Overview

Dataset statistics

Number of variables18
Number of observations151
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 KiB
Average record size in memory161.9 B

Variable types

Categorical5
Numeric13

Dataset

DescriptionSample
Author한국인터넷진흥원
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KIS00000000000000020

Alerts

운전자 has constant value ""Constant
생성년도 has constant value ""Constant
차량흡입압력값 is highly overall correlated with 차량연료소비값 and 3 other fieldsHigh correlation
차량왼쪽앞바퀴속도값 is highly overall correlated with 차량오른쪽앞바퀴속도값 and 5 other fieldsHigh correlation
차량오른쪽앞바퀴속도값 is highly overall correlated with 차량왼쪽앞바퀴속도값 and 5 other fieldsHigh correlation
차량왼쪽뒷바큇속도값 is highly overall correlated with 차량왼쪽앞바퀴속도값 and 5 other fieldsHigh correlation
차량토크변환값 is highly overall correlated with 차량왼쪽앞바퀴속도값 and 5 other fieldsHigh correlation
주행시간 is highly overall correlated with 차량엔진냉각재온도값 and 2 other fieldsHigh correlation
차량가속값 is highly overall correlated with 차량왼쪽앞바퀴속도값 and 7 other fieldsHigh correlation
차량연료소비값 is highly overall correlated with 차량흡입압력값 and 9 other fieldsHigh correlation
차량마찰토크값 is highly overall correlated with 차량변속기온도값 and 1 other fieldsHigh correlation
차량최대엔진토크값 is highly overall correlated with 차량왼쪽앞바퀴속도값 and 5 other fieldsHigh correlation
차량엔진토크값 is highly overall correlated with 차량흡입압력값 and 4 other fieldsHigh correlation
차량가용토크값 is highly overall correlated with 차량흡입압력값 and 3 other fieldsHigh correlation
차량엔진냉각재온도값 is highly overall correlated with 주행시간 and 2 other fieldsHigh correlation
차량장기연료보정값 is highly overall correlated with 차량흡입압력값 and 5 other fieldsHigh correlation
차량변속기온도값 is highly overall correlated with 주행시간 and 2 other fieldsHigh correlation
차량공기압축여부 is highly overall correlated with 차량마찰토크값 and 1 other fieldsHigh correlation
차량공기압축여부 is highly imbalanced (82.4%)Imbalance
주행시간 has unique valuesUnique
차량왼쪽앞바퀴속도값 has 13 (8.6%) zerosZeros
차량오른쪽앞바퀴속도값 has 23 (15.2%) zerosZeros
차량왼쪽뒷바큇속도값 has 15 (9.9%) zerosZeros
차량가속값 has 96 (63.6%) zerosZeros
차량연료소비값 has 5 (3.3%) zerosZeros
차량엔진토크값 has 5 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-10 06:40:46.163713
Analysis finished2023-12-10 06:41:13.083672
Duration26.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차량장기연료보정값
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0.0
118 
0.8
18 
-0.8
15 

Length

Max length4
Median length3
Mean length3.0993377
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row-0.8
3rd row-0.8
4th row-0.8
5th row-0.8

Common Values

ValueCountFrequency (%)
0.0 118
78.1%
0.8 18
 
11.9%
-0.8 15
 
9.9%

Length

2023-12-10T15:41:13.209414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:13.383115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 118
78.1%
0.8 33
 
21.9%

차량흡입압력값
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.774834
Minimum24
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:13.583932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile32.5
Q152
median62
Q368
95-th percentile86
Maximum99
Range75
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.752705
Coefficient of variation (CV)0.24274364
Kurtosis0.75904122
Mean60.774834
Median Absolute Deviation (MAD)7
Skewness0.1208103
Sum9177
Variance217.6423
MonotonicityNot monotonic
2023-12-10T15:41:13.853015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 12
 
7.9%
67 12
 
7.9%
66 7
 
4.6%
62 6
 
4.0%
65 6
 
4.0%
59 6
 
4.0%
58 5
 
3.3%
64 5
 
3.3%
54 5
 
3.3%
52 5
 
3.3%
Other values (44) 82
54.3%
ValueCountFrequency (%)
24 1
 
0.7%
27 1
 
0.7%
28 1
 
0.7%
29 1
 
0.7%
31 3
2.0%
32 1
 
0.7%
33 1
 
0.7%
37 2
1.3%
39 1
 
0.7%
40 1
 
0.7%
ValueCountFrequency (%)
99 4
2.6%
98 1
 
0.7%
97 1
 
0.7%
88 1
 
0.7%
87 1
 
0.7%
85 1
 
0.7%
84 2
1.3%
80 2
1.3%
78 1
 
0.7%
77 2
1.3%

차량변속기온도값
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
94
124 
95
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row95
2nd row95
3rd row95
4th row95
5th row95

Common Values

ValueCountFrequency (%)
94 124
82.1%
95 27
 
17.9%

Length

2023-12-10T15:41:14.078510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:14.230473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
94 124
82.1%
95 27
 
17.9%

차량왼쪽앞바퀴속도값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.531126
Minimum0
Maximum42.9
Zeros13
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:14.420561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.8
median7.9
Q317.2
95-th percentile39.2
Maximum42.9
Range42.9
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation12.784085
Coefficient of variation (CV)1.0201865
Kurtosis-0.17129614
Mean12.531126
Median Absolute Deviation (MAD)6
Skewness1.0649173
Sum1892.2
Variance163.43282
MonotonicityNot monotonic
2023-12-10T15:41:14.639584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
8.6%
10.0 4
 
2.6%
7.1 3
 
2.0%
10.4 3
 
2.0%
0.1 3
 
2.0%
9.4 3
 
2.0%
5.4 3
 
2.0%
3.0 3
 
2.0%
0.6 3
 
2.0%
0.5 2
 
1.3%
Other values (94) 111
73.5%
ValueCountFrequency (%)
0.0 13
8.6%
0.1 3
 
2.0%
0.3 1
 
0.7%
0.4 1
 
0.7%
0.5 2
 
1.3%
0.6 3
 
2.0%
1.0 1
 
0.7%
1.3 2
 
1.3%
1.4 1
 
0.7%
1.5 1
 
0.7%
ValueCountFrequency (%)
42.9 1
0.7%
42.3 1
0.7%
41.8 1
0.7%
41.6 1
0.7%
40.4 1
0.7%
40.3 1
0.7%
40.1 1
0.7%
39.8 1
0.7%
38.6 1
0.7%
38.3 1
0.7%

차량오른쪽앞바퀴속도값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.664901
Minimum0
Maximum42
Zeros23
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:14.870416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7.1
Q316.3
95-th percentile38.4
Maximum42
Range42
Interquartile range (IQR)14.3

Descriptive statistics

Standard deviation12.671533
Coefficient of variation (CV)1.0862959
Kurtosis-0.081452846
Mean11.664901
Median Absolute Deviation (MAD)5.9
Skewness1.1211613
Sum1761.4
Variance160.56776
MonotonicityNot monotonic
2023-12-10T15:41:15.030150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
15.2%
4.0 8
 
5.3%
2.0 7
 
4.6%
8.0 5
 
3.3%
4.1 5
 
3.3%
8.1 5
 
3.3%
0.1 4
 
2.6%
2.1 4
 
2.6%
6.1 3
 
2.0%
6.0 3
 
2.0%
Other values (77) 84
55.6%
ValueCountFrequency (%)
0.0 23
15.2%
0.1 4
 
2.6%
0.5 1
 
0.7%
0.6 1
 
0.7%
1.2 2
 
1.3%
1.7 1
 
0.7%
2.0 7
 
4.6%
2.1 4
 
2.6%
2.2 1
 
0.7%
2.8 1
 
0.7%
ValueCountFrequency (%)
42.0 2
1.3%
40.9 1
0.7%
40.7 1
0.7%
40.0 1
0.7%
39.9 1
0.7%
39.1 1
0.7%
38.8 1
0.7%
38.0 1
0.7%
37.5 1
0.7%
36.7 1
0.7%

차량왼쪽뒷바큇속도값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.39404
Minimum0
Maximum42.5
Zeros15
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:15.252178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.7
median7.8
Q317.15
95-th percentile39
Maximum42.5
Range42.5
Interquartile range (IQR)14.45

Descriptive statistics

Standard deviation12.723245
Coefficient of variation (CV)1.0265616
Kurtosis-0.16645468
Mean12.39404
Median Absolute Deviation (MAD)5.9
Skewness1.071826
Sum1871.5
Variance161.88096
MonotonicityNot monotonic
2023-12-10T15:41:15.461776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
9.9%
9.3 4
 
2.6%
6.8 3
 
2.0%
10.0 3
 
2.0%
0.5 3
 
2.0%
5.4 3
 
2.0%
2.5 3
 
2.0%
1.9 3
 
2.0%
1.3 2
 
1.3%
2.8 2
 
1.3%
Other values (92) 110
72.8%
ValueCountFrequency (%)
0.0 15
9.9%
0.1 1
 
0.7%
0.3 1
 
0.7%
0.4 1
 
0.7%
0.5 3
 
2.0%
0.6 2
 
1.3%
1.0 1
 
0.7%
1.3 2
 
1.3%
1.4 2
 
1.3%
1.6 1
 
0.7%
ValueCountFrequency (%)
42.5 1
0.7%
42.0 1
0.7%
41.6 1
0.7%
41.3 1
0.7%
40.1 1
0.7%
40.0 1
0.7%
39.9 1
0.7%
39.6 1
0.7%
38.4 1
0.7%
38.1 1
0.7%

차량토크변환값
Real number (ℝ)

HIGH CORRELATION 

Distinct149
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1023.2517
Minimum0
Maximum3201.3
Zeros1
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:15.643718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126.4
Q1629.4
median900.8
Q31514
95-th percentile1947.5
Maximum3201.3
Range3201.3
Interquartile range (IQR)884.6

Descriptive statistics

Standard deviation608.16416
Coefficient of variation (CV)0.59434466
Kurtosis0.26571729
Mean1023.2517
Median Absolute Deviation (MAD)452
Skewness0.58203859
Sum154511
Variance369863.65
MonotonicityNot monotonic
2023-12-10T15:41:15.856925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1512.5 2
 
1.3%
721.0 2
 
1.3%
1121.8 1
 
0.7%
0.0 1
 
0.7%
455.5 1
 
0.7%
434.3 1
 
0.7%
379.8 1
 
0.7%
287.3 1
 
0.7%
180.3 1
 
0.7%
69.5 1
 
0.7%
Other values (139) 139
92.1%
ValueCountFrequency (%)
0.0 1
0.7%
9.8 1
0.7%
54.8 1
0.7%
67.3 1
0.7%
69.5 1
0.7%
81.5 1
0.7%
109.0 1
0.7%
117.3 1
0.7%
135.5 1
0.7%
161.8 1
0.7%
ValueCountFrequency (%)
3201.3 1
0.7%
2804.5 1
0.7%
2467.8 1
0.7%
2338.0 1
0.7%
2036.0 1
0.7%
2019.8 1
0.7%
1974.8 1
0.7%
1956.0 1
0.7%
1939.0 1
0.7%
1935.3 1
0.7%

주행시간
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76
Minimum1
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:16.071259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.5
Q138.5
median76
Q3113.5
95-th percentile143.5
Maximum151
Range150
Interquartile range (IQR)75

Descriptive statistics

Standard deviation43.734045
Coefficient of variation (CV)0.57544796
Kurtosis-1.2
Mean76
Median Absolute Deviation (MAD)38
Skewness0
Sum11476
Variance1912.6667
MonotonicityStrictly increasing
2023-12-10T15:41:16.275373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
105 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
106 1
 
0.7%
Other values (141) 141
93.4%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
151 1
0.7%
150 1
0.7%
149 1
0.7%
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%
143 1
0.7%
142 1
0.7%

운전자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
A
151 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 151
100.0%

Length

2023-12-10T15:41:16.470080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:16.593112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 151
100.0%

생성년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2018
151 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 151
100.0%

Length

2023-12-10T15:41:16.736602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:16.888494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 151
100.0%

차량가속값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4092715
Minimum0
Maximum48.4
Zeros96
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:17.095140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.1
95-th percentile18.7
Maximum48.4
Range48.4
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation8.9521048
Coefficient of variation (CV)2.0302911
Kurtosis10.275153
Mean4.4092715
Median Absolute Deviation (MAD)0
Skewness3.0008305
Sum665.8
Variance80.14018
MonotonicityNot monotonic
2023-12-10T15:41:17.322895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 96
63.6%
4.3 5
 
3.3%
11.7 3
 
2.0%
5.1 3
 
2.0%
16.4 2
 
1.3%
13.3 2
 
1.3%
2.7 2
 
1.3%
7.0 2
 
1.3%
8.2 2
 
1.3%
6.6 2
 
1.3%
Other values (28) 32
 
21.2%
ValueCountFrequency (%)
0.0 96
63.6%
0.4 1
 
0.7%
0.8 2
 
1.3%
1.2 1
 
0.7%
1.6 1
 
0.7%
2.7 2
 
1.3%
3.5 1
 
0.7%
3.9 1
 
0.7%
4.3 5
 
3.3%
4.7 1
 
0.7%
ValueCountFrequency (%)
48.4 1
0.7%
47.3 1
0.7%
43.0 1
0.7%
41.8 1
0.7%
32.0 1
0.7%
21.1 2
1.3%
18.7 2
1.3%
18.0 1
0.7%
17.6 1
0.7%
16.4 2
1.3%

차량연료소비값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.28344
Minimum0
Maximum4044.8
Zeros5
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:17.541385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile243.2
Q1384
median448
Q3742.4
95-th percentile1798.4
Maximum4044.8
Range4044.8
Interquartile range (IQR)358.4

Descriptive statistics

Standard deviation626.63003
Coefficient of variation (CV)0.91307759
Kurtosis10.190361
Mean686.28344
Median Absolute Deviation (MAD)89.6
Skewness2.8791227
Sum103628.8
Variance392665.19
MonotonicityNot monotonic
2023-12-10T15:41:17.832723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
384.0 11
 
7.3%
435.2 8
 
5.3%
358.4 7
 
4.6%
448.0 7
 
4.6%
396.8 7
 
4.6%
409.6 6
 
4.0%
422.4 6
 
4.0%
524.8 6
 
4.0%
371.2 6
 
4.0%
0.0 5
 
3.3%
Other values (51) 82
54.3%
ValueCountFrequency (%)
0.0 5
3.3%
38.4 1
 
0.7%
243.2 5
3.3%
268.8 3
 
2.0%
294.4 1
 
0.7%
320.0 2
 
1.3%
345.6 2
 
1.3%
358.4 7
4.6%
371.2 6
4.0%
384.0 11
7.3%
ValueCountFrequency (%)
4044.8 1
0.7%
3750.4 1
0.7%
3174.4 1
0.7%
2534.4 1
0.7%
2329.6 1
0.7%
2252.8 1
0.7%
2214.4 1
0.7%
1817.6 1
0.7%
1779.2 1
0.7%
1728.0 1
0.7%

차량마찰토크값
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.806623
Minimum7.4
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:18.065317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile16.8
Q118.8
median19.1
Q319.9
95-th percentile20.7
Maximum21.5
Range14.1
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation2.0943629
Coefficient of variation (CV)0.11136305
Kurtosis16.458168
Mean18.806623
Median Absolute Deviation (MAD)0.7
Skewness-3.7122834
Sum2839.8
Variance4.3863558
MonotonicityNot monotonic
2023-12-10T15:41:18.267853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
18.8 31
20.5%
19.9 29
19.2%
19.1 25
16.6%
18.0 16
10.6%
19.5 13
8.6%
18.4 8
 
5.3%
20.7 5
 
3.3%
20.3 4
 
2.6%
21.1 4
 
2.6%
16.4 3
 
2.0%
Other values (8) 13
8.6%
ValueCountFrequency (%)
7.4 1
 
0.7%
8.2 1
 
0.7%
8.6 1
 
0.7%
9.0 1
 
0.7%
10.9 1
 
0.7%
16.4 3
 
2.0%
17.2 3
 
2.0%
17.6 2
 
1.3%
18.0 16
10.6%
18.4 8
5.3%
ValueCountFrequency (%)
21.5 3
 
2.0%
21.1 4
 
2.6%
20.7 5
 
3.3%
20.3 4
 
2.6%
19.9 29
19.2%
19.5 13
8.6%
19.1 25
16.6%
18.8 31
20.5%
18.4 8
 
5.3%
18.0 16
10.6%

차량최대엔진토크값
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.984768
Minimum53.1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:18.512576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.1
5-th percentile53.3
Q154.7
median58.6
Q369.7
95-th percentile76.8
Maximum82
Range28.9
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.51484
Coefficient of variation (CV)0.13736988
Kurtosis-1.0754507
Mean61.984768
Median Absolute Deviation (MAD)4.7
Skewness0.65919296
Sum9359.7
Variance72.5025
MonotonicityNot monotonic
2023-12-10T15:41:19.077847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.7 15
 
9.9%
53.9 15
 
9.9%
55.1 11
 
7.3%
53.1 8
 
5.3%
76.6 6
 
4.0%
62.9 4
 
2.6%
55.5 4
 
2.6%
58.6 4
 
2.6%
73.4 4
 
2.6%
77.0 4
 
2.6%
Other values (43) 76
50.3%
ValueCountFrequency (%)
53.1 8
5.3%
53.5 3
 
2.0%
53.9 15
9.9%
54.3 4
 
2.6%
54.7 15
9.9%
55.1 11
7.3%
55.5 4
 
2.6%
55.9 4
 
2.6%
56.3 2
 
1.3%
56.6 3
 
2.0%
ValueCountFrequency (%)
82.0 1
 
0.7%
79.7 1
 
0.7%
78.1 1
 
0.7%
77.3 1
 
0.7%
77.0 4
2.6%
76.6 6
4.0%
76.2 1
 
0.7%
75.8 1
 
0.7%
75.4 1
 
0.7%
75.0 1
 
0.7%

차량엔진토크값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.425828
Minimum0
Maximum61.7
Zeros5
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:19.298884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.05
Q120.5
median24.6
Q327.5
95-th percentile45.1
Maximum61.7
Range61.7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.17001
Coefficient of variation (CV)0.43931747
Kurtosis1.7041637
Mean25.425828
Median Absolute Deviation (MAD)3.9
Skewness0.53106743
Sum3839.3
Variance124.76913
MonotonicityNot monotonic
2023-12-10T15:41:19.532876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.6 19
 
12.6%
25.4 7
 
4.6%
25.0 7
 
4.6%
25.8 7
 
4.6%
24.2 5
 
3.3%
0.0 5
 
3.3%
19.9 5
 
3.3%
27.0 4
 
2.6%
20.7 4
 
2.6%
23.4 4
 
2.6%
Other values (59) 84
55.6%
ValueCountFrequency (%)
0.0 5
3.3%
0.4 1
 
0.7%
3.9 1
 
0.7%
5.1 1
 
0.7%
7.0 1
 
0.7%
7.8 1
 
0.7%
8.2 1
 
0.7%
8.6 1
 
0.7%
10.2 1
 
0.7%
13.7 1
 
0.7%
ValueCountFrequency (%)
61.7 1
0.7%
60.2 1
0.7%
56.3 1
0.7%
54.3 1
0.7%
52.3 2
1.3%
48.8 1
0.7%
45.7 1
0.7%
44.5 2
1.3%
43.8 1
0.7%
43.0 1
0.7%

차량가용토크값
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.016556
Minimum16.1
Maximum96.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:19.776175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.1
5-th percentile22.95
Q140.6
median51.4
Q356.9
95-th percentile75.1
Maximum96.1
Range80
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation14.897447
Coefficient of variation (CV)0.29785031
Kurtosis1.2433089
Mean50.016556
Median Absolute Deviation (MAD)7.5
Skewness0.44763618
Sum7552.5
Variance221.93392
MonotonicityNot monotonic
2023-12-10T15:41:20.046000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.3 7
 
4.6%
56.9 6
 
4.0%
48.2 5
 
3.3%
56.5 5
 
3.3%
39.6 4
 
2.6%
58.4 3
 
2.0%
57.3 3
 
2.0%
53.3 3
 
2.0%
55.7 3
 
2.0%
37.3 3
 
2.0%
Other values (78) 109
72.2%
ValueCountFrequency (%)
16.1 1
0.7%
18.4 2
1.3%
19.2 1
0.7%
21.2 1
0.7%
22.0 2
1.3%
22.4 1
0.7%
23.5 1
0.7%
27.8 1
0.7%
28.2 1
0.7%
29.0 1
0.7%
ValueCountFrequency (%)
96.1 1
0.7%
93.7 1
0.7%
93.3 1
0.7%
92.2 1
0.7%
89.0 1
0.7%
86.3 1
0.7%
76.5 1
0.7%
76.1 1
0.7%
74.1 2
1.3%
71.4 1
0.7%

차량공기압축여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
147 
0
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 147
97.4%
0 4
 
2.6%

Length

2023-12-10T15:41:20.263850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:20.429967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 147
97.4%
0 4
 
2.6%

차량엔진냉각재온도값
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.496689
Minimum85
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-10T15:41:20.569004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile85
Q186
median87
Q387
95-th percentile93
Maximum94
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6630163
Coefficient of variation (CV)0.030435623
Kurtosis0.35379787
Mean87.496689
Median Absolute Deviation (MAD)1
Skewness1.2819714
Sum13212
Variance7.0916556
MonotonicityNot monotonic
2023-12-10T15:41:20.713179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
86 44
29.1%
87 43
28.5%
85 28
18.5%
91 9
 
6.0%
93 8
 
5.3%
94 7
 
4.6%
92 5
 
3.3%
90 4
 
2.6%
89 2
 
1.3%
88 1
 
0.7%
ValueCountFrequency (%)
85 28
18.5%
86 44
29.1%
87 43
28.5%
88 1
 
0.7%
89 2
 
1.3%
90 4
 
2.6%
91 9
 
6.0%
92 5
 
3.3%
93 8
 
5.3%
94 7
 
4.6%
ValueCountFrequency (%)
94 7
 
4.6%
93 8
 
5.3%
92 5
 
3.3%
91 9
 
6.0%
90 4
 
2.6%
89 2
 
1.3%
88 1
 
0.7%
87 43
28.5%
86 44
29.1%
85 28
18.5%

Interactions

2023-12-10T15:41:10.484482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:47.270097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.226264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.034151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.587595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:54.364573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.628990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.483624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.339849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.252564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.529634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.487052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.474576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:10.628535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:47.423687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.373362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.180400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.722226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:54.867062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.765444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.608395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.503160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.395297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.679410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.619563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.611893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:10.746611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:47.553731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.500370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.290317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.853671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:54.998726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.901045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.752864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.631605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.533807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.828058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.781049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.725457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:10.872904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:47.690985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.625761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.404979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.020168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:55.132583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.055458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.878797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.760484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.673807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.972724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.895783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.868094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.020821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:47.828039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.774267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.495379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.153702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:55.267536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.186779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.004906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.897702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.810905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:05.143641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:07.021036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.986186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.149232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:47.973628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.920792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.620311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.266896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:55.440175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.328998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.144598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.083164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.940931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:05.307679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:07.177453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:09.097462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.262210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:48.110038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.072111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.746666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.414353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:55.610065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.483768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.296739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.230701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:03.090228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:05.493174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:07.338376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:09.210935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.374434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:48.249120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.197187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.847074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.545574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:55.722182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.600008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.446780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.371897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:03.586241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:05.631076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:07.503948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:09.377098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.519068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:48.418593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.358773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:51.985028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.710056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:55.866522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.733449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.597558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.539640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:03.753692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:05.775718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:07.682252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:09.584392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.666122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:48.583089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.509528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.111182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.854101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.039176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:57.885430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.741455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.706667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:03.922467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:05.923682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:07.841506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:09.752362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.790481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:48.731658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.643177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.231248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:53.986935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.193713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.032232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:59.882238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.844944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.084645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.049316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.011289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:09.907792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:11.935081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:48.907218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.771224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.349524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:54.121468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.348395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.190672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.054565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:01.982523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.216238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.167440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.168756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:10.063462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:12.103733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:49.081578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:50.905088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:52.488145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:54.252366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:56.505297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:58.349275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:00.208089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:02.120676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:04.379414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:06.336401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:08.332962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:10.244545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:41:20.858351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량장기연료보정값차량흡입압력값차량변속기온도값차량왼쪽앞바퀴속도값차량오른쪽앞바퀴속도값차량왼쪽뒷바큇속도값차량토크변환값주행시간차량가속값차량연료소비값차량마찰토크값차량최대엔진토크값차량엔진토크값차량가용토크값차량공기압축여부차량엔진냉각재온도값
차량장기연료보정값1.0000.7780.2920.5500.5640.5440.5430.6980.6800.8790.5690.5880.8510.7340.2090.606
차량흡입압력값0.7781.0000.3910.7010.7450.6990.7250.7310.7100.6770.5080.7520.9400.9760.6050.517
차량변속기온도값0.2920.3911.0000.5770.4580.5530.0000.9520.0000.1010.5250.1820.4260.0000.4390.967
차량왼쪽앞바퀴속도값0.5500.7010.5771.0000.9870.9990.8570.8980.5740.5970.0000.8120.6390.7300.0000.632
차량오른쪽앞바퀴속도값0.5640.7450.4580.9871.0000.9860.8540.9010.6170.6210.0000.8490.6660.7490.0000.639
차량왼쪽뒷바큇속도값0.5440.6990.5530.9990.9861.0000.8480.8960.5720.5690.0000.8090.6410.7320.0000.633
차량토크변환값0.5430.7250.0000.8570.8540.8481.0000.7710.8010.7760.0000.9410.8010.8510.0000.211
주행시간0.6980.7310.9520.8980.9010.8960.7711.0000.5410.4630.5940.7600.7160.7020.5770.933
차량가속값0.6800.7100.0000.5740.6170.5720.8010.5411.0000.8470.0000.7080.7930.8030.0000.143
차량연료소비값0.8790.6770.1010.5970.6210.5690.7760.4630.8471.0000.1570.7530.8380.7250.0000.000
차량마찰토크값0.5690.5080.5250.0000.0000.0000.0000.5940.0000.1571.0000.0000.6090.3581.0000.697
차량최대엔진토크값0.5880.7520.1820.8120.8490.8090.9410.7600.7080.7530.0001.0000.8110.7700.0000.404
차량엔진토크값0.8510.9400.4260.6390.6660.6410.8010.7160.7930.8380.6090.8111.0000.9400.5610.687
차량가용토크값0.7340.9760.0000.7300.7490.7320.8510.7020.8030.7250.3580.7700.9401.0000.1390.221
차량공기압축여부0.2090.6050.4390.0000.0000.0000.0000.5770.0000.0001.0000.0000.5610.1391.0000.893
차량엔진냉각재온도값0.6060.5170.9670.6320.6390.6330.2110.9330.1430.0000.6970.4040.6870.2210.8931.000
2023-12-10T15:41:21.102588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량공기압축여부차량변속기온도값차량장기연료보정값
차량공기압축여부1.0000.2890.341
차량변속기온도값0.2891.0000.470
차량장기연료보정값0.3410.4701.000
2023-12-10T15:41:21.258071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량흡입압력값차량왼쪽앞바퀴속도값차량오른쪽앞바퀴속도값차량왼쪽뒷바큇속도값차량토크변환값주행시간차량가속값차량연료소비값차량마찰토크값차량최대엔진토크값차량엔진토크값차량가용토크값차량엔진냉각재온도값차량장기연료보정값차량변속기온도값차량공기압축여부
차량흡입압력값1.000-0.144-0.157-0.148-0.2070.2540.3760.528-0.055-0.0180.7670.992-0.1630.6450.2890.398
차량왼쪽앞바퀴속도값-0.1441.0000.9960.9990.8910.2480.5880.516-0.1310.9020.239-0.146-0.4300.3840.4330.000
차량오른쪽앞바퀴속도값-0.1570.9961.0000.9950.8960.2340.5770.506-0.1430.8990.218-0.158-0.4180.3980.3400.000
차량왼쪽뒷바큇속도값-0.1480.9990.9951.0000.8890.2550.5800.509-0.1370.8980.230-0.149-0.4380.3850.4140.000
차량토크변환값-0.2070.8910.8960.8891.0000.0700.6840.546-0.2220.9440.233-0.211-0.1590.3780.0000.000
주행시간0.2540.2480.2340.2550.0701.0000.1520.235-0.3400.1950.2600.268-0.5990.5210.7950.413
차량가속값0.3760.5880.5770.5800.6840.1521.0000.842-0.2210.7640.7920.358-0.1400.5520.0000.000
차량연료소비값0.5280.5160.5060.5090.5460.2350.8421.000-0.1530.6920.8380.513-0.1770.5930.0970.000
차량마찰토크값-0.055-0.131-0.143-0.137-0.222-0.340-0.221-0.1531.000-0.199-0.073-0.0660.2050.4520.5540.983
차량최대엔진토크값-0.0180.9020.8990.8980.9440.1950.7640.692-0.1991.0000.417-0.026-0.2810.4210.1340.000
차량엔진토크값0.7670.2390.2180.2300.2330.2600.7920.838-0.0730.4171.0000.745-0.1710.7500.3180.421
차량가용토크값0.992-0.146-0.158-0.149-0.2110.2680.3580.513-0.066-0.0260.7451.000-0.1710.5860.0490.099
차량엔진냉각재온도값-0.163-0.430-0.418-0.438-0.159-0.599-0.140-0.1770.205-0.281-0.171-0.1711.0000.4390.8210.709
차량장기연료보정값0.6450.3840.3980.3850.3780.5210.5520.5930.4520.4210.7500.5860.4391.0000.4700.341
차량변속기온도값0.2890.4330.3400.4140.0000.7950.0000.0970.5540.1340.3180.0490.8210.4701.0000.289
차량공기압축여부0.3980.0000.0000.0000.0000.4130.0000.0000.9830.0000.4210.0990.7090.3410.2891.000

Missing values

2023-12-10T15:41:12.568355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:41:12.937766image/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

차량장기연료보정값차량흡입압력값차량변속기온도값차량왼쪽앞바퀴속도값차량오른쪽앞바퀴속도값차량왼쪽뒷바큇속도값차량토크변환값주행시간운전자생성년도차량가속값차량연료소비값차량마찰토크값차량최대엔진토크값차량엔진토크값차량가용토크값차량공기압축여부차량엔진냉각재온도값
00.059950.00.00.01121.81A20180.0627.28.662.914.548.6094
1-0.833950.00.00.01095.82A20180.0320.09.061.35.123.5094
2-0.837950.00.00.0774.83A20180.0243.28.255.17.027.8094
3-0.843950.10.00.0672.04A20180.0243.27.453.57.832.9094
4-0.843950.00.00.0698.55A20180.0243.210.953.58.633.3194
5-0.845950.00.00.0721.86A20180.0268.816.453.910.234.5194
6-0.848950.00.00.0656.37A20180.0268.816.453.114.138.0194
7-0.846950.00.00.0651.08A20180.0243.216.453.113.735.7193
8-0.848950.00.00.0638.09A20180.0243.217.253.115.637.3193
9-0.854950.00.00.0629.010A20180.0294.418.453.119.143.1193
차량장기연료보정값차량흡입압력값차량변속기온도값차량왼쪽앞바퀴속도값차량오른쪽앞바퀴속도값차량왼쪽뒷바큇속도값차량토크변환값주행시간운전자생성년도차량가속값차량연료소비값차량마찰토크값차량최대엔진토크값차량엔진토크값차량가용토크값차량공기압축여부차량엔진냉각재온도값
1410.8809426.925.626.61611.5142A20188.61728.018.075.443.071.4185
1420.0419427.426.227.31648.3143A20180.0665.619.171.919.931.4185
1430.0749427.526.327.41662.5144A201818.01420.818.075.041.462.7185
1440.8979430.028.929.81799.3145A201821.12214.417.277.052.389.0185
1450.0689432.431.532.31939.0146A20187.01702.418.076.634.858.4185
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