Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory99.4 B

Variable types

DateTime1
Text2
Numeric7
Categorical1

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/9ef2a160-449e-4bac-87d6-c99d0a1fbd02

Alerts

충전일자 has constant value ""Constant
시도코드 is highly overall correlated with 시군구코드High correlation
시군구코드 is highly overall correlated with 시도코드High correlation
한국전력공사고지사용량 is highly overall correlated with 전력사용량(경부하) and 2 other fieldsHigh correlation
전력사용량(경부하) is highly overall correlated with 한국전력공사고지사용량 and 2 other fieldsHigh correlation
전력사용량(중부하) is highly overall correlated with 한국전력공사고지사용량 and 2 other fieldsHigh correlation
전력사용량(최대부하) is highly overall correlated with 한국전력공사고지사용량 and 2 other fieldsHigh correlation
충전소ID has unique valuesUnique
한국전력공사고지사용량 has unique valuesUnique
전력사용량(경부하) has unique valuesUnique
전력사용량(중부하) has unique valuesUnique
총전력사용량 has 23 (76.7%) zerosZeros
전력사용량(중부하) has 1 (3.3%) zerosZeros
전력사용량(최대부하) has 2 (6.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:25:17.381512
Analysis finished2023-12-10 14:25:23.378914
Duration6 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
2023-12-10T23:25:23.416138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:23.499828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

충전소ID
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:25:23.691387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters330
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowKRPPKCS0008
2nd rowKRPPKCS0059
3rd rowKRPPKCS0011
4th rowKRPPKCS0032
5th rowKRPPKCS0019
ValueCountFrequency (%)
krppkcs0008 1
 
3.3%
krppkcs0059 1
 
3.3%
krppkcs0051 1
 
3.3%
krppkcs0039 1
 
3.3%
krppkcs0017 1
 
3.3%
krppkcs0033 1
 
3.3%
krppkcs0045 1
 
3.3%
krppkcs0036 1
 
3.3%
krppkcs0023 1
 
3.3%
krppkcs0018 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:25:23.978099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66
20.0%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
3 11
 
3.3%
1 10
 
3.0%
5 9
 
2.7%
4 6
 
1.8%
Other values (5) 18
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 210
63.6%
Decimal Number 120
36.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66
55.0%
3 11
 
9.2%
1 10
 
8.3%
5 9
 
7.5%
4 6
 
5.0%
2 5
 
4.2%
7 4
 
3.3%
6 4
 
3.3%
9 3
 
2.5%
8 2
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
K 60
28.6%
P 60
28.6%
R 30
14.3%
C 30
14.3%
S 30
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 210
63.6%
Common 120
36.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66
55.0%
3 11
 
9.2%
1 10
 
8.3%
5 9
 
7.5%
4 6
 
5.0%
2 5
 
4.2%
7 4
 
3.3%
6 4
 
3.3%
9 3
 
2.5%
8 2
 
1.7%
Latin
ValueCountFrequency (%)
K 60
28.6%
P 60
28.6%
R 30
14.3%
C 30
14.3%
S 30
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66
20.0%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
3 11
 
3.3%
1 10
 
3.0%
5 9
 
2.7%
4 6
 
1.8%
Other values (5) 18
 
5.5%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.333333
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:24.116829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q348
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.819859
Coefficient of variation (CV)0.39112809
Kurtosis-0.88035324
Mean35.333333
Median Absolute Deviation (MAD)8
Skewness-0.72350617
Sum1060
Variance190.98851
MonotonicityNot monotonic
2023-12-10T23:25:24.235264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
48 6
20.0%
41 5
16.7%
11 5
16.7%
26 4
13.3%
50 3
10.0%
44 3
10.0%
28 2
 
6.7%
43 1
 
3.3%
27 1
 
3.3%
ValueCountFrequency (%)
11 5
16.7%
26 4
13.3%
27 1
 
3.3%
28 2
 
6.7%
41 5
16.7%
43 1
 
3.3%
44 3
10.0%
48 6
20.0%
50 3
10.0%
ValueCountFrequency (%)
50 3
10.0%
48 6
20.0%
44 3
10.0%
43 1
 
3.3%
41 5
16.7%
28 2
 
6.7%
27 1
 
3.3%
26 4
13.3%
11 5
16.7%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.66667
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:24.335435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110
Q1123.5
median200
Q3320
95-th percentile635.75
Maximum710
Range600
Interquartile range (IQR)196.5

Descriptive statistics

Standard deviation169.45518
Coefficient of variation (CV)0.67066693
Kurtosis1.8464875
Mean252.66667
Median Absolute Deviation (MAD)86.5
Skewness1.5129472
Sum7580
Variance28715.057
MonotonicityNot monotonic
2023-12-10T23:25:24.443243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
110 4
13.3%
410 3
 
10.0%
123 2
 
6.7%
210 2
 
6.7%
200 2
 
6.7%
710 2
 
6.7%
290 2
 
6.7%
125 2
 
6.7%
190 1
 
3.3%
545 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
110 4
13.3%
112 1
 
3.3%
117 1
 
3.3%
123 2
6.7%
125 2
6.7%
129 1
 
3.3%
133 1
 
3.3%
173 1
 
3.3%
190 1
 
3.3%
200 2
6.7%
ValueCountFrequency (%)
710 2
6.7%
545 1
 
3.3%
410 3
10.0%
350 1
 
3.3%
330 1
 
3.3%
290 2
6.7%
285 1
 
3.3%
230 1
 
3.3%
210 2
6.7%
200 2
6.7%

충전지역명
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:25:24.650653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length14.7
Min length11

Characters and Unicode

Total characters441
Distinct characters91
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

Unique26 ?
Unique (%)86.7%

Sample

1st row경기도 부천시 대장동
2nd row제주특별자치도 제주시 오라일동
3rd row경기도 광명시 철산동
4th row충청남도 아산시 모종동
5th row서울특별시 서대문구 홍은동
ValueCountFrequency (%)
경상남도 6
 
5.8%
창원시 5
 
4.9%
서울특별시 5
 
4.9%
경기도 5
 
4.9%
부산광역시 4
 
3.9%
충청남도 3
 
2.9%
제주시 3
 
2.9%
제주특별자치도 3
 
2.9%
인천광역시 2
 
1.9%
마산합포구 2
 
1.9%
Other values (61) 65
63.1%
2023-12-10T23:25:25.029814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
16.6%
시 30
 
6.8%
동 30
 
6.8%
구 21
 
4.8%
도 19
 
4.3%
산 17
 
3.9%
경 11
 
2.5%
서 11
 
2.5%
남 10
 
2.3%
주 9
 
2.0%
Other values (81) 210
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
83.0%
Space Separator 73
 
16.6%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
 
8.2%
동 30
 
8.2%
구 21
 
5.7%
도 19
 
5.2%
산 17
 
4.6%
경 11
 
3.0%
서 11
 
3.0%
남 10
 
2.7%
주 9
 
2.5%
성 8
 
2.2%
Other values (78) 200
54.6%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
7 1
50.0%
Space Separator
ValueCountFrequency (%)
73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
83.0%
Common 75
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
 
8.2%
동 30
 
8.2%
구 21
 
5.7%
도 19
 
5.2%
산 17
 
4.6%
경 11
 
3.0%
서 11
 
3.0%
남 10
 
2.7%
주 9
 
2.5%
성 8
 
2.2%
Other values (78) 200
54.6%
Common
ValueCountFrequency (%)
73
97.3%
2 1
 
1.3%
7 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
83.0%
ASCII 75
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
97.3%
2 1
 
1.3%
7 1
 
1.3%
Hangul
ValueCountFrequency (%)
시 30
 
8.2%
동 30
 
8.2%
구 21
 
5.7%
도 19
 
5.2%
산 17
 
4.6%
경 11
 
3.0%
서 11
 
3.0%
남 10
 
2.7%
주 9
 
2.5%
성 8
 
2.2%
Other values (78) 200
54.6%

충전소명
Categorical

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
**** ***
9 
**** *****
8 
**** ******
3 
***** *****
2 
**** *** *****
1 
Other values (7)
7 

Length

Max length18
Median length16
Mean length10.733333
Min length7

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row**** *** *****
2nd row**** *****
3rd row**** ******
4th row**** *************
5th row**** ***

Common Values

ValueCountFrequency (%)
**** *** 9
30.0%
**** ***** 8
26.7%
**** ****** 3
 
10.0%
***** ***** 2
 
6.7%
**** *** ***** 1
 
3.3%
**** ************* 1
 
3.3%
**** ******* 1
 
3.3%
**** ** 1
 
3.3%
**** ***** *** *** 1
 
3.3%
**** ** *** *** 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:25:25.161334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65
100.0%

총전력사용량
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34929.267
Minimum0
Maximum373788
Zeros23
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:25.255496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile214328.45
Maximum373788
Range373788
Interquartile range (IQR)0

Descriptive statistics

Standard deviation87692.615
Coefficient of variation (CV)2.510577
Kurtosis7.9594762
Mean34929.267
Median Absolute Deviation (MAD)0
Skewness2.8306388
Sum1047878
Variance7.6899946 × 109
MonotonicityNot monotonic
2023-12-10T23:25:25.349135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 23
76.7%
2801 1
 
3.3%
154001 1
 
3.3%
33372 1
 
3.3%
61819 1
 
3.3%
178249 1
 
3.3%
243848 1
 
3.3%
373788 1
 
3.3%
ValueCountFrequency (%)
0 23
76.7%
2801 1
 
3.3%
33372 1
 
3.3%
61819 1
 
3.3%
154001 1
 
3.3%
178249 1
 
3.3%
243848 1
 
3.3%
373788 1
 
3.3%
ValueCountFrequency (%)
373788 1
 
3.3%
243848 1
 
3.3%
178249 1
 
3.3%
154001 1
 
3.3%
61819 1
 
3.3%
33372 1
 
3.3%
2801 1
 
3.3%
0 23
76.7%

한국전력공사고지사용량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144008.37
Minimum98
Maximum503099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:25.464844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile10505
Q145810.25
median135658.5
Q3176931
95-th percentile354247.2
Maximum503099
Range503001
Interquartile range (IQR)131120.75

Descriptive statistics

Standard deviation122660.85
Coefficient of variation (CV)0.85176195
Kurtosis1.2101177
Mean144008.37
Median Absolute Deviation (MAD)78192.5
Skewness1.1569965
Sum4320251
Variance1.5045684 × 1010
MonotonicityNot monotonic
2023-12-10T23:25:25.595226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
503099 1
 
3.3%
55708 1
 
3.3%
361089 1
 
3.3%
178549 1
 
3.3%
26159 1
 
3.3%
111669 1
 
3.3%
22766 1
 
3.3%
237807 1
 
3.3%
25352 1
 
3.3%
172077 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
98 1
3.3%
5663 1
3.3%
16423 1
3.3%
22766 1
3.3%
25352 1
3.3%
26159 1
3.3%
30502 1
3.3%
42511 1
3.3%
55708 1
3.3%
59224 1
3.3%
ValueCountFrequency (%)
503099 1
3.3%
361089 1
3.3%
345885 1
3.3%
327345 1
3.3%
288836 1
3.3%
237807 1
3.3%
205765 1
3.3%
178549 1
3.3%
172077 1
3.3%
167721 1
3.3%

전력사용량(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58354.533
Minimum38
Maximum198287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:25.738372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile3501.15
Q118807.75
median49022.5
Q370075.5
95-th percentile158447.85
Maximum198287
Range198249
Interquartile range (IQR)51267.75

Descriptive statistics

Standard deviation52387.344
Coefficient of variation (CV)0.89774248
Kurtosis0.75311758
Mean58354.533
Median Absolute Deviation (MAD)26241
Skewness1.2068828
Sum1750636
Variance2.7444338 × 109
MonotonicityNot monotonic
2023-12-10T23:25:25.854471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
198287 1
 
3.3%
14305 1
 
3.3%
140722 1
 
3.3%
71691 1
 
3.3%
6924 1
 
3.3%
40491 1
 
3.3%
2913 1
 
3.3%
76116 1
 
3.3%
11680 1
 
3.3%
65229 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
38 1
3.3%
2913 1
3.3%
4220 1
3.3%
6924 1
3.3%
11680 1
3.3%
12554 1
3.3%
14305 1
3.3%
17199 1
3.3%
23634 1
3.3%
24668 1
3.3%
ValueCountFrequency (%)
198287 1
3.3%
165462 1
3.3%
149875 1
3.3%
140722 1
3.3%
134092 1
3.3%
106667 1
3.3%
76116 1
3.3%
71691 1
3.3%
65229 1
3.3%
64725 1
3.3%

전력사용량(중부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53949.033
Minimum0
Maximum188566
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:25.971948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile675.65
Q117622.5
median46910.5
Q368042.75
95-th percentile130485.9
Maximum188566
Range188566
Interquartile range (IQR)50420.25

Descriptive statistics

Standard deviation45777.316
Coefficient of variation (CV)0.84852894
Kurtosis1.1975407
Mean53949.033
Median Absolute Deviation (MAD)22889.5
Skewness1.0880626
Sum1618471
Variance2.0955627 × 109
MonotonicityNot monotonic
2023-12-10T23:25:26.117903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
188566 1
 
3.3%
23117 1
 
3.3%
134220 1
 
3.3%
63488 1
 
3.3%
12221 1
 
3.3%
44669 1
 
3.3%
15791 1
 
3.3%
98345 1
 
3.3%
10943 1
 
3.3%
65669 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
47 1
3.3%
1444 1
3.3%
3877 1
3.3%
5708 1
3.3%
10943 1
3.3%
12221 1
3.3%
15791 1
3.3%
23117 1
3.3%
25087 1
3.3%
ValueCountFrequency (%)
188566 1
3.3%
134220 1
3.3%
125922 1
3.3%
114376 1
3.3%
107927 1
3.3%
98345 1
3.3%
68896 1
3.3%
68834 1
3.3%
65669 1
3.3%
65075 1
3.3%

전력사용량(최대부하)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31718.333
Minimum0
Maximum116287
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:25:26.243962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.05
Q17706.75
median26456.5
Q342860
95-th percentile80032.2
Maximum116287
Range116287
Interquartile range (IQR)35153.25

Descriptive statistics

Standard deviation29039.917
Coefficient of variation (CV)0.91555621
Kurtosis1.0879405
Mean31718.333
Median Absolute Deviation (MAD)17691
Skewness1.090455
Sum951550
Variance8.4331678 × 108
MonotonicityNot monotonic
2023-12-10T23:25:26.374461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.7%
116287 1
 
3.3%
18294 1
 
3.3%
86235 1
 
3.3%
43431 1
 
3.3%
7012 1
 
3.3%
26562 1
 
3.3%
4069 1
 
3.3%
63359 1
 
3.3%
2719 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 2
6.7%
49 1
3.3%
60 1
3.3%
136 1
3.3%
2719 1
3.3%
4069 1
3.3%
7012 1
3.3%
9791 1
3.3%
16088 1
3.3%
16941 1
3.3%
ValueCountFrequency (%)
116287 1
3.3%
86235 1
3.3%
72451 1
3.3%
67888 1
3.3%
67301 1
3.3%
63359 1
3.3%
44864 1
3.3%
43431 1
3.3%
41147 1
3.3%
39529 1
3.3%

Interactions

2023-12-10T23:25:22.287601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:17.725281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.614234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.317601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.049545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.822453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.593165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.389503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:17.902257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.709541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.410581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.144675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.968502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.696542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.492582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.049578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.791679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.499462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.239362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.073807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.790587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.589853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.185351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.882047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.601416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.369278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.178828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.895819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.669898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.281620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.966121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.709001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.496463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.273830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.018994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.991038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.389710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.076294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.832703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.637238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.384442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.108780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:23.087425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:18.514700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.183935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:19.942955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:20.726565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:21.501184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:25:22.198879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:25:26.493039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID시도코드시군구코드충전지역명충전소명총전력사용량한국전력공사고지사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
충전소ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시도코드1.0001.0000.5121.0000.6970.2220.0000.0000.0000.000
시군구코드1.0000.5121.0001.0000.0000.4090.5340.6600.2990.000
충전지역명1.0001.0001.0001.0001.0000.8210.9461.0000.9410.943
충전소명1.0000.6970.0001.0001.0000.0000.5920.8250.6110.795
총전력사용량1.0000.2220.4090.8210.0001.0000.6950.0000.7340.705
한국전력공사고지사용량1.0000.0000.5340.9460.5920.6951.0000.9650.9610.946
전력사용량(경부하)1.0000.0000.6601.0000.8250.0000.9651.0000.8700.860
전력사용량(중부하)1.0000.0000.2990.9410.6110.7340.9610.8701.0000.994
전력사용량(최대부하)1.0000.0000.0000.9430.7950.7050.9460.8600.9941.000
2023-12-10T23:25:26.630926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드총전력사용량한국전력공사고지사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)충전소명
시도코드1.000-0.723-0.110-0.013-0.0210.0090.0120.278
시군구코드-0.7231.0000.2780.1740.1440.2300.2050.000
총전력사용량-0.1100.2781.0000.1110.0770.1220.1340.000
한국전력공사고지사용량-0.0130.1740.1111.0000.9640.9540.9300.247
전력사용량(경부하)-0.0210.1440.0770.9641.0000.8730.8330.484
전력사용량(중부하)0.0090.2300.1220.9540.8731.0000.9710.253
전력사용량(최대부하)0.0120.2050.1340.9300.8330.9711.0000.427
충전소명0.2780.0000.0000.2470.4840.2530.4271.000

Missing values

2023-12-10T23:25:23.198844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:25:23.330066image/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

충전일자충전소ID시도코드시군구코드충전지역명충전소명총전력사용량한국전력공사고지사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
02021-08KRPPKCS000841190경기도 부천시 대장동**** *** *****0503099198287188566116287
12021-08KRPPKCS005950110제주특별자치도 제주시 오라일동**** *****28019838060
22021-08KRPPKCS001141210경기도 광명시 철산동**** ******0163867578616883437132
32021-08KRPPKCS003244200충청남도 아산시 모종동**** *************082731236343672522394
42021-08KRPPKCS001911410서울특별시 서대문구 홍은동**** ***154001150656495146507536050
52021-08KRPPKCS003444133충청남도 천안시 서북구 차암동**** ***0167721539826889644864
62021-08KRPPKCS003748129경상남도 창원시 진해구 여좌동**** ******0132076494475630426351
72021-08KRPPKCS004326350부산광역시 해운대구 송정동**** ******0152706485986458739529
82021-08KRPPKCS005348125경상남도 창원시 마산합포구 해운동**** *******034588516546210792772451
92021-08KRPPKCS005750110제주특별자치도 제주시 도두일동**** **078582277283478316088
충전일자충전소ID시도코드시군구코드충전지역명충전소명총전력사용량한국전력공사고지사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
202021-08KRPPKCS001411410서울특별시 서대문구 홍은동**** ***042511424164749
212021-08KRPPKCS001811545서울특별시 금천구 가산동**** *****028883610666711437667888
222021-08KRPPKCS002328710인천광역시 강화군 강화읍 남산리**** **********178249172077652296566941147
232021-08KRPPKCS003627290대구광역시 달서구 갈산동**** ***02535211680109432719
242021-08KRPPKCS004526710부산광역시 기장군 정관읍 용수리**** *****243848237807761169834563359
252021-08KRPPKCS003344210충청남도 서산시 잠홍동**** ***0227662913157914069
262021-08KRPPKCS001711200서울특별시 성동구 성수동2가**** ***0111669404914466926562
272021-08KRPPKCS003926410부산광역시 금정구 노포동**** ***********0261596924122217012
282021-08KRPPKCS005148123경상남도 창원시 성산구 성주동***** *****0178549716916348843431
292021-08KRPPKCS005548125경상남도 창원시 마산합포구 가포동**** *****37378836108914072213422086235