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 07:50:51.187181
Analysis finished2024-04-16 07:50:52.443817
Duration1.26 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%
Mean7739.3928
Minimum1
Maximum15469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:52.519253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile761.95
Q13884.75
median7772.5
Q311601.25
95-th percentile14701.05
Maximum15469
Range15468
Interquartile range (IQR)7716.5

Descriptive statistics

Standard deviation4466.8199
Coefficient of variation (CV)0.57715379
Kurtosis-1.1977395
Mean7739.3928
Median Absolute Deviation (MAD)3858.5
Skewness-0.0086107543
Sum77393928
Variance19952480
MonotonicityNot monotonic
2024-04-16T16:50:52.640260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7225 1
 
< 0.1%
3913 1
 
< 0.1%
9371 1
 
< 0.1%
10805 1
 
< 0.1%
13799 1
 
< 0.1%
4679 1
 
< 0.1%
996 1
 
< 0.1%
7509 1
 
< 0.1%
5157 1
 
< 0.1%
563 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
15469 1
< 0.1%
15468 1
< 0.1%
15467 1
< 0.1%
15466 1
< 0.1%
15465 1
< 0.1%
15462 1
< 0.1%
15460 1
< 0.1%
15459 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
2015
2030 
2018
2015 
2017
2000 
2016
1986 
2019
1969 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2018
3rd row2017
4th row2017
5th row2015

Common Values

ValueCountFrequency (%)
2015 2030
20.3%
2018 2015
20.2%
2017 2000
20.0%
2016 1986
19.9%
2019 1969
19.7%

Length

2024-04-16T16:50:52.757721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:52.857310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 2030
20.3%
2018 2015
20.2%
2017 2000
20.0%
2016 1986
19.9%
2019 1969
19.7%

d_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4981
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:52.948125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4505343
Coefficient of variation (CV)0.53100665
Kurtosis-1.2188098
Mean6.4981
Median Absolute Deviation (MAD)3
Skewness0.0069949065
Sum64981
Variance11.906187
MonotonicityNot monotonic
2024-04-16T16:50:53.049812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 856
8.6%
5 855
8.6%
3 851
8.5%
4 843
8.4%
11 840
8.4%
12 838
8.4%
2 832
8.3%
8 827
8.3%
7 823
8.2%
1 818
8.2%
Other values (2) 1617
16.2%
ValueCountFrequency (%)
1 818
8.2%
2 832
8.3%
3 851
8.5%
4 843
8.4%
5 855
8.6%
6 812
8.1%
7 823
8.2%
8 827
8.3%
9 856
8.6%
10 805
8.1%
ValueCountFrequency (%)
12 838
8.4%
11 840
8.4%
10 805
8.1%
9 856
8.6%
8 827
8.3%
7 823
8.2%
6 812
8.1%
5 855
8.6%
4 843
8.4%
3 851
8.5%

sigungu
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 기장군
2744 
부산광역시 중구
1561 
부산광역시 서구
955 
부산광역시 강서구
862 
부산광역시 영도구
780 
Other values (11)
3098 

Length

Max length10
Median length9
Mean length8.7611
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 영도구
2nd row부산광역시 금정구
3rd row부산광역시 기장군
4th row부산광역시 동래구
5th row부산광역시 기장군

Common Values

ValueCountFrequency (%)
부산광역시 기장군 2744
27.4%
부산광역시 중구 1561
15.6%
부산광역시 서구 955
 
9.6%
부산광역시 강서구 862
 
8.6%
부산광역시 영도구 780
 
7.8%
부산광역시 금정구 489
 
4.9%
부산광역시 부산진구 430
 
4.3%
부산광역시 동래구 356
 
3.6%
부산광역시 사상구 321
 
3.2%
부산광역시 사하구 315
 
3.1%
Other values (6) 1187
11.9%

Length

2024-04-16T16:50:53.163890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
기장군 2744
 
13.7%
중구 1561
 
7.8%
서구 955
 
4.8%
강서구 862
 
4.3%
영도구 780
 
3.9%
금정구 489
 
2.4%
부산진구 430
 
2.1%
동래구 356
 
1.8%
사상구 321
 
1.6%
Other values (7) 1502
 
7.5%

area
Text

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:53.412735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.664
Min length2

Characters and Unicode

Total characters46640
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row영선동3가
2nd row남산동
3rd row철마면 웅천리
4th row온천동
5th row장안읍 덕선리
ValueCountFrequency (%)
기장읍 624
 
4.9%
장안읍 498
 
3.9%
일광면 496
 
3.9%
철마면 426
 
3.4%
정관읍 378
 
3.0%
정관면 322
 
2.5%
모전리 80
 
0.6%
예림리 80
 
0.6%
용수리 76
 
0.6%
달산리 76
 
0.6%
Other values (244) 9590
75.8%
2024-04-16T16:50:53.778320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7905
 
16.9%
가 3095
 
6.6%
리 2688
 
5.8%
2646
 
5.7%
읍 1546
 
3.3%
장 1394
 
3.0%
면 1244
 
2.7%
정 928
 
2.0%
대 921
 
2.0%
2 918
 
2.0%
Other values (134) 23355
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40864
87.6%
Decimal Number 3130
 
6.7%
Space Separator 2646
 
5.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7905
19.3%
가 3095
 
7.6%
리 2688
 
6.6%
읍 1546
 
3.8%
장 1394
 
3.4%
면 1244
 
3.0%
정 928
 
2.3%
대 921
 
2.3%
광 885
 
2.2%
관 700
 
1.7%
Other values (126) 19558
47.9%
Decimal Number
ValueCountFrequency (%)
2 918
29.3%
1 901
28.8%
3 671
21.4%
4 332
 
10.6%
5 198
 
6.3%
6 73
 
2.3%
7 37
 
1.2%
Space Separator
ValueCountFrequency (%)
2646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40864
87.6%
Common 5776
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7905
19.3%
가 3095
 
7.6%
리 2688
 
6.6%
읍 1546
 
3.8%
장 1394
 
3.4%
면 1244
 
3.0%
정 928
 
2.3%
대 921
 
2.3%
광 885
 
2.2%
관 700
 
1.7%
Other values (126) 19558
47.9%
Common
ValueCountFrequency (%)
2646
45.8%
2 918
 
15.9%
1 901
 
15.6%
3 671
 
11.6%
4 332
 
5.7%
5 198
 
3.4%
6 73
 
1.3%
7 37
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40864
87.6%
ASCII 5776
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7905
19.3%
가 3095
 
7.6%
리 2688
 
6.6%
읍 1546
 
3.8%
장 1394
 
3.4%
면 1244
 
3.0%
정 928
 
2.3%
대 921
 
2.3%
광 885
 
2.2%
관 700
 
1.7%
Other values (126) 19558
47.9%
ASCII
ValueCountFrequency (%)
2646
45.8%
2 918
 
15.9%
1 901
 
15.6%
3 671
 
11.6%
4 332
 
5.7%
5 198
 
3.4%
6 73
 
1.3%
7 37
 
0.6%

elect
Text

Distinct4895
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:54.080089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6213
Min length1

Characters and Unicode

Total characters36213
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

Unique3761 ?
Unique (%)37.6%

Sample

1st row253
2nd row4,821
3rd row151
4th row10348
5th row60
ValueCountFrequency (%)
0 173
 
1.7%
2 53
 
0.5%
1 29
 
0.3%
197 25
 
0.2%
85 24
 
0.2%
88 23
 
0.2%
91 22
 
0.2%
67 21
 
0.2%
98 21
 
0.2%
233 20
 
0.2%
Other values (4885) 9589
95.9%
2024-04-16T16:50:54.478524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5116
14.1%
2 4208
11.6%
3 3545
9.8%
6 3239
8.9%
5 3187
8.8%
4 3168
8.7%
7 3035
8.4%
8 2770
7.6%
0 2699
7.5%
, 2653
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33560
92.7%
Other Punctuation 2653
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5116
15.2%
2 4208
12.5%
3 3545
10.6%
6 3239
9.7%
5 3187
9.5%
4 3168
9.4%
7 3035
9.0%
8 2770
8.3%
0 2699
8.0%
9 2593
7.7%
Other Punctuation
ValueCountFrequency (%)
, 2653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36213
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5116
14.1%
2 4208
11.6%
3 3545
9.8%
6 3239
8.9%
5 3187
8.8%
4 3168
8.7%
7 3035
8.4%
8 2770
7.6%
0 2699
7.5%
, 2653
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36213
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5116
14.1%
2 4208
11.6%
3 3545
9.8%
6 3239
8.9%
5 3187
8.8%
4 3168
8.7%
7 3035
8.4%
8 2770
7.6%
0 2699
7.5%
, 2653
7.3%

gas
Text

Distinct2922
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:54.781471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.6007
Min length1

Characters and Unicode

Total characters26007
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

Unique1978 ?
Unique (%)19.8%

Sample

1st row19
2nd row430
3rd row0
4th row1811
5th row0
ValueCountFrequency (%)
0 2263
 
22.6%
2 107
 
1.1%
1 81
 
0.8%
3 73
 
0.7%
4 69
 
0.7%
11 59
 
0.6%
7 59
 
0.6%
6 57
 
0.6%
5 57
 
0.6%
13 52
 
0.5%
Other values (2912) 7123
71.2%
2024-04-16T16:50:55.421074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3958
15.2%
0 3913
15.0%
2 3028
11.6%
3 2477
9.5%
4 2075
8.0%
5 1989
7.6%
6 1876
7.2%
7 1802
6.9%
9 1752
6.7%
8 1738
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24608
94.6%
Other Punctuation 1399
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3958
16.1%
0 3913
15.9%
2 3028
12.3%
3 2477
10.1%
4 2075
8.4%
5 1989
8.1%
6 1876
7.6%
7 1802
7.3%
9 1752
7.1%
8 1738
7.1%
Other Punctuation
ValueCountFrequency (%)
, 1399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3958
15.2%
0 3913
15.0%
2 3028
11.6%
3 2477
9.5%
4 2075
8.0%
5 1989
7.6%
6 1876
7.2%
7 1802
6.9%
9 1752
6.7%
8 1738
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3958
15.2%
0 3913
15.0%
2 3028
11.6%
3 2477
9.5%
4 2075
8.0%
5 1989
7.6%
6 1876
7.2%
7 1802
6.9%
9 1752
6.7%
8 1738
6.7%

heating
Text

Distinct287
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:55.739455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.0724
Min length1

Characters and Unicode

Total characters10724
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

Unique235 ?
Unique (%)2.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9652
96.5%
151 4
 
< 0.1%
124 4
 
< 0.1%
277 3
 
< 0.1%
64 3
 
< 0.1%
73 3
 
< 0.1%
144 3
 
< 0.1%
33 3
 
< 0.1%
65 3
 
< 0.1%
9 3
 
< 0.1%
Other values (277) 319
 
3.2%
2024-04-16T16:50:56.123732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9727
90.7%
1 206
 
1.9%
2 125
 
1.2%
4 104
 
1.0%
3 98
 
0.9%
5 96
 
0.9%
9 83
 
0.8%
7 83
 
0.8%
6 81
 
0.8%
8 81
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10684
99.6%
Other Punctuation 40
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9727
91.0%
1 206
 
1.9%
2 125
 
1.2%
4 104
 
1.0%
3 98
 
0.9%
5 96
 
0.9%
9 83
 
0.8%
7 83
 
0.8%
6 81
 
0.8%
8 81
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9727
90.7%
1 206
 
1.9%
2 125
 
1.2%
4 104
 
1.0%
3 98
 
0.9%
5 96
 
0.9%
9 83
 
0.8%
7 83
 
0.8%
6 81
 
0.8%
8 81
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9727
90.7%
1 206
 
1.9%
2 125
 
1.2%
4 104
 
1.0%
3 98
 
0.9%
5 96
 
0.9%
9 83
 
0.8%
7 83
 
0.8%
6 81
 
0.8%
8 81
 
0.8%

total
Text

Distinct5158
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:56.461236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7451
Min length1

Characters and Unicode

Total characters37451
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

Unique3998 ?
Unique (%)40.0%

Sample

1st row272
2nd row5,251
3rd row151
4th row12159
5th row60
ValueCountFrequency (%)
0 75
 
0.8%
2 43
 
0.4%
63 24
 
0.2%
1 22
 
0.2%
107 21
 
0.2%
88 21
 
0.2%
108 21
 
0.2%
67 20
 
0.2%
91 20
 
0.2%
85 19
 
0.2%
Other values (5148) 9714
97.1%
2024-04-16T16:50:56.887478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5379
14.4%
2 4336
11.6%
3 3653
9.8%
4 3395
9.1%
5 3161
8.4%
6 3101
8.3%
7 3015
8.1%
8 2945
7.9%
9 2891
7.7%
, 2803
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34648
92.5%
Other Punctuation 2803
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5379
15.5%
2 4336
12.5%
3 3653
10.5%
4 3395
9.8%
5 3161
9.1%
6 3101
9.0%
7 3015
8.7%
8 2945
8.5%
9 2891
8.3%
0 2772
8.0%
Other Punctuation
ValueCountFrequency (%)
, 2803
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37451
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5379
14.4%
2 4336
11.6%
3 3653
9.8%
4 3395
9.1%
5 3161
8.4%
6 3101
8.3%
7 3015
8.1%
8 2945
7.9%
9 2891
7.7%
, 2803
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5379
14.4%
2 4336
11.6%
3 3653
9.8%
4 3395
9.1%
5 3161
8.4%
6 3101
8.3%
7 3015
8.1%
8 2945
7.9%
9 2891
7.7%
, 2803
7.5%

last_load_dttm
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-12-22 14:29:57
Maximum2020-12-22 14:29:58
2024-04-16T16:50:56.981262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:57.059741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2024-04-16T16:50:52.031828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:51.879678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:52.108949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:51.958499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T16:50:57.124632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungulast_load_dttm
skey1.0001.0000.0000.6511.000
d_year1.0001.0000.0000.0000.926
d_month0.0000.0001.0000.0000.000
sigungu0.6510.0000.0001.0000.000
last_load_dttm1.0000.9260.0000.0001.000
2024-04-16T16:50:57.211460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
d_yearsigungu
d_year1.0000.000
sigungu0.0001.000
2024-04-16T16:50:57.284068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_monthd_yearsigungu
skey1.0000.0340.9890.322
d_month0.0341.0000.0000.000
d_year0.9890.0001.0000.000
sigungu0.3220.0000.0001.000

Missing values

2024-04-16T16:50:52.217982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:50:52.369212image/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
7227722520178부산광역시 영도구영선동3가2531902722020-12-22 14:29:57
110671106620188부산광역시 금정구남산동4,82143005,2512020-12-22 14:29:57
9117911420179부산광역시 기장군철마면 웅천리151001512020-12-22 14:29:57
75447541201710부산광역시 동래구온천동1034818110121592020-12-22 14:29:57
2704270320157부산광역시 기장군장안읍 덕선리6000602020-12-22 14:29:57
8603860020172부산광역시 기장군일광면 동백리258002582020-12-22 14:29:57
107521075020187부산광역시 북구금곡동3,8772,08705,9642020-12-22 14:29:57
155212920154부산광역시 중구중앙동3가1842402082020-12-22 14:29:57
4883488120169부산광역시 금정구금성동266002662020-12-22 14:29:57
41944192201612부산광역시 영도구신선동2가3288204102020-12-22 14:29:57
skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
61846181201612부산광역시 기장군기장읍 시랑리8013408352020-12-22 14:29:57
97461520156부산광역시 서구동대신동3가1,51133501,8462020-12-22 14:29:57
7491748820174부산광역시 동래구사직동57073763094702020-12-22 14:29:57
135741357420199부산광역시 부산진구초읍동2,75630903,0652020-12-22 14:29:58
51825180201612부산광역시 강서구동선동246002462020-12-22 14:29:57
133061330620194부산광역시 영도구봉래동2가39917705762020-12-22 14:29:58
1535415355201911부산광역시 기장군장안읍 기룡리431004312020-12-22 14:29:58
9568956620186부산광역시 중구광복동2가271002712020-12-22 14:29:57
137021520156부산광역시 중구중앙동7가1,95424002,1942020-12-22 14:29:57
1222212220201810부산광역시 기장군장안읍 오리9800982020-12-22 14:29:57