Overview

Dataset statistics

Number of variables6
Number of observations595
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.8 KiB
Average record size in memory51.2 B

Variable types

DateTime1
Categorical2
Numeric3

Dataset

Description영업통계 정보(광역시도별 판매량, 판매수입, 고객호수 (16.11~17.3))
Author한국전력공사
URLhttps://www.data.go.kr/data/15069182/fileData.do

Alerts

판매량 is highly overall correlated with 판매수입High correlation
판매수입 is highly overall correlated with 판매량High correlation
판매량 has unique valuesUnique
판매수입 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:54:50.269722
Analysis finished2023-12-12 09:54:52.475894
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조회기간
Date

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum2016-11-01 00:00:00
Maximum2017-03-01 00:00:00
2023-12-12T18:54:52.540579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:52.680675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

시도
Categorical

Distinct17
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
서울특별시
 
35
부산광역시
 
35
대구광역시
 
35
인천광역시
 
35
광주광역시
 
35
Other values (12)
420 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 35
 
5.9%
부산광역시 35
 
5.9%
대구광역시 35
 
5.9%
인천광역시 35
 
5.9%
광주광역시 35
 
5.9%
대전광역시 35
 
5.9%
울산광역시 35
 
5.9%
경기도 35
 
5.9%
강원도 35
 
5.9%
충청북도 35
 
5.9%
Other values (7) 245
41.2%

Length

2023-12-12T18:54:52.814780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 35
 
5.9%
충청북도 35
 
5.9%
제주특별자치도 35
 
5.9%
경상남도 35
 
5.9%
경상북도 35
 
5.9%
전라남도 35
 
5.9%
전라북도 35
 
5.9%
충청남도 35
 
5.9%
강원도 35
 
5.9%
부산광역시 35
 
5.9%
Other values (7) 245
41.2%

계약종별(대)
Categorical

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
주택용
85 
일반용
85 
교육용
85 
산업용
85 
농사용
85 
Other values (2)
170 

Length

Max length4
Median length3
Mean length3.1428571
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주택용
2nd row일반용
3rd row교육용
4th row산업용
5th row농사용

Common Values

ValueCountFrequency (%)
주택용 85
14.3%
일반용 85
14.3%
교육용 85
14.3%
산업용 85
14.3%
농사용 85
14.3%
가로등 85
14.3%
심 야 85
14.3%

Length

2023-12-12T18:54:52.945923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:54:53.059849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택용 85
12.5%
일반용 85
12.5%
교육용 85
12.5%
산업용 85
12.5%
농사용 85
12.5%
가로등 85
12.5%
심 85
12.5%
야 85
12.5%

고객호수
Real number (ℝ)

Distinct576
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191059.76
Minimum105
Maximum2811285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:54:53.201109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile808.1
Q110508
median49574
Q3158065.5
95-th percentile730514.9
Maximum2811285
Range2811180
Interquartile range (IQR)147557.5

Descriptive statistics

Standard deviation407310.18
Coefficient of variation (CV)2.1318471
Kurtosis24.662649
Mean191059.76
Median Absolute Deviation (MAD)48094
Skewness4.5725144
Sum1.1368056 × 108
Variance1.6590158 × 1011
MonotonicityNot monotonic
2023-12-12T18:54:53.360620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1109 3
 
0.5%
427 3
 
0.5%
105 2
 
0.3%
773 2
 
0.3%
5475 2
 
0.3%
133130 2
 
0.3%
25097 2
 
0.3%
1484 2
 
0.3%
122 2
 
0.3%
1642 2
 
0.3%
Other values (566) 573
96.3%
ValueCountFrequency (%)
105 2
0.3%
107 1
 
0.2%
122 2
0.3%
336 1
 
0.2%
337 2
0.3%
338 2
0.3%
425 1
 
0.2%
427 3
0.5%
429 1
 
0.2%
612 1
 
0.2%
ValueCountFrequency (%)
2811285 1
0.2%
2804344 1
0.2%
2797971 1
0.2%
2789223 1
0.2%
2781797 1
0.2%
2730453 1
0.2%
2726031 1
0.2%
2723537 1
0.2%
2718481 1
0.2%
2715830 1
0.2%

판매량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct595
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6297161 × 108
Minimum1308461
Maximum5.3200203 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:54:53.518034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1308461
5-th percentile6311981.2
Q124458734
median1.2868539 × 108
Q33.0988578 × 108
95-th percentile1.982219 × 109
Maximum5.3200203 × 109
Range5.3187119 × 109
Interquartile range (IQR)2.8542705 × 108

Descriptive statistics

Standard deviation7.2818414 × 108
Coefficient of variation (CV)2.0061738
Kurtosis17.54165
Mean3.6297161 × 108
Median Absolute Deviation (MAD)1.1286001 × 108
Skewness3.8055269
Sum2.1596811 × 1011
Variance5.3025214 × 1017
MonotonicityNot monotonic
2023-12-12T18:54:53.649765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1049004254 1
 
0.2%
176255400 1
 
0.2%
37008551 1
 
0.2%
270285709 1
 
0.2%
3174790 1
 
0.2%
7913613 1
 
0.2%
39203690 1
 
0.2%
134343690 1
 
0.2%
189783549 1
 
0.2%
15841948 1
 
0.2%
Other values (585) 585
98.3%
ValueCountFrequency (%)
1308461 1
0.2%
1386471 1
0.2%
1486212 1
0.2%
1526193 1
0.2%
1641434 1
0.2%
2421471 1
0.2%
2561359 1
0.2%
2580886 1
0.2%
2663312 1
0.2%
2691393 1
0.2%
ValueCountFrequency (%)
5320020341 1
0.2%
5242897188 1
0.2%
5211220748 1
0.2%
5104867247 1
0.2%
4941737976 1
0.2%
3261837577 1
0.2%
3252717500 1
0.2%
3217594364 1
0.2%
3147657243 1
0.2%
3026453135 1
0.2%

판매수입
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct595
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0779357 × 1010
Minimum63742321
Maximum6.2963297 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:54:53.785295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63742321
5-th percentile3.9560962 × 108
Q12.3577816 × 109
median8.3266935 × 109
Q33.5487097 × 1010
95-th percentile2.1730114 × 1011
Maximum6.2963297 × 1011
Range6.2956923 × 1011
Interquartile range (IQR)3.3129315 × 1010

Descriptive statistics

Standard deviation8.4258018 × 1010
Coefficient of variation (CV)2.0661929
Kurtosis17.311396
Mean4.0779357 × 1010
Median Absolute Deviation (MAD)7.6775 × 109
Skewness3.7659385
Sum2.4263717 × 1013
Variance7.0994136 × 1021
MonotonicityNot monotonic
2023-12-12T18:54:53.937277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128085741497 1
 
0.2%
19093044325 1
 
0.2%
3506657831 1
 
0.2%
32741583740 1
 
0.2%
159196075 1
 
0.2%
865294794 1
 
0.2%
2948205193 1
 
0.2%
15204900825 1
 
0.2%
26101094843 1
 
0.2%
1598989833 1
 
0.2%
Other values (585) 585
98.3%
ValueCountFrequency (%)
63742321 1
0.2%
68263694 1
0.2%
71540142 1
0.2%
74297558 1
0.2%
79420503 1
0.2%
133467357 1
0.2%
134633087 1
0.2%
140705835 1
0.2%
149323658 1
0.2%
159196075 1
0.2%
ValueCountFrequency (%)
629632968263 1
0.2%
620945307019 1
0.2%
610103340059 1
0.2%
578374488929 1
0.2%
517383980981 1
0.2%
370513520190 1
0.2%
356187048329 1
0.2%
356027042246 1
0.2%
346916852658 1
0.2%
341495300434 1
0.2%

Interactions

2023-12-12T18:54:51.562105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:50.706843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:51.183882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:51.700899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:50.910488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:51.319488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:51.824369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:51.047652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:54:51.440583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:54:54.081532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회기간시도계약종별(대)고객호수판매량판매수입
조회기간1.0000.0000.0000.0000.0000.000
시도0.0001.0000.0000.5190.5470.486
계약종별(대)0.0000.0001.0000.5450.4800.473
고객호수0.0000.5190.5451.0000.4030.431
판매량0.0000.5470.4800.4031.0000.975
판매수입0.0000.4860.4730.4310.9751.000
2023-12-12T18:54:54.212016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약종별(대)시도
계약종별(대)1.0000.000
시도0.0001.000
2023-12-12T18:54:54.314534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객호수판매량판매수입시도계약종별(대)
고객호수1.0000.4540.4340.2730.363
판매량0.4541.0000.9860.2630.282
판매수입0.4340.9861.0000.2250.277
시도0.2730.2630.2251.0000.000
계약종별(대)0.3630.2820.2770.0001.000

Missing values

2023-12-12T18:54:51.999618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:54:52.429039image/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

조회기간시도계약종별(대)고객호수판매량판매수입
02016-11서울특별시주택용27158301049004254128085741497
12016-11서울특별시일반용5405371843444173245877194342
22016-11서울특별시교육용264812868538614449062768
32016-11서울특별시산업용4616140628859949279282415
42016-11서울특별시농사용2241130846163742321
52016-11서울특별시가로등178566318702583371122170
62016-11서울특별시심 야16754190736851501598626
72016-11부산광역시주택용94408736603539343568149585
82016-11부산광역시일반용18521247085924462882230951
92016-11부산광역시교육용1161358292524116051911
조회기간시도계약종별(대)고객호수판매량판매수입
5852017-03제주특별자치도농사용441011301673245950270812
5862017-03제주특별자치도가로등219294316701486131724
5872017-03제주특별자치도심 야727315154169994835328
5882017-03세종특별자치시주택용49122287876372890601922
5892017-03세종특별자치시일반용11634444616675726233451
5902017-03세종특별자치시교육용1226772507684893369
5912017-03세종특별자치시산업용148414670878914338680496
5922017-03세종특별자치시농사용134425828650285994665
5932017-03세종특별자치시가로등79182421471277317518
5942017-03세종특별자치시심 야787812267579809325731