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/adaf8706-8cd6-4701-ac91-dbe1fc1ce1d8

Alerts

충전일자 has constant value ""Constant
전력사용량(중부하) has constant value ""Constant
전력사용량(최대부하) has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High 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 14:04:00.478681
Analysis finished2023-12-10 14:04:10.656056
Duration10.18 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

2023-12-10T23:04:10.759594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:10.917870image/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
2023-12-10T23:04:11.214525image/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 rowKRPPKCS0047
2nd rowKRPPKCS0008
3rd rowKRPPKCS0044
4th rowKRPPKCS0030
5th rowKRPPKCS0007
ValueCountFrequency (%)
krppkcs0047 1
 
3.3%
krppkcs0008 1
 
3.3%
krppkcs0048 1
 
3.3%
krppkcs0025 1
 
3.3%
krppkcs0015 1
 
3.3%
krppkcs0039 1
 
3.3%
krppkcs0034 1
 
3.3%
krppkcs0041 1
 
3.3%
krppkcs0022 1
 
3.3%
krppkcs0004 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:04:11.778318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68
20.6%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
4 11
 
3.3%
1 9
 
2.7%
3 7
 
2.1%
2 7
 
2.1%
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 68
56.7%
4 11
 
9.2%
1 9
 
7.5%
3 7
 
5.8%
2 7
 
5.8%
5 7
 
5.8%
7 4
 
3.3%
8 4
 
3.3%
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 68
56.7%
4 11
 
9.2%
1 9
 
7.5%
3 7
 
5.8%
2 7
 
5.8%
5 7
 
5.8%
7 4
 
3.3%
8 4
 
3.3%
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 68
20.6%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
4 11
 
3.3%
1 9
 
2.7%
3 7
 
2.1%
2 7
 
2.1%
Other values (5) 18
 
5.5%

시도코드
Real number (ℝ)

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.2
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:11.989383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q343
95-th percentile48
Maximum48
Range37
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.282311
Coefficient of variation (CV)0.35913191
Kurtosis-0.68141229
Mean34.2
Median Absolute Deviation (MAD)7
Skewness-0.69564879
Sum1026
Variance150.85517
MonotonicityNot monotonic
2023-12-10T23:04:12.322884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
41 8
26.7%
26 6
20.0%
48 5
16.7%
11 4
13.3%
43 2
 
6.7%
28 2
 
6.7%
45 1
 
3.3%
27 1
 
3.3%
44 1
 
3.3%
ValueCountFrequency (%)
11 4
13.3%
26 6
20.0%
27 1
 
3.3%
28 2
 
6.7%
41 8
26.7%
43 2
 
6.7%
44 1
 
3.3%
45 1
 
3.3%
48 5
16.7%
ValueCountFrequency (%)
48 5
16.7%
45 1
 
3.3%
44 1
 
3.3%
43 2
 
6.7%
41 8
26.7%
28 2
 
6.7%
27 1
 
3.3%
26 6
20.0%
11 4
13.3%

시군구코드
Real number (ℝ)

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.93333
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:12.626102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile111
Q1142.25
median205
Q3345
95-th percentile558.75
Maximum710
Range600
Interquartile range (IQR)202.75

Descriptive statistics

Standard deviation159.2703
Coefficient of variation (CV)0.58785792
Kurtosis0.6477509
Mean270.93333
Median Absolute Deviation (MAD)83.5
Skewness1.1255077
Sum8128
Variance25367.03
MonotonicityNot monotonic
2023-12-10T23:04:13.027283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
200 2
 
6.7%
111 2
 
6.7%
260 2
 
6.7%
330 2
 
6.7%
210 1
 
3.3%
710 1
 
3.3%
110 1
 
3.3%
470 1
 
3.3%
410 1
 
3.3%
133 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
110 1
3.3%
111 2
6.7%
112 1
3.3%
123 1
3.3%
125 1
3.3%
129 1
3.3%
133 1
3.3%
170 1
3.3%
171 1
3.3%
173 1
3.3%
ValueCountFrequency (%)
710 1
3.3%
570 1
3.3%
545 1
3.3%
500 1
3.3%
470 1
3.3%
410 1
3.3%
370 1
3.3%
350 1
3.3%
330 2
6.7%
290 1
3.3%

읍면동코드
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9
Minimum101
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:13.305075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103.25
median109.5
Q3118.75
95-th percentile133.85
Maximum256
Range155
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation28.237112
Coefficient of variation (CV)0.24154929
Kurtosis21.696175
Mean116.9
Median Absolute Deviation (MAD)8
Skewness4.3774367
Sum3507
Variance797.33448
MonotonicityNot monotonic
2023-12-10T23:04:13.561225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
101 5
16.7%
106 3
 
10.0%
102 2
 
6.7%
113 2
 
6.7%
128 2
 
6.7%
118 2
 
6.7%
256 1
 
3.3%
109 1
 
3.3%
115 1
 
3.3%
104 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
101 5
16.7%
102 2
 
6.7%
103 1
 
3.3%
104 1
 
3.3%
105 1
 
3.3%
106 3
10.0%
108 1
 
3.3%
109 1
 
3.3%
110 1
 
3.3%
113 2
 
6.7%
ValueCountFrequency (%)
256 1
3.3%
137 1
3.3%
130 1
3.3%
129 1
3.3%
128 2
6.7%
120 1
3.3%
119 1
3.3%
118 2
6.7%
117 1
3.3%
115 1
3.3%

충전지역명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:04:13.987266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.866667
Min length11

Characters and Unicode

Total characters416
Distinct characters90
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 row부산광역시 영도구 청학동
2nd row경기도 부천시 대장동
3rd row부산광역시 수영구 민락동
4th row충청북도 청주시 상당구 월오동
5th row경기도 고양시 일산동구 성석동
ValueCountFrequency (%)
경기도 8
 
7.9%
부산광역시 6
 
5.9%
경상남도 5
 
5.0%
서울특별시 4
 
4.0%
창원시 3
 
3.0%
양산시 2
 
2.0%
충청북도 2
 
2.0%
청주시 2
 
2.0%
안양시 2
 
2.0%
인천광역시 2
 
2.0%
Other values (65) 65
64.4%
2023-12-10T23:04:14.597269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
17.1%
동 32
 
7.7%
시 30
 
7.2%
구 23
 
5.5%
도 18
 
4.3%
산 16
 
3.8%
경 13
 
3.1%
광 11
 
2.6%
역 9
 
2.2%
기 9
 
2.2%
Other values (80) 184
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 342
82.2%
Space Separator 71
 
17.1%
Decimal Number 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 32
 
9.4%
시 30
 
8.8%
구 23
 
6.7%
도 18
 
5.3%
산 16
 
4.7%
경 13
 
3.8%
광 11
 
3.2%
역 9
 
2.6%
기 9
 
2.6%
서 8
 
2.3%
Other values (76) 173
50.6%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
3 1
33.3%
7 1
33.3%
Space Separator
ValueCountFrequency (%)
71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 342
82.2%
Common 74
 
17.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 32
 
9.4%
시 30
 
8.8%
구 23
 
6.7%
도 18
 
5.3%
산 16
 
4.7%
경 13
 
3.8%
광 11
 
3.2%
역 9
 
2.6%
기 9
 
2.6%
서 8
 
2.3%
Other values (76) 173
50.6%
Common
ValueCountFrequency (%)
71
95.9%
2 1
 
1.4%
3 1
 
1.4%
7 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 342
82.2%
ASCII 74
 
17.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
95.9%
2 1
 
1.4%
3 1
 
1.4%
7 1
 
1.4%
Hangul
ValueCountFrequency (%)
동 32
 
9.4%
시 30
 
8.8%
구 23
 
6.7%
도 18
 
5.3%
산 16
 
4.7%
경 13
 
3.8%
광 11
 
3.2%
역 9
 
2.6%
기 9
 
2.6%
서 8
 
2.3%
Other values (76) 173
50.6%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.454545
Minimum35.08454
Maximum37.714936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:14.802467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.08454
5-th percentile35.154831
Q135.299975
median36.724249
Q337.479474
95-th percentile37.586477
Maximum37.714936
Range2.630396
Interquartile range (IQR)2.1794988

Descriptive statistics

Standard deviation1.060076
Coefficient of variation (CV)0.029079392
Kurtosis-1.9043471
Mean36.454545
Median Absolute Deviation (MAD)0.85069265
Skewness-0.18073366
Sum1093.6363
Variance1.1237611
MonotonicityNot monotonic
2023-12-10T23:04:15.020845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.0845399 1
 
3.3%
37.4682424 1
 
3.3%
35.3313602 1
 
3.3%
35.1991779 1
 
3.3%
37.4455086 1
 
3.3%
37.5074444 1
 
3.3%
35.2895131 1
 
3.3%
36.838553 1
 
3.3%
35.4267991 1
 
3.3%
37.6267799 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
35.0845399 1
3.3%
35.1525639 1
3.3%
35.1576012 1
3.3%
35.1683814 1
3.3%
35.1848373 1
3.3%
35.1879915 1
3.3%
35.1991779 1
3.3%
35.2895131 1
3.3%
35.3313602 1
3.3%
35.388039 1
3.3%
ValueCountFrequency (%)
37.7149359 1
3.3%
37.6267799 1
3.3%
37.5372182 1
3.3%
37.5356684 1
3.3%
37.53167 1
3.3%
37.5074444 1
3.3%
37.5027529 1
3.3%
37.4832174 1
3.3%
37.4682424 1
3.3%
37.4455086 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.74568
Minimum126.59816
Maximum129.1999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:15.284756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.59816
5-th percentile126.6792
Q1126.85578
median127.08073
Q3128.96808
95-th percentile129.18039
Maximum129.1999
Range2.6017408
Interquartile range (IQR)2.1123022

Descriptive statistics

Standard deviation1.0353582
Coefficient of variation (CV)0.0081048391
Kurtosis-1.7514347
Mean127.74568
Median Absolute Deviation (MAD)0.4112296
Skewness0.40912491
Sum3832.3703
Variance1.0719665
MonotonicityNot monotonic
2023-12-10T23:04:15.504499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
129.0604747 1
 
3.3%
126.8470481 1
 
3.3%
129.1786713 1
 
3.3%
129.0685238 1
 
3.3%
126.5981554 1
 
3.3%
126.8343172 1
 
3.3%
129.0969073 1
 
3.3%
127.107725 1
 
3.3%
129.1817975 1
 
3.3%
126.7092977 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126.5981554 1
3.3%
126.6545758 1
3.3%
126.7092977 1
3.3%
126.7613074 1
3.3%
126.7873808 1
3.3%
126.8343172 1
3.3%
126.8344255 1
3.3%
126.8470481 1
3.3%
126.8819566 1
3.3%
126.8937052 1
3.3%
ValueCountFrequency (%)
129.1998962 1
3.3%
129.1817975 1
3.3%
129.1786713 1
3.3%
129.140194 1
3.3%
129.1240447 1
3.3%
129.0969073 1
3.3%
129.0685238 1
3.3%
129.0604747 1
3.3%
128.6908854 1
3.3%
128.661613 1
3.3%

총전력사용량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4247.0217
Minimum89.46
Maximum12791.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:15.707556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89.46
5-th percentile157.0845
Q11548.54
median2815.8
Q36268.76
95-th percentile12386.873
Maximum12791.96
Range12702.5
Interquartile range (IQR)4720.22

Descriptive statistics

Standard deviation3866.5862
Coefficient of variation (CV)0.91042299
Kurtosis0.016508791
Mean4247.0217
Median Absolute Deviation (MAD)2153.85
Skewness1.0008227
Sum127410.65
Variance14950488
MonotonicityNot monotonic
2023-12-10T23:04:15.952003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1838.46 1
 
3.3%
7931.52 1
 
3.3%
7663.16 1
 
3.3%
2204.28 1
 
3.3%
2179.24 1
 
3.3%
2668.62 1
 
3.3%
135.39 1
 
3.3%
4892.34 1
 
3.3%
10266.04 1
 
3.3%
1469.44 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
89.46 1
3.3%
135.39 1
3.3%
183.6 1
3.3%
237.96 1
3.3%
438.78 1
3.3%
846.08 1
3.3%
1105.62 1
3.3%
1469.44 1
3.3%
1785.84 1
3.3%
1822.2 1
3.3%
ValueCountFrequency (%)
12791.96 1
3.3%
12569.24 1
3.3%
12163.98 1
3.3%
10266.04 1
3.3%
7931.52 1
3.3%
7808.76 1
3.3%
7663.16 1
3.3%
6482.16 1
3.3%
5628.56 1
3.3%
5046.96 1
3.3%

전력사용량(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4247.3333
Minimum89
Maximum12784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:04:16.164823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile156.6
Q11549.25
median2816
Q36269
95-th percentile12387.2
Maximum12784
Range12695
Interquartile range (IQR)4719.75

Descriptive statistics

Standard deviation3866.14
Coefficient of variation (CV)0.91025114
Kurtosis0.01424524
Mean4247.3333
Median Absolute Deviation (MAD)2153
Skewness1.0000496
Sum127420
Variance14947038
MonotonicityNot monotonic
2023-12-10T23:04:16.413021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1842 1
 
3.3%
7931 1
 
3.3%
7669 1
 
3.3%
2206 1
 
3.3%
2180 1
 
3.3%
2666 1
 
3.3%
135 1
 
3.3%
4891 1
 
3.3%
10264 1
 
3.3%
1471 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
89 1
3.3%
135 1
3.3%
183 1
3.3%
237 1
3.3%
439 1
3.3%
847 1
3.3%
1106 1
3.3%
1471 1
3.3%
1784 1
3.3%
1823 1
3.3%
ValueCountFrequency (%)
12784 1
3.3%
12569 1
3.3%
12165 1
3.3%
10264 1
3.3%
7931 1
3.3%
7815 1
3.3%
7669 1
3.3%
6484 1
3.3%
5624 1
3.3%
5047 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

2023-12-10T23:04:16.635769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:16.817375image/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

2023-12-10T23:04:16.993823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:04:17.274264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T23:04:09.091209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:00.878689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:02.006019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:03.227168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:04.467301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.521500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:06.439887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:09.255589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:01.008097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:02.167997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:03.435158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:04.609638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.662496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:06.587797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:09.402202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:01.152245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:02.307435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:03.583307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:04.755312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.792804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:07.618722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:09.565985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:01.327070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:02.580282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:03.802534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:04.903338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.925431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:08.010865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:09.726145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:01.481073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:02.725338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:03.974101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.080498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:06.049375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:08.392299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:09.904865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:01.647370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:02.904380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:04.121807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.242199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:06.167989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:08.658885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:10.056564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:01.836957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:03.071367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:04.299872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:05.388835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:06.294249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:04:08.904424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:04:17.378840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용량전력사용량(경부하)
충전소ID1.0001.0001.0001.0001.0001.0001.0001.0001.000
시도코드1.0001.0000.3640.0001.0000.8790.6380.2490.249
시군구코드1.0000.3641.0000.6621.0000.1600.0000.5600.560
읍면동코드1.0000.0000.6621.0001.0000.0000.1490.4620.462
충전지역명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0000.8790.1600.0001.0001.0000.7840.0000.000
경도1.0000.6380.0000.1491.0000.7841.0000.0000.000
총전력사용량1.0000.2490.5600.4621.0000.0000.0001.0001.000
전력사용량(경부하)1.0000.2490.5600.4621.0000.0000.0001.0001.000
2023-12-10T23:04:17.606769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드위도경도총전력사용량전력사용량(경부하)
시도코드1.000-0.4570.145-0.2440.0830.1120.112
시군구코드-0.4571.000-0.3320.0400.1830.0070.007
읍면동코드0.145-0.3321.000-0.1870.2580.0730.073
위도-0.2440.040-0.1871.000-0.823-0.105-0.105
경도0.0830.1830.258-0.8231.0000.1600.160
총전력사용량0.1120.0070.073-0.1050.1601.0001.000
전력사용량(경부하)0.1120.0070.073-0.1050.1601.0001.000

Missing values

2023-12-10T23:04:10.274968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:04:10.554259image/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)
02021-08-01KRPPKCS004726200120부산광역시 영도구 청학동35.08454129.0604751838.46184200
12021-08-01KRPPKCS000841190117경기도 부천시 대장동37.537218126.76130712791.961278400
22021-08-01KRPPKCS004426500103부산광역시 수영구 민락동35.157601129.1240454519.94452000
32021-08-01KRPPKCS003043111130충청북도 청주시 상당구 월오동36.609944127.5319491785.84178400
42021-08-01KRPPKCS000741285113경기도 고양시 일산동구 성석동37.714936126.787381438.7843900
52021-08-01KRPPKCS002428260110인천광역시 서구 석남동37.502753126.6545761822.2182300
62021-08-01KRPPKCS005548125101경상남도 창원시 마산합포구 가포동35.168381128.56983212163.981216500
72021-08-01KRPPKCS004048330119경상남도 양산시 평산동35.388039129.1401941994.96199600
82021-08-01KRPPKCS001041111129경기도 수원시 장안구 파장동37.321371126.98747212569.241256900
92021-08-01KRPPKCS003748129137경상남도 창원시 진해구 여좌동35.152564128.6616134202.4420600
충전일자충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)
202021-08-01KRPPKCS001711200115서울특별시 성동구 성수동2가37.535668127.0537432962.98296600
212021-08-01KRPPKCS000411170128서울특별시 용산구 한강로3가37.53167126.9641105.62110600
222021-08-01KRPPKCS002241570101경기도 김포시 북변동37.62678126.7092981469.44147100
232021-08-01KRPPKCS004148330113경상남도 양산시 용당동35.426799129.18179710266.041026400
242021-08-01KRPPKCS003444133106충청남도 천안시 서북구 차암동36.838553127.1077254892.34489100
252021-08-01KRPPKCS003926410102부산광역시 금정구 노포동35.289513129.096907135.3913500
262021-08-01KRPPKCS001511470101서울특별시 양천구 신정동37.507444126.8343172668.62266600
272021-08-01KRPPKCS002528110118인천광역시 중구 항동7가37.445509126.5981552179.24218000
282021-08-01KRPPKCS004826260109부산광역시 동래구 사직동35.199178129.0685242204.28220600
292021-08-01KRPPKCS004526710256부산광역시 기장군 정관읍 용수리35.33136129.1786717663.16766900