Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory888.7 KiB
Average record size in memory91.0 B

Variable types

Numeric2
Categorical2
Text5
DateTime1

Alerts

skey is highly overall correlated with d_yearHigh correlation
d_year is highly overall correlated with skeyHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 07:50:44.258543
Analysis finished2024-04-16 07:50:45.255073
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7773.8391
Minimum1
Maximum15470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:45.312416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile790.85
Q13890.75
median7801.5
Q311642.25
95-th percentile14683.2
Maximum15470
Range15469
Interquartile range (IQR)7751.5

Descriptive statistics

Standard deviation4467.675
Coefficient of variation (CV)0.57470639
Kurtosis-1.209122
Mean7773.8391
Median Absolute Deviation (MAD)3874.5
Skewness-0.014225981
Sum77738391
Variance19960120
MonotonicityNot monotonic
2024-04-16T16:50:45.444559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2606 1
 
< 0.1%
15021 1
 
< 0.1%
10552 1
 
< 0.1%
1657 1
 
< 0.1%
1545 1
 
< 0.1%
2121 1
 
< 0.1%
5157 1
 
< 0.1%
6783 1
 
< 0.1%
12975 1
 
< 0.1%
3603 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
15470 1
< 0.1%
15469 1
< 0.1%
15468 1
< 0.1%
15467 1
< 0.1%
15465 1
< 0.1%
15460 1
< 0.1%
15458 1
< 0.1%
15456 1
< 0.1%
15455 1
< 0.1%
15454 1
< 0.1%

d_year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2018
2022 
2015
2012 
2019
2010 
2016
1986 
2017
1970 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2017
3rd row2017
4th row2019
5th row2017

Common Values

ValueCountFrequency (%)
2018 2022
20.2%
2015 2012
20.1%
2019 2010
20.1%
2016 1986
19.9%
2017 1970
19.7%

Length

2024-04-16T16:50:45.583659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:45.669094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 2022
20.2%
2015 2012
20.1%
2019 2010
20.1%
2016 1986
19.9%
2017 1970
19.7%

d_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5141
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:45.750389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4525633
Coefficient of variation (CV)0.53001386
Kurtosis-1.2261516
Mean6.5141
Median Absolute Deviation (MAD)3
Skewness-0.0074436108
Sum65141
Variance11.920193
MonotonicityNot monotonic
2024-04-16T16:50:45.830545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 859
8.6%
8 854
8.5%
3 850
8.5%
9 847
8.5%
2 838
8.4%
11 837
8.4%
4 830
8.3%
5 827
8.3%
12 825
8.2%
6 821
8.2%
Other values (2) 1612
16.1%
ValueCountFrequency (%)
1 817
8.2%
2 838
8.4%
3 850
8.5%
4 830
8.3%
5 827
8.3%
6 821
8.2%
7 795
8.0%
8 854
8.5%
9 847
8.5%
10 859
8.6%
ValueCountFrequency (%)
12 825
8.2%
11 837
8.4%
10 859
8.6%
9 847
8.5%
8 854
8.5%
7 795
8.0%
6 821
8.2%
5 827
8.3%
4 830
8.3%
3 850
8.5%

sigungu
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 기장군
2734 
부산광역시 중구
1577 
부산광역시 서구
947 
부산광역시 강서구
869 
부산광역시 영도구
825 
Other values (11)
3048 

Length

Max length10
Median length9
Mean length8.7644
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 기장군
2nd row부산광역시 기장군
3rd row부산광역시 남구
4th row부산광역시 기장군
5th row부산광역시 강서구

Common Values

ValueCountFrequency (%)
부산광역시 기장군 2734
27.3%
부산광역시 중구 1577
15.8%
부산광역시 서구 947
 
9.5%
부산광역시 강서구 869
 
8.7%
부산광역시 영도구 825
 
8.2%
부산광역시 금정구 539
 
5.4%
부산광역시 부산진구 429
 
4.3%
부산광역시 동래구 354
 
3.5%
부산광역시 해운대구 308
 
3.1%
부산광역시 사상구 298
 
3.0%
Other values (6) 1120
11.2%

Length

2024-04-16T16:50:45.932356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
기장군 2734
 
13.7%
중구 1577
 
7.9%
서구 947
 
4.7%
강서구 869
 
4.3%
영도구 825
 
4.1%
금정구 539
 
2.7%
부산진구 429
 
2.1%
동래구 354
 
1.8%
해운대구 308
 
1.5%
Other values (7) 1418
 
7.1%

area
Text

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:46.191318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6679
Min length2

Characters and Unicode

Total characters46679
Distinct characters144
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row철마면 이곡리
2nd row일광면 청광리
3rd row우암동
4th row정관면 매학리
5th row동선동
ValueCountFrequency (%)
기장읍 626
 
5.0%
장안읍 532
 
4.2%
일광면 482
 
3.8%
철마면 409
 
3.2%
정관읍 384
 
3.0%
정관면 301
 
2.4%
예림리 82
 
0.6%
방곡리 79
 
0.6%
송정동 78
 
0.6%
용수리 77
 
0.6%
Other values (244) 9580
75.9%
2024-04-16T16:50:46.540597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7923
 
17.0%
가 3158
 
6.8%
리 2664
 
5.7%
2630
 
5.6%
읍 1582
 
3.4%
장 1430
 
3.1%
면 1192
 
2.6%
2 936
 
2.0%
정 924
 
2.0%
대 922
 
2.0%
Other values (134) 23318
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40854
87.5%
Decimal Number 3195
 
6.8%
Space Separator 2630
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7923
19.4%
가 3158
 
7.7%
리 2664
 
6.5%
읍 1582
 
3.9%
장 1430
 
3.5%
면 1192
 
2.9%
정 924
 
2.3%
대 922
 
2.3%
광 856
 
2.1%
안 725
 
1.8%
Other values (126) 19478
47.7%
Decimal Number
ValueCountFrequency (%)
2 936
29.3%
1 915
28.6%
3 688
21.5%
4 341
 
10.7%
5 203
 
6.4%
6 73
 
2.3%
7 39
 
1.2%
Space Separator
ValueCountFrequency (%)
2630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40854
87.5%
Common 5825
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7923
19.4%
가 3158
 
7.7%
리 2664
 
6.5%
읍 1582
 
3.9%
장 1430
 
3.5%
면 1192
 
2.9%
정 924
 
2.3%
대 922
 
2.3%
광 856
 
2.1%
안 725
 
1.8%
Other values (126) 19478
47.7%
Common
ValueCountFrequency (%)
2630
45.2%
2 936
 
16.1%
1 915
 
15.7%
3 688
 
11.8%
4 341
 
5.9%
5 203
 
3.5%
6 73
 
1.3%
7 39
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40854
87.5%
ASCII 5825
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7923
19.4%
가 3158
 
7.7%
리 2664
 
6.5%
읍 1582
 
3.9%
장 1430
 
3.5%
면 1192
 
2.9%
정 924
 
2.3%
대 922
 
2.3%
광 856
 
2.1%
안 725
 
1.8%
Other values (126) 19478
47.7%
ASCII
ValueCountFrequency (%)
2630
45.2%
2 936
 
16.1%
1 915
 
15.7%
3 688
 
11.8%
4 341
 
5.9%
5 203
 
3.5%
6 73
 
1.3%
7 39
 
0.7%

elect
Text

Distinct4807
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:46.890904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6199
Min length1

Characters and Unicode

Total characters36199
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3616 ?
Unique (%)36.2%

Sample

1st row50
2nd row61
3rd row1416
4th row0
5th row162
ValueCountFrequency (%)
0 168
 
1.7%
2 56
 
0.6%
91 28
 
0.3%
197 26
 
0.3%
50 22
 
0.2%
188 21
 
0.2%
1 21
 
0.2%
119 21
 
0.2%
88 21
 
0.2%
102 21
 
0.2%
Other values (4797) 9595
96.0%
2024-04-16T16:50:47.284558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5169
14.3%
2 4179
11.5%
3 3521
9.7%
6 3212
8.9%
5 3189
8.8%
4 3116
8.6%
7 2980
8.2%
8 2818
7.8%
0 2746
7.6%
9 2636
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33566
92.7%
Other Punctuation 2633
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5169
15.4%
2 4179
12.5%
3 3521
10.5%
6 3212
9.6%
5 3189
9.5%
4 3116
9.3%
7 2980
8.9%
8 2818
8.4%
0 2746
8.2%
9 2636
7.9%
Other Punctuation
ValueCountFrequency (%)
, 2633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5169
14.3%
2 4179
11.5%
3 3521
9.7%
6 3212
8.9%
5 3189
8.8%
4 3116
8.6%
7 2980
8.2%
8 2818
7.8%
0 2746
7.6%
9 2636
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5169
14.3%
2 4179
11.5%
3 3521
9.7%
6 3212
8.9%
5 3189
8.8%
4 3116
8.6%
7 2980
8.2%
8 2818
7.8%
0 2746
7.6%
9 2636
7.3%

gas
Text

Distinct2882
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:47.571769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5951
Min length1

Characters and Unicode

Total characters25951
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1960 ?
Unique (%)19.6%

Sample

1st row0
2nd row0
3rd row185
4th row0
5th row0
ValueCountFrequency (%)
0 2241
 
22.4%
2 110
 
1.1%
1 81
 
0.8%
4 74
 
0.7%
3 71
 
0.7%
11 66
 
0.7%
7 61
 
0.6%
6 60
 
0.6%
8 56
 
0.6%
5 55
 
0.5%
Other values (2872) 7125
71.2%
2024-04-16T16:50:47.970138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3978
15.3%
0 3863
14.9%
2 3090
11.9%
3 2427
9.4%
4 2111
8.1%
5 1979
7.6%
6 1862
7.2%
7 1813
7.0%
8 1751
6.7%
9 1681
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24555
94.6%
Other Punctuation 1396
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3978
16.2%
0 3863
15.7%
2 3090
12.6%
3 2427
9.9%
4 2111
8.6%
5 1979
8.1%
6 1862
7.6%
7 1813
7.4%
8 1751
7.1%
9 1681
6.8%
Other Punctuation
ValueCountFrequency (%)
, 1396
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25951
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3978
15.3%
0 3863
14.9%
2 3090
11.9%
3 2427
9.4%
4 2111
8.1%
5 1979
7.6%
6 1862
7.2%
7 1813
7.0%
8 1751
6.7%
9 1681
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3978
15.3%
0 3863
14.9%
2 3090
11.9%
3 2427
9.4%
4 2111
8.1%
5 1979
7.6%
6 1862
7.2%
7 1813
7.0%
8 1751
6.7%
9 1681
6.5%

heating
Text

Distinct299
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:48.269240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.076
Min length1

Characters and Unicode

Total characters10760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)2.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9640
96.4%
55 4
 
< 0.1%
144 3
 
< 0.1%
1 3
 
< 0.1%
182 3
 
< 0.1%
82 3
 
< 0.1%
22 3
 
< 0.1%
277 3
 
< 0.1%
124 3
 
< 0.1%
337 2
 
< 0.1%
Other values (289) 333
 
3.3%
2024-04-16T16:50:48.652316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9726
90.4%
1 203
 
1.9%
2 138
 
1.3%
3 102
 
0.9%
4 100
 
0.9%
7 100
 
0.9%
5 87
 
0.8%
6 87
 
0.8%
8 86
 
0.8%
9 86
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10715
99.6%
Other Punctuation 45
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9726
90.8%
1 203
 
1.9%
2 138
 
1.3%
3 102
 
1.0%
4 100
 
0.9%
7 100
 
0.9%
5 87
 
0.8%
6 87
 
0.8%
8 86
 
0.8%
9 86
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9726
90.4%
1 203
 
1.9%
2 138
 
1.3%
3 102
 
0.9%
4 100
 
0.9%
7 100
 
0.9%
5 87
 
0.8%
6 87
 
0.8%
8 86
 
0.8%
9 86
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9726
90.4%
1 203
 
1.9%
2 138
 
1.3%
3 102
 
0.9%
4 100
 
0.9%
7 100
 
0.9%
5 87
 
0.8%
6 87
 
0.8%
8 86
 
0.8%
9 86
 
0.8%

total
Text

Distinct5124
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:48.978033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7443
Min length1

Characters and Unicode

Total characters37443
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3932 ?
Unique (%)39.3%

Sample

1st row50
2nd row61
3rd row1601
4th row0
5th row162
ValueCountFrequency (%)
0 71
 
0.7%
2 46
 
0.5%
88 25
 
0.2%
234 22
 
0.2%
91 21
 
0.2%
50 20
 
0.2%
103 20
 
0.2%
107 20
 
0.2%
94 20
 
0.2%
62 20
 
0.2%
Other values (5114) 9715
97.2%
2024-04-16T16:50:49.397728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5406
14.4%
2 4302
11.5%
3 3711
9.9%
4 3308
8.8%
5 3185
8.5%
6 3107
8.3%
7 3060
8.2%
8 2916
7.8%
9 2870
7.7%
0 2793
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34658
92.6%
Other Punctuation 2785
 
7.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5406
15.6%
2 4302
12.4%
3 3711
10.7%
4 3308
9.5%
5 3185
9.2%
6 3107
9.0%
7 3060
8.8%
8 2916
8.4%
9 2870
8.3%
0 2793
8.1%
Other Punctuation
ValueCountFrequency (%)
, 2785
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37443
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5406
14.4%
2 4302
11.5%
3 3711
9.9%
4 3308
8.8%
5 3185
8.5%
6 3107
8.3%
7 3060
8.2%
8 2916
7.8%
9 2870
7.7%
0 2793
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5406
14.4%
2 4302
11.5%
3 3711
9.9%
4 3308
8.8%
5 3185
8.5%
6 3107
8.3%
7 3060
8.2%
8 2916
7.8%
9 2870
7.7%
0 2793
7.5%

last_load_dttm
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-01-05 12:12:19
Maximum2021-01-05 12:12:20
2024-04-16T16:50:49.491768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:49.576483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2024-04-16T16:50:44.908117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:44.755601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:44.984722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:44.835316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T16:50:49.637199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungulast_load_dttm
skey1.0001.0000.0000.6520.996
d_year1.0001.0000.0000.0000.793
d_month0.0000.0001.0000.0000.000
sigungu0.6520.0000.0001.0000.196
last_load_dttm0.9960.7930.0000.1961.000
2024-04-16T16:50:49.730128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
d_yearsigungu
d_year1.0000.000
sigungu0.0001.000
2024-04-16T16:50:49.792653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_monthd_yearsigungu
skey1.0000.0350.9890.322
d_month0.0351.0000.0000.000
d_year0.9890.0001.0000.000
sigungu0.3220.0000.0001.000

Missing values

2024-04-16T16:50:45.086062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:50:45.200826image/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

skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
2607260620155부산광역시 기장군철마면 이곡리5000502021-01-05 12:12:19
8823882020175부산광역시 기장군일광면 청광리6100612021-01-05 12:12:19
7604760120177부산광역시 남구우암동1416185016012021-01-05 12:12:19
1531615317201910부산광역시 기장군정관면 매학리00002021-01-05 12:12:20
8060805820171부산광역시 강서구동선동162001622021-01-05 12:12:19
6764676220172부산광역시 서구서대신동3가20341493035272021-01-05 12:12:19
74150720157부산광역시 해운대구좌동11,6223,5951,74416,9612021-01-05 12:12:19
116901168820182부산광역시 기장군일광면 청광리9000902021-01-05 12:12:20
1008910087201812부산광역시 서구서대신동1가35625106072021-01-05 12:12:20
1419914199201912부산광역시 금정구금성동370003702021-01-05 12:12:20
skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
131861318620192부산광역시 동구좌천동3,0111,11404,1252021-01-05 12:12:20
20502051201512부산광역시 강서구송정동65,60131,826097,4272021-01-05 12:12:19
9335933220181부산광역시 중구중앙동2가2923303252021-01-05 12:12:19
9015901220178부산광역시 기장군장안읍 길천리663006632021-01-05 12:12:19
35903589201612부산광역시 중구대청동3가971101082021-01-05 12:12:19
141531415320199부산광역시 금정구오륜동209002092021-01-05 12:12:20
5116511420169부산광역시 강서구동선동131001312021-01-05 12:12:19
6536653320178부산광역시 중구중앙동3가2813803192021-01-05 12:12:19
120131420158부산광역시 중구신창동1가3431803612021-01-05 12:12:19
76897687201711부산광역시 북구구포동759731250107222021-01-05 12:12:19