Overview

Dataset statistics

Number of variables20
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory175.4 B

Variable types

Categorical7
Text1
DateTime3
Numeric9

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/1bcd9419-8f89-4d3f-b6dd-a43027c91a28

Alerts

시도명 has constant value ""Constant
비고 has constant value ""Constant
생산일시 has constant value ""Constant
시군구명 is highly overall correlated with 최저기온 and 2 other fieldsHigh correlation
충전소ID is highly overall correlated with 최저기온 and 2 other fieldsHigh correlation
소요시간 is highly overall correlated with 전력사용량High correlation
시작SOC is highly overall correlated with 종료SOC and 1 other fieldsHigh correlation
종료SOC is highly overall correlated with 시작SOC and 2 other fieldsHigh correlation
전력사용량 is highly overall correlated with 소요시간High correlation
전력사용량경부하 is highly overall correlated with 전력사용량중부하High correlation
전력사용량중부하 is highly overall correlated with 전력사용량경부하High correlation
최저기온 is highly overall correlated with 평균기온 and 3 other fieldsHigh correlation
최고기온 is highly overall correlated with 평균기온High correlation
평균기온 is highly overall correlated with 최저기온 and 1 other fieldsHigh correlation
차량번호 is highly overall correlated with 시작SOC and 4 other fieldsHigh correlation
전력사용량최대부하 is highly overall correlated with 종료SOCHigh correlation
전력사용량최대부하 is highly imbalanced (73.5%)Imbalance
강수량 is highly imbalanced (56.1%)Imbalance
시작일시 has unique valuesUnique
종료일시 has unique valuesUnique
소요시간 has unique valuesUnique
전력사용량경부하 has 17 (56.7%) zerosZeros
전력사용량중부하 has 14 (46.7%) zerosZeros

Reproduction

Analysis started2024-03-13 11:49:37.300681
Analysis finished2024-03-13 11:49:48.296697
Duration11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2024-03-13T20:49:48.361314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:48.458025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
안양시
17 
수원시
5 
남양주시
3 
시흥시
2 
광명시
 
1
Other values (2)
2 

Length

Max length4
Median length3
Mean length3.1
Min length3

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row안양시
2nd row남양주시
3rd row광명시
4th row안양시
5th row수원시

Common Values

ValueCountFrequency (%)
안양시 17
56.7%
수원시 5
 
16.7%
남양주시 3
 
10.0%
시흥시 2
 
6.7%
광명시 1
 
3.3%
부천시 1
 
3.3%
김포시 1
 
3.3%

Length

2024-03-13T20:49:48.600415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:48.753417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안양시 17
56.7%
수원시 5
 
16.7%
남양주시 3
 
10.0%
시흥시 2
 
6.7%
광명시 1
 
3.3%
부천시 1
 
3.3%
김포시 1
 
3.3%

충전소ID
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
KRPPKCS0012
17 
KRPPKCS0010
5 
KRPPKCS0004
2 
KRPPKCS0020
2 
KRPPKCS0011
 
1
Other values (3)
3 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st rowKRPPKCS0012
2nd rowKRPPKCS0004
3rd rowKRPPKCS0011
4th rowKRPPKCS0012
5th rowKRPPKCS0010

Common Values

ValueCountFrequency (%)
KRPPKCS0012 17
56.7%
KRPPKCS0010 5
 
16.7%
KRPPKCS0004 2
 
6.7%
KRPPKCS0020 2
 
6.7%
KRPPKCS0011 1
 
3.3%
KRPPKCS0090 1
 
3.3%
KRPPKCS0005 1
 
3.3%
KRPPKCS0072 1
 
3.3%

Length

2024-03-13T20:49:48.942964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:49.100238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
krppkcs0012 17
56.7%
krppkcs0010 5
 
16.7%
krppkcs0004 2
 
6.7%
krppkcs0020 2
 
6.7%
krppkcs0011 1
 
3.3%
krppkcs0090 1
 
3.3%
krppkcs0005 1
 
3.3%
krppkcs0072 1
 
3.3%

충전기ID
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:49:49.308323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters330
Distinct characters13
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

Unique18 ?
Unique (%)60.0%

Sample

1st rowKRPPKCP0092
2nd rowKRPPKCP0023
3rd rowKRPPKCP0085
4th rowKRPPKCP0094
5th rowKRPPKCP0401
ValueCountFrequency (%)
krppkcp0092 6
20.0%
krppkcp0093 4
 
13.3%
krppkcp0089 2
 
6.7%
krppkcp0584 1
 
3.3%
krppkcp0405 1
 
3.3%
krppkcp0512 1
 
3.3%
krppkcp0086 1
 
3.3%
krppkcp0155 1
 
3.3%
krppkcp0088 1
 
3.3%
krppkcp0154 1
 
3.3%
Other values (11) 11
36.7%
2024-03-13T20:49:49.722012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 90
27.3%
K 60
18.2%
0 55
16.7%
R 30
 
9.1%
C 30
 
9.1%
9 15
 
4.5%
2 10
 
3.0%
3 8
 
2.4%
4 8
 
2.4%
8 7
 
2.1%
Other values (3) 17
 
5.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55
45.8%
9 15
 
12.5%
2 10
 
8.3%
3 8
 
6.7%
4 8
 
6.7%
8 7
 
5.8%
5 7
 
5.8%
1 6
 
5.0%
6 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 55
45.8%
9 15
 
12.5%
2 10
 
8.3%
3 8
 
6.7%
4 8
 
6.7%
8 7
 
5.8%
5 7
 
5.8%
1 6
 
5.0%
6 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 55
16.7%
R 30
 
9.1%
C 30
 
9.1%
9 15
 
4.5%
2 10
 
3.0%
3 8
 
2.4%
4 8
 
2.4%
8 7
 
2.1%
Other values (3) 17
 
5.2%

시작일시
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-10-04 19:05:16
Maximum2023-10-31 21:56:29
2024-03-13T20:49:49.913239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:50.060795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

종료일시
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-10-04 19:22:34
Maximum2023-10-31 22:15:29
2024-03-13T20:49:50.176008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:50.311997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

소요시간
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1835.6667
Minimum125
Maximum3209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:50.495495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile464.05
Q11257.25
median1871.5
Q32410.5
95-th percentile3031
Maximum3209
Range3084
Interquartile range (IQR)1153.25

Descriptive statistics

Standard deviation805.06304
Coefficient of variation (CV)0.43856712
Kurtosis-0.48128343
Mean1835.6667
Median Absolute Deviation (MAD)621
Skewness-0.2755314
Sum55070
Variance648126.51
MonotonicityNot monotonic
2024-03-13T20:49:50.683896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2220 1
 
3.3%
2730 1
 
3.3%
2253 1
 
3.3%
125 1
 
3.3%
2802 1
 
3.3%
2558 1
 
3.3%
741 1
 
3.3%
1121 1
 
3.3%
1137 1
 
3.3%
1156 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
125 1
3.3%
343 1
3.3%
612 1
3.3%
741 1
3.3%
1121 1
3.3%
1137 1
3.3%
1156 1
3.3%
1200 1
3.3%
1429 1
3.3%
1501 1
3.3%
ValueCountFrequency (%)
3209 1
3.3%
3130 1
3.3%
2910 1
3.3%
2802 1
3.3%
2730 1
3.3%
2643 1
3.3%
2558 1
3.3%
2442 1
3.3%
2316 1
3.3%
2253 1
3.3%

차량번호
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e
8 
fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b
8 
d1ee96bae6fabb69e58ae7c3c44a12b8e4731fef0c928d6793829df3d2fbd8bb
2 
db706b121d03a7bf5ffbb1fcf90cc2ab2a6e5dee3b7817becbab3581aac390af
2 
1a532fbade1d82faf096a9d112603c16bd4e9f1abaae4ba4c734155b79d008c7
1 
Other values (9)
9 

Length

Max length64
Median length64
Mean length64
Min length64

Unique

Unique10 ?
Unique (%)33.3%

Sample

1st rowfd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e
2nd rowd1ee96bae6fabb69e58ae7c3c44a12b8e4731fef0c928d6793829df3d2fbd8bb
3rd row1a532fbade1d82faf096a9d112603c16bd4e9f1abaae4ba4c734155b79d008c7
4th rowfd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e
5th rowf6eb9b769306daac82c77e89cb259814bb534e1022842b71e51155a9ae0c77e0

Common Values

ValueCountFrequency (%)
fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e 8
26.7%
fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b 8
26.7%
d1ee96bae6fabb69e58ae7c3c44a12b8e4731fef0c928d6793829df3d2fbd8bb 2
 
6.7%
db706b121d03a7bf5ffbb1fcf90cc2ab2a6e5dee3b7817becbab3581aac390af 2
 
6.7%
1a532fbade1d82faf096a9d112603c16bd4e9f1abaae4ba4c734155b79d008c7 1
 
3.3%
f6eb9b769306daac82c77e89cb259814bb534e1022842b71e51155a9ae0c77e0 1
 
3.3%
10842b660d8fe6c87df9351663b9292d09ae7f45bbced214c891ab90ef449cb5 1
 
3.3%
f783bbebecf095c2180805bae5dcfb1867900b49fef35e3dbf934d849f9b5e1b 1
 
3.3%
5cd55b90b807b30cf9b23985168c3d28de3f2b21df2989e476ffdc71fb7c44ff 1
 
3.3%
8e1b0e4e978f41ac51c472719e3f2ad5cf0c4fac43489193d45c966805c17c7f 1
 
3.3%
Other values (4) 4
13.3%

Length

2024-03-13T20:49:50.826945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e 8
26.7%
fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b 8
26.7%
d1ee96bae6fabb69e58ae7c3c44a12b8e4731fef0c928d6793829df3d2fbd8bb 2
 
6.7%
db706b121d03a7bf5ffbb1fcf90cc2ab2a6e5dee3b7817becbab3581aac390af 2
 
6.7%
1a532fbade1d82faf096a9d112603c16bd4e9f1abaae4ba4c734155b79d008c7 1
 
3.3%
f6eb9b769306daac82c77e89cb259814bb534e1022842b71e51155a9ae0c77e0 1
 
3.3%
10842b660d8fe6c87df9351663b9292d09ae7f45bbced214c891ab90ef449cb5 1
 
3.3%
f783bbebecf095c2180805bae5dcfb1867900b49fef35e3dbf934d849f9b5e1b 1
 
3.3%
5cd55b90b807b30cf9b23985168c3d28de3f2b21df2989e476ffdc71fb7c44ff 1
 
3.3%
8e1b0e4e978f41ac51c472719e3f2ad5cf0c4fac43489193d45c966805c17c7f 1
 
3.3%
Other values (4) 4
13.3%

시작SOC
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.766667
Minimum18
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:50.963389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile33.6
Q176.5
median84.5
Q392
95-th percentile93.55
Maximum94
Range76
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation20.095776
Coefficient of variation (CV)0.25841118
Kurtosis3.2250805
Mean77.766667
Median Absolute Deviation (MAD)7.5
Skewness-1.9039353
Sum2333
Variance403.84023
MonotonicityNot monotonic
2024-03-13T20:49:51.104582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
85 5
16.7%
93 4
13.3%
92 3
 
10.0%
84 3
 
10.0%
94 2
 
6.7%
83 1
 
3.3%
91 1
 
3.3%
76 1
 
3.3%
50 1
 
3.3%
81 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
18 1
3.3%
21 1
3.3%
49 1
3.3%
50 1
3.3%
55 1
3.3%
68 1
3.3%
71 1
3.3%
76 1
3.3%
78 1
3.3%
79 1
3.3%
ValueCountFrequency (%)
94 2
 
6.7%
93 4
13.3%
92 3
10.0%
91 1
 
3.3%
85 5
16.7%
84 3
10.0%
83 1
 
3.3%
81 1
 
3.3%
79 1
 
3.3%
78 1
 
3.3%

종료SOC
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.4
Minimum24
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:51.250031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile57.35
Q193.25
median95.5
Q399
95-th percentile100
Maximum100
Range76
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation17.175765
Coefficient of variation (CV)0.1899974
Kurtosis8.0902297
Mean90.4
Median Absolute Deviation (MAD)3.5
Skewness-2.7866089
Sum2712
Variance295.0069
MonotonicityNot monotonic
2024-03-13T20:49:51.372188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
100 7
23.3%
95 6
20.0%
99 5
16.7%
96 2
 
6.7%
70 2
 
6.7%
93 2
 
6.7%
24 1
 
3.3%
47 1
 
3.3%
87 1
 
3.3%
98 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
24 1
 
3.3%
47 1
 
3.3%
70 2
 
6.7%
79 1
 
3.3%
87 1
 
3.3%
93 2
 
6.7%
94 1
 
3.3%
95 6
20.0%
96 2
 
6.7%
98 1
 
3.3%
ValueCountFrequency (%)
100 7
23.3%
99 5
16.7%
98 1
 
3.3%
96 2
 
6.7%
95 6
20.0%
94 1
 
3.3%
93 2
 
6.7%
87 1
 
3.3%
79 1
 
3.3%
70 2
 
6.7%

전력사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.066667
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:51.528125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.45
Q115.75
median24.5
Q330.5
95-th percentile46.7
Maximum55
Range54
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation12.870531
Coefficient of variation (CV)0.53478662
Kurtosis0.45828638
Mean24.066667
Median Absolute Deviation (MAD)7.5
Skewness0.43328306
Sum722
Variance165.65057
MonotonicityNot monotonic
2024-03-13T20:49:51.712901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
26 2
 
6.7%
18 2
 
6.7%
29 2
 
6.7%
9 2
 
6.7%
34 2
 
6.7%
14 1
 
3.3%
33 1
 
3.3%
15 1
 
3.3%
1 1
 
3.3%
38 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
1 1
3.3%
4 1
3.3%
5 1
3.3%
9 2
6.7%
12 1
3.3%
14 1
3.3%
15 1
3.3%
18 2
6.7%
20 1
3.3%
21 1
3.3%
ValueCountFrequency (%)
55 1
3.3%
53 1
3.3%
39 1
3.3%
38 1
3.3%
34 2
6.7%
33 1
3.3%
31 1
3.3%
29 2
6.7%
28 1
3.3%
27 1
3.3%

전력사용량경부하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.566667
Minimum0
Maximum55
Zeros17
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:51.864591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324.25
95-th percentile38.55
Maximum55
Range55
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation16.194259
Coefficient of variation (CV)1.40008
Kurtosis0.14462898
Mean11.566667
Median Absolute Deviation (MAD)0
Skewness1.1411543
Sum347
Variance262.25402
MonotonicityNot monotonic
2024-03-13T20:49:51.995196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
56.7%
39 1
 
3.3%
10 1
 
3.3%
5 1
 
3.3%
34 1
 
3.3%
25 1
 
3.3%
9 1
 
3.3%
28 1
 
3.3%
55 1
 
3.3%
33 1
 
3.3%
Other values (4) 4
 
13.3%
ValueCountFrequency (%)
0 17
56.7%
5 1
 
3.3%
9 1
 
3.3%
10 1
 
3.3%
18 1
 
3.3%
22 1
 
3.3%
25 1
 
3.3%
28 1
 
3.3%
31 1
 
3.3%
33 1
 
3.3%
ValueCountFrequency (%)
55 1
3.3%
39 1
3.3%
38 1
3.3%
34 1
3.3%
33 1
3.3%
31 1
3.3%
28 1
3.3%
25 1
3.3%
22 1
3.3%
18 1
3.3%

전력사용량중부하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.7
Minimum0
Maximum53
Zeros14
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:52.180260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q323.75
95-th percentile31.75
Maximum53
Range53
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation14.513133
Coefficient of variation (CV)1.2404387
Kurtosis0.31044115
Mean11.7
Median Absolute Deviation (MAD)1.5
Skewness0.98192841
Sum351
Variance210.63103
MonotonicityNot monotonic
2024-03-13T20:49:52.361782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 14
46.7%
26 2
 
6.7%
29 2
 
6.7%
14 1
 
3.3%
34 1
 
3.3%
23 1
 
3.3%
4 1
 
3.3%
2 1
 
3.3%
27 1
 
3.3%
21 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0 14
46.7%
1 1
 
3.3%
2 1
 
3.3%
4 1
 
3.3%
14 1
 
3.3%
18 1
 
3.3%
20 1
 
3.3%
21 1
 
3.3%
23 1
 
3.3%
24 1
 
3.3%
ValueCountFrequency (%)
53 1
3.3%
34 1
3.3%
29 2
6.7%
27 1
3.3%
26 2
6.7%
24 1
3.3%
23 1
3.3%
21 1
3.3%
20 1
3.3%
18 1
3.3%

전력사용량최대부하
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
28 
9
 
1
15
 
1

Length

Max length2
Median length1
Mean length1.0333333
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
93.3%
9 1
 
3.3%
15 1
 
3.3%

Length

2024-03-13T20:49:52.538809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:52.669829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
93.3%
9 1
 
3.3%
15 1
 
3.3%

최저기온
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2333333
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:52.781931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.9
Q17.25
median9
Q311
95-th percentile13
Maximum13
Range9
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation2.3146212
Coefficient of variation (CV)0.250681
Kurtosis-0.19487422
Mean9.2333333
Median Absolute Deviation (MAD)2
Skewness-0.0897918
Sum277
Variance5.3574713
MonotonicityNot monotonic
2024-03-13T20:49:52.904514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 10
33.3%
7 6
20.0%
11 4
 
13.3%
13 4
 
13.3%
10 2
 
6.7%
4 1
 
3.3%
8 1
 
3.3%
12 1
 
3.3%
5 1
 
3.3%
ValueCountFrequency (%)
4 1
 
3.3%
5 1
 
3.3%
7 6
20.0%
8 1
 
3.3%
9 10
33.3%
10 2
 
6.7%
11 4
 
13.3%
12 1
 
3.3%
13 4
 
13.3%
ValueCountFrequency (%)
13 4
 
13.3%
12 1
 
3.3%
11 4
 
13.3%
10 2
 
6.7%
9 10
33.3%
8 1
 
3.3%
7 6
20.0%
5 1
 
3.3%
4 1
 
3.3%

최고기온
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.033333
Minimum15
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:53.021802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15.9
Q119.25
median20
Q321
95-th percentile23
Maximum24
Range9
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation2.1573185
Coefficient of variation (CV)0.10768645
Kurtosis0.5884841
Mean20.033333
Median Absolute Deviation (MAD)1
Skewness-0.62003559
Sum601
Variance4.654023
MonotonicityNot monotonic
2024-03-13T20:49:53.166474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20 10
33.3%
21 6
20.0%
23 3
 
10.0%
22 2
 
6.7%
15 2
 
6.7%
19 2
 
6.7%
17 2
 
6.7%
18 2
 
6.7%
24 1
 
3.3%
ValueCountFrequency (%)
15 2
 
6.7%
17 2
 
6.7%
18 2
 
6.7%
19 2
 
6.7%
20 10
33.3%
21 6
20.0%
22 2
 
6.7%
23 3
 
10.0%
24 1
 
3.3%
ValueCountFrequency (%)
24 1
 
3.3%
23 3
 
10.0%
22 2
 
6.7%
21 6
20.0%
20 10
33.3%
19 2
 
6.7%
18 2
 
6.7%
17 2
 
6.7%
15 2
 
6.7%

평균기온
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.133333
Minimum11
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:49:53.267467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median14
Q315
95-th percentile17
Maximum17
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7759569
Coefficient of variation (CV)0.12565733
Kurtosis-0.61313662
Mean14.133333
Median Absolute Deviation (MAD)1
Skewness-0.057105032
Sum424
Variance3.154023
MonotonicityNot monotonic
2024-03-13T20:49:53.385890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15 7
23.3%
13 6
20.0%
14 6
20.0%
17 4
13.3%
11 3
10.0%
12 2
 
6.7%
16 2
 
6.7%
ValueCountFrequency (%)
11 3
10.0%
12 2
 
6.7%
13 6
20.0%
14 6
20.0%
15 7
23.3%
16 2
 
6.7%
17 4
13.3%
ValueCountFrequency (%)
17 4
13.3%
16 2
 
6.7%
15 7
23.3%
14 6
20.0%
13 6
20.0%
12 2
 
6.7%
11 3
10.0%

강수량
Categorical

IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
25 
1
3 
13
 
1
24
 
1

Length

Max length2
Median length1
Mean length1.0666667
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25
83.3%
1 3
 
10.0%
13 1
 
3.3%
24 1
 
3.3%

Length

2024-03-13T20:49:53.536046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:53.688170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25
83.3%
1 3
 
10.0%
13 1
 
3.3%
24 1
 
3.3%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-15 기온/강수량과 충전전력사용량 데이터
30 

Length

Max length30
Median length30
Mean length30
Min length30

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-15 기온/강수량과 충전전력사용량 데이터
2nd row2024-01-15 기온/강수량과 충전전력사용량 데이터
3rd row2024-01-15 기온/강수량과 충전전력사용량 데이터
4th row2024-01-15 기온/강수량과 충전전력사용량 데이터
5th row2024-01-15 기온/강수량과 충전전력사용량 데이터

Common Values

ValueCountFrequency (%)
2024-01-15 기온/강수량과 충전전력사용량 데이터 30
100.0%

Length

2024-03-13T20:49:53.851280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:49:53.989253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-15 30
25.0%
기온/강수량과 30
25.0%
충전전력사용량 30
25.0%
데이터 30
25.0%

생산일시
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2024-01-15 14:18:41
Maximum2024-01-15 14:18:41
2024-03-13T20:49:54.083421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:54.192148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-13T20:49:46.343531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.184071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.115872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.958771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.293798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.123147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.227347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.121018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:45.054167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.451332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.323390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.206128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.049225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.391268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.221829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.330945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.241502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:45.232838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.572237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.508074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.307124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.156215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.475276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.325354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.430964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.344209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:45.404635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.700228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.595982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.409168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.265496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.575372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.434155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.550200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.462826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:45.529146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.793534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.698357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.501764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.368312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.678563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.515465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.652514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.561865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:45.667142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.878873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.786223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.595031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.464611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.778070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.619763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.745255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.658223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:45.892840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.986085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.882976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.689654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.653856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.869976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.726068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.845982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.761875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.014907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:47.072730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:38.965403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.780949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:40.793066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.953415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.936132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.931587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.849983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.127526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:47.175977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.037065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:39.864028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:41.188263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:42.030693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:43.121414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.023935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:44.945991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:49:46.235235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:49:54.336728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명충전소ID충전기ID시작일시종료일시소요시간차량번호시작SOC종료SOC전력사용량전력사용량경부하전력사용량중부하전력사용량최대부하최저기온최고기온평균기온강수량
시군구명1.0001.0001.0001.0001.0000.3751.0000.6660.5610.4550.0000.8300.0000.7860.5670.7200.260
충전소ID1.0001.0001.0001.0001.0000.3911.0000.6830.5240.4060.0000.6750.0000.7750.4900.5490.526
충전기ID1.0001.0001.0001.0001.0000.7560.9970.9741.0000.8000.6500.8420.0000.9590.8810.9570.596
시작일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
종료일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소요시간0.3750.3910.7561.0001.0001.0000.6810.4720.7100.8870.3720.0000.5470.0000.5760.0000.000
차량번호1.0001.0000.9971.0001.0000.6811.0000.9331.0000.7310.0000.4440.6330.8740.7450.8500.000
시작SOC0.6660.6830.9741.0001.0000.4720.9331.0000.9340.7690.4670.0000.6740.5050.4790.0000.457
종료SOC0.5610.5241.0001.0001.0000.7101.0000.9341.0000.7520.4810.0000.9080.0000.5520.4000.000
전력사용량0.4550.4060.8001.0001.0000.8870.7310.7690.7521.0000.7720.6750.7130.0000.0000.0000.140
전력사용량경부하0.0000.0000.6501.0001.0000.3720.0000.4670.4810.7721.0000.0000.0000.0000.0390.1580.651
전력사용량중부하0.8300.6750.8421.0001.0000.0000.4440.0000.0000.6750.0001.0000.0000.0000.0000.0000.000
전력사용량최대부하0.0000.0000.0001.0001.0000.5470.6330.6740.9080.7130.0000.0001.0000.2370.0000.0000.000
최저기온0.7860.7750.9591.0001.0000.0000.8740.5050.0000.0000.0000.0000.2371.0000.5580.7870.536
최고기온0.5670.4900.8811.0001.0000.5760.7450.4790.5520.0000.0390.0000.0000.5581.0000.6550.605
평균기온0.7200.5490.9571.0001.0000.0000.8500.0000.4000.0000.1580.0000.0000.7870.6551.0000.075
강수량0.2600.5260.5961.0001.0000.0000.0000.4570.0000.1400.6510.0000.0000.5360.6050.0751.000
2024-03-13T20:49:54.600491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명충전소ID강수량전력사용량최대부하차량번호
시군구명1.0000.9780.1470.0000.834
충전소ID0.9781.0000.2180.0000.853
강수량0.1470.2181.0000.0000.000
전력사용량최대부하0.0000.0000.0001.0000.325
차량번호0.8340.8530.0000.3251.000
2024-03-13T20:49:54.782686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소요시간시작SOC종료SOC전력사용량전력사용량경부하전력사용량중부하최저기온최고기온평균기온시군구명충전소ID차량번호전력사용량최대부하강수량
소요시간1.000-0.2900.1140.6660.3270.015-0.2560.216-0.1630.1690.1670.2940.2340.000
시작SOC-0.2901.0000.730-0.481-0.3650.149-0.172-0.270-0.1330.4580.4390.6500.3320.289
종료SOC0.1140.7301.000-0.194-0.2230.156-0.035-0.236-0.0470.3600.2960.8160.5970.000
전력사용량0.666-0.481-0.1941.0000.4230.133-0.2450.413-0.1580.2240.1710.3330.3590.155
전력사용량경부하0.327-0.365-0.2230.4231.000-0.7610.0240.2340.0930.0000.0000.1580.0000.139
전력사용량중부하0.0150.1490.1560.133-0.7611.000-0.165-0.023-0.1480.4220.4260.0650.0000.000
최저기온-0.256-0.172-0.035-0.2450.024-0.1651.0000.3900.8630.5420.5050.5320.0000.322
최고기온0.216-0.270-0.2360.4130.234-0.0230.3901.0000.6140.3120.2370.3560.0000.381
평균기온-0.163-0.133-0.047-0.1580.093-0.1480.8630.6141.0000.3120.3120.3920.0000.000
시군구명0.1690.4580.3600.2240.0000.4220.5420.3120.3121.0000.9780.8340.0000.147
충전소ID0.1670.4390.2960.1710.0000.4260.5050.2370.3120.9781.0000.8530.0000.218
차량번호0.2940.6500.8160.3330.1580.0650.5320.3560.3920.8340.8531.0000.3250.000
전력사용량최대부하0.2340.3320.5970.3590.0000.0000.0000.0000.0000.0000.0000.3251.0000.000
강수량0.0000.2890.0000.1550.1390.0000.3220.3810.0000.1470.2180.0000.0001.000

Missing values

2024-03-13T20:49:47.373132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:49:47.836453image/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

시도명시군구명충전소ID충전기ID시작일시종료일시소요시간차량번호시작SOC종료SOC전력사용량전력사용량경부하전력사용량중부하전력사용량최대부하최저기온최고기온평균기온강수량비고생산일시
0경기도안양시KRPPKCS0012KRPPKCP00922023-10-31 19:35:452023-10-31 19:58:052220fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e931001401409201502024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
1경기도남양주시KRPPKCS0004KRPPKCP00232023-10-10 18:36:372023-10-10 18:57:282051d1ee96bae6fabb69e58ae7c3c44a12b8e4731fef0c928d6793829df3d2fbd8bb849534034011221502024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
2경기도광명시KRPPKCS0011KRPPKCP00852023-10-06 18:47:442023-10-06 19:08:0820241a532fbade1d82faf096a9d112603c16bd4e9f1abaae4ba4c734155b79d008c785952602604201202024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
3경기도안양시KRPPKCS0012KRPPKCP00942023-10-11 09:06:112023-10-11 09:18:111200fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e9310023023011231602024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
4경기도수원시KRPPKCS0010KRPPKCP04012023-10-29 08:21:502023-10-29 08:46:322442f6eb9b769306daac82c77e89cb259814bb534e1022842b71e51155a9ae0c77e071953939007211302024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
5경기도안양시KRPPKCS0012KRPPKCP00932023-10-31 09:12:592023-10-31 09:19:11612fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e929640409201502024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
6경기도안양시KRPPKCS0012KRPPKCP00922023-10-31 21:56:292023-10-31 22:15:291900fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e941001210209201502024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
7경기도남양주시KRPPKCS0004KRPPKCP00212023-10-04 19:05:162023-10-04 19:22:341718d1ee96bae6fabb69e58ae7c3c44a12b8e4731fef0c928d6793829df3d2fbd8bb7895290290112115132024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
8경기도부천시KRPPKCS0090KRPPKCP06612023-10-20 19:43:172023-10-20 20:02:00184310842b660d8fe6c87df9351663b9292d09ae7f45bbced214c891ab90ef449cb5851002602607151102024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
9경기도남양주시KRPPKCS0005KRPPKCP00322023-10-11 19:45:232023-10-11 20:00:241501f783bbebecf095c2180805bae5dcfb1867900b49fef35e3dbf934d849f9b5e1b85952702709221402024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
시도명시군구명충전소ID충전기ID시작일시종료일시소요시간차량번호시작SOC종료SOC전력사용량전력사용량경부하전력사용량중부하전력사용량최대부하최저기온최고기온평균기온강수량비고생산일시
20경기도안양시KRPPKCS0012KRPPKCP00922023-10-25 06:15:242023-10-25 06:44:342910fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b81983131009211402024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
21경기도시흥시KRPPKCS0020KRPPKCP01552023-10-28 18:04:482023-10-28 18:36:183130db706b121d03a7bf5ffbb1fcf90cc2ab2a6e5dee3b7817becbab3581aac390af50705305307201302024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
22경기도안양시KRPPKCS0012KRPPKCP00932023-10-31 09:12:402023-10-31 09:24:361156fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e92992002009201502024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
23경기도안양시KRPPKCS0012KRPPKCP00862023-10-24 04:46:252023-10-24 04:58:021137fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b931002222009181302024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
24경기도안양시KRPPKCS0012KRPPKCP00892023-10-09 16:55:552023-10-09 17:07:161121fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e849318180013231712024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
25경기도안양시KRPPKCS0012KRPPKCP05122023-10-23 15:22:272023-10-23 15:30:087415b3787530f5a91d0475ce2d85e9da1752a5fc2dde11b9950697231cc830553ca767990099201402024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
26경기도안양시KRPPKCS0012KRPPKCP00892023-10-09 16:56:142023-10-09 17:22:122558fd1a467798a052d5b934bb027c4838e4d828b5a2142c852171e6c572bc43ee7e849938380013231712024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
27경기도안양시KRPPKCS0012KRPPKCP00932023-10-25 08:23:012023-10-25 08:51:032802fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b91991801809211402024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
28경기도안양시KRPPKCS0012KRPPKCP00902023-10-20 10:58:362023-10-20 11:00:01125fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b949410107151102024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41
29경기도안양시KRPPKCS0012KRPPKCP00922023-10-27 14:51:082023-10-27 15:14:012253fe8b29064f99c3a93367ff05d7f14470e9bd41d078d9965462acbdada5c87a6b9310015001510201402024-01-15 기온/강수량과 충전전력사용량 데이터2024-01-15 14:18:41