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/86a7e028-adb1-47a7-b42d-4a6566f7ce7b

Alerts

충전일자 has constant value ""Constant
전력사용요금(중부하) has constant value ""Constant
전력사용요금(최대부하) has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
총전력사용요금 is highly overall correlated with 전력사용요금(경부하)High correlation
전력사용요금(경부하) is highly overall correlated with 총전력사용요금High correlation
충전소ID has unique valuesUnique
충전지역명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
총전력사용요금 has unique valuesUnique
전력사용요금(경부하) has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:42:16.391685
Analysis finished2023-12-10 13:42:27.170745
Duration10.78 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

2023-12-10T22:42:27.296947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:27.448981image/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
2023-12-10T22:42:27.727079image/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%
2023-12-10T22:42:28.361339image/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
2023-12-10T22:42:28.565912image/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
2023-12-10T22:42:28.763088image/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
2023-12-10T22:42:28.965690image/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
2023-12-10T22:42:29.169087image/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
2023-12-10T22:42:29.364860image/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
2023-12-10T22:42:29.566542image/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
2023-12-10T22:42:30.087568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.5
Min length15

Characters and Unicode

Total characters585
Distinct characters98
Distinct categories4 ?
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 84-103
2nd row경기도 부천시 대장동 688
3rd row부산광역시 수영구 민락동 34-5
4th row충청북도 청주시 상당구 월오동 517
5th row경기도 고양시 일산동구 성석동 1221-8
ValueCountFrequency (%)
경기도 8
 
6.1%
부산광역시 6
 
4.6%
경상남도 5
 
3.8%
서울특별시 4
 
3.1%
창원시 3
 
2.3%
양산시 2
 
1.5%
충청북도 2
 
1.5%
청주시 2
 
1.5%
안양시 2
 
1.5%
인천광역시 2
 
1.5%
Other values (95) 95
72.5%
2023-12-10T22:42:30.692559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
 
17.4%
동 32
 
5.5%
시 30
 
5.1%
구 23
 
3.9%
1 22
 
3.8%
- 20
 
3.4%
도 18
 
3.1%
산 16
 
2.7%
3 16
 
2.7%
2 14
 
2.4%
Other values (88) 292
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 342
58.5%
Decimal Number 121
 
20.7%
Space Separator 102
 
17.4%
Dash Punctuation 20
 
3.4%

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%
기 9
 
2.6%
역 9
 
2.6%
서 8
 
2.3%
Other values (76) 173
50.6%
Decimal Number
ValueCountFrequency (%)
1 22
18.2%
3 16
13.2%
2 14
11.6%
7 12
9.9%
0 12
9.9%
8 11
9.1%
6 10
8.3%
4 9
7.4%
9 8
 
6.6%
5 7
 
5.8%
Space Separator
ValueCountFrequency (%)
102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 342
58.5%
Common 243
41.5%

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%
기 9
 
2.6%
역 9
 
2.6%
서 8
 
2.3%
Other values (76) 173
50.6%
Common
ValueCountFrequency (%)
102
42.0%
1 22
 
9.1%
- 20
 
8.2%
3 16
 
6.6%
2 14
 
5.8%
7 12
 
4.9%
0 12
 
4.9%
8 11
 
4.5%
6 10
 
4.1%
4 9
 
3.7%
Other values (2) 15
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 342
58.5%
ASCII 243
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102
42.0%
1 22
 
9.1%
- 20
 
8.2%
3 16
 
6.6%
2 14
 
5.8%
7 12
 
4.9%
0 12
 
4.9%
8 11
 
4.5%
6 10
 
4.1%
4 9
 
3.7%
Other values (2) 15
 
6.2%
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%
기 9
 
2.6%
역 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
2023-12-10T22:42:30.898516image/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
2023-12-10T22:42:31.091101image/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
2023-12-10T22:42:31.293583image/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
2023-12-10T22:42:31.483041image/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%

총전력사용요금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214903.5
Minimum6295
Maximum666359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:31.680848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6295
5-th percentile8459.1
Q180049.25
median132418
Q3323419.75
95-th percentile629817.2
Maximum666359
Range660064
Interquartile range (IQR)243370.5

Descriptive statistics

Standard deviation194659.91
Coefficient of variation (CV)0.90580147
Kurtosis0.22442429
Mean214903.5
Median Absolute Deviation (MAD)101233.5
Skewness1.0545743
Sum6447105
Variance3.7892479 × 1010
MonotonicityNot monotonic
2023-12-10T22:42:31.879103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
98863 1
 
3.3%
377099 1
 
3.3%
366983 1
 
3.3%
103589 1
 
3.3%
113322 1
 
3.3%
124091 1
 
3.3%
6295 1
 
3.3%
271486 1
 
3.3%
500697 1
 
3.3%
77880 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
6295 1
3.3%
7569 1
3.3%
9547 1
3.3%
17952 1
3.3%
39344 1
3.3%
48824 1
3.3%
52518 1
3.3%
77880 1
3.3%
86557 1
3.3%
92768 1
3.3%
ValueCountFrequency (%)
666359 1
3.3%
632528 1
3.3%
626504 1
3.3%
500697 1
3.3%
377569 1
3.3%
377099 1
3.3%
366983 1
3.3%
326462 1
3.3%
314293 1
3.3%
271486 1
3.3%

전력사용요금(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214903.5
Minimum6295
Maximum666359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:32.100955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6295
5-th percentile8459.1
Q180049.25
median132418
Q3323419.75
95-th percentile629817.2
Maximum666359
Range660064
Interquartile range (IQR)243370.5

Descriptive statistics

Standard deviation194659.91
Coefficient of variation (CV)0.90580147
Kurtosis0.22442429
Mean214903.5
Median Absolute Deviation (MAD)101233.5
Skewness1.0545743
Sum6447105
Variance3.7892479 × 1010
MonotonicityNot monotonic
2023-12-10T22:42:32.299140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
98863 1
 
3.3%
377099 1
 
3.3%
366983 1
 
3.3%
103589 1
 
3.3%
113322 1
 
3.3%
124091 1
 
3.3%
6295 1
 
3.3%
271486 1
 
3.3%
500697 1
 
3.3%
77880 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
6295 1
3.3%
7569 1
3.3%
9547 1
3.3%
17952 1
3.3%
39344 1
3.3%
48824 1
3.3%
52518 1
3.3%
77880 1
3.3%
86557 1
3.3%
92768 1
3.3%
ValueCountFrequency (%)
666359 1
3.3%
632528 1
3.3%
626504 1
3.3%
500697 1
3.3%
377569 1
3.3%
377099 1
3.3%
366983 1
3.3%
326462 1
3.3%
314293 1
3.3%
271486 1
3.3%

전력사용요금(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

2023-12-10T22:42:32.536683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:32.692360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

전력사용요금(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

2023-12-10T22:42:32.865098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:33.045965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

Interactions

2023-12-10T22:42:25.398494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:18.892254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.118034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.286762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.657312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.454367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.397405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:25.862120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.135375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.318083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.557481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.776218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.574497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.558275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:26.010515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.289469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.464427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.787675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.884501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.697591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.687830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:26.176338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.453348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.616551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.020195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.015605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.828656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.828755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:26.305629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.613094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.761527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.231674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.110690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.952027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.962004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:26.435194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.748172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.916978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.391856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.220275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.084374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:25.092913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:26.563619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.957200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.077820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.537307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.342829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:24.221775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:25.239059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:42:33.246258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용요금전력사용요금(경부하)
충전소ID1.0001.0001.0001.0001.0001.0001.0001.0001.000
시도코드1.0001.0000.3640.0001.0000.8790.6380.0000.000
시군구코드1.0000.3641.0000.6621.0000.1600.0000.3440.344
읍면동코드1.0000.0000.6621.0001.0000.0000.1490.4230.423
충전지역명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0000.8790.1600.0001.0001.0000.7840.2680.268
경도1.0000.6380.0000.1491.0000.7841.0000.0000.000
총전력사용요금1.0000.0000.3440.4231.0000.2680.0001.0001.000
전력사용요금(경부하)1.0000.0000.3440.4231.0000.2680.0001.0001.000
2023-12-10T22:42:33.714835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드위도경도총전력사용요금전력사용요금(경부하)
시도코드1.000-0.4570.145-0.2440.0830.1180.118
시군구코드-0.4571.000-0.3320.0400.183-0.023-0.023
읍면동코드0.145-0.3321.000-0.1870.2580.0840.084
위도-0.2440.040-0.1871.000-0.823-0.103-0.103
경도0.0830.1830.258-0.8231.0000.1490.149
총전력사용요금0.118-0.0230.084-0.1030.1491.0001.000
전력사용요금(경부하)0.118-0.0230.084-0.1030.1491.0001.000

Missing values

2023-12-10T22:42:26.766021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:42:27.055171image/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

충전일자충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용요금전력사용요금(경부하)전력사용요금(중부하)전력사용요금(최대부하)
02021-08-01KRPPKCS004726200120부산광역시 영도구 청학동 84-10335.08454129.060475988639886300
12021-08-01KRPPKCS000841190117경기도 부천시 대장동 68837.537218126.76130766635966635900
22021-08-01KRPPKCS004426500103부산광역시 수영구 민락동 34-535.157601129.12404521275021275000
32021-08-01KRPPKCS003043111130충청북도 청주시 상당구 월오동 51736.609944127.531949928649286400
42021-08-01KRPPKCS000741285113경기도 고양시 일산동구 성석동 1221-837.714936126.787381488244882400
52021-08-01KRPPKCS002428260110인천광역시 서구 석남동 223-39437.502753126.654576865578655700
62021-08-01KRPPKCS005548125101경상남도 창원시 마산합포구 가포동 499-1235.168381128.56983263252863252800
72021-08-01KRPPKCS004048330119경상남도 양산시 평산동 568-1035.388039129.140194927689276800
82021-08-01KRPPKCS001041111129경기도 수원시 장안구 파장동 23-537.321371126.98747262650462650400
92021-08-01KRPPKCS003748129137경상남도 창원시 진해구 여좌동 761-28035.152564128.66161321852621852600
충전일자충전소ID시도코드시군구코드읍면동코드충전지역명위도경도총전력사용요금전력사용요금(경부하)전력사용요금(중부하)전력사용요금(최대부하)
202021-08-01KRPPKCS001711200115서울특별시 성동구 성수동2가 649-137.535668127.05374314074514074500
212021-08-01KRPPKCS000411170128서울특별시 용산구 한강로3가 40-105137.53167126.964525185251800
222021-08-01KRPPKCS002241570101경기도 김포시 북변동 387-837.62678126.709298778807788000
232021-08-01KRPPKCS004148330113경상남도 양산시 용당동 73035.426799129.18179750069750069700
242021-08-01KRPPKCS003444133106충청남도 천안시 서북구 차암동 43136.838553127.10772527148627148600
252021-08-01KRPPKCS003926410102부산광역시 금정구 노포동 117635.289513129.0969076295629500
262021-08-01KRPPKCS001511470101서울특별시 양천구 신정동 131237.507444126.83431712409112409100
272021-08-01KRPPKCS002528110118인천광역시 중구 항동7가 7337.445509126.59815511332211332200
282021-08-01KRPPKCS004826260109부산광역시 동래구 사직동 151-135.199178129.06852410358910358900
292021-08-01KRPPKCS004526710256부산광역시 기장군 정관읍 용수리 306-135.33136129.17867136698336698300