Overview

Dataset statistics

Number of variables10
Number of observations451
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.1 KiB
Average record size in memory84.3 B

Variable types

DateTime2
Categorical7
Numeric1

Dataset

Description년월,지사코드,지사명,지역코드,지역명,설비코드,설비명,열생산량 합계,단위,작업일시
Author서울에너지공사
URLhttps://data.seoul.go.kr/dataList/OA-20442/S/1/datasetView.do

Alerts

단위 has constant value ""Constant
지역코드 is highly overall correlated with 지사코드 and 2 other fieldsHigh correlation
지사코드 is highly overall correlated with 지사명 and 2 other fieldsHigh correlation
지역명 is highly overall correlated with 지사코드 and 2 other fieldsHigh correlation
지사명 is highly overall correlated with 지사코드 and 2 other fieldsHigh correlation
설비코드 is highly overall correlated with 설비명High correlation
설비명 is highly overall correlated with 설비코드High correlation
열생산량 합계 has 28 (6.2%) zerosZeros

Reproduction

Analysis started2024-05-18 06:29:57.125332
Analysis finished2024-05-18 06:29:59.767537
Duration2.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Date

Distinct67
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2016-12-01 00:00:00
Maximum2022-06-01 00:00:00
2024-05-18T15:30:00.013039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:30:00.673439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지사코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
226 
2
225 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 226
50.1%
2 225
49.9%

Length

2024-05-18T15:30:01.269460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:01.666197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 226
50.1%
2 225
49.9%

지사명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
서부지사
226 
동부지사
225 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동부지사
2nd row서부지사
3rd row동부지사
4th row동부지사
5th row서부지사

Common Values

ValueCountFrequency (%)
서부지사 226
50.1%
동부지사 225
49.9%

Length

2024-05-18T15:30:02.401911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:02.877084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서부지사 226
50.1%
동부지사 225
49.9%

지역코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2
225 
1
160 
4
66 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 225
49.9%
1 160
35.5%
4 66
 
14.6%

Length

2024-05-18T15:30:03.260019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:03.606025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 225
49.9%
1 160
35.5%
4 66
 
14.6%

지역명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
노원
225 
목동
160 
마곡
66 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노원
2nd row마곡
3rd row노원
4th row노원
5th row목동

Common Values

ValueCountFrequency (%)
노원 225
49.9%
목동 160
35.5%
마곡 66
 
14.6%

Length

2024-05-18T15:30:03.927340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:04.282196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노원 225
49.9%
목동 160
35.5%
마곡 66
 
14.6%

설비코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2
167 
3
134 
1
79 
5
71 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 167
37.0%
3 134
29.7%
1 79
17.5%
5 71
15.7%

Length

2024-05-18T15:30:04.711567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:05.069632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 167
37.0%
3 134
29.7%
1 79
17.5%
5 71
15.7%

설비명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
PLB
167 
CHP
147 
INC
134 
기타
 
3

Length

Max length3
Median length3
Mean length2.9933481
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd rowPLB
3rd rowINC
4th rowPLB
5th rowINC

Common Values

ValueCountFrequency (%)
PLB 167
37.0%
CHP 147
32.6%
INC 134
29.7%
기타 3
 
0.7%

Length

2024-05-18T15:30:05.457080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:05.884542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
plb 167
37.0%
chp 147
32.6%
inc 134
29.7%
기타 3
 
0.7%

열생산량 합계
Real number (ℝ)

ZEROS 

Distinct423
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20173.838
Minimum0
Maximum121602.56
Zeros28
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-05-18T15:30:06.245858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1764.28
median15104
Q329027.05
95-th percentile63815.845
Maximum121602.56
Range121602.56
Interquartile range (IQR)28262.77

Descriptive statistics

Standard deviation21574.592
Coefficient of variation (CV)1.0694342
Kurtosis2.3429814
Mean20173.838
Median Absolute Deviation (MAD)14307.06
Skewness1.4494631
Sum9098400.9
Variance4.6546303 × 108
MonotonicityNot monotonic
2024-05-18T15:30:06.699360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
 
6.2%
19070.0 2
 
0.4%
21716.1 1
 
0.2%
32440.8 1
 
0.2%
466.35 1
 
0.2%
20895.3 1
 
0.2%
581.13 1
 
0.2%
1736.0 1
 
0.2%
14686.94 1
 
0.2%
743.12 1
 
0.2%
Other values (413) 413
91.6%
ValueCountFrequency (%)
0.0 28
6.2%
1.52 1
 
0.2%
2.49 1
 
0.2%
5.43 1
 
0.2%
13.76 1
 
0.2%
14.0 1
 
0.2%
14.31 1
 
0.2%
22.55 1
 
0.2%
23.08 1
 
0.2%
30.0 1
 
0.2%
ValueCountFrequency (%)
121602.56 1
0.2%
118464.78 1
0.2%
97396.83 1
0.2%
88662.92 1
0.2%
88053.59 1
0.2%
87950.29 1
0.2%
84330.17 1
0.2%
83156.89 1
0.2%
82868.71 1
0.2%
76783.05 1
0.2%

단위
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
GCAL
451 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGCAL
2nd rowGCAL
3rd rowGCAL
4th rowGCAL
5th rowGCAL

Common Values

ValueCountFrequency (%)
GCAL 451
100.0%

Length

2024-05-18T15:30:07.141028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:30:07.412590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gcal 451
100.0%

작업일시
Date

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2024-05-10 20:26:50
Maximum2024-05-10 20:26:51
2024-05-18T15:30:07.631156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:30:07.953993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2024-05-18T15:29:58.102553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T15:30:08.183859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월지사코드지사명지역코드지역명설비코드설비명열생산량 합계작업일시
년월1.0000.0000.0000.0000.0000.0000.0000.0000.529
지사코드0.0001.0001.0001.0001.0000.5240.4960.3960.781
지사명0.0001.0001.0001.0001.0000.5240.4960.3960.781
지역코드0.0001.0001.0001.0001.0000.3830.3430.4510.390
지역명0.0001.0001.0001.0001.0000.3830.3430.4510.390
설비코드0.0000.5240.5240.3830.3831.0000.9820.6560.339
설비명0.0000.4960.4960.3430.3430.9821.0000.5860.218
열생산량 합계0.0000.3960.3960.4510.4510.6560.5861.0000.194
작업일시0.5290.7810.7810.3900.3900.3390.2180.1941.000
2024-05-18T15:30:08.520726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역코드설비코드지사코드지역명설비명지사명
지역코드1.0000.3730.9991.0000.3310.999
설비코드0.3731.0000.3560.3730.8200.356
지사코드0.9990.3561.0000.9990.3350.996
지역명1.0000.3730.9991.0000.3310.999
설비명0.3310.8200.3350.3311.0000.335
지사명0.9990.3560.9960.9990.3351.000
2024-05-18T15:30:08.806331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
열생산량 합계지사코드지사명지역코드지역명설비코드설비명
열생산량 합계1.0000.3010.3010.3020.3020.4530.389
지사코드0.3011.0000.9960.9990.9990.3560.335
지사명0.3010.9961.0000.9990.9990.3560.335
지역코드0.3020.9990.9991.0001.0000.3730.331
지역명0.3020.9990.9991.0001.0000.3730.331
설비코드0.4530.3560.3560.3730.3731.0000.820
설비명0.3890.3350.3350.3310.3310.8201.000

Missing values

2024-05-18T15:29:58.610701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T15:29:59.397462image/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

년월지사코드지사명지역코드지역명설비코드설비명열생산량 합계단위작업일시
02022-062동부지사2노원5기타27825.45GCAL2024-05-10 20:26:50.0
12022-061서부지사4마곡2PLB1542.0GCAL2024-05-10 20:26:50.0
22022-062동부지사2노원3INC25544.5GCAL2024-05-10 20:26:50.0
32022-062동부지사2노원2PLB253.58GCAL2024-05-10 20:26:50.0
42022-061서부지사1목동3INC17697.0GCAL2024-05-10 20:26:50.0
52022-061서부지사1목동2PLB14.31GCAL2024-05-10 20:26:50.0
62022-051서부지사4마곡2PLB11042.0GCAL2024-05-10 20:26:50.0
72022-052동부지사2노원5CHP356.36GCAL2024-05-10 20:26:50.0
82022-052동부지사2노원3INC23947.6GCAL2024-05-10 20:26:50.0
92022-052동부지사2노원1CHP12738.82GCAL2024-05-10 20:26:50.0
년월지사코드지사명지역코드지역명설비코드설비명열생산량 합계단위작업일시
4412017-011서부지사1목동3INC14800.0GCAL2024-05-10 20:26:51.0
4422017-012동부지사2노원1CHP39666.1GCAL2024-05-10 20:26:50.0
4432017-012동부지사2노원2PLB84330.17GCAL2024-05-10 20:26:50.0
4442016-122동부지사2노원1CHP65685.62GCAL2024-05-10 20:26:50.0
4452016-122동부지사2노원5CHP860.58GCAL2024-05-10 20:26:50.0
4462016-122동부지사2노원3INC24233.39GCAL2024-05-10 20:26:50.0
4472016-121서부지사1목동2PLB34660.12GCAL2024-05-10 20:26:51.0
4482016-121서부지사1목동3INC14896.0GCAL2024-05-10 20:26:51.0
4492016-122동부지사2노원2PLB49323.25GCAL2024-05-10 20:26:50.0
4502016-121서부지사1목동1CHP44959.4GCAL2024-05-10 20:26:51.0