Overview

Dataset statistics

Number of variables16
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory142.4 B

Variable types

DateTime1
Categorical6
Text1
Numeric8

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/a90601d6-07fd-4258-be41-f2fe14336bca

Alerts

전력사용요금(중부하) has constant value ""Constant
전력사용요금(최대부하) has constant value ""Constant
모델명 is highly overall correlated with 시도코드 and 10 other fieldsHigh correlation
충전지역명 is highly overall correlated with 시도코드 and 6 other fieldsHigh correlation
충전소ID is highly overall correlated with 시도코드 and 6 other fieldsHigh correlation
제조사 is highly overall correlated with 시도코드 and 10 other fieldsHigh correlation
시도코드 is highly overall correlated with 시군구코드 and 4 other fieldsHigh correlation
시군구코드 is highly overall correlated with 시도코드 and 5 other fieldsHigh correlation
읍면동코드 is highly overall correlated with 충전소ID and 3 other fieldsHigh correlation
시작SOC is highly overall correlated with 총전력사용요금 and 4 other fieldsHigh correlation
종료SOC is highly overall correlated with 시군구코드 and 4 other fieldsHigh correlation
총전력사용요금 is highly overall correlated with 시작SOC and 4 other fieldsHigh correlation
전력사용요금(경부하) is highly overall correlated with 시작SOC and 4 other fieldsHigh correlation
총충전시간 is highly overall correlated with 시작SOC and 4 other fieldsHigh correlation
제조사 is highly imbalanced (64.7%)Imbalance
모델명 is highly imbalanced (64.7%)Imbalance
총전력사용요금 has unique valuesUnique
전력사용요금(경부하) has unique valuesUnique
총충전시간 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:19:28.992317
Analysis finished2023-12-10 14:19:39.731605
Duration10.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전일시
Date

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-08-01 00:00:01
Maximum2021-08-01 00:05:10
2023-12-10T23:19:39.800000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:39.941715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

충전소ID
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
KRPPKCS0010
7 
KRPPKCS0001
4 
KRPPKCS0045
3 
KRPPKCS0018
2 
KRPPKCS0057
2 
Other values (9)
12 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st rowKRPPKCS0010
2nd rowKRPPKCS0045
3rd rowKRPPKCS0010
4th rowKRPPKCS0008
5th rowKRPPKCS0010

Common Values

ValueCountFrequency (%)
KRPPKCS0010 7
23.3%
KRPPKCS0001 4
13.3%
KRPPKCS0045 3
10.0%
KRPPKCS0018 2
 
6.7%
KRPPKCS0057 2
 
6.7%
KRPPKCS0042 2
 
6.7%
KRPPKCS0024 2
 
6.7%
KRPPKCS0004 2
 
6.7%
KRPPKCS0008 1
 
3.3%
KRPPKCS0036 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:19:40.106246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
krppkcs0010 7
23.3%
krppkcs0001 4
13.3%
krppkcs0045 3
10.0%
krppkcs0018 2
 
6.7%
krppkcs0057 2
 
6.7%
krppkcs0042 2
 
6.7%
krppkcs0024 2
 
6.7%
krppkcs0004 2
 
6.7%
krppkcs0008 1
 
3.3%
krppkcs0036 1
 
3.3%
Other values (4) 4
13.3%

차량번호
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:19:40.418904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters1920
Distinct characters16
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

Unique28 ?
Unique (%)93.3%

Sample

1st row23a1c6b1b6d9d4519c15c8837c93bcafc2deb8429f2aa5335e3271cc9c2f0538
2nd row116510ef47ea1efa7a356531dafe0a638706b0f3a114eab8b7f639e77e6f9b6f
3rd rowd03eb2ba247c62bc8e5d1b61735720c017310aaeef4d01e7933c4e43fc818043
4th row8e94f7eb22ecdae8e72425ae58acab4410419ca0e73c305d460bc9a1bcbbd970
5th row864b58f6beebff5eebd5279c5d9dda7595dadf18cfc9da31040941568d6f72c9
ValueCountFrequency (%)
62ce11f85852dc47a0104835ece515de3553712d37a61a415eaedfc25a239611 2
 
6.7%
23a1c6b1b6d9d4519c15c8837c93bcafc2deb8429f2aa5335e3271cc9c2f0538 1
 
3.3%
7900e1ffda71cc83a3c8670aaac6cc03a23b65f1904c467e7e9c0fe2f2a2dbc6 1
 
3.3%
c5bb2339247df5b2f650dcdaa32992db30f95157159fe5aebee93f59f1768409 1
 
3.3%
421007e267c4eb716ba569d2292b6197720db783231bbb77798dbb542e7ace5a 1
 
3.3%
5dd03b4a227fc9e6bd74e16b57db4667c61b26024b40d07173267bc97dbfd9a8 1
 
3.3%
fab1554e801cab8c66a781d2ff99ef11b6f0702690956190eaaece986967366c 1
 
3.3%
4232846ce7df4b22e3fc7f980b6deed44961e113b3d8f44c2c5f5a89708acf7e 1
 
3.3%
db80cca3f15e218f3e37113da11312c3100d3216029b9042f080a8f49d010078 1
 
3.3%
e56a8da2d8760d14f0049c4927304c2d5d5280e066c3a6decbdd5685d48f0dbc 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:19:40.859079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 135
 
7.0%
1 130
 
6.8%
2 129
 
6.7%
7 128
 
6.7%
6 123
 
6.4%
c 123
 
6.4%
5 121
 
6.3%
d 121
 
6.3%
8 117
 
6.1%
a 117
 
6.1%
Other values (6) 676
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1224
63.7%
Lowercase Letter 696
36.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 135
11.0%
1 130
10.6%
2 129
10.5%
7 128
10.5%
6 123
10.0%
5 121
9.9%
8 117
9.6%
4 116
9.5%
3 113
9.2%
9 112
9.2%
Lowercase Letter
ValueCountFrequency (%)
c 123
17.7%
d 121
17.4%
a 117
16.8%
f 113
16.2%
e 112
16.1%
b 110
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1224
63.7%
Latin 696
36.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 135
11.0%
1 130
10.6%
2 129
10.5%
7 128
10.5%
6 123
10.0%
5 121
9.9%
8 117
9.6%
4 116
9.5%
3 113
9.2%
9 112
9.2%
Latin
ValueCountFrequency (%)
c 123
17.7%
d 121
17.4%
a 117
16.8%
f 113
16.2%
e 112
16.1%
b 110
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 135
 
7.0%
1 130
 
6.8%
2 129
 
6.7%
7 128
 
6.7%
6 123
 
6.4%
c 123
 
6.4%
5 121
 
6.3%
d 121
 
6.3%
8 117
 
6.1%
a 117
 
6.1%
Other values (6) 676
35.2%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.566667
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:40.989466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q341
95-th percentile49.1
Maximum50
Range39
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.325145
Coefficient of variation (CV)0.3784589
Kurtosis-0.76293203
Mean32.566667
Median Absolute Deviation (MAD)9
Skewness-0.59118701
Sum977
Variance151.9092
MonotonicityNot monotonic
2023-12-10T23:19:41.125548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
41 13
43.3%
26 5
 
16.7%
11 5
 
16.7%
28 3
 
10.0%
50 2
 
6.7%
27 1
 
3.3%
48 1
 
3.3%
ValueCountFrequency (%)
11 5
 
16.7%
26 5
 
16.7%
27 1
 
3.3%
28 3
 
10.0%
41 13
43.3%
48 1
 
3.3%
50 2
 
6.7%
ValueCountFrequency (%)
50 2
 
6.7%
48 1
 
3.3%
41 13
43.3%
28 3
 
10.0%
27 1
 
3.3%
26 5
 
16.7%
11 5
 
16.7%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.5
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:41.241946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile110.45
Q1111
median180
Q3282.5
95-th percentile710
Maximum710
Range600
Interquartile range (IQR)171.5

Descriptive statistics

Standard deviation195.24392
Coefficient of variation (CV)0.76118489
Kurtosis0.95504833
Mean256.5
Median Absolute Deviation (MAD)69
Skewness1.4467342
Sum7695
Variance38120.19
MonotonicityNot monotonic
2023-12-10T23:19:41.349849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
111 7
23.3%
117 4
13.3%
710 3
10.0%
260 3
10.0%
545 2
 
6.7%
110 2
 
6.7%
230 2
 
6.7%
170 2
 
6.7%
190 1
 
3.3%
290 1
 
3.3%
Other values (3) 3
10.0%
ValueCountFrequency (%)
110 2
 
6.7%
111 7
23.3%
117 4
13.3%
170 2
 
6.7%
190 1
 
3.3%
210 1
 
3.3%
230 2
 
6.7%
260 3
10.0%
290 1
 
3.3%
330 1
 
3.3%
ValueCountFrequency (%)
710 3
10.0%
545 2
6.7%
410 1
 
3.3%
330 1
 
3.3%
290 1
 
3.3%
260 3
10.0%
230 2
6.7%
210 1
 
3.3%
190 1
 
3.3%
170 2
6.7%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.9
Minimum101
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:41.540920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101.45
Q1106
median117.5
Q3129
95-th percentile256
Maximum256
Range155
Interquartile range (IQR)23

Descriptive statistics

Standard deviation44.098909
Coefficient of variation (CV)0.33948352
Kurtosis5.2325583
Mean129.9
Median Absolute Deviation (MAD)11.5
Skewness2.498684
Sum3897
Variance1944.7138
MonotonicityNot monotonic
2023-12-10T23:19:41.726341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
129 7
23.3%
110 4
13.3%
103 4
13.3%
256 3
10.0%
101 2
 
6.7%
127 2
 
6.7%
106 2
 
6.7%
128 2
 
6.7%
117 1
 
3.3%
113 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
101 2
6.7%
102 1
 
3.3%
103 4
13.3%
106 2
6.7%
110 4
13.3%
113 1
 
3.3%
117 1
 
3.3%
118 1
 
3.3%
127 2
6.7%
128 2
6.7%
ValueCountFrequency (%)
256 3
10.0%
129 7
23.3%
128 2
 
6.7%
127 2
 
6.7%
118 1
 
3.3%
117 1
 
3.3%
113 1
 
3.3%
110 4
13.3%
106 2
 
6.7%
103 4
13.3%

충전지역명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도 수원시 장안구 파장동
7 
경기도 수원시 영통구 이의동
4 
부산광역시 기장군 정관읍 용수리
3 
서울특별시 금천구 가산동
2 
제주특별자치도 제주시 도두일동
2 
Other values (9)
12 

Length

Max length17
Median length16
Mean length14.3
Min length11

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row경기도 수원시 장안구 파장동
2nd row부산광역시 기장군 정관읍 용수리
3rd row경기도 수원시 장안구 파장동
4th row경기도 부천시 대장동
5th row경기도 수원시 장안구 파장동

Common Values

ValueCountFrequency (%)
경기도 수원시 장안구 파장동 7
23.3%
경기도 수원시 영통구 이의동 4
13.3%
부산광역시 기장군 정관읍 용수리 3
10.0%
서울특별시 금천구 가산동 2
 
6.7%
제주특별자치도 제주시 도두일동 2
 
6.7%
부산광역시 부산진구 가야동 2
 
6.7%
인천광역시 서구 석남동 2
 
6.7%
서울특별시 용산구 한강로3가 2
 
6.7%
경기도 부천시 대장동 1
 
3.3%
대구광역시 달서구 갈산동 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:19:41.942593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 13
 
12.5%
수원시 11
 
10.6%
장안구 7
 
6.7%
파장동 7
 
6.7%
서울특별시 5
 
4.8%
부산광역시 5
 
4.8%
영통구 4
 
3.8%
이의동 4
 
3.8%
서구 3
 
2.9%
인천광역시 3
 
2.9%
Other values (26) 42
40.4%

제조사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
HYD
28 
<NA>
 
2

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
HYD 28
93.3%
<NA> 2
 
6.7%

Length

2023-12-10T23:19:42.155388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:42.274605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
hyd 28
93.3%
na 2
 
6.7%

모델명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
일렉시티
28 
<NA>
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일렉시티 28
93.3%
<NA> 2
 
6.7%

Length

2023-12-10T23:19:42.388725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:42.488349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일렉시티 28
93.3%
na 2
 
6.7%

시작SOC
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:42.595805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile17.85
Q152.5
median73.5
Q389.75
95-th percentile97
Maximum99
Range88
Interquartile range (IQR)37.25

Descriptive statistics

Standard deviation26.864603
Coefficient of variation (CV)0.39218399
Kurtosis-0.40028164
Mean68.5
Median Absolute Deviation (MAD)17.5
Skewness-0.86647409
Sum2055
Variance721.7069
MonotonicityNot monotonic
2023-12-10T23:19:42.718808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
89 3
 
10.0%
72 2
 
6.7%
93 2
 
6.7%
44 2
 
6.7%
97 2
 
6.7%
25 2
 
6.7%
84 2
 
6.7%
62 1
 
3.3%
63 1
 
3.3%
71 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
11 1
3.3%
12 1
3.3%
25 2
6.7%
29 1
3.3%
44 2
6.7%
51 1
3.3%
57 1
3.3%
62 1
3.3%
63 1
3.3%
71 1
3.3%
ValueCountFrequency (%)
99 1
 
3.3%
97 2
6.7%
94 1
 
3.3%
93 2
6.7%
92 1
 
3.3%
90 1
 
3.3%
89 3
10.0%
84 2
6.7%
80 1
 
3.3%
74 1
 
3.3%

종료SOC
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.033333
Minimum90
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:42.876990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90.45
Q195
median97.5
Q3100
95-th percentile100
Maximum100
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2640501
Coefficient of variation (CV)0.033638441
Kurtosis-0.53158149
Mean97.033333
Median Absolute Deviation (MAD)2.5
Skewness-0.67558825
Sum2911
Variance10.654023
MonotonicityNot monotonic
2023-12-10T23:19:43.011985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
100 14
46.7%
95 11
36.7%
90 2
 
6.7%
99 1
 
3.3%
96 1
 
3.3%
91 1
 
3.3%
ValueCountFrequency (%)
90 2
 
6.7%
91 1
 
3.3%
95 11
36.7%
96 1
 
3.3%
99 1
 
3.3%
100 14
46.7%
ValueCountFrequency (%)
100 14
46.7%
99 1
 
3.3%
96 1
 
3.3%
95 11
36.7%
91 1
 
3.3%
90 2
 
6.7%

총전력사용요금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3107.4
Minimum29
Maximum8891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:43.151709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile143.35
Q1777.5
median2668
Q34718.75
95-th percentile8217.2
Maximum8891
Range8862
Interquartile range (IQR)3941.25

Descriptive statistics

Standard deviation2652.3969
Coefficient of variation (CV)0.85357432
Kurtosis-0.47725139
Mean3107.4
Median Absolute Deviation (MAD)2033.5
Skewness0.72048038
Sum93222
Variance7035209.1
MonotonicityNot monotonic
2023-12-10T23:19:43.254608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2825 1
 
3.3%
978 1
 
3.3%
4785 1
 
3.3%
4667 1
 
3.3%
1576 1
 
3.3%
3209 1
 
3.3%
8891 1
 
3.3%
8480 1
 
3.3%
7896 1
 
3.3%
5878 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
29 1
3.3%
43 1
3.3%
266 1
3.3%
359 1
3.3%
407 1
3.3%
500 1
3.3%
540 1
3.3%
737 1
3.3%
899 1
3.3%
978 1
3.3%
ValueCountFrequency (%)
8891 1
3.3%
8480 1
3.3%
7896 1
3.3%
6651 1
3.3%
6086 1
3.3%
5878 1
3.3%
4785 1
3.3%
4736 1
3.3%
4667 1
3.3%
4291 1
3.3%

전력사용요금(경부하)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3107.4
Minimum29
Maximum8891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:43.363470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile143.35
Q1777.5
median2668
Q34718.75
95-th percentile8217.2
Maximum8891
Range8862
Interquartile range (IQR)3941.25

Descriptive statistics

Standard deviation2652.3969
Coefficient of variation (CV)0.85357432
Kurtosis-0.47725139
Mean3107.4
Median Absolute Deviation (MAD)2033.5
Skewness0.72048038
Sum93222
Variance7035209.1
MonotonicityNot monotonic
2023-12-10T23:19:43.515715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2825 1
 
3.3%
978 1
 
3.3%
4785 1
 
3.3%
4667 1
 
3.3%
1576 1
 
3.3%
3209 1
 
3.3%
8891 1
 
3.3%
8480 1
 
3.3%
7896 1
 
3.3%
5878 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
29 1
3.3%
43 1
3.3%
266 1
3.3%
359 1
3.3%
407 1
3.3%
500 1
3.3%
540 1
3.3%
737 1
3.3%
899 1
3.3%
978 1
3.3%
ValueCountFrequency (%)
8891 1
3.3%
8480 1
3.3%
7896 1
3.3%
6651 1
3.3%
6086 1
3.3%
5878 1
3.3%
4785 1
3.3%
4736 1
3.3%
4667 1
3.3%
4291 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

2023-12-10T23:19:43.683551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:43.816542image/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

2023-12-10T23:19:43.979917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:44.139803image/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%
Mean7039.8667
Minimum19
Maximum33034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:44.283778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile163.6
Q11006.75
median3366
Q310554.5
95-th percentile22518.45
Maximum33034
Range33015
Interquartile range (IQR)9547.75

Descriptive statistics

Standard deviation8295.0567
Coefficient of variation (CV)1.1782974
Kurtosis2.3114339
Mean7039.8667
Median Absolute Deviation (MAD)2559
Skewness1.6228741
Sum211196
Variance68807965
MonotonicityNot monotonic
2023-12-10T23:19:44.446978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5047 1
 
3.3%
2823 1
 
3.3%
13127 1
 
3.3%
11749 1
 
3.3%
1728 1
 
3.3%
3409 1
 
3.3%
33034 1
 
3.3%
24309 1
 
3.3%
20308 1
 
3.3%
20330 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
19 1
3.3%
52 1
3.3%
300 1
3.3%
727 1
3.3%
818 1
3.3%
937 1
3.3%
1005 1
3.3%
1006 1
3.3%
1009 1
3.3%
1518 1
3.3%
ValueCountFrequency (%)
33034 1
3.3%
24309 1
3.3%
20330 1
3.3%
20308 1
3.3%
13738 1
3.3%
13127 1
3.3%
11749 1
3.3%
10558 1
3.3%
10544 1
3.3%
10144 1
3.3%

Interactions

2023-12-10T23:19:38.114673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.657465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.614074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.429763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.373779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:34.690186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.715455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.055483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:38.230890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.769815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.730704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.538462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.520768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:34.856550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.859470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.214907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:38.374543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.891906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.860855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.659088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.710222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:34.996785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:36.001022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.337750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:38.511622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.041521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.963343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.793901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:33.215223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.118462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:36.136775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.511064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:38.643667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.166614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.049176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.896677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:33.599561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.232475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:36.265754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.632555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:38.809183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.277740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.153235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.022330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:33.879981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.367625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:36.408957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.770294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:38.924778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.416322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.254498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.152484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:34.219694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.472192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:36.530011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.877638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:39.033811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.515702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.336896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.265705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:34.533404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.582302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:36.939803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:37.995848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:19:44.580983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전일시충전소ID차량번호시도코드시군구코드읍면동코드충전지역명시작SOC종료SOC총전력사용요금전력사용요금(경부하)총충전시간
충전일시1.0000.7420.0000.0000.4880.0000.7420.7510.2760.8150.8150.909
충전소ID0.7421.0001.0001.0001.0001.0001.0000.7850.8190.4580.4580.830
차량번호0.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.000
시도코드0.0001.0001.0001.0000.8410.5931.0000.7400.4370.0000.0000.000
시군구코드0.4881.0001.0000.8411.0000.8401.0000.7790.4990.0880.0880.726
읍면동코드0.0001.0001.0000.5930.8401.0001.0000.4680.6690.3920.3920.000
충전지역명0.7421.0001.0001.0001.0001.0001.0000.7850.8190.4580.4580.830
시작SOC0.7510.7851.0000.7400.7790.4680.7851.0000.4870.9230.9230.873
종료SOC0.2760.8190.0000.4370.4990.6690.8190.4871.0000.4020.4020.224
총전력사용요금0.8150.4580.0000.0000.0880.3920.4580.9230.4021.0001.0000.923
전력사용요금(경부하)0.8150.4580.0000.0000.0880.3920.4580.9230.4021.0001.0000.923
총충전시간0.9090.8301.0000.0000.7260.0000.8300.8730.2240.9230.9231.000
2023-12-10T23:19:44.751910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
모델명충전지역명충전소ID제조사
모델명1.0001.0001.0001.000
충전지역명1.0001.0001.0001.000
충전소ID1.0001.0001.0001.000
제조사1.0001.0001.0001.000
2023-12-10T23:19:45.214508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드시군구코드읍면동코드시작SOC종료SOC총전력사용요금전력사용요금(경부하)총충전시간충전소ID충전지역명제조사모델명
시도코드1.000-0.7160.067-0.087-0.194-0.102-0.102-0.0890.8000.8001.0001.000
시군구코드-0.7161.000-0.1890.1010.5130.1750.1750.1140.8340.8341.0001.000
읍면동코드0.067-0.1891.0000.0480.067-0.101-0.101-0.1160.7700.7701.0001.000
시작SOC-0.0870.1010.0481.0000.428-0.909-0.909-0.9160.3950.3951.0001.000
종료SOC-0.1940.5130.0670.4281.000-0.127-0.127-0.1830.6240.6241.0001.000
총전력사용요금-0.1020.175-0.101-0.909-0.1271.0001.0000.9710.1120.1121.0001.000
전력사용요금(경부하)-0.1020.175-0.101-0.909-0.1271.0001.0000.9710.1120.1121.0001.000
총충전시간-0.0890.114-0.116-0.916-0.1830.9710.9711.0000.3690.3691.0001.000
충전소ID0.8000.8340.7700.3950.6240.1120.1120.3691.0001.0001.0001.000
충전지역명0.8000.8340.7700.3950.6240.1120.1120.3691.0001.0001.0001.000
제조사1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
모델명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T23:19:39.279769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:19:39.617053image/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

충전일시충전소ID차량번호시도코드시군구코드읍면동코드충전지역명제조사모델명시작SOC종료SOC총전력사용요금전력사용요금(경부하)전력사용요금(중부하)전력사용요금(최대부하)총충전시간
02021-08-01 00:00:01KRPPKCS001023a1c6b1b6d9d4519c15c8837c93bcafc2deb8429f2aa5335e3271cc9c2f053841111129경기도 수원시 장안구 파장동HYD일렉시티629528252825005047
12021-08-01 00:00:01KRPPKCS0045116510ef47ea1efa7a356531dafe0a638706b0f3a114eab8b7f639e77e6f9b6f26710256부산광역시 기장군 정관읍 용수리HYD일렉시티9410073773700937
22021-08-01 00:00:01KRPPKCS0010d03eb2ba247c62bc8e5d1b61735720c017310aaeef4d01e7933c4e43fc81804341111129경기도 수원시 장안구 파장동HYD일렉시티909540740700727
32021-08-01 00:00:01KRPPKCS00088e94f7eb22ecdae8e72425ae58acab4410419ca0e73c305d460bc9a1bcbbd97041190117경기도 부천시 대장동HYD일렉시티899912831283001518
42021-08-01 00:00:01KRPPKCS0010864b58f6beebff5eebd5279c5d9dda7595dadf18cfc9da31040941568d6f72c941111129경기도 수원시 장안구 파장동HYD일렉시티729519381938003125
52021-08-01 00:00:01KRPPKCS00187529de2b3aaf2a5b64dec7e6c29c94a4c981bdbd19eb2fce2981cecba0141a6a11545101서울특별시 금천구 가산동HYD일렉시티939654054000818
62021-08-01 00:00:01KRPPKCS0057b3577381f77303bee0729805b6f8466815748acf7764cffdb455924157697c0750110127제주특별자치도 제주시 도두일동HYD일렉시티9910029290019
72021-08-01 00:00:01KRPPKCS00104f9649a2fb5a8b506cd41846db6ad77bf53a892b9a08f3a5e91a238886ad157341111129경기도 수원시 장안구 파장동HYD일렉시티4495429142910010544
82021-08-01 00:00:01KRPPKCS003657e6b47df464b35b9a04e9437c92701cd954172bf0427ebb5dd6feb50ec7524827290106대구광역시 달서구 갈산동HYD일렉시티8010025112511002803
92021-08-01 00:00:01KRPPKCS005763fa2851bca60012c82cbcb459ffcd970e0512c0397b738d24d352c66229f79250110127제주특별자치도 제주시 도두일동HYD일렉시티93100899899001005
충전일시충전소ID차량번호시도코드시군구코드읍면동코드충전지역명제조사모델명시작SOC종료SOC총전력사용요금전력사용요금(경부하)전력사용요금(중부하)전력사용요금(최대부하)총충전시간
202021-08-01 00:00:13KRPPKCS0011e56a8da2d8760d14f0049c4927304c2d5d5280e066c3a6decbdd5685d48f0dbc41210102경기도 광명시 철산동HYD일렉시티8410019731973003323
212021-08-01 00:00:34KRPPKCS0045db80cca3f15e218f3e37113da11312c3100d3216029b9042f080a8f49d01007826710256부산광역시 기장군 정관읍 용수리HYD일렉시티7410032073207004025
222021-08-01 00:01:21KRPPKCS000162ce11f85852dc47a0104835ece515de3553712d37a61a415eaedfc25a23961141117103경기도 수원시 영통구 이의동HYD일렉시티2591587858780020330
232021-08-01 00:01:43KRPPKCS000162ce11f85852dc47a0104835ece515de3553712d37a61a415eaedfc25a23961141117103경기도 수원시 영통구 이의동HYD일렉시티2590789678960020308
242021-08-01 00:01:46KRPPKCS00274232846ce7df4b22e3fc7f980b6deed44961e113b3d8f44c2c5f5a89708acf7e28260106인천광역시 서구 연희동HYD일렉시티57100848084800024309
252021-08-01 00:02:14KRPPKCS0014fab1554e801cab8c66a781d2ff99ef11b6f0702690956190eaaece986967366c11410118서울특별시 서대문구 홍은동HYD일렉시티29100889188910033034
262021-08-01 00:02:18KRPPKCS00045dd03b4a227fc9e6bd74e16b57db4667c61b26024b40d07173267bc97dbfd9a811170128서울특별시 용산구 한강로3가HYD일렉시티729532093209003409
272021-08-01 00:03:28KRPPKCS0004421007e267c4eb716ba569d2292b6197720db783231bbb77798dbb542e7ace5a11170128서울특별시 용산구 한강로3가HYD일렉시티849515761576001728
282021-08-01 00:04:58KRPPKCS0001c5bb2339247df5b2f650dcdaa32992db30f95157159fe5aebee93f59f176840941117103경기도 수원시 영통구 이의동HYD일렉시티1195466746670011749
292021-08-01 00:05:10KRPPKCS0018044f5cacb1175807a04f67a867c143ed4fb7dfcc64a6b4a70eb12000fda0d1ba11545101서울특별시 금천구 가산동HYD일렉시티7195478547850013127