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 07:50:29.793785
Analysis finished2024-04-16 07:50:30.825315
Duration1.03 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%
Mean7714.6856
Minimum1
Maximum15470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:30.883209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile760.95
Q13834.75
median7707.5
Q311597.25
95-th percentile14671.1
Maximum15470
Range15469
Interquartile range (IQR)7762.5

Descriptive statistics

Standard deviation4471.6376
Coefficient of variation (CV)0.57962667
Kurtosis-1.2036136
Mean7714.6856
Median Absolute Deviation (MAD)3882
Skewness0.004336063
Sum77146856
Variance19995542
MonotonicityNot monotonic
2024-04-16T16:50:31.216185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2868 1
 
< 0.1%
7727 1
 
< 0.1%
11201 1
 
< 0.1%
1736 1
 
< 0.1%
1307 1
 
< 0.1%
1523 1
 
< 0.1%
6915 1
 
< 0.1%
6794 1
 
< 0.1%
7848 1
 
< 0.1%
2305 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
15470 1
< 0.1%
15468 1
< 0.1%
15466 1
< 0.1%
15465 1
< 0.1%
15464 1
< 0.1%
15463 1
< 0.1%
15461 1
< 0.1%
15460 1
< 0.1%
15458 1
< 0.1%
15457 1
< 0.1%

d_year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2016
2040 
2015
2024 
2019
1989 
2017
1988 
2018
1959 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2018
3rd row2016
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2016 2040
20.4%
2015 2024
20.2%
2019 1989
19.9%
2017 1988
19.9%
2018 1959
19.6%

Length

2024-04-16T16:50:31.316920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:31.398150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 2040
20.4%
2015 2024
20.2%
2019 1989
19.9%
2017 1988
19.9%
2018 1959
19.6%

d_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4966
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:31.481678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4432215
Coefficient of variation (CV)0.53000361
Kurtosis-1.2118527
Mean6.4966
Median Absolute Deviation (MAD)3
Skewness0.0040668369
Sum64966
Variance11.855774
MonotonicityNot monotonic
2024-04-16T16:50:31.564036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 861
8.6%
7 852
8.5%
6 852
8.5%
9 847
8.5%
5 840
8.4%
10 835
8.3%
11 832
8.3%
4 828
8.3%
1 828
8.3%
12 824
8.2%
Other values (2) 1601
16.0%
ValueCountFrequency (%)
1 828
8.3%
2 809
8.1%
3 861
8.6%
4 828
8.3%
5 840
8.4%
6 852
8.5%
7 852
8.5%
8 792
7.9%
9 847
8.5%
10 835
8.3%
ValueCountFrequency (%)
12 824
8.2%
11 832
8.3%
10 835
8.3%
9 847
8.5%
8 792
7.9%
7 852
8.5%
6 852
8.5%
5 840
8.4%
4 828
8.3%
3 861
8.6%

sigungu
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 기장군
2699 
부산광역시 중구
1599 
부산광역시 서구
938 
부산광역시 강서구
907 
부산광역시 영도구
802 
Other values (11)
3055 

Length

Max length10
Median length9
Mean length8.7624
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 기장군
2nd row부산광역시 사하구
3rd row부산광역시 중구
4th row부산광역시 중구
5th row부산광역시 기장군

Common Values

ValueCountFrequency (%)
부산광역시 기장군 2699
27.0%
부산광역시 중구 1599
16.0%
부산광역시 서구 938
 
9.4%
부산광역시 강서구 907
 
9.1%
부산광역시 영도구 802
 
8.0%
부산광역시 금정구 513
 
5.1%
부산광역시 부산진구 420
 
4.2%
부산광역시 동래구 351
 
3.5%
부산광역시 사하구 320
 
3.2%
부산광역시 사상구 315
 
3.1%
Other values (6) 1136
11.4%

Length

2024-04-16T16:50:31.675152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
기장군 2699
 
13.5%
중구 1599
 
8.0%
서구 938
 
4.7%
강서구 907
 
4.5%
영도구 802
 
4.0%
금정구 513
 
2.6%
부산진구 420
 
2.1%
동래구 351
 
1.8%
사하구 320
 
1.6%
Other values (7) 1451
 
7.3%

area
Text

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:31.944831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6512
Min length2

Characters and Unicode

Total characters46512
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

Unique0 ?
Unique (%)0.0%

Sample

1st row일광면 삼성리
2nd row구평동
3rd row광복동2가
4th row대창동2가
5th row정관읍 병산리
ValueCountFrequency (%)
기장읍 638
 
5.1%
장안읍 534
 
4.2%
일광면 483
 
3.8%
철마면 404
 
3.2%
정관읍 361
 
2.9%
정관면 279
 
2.2%
송정동 78
 
0.6%
매학리 77
 
0.6%
예림리 75
 
0.6%
달산리 73
 
0.6%
Other values (244) 9591
76.2%
2024-04-16T16:50:32.313024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7958
 
17.1%
가 3141
 
6.8%
리 2627
 
5.6%
2593
 
5.6%
읍 1569
 
3.4%
장 1439
 
3.1%
면 1166
 
2.5%
대 968
 
2.1%
1 961
 
2.1%
2 946
 
2.0%
Other values (134) 23144
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40728
87.6%
Decimal Number 3191
 
6.9%
Space Separator 2593
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7958
19.5%
가 3141
 
7.7%
리 2627
 
6.5%
읍 1569
 
3.9%
장 1439
 
3.5%
면 1166
 
2.9%
대 968
 
2.4%
정 877
 
2.2%
광 861
 
2.1%
안 717
 
1.8%
Other values (126) 19405
47.6%
Decimal Number
ValueCountFrequency (%)
1 961
30.1%
2 946
29.6%
3 649
20.3%
4 338
 
10.6%
5 190
 
6.0%
6 77
 
2.4%
7 30
 
0.9%
Space Separator
ValueCountFrequency (%)
2593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40728
87.6%
Common 5784
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7958
19.5%
가 3141
 
7.7%
리 2627
 
6.5%
읍 1569
 
3.9%
장 1439
 
3.5%
면 1166
 
2.9%
대 968
 
2.4%
정 877
 
2.2%
광 861
 
2.1%
안 717
 
1.8%
Other values (126) 19405
47.6%
Common
ValueCountFrequency (%)
2593
44.8%
1 961
 
16.6%
2 946
 
16.4%
3 649
 
11.2%
4 338
 
5.8%
5 190
 
3.3%
6 77
 
1.3%
7 30
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40728
87.6%
ASCII 5784
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7958
19.5%
가 3141
 
7.7%
리 2627
 
6.5%
읍 1569
 
3.9%
장 1439
 
3.5%
면 1166
 
2.9%
대 968
 
2.4%
정 877
 
2.2%
광 861
 
2.1%
안 717
 
1.8%
Other values (126) 19405
47.6%
ASCII
ValueCountFrequency (%)
2593
44.8%
1 961
 
16.6%
2 946
 
16.4%
3 649
 
11.2%
4 338
 
5.8%
5 190
 
3.3%
6 77
 
1.3%
7 30
 
0.5%

elect
Text

Distinct4815
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:32.618297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6192
Min length1

Characters and Unicode

Total characters36192
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

Unique3649 ?
Unique (%)36.5%

Sample

1st row423
2nd row29,997
3rd row270
4th row58
5th row261
ValueCountFrequency (%)
0 144
 
1.4%
2 52
 
0.5%
1 29
 
0.3%
197 23
 
0.2%
67 22
 
0.2%
79 22
 
0.2%
88 22
 
0.2%
91 21
 
0.2%
98 21
 
0.2%
94 21
 
0.2%
Other values (4805) 9623
96.2%
2024-04-16T16:50:33.081681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5200
14.4%
2 4220
11.7%
3 3548
9.8%
6 3213
8.9%
5 3208
8.9%
4 3163
8.7%
7 2983
8.2%
8 2768
7.6%
0 2687
7.4%
9 2608
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33598
92.8%
Other Punctuation 2594
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5200
15.5%
2 4220
12.6%
3 3548
10.6%
6 3213
9.6%
5 3208
9.5%
4 3163
9.4%
7 2983
8.9%
8 2768
8.2%
0 2687
8.0%
9 2608
7.8%
Other Punctuation
ValueCountFrequency (%)
, 2594
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5200
14.4%
2 4220
11.7%
3 3548
9.8%
6 3213
8.9%
5 3208
8.9%
4 3163
8.7%
7 2983
8.2%
8 2768
7.6%
0 2687
7.4%
9 2608
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5200
14.4%
2 4220
11.7%
3 3548
9.8%
6 3213
8.9%
5 3208
8.9%
4 3163
8.7%
7 2983
8.2%
8 2768
7.6%
0 2687
7.4%
9 2608
7.2%

gas
Text

Distinct2909
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:33.386580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5896
Min length1

Characters and Unicode

Total characters25896
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

Unique1997 ?
Unique (%)20.0%

Sample

1st row46
2nd row1,276
3rd row0
4th row4
5th row0
ValueCountFrequency (%)
0 2234
 
22.3%
2 101
 
1.0%
1 82
 
0.8%
4 75
 
0.8%
3 73
 
0.7%
5 64
 
0.6%
7 63
 
0.6%
11 59
 
0.6%
6 55
 
0.5%
9 55
 
0.5%
Other values (2899) 7139
71.4%
2024-04-16T16:50:33.780481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3952
15.3%
0 3827
14.8%
2 3050
11.8%
3 2421
9.3%
4 2095
8.1%
5 1966
7.6%
6 1892
7.3%
7 1782
6.9%
9 1781
6.9%
8 1762
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24528
94.7%
Other Punctuation 1368
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3952
16.1%
0 3827
15.6%
2 3050
12.4%
3 2421
9.9%
4 2095
8.5%
5 1966
8.0%
6 1892
7.7%
7 1782
7.3%
9 1781
7.3%
8 1762
7.2%
Other Punctuation
ValueCountFrequency (%)
, 1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25896
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3952
15.3%
0 3827
14.8%
2 3050
11.8%
3 2421
9.3%
4 2095
8.1%
5 1966
7.6%
6 1892
7.3%
7 1782
6.9%
9 1781
6.9%
8 1762
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3952
15.3%
0 3827
14.8%
2 3050
11.8%
3 2421
9.3%
4 2095
8.1%
5 1966
7.6%
6 1892
7.3%
7 1782
6.9%
9 1781
6.9%
8 1762
6.8%

heating
Text

Distinct275
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:34.082141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.0685
Min length1

Characters and Unicode

Total characters10685
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

Unique237 ?
Unique (%)2.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9681
96.8%
344 3
 
< 0.1%
1 3
 
< 0.1%
55 3
 
< 0.1%
277 3
 
< 0.1%
67 3
 
< 0.1%
77 3
 
< 0.1%
102 3
 
< 0.1%
151 3
 
< 0.1%
180 2
 
< 0.1%
Other values (265) 293
 
2.9%
2024-04-16T16:50:34.516843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9749
91.2%
1 186
 
1.7%
2 113
 
1.1%
3 105
 
1.0%
7 93
 
0.9%
4 92
 
0.9%
6 88
 
0.8%
5 80
 
0.7%
8 70
 
0.7%
9 63
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10639
99.6%
Other Punctuation 46
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9749
91.6%
1 186
 
1.7%
2 113
 
1.1%
3 105
 
1.0%
7 93
 
0.9%
4 92
 
0.9%
6 88
 
0.8%
5 80
 
0.8%
8 70
 
0.7%
9 63
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9749
91.2%
1 186
 
1.7%
2 113
 
1.1%
3 105
 
1.0%
7 93
 
0.9%
4 92
 
0.9%
6 88
 
0.8%
5 80
 
0.7%
8 70
 
0.7%
9 63
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9749
91.2%
1 186
 
1.7%
2 113
 
1.1%
3 105
 
1.0%
7 93
 
0.9%
4 92
 
0.9%
6 88
 
0.8%
5 80
 
0.7%
8 70
 
0.7%
9 63
 
0.6%

total
Text

Distinct5121
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:34.822748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7356
Min length1

Characters and Unicode

Total characters37356
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

Unique3965 ?
Unique (%)39.6%

Sample

1st row469
2nd row31,273
3rd row270
4th row62
5th row261
ValueCountFrequency (%)
0 63
 
0.6%
2 42
 
0.4%
102 21
 
0.2%
94 21
 
0.2%
78 21
 
0.2%
79 21
 
0.2%
61 21
 
0.2%
88 21
 
0.2%
1 20
 
0.2%
98 20
 
0.2%
Other values (5111) 9729
97.3%
2024-04-16T16:50:35.248978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5377
14.4%
2 4312
11.5%
3 3578
9.6%
4 3379
9.0%
5 3189
8.5%
6 3135
8.4%
7 3082
8.3%
8 2952
7.9%
0 2809
7.5%
9 2800
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34613
92.7%
Other Punctuation 2743
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5377
15.5%
2 4312
12.5%
3 3578
10.3%
4 3379
9.8%
5 3189
9.2%
6 3135
9.1%
7 3082
8.9%
8 2952
8.5%
0 2809
8.1%
9 2800
8.1%
Other Punctuation
ValueCountFrequency (%)
, 2743
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5377
14.4%
2 4312
11.5%
3 3578
9.6%
4 3379
9.0%
5 3189
8.5%
6 3135
8.4%
7 3082
8.3%
8 2952
7.9%
0 2809
7.5%
9 2800
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5377
14.4%
2 4312
11.5%
3 3578
9.6%
4 3379
9.0%
5 3189
8.5%
6 3135
8.4%
7 3082
8.3%
8 2952
7.9%
0 2809
7.5%
9 2800
7.5%

last_load_dttm
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-03-01 05:37:03
Maximum2021-03-01 05:37:04
2024-04-16T16:50:35.351754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:35.427629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2024-04-16T16:50:30.459888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:30.307225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:30.543746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:30.385653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T16:50:35.496848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungulast_load_dttm
skey1.0001.0000.0000.6520.996
d_year1.0001.0000.0000.0000.794
d_month0.0000.0001.0000.0000.000
sigungu0.6520.0000.0001.0000.191
last_load_dttm0.9960.7940.0000.1911.000
2024-04-16T16:50:35.571087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
d_yearsigungu
d_year1.0000.000
sigungu0.0001.000
2024-04-16T16:50:35.648067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_monthd_yearsigungu
skey1.0000.0300.9890.322
d_month0.0301.0000.0000.000
d_year0.9890.0001.0000.000
sigungu0.3220.0000.0001.000

Missing values

2024-04-16T16:50:30.652101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:50:30.769075image/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
2870286820159부산광역시 기장군일광면 삼성리4234604692021-03-01 05:37:03
1095610954201810부산광역시 사하구구평동29,9971,276031,2732021-03-01 05:37:04
3401340020167부산광역시 중구광복동2가270002702021-03-01 05:37:03
125271252520194부산광역시 중구대창동2가5840622021-03-01 05:37:04
1529715298201910부산광역시 기장군정관읍 병산리261002612021-03-01 05:37:04
149171491820194부산광역시 기장군철마면 안평리512005122021-03-01 05:37:04
2840283820159부산광역시 기장군기장읍 연화리224302272021-03-01 05:37:03
3397339620167부산광역시 중구신창동4가109001092021-03-01 05:37:03
2541254020155부산광역시 기장군기장읍 신천리208302112021-03-01 05:37:03
1442114422201911부산광역시 강서구강동동2,942202,9442021-03-01 05:37:04
skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
9001899820178부산광역시 기장군기장읍 내리5034305462021-03-01 05:37:03
3311331020165부산광역시 중구부평동4가2467803242021-03-01 05:37:03
2687268620157부산광역시 기장군기장읍 신천리221202232021-03-01 05:37:03
2303230320151부산광역시 기장군정관면 모전리001,6771,6772021-03-01 05:37:03
89553620152부산광역시 서구충무동1가7455808032021-03-01 05:37:03
149941499520196부산광역시 기장군기장읍 대라리1,69843502,1332021-03-01 05:37:04
8537853420171부산광역시 기장군일광면 화전리260002602021-03-01 05:37:03
61056102201611부산광역시 기장군기장읍 청강리1166639018052021-03-01 05:37:03
1314113141201911부산광역시 서구부민동2가2802003002021-03-01 05:37:04
126111260920196부산광역시 중구중앙동2가1911402052021-03-01 05:37:04