2
 
6.2%
3.6 2
 
6.2%
3.5 2
 
6.2%
3.4 2
 
6.2%
4.8 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
2.9 1
 
3.1%
3.0 1
 
3.1%
3.1 2
 
6.2%
3.2 2
 
6.2%
3.3 6
18.8%
3.4 2
 
6.2%
3.5 2
 
6.2%
3.6 2
 
6.2%
3.9 1
 
3.1%
4.0 2
 
6.2%
ValueCountFrequency (%)
6.5 2
6.2%
6.1 1
3.1%
5.7 1
3.1%
5.6 1
3.1%
4.9 1
3.1%
4.8 1
3.1%
4.6 1
3.1%
4.5 2
6.2%
4.4 1
3.1%
4.0 2
6.2%

발전용
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.75
Minimum0
Maximum3
Zeros5
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-23T06:55:43.855815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.6
median1.9
Q32.225
95-th percentile2.845
Maximum3
Range3
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation0.85609767
Coefficient of variation (CV)0.48919867
Kurtosis0.61002841
Mean1.75
Median Absolute Deviation (MAD)0.3
Skewness-1.0999798
Sum56
Variance0.73290323
MonotonicityNot monotonic
2023-12-23T06:55:44.394906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 5
15.6%
1.8 4
12.5%
1.9 4
12.5%
1.6 3
9.4%
2.3 2
 
6.2%
2.6 2
 
6.2%
2.1 2
 
6.2%
1.7 2
 
6.2%
2.2 2
 
6.2%
2.9 1
 
3.1%
Other values (5) 5
15.6%
ValueCountFrequency (%)
0.0 5
15.6%
1.5 1
 
3.1%
1.6 3
9.4%
1.7 2
 
6.2%
1.8 4
12.5%
1.9 4
12.5%
2.0 1
 
3.1%
2.1 2
 
6.2%
2.2 2
 
6.2%
2.3 2
 
6.2%
ValueCountFrequency (%)
3.0 1
 
3.1%
2.9 1
 
3.1%
2.8 1
 
3.1%
2.6 2
6.2%
2.4 1
 
3.1%
2.3 2
6.2%
2.2 2
6.2%
2.1 2
6.2%
2.0 1
 
3.1%
1.9 4
12.5%

Interactions

2023-12-23T06:55:21.107589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:43.997572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:47.505882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:50.888718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:54.495331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:59.171649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:03.300242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:06.727672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:10.793584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:14.163933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:17.526777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:21.481882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:44.368725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:47.899410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:51.197470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:54.805482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:59.432563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:03.659735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:07.076456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:11.054327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:14.450488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:17.819596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:21.822325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:44.712050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:48.238638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:51.521509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:55.136566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:59.925511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:03.880076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:07.469072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:11.322548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:14.809754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:18.145358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:22.702982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:45.008029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:48.506895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:51.848976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:55.550236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:00.273219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:04.205946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:07.790812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:11.584432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:15.044135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:18.394809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:23.118890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:45.341643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:48.859963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:52.112174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:55.996019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:00.702989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:04.539485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:08.150592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:11.925846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:15.356495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:18.798773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:23.662331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:45.616078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:49.199832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:52.393547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:56.463198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:01.081838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:04.842013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:08.592240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:12.248734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:15.700940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:19.161985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:23.990548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:45.845895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:49.434526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:52.658026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:56.825770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:01.316328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:05.158548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:08.920311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:12.567422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:15.960986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:19.447166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:24.291012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:46.173258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:49.815294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:53.002322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:57.453804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:01.735477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:05.464556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:09.261550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:12.894420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:16.321930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:19.758328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:24.646421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:46.502298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:50.030463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:53.301464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:57.803165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:02.058127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:05.753869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:09.641363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:13.215655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:16.541919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:19.999674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:25.072682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:46.813453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:50.323197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:53.738570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:58.193591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:02.297752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:06.045563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:10.016583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:13.550832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:16.918635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:20.384928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:25.437195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:47.150737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:50.615208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:54.179319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:54:58.820611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:02.629755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:06.377414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:10.371521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:13.846919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:17.238645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:55:20.750965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T06:55:44.964887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도주택용산업용일반용열병합용수송용직공급업무난방용냉방용도시가스 통합발전용
연도1.0000.0000.4430.9380.6280.9270.8150.7600.6010.7040.517
주택용0.0001.0000.1130.0000.7830.0000.7130.5630.0000.0000.241
산업용0.4430.1131.0000.8390.0000.0000.0000.0000.0000.9450.729
일반용0.9380.0000.8391.0000.2250.5520.4040.0000.0000.9080.659
열병합용0.6280.7830.0000.2251.0000.5950.6310.6400.8070.7390.457
수송용0.9270.0000.0000.5520.5951.0000.9470.5240.2860.6520.330
직공급0.8150.7130.0000.4040.6310.9471.0000.3570.0000.0000.466
업무난방용0.7600.5630.0000.0000.6400.5240.3571.0000.8920.0000.486
냉방용0.6010.0000.0000.0000.8070.2860.0000.8921.0000.0000.000
도시가스 통합0.7040.0000.9450.9080.7390.6520.0000.0000.0001.0000.522
발전용0.5170.2410.7290.6590.4570.3300.4660.4860.0000.5221.000
2023-12-23T06:55:45.808938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도주택용산업용일반용열병합용수송용직공급업무난방용냉방용도시가스 통합발전용
연도1.0000.136-0.719-0.9500.3230.4580.6960.7510.782-0.8840.270
주택용0.1361.000-0.044-0.0140.4250.0690.1140.122-0.0190.0830.001
산업용-0.719-0.0441.0000.771-0.311-0.518-0.761-0.500-0.5710.8120.039
일반용-0.950-0.0140.7711.000-0.289-0.433-0.679-0.695-0.7670.915-0.169
열병합용0.3230.425-0.311-0.2891.0000.5810.3790.160-0.102-0.2880.345
수송용0.4580.069-0.518-0.4330.5811.0000.7260.2870.022-0.4650.260
직공급0.6960.114-0.761-0.6790.3790.7261.0000.3920.537-0.6660.084
업무난방용0.7510.122-0.500-0.6950.1600.2870.3921.0000.569-0.6720.146
냉방용0.782-0.019-0.571-0.767-0.1020.0220.5370.5691.000-0.674-0.017
도시가스 통합-0.8840.0830.8120.915-0.288-0.465-0.666-0.672-0.6741.000-0.243
발전용0.2700.0010.039-0.1690.3450.2600.0840.146-0.017-0.2431.000

Missing values

2023-12-23T06:55:26.032443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T06:55:26.861825image/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

연도주택용산업용일반용열병합용수송용직공급업무난방용냉방용도시가스 통합발전용
019918.81.85.10.00.00.00.00.04.80.0
119928.42.98.30.00.00.00.00.05.70.0
219937.73.48.40.00.00.00.00.05.60.0
3199412.53.88.20.00.00.00.00.06.50.0
4199510.43.99.70.00.00.00.00.06.50.0
5199610.03.67.90.00.00.00.00.06.12.3
6199710.22.58.30.00.00.00.00.04.91.6
719988.82.09.60.00.00.00.00.04.52.9
8199910.12.33.311.60.00.00.00.04.62.6
9200011.51.93.114.20.00.00.00.04.02.1
연도주택용산업용일반용열병합용수송용직공급업무난방용냉방용도시가스 통합발전용
22201310.61.72.411.11.41.511.36.33.21.7
2320148.61.52.46.81.51.59.79.82.91.8
2420159.11.52.15.81.41.68.75.83.01.5
25201610.41.72.35.11.41.79.99.03.31.9
2620179.81.92.35.11.41.59.57.13.32.4
27201812.61.62.44.11.41.512.28.43.32.1
2820199.91.62.17.71.41.49.55.13.11.9
2920207.71.92.08.71.41.38.67.13.22.8
30202111.21.62.312.61.52.110.67.83.41.6
31202210.81.72.211.41.51.59.66.93.31.6