Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory83.4 B

Variable types

DateTime1
Numeric5
Text1
Categorical2

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/e009b13b-f3af-4f30-97cb-db55760a5605

Alerts

충전일자 has constant value ""Constant
전력사용량(중부하) has constant value ""Constant
전력사용량(최대부하) has constant value ""Constant
시도코드 is highly overall correlated with 시군구코드High correlation
시군구코드 is highly overall correlated with 시도코드High correlation
총전력사용량 is highly overall correlated with 전력사용량(경부하)High correlation
전력사용량(경부하) is highly overall correlated with 총전력사용량High correlation
충전지역명 has unique valuesUnique
총전력사용량 has unique valuesUnique
전력사용량(경부하) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:19:41.271818
Analysis finished2023-12-10 14:19:45.109962
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-08-01 00:00:00
Maximum2021-08-01 00:00:00
2023-12-10T23:19:45.159052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:45.264981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.533333
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:45.401704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q344
95-th percentile49.1
Maximum50
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.224151
Coefficient of variation (CV)0.39435837
Kurtosis-1.0134125
Mean33.533333
Median Absolute Deviation (MAD)9
Skewness-0.53759908
Sum1006
Variance174.87816
MonotonicityNot monotonic
2023-12-10T23:19:45.530179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
26 6
20.0%
41 5
16.7%
11 5
16.7%
44 3
10.0%
48 3
10.0%
28 2
 
6.7%
43 2
 
6.7%
50 2
 
6.7%
45 1
 
3.3%
27 1
 
3.3%
ValueCountFrequency (%)
11 5
16.7%
26 6
20.0%
27 1
 
3.3%
28 2
 
6.7%
41 5
16.7%
43 2
 
6.7%
44 3
10.0%
45 1
 
3.3%
48 3
10.0%
50 2
 
6.7%
ValueCountFrequency (%)
50 2
 
6.7%
48 3
10.0%
45 1
 
3.3%
44 3
10.0%
43 2
 
6.7%
41 5
16.7%
28 2
 
6.7%
27 1
 
3.3%
26 6
20.0%
11 5
16.7%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259.73333
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:45.646430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110.45
Q1130
median205
Q3320
95-th percentile635.75
Maximum710
Range600
Interquartile range (IQR)190

Descriptive statistics

Standard deviation168.12945
Coefficient of variation (CV)0.64731565
Kurtosis1.8109518
Mean259.73333
Median Absolute Deviation (MAD)83.5
Skewness1.5287679
Sum7792
Variance28267.513
MonotonicityNot monotonic
2023-12-10T23:19:45.754837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
710 2
 
6.7%
210 2
 
6.7%
111 2
 
6.7%
260 2
 
6.7%
110 2
 
6.7%
200 2
 
6.7%
230 2
 
6.7%
171 1
 
3.3%
133 1
 
3.3%
290 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
110 2
6.7%
111 2
6.7%
112 1
3.3%
117 1
3.3%
123 1
3.3%
129 1
3.3%
133 1
3.3%
170 1
3.3%
171 1
3.3%
180 1
3.3%
ValueCountFrequency (%)
710 2
6.7%
545 1
3.3%
500 1
3.3%
410 1
3.3%
380 1
3.3%
350 1
3.3%
330 1
3.3%
290 1
3.3%
260 2
6.7%
230 2
6.7%

읍면동코드
Real number (ℝ)

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.8
Minimum101
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:45.870750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101.45
Q1105.25
median110.5
Q3127.75
95-th percentile251.65
Maximum256
Range155
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation43.967386
Coefficient of variation (CV)0.34674595
Kurtosis5.3646442
Mean126.8
Median Absolute Deviation (MAD)7.5
Skewness2.5480297
Sum3804
Variance1933.131
MonotonicityNot monotonic
2023-12-10T23:19:45.989730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
106 4
 
13.3%
102 2
 
6.7%
128 2
 
6.7%
101 2
 
6.7%
103 2
 
6.7%
105 2
 
6.7%
117 1
 
3.3%
253 1
 
3.3%
108 1
 
3.3%
118 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
101 2
6.7%
102 2
6.7%
103 2
6.7%
105 2
6.7%
106 4
13.3%
108 1
 
3.3%
109 1
 
3.3%
110 1
 
3.3%
111 1
 
3.3%
113 1
 
3.3%
ValueCountFrequency (%)
256 1
3.3%
253 1
3.3%
250 1
3.3%
137 1
3.3%
130 1
3.3%
129 1
3.3%
128 2
6.7%
127 1
3.3%
119 1
3.3%
118 1
3.3%

충전지역명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:19:46.245218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length14.233333
Min length11

Characters and Unicode

Total characters427
Distinct characters96
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

Unique30 ?
Unique (%)100.0%

Sample

1st row경기도 안양시 만안구 석수동
2nd row부산광역시 부산진구 개금동
3rd row서울특별시 용산구 한강로3가
4th row서울특별시 은평구 수색동
5th row부산광역시 부산진구 가야동
ValueCountFrequency (%)
부산광역시 6
 
5.9%
경기도 5
 
5.0%
서울특별시 5
 
5.0%
충청남도 3
 
3.0%
경상남도 3
 
3.0%
부산진구 2
 
2.0%
인천광역시 2
 
2.0%
창원시 2
 
2.0%
제주특별자치도 2
 
2.0%
제주시 2
 
2.0%
Other values (66) 69
68.3%
2023-12-10T23:19:46.594092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
16.6%
시 30
 
7.0%
동 28
 
6.6%
구 21
 
4.9%
산 18
 
4.2%
도 17
 
4.0%
서 11
 
2.6%
광 10
 
2.3%
역 9
 
2.1%
부 9
 
2.1%
Other values (86) 203
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 354
82.9%
Space Separator 71
 
16.6%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
 
8.5%
동 28
 
7.9%
구 21
 
5.9%
산 18
 
5.1%
도 17
 
4.8%
서 11
 
3.1%
광 10
 
2.8%
역 9
 
2.5%
부 9
 
2.5%
경 8
 
2.3%
Other values (83) 193
54.5%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
3 1
50.0%
Space Separator
ValueCountFrequency (%)
71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 354
82.9%
Common 73
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
 
8.5%
동 28
 
7.9%
구 21
 
5.9%
산 18
 
5.1%
도 17
 
4.8%
서 11
 
3.1%
광 10
 
2.8%
역 9
 
2.5%
부 9
 
2.5%
경 8
 
2.3%
Other values (83) 193
54.5%
Common
ValueCountFrequency (%)
71
97.3%
2 1
 
1.4%
3 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 354
82.9%
ASCII 73
 
17.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
97.3%
2 1
 
1.4%
3 1
 
1.4%
Hangul
ValueCountFrequency (%)
시 30
 
8.5%
동 28
 
7.9%
구 21
 
5.9%
산 18
 
5.1%
도 17
 
4.8%
서 11
 
3.1%
광 10
 
2.8%
역 9
 
2.5%
부 9
 
2.5%
경 8
 
2.3%
Other values (83) 193
54.5%

총전력사용량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4326.0667
Minimum89.46
Maximum12791.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:46.708813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89.46
5-th percentile398.79
Q11858.055
median4119.02
Q35428.08
95-th percentile12061.334
Maximum12791.96
Range12702.5
Interquartile range (IQR)3570.025

Descriptive statistics

Standard deviation3377.6251
Coefficient of variation (CV)0.78076123
Kurtosis1.1759194
Mean4326.0667
Median Absolute Deviation (MAD)1778.32
Skewness1.2021874
Sum129782
Variance11408352
MonotonicityNot monotonic
2023-12-10T23:19:46.817544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5046.96 1
 
3.3%
12791.96 1
 
3.3%
1796.7 1
 
3.3%
846.08 1
 
3.3%
5628.56 1
 
3.3%
4035.64 1
 
3.3%
4930.32 1
 
3.3%
4752.64 1
 
3.3%
661.8 1
 
3.3%
2962.98 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
89.46 1
3.3%
183.6 1
3.3%
661.8 1
3.3%
846.08 1
3.3%
1105.62 1
3.3%
1785.84 1
3.3%
1796.7 1
3.3%
1812.42 1
3.3%
1994.96 1
3.3%
2204.28 1
3.3%
ValueCountFrequency (%)
12791.96 1
3.3%
12569.24 1
3.3%
11440.56 1
3.3%
7808.76 1
3.3%
7663.16 1
3.3%
5628.56 1
3.3%
5575.12 1
3.3%
5555.12 1
3.3%
5046.96 1
3.3%
5013.2 1
3.3%

전력사용량(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4326.6333
Minimum89
Maximum12784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:46.934236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile399
Q11858
median4120.5
Q35431.75
95-th percentile12061.4
Maximum12784
Range12695
Interquartile range (IQR)3573.75

Descriptive statistics

Standard deviation3377.2097
Coefficient of variation (CV)0.78056296
Kurtosis1.1714965
Mean4326.6333
Median Absolute Deviation (MAD)1778.5
Skewness1.2009546
Sum129799
Variance11405546
MonotonicityNot monotonic
2023-12-10T23:19:47.082278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5047 1
 
3.3%
12784 1
 
3.3%
1798 1
 
3.3%
847 1
 
3.3%
5624 1
 
3.3%
4035 1
 
3.3%
4933 1
 
3.3%
4754 1
 
3.3%
663 1
 
3.3%
2966 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
89 1
3.3%
183 1
3.3%
663 1
3.3%
847 1
3.3%
1106 1
3.3%
1784 1
3.3%
1798 1
3.3%
1812 1
3.3%
1996 1
3.3%
2206 1
3.3%
ValueCountFrequency (%)
12784 1
3.3%
12569 1
3.3%
11441 1
3.3%
7815 1
3.3%
7669 1
3.3%
5624 1
3.3%
5573 1
3.3%
5560 1
3.3%
5047 1
3.3%
5014 1
3.3%

전력사용량(중부하)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T23:19:47.240625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:47.339909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

전력사용량(최대부하)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
100.0%

Length

2023-12-10T23:19:47.445932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:47.585845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:19:44.136540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:41.564924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.246262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.741244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.503406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:44.286268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:41.712805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.345495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.865322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.628206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:44.412498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:41.845924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.451474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.966797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.734268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:44.556837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.019410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.556336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.055241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.856488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:44.666187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.146052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:42.649973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.373445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:43.994703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:19:47.665333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드충전지역명총전력사용량전력사용량(경부하)
시도코드1.0000.5970.3261.0000.6220.622
시군구코드0.5971.0000.7041.0000.5390.539
읍면동코드0.3260.7041.0001.0000.4360.436
충전지역명1.0001.0001.0001.0001.0001.000
총전력사용량0.6220.5390.4361.0001.0001.000
전력사용량(경부하)0.6220.5390.4361.0001.0001.000
2023-12-10T23:19:47.780729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드총전력사용량전력사용량(경부하)
시도코드1.000-0.6410.259-0.217-0.217
시군구코드-0.6411.000-0.2280.2290.229
읍면동코드0.259-0.2281.0000.1010.101
총전력사용량-0.2170.2290.1011.0001.000
전력사용량(경부하)-0.2170.2290.1011.0001.000

Missing values

2023-12-10T23:19:44.841697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:19:45.038268image/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

충전일자시도코드시군구코드읍면동코드충전지역명총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
02021-08-0141171102경기도 안양시 만안구 석수동5046.96504700
12021-08-0126230111부산광역시 부산진구 개금동1812.42181200
22021-08-0111170128서울특별시 용산구 한강로3가1105.62110600
32021-08-0111380101서울특별시 은평구 수색동2682.36268200
42021-08-0126230110부산광역시 부산진구 가야동5555.12556000
52021-08-0145180106전라북도 정읍시 농소동183.618300
62021-08-0128710250인천광역시 강화군 강화읍 남산리5575.12557300
72021-08-0141117103경기도 수원시 영통구 이의동5013.2501400
82021-08-0143111130충청북도 청주시 상당구 월오동1785.84178400
92021-08-0126260109부산광역시 동래구 사직동2204.28220600
충전일자시도코드시군구코드읍면동코드충전지역명총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
202021-08-0148123128경상남도 창원시 성산구 성주동11440.561144100
212021-08-0150110127제주특별자치도 제주시 도두일동2477.12247800
222021-08-0111200115서울특별시 성동구 성수동2가2962.98296600
232021-08-0144210105충청남도 서산시 잠홍동661.866300
242021-08-0128260106인천광역시 서구 연희동4752.64475400
252021-08-0111410118서울특별시 서대문구 홍은동4930.32493300
262021-08-0141210102경기도 광명시 철산동4035.64403500
272021-08-0126350108부산광역시 해운대구 송정동5628.56562400
282021-08-0127290106대구광역시 달서구 갈산동846.0884700
292021-08-0150110253제주특별자치도 제주시 애월읍 장전리1796.7179800