Overview

Dataset statistics

Number of variables14
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory125.4 B

Variable types

Categorical5
Text3
Numeric6

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/cfdfcfdf-93f7-42b4-bb04-765e19e9aa78

Alerts

충전일자 has constant value ""Constant
충전유형 has constant value ""Constant
충전방식 has constant value ""Constant
전력사용량(중부하) has constant value ""Constant
전력사용량(최대부하) has constant value ""Constant
시도코드 is highly overall correlated with 시군구코드High correlation
시군구코드 is highly overall correlated with 시도코드High correlation
총전력사용량 is highly overall correlated with 전력사용량(경부하) and 1 other fieldsHigh correlation
전력사용량(경부하) is highly overall correlated with 총전력사용량 and 1 other fieldsHigh correlation
총충전시간 is highly overall correlated with 총전력사용량 and 1 other fieldsHigh correlation
충전기ID has unique valuesUnique
총전력사용량 has unique valuesUnique
전력사용량(경부하) has unique valuesUnique
총충전시간 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:19:53.822267
Analysis finished2023-12-10 14:19:58.857953
Duration5.04 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

2023-12-10T23:19:58.968453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:59.108449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-01 30
100.0%

충전소ID
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:19:59.304753image/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

Unique12 ?
Unique (%)40.0%

Sample

1st rowKRPPKCS0053
2nd rowKRPPKCS0018
3rd rowKRPPKCS0008
4th rowKRPPKCS0010
5th rowKRPPKCS0057
ValueCountFrequency (%)
krppkcs0018 3
 
10.0%
krppkcs0010 3
 
10.0%
krppkcs0053 2
 
6.7%
krppkcs0044 2
 
6.7%
krppkcs0051 2
 
6.7%
krppkcs0016 2
 
6.7%
krppkcs0020 2
 
6.7%
krppkcs0006 2
 
6.7%
krppkcs0032 1
 
3.3%
krppkcs0003 1
 
3.3%
Other values (10) 10
33.3%
2023-12-10T23:19:59.710083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70
21.2%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
1 14
 
4.2%
5 7
 
2.1%
4 7
 
2.1%
3 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 70
58.3%
1 14
 
11.7%
5 7
 
5.8%
4 7
 
5.8%
3 6
 
5.0%
6 5
 
4.2%
2 5
 
4.2%
8 4
 
3.3%
7 1
 
0.8%
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 70
58.3%
1 14
 
11.7%
5 7
 
5.8%
4 7
 
5.8%
3 6
 
5.0%
6 5
 
4.2%
2 5
 
4.2%
8 4
 
3.3%
7 1
 
0.8%
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 70
21.2%
K 60
18.2%
P 60
18.2%
R 30
9.1%
C 30
9.1%
S 30
9.1%
1 14
 
4.2%
5 7
 
2.1%
4 7
 
2.1%
3 6
 
1.8%
Other values (5) 16
 
4.8%

충전기ID
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:19:59.964472image/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 rowKRPPKCP0332
2nd rowKRPPKCP0130
3rd rowKRPPKCP0056
4th rowKRPPKCP0399
5th rowKRPPKCP0370
ValueCountFrequency (%)
krppkcp0332 1
 
3.3%
krppkcp0130 1
 
3.3%
krppkcp0085 1
 
3.3%
krppkcp0135 1
 
3.3%
krppkcp0416 1
 
3.3%
krppkcp0276 1
 
3.3%
krppkcp0145 1
 
3.3%
krppkcp0173 1
 
3.3%
krppkcp0277 1
 
3.3%
krppkcp0354 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:20:00.425343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 90
27.3%
K 60
18.2%
0 40
12.1%
R 30
 
9.1%
C 30
 
9.1%
3 19
 
5.8%
1 16
 
4.8%
2 11
 
3.3%
5 7
 
2.1%
7 7
 
2.1%
Other values (4) 20
 
6.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
33.3%
3 19
15.8%
1 16
 
13.3%
2 11
 
9.2%
5 7
 
5.8%
7 7
 
5.8%
4 7
 
5.8%
6 5
 
4.2%
8 5
 
4.2%
9 3
 
2.5%
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 40
33.3%
3 19
15.8%
1 16
 
13.3%
2 11
 
9.2%
5 7
 
5.8%
7 7
 
5.8%
4 7
 
5.8%
6 5
 
4.2%
8 5
 
4.2%
9 3
 
2.5%
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 40
12.1%
R 30
 
9.1%
C 30
 
9.1%
3 19
 
5.8%
1 16
 
4.8%
2 11
 
3.3%
5 7
 
2.1%
7 7
 
2.1%
Other values (4) 20
 
6.1%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.033333
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:00.573667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q344
95-th percentile49.1
Maximum50
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.897366
Coefficient of variation (CV)0.3966898
Kurtosis-0.73002996
Mean35.033333
Median Absolute Deviation (MAD)7
Skewness-0.88734475
Sum1051
Variance193.13678
MonotonicityNot monotonic
2023-12-10T23:20:00.708323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
41 11
36.7%
11 6
20.0%
48 5
16.7%
26 3
 
10.0%
50 2
 
6.7%
44 2
 
6.7%
28 1
 
3.3%
ValueCountFrequency (%)
11 6
20.0%
26 3
 
10.0%
28 1
 
3.3%
41 11
36.7%
44 2
 
6.7%
48 5
16.7%
50 2
 
6.7%
ValueCountFrequency (%)
50 2
 
6.7%
48 5
16.7%
44 2
 
6.7%
41 11
36.7%
28 1
 
3.3%
26 3
 
10.0%
11 6
20.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.1
Minimum110
Maximum545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:00.883080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110.45
Q1125
median210
Q3370
95-th percentile545
Maximum545
Range435
Interquartile range (IQR)245

Descriptive statistics

Standard deviation152.68953
Coefficient of variation (CV)0.58256212
Kurtosis-0.87481815
Mean262.1
Median Absolute Deviation (MAD)93
Skewness0.71578474
Sum7863
Variance23314.093
MonotonicityNot monotonic
2023-12-10T23:20:01.028942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
125 3
10.0%
111 3
10.0%
545 3
10.0%
210 3
10.0%
110 2
 
6.7%
290 2
 
6.7%
370 2
 
6.7%
123 2
 
6.7%
500 2
 
6.7%
390 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
110 2
6.7%
111 3
10.0%
123 2
6.7%
125 3
10.0%
133 1
 
3.3%
171 1
 
3.3%
190 1
 
3.3%
200 1
 
3.3%
210 3
10.0%
260 1
 
3.3%
ValueCountFrequency (%)
545 3
10.0%
500 2
6.7%
410 1
 
3.3%
390 1
 
3.3%
370 2
6.7%
350 1
 
3.3%
290 2
6.7%
260 1
 
3.3%
210 3
10.0%
200 1
 
3.3%

읍면동코드
Real number (ℝ)

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.03333
Minimum101
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:01.179493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102.25
median117.5
Q3129
95-th percentile158
Maximum253
Range152
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation29.511521
Coefficient of variation (CV)0.24183164
Kurtosis13.411263
Mean122.03333
Median Absolute Deviation (MAD)14.5
Skewness3.2331346
Sum3661
Variance870.92989
MonotonicityNot monotonic
2023-12-10T23:20:01.326488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
101 6
20.0%
129 3
10.0%
118 3
10.0%
158 2
 
6.7%
102 2
 
6.7%
103 2
 
6.7%
133 2
 
6.7%
128 2
 
6.7%
110 1
 
3.3%
113 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
101 6
20.0%
102 2
 
6.7%
103 2
 
6.7%
106 1
 
3.3%
108 1
 
3.3%
110 1
 
3.3%
113 1
 
3.3%
117 1
 
3.3%
118 3
10.0%
127 1
 
3.3%
ValueCountFrequency (%)
253 1
 
3.3%
158 2
6.7%
133 2
6.7%
132 1
 
3.3%
129 3
10.0%
128 2
6.7%
127 1
 
3.3%
118 3
10.0%
117 1
 
3.3%
113 1
 
3.3%

충전지역명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:20:01.603389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length13.9
Min length11

Characters and Unicode

Total characters417
Distinct characters71
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row경상남도 창원시 마산합포구 해운동
2nd row서울특별시 금천구 가산동
3rd row경기도 부천시 대장동
4th row경기도 수원시 장안구 파장동
5th row제주특별자치도 제주시 도두일동
ValueCountFrequency (%)
경기도 11
 
10.9%
서울특별시 6
 
5.9%
경상남도 5
 
5.0%
창원시 5
 
5.0%
금천구 3
 
3.0%
부산광역시 3
 
3.0%
마산합포구 3
 
3.0%
광명시 3
 
3.0%
파장동 3
 
3.0%
수원시 3
 
3.0%
Other values (39) 56
55.4%
2023-12-10T23:20:02.000795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
17.0%
시 31
 
7.4%
동 29
 
7.0%
도 21
 
5.0%
구 20
 
4.8%
경 16
 
3.8%
산 15
 
3.6%
기 11
 
2.6%
서 9
 
2.2%
광 9
 
2.2%
Other values (61) 185
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 346
83.0%
Space Separator 71
 
17.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
 
9.0%
동 29
 
8.4%
도 21
 
6.1%
구 20
 
5.8%
경 16
 
4.6%
산 15
 
4.3%
기 11
 
3.2%
서 9
 
2.6%
광 9
 
2.6%
별 8
 
2.3%
Other values (60) 177
51.2%
Space Separator
ValueCountFrequency (%)
71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 346
83.0%
Common 71
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
 
9.0%
동 29
 
8.4%
도 21
 
6.1%
구 20
 
5.8%
경 16
 
4.6%
산 15
 
4.3%
기 11
 
3.2%
서 9
 
2.6%
광 9
 
2.6%
별 8
 
2.3%
Other values (60) 177
51.2%
Common
ValueCountFrequency (%)
71
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 346
83.0%
ASCII 71
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
100.0%
Hangul
ValueCountFrequency (%)
시 31
 
9.0%
동 29
 
8.4%
도 21
 
6.1%
구 20
 
5.8%
경 16
 
4.6%
산 15
 
4.3%
기 11
 
3.2%
서 9
 
2.6%
광 9
 
2.6%
별 8
 
2.3%
Other values (60) 177
51.2%

충전유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
13
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 30
100.0%

Length

2023-12-10T23:20:02.159283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:02.288101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 30
100.0%

충전방식
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
D
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 30
100.0%

Length

2023-12-10T23:20:02.411890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:02.519822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 30
100.0%

총전력사용량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean609.12933
Minimum79.44
Maximum1759.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:02.610993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79.44
5-th percentile96.022
Q1245.06
median486.52
Q3876.57
95-th percentile1273.935
Maximum1759.44
Range1680
Interquartile range (IQR)631.51

Descriptive statistics

Standard deviation424.87448
Coefficient of variation (CV)0.69751112
Kurtosis0.17763062
Mean609.12933
Median Absolute Deviation (MAD)306.3
Skewness0.79092396
Sum18273.88
Variance180518.32
MonotonicityNot monotonic
2023-12-10T23:20:02.780854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
207.0 1
 
3.3%
1102.38 1
 
3.3%
1324.74 1
 
3.3%
501.92 1
 
3.3%
403.8 1
 
3.3%
363.64 1
 
3.3%
625.52 1
 
3.3%
551.84 1
 
3.3%
876.72 1
 
3.3%
233.12 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
79.44 1
3.3%
88.48 1
3.3%
105.24 1
3.3%
161.04 1
3.3%
196.38 1
3.3%
199.68 1
3.3%
207.0 1
3.3%
233.12 1
3.3%
280.88 1
3.3%
313.74 1
3.3%
ValueCountFrequency (%)
1759.44 1
3.3%
1324.74 1
3.3%
1211.84 1
3.3%
1102.38 1
3.3%
1085.72 1
3.3%
1038.44 1
3.3%
915.96 1
3.3%
876.72 1
3.3%
876.12 1
3.3%
822.9 1
3.3%

전력사용량(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean609.33333
Minimum80
Maximum1761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:03.206820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile95.65
Q1244.25
median487.5
Q3875.75
95-th percentile1274.6
Maximum1761
Range1681
Interquartile range (IQR)631.5

Descriptive statistics

Standard deviation425.06854
Coefficient of variation (CV)0.69759607
Kurtosis0.18374053
Mean609.33333
Median Absolute Deviation (MAD)306.5
Skewness0.79171796
Sum18280
Variance180683.26
MonotonicityNot monotonic
2023-12-10T23:20:03.365250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
207 1
 
3.3%
1102 1
 
3.3%
1325 1
 
3.3%
503 1
 
3.3%
404 1
 
3.3%
364 1
 
3.3%
627 1
 
3.3%
551 1
 
3.3%
875 1
 
3.3%
232 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
80 1
3.3%
88 1
3.3%
105 1
3.3%
161 1
3.3%
197 1
3.3%
199 1
3.3%
207 1
3.3%
232 1
3.3%
281 1
3.3%
313 1
3.3%
ValueCountFrequency (%)
1761 1
3.3%
1325 1
3.3%
1213 1
3.3%
1102 1
3.3%
1084 1
3.3%
1041 1
3.3%
916 1
3.3%
876 1
3.3%
875 1
3.3%
822 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

2023-12-10T23:20:03.503567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:03.602636image/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

2023-12-10T23:20:03.718978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:03.836145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
100.0%

총충전시간
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46667.333
Minimum3859
Maximum127475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:03.968616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3859
5-th percentile9841.25
Q127466.5
median40904
Q362848.75
95-th percentile92829.45
Maximum127475
Range123616
Interquartile range (IQR)35382.25

Descriptive statistics

Standard deviation29813.006
Coefficient of variation (CV)0.63884099
Kurtosis0.3486938
Mean46667.333
Median Absolute Deviation (MAD)19843
Skewness0.715536
Sum1400020
Variance8.8881531 × 108
MonotonicityNot monotonic
2023-12-10T23:20:04.102704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
17330 1
 
3.3%
64793 1
 
3.3%
84091 1
 
3.3%
39249 1
 
3.3%
44414 1
 
3.3%
29946 1
 
3.3%
39509 1
 
3.3%
38677 1
 
3.3%
55727 1
 
3.3%
12297 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
3859 1
3.3%
9677 1
3.3%
10042 1
3.3%
10318 1
3.3%
12297 1
3.3%
14188 1
3.3%
17330 1
3.3%
26780 1
3.3%
29526 1
3.3%
29946 1
3.3%
ValueCountFrequency (%)
127475 1
3.3%
95484 1
3.3%
89585 1
3.3%
84091 1
3.3%
81493 1
3.3%
75584 1
3.3%
69242 1
3.3%
64793 1
3.3%
57016 1
3.3%
55727 1
3.3%

Interactions

2023-12-10T23:19:57.877552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.180251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.833830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.800935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.479400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.224522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.996548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.287866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.950043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.902750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.596805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.345185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:58.104546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.378870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.337827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.997164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.690449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.463669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:58.202728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.487808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.461225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.135870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.847476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.565055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:58.297419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.605612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.581415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.288968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.987446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.683272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:58.399138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:54.722703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:55.686518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:56.386405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.108657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:57.778823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:20:04.220616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID충전기ID시도코드시군구코드읍면동코드충전지역명총전력사용량전력사용량(경부하)총충전시간
충전소ID1.0001.0001.0001.0001.0001.0000.0000.0000.289
충전기ID1.0001.0001.0001.0001.0001.0001.0001.0001.000
시도코드1.0001.0001.0000.8950.2921.0000.0000.0000.398
시군구코드1.0001.0000.8951.0000.5641.0000.0000.0000.000
읍면동코드1.0001.0000.2920.5641.0001.0000.0000.0000.000
충전지역명1.0001.0001.0001.0001.0001.0000.0000.0000.289
총전력사용량0.0001.0000.0000.0000.0000.0001.0001.0000.940
전력사용량(경부하)0.0001.0000.0000.0000.0000.0001.0001.0000.940
총충전시간0.2891.0000.3980.0000.0000.2890.9400.9401.000
2023-12-10T23:20:04.349859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드총전력사용량전력사용량(경부하)총충전시간
시도코드1.000-0.8070.3420.1660.1630.105
시군구코드-0.8071.000-0.491-0.062-0.061-0.034
읍면동코드0.342-0.4911.000-0.095-0.089-0.087
총전력사용량0.166-0.062-0.0951.0001.0000.934
전력사용량(경부하)0.163-0.061-0.0891.0001.0000.935
총충전시간0.105-0.034-0.0870.9340.9351.000

Missing values

2023-12-10T23:19:58.534403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:19:58.758943image/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

충전일자충전소ID충전기ID시도코드시군구코드읍면동코드충전지역명충전유형충전방식총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)총충전시간
02021-08-01KRPPKCS0053KRPPKCP033248125158경상남도 창원시 마산합포구 해운동13D207.02070017330
12021-08-01KRPPKCS0018KRPPKCP013011545101서울특별시 금천구 가산동13D105.241050010042
22021-08-01KRPPKCS0008KRPPKCP005641190117경기도 부천시 대장동13D313.743130051364
32021-08-01KRPPKCS0010KRPPKCP039941111129경기도 수원시 장안구 파장동13D464.84660041996
42021-08-01KRPPKCS0057KRPPKCP037050110127제주특별자치도 제주시 도두일동13D199.68199009677
52021-08-01KRPPKCS0006KRPPKCP003511290133서울특별시 성북구 정릉동13D88.48880014188
62021-08-01KRPPKCS0060KRPPKCP037850110253제주특별자치도 제주시 애월읍 장전리13D808.988100054508
72021-08-01KRPPKCS0003KRPPKCP001841390132경기도 시흥시 정왕동13D822.98220054429
82021-08-01KRPPKCS0010KRPPKCP038241111129경기도 수원시 장안구 파장동13D280.882810029526
92021-08-01KRPPKCS0018KRPPKCP012311545101서울특별시 금천구 가산동13D1038.4410410081493
충전일자충전소ID충전기ID시도코드시군구코드읍면동코드충전지역명충전유형충전방식총전력사용량전력사용량(경부하)전력사용량(중부하)전력사용량(최대부하)총충전시간
202021-08-01KRPPKCS0053KRPPKCP033448125158경상남도 창원시 마산합포구 해운동13D1085.7210840089585
212021-08-01KRPPKCS0055KRPPKCP035448125101경상남도 창원시 마산합포구 가포동13D1211.8412130095484
222021-08-01KRPPKCS0044KRPPKCP027726500103부산광역시 수영구 민락동13D233.122320012297
232021-08-01KRPPKCS0024KRPPKCP017328260110인천광역시 서구 석남동13D876.728750055727
242021-08-01KRPPKCS0019KRPPKCP014511410118서울특별시 서대문구 홍은동13D551.845510038677
252021-08-01KRPPKCS0044KRPPKCP027626500103부산광역시 수영구 민락동13D625.526270039509
262021-08-01KRPPKCS0010KRPPKCP041641111129경기도 수원시 장안구 파장동13D363.643640029946
272021-08-01KRPPKCS0018KRPPKCP013511545101서울특별시 금천구 가산동13D403.84040044414
282021-08-01KRPPKCS0011KRPPKCP008541210102경기도 광명시 철산동13D501.925030039249
292021-08-01KRPPKCS0051KRPPKCP032348123128경상남도 창원시 성산구 성주동13D1324.7413250084091