Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory105.4 B

Variable types

Categorical6
Text3
Numeric3

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/a34a6c33-4ac6-4728-9c00-bea83eeafa24

Alerts

비고 has constant value ""Constant
생산_일시 has constant value ""Constant
시도_코드 is highly overall correlated with 시군구_코드 and 1 other fieldsHigh correlation
시군구_코드 is highly overall correlated with 시도_코드High correlation
청구_전력_사용량 is highly overall correlated with 시도_명High correlation
시도_명 is highly overall correlated with 시도_코드 and 1 other fieldsHigh correlation
충전_전력_사용량 is highly imbalanced (53.1%)Imbalance
청구_전력_사용량 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:24:31.153798
Analysis finished2023-12-10 14:24:32.737583
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전_일시
Categorical

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-01-01 01:01:00
16 
2022-01-01 01:00:00
9 
2022-01-01 01:02:00
5 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01 01:00:00
2nd row2022-01-01 01:00:00
3rd row2022-01-01 01:00:00
4th row2022-01-01 01:00:00
5th row2022-01-01 01:00:00

Common Values

ValueCountFrequency (%)
2022-01-01 01:01:00 16
53.3%
2022-01-01 01:00:00 9
30.0%
2022-01-01 01:02:00 5
 
16.7%

Length

2023-12-10T23:24:32.804435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:32.891975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-01-01 30
50.0%
01:01:00 16
26.7%
01:00:00 9
 
15.0%
01:02:00 5
 
8.3%

충전소_ID
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:24:33.047950image/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

Unique13 ?
Unique (%)43.3%

Sample

1st rowKRPPKCS0042
2nd rowKRPPKCS0060
3rd rowKRPPKCS0108
4th rowKRPPKCS0043
5th rowKRPPKCS0008
ValueCountFrequency (%)
krppkcs0010 3
 
10.0%
krppkcs0051 2
 
6.7%
krppkcs0053 2
 
6.7%
krppkcs0104 2
 
6.7%
krppkcs0008 2
 
6.7%
krppkcs0043 2
 
6.7%
krppkcs0042 2
 
6.7%
krppkcs0020 2
 
6.7%
krppkcs0060 1
 
3.3%
krppkcs0015 1
 
3.3%
Other values (11) 11
36.7%
2023-12-10T23:24:33.338697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 69
20.9%
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 10
 
3.0%
5 8
 
2.4%
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 69
57.5%
4 11
 
9.2%
1 10
 
8.3%
5 8
 
6.7%
2 6
 
5.0%
8 5
 
4.2%
3 4
 
3.3%
6 4
 
3.3%
7 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 69
57.5%
4 11
 
9.2%
1 10
 
8.3%
5 8
 
6.7%
2 6
 
5.0%
8 5
 
4.2%
3 4
 
3.3%
6 4
 
3.3%
7 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 69
20.9%
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 10
 
3.0%
5 8
 
2.4%
2 6
 
1.8%
Other values (5) 16
 
4.8%

충전기_ID
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:24:33.575106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters330
Distinct characters14
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

Unique22 ?
Unique (%)73.3%

Sample

1st rowKRPPKCP0264
2nd rowKRPPKCP0377
3rd rowKRPPKCP0772
4th rowKRPPKCP0271
5th rowKRPPKCP0497
ValueCountFrequency (%)
krppkcp0264 2
 
6.7%
krppkcp0751 2
 
6.7%
krppkcp0271 2
 
6.7%
krppkcp0406 2
 
6.7%
krppkcp0426 1
 
3.3%
krppkcp0147 1
 
3.3%
krppkcp0298 1
 
3.3%
krppkcp0021 1
 
3.3%
krppkcp0151 1
 
3.3%
krppkcp0468 1
 
3.3%
Other values (16) 16
53.3%
2023-12-10T23:24:33.887096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 90
27.3%
K 60
18.2%
0 37
11.2%
R 30
 
9.1%
C 30
 
9.1%
4 15
 
4.5%
2 13
 
3.9%
7 12
 
3.6%
1 12
 
3.6%
6 8
 
2.4%
Other values (4) 23
 
7.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37
30.8%
4 15
12.5%
2 13
 
10.8%
7 12
 
10.0%
1 12
 
10.0%
6 8
 
6.7%
3 8
 
6.7%
5 6
 
5.0%
9 5
 
4.2%
8 4
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
P 90
42.9%
K 60
28.6%
R 30
 
14.3%
C 30
 
14.3%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 37
30.8%
4 15
12.5%
2 13
 
10.8%
7 12
 
10.0%
1 12
 
10.0%
6 8
 
6.7%
3 8
 
6.7%
5 6
 
5.0%
9 5
 
4.2%
8 4
 
3.3%
Latin
ValueCountFrequency (%)
P 90
42.9%
K 60
28.6%
R 30
 
14.3%
C 30
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 90
27.3%
K 60
18.2%
0 37
11.2%
R 30
 
9.1%
C 30
 
9.1%
4 15
 
4.5%
2 13
 
3.9%
7 12
 
3.6%
1 12
 
3.6%
6 8
 
2.4%
Other values (4) 23
 
7.0%

시도_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.466667
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:24:33.992761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q341
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.691929
Coefficient of variation (CV)0.36823778
Kurtosis-0.75515233
Mean34.466667
Median Absolute Deviation (MAD)9
Skewness-0.5879059
Sum1034
Variance161.08506
MonotonicityNot monotonic
2023-12-10T23:24:34.091246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
41 10
33.3%
26 7
23.3%
11 4
 
13.3%
48 4
 
13.3%
50 3
 
10.0%
28 2
 
6.7%
ValueCountFrequency (%)
11 4
 
13.3%
26 7
23.3%
28 2
 
6.7%
41 10
33.3%
48 4
 
13.3%
50 3
 
10.0%
ValueCountFrequency (%)
50 3
 
10.0%
48 4
 
13.3%
41 10
33.3%
28 2
 
6.7%
26 7
23.3%
11 4
 
13.3%

시군구_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.7
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:24:34.191062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110
Q1123
median200
Q3327.5
95-th percentile656
Maximum710
Range600
Interquartile range (IQR)204.5

Descriptive statistics

Standard deviation182.44983
Coefficient of variation (CV)0.69984592
Kurtosis0.97351319
Mean260.7
Median Absolute Deviation (MAD)80
Skewness1.4051451
Sum7821
Variance33287.941
MonotonicityNot monotonic
2023-12-10T23:24:34.283976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
230 3
10.0%
111 3
10.0%
110 3
10.0%
210 2
 
6.7%
710 2
 
6.7%
350 2
 
6.7%
190 2
 
6.7%
125 2
 
6.7%
123 2
 
6.7%
260 2
 
6.7%
Other values (6) 7
23.3%
ValueCountFrequency (%)
110 3
10.0%
111 3
10.0%
117 1
 
3.3%
123 2
6.7%
125 2
6.7%
170 1
 
3.3%
185 1
 
3.3%
190 2
6.7%
210 2
6.7%
230 3
10.0%
ValueCountFrequency (%)
710 2
6.7%
590 2
6.7%
470 1
 
3.3%
410 1
 
3.3%
350 2
6.7%
260 2
6.7%
230 3
10.0%
210 2
6.7%
190 2
6.7%
185 1
 
3.3%

시도_명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
10 
부산광역시
7 
서울특별시
4 
경상남도
4 
제주특별자치도
3 

Length

Max length7
Median length5
Mean length4.4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시
2nd row제주특별자치도
3rd row서울특별시
4th row부산광역시
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 10
33.3%
부산광역시 7
23.3%
서울특별시 4
 
13.3%
경상남도 4
 
13.3%
제주특별자치도 3
 
10.0%
인천광역시 2
 
6.7%

Length

2023-12-10T23:24:34.393206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:34.499516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 10
33.3%
부산광역시 7
23.3%
서울특별시 4
 
13.3%
경상남도 4
 
13.3%
제주특별자치도 3
 
10.0%
인천광역시 2
 
6.7%

시군구_명
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:24:34.670878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1666667
Min length2

Characters and Unicode

Total characters95
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row부산진구
2nd row제주시
3rd row강서구
4th row해운대구
5th row부천시
ValueCountFrequency (%)
수원시 4
13.3%
창원시 4
13.3%
부산진구 3
10.0%
제주시 3
10.0%
해운대구 2
 
6.7%
부천시 2
 
6.7%
화성시 2
 
6.7%
광명시 2
 
6.7%
강서구 1
 
3.3%
양천구 1
 
3.3%
Other values (6) 6
20.0%
2023-12-10T23:24:34.940639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 17
17.9%
구 12
 
12.6%
원 8
 
8.4%
수 5
 
5.3%
부 5
 
5.3%
창 4
 
4.2%
산 4
 
4.2%
진 3
 
3.2%
제 3
 
3.2%
주 3
 
3.2%
Other values (19) 31
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 95
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 17
17.9%
구 12
 
12.6%
원 8
 
8.4%
수 5
 
5.3%
부 5
 
5.3%
창 4
 
4.2%
산 4
 
4.2%
진 3
 
3.2%
제 3
 
3.2%
주 3
 
3.2%
Other values (19) 31
32.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 95
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 17
17.9%
구 12
 
12.6%
원 8
 
8.4%
수 5
 
5.3%
부 5
 
5.3%
창 4
 
4.2%
산 4
 
4.2%
진 3
 
3.2%
제 3
 
3.2%
주 3
 
3.2%
Other values (19) 31
32.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 95
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 17
17.9%
구 12
 
12.6%
원 8
 
8.4%
수 5
 
5.3%
부 5
 
5.3%
창 4
 
4.2%
산 4
 
4.2%
진 3
 
3.2%
제 3
 
3.2%
주 3
 
3.2%
Other values (19) 31
32.6%

충전_전력_사용량
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
27 
2
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 27
90.0%
2 3
 
10.0%

Length

2023-12-10T23:24:35.079044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:35.177383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 27
90.0%
2 3
 
10.0%

청구_전력_사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3299.5667
Minimum0
Maximum9484
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:24:35.279544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile472.85
Q11496.25
median2679.5
Q34982.25
95-th percentile8007.3
Maximum9484
Range9484
Interquartile range (IQR)3486

Descriptive statistics

Standard deviation2428.0952
Coefficient of variation (CV)0.73588307
Kurtosis0.31896977
Mean3299.5667
Median Absolute Deviation (MAD)1421
Skewness0.94480166
Sum98987
Variance5895646.5
MonotonicityNot monotonic
2023-12-10T23:24:35.468162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6108 2
 
6.7%
3273 2
 
6.7%
836 1
 
3.3%
458 1
 
3.3%
4737 1
 
3.3%
1394 1
 
3.3%
3466 1
 
3.3%
3275 1
 
3.3%
0 1
 
3.3%
1983 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 1
3.3%
458 1
3.3%
491 1
3.3%
809 1
3.3%
836 1
3.3%
1138 1
3.3%
1379 1
3.3%
1394 1
3.3%
1803 1
3.3%
1983 1
3.3%
ValueCountFrequency (%)
9484 1
3.3%
8280 1
3.3%
7674 1
3.3%
6108 2
6.7%
5425 1
3.3%
5093 1
3.3%
5064 1
3.3%
4737 1
3.3%
3466 1
3.3%
3275 1
3.3%

대기_전력_량
Categorical

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
4
16 
3
10 
2
3 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 16
53.3%
3 10
33.3%
2 3
 
10.0%
5 1
 
3.3%

Length

2023-12-10T23:24:35.598011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:35.729242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 16
53.3%
3 10
33.3%
2 3
 
10.0%
5 1
 
3.3%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-08-24 전기차 충전기 대기 전력량
30 

Length

Max length25
Median length25
Mean length25
Min length25

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-24 전기차 충전기 대기 전력량
2nd row2022-08-24 전기차 충전기 대기 전력량
3rd row2022-08-24 전기차 충전기 대기 전력량
4th row2022-08-24 전기차 충전기 대기 전력량
5th row2022-08-24 전기차 충전기 대기 전력량

Common Values

ValueCountFrequency (%)
2022-08-24 전기차 충전기 대기 전력량 30
100.0%

Length

2023-12-10T23:24:35.855635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:35.952520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-24 30
20.0%
전기차 30
20.0%
충전기 30
20.0%
대기 30
20.0%
전력량 30
20.0%

생산_일시
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2022-08-24 09:25:23
30 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-24 09:25:23
2nd row2022-08-24 09:25:23
3rd row2022-08-24 09:25:23
4th row2022-08-24 09:25:23
5th row2022-08-24 09:25:23

Common Values

ValueCountFrequency (%)
2022-08-24 09:25:23 30
100.0%

Length

2023-12-10T23:24:36.052314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:24:36.147057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-24 30
50.0%
09:25:23 30
50.0%

Interactions

2023-12-10T23:24:32.033779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:31.521525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:31.775414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:32.121325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:31.606179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:31.874621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:32.199703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:31.692803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:24:31.962085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:24:36.224978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전_일시충전소_ID충전기_ID시도_코드시군구_코드시도_명시군구_명충전_전력_사용량청구_전력_사용량대기_전력_량
충전_일시1.0000.0000.0000.0000.0000.0000.0000.0620.2550.252
충전소_ID0.0001.0001.0001.0001.0001.0001.0000.7400.8470.205
충전기_ID0.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
시도_코드0.0001.0001.0001.0000.6641.0001.0000.1250.7600.186
시군구_코드0.0001.0001.0000.6641.0000.5681.0000.0000.2330.620
시도_명0.0001.0001.0001.0000.5681.0001.0000.0000.8050.091
시군구_명0.0001.0001.0001.0001.0001.0001.0000.0000.8140.181
충전_전력_사용량0.0620.7401.0000.1250.0000.0000.0001.0000.0000.540
청구_전력_사용량0.2550.8471.0000.7600.2330.8050.8140.0001.0000.000
대기_전력_량0.2520.2050.0000.1860.6200.0910.1810.5400.0001.000
2023-12-10T23:24:36.373480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전_전력_사용량충전_일시대기_전력_량시도_명
충전_전력_사용량1.0000.0890.3510.000
충전_일시0.0891.0000.2290.000
대기_전력_량0.3510.2291.0000.000
시도_명0.0000.0000.0001.000
2023-12-10T23:24:36.473686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도_코드시군구_코드청구_전력_사용량충전_일시시도_명충전_전력_사용량대기_전력_량
시도_코드1.000-0.7290.3670.0000.9800.1270.131
시군구_코드-0.7291.000-0.4140.0000.3320.0000.276
청구_전력_사용량0.367-0.4141.0000.0260.5150.0000.000
충전_일시0.0000.0000.0261.0000.0000.0890.229
시도_명0.9800.3320.5150.0001.0000.0000.000
충전_전력_사용량0.1270.0000.0000.0890.0001.0000.351
대기_전력_량0.1310.2760.0000.2290.0000.3511.000

Missing values

2023-12-10T23:24:32.530464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:24:32.675408image/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

충전_일시충전소_ID충전기_ID시도_코드시군구_코드시도_명시군구_명충전_전력_사용량청구_전력_사용량대기_전력_량비고생산_일시
02022-01-01 01:00:00KRPPKCS0042KRPPKCP026426230부산광역시부산진구183642022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
12022-01-01 01:00:00KRPPKCS0060KRPPKCP037750110제주특별자치도제주시2767432022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
22022-01-01 01:00:00KRPPKCS0108KRPPKCP077211710서울특별시강서구1180342022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
32022-01-01 01:00:00KRPPKCS0043KRPPKCP027126350부산광역시해운대구1209432022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
42022-01-01 01:00:00KRPPKCS0008KRPPKCP049741190경기도부천시1506442022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
52022-01-01 01:00:00KRPPKCS0010KRPPKCP040641111경기도수원시1241652022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
62022-01-01 01:00:00KRPPKCS0053KRPPKCP034048125경상남도창원시1272742022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
72022-01-01 01:00:00KRPPKCS0051KRPPKCP032148123경상남도창원시2509332022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
82022-01-01 01:00:00KRPPKCS0015KRPPKCP010411470서울특별시양천구1137932022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
92022-01-01 01:01:00KRPPKCS0048KRPPKCP030626260부산광역시동래구2234032022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
충전_일시충전소_ID충전기_ID시도_코드시군구_코드시도_명시군구_명충전_전력_사용량청구_전력_사용량대기_전력_량비고생산_일시
202022-01-01 01:01:00KRPPKCS0020KRPPKCP015241210경기도광명시1285042022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
212022-01-01 01:01:00KRPPKCS0056KRPPKCP036450110제주특별자치도제주시1327342022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
222022-01-01 01:01:00KRPPKCS0057KRPPKCP044550110제주특별자치도제주시1327332022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
232022-01-01 01:01:00KRPPKCS0043KRPPKCP027126350부산광역시해운대구1198332022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
242022-01-01 01:01:00KRPPKCS0064KRPPKCP046841117경기도수원시1042022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
252022-01-01 01:02:00KRPPKCS0020KRPPKCP015141210경기도광명시1327522022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
262022-01-01 01:02:00KRPPKCS0004KRPPKCP002111170서울특별시용산구1346632022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
272022-01-01 01:02:00KRPPKCS0046KRPPKCP029826230부산광역시부산진구1139422022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
282022-01-01 01:02:00KRPPKCS0008KRPPKCP048941190경기도부천시1473742022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23
292022-01-01 01:02:00KRPPKCS0104KRPPKCP075141590경기도화성시145842022-08-24 전기차 충전기 대기 전력량2022-08-24 09:25:23