Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells21
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory130.4 B

Variable types

DateTime4
Text3
Categorical4
Numeric4

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/66e99eb3-a895-4133-b183-477d347e836e

Alerts

충전_일시 has constant value ""Constant
종료_일시 has constant value ""Constant
비고 has constant value ""Constant
생산_일시 has constant value ""Constant
시도_명 is highly overall correlated with 시군구_코드 and 1 other fieldsHigh correlation
시도_코드 is highly overall correlated with 시군구_코드 and 1 other fieldsHigh correlation
시군구_코드 is highly overall correlated with 시작_SOC and 2 other fieldsHigh correlation
시작_SOC is highly overall correlated with 시군구_코드 and 1 other fieldsHigh correlation
공급_전압 is highly overall correlated with 시작_SOCHigh correlation
종료_일시 has 21 (70.0%) missing valuesMissing
충전기_ID has unique valuesUnique
시작_일시 has unique valuesUnique
공급_전압 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:14:11.225732
Analysis finished2023-12-10 14:14:16.143729
Duration4.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전_일시
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-10T23:14:16.212937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:16.386725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

충전소_ID
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:14:16.677834image/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

Unique19 ?
Unique (%)63.3%

Sample

1st rowKRPPKCS0049
2nd rowKRPPKCS0010
3rd rowKRPPKCS0027
4th rowKRPPKCS0085
5th rowKRPPKCS0027
ValueCountFrequency (%)
krppkcs0027 3
 
10.0%
krppkcs0064 3
 
10.0%
krppkcs0010 3
 
10.0%
krppkcs0020 2
 
6.7%
krppkcs0115 1
 
3.3%
krppkcs0049 1
 
3.3%
krppkcs0105 1
 
3.3%
krppkcs0041 1
 
3.3%
krppkcs0089 1
 
3.3%
krppkcs0090 1
 
3.3%
Other values (13) 13
43.3%
2023-12-10T23:14:17.224493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63
19.1%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
1 14
 
4.2%
4 9
 
2.7%
9 8
 
2.4%
2 7
 
2.1%
Other values (5) 19
 
5.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63
52.5%
1 14
 
11.7%
4 9
 
7.5%
9 8
 
6.7%
2 7
 
5.8%
7 5
 
4.2%
6 5
 
4.2%
8 4
 
3.3%
5 3
 
2.5%
3 2
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
K 60
28.6%
P 60
28.6%
R 30
14.3%
C 30
14.3%
S 30
14.3%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 63
52.5%
1 14
 
11.7%
4 9
 
7.5%
9 8
 
6.7%
2 7
 
5.8%
7 5
 
4.2%
6 5
 
4.2%
8 4
 
3.3%
5 3
 
2.5%
3 2
 
1.7%
Latin
ValueCountFrequency (%)
K 60
28.6%
P 60
28.6%
R 30
14.3%
C 30
14.3%
S 30
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63
19.1%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
1 14
 
4.2%
4 9
 
2.7%
9 8
 
2.4%
2 7
 
2.1%
Other values (5) 19
 
5.8%

충전기_ID
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:14:17.538893image/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

Unique30 ?
Unique (%)100.0%

Sample

1st rowKRPPKCP0859
2nd rowKRPPKCP0387
3rd rowKRPPKCP0547
4th rowKRPPKCP0645
5th rowKRPPKCP0544
ValueCountFrequency (%)
krppkcp0859 1
 
3.3%
krppkcp0387 1
 
3.3%
krppkcp0908 1
 
3.3%
krppkcp0660 1
 
3.3%
krppkcp0664 1
 
3.3%
krppkcp0383 1
 
3.3%
krppkcp0528 1
 
3.3%
krppkcp0789 1
 
3.3%
krppkcp0074 1
 
3.3%
krppkcp0148 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:14:18.188596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 90
27.3%
K 60
18.2%
0 38
11.5%
R 30
 
9.1%
C 30
 
9.1%
4 15
 
4.5%
7 13
 
3.9%
6 13
 
3.9%
8 10
 
3.0%
5 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 38
31.7%
4 15
 
12.5%
7 13
 
10.8%
6 13
 
10.8%
8 10
 
8.3%
5 8
 
6.7%
2 7
 
5.8%
9 6
 
5.0%
3 5
 
4.2%
1 5
 
4.2%
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 38
31.7%
4 15
 
12.5%
7 13
 
10.8%
6 13
 
10.8%
8 10
 
8.3%
5 8
 
6.7%
2 7
 
5.8%
9 6
 
5.0%
3 5
 
4.2%
1 5
 
4.2%
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 38
11.5%
R 30
 
9.1%
C 30
 
9.1%
4 15
 
4.5%
7 13
 
3.9%
6 13
 
3.9%
8 10
 
3.0%
5 8
 
2.4%
Other values (4) 23
 
7.0%

충전건
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
17 
2
13 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 17
56.7%
2 13
43.3%

Length

2023-12-10T23:14:18.520427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:18.774607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 17
56.7%
2 13
43.3%

시도_코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
41
16 
28
4 
48
4 
26
3 
11
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26
2nd row41
3rd row28
4th row41
5th row28

Common Values

ValueCountFrequency (%)
41 16
53.3%
28 4
 
13.3%
48 4
 
13.3%
26 3
 
10.0%
11 3
 
10.0%

Length

2023-12-10T23:14:18.985330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:19.252447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41 16
53.3%
28 4
 
13.3%
48 4
 
13.3%
26 3
 
10.0%
11 3
 
10.0%

시군구_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.83333
Minimum111
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:19.599912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile111
Q1136.25
median215
Q3312.5
95-th percentile661
Maximum710
Range599
Interquartile range (IQR)176.25

Descriptive statistics

Standard deviation186.66586
Coefficient of variation (CV)0.65998537
Kurtosis0.13327376
Mean282.83333
Median Absolute Deviation (MAD)90
Skewness1.1953648
Sum8485
Variance34844.144
MonotonicityNot monotonic
2023-12-10T23:14:19.896493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
260 4
13.3%
117 3
 
10.0%
210 3
 
10.0%
111 3
 
10.0%
230 2
 
6.7%
125 2
 
6.7%
570 1
 
3.3%
500 1
 
3.3%
330 1
 
3.3%
670 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
111 3
10.0%
117 3
10.0%
125 2
6.7%
170 1
 
3.3%
171 1
 
3.3%
190 1
 
3.3%
200 1
 
3.3%
210 3
10.0%
220 1
 
3.3%
230 2
6.7%
ValueCountFrequency (%)
710 1
 
3.3%
670 1
 
3.3%
650 1
 
3.3%
590 1
 
3.3%
570 1
 
3.3%
500 1
 
3.3%
450 1
 
3.3%
330 1
 
3.3%
260 4
13.3%
230 2
6.7%

시도_명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
16 
인천광역시
4 
경상남도
4 
부산광역시
3 
서울특별시
3 

Length

Max length5
Median length3
Mean length3.8
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시
2nd row경기도
3rd row인천광역시
4th row경기도
5th row인천광역시

Common Values

ValueCountFrequency (%)
경기도 16
53.3%
인천광역시 4
 
13.3%
경상남도 4
 
13.3%
부산광역시 3
 
10.0%
서울특별시 3
 
10.0%

Length

2023-12-10T23:14:20.175788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:20.402662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 16
53.3%
인천광역시 4
 
13.3%
경상남도 4
 
13.3%
부산광역시 3
 
10.0%
서울특별시 3
 
10.0%

시군구_명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:14:20.654989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters28
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

Unique11 ?
Unique (%)36.7%

Sample

1st row부산진구
2nd row수원시
3rd row서구
4th row화성시
5th row서구
ValueCountFrequency (%)
수원시 6
20.0%
서구 4
13.3%
광명시 3
10.0%
부산진구 2
 
6.7%
창원시 2
 
6.7%
강서구 2
 
6.7%
하남시 1
 
3.3%
서초구 1
 
3.3%
여주시 1
 
3.3%
안양시 1
 
3.3%
Other values (7) 7
23.3%
2023-12-10T23:14:21.197353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 20
22.7%
구 10
11.4%
원 8
 
9.1%
서 7
 
8.0%
수 6
 
6.8%
광 3
 
3.4%
명 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
진 3
 
3.4%
Other values (18) 22
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 20
22.7%
구 10
11.4%
원 8
 
9.1%
서 7
 
8.0%
수 6
 
6.8%
광 3
 
3.4%
명 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
진 3
 
3.4%
Other values (18) 22
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 20
22.7%
구 10
11.4%
원 8
 
9.1%
서 7
 
8.0%
수 6
 
6.8%
광 3
 
3.4%
명 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
진 3
 
3.4%
Other values (18) 22
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 20
22.7%
구 10
11.4%
원 8
 
9.1%
서 7
 
8.0%
수 6
 
6.8%
광 3
 
3.4%
명 3
 
3.4%
부 3
 
3.4%
산 3
 
3.4%
진 3
 
3.4%
Other values (18) 22
25.0%

시작_일시
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-03-31 22:25:44
Maximum2023-04-01 00:00:00
2023-12-10T23:14:21.422396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:21.593847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

종료_일시
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)11.1%
Missing21
Missing (%)70.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-10T23:14:21.742835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:21.891452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시작_SOC
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.633333
Minimum17
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:22.055014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile23.8
Q131.5
median55.5
Q363
95-th percentile74.75
Maximum85
Range68
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.193374
Coefficient of variation (CV)0.35235714
Kurtosis-0.87407465
Mean51.633333
Median Absolute Deviation (MAD)11.5
Skewness-0.36003944
Sum1549
Variance330.99885
MonotonicityNot monotonic
2023-12-10T23:14:22.219187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
67 2
 
6.7%
59 2
 
6.7%
52 2
 
6.7%
53 2
 
6.7%
72 2
 
6.7%
63 2
 
6.7%
30 2
 
6.7%
27 1
 
3.3%
36 1
 
3.3%
17 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
17 1
3.3%
22 1
3.3%
26 1
3.3%
27 1
3.3%
28 1
3.3%
29 1
3.3%
30 2
6.7%
36 1
3.3%
50 1
3.3%
52 2
6.7%
ValueCountFrequency (%)
85 1
3.3%
77 1
3.3%
72 2
6.7%
70 1
3.3%
67 2
6.7%
63 2
6.7%
62 1
3.3%
60 1
3.3%
59 2
6.7%
57 1
3.3%

공급_전압
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean693.66667
Minimum591
Maximum811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:22.389439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum591
5-th percentile608.45
Q1655.5
median693.5
Q3725.5
95-th percentile781.3
Maximum811
Range220
Interquartile range (IQR)70

Descriptive statistics

Standard deviation54.858019
Coefficient of variation (CV)0.079084122
Kurtosis-0.40495245
Mean693.66667
Median Absolute Deviation (MAD)37
Skewness0.062932094
Sum20810
Variance3009.4023
MonotonicityNot monotonic
2023-12-10T23:14:22.574405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
745 1
 
3.3%
706 1
 
3.3%
591 1
 
3.3%
784 1
 
3.3%
609 1
 
3.3%
608 1
 
3.3%
679 1
 
3.3%
705 1
 
3.3%
750 1
 
3.3%
680 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
591 1
3.3%
608 1
3.3%
609 1
3.3%
616 1
3.3%
632 1
3.3%
641 1
3.3%
648 1
3.3%
652 1
3.3%
666 1
3.3%
670 1
3.3%
ValueCountFrequency (%)
811 1
3.3%
784 1
3.3%
778 1
3.3%
753 1
3.3%
750 1
3.3%
745 1
3.3%
739 1
3.3%
726 1
3.3%
724 1
3.3%
715 1
3.3%

공급_전류
Real number (ℝ)

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.66667
Minimum117
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:14:22.814389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile118.45
Q1126.75
median149
Q3149
95-th percentile150
Maximum150
Range33
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation12.598121
Coefficient of variation (CV)0.090851832
Kurtosis-1.621404
Mean138.66667
Median Absolute Deviation (MAD)1
Skewness-0.46978118
Sum4160
Variance158.71264
MonotonicityNot monotonic
2023-12-10T23:14:23.022112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
149 12
40.0%
150 4
 
13.3%
129 3
 
10.0%
125 2
 
6.7%
126 2
 
6.7%
130 2
 
6.7%
117 1
 
3.3%
148 1
 
3.3%
118 1
 
3.3%
121 1
 
3.3%
ValueCountFrequency (%)
117 1
 
3.3%
118 1
 
3.3%
119 1
 
3.3%
121 1
 
3.3%
125 2
 
6.7%
126 2
 
6.7%
129 3
 
10.0%
130 2
 
6.7%
148 1
 
3.3%
149 12
40.0%
ValueCountFrequency (%)
150 4
 
13.3%
149 12
40.0%
148 1
 
3.3%
130 2
 
6.7%
129 3
 
10.0%
126 2
 
6.7%
125 2
 
6.7%
121 1
 
3.3%
119 1
 
3.3%
118 1
 
3.3%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-07-10 전기차 배터리 충전 공급 전압/전류
30 

Length

Max length30
Median length30
Mean length30
Min length30

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-10 전기차 배터리 충전 공급 전압/전류
2nd row2023-07-10 전기차 배터리 충전 공급 전압/전류
3rd row2023-07-10 전기차 배터리 충전 공급 전압/전류
4th row2023-07-10 전기차 배터리 충전 공급 전압/전류
5th row2023-07-10 전기차 배터리 충전 공급 전압/전류

Common Values

ValueCountFrequency (%)
2023-07-10 전기차 배터리 충전 공급 전압/전류 30
100.0%

Length

2023-12-10T23:14:23.277604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:23.491780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-10 30
16.7%
전기차 30
16.7%
배터리 30
16.7%
충전 30
16.7%
공급 30
16.7%
전압/전류 30
16.7%

생산_일시
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-07-10 11:22:03
Maximum2023-07-10 11:22:03
2023-12-10T23:14:23.632952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:23.816644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T23:14:14.698535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:12.125410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:13.334447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:14.068648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:14.890524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:12.340358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:13.484310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:14.247641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:15.044560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:12.954855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:13.721532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:14.425462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:15.177486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:13.181527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:13.888309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:14:14.554204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:14:23.985706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소_ID충전기_ID충전건시도_코드시군구_코드시도_명시군구_명시작_일시시작_SOC공급_전압공급_전류
충전소_ID1.0001.0000.0001.0001.0001.0001.0001.0000.9570.8790.821
충전기_ID1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
충전건0.0001.0001.0000.3160.1960.3160.4331.0000.0000.0000.000
시도_코드1.0001.0000.3161.0000.8301.0001.0001.0000.2000.4250.732
시군구_코드1.0001.0000.1960.8301.0000.8300.9791.0000.3300.6000.000
시도_명1.0001.0000.3161.0000.8301.0001.0001.0000.2000.4250.732
시군구_명1.0001.0000.4331.0000.9791.0001.0001.0000.7430.8250.238
시작_일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시작_SOC0.9571.0000.0000.2000.3300.2000.7431.0001.0000.6970.520
공급_전압0.8791.0000.0000.4250.6000.4250.8251.0000.6971.0000.563
공급_전류0.8211.0000.0000.7320.0000.7320.2381.0000.5200.5631.000
2023-12-10T23:14:24.275925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전건시도_명시도_코드
충전건1.0000.3580.358
시도_명0.3581.0001.000
시도_코드0.3581.0001.000
2023-12-10T23:14:24.535005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드시작_SOC공급_전압공급_전류충전건시도_코드시도_명
시군구_코드1.0000.5110.2910.3070.1400.6100.610
시작_SOC0.5111.0000.5040.0320.0000.0300.030
공급_전압0.2910.5041.000-0.3740.0000.1720.172
공급_전류0.3070.032-0.3741.0000.0000.3500.350
충전건0.1400.0000.0000.0001.0000.3580.358
시도_코드0.6100.0300.1720.3500.3581.0001.000
시도_명0.6100.0300.1720.3500.3581.0001.000

Missing values

2023-12-10T23:14:15.545520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:16.003847image/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_ID충전건시도_코드시군구_코드시도_명시군구_명시작_일시종료_일시시작_SOC공급_전압공급_전류비고생산_일시
02023-04-01 00:00:00KRPPKCS0049KRPPKCP0859126230부산광역시부산진구2023-03-31 23:40:10<NA>677451172023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
12023-04-01 00:00:00KRPPKCS0010KRPPKCP0387141111경기도수원시2023-03-31 23:46:53<NA>306481252023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
22023-04-01 00:00:00KRPPKCS0027KRPPKCP0547228260인천광역시서구2023-03-31 23:56:16<NA>596911492023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
32023-04-01 00:00:00KRPPKCS0085KRPPKCP0645241590경기도화성시2023-03-31 22:55:282023-04-01 00:00:00527781262023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
42023-04-01 00:00:00KRPPKCS0027KRPPKCP0544228260인천광역시서구2023-03-31 23:24:19<NA>627261492023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
52023-04-01 00:00:00KRPPKCS0064KRPPKCP0458241117경기도수원시2023-03-31 22:25:44<NA>266411302023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
62023-04-01 00:00:00KRPPKCS0096KRPPKCP0707228260인천광역시서구2023-03-31 23:49:00<NA>727141482023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
72023-04-01 00:00:00KRPPKCS0047KRPPKCP0304126200부산광역시영도구2023-03-31 23:40:41<NA>858111182023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
82023-04-01 00:00:00KRPPKCS0020KRPPKCP0149141210경기도광명시2023-03-31 23:46:392023-04-01 00:00:00677121292023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
92023-04-01 00:00:00KRPPKCS0064KRPPKCP0462241117경기도수원시2023-03-31 23:02:40<NA>286161292023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
충전_일시충전소_ID충전기_ID충전건시도_코드시군구_코드시도_명시군구_명시작_일시종료_일시시작_SOC공급_전압공급_전류비고생산_일시
202023-04-01 00:00:00KRPPKCS0115KRPPKCP0811148125경상남도창원시2023-03-31 23:33:56<NA>306701492023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
212023-04-01 00:00:00KRPPKCS0020KRPPKCP0148241210경기도광명시2023-03-31 23:33:452023-04-01 00:00:00727241292023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
222023-04-01 00:00:00KRPPKCS0010KRPPKCP0074141111경기도수원시2023-03-31 23:11:56<NA>226801252023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
232023-04-01 00:00:00KRPPKCS0112KRPPKCP0789241570경기도김포시2023-03-31 23:58:512023-04-01 00:00:00637501192023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
242023-04-01 00:00:00KRPPKCS0069KRPPKCP0528241171경기도안양시2023-03-31 23:34:33<NA>527051492023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
252023-04-01 00:00:00KRPPKCS0010KRPPKCP0383241111경기도수원시2023-03-31 23:04:35<NA>176791262023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
262023-04-01 00:00:00KRPPKCS0090KRPPKCP0664141450경기도하남시2023-03-31 23:56:322023-04-01 00:00:00596081502023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
272023-04-01 00:00:00KRPPKCS0089KRPPKCP0660141670경기도여주시2023-03-31 23:00:332023-04-01 00:00:00366091492023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
282023-04-01 00:00:00KRPPKCS0041KRPPKCP0908148330경상남도양산시2023-03-31 23:26:51<NA>537841492023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03
292023-04-01 00:00:00KRPPKCS0103KRPPKCP0746111500서울특별시강서구2023-03-31 22:44:01<NA>535911502023-07-10 전기차 배터리 충전 공급 전압/전류2023-07-10 11:22:03