Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory109.4 B

Variable types

Categorical3
Text2
Numeric7

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/609e4eba-9f84-4c55-8bfd-575fee8809e1

Alerts

충전일자 has constant value ""Constant
전력사용량(중부하) has constant value ""Constant
전력사용량(최대부하) has constant value ""Constant
시도코드 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
시군구코드 is highly overall correlated with 시도코드High correlation
위도 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
충전소ID has unique valuesUnique
충전지역명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
총전력사용량 has unique valuesUnique
전력사용량(경부하) has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:54:09.269891
Analysis finished2023-12-10 13:54:18.349888
Duration9.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-08-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-01
2nd row2021-08-01
3rd row2021-08-01
4th row2021-08-01
5th row2021-08-01

Common Values

ValueCountFrequency (%)
2021-08-01 30
100.0%

Length

2023-12-10T22:54:18.579464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:18.734648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-01 30
100.0%

충전소ID
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:54:19.014983image/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 rowKRPPKCS0040
2nd rowKRPPKCS0018
3rd rowKRPPKCS0044
4th rowKRPPKCS0047
5th rowKRPPKCS0003
ValueCountFrequency (%)
krppkcs0040 1
 
3.3%
krppkcs0018 1
 
3.3%
krppkcs0027 1
 
3.3%
krppkcs0009 1
 
3.3%
krppkcs0031 1
 
3.3%
krppkcs0006 1
 
3.3%
krppkcs0020 1
 
3.3%
krppkcs0048 1
 
3.3%
krppkcs0008 1
 
3.3%
krppkcs0045 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:54:19.614055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 71
21.5%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
1 11
 
3.3%
4 10
 
3.0%
5 6
 
1.8%
2 6
 
1.8%
Other values (5) 16
 
4.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71
59.2%
1 11
 
9.2%
4 10
 
8.3%
5 6
 
5.0%
2 6
 
5.0%
7 5
 
4.2%
3 5
 
4.2%
8 3
 
2.5%
6 2
 
1.7%
9 1
 
0.8%
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 71
59.2%
1 11
 
9.2%
4 10
 
8.3%
5 6
 
5.0%
2 6
 
5.0%
7 5
 
4.2%
3 5
 
4.2%
8 3
 
2.5%
6 2
 
1.7%
9 1
 
0.8%
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 71
21.5%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
1 11
 
3.3%
4 10
 
3.0%
5 6
 
1.8%
2 6
 
1.8%
Other values (5) 16
 
4.8%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.466667
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:19.826298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q114.75
median34.5
Q342.5
95-th percentile49.1
Maximum50
Range39
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation14.794531
Coefficient of variation (CV)0.47016519
Kurtosis-1.4835936
Mean31.466667
Median Absolute Deviation (MAD)11
Skewness-0.30300431
Sum944
Variance218.87816
MonotonicityNot monotonic
2023-12-10T22:54:20.026880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
11 8
26.7%
41 7
23.3%
48 5
16.7%
26 5
16.7%
50 2
 
6.7%
28 2
 
6.7%
43 1
 
3.3%
ValueCountFrequency (%)
11 8
26.7%
26 5
16.7%
28 2
 
6.7%
41 7
23.3%
43 1
 
3.3%
48 5
16.7%
50 2
 
6.7%
ValueCountFrequency (%)
50 2
 
6.7%
48 5
16.7%
43 1
 
3.3%
41 7
23.3%
28 2
 
6.7%
26 5
16.7%
11 8
26.7%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.83333
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:20.224536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110.45
Q1139.25
median220
Q3387.5
95-th percentile635.75
Maximum710
Range600
Interquartile range (IQR)248.25

Descriptive statistics

Standard deviation171.73176
Coefficient of variation (CV)0.60504438
Kurtosis0.6397022
Mean283.83333
Median Absolute Deviation (MAD)105.5
Skewness1.1115193
Sum8515
Variance29491.799
MonotonicityNot monotonic
2023-12-10T22:54:20.812128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
330 2
 
6.7%
410 2
 
6.7%
260 2
 
6.7%
710 2
 
6.7%
110 2
 
6.7%
210 2
 
6.7%
200 2
 
6.7%
470 1
 
3.3%
129 1
 
3.3%
112 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
110 2
6.7%
111 1
3.3%
112 1
3.3%
117 1
3.3%
123 1
3.3%
125 1
3.3%
129 1
3.3%
170 1
3.3%
173 1
3.3%
190 1
3.3%
ValueCountFrequency (%)
710 2
6.7%
545 1
3.3%
500 1
3.3%
470 1
3.3%
410 2
6.7%
390 1
3.3%
380 1
3.3%
330 2
6.7%
290 1
3.3%
260 2
6.7%

읍면동코드
Real number (ℝ)

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.03333
Minimum101
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:20.993685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103.25
median116
Q3128
95-th percentile251.65
Maximum256
Range155
Interquartile range (IQR)24.75

Descriptive statistics

Standard deviation43.826841
Coefficient of variation (CV)0.34230805
Kurtosis5.1531272
Mean128.03333
Median Absolute Deviation (MAD)12.5
Skewness2.481873
Sum3841
Variance1920.792
MonotonicityNot monotonic
2023-12-10T22:54:21.264673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
101 5
 
16.7%
119 2
 
6.7%
103 2
 
6.7%
128 2
 
6.7%
102 1
 
3.3%
250 1
 
3.3%
106 1
 
3.3%
105 1
 
3.3%
133 1
 
3.3%
109 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
101 5
16.7%
102 1
 
3.3%
103 2
 
6.7%
104 1
 
3.3%
105 1
 
3.3%
106 1
 
3.3%
109 1
 
3.3%
110 1
 
3.3%
113 1
 
3.3%
115 1
 
3.3%
ValueCountFrequency (%)
256 1
3.3%
253 1
3.3%
250 1
3.3%
137 1
3.3%
133 1
3.3%
132 1
3.3%
129 1
3.3%
128 2
6.7%
127 1
3.3%
120 1
3.3%

충전지역명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:54:21.719486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length14.133333
Min length11

Characters and Unicode

Total characters424
Distinct characters89
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 row경상남도 양산시 평산동
2nd row서울특별시 금천구 가산동
3rd row부산광역시 수영구 민락동
4th row부산광역시 영도구 청학동
5th row경기도 시흥시 정왕동
ValueCountFrequency (%)
서울특별시 8
 
8.0%
경기도 7
 
7.0%
부산광역시 5
 
5.0%
경상남도 5
 
5.0%
창원시 3
 
3.0%
서대문구 2
 
2.0%
양산시 2
 
2.0%
인천광역시 2
 
2.0%
제주특별자치도 2
 
2.0%
수원시 2
 
2.0%
Other values (60) 62
62.0%
2023-12-10T22:54:22.407250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
 
16.5%
시 31
 
7.3%
동 29
 
6.8%
구 20
 
4.7%
도 17
 
4.0%
산 15
 
3.5%
경 12
 
2.8%
서 12
 
2.8%
특 10
 
2.4%
광 10
 
2.4%
Other values (79) 198
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 352
83.0%
Space Separator 70
 
16.5%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
 
8.8%
동 29
 
8.2%
구 20
 
5.7%
도 17
 
4.8%
산 15
 
4.3%
경 12
 
3.4%
서 12
 
3.4%
특 10
 
2.8%
광 10
 
2.8%
별 10
 
2.8%
Other values (76) 186
52.8%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 352
83.0%
Common 72
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
 
8.8%
동 29
 
8.2%
구 20
 
5.7%
도 17
 
4.8%
산 15
 
4.3%
경 12
 
3.4%
서 12
 
3.4%
특 10
 
2.8%
광 10
 
2.8%
별 10
 
2.8%
Other values (76) 186
52.8%
Common
ValueCountFrequency (%)
70
97.2%
3 1
 
1.4%
2 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 352
83.0%
ASCII 72
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70
97.2%
3 1
 
1.4%
2 1
 
1.4%
Hangul
ValueCountFrequency (%)
시 31
 
8.8%
동 29
 
8.2%
구 20
 
5.7%
도 17
 
4.8%
산 15
 
4.3%
경 12
 
3.4%
서 12
 
3.4%
특 10
 
2.8%
광 10
 
2.8%
별 10
 
2.8%
Other values (76) 186
52.8%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.445204
Minimum33.452069
Maximum37.740645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:22.617970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.452069
5-th percentile34.211181
Q135.190788
median37.331216
Q337.534669
95-th percentile37.606432
Maximum37.740645
Range4.2885763
Interquartile range (IQR)2.3438807

Descriptive statistics

Standard deviation1.3385333
Coefficient of variation (CV)0.036727283
Kurtosis-0.67018146
Mean36.445204
Median Absolute Deviation (MAD)0.27391605
Skewness-0.77396766
Sum1093.3561
Variance1.7916715
MonotonicityNot monotonic
2023-12-10T22:54:22.813900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.388039 1
 
3.3%
37.38665 1
 
3.3%
37.7406451 1
 
3.3%
37.548788 1
 
3.3%
37.5816786 1
 
3.3%
36.600025 1
 
3.3%
37.61814 1
 
3.3%
37.4682424 1
 
3.3%
35.1991779 1
 
3.3%
37.5372182 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
33.4520688 1
3.3%
33.4966142 1
3.3%
35.0845399 1
3.3%
35.1476864 1
3.3%
35.1525639 1
3.3%
35.1576012 1
3.3%
35.1683814 1
3.3%
35.1879915 1
3.3%
35.1991779 1
3.3%
35.3313602 1
3.3%
ValueCountFrequency (%)
37.7406451 1
3.3%
37.61814 1
3.3%
37.5921233 1
3.3%
37.5816786 1
3.3%
37.5799143 1
3.3%
37.548788 1
3.3%
37.5372182 1
3.3%
37.5356684 1
3.3%
37.53167 1
3.3%
37.5074444 1
3.3%

경도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.55964
Minimum126.38353
Maximum129.1818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:23.002252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.38353
5-th percentile126.47617
Q1126.85259
median126.97574
Q3128.68357
95-th percentile129.16136
Maximum129.1818
Range2.7982656
Interquartile range (IQR)1.8309798

Descriptive statistics

Standard deviation1.0414886
Coefficient of variation (CV)0.0081647185
Kurtosis-1.4094362
Mean127.55964
Median Absolute Deviation (MAD)0.29297275
Skewness0.6793922
Sum3826.7893
Variance1.0846984
MonotonicityNot monotonic
2023-12-10T22:54:23.277139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
129.140194 1
 
3.3%
126.94412 1
 
3.3%
126.4879187 1
 
3.3%
126.668207 1
 
3.3%
126.9089355 1
 
3.3%
127.477035 1
 
3.3%
126.9966662 1
 
3.3%
126.8470481 1
 
3.3%
129.0685238 1
 
3.3%
126.7613074 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126.3835319 1
3.3%
126.4665499 1
3.3%
126.4879187 1
3.3%
126.668207 1
3.3%
126.69732 1
3.3%
126.7613074 1
3.3%
126.8343172 1
3.3%
126.8470481 1
3.3%
126.8692055 1
3.3%
126.8819566 1
3.3%
ValueCountFrequency (%)
129.1817975 1
3.3%
129.1786713 1
3.3%
129.140194 1
3.3%
129.1240447 1
3.3%
129.0685238 1
3.3%
129.0604747 1
3.3%
129.0285067 1
3.3%
128.6908854 1
3.3%
128.661613 1
3.3%
128.5698316 1
3.3%

총전력사용량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4848.4663
Minimum145.56
Maximum13473.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:23.490294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum145.56
5-th percentile705.396
Q12151.51
median4193.62
Q35902.3
95-th percentile12590.345
Maximum13473.28
Range13327.72
Interquartile range (IQR)3750.79

Descriptive statistics

Standard deviation3674.9824
Coefficient of variation (CV)0.75796802
Kurtosis0.3592646
Mean4848.4663
Median Absolute Deviation (MAD)2016.7
Skewness1.0427272
Sum145453.99
Variance13505496
MonotonicityNot monotonic
2023-12-10T22:54:23.771554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1994.96 1
 
3.3%
377.94 1
 
3.3%
5607.28 1
 
3.3%
5983.12 1
 
3.3%
1249.2 1
 
3.3%
145.56 1
 
3.3%
4611.12 1
 
3.3%
7938.84 1
 
3.3%
2227.74 1
 
3.3%
12939.19 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
145.56 1
3.3%
377.94 1
3.3%
1105.62 1
3.3%
1249.2 1
3.3%
1542.04 1
3.3%
1796.7 1
3.3%
1994.96 1
3.3%
2126.1 1
3.3%
2227.74 1
3.3%
2579.28 1
3.3%
ValueCountFrequency (%)
13473.28 1
3.3%
12939.19 1
3.3%
12163.98 1
3.3%
10767.56 1
3.3%
7948.62 1
3.3%
7938.84 1
3.3%
7892.04 1
3.3%
5983.12 1
3.3%
5659.84 1
3.3%
5607.28 1
3.3%

전력사용량(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4848.6333
Minimum145
Maximum13473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:23.986087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum145
5-th percentile705.05
Q12154.75
median4195.5
Q35904
95-th percentile12586.85
Maximum13473
Range13328
Interquartile range (IQR)3749.25

Descriptive statistics

Standard deviation3674.6056
Coefficient of variation (CV)0.75786419
Kurtosis0.35704601
Mean4848.6333
Median Absolute Deviation (MAD)2016
Skewness1.0420566
Sum145459
Variance13502726
MonotonicityNot monotonic
2023-12-10T22:54:24.261629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1996 1
 
3.3%
377 1
 
3.3%
5604 1
 
3.3%
5984 1
 
3.3%
1250 1
 
3.3%
145 1
 
3.3%
4606 1
 
3.3%
7939 1
 
3.3%
2229 1
 
3.3%
12932 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
145 1
3.3%
377 1
3.3%
1106 1
3.3%
1250 1
3.3%
1541 1
3.3%
1798 1
3.3%
1996 1
3.3%
2130 1
3.3%
2229 1
3.3%
2580 1
3.3%
ValueCountFrequency (%)
13473 1
3.3%
12932 1
3.3%
12165 1
3.3%
10765 1
3.3%
7955 1
3.3%
7939 1
3.3%
7898 1
3.3%
5984 1
3.3%
5664 1
3.3%
5604 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

2023-12-10T22:54:24.534725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:24.780992image/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

2023-12-10T22:54:24.945592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:25.134545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T22:54:16.746287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:09.635645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.202077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:12.281719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.353889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.439643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:15.541249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:16.988245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:09.797313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.365047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:12.459217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.496875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.580169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:15.719268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:17.129246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:09.963905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.515505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:12.591138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.614833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.700256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:15.904808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:17.285342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:10.117922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.652351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:12.766602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.760677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.863850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:16.066072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:17.412427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:10.695676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.825391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:12.893093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.887707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.990594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:16.187727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:17.554173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:10.828651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.977119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.056506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.029344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:15.137238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:16.345695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:17.704428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:11.027374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:12.129464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:13.203016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:14.278108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:15.366122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:16.484403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:54:25.240285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용량전력사용량(경부하)
충전소ID1.0001.0001.0001.0001.0001.0001.0001.0001.000
시도코드1.0001.0000.5700.0001.0000.9260.7940.4720.472
시군구코드1.0000.5701.0000.5161.0000.0000.0000.0000.000
읍면동코드1.0000.0000.5161.0001.0000.0000.4200.0000.000
충전지역명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0000.9260.0000.0001.0001.0000.8270.0000.000
경도1.0000.7940.0000.4201.0000.8271.0000.1940.194
총전력사용량1.0000.4720.0000.0001.0000.0000.1941.0001.000
전력사용량(경부하)1.0000.4720.0000.0001.0000.0000.1941.0001.000
2023-12-10T22:54:25.425742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드위도경도총전력사용량전력사용량(경부하)
시도코드1.000-0.6010.176-0.5990.0090.1390.139
시군구코드-0.6011.000-0.0970.4420.0620.0960.096
읍면동코드0.176-0.0971.000-0.1350.016-0.097-0.097
위도-0.5990.442-0.1351.000-0.4190.0090.009
경도0.0090.0620.016-0.4191.0000.0540.054
총전력사용량0.1390.096-0.0970.0090.0541.0001.000
전력사용량(경부하)0.1390.096-0.0970.0090.0541.0001.000

Missing values

2023-12-10T22:54:17.902372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:54:18.226510image/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

충전일자충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
02021-08-01KRPPKCS004048330119경상남도 양산시 평산동35.388039129.1401941994.96199600
12021-08-01KRPPKCS001811545101서울특별시 금천구 가산동37.483217126.8819577948.62795500
22021-08-01KRPPKCS004426500103부산광역시 수영구 민락동35.157601129.1240454575.32457500
32021-08-01KRPPKCS004726200120부산광역시 영도구 청학동35.08454129.0604752126.1213000
42021-08-01KRPPKCS000341390132경기도 시흥시 정왕동37.34106126.697323733.26372900
52021-08-01KRPPKCS005548125101경상남도 창원시 마산합포구 가포동35.168381128.56983212163.981216500
62021-08-01KRPPKCS001711200115서울특별시 성동구 성수동2가37.535668127.0537432962.98296600
72021-08-01KRPPKCS001511470101서울특별시 양천구 신정동37.507444126.8343172668.62266600
82021-08-01KRPPKCS004148330113경상남도 양산시 용당동35.426799129.18179710767.561076500
92021-08-01KRPPKCS003748129137경상남도 창원시 진해구 여좌동35.152564128.6616134202.4420600
충전일자충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
202021-08-01KRPPKCS000411170128서울특별시 용산구 한강로3가37.53167126.9641105.62110600
212021-08-01KRPPKCS004526710256부산광역시 기장군 정관읍 용수리35.33136129.1786717892.04789800
222021-08-01KRPPKCS000841190117경기도 부천시 대장동37.537218126.76130712939.191293200
232021-08-01KRPPKCS004826260109부산광역시 동래구 사직동35.199178129.0685242227.74222900
242021-08-01KRPPKCS002041210101경기도 광명시 광명동37.468242126.8470487938.84793900
252021-08-01KRPPKCS000611290133서울특별시 성북구 정릉동37.61814126.9966664611.12460600
262021-08-01KRPPKCS003143112105충청북도 청주시 서원구 미평동36.600025127.477035145.5614500
272021-08-01KRPPKCS000911410119서울특별시 서대문구 북가좌동37.581679126.9089351249.2125000
282021-08-01KRPPKCS002728260106인천광역시 서구 연희동37.548788126.6682075983.12598400
292021-08-01KRPPKCS002328710250인천광역시 강화군 강화읍 남산리37.740645126.4879195607.28560400