Overview

Dataset statistics

Number of variables16
Number of observations300
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)1.0%
Total size in memory42.3 KiB
Average record size in memory144.4 B

Variable types

Numeric10
Categorical6

Dataset

Description자동차관리법 및 자동차종합검사 시행등에 관한 규칙에 따라 한국교통안전공단(KOTSA)에서 관리하는 자동차검사 자료입니다.
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15088042/fileData.do

Alerts

질소산화물데이터 has constant value ""Constant
산소비율(OCV) has constant value ""Constant
Dataset has 3 (1.0%) duplicate rowsDuplicates
이산화탄소데이터 is highly overall correlated with 이산화탄소데이터(고속_무부후)High correlation
산소데이터 is highly overall correlated with 공기과잉률데이터High correlation
공기과잉률데이터 is highly overall correlated with 산소데이터 and 1 other fieldsHigh correlation
무부하고속HC is highly overall correlated with 이산화탄소데이터(고속_무부후)High correlation
공기과잉률데이터(고속_무부하) is highly overall correlated with 이산화탄소데이터(고속_무부후) and 2 other fieldsHigh correlation
이산화탄소데이터(고속_무부후) is highly overall correlated with 이산화탄소데이터 and 2 other fieldsHigh correlation
산소데이터(고속_무부하) is highly overall correlated with 공기과잉률데이터(고속_무부하)High correlation
차종구분(1CO_HC) is highly overall correlated with 공기과잉률데이터(고속_무부하) and 1 other fieldsHigh correlation
차종구분(2LAM) is highly overall correlated with 공기과잉률데이터High correlation
진동자여부 is highly overall correlated with 차종구분(1CO_HC)High correlation
차종구분(2LAM) is highly imbalanced (82.1%)Imbalance
진동자여부 is highly imbalanced (66.1%)Imbalance
일산화탄소데이터 has 252 (84.0%) zerosZeros
탄화수소데이터 has 122 (40.7%) zerosZeros
산소데이터 has 60 (20.0%) zerosZeros
무부하고속챼 has 240 (80.0%) zerosZeros
무부하고속HC has 133 (44.3%) zerosZeros
공기과잉률데이터(고속_무부하) has 73 (24.3%) zerosZeros
이산화탄소데이터(고속_무부후) has 73 (24.3%) zerosZeros
산소데이터(고속_무부하) has 150 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-12 16:03:16.747862
Analysis finished2023-12-12 16:03:29.235375
Duration12.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일산화탄소데이터
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0733333
Minimum0
Maximum70
Zeros252
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:29.289538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.541086
Coefficient of variation (CV)5.1625025
Kurtosis92.658942
Mean1.0733333
Median Absolute Deviation (MAD)0
Skewness8.8277969
Sum322
Variance30.703634
MonotonicityNot monotonic
2023-12-13T01:03:29.408901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 252
84.0%
1 23
 
7.7%
10 5
 
1.7%
3 4
 
1.3%
4 4
 
1.3%
2 4
 
1.3%
9 2
 
0.7%
30 2
 
0.7%
5 1
 
0.3%
20 1
 
0.3%
Other values (2) 2
 
0.7%
ValueCountFrequency (%)
0 252
84.0%
1 23
 
7.7%
2 4
 
1.3%
3 4
 
1.3%
4 4
 
1.3%
5 1
 
0.3%
9 2
 
0.7%
10 5
 
1.7%
20 1
 
0.3%
30 2
 
0.7%
ValueCountFrequency (%)
70 1
 
0.3%
40 1
 
0.3%
30 2
 
0.7%
20 1
 
0.3%
10 5
1.7%
9 2
 
0.7%
5 1
 
0.3%
4 4
1.3%
3 4
1.3%
2 4
1.3%

탄화수소데이터
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.293333
Minimum0
Maximum277
Zeros122
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:29.544479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile50.05
Maximum277
Range277
Interquartile range (IQR)8

Descriptive statistics

Standard deviation31.179177
Coefficient of variation (CV)2.760848
Kurtosis36.782393
Mean11.293333
Median Absolute Deviation (MAD)1
Skewness5.4455379
Sum3388
Variance972.14109
MonotonicityNot monotonic
2023-12-13T01:03:29.766824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 122
40.7%
1 36
 
12.0%
2 22
 
7.3%
3 13
 
4.3%
5 12
 
4.0%
4 10
 
3.3%
17 6
 
2.0%
16 6
 
2.0%
8 6
 
2.0%
6 5
 
1.7%
Other values (37) 62
20.7%
ValueCountFrequency (%)
0 122
40.7%
1 36
 
12.0%
2 22
 
7.3%
3 13
 
4.3%
4 10
 
3.3%
5 12
 
4.0%
6 5
 
1.7%
7 3
 
1.0%
8 6
 
2.0%
9 5
 
1.7%
ValueCountFrequency (%)
277 1
0.3%
270 1
0.3%
172 1
0.3%
146 1
0.3%
117 1
0.3%
110 1
0.3%
107 1
0.3%
104 1
0.3%
93 1
0.3%
87 1
0.3%

이산화탄소데이터
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1326.97
Minimum0
Maximum1600
Zeros3
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:29.931340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile930
Q11250
median1400
Q31460
95-th percentile1600
Maximum1600
Range1600
Interquartile range (IQR)210

Descriptive statistics

Standard deviation233.05701
Coefficient of variation (CV)0.17563095
Kurtosis12.381585
Mean1326.97
Median Absolute Deviation (MAD)88
Skewness-2.8580452
Sum398091
Variance54315.568
MonotonicityNot monotonic
2023-12-13T01:03:30.111283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 16
 
5.3%
1470 14
 
4.7%
1460 14
 
4.7%
1430 13
 
4.3%
1410 12
 
4.0%
1490 11
 
3.7%
1390 10
 
3.3%
1420 10
 
3.3%
1450 10
 
3.3%
1440 9
 
3.0%
Other values (75) 181
60.3%
ValueCountFrequency (%)
0 3
1.0%
50 1
 
0.3%
500 1
 
0.3%
750 1
 
0.3%
760 1
 
0.3%
830 1
 
0.3%
840 2
0.7%
870 1
 
0.3%
910 2
0.7%
930 3
1.0%
ValueCountFrequency (%)
1600 16
5.3%
1560 3
 
1.0%
1550 1
 
0.3%
1540 1
 
0.3%
1520 2
 
0.7%
1510 7
2.3%
1500 2
 
0.7%
1495 1
 
0.3%
1490 11
3.7%
1486 1
 
0.3%

산소데이터
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.44
Minimum0
Maximum620
Zeros60
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:30.267519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median30
Q370
95-th percentile231
Maximum620
Range620
Interquartile range (IQR)60

Descriptive statistics

Standard deviation98.856538
Coefficient of variation (CV)1.5106439
Kurtosis11.141051
Mean65.44
Median Absolute Deviation (MAD)30
Skewness3.1393071
Sum19632
Variance9772.6151
MonotonicityNot monotonic
2023-12-13T01:03:30.417368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
20.0%
20 39
13.0%
30 36
12.0%
50 24
 
8.0%
40 19
 
6.3%
70 17
 
5.7%
10 16
 
5.3%
60 9
 
3.0%
80 7
 
2.3%
90 7
 
2.3%
Other values (41) 66
22.0%
ValueCountFrequency (%)
0 60
20.0%
10 16
 
5.3%
12 1
 
0.3%
20 39
13.0%
21 1
 
0.3%
29 1
 
0.3%
30 36
12.0%
31 1
 
0.3%
32 1
 
0.3%
37 1
 
0.3%
ValueCountFrequency (%)
620 1
0.3%
600 1
0.3%
480 2
0.7%
470 2
0.7%
450 2
0.7%
440 1
0.3%
420 1
0.3%
410 1
0.3%
310 1
0.3%
270 1
0.3%

질소산화물데이터
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
300 

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 (%)
0 300
100.0%

Length

2023-12-13T01:03:30.885539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:31.005725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 300
100.0%

공기과잉률데이터
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.23667
Minimum0
Maximum153
Zeros3
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:31.096972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q1100
median101
Q3103
95-th percentile110
Maximum153
Range153
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.530391
Coefficient of variation (CV)0.12256259
Kurtosis45.154274
Mean102.23667
Median Absolute Deviation (MAD)2
Skewness-4.6156783
Sum30671
Variance157.01069
MonotonicityNot monotonic
2023-12-13T01:03:31.220575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
101 64
21.3%
99 48
16.0%
100 45
15.0%
102 35
11.7%
103 30
10.0%
104 19
 
6.3%
105 13
 
4.3%
109 9
 
3.0%
108 6
 
2.0%
107 5
 
1.7%
Other values (13) 26
8.7%
ValueCountFrequency (%)
0 3
 
1.0%
98 2
 
0.7%
99 48
16.0%
100 45
15.0%
101 64
21.3%
102 35
11.7%
103 30
10.0%
104 19
 
6.3%
105 13
 
4.3%
106 3
 
1.0%
ValueCountFrequency (%)
153 1
 
0.3%
151 1
 
0.3%
138 1
 
0.3%
136 2
0.7%
134 1
 
0.3%
132 2
0.7%
129 2
0.7%
117 2
0.7%
116 2
0.7%
110 4
1.3%

수소비율(HCV)
Categorical

Distinct5
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
197 
185
79 
18500
 
12
250
 
11
25000
 
1

Length

Max length5
Median length1
Mean length1.7733333
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row185
2nd row0
3rd row185
4th row185
5th row185

Common Values

ValueCountFrequency (%)
0 197
65.7%
185 79
26.3%
18500 12
 
4.0%
250 11
 
3.7%
25000 1
 
0.3%

Length

2023-12-13T01:03:31.380537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:31.526981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 197
65.7%
185 79
26.3%
18500 12
 
4.0%
250 11
 
3.7%
25000 1
 
0.3%

산소비율(OCV)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
300 

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 (%)
0 300
100.0%

Length

2023-12-13T01:03:31.640071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:31.747108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 300
100.0%

차종구분(1CO_HC)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
6
222 
5
43 
4
27 
1
 
7
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 222
74.0%
5 43
 
14.3%
4 27
 
9.0%
1 7
 
2.3%
0 1
 
0.3%

Length

2023-12-13T01:03:31.873167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:32.026943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 222
74.0%
5 43
 
14.3%
4 27
 
9.0%
1 7
 
2.3%
0 1
 
0.3%

차종구분(2LAM)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
7
287 
0
 
11
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 287
95.7%
0 11
 
3.7%
8 2
 
0.7%

Length

2023-12-13T01:03:32.161441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:32.268421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 287
95.7%
0 11
 
3.7%
8 2
 
0.7%

무부하고속챼
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3533333
Minimum0
Maximum360
Zeros240
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:32.358342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum360
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.078648
Coefficient of variation (CV)6.58409
Kurtosis230.30045
Mean3.3533333
Median Absolute Deviation (MAD)0
Skewness14.483197
Sum1006
Variance487.46671
MonotonicityNot monotonic
2023-12-13T01:03:32.484113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 240
80.0%
10 16
 
5.3%
1 12
 
4.0%
2 5
 
1.7%
20 4
 
1.3%
5 3
 
1.0%
4 3
 
1.0%
7 2
 
0.7%
12 2
 
0.7%
6 2
 
0.7%
Other values (11) 11
 
3.7%
ValueCountFrequency (%)
0 240
80.0%
1 12
 
4.0%
2 5
 
1.7%
3 1
 
0.3%
4 3
 
1.0%
5 3
 
1.0%
6 2
 
0.7%
7 2
 
0.7%
10 16
 
5.3%
12 2
 
0.7%
ValueCountFrequency (%)
360 1
 
0.3%
90 1
 
0.3%
60 1
 
0.3%
40 1
 
0.3%
30 1
 
0.3%
25 1
 
0.3%
20 4
1.3%
17 1
 
0.3%
15 1
 
0.3%
14 1
 
0.3%

무부하고속HC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7966667
Minimum0
Maximum372
Zeros133
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:32.604156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile31.05
Maximum372
Range372
Interquartile range (IQR)8

Descriptive statistics

Standard deviation26.811531
Coefficient of variation (CV)3.0479194
Kurtosis115.25314
Mean8.7966667
Median Absolute Deviation (MAD)1
Skewness9.3250535
Sum2639
Variance718.85818
MonotonicityNot monotonic
2023-12-13T01:03:32.729572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 133
44.3%
1 24
 
8.0%
3 19
 
6.3%
4 15
 
5.0%
5 15
 
5.0%
2 8
 
2.7%
9 8
 
2.7%
20 6
 
2.0%
6 6
 
2.0%
26 5
 
1.7%
Other values (31) 61
20.3%
ValueCountFrequency (%)
0 133
44.3%
1 24
 
8.0%
2 8
 
2.7%
3 19
 
6.3%
4 15
 
5.0%
5 15
 
5.0%
6 6
 
2.0%
7 2
 
0.7%
8 5
 
1.7%
9 8
 
2.7%
ValueCountFrequency (%)
372 1
0.3%
118 1
0.3%
114 1
0.3%
105 1
0.3%
98 1
0.3%
94 1
0.3%
68 2
0.7%
64 1
0.3%
53 1
0.3%
46 1
0.3%

공기과잉률데이터(고속_무부하)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.233333
Minimum0
Maximum109
Zeros73
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:32.850153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q196.5
median100
Q3101
95-th percentile105
Maximum109
Range109
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation43.351317
Coefficient of variation (CV)0.56866616
Kurtosis-0.56446732
Mean76.233333
Median Absolute Deviation (MAD)1
Skewness-1.194408
Sum22870
Variance1879.3367
MonotonicityNot monotonic
2023-12-13T01:03:32.966198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 73
24.3%
100 65
21.7%
99 58
19.3%
101 55
18.3%
102 15
 
5.0%
103 9
 
3.0%
105 7
 
2.3%
107 5
 
1.7%
109 4
 
1.3%
106 4
 
1.3%
Other values (4) 5
 
1.7%
ValueCountFrequency (%)
0 73
24.3%
90 1
 
0.3%
92 1
 
0.3%
98 2
 
0.7%
99 58
19.3%
100 65
21.7%
101 55
18.3%
102 15
 
5.0%
103 9
 
3.0%
105 7
 
2.3%
ValueCountFrequency (%)
109 4
 
1.3%
108 1
 
0.3%
107 5
 
1.7%
106 4
 
1.3%
105 7
 
2.3%
103 9
 
3.0%
102 15
 
5.0%
101 55
18.3%
100 65
21.7%
99 58
19.3%

진동자여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
268 
2
29 
1
 
3

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 (%)
0 268
89.3%
2 29
 
9.7%
1 3
 
1.0%

Length

2023-12-13T01:03:33.073692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:33.159377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 268
89.3%
2 29
 
9.7%
1 3
 
1.0%

이산화탄소데이터(고속_무부후)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1045.05
Minimum0
Maximum1670
Zeros73
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:33.252748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1820
median1370
Q31460
95-th percentile1580
Maximum1670
Range1670
Interquartile range (IQR)640

Descriptive statistics

Standard deviation611.167
Coefficient of variation (CV)0.58482082
Kurtosis-0.74973281
Mean1045.05
Median Absolute Deviation (MAD)120
Skewness-1.0438645
Sum313515
Variance373525.1
MonotonicityNot monotonic
2023-12-13T01:03:33.376212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
24.3%
1420 15
 
5.0%
1460 14
 
4.7%
1600 13
 
4.3%
1440 13
 
4.3%
1470 12
 
4.0%
1450 10
 
3.3%
1490 9
 
3.0%
1230 7
 
2.3%
1480 6
 
2.0%
Other values (58) 128
42.7%
ValueCountFrequency (%)
0 73
24.3%
290 1
 
0.3%
520 1
 
0.3%
920 1
 
0.3%
930 1
 
0.3%
980 2
 
0.7%
1000 2
 
0.7%
1030 2
 
0.7%
1060 1
 
0.3%
1080 1
 
0.3%
ValueCountFrequency (%)
1670 1
 
0.3%
1600 13
4.3%
1580 2
 
0.7%
1560 1
 
0.3%
1550 2
 
0.7%
1530 3
 
1.0%
1520 5
 
1.7%
1510 3
 
1.0%
1500 6
2.0%
1490 9
3.0%

산소데이터(고속_무부하)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.38
Minimum0
Maximum220
Zeros150
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:03:33.493973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q327.25
95-th percentile110.15
Maximum220
Range220
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation38.353604
Coefficient of variation (CV)1.793901
Kurtosis8.2850126
Mean21.38
Median Absolute Deviation (MAD)1
Skewness2.785707
Sum6414
Variance1470.9989
MonotonicityNot monotonic
2023-12-13T01:03:33.606428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 150
50.0%
20 38
 
12.7%
10 29
 
9.7%
30 25
 
8.3%
40 8
 
2.7%
50 5
 
1.7%
60 4
 
1.3%
70 3
 
1.0%
140 3
 
1.0%
16 2
 
0.7%
Other values (27) 33
 
11.0%
ValueCountFrequency (%)
0 150
50.0%
2 1
 
0.3%
5 1
 
0.3%
10 29
 
9.7%
13 1
 
0.3%
16 2
 
0.7%
17 1
 
0.3%
20 38
 
12.7%
26 1
 
0.3%
27 1
 
0.3%
ValueCountFrequency (%)
220 1
 
0.3%
200 2
0.7%
190 1
 
0.3%
170 1
 
0.3%
160 1
 
0.3%
150 1
 
0.3%
141 1
 
0.3%
140 3
1.0%
130 2
0.7%
119 1
 
0.3%

Interactions

2023-12-13T01:03:27.559409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:17.334652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.591909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.642709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.752298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.066619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.227263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.167994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.370394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.365002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.681761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:17.413424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.705593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.750069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.890597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.247325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.358068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.274395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.478904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.534489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.766547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:17.492219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.798514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.843458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.983184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.397934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.442869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.612393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.573219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.652667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.866867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:17.582472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.932130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.978067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.100501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.536115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.549259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.690428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.666545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.774969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.965734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:17.664092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.043430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.073873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.207273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.628813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.654884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.787048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.753014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.875580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:28.142377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:17.743577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.141668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.191589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.343875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.730726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.746400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.887084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.847868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.998955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:28.256623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.165868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.256595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.288932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.464958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.845309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.830652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.988996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.939511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.120750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:28.377848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.278365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.351680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.392467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.573317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:22.951711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.916953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.084678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.048584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.250651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:28.485757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.384971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.445749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.505929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.706724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.034657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.990637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.168244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.147715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.363153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:28.608103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:18.492489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:19.548216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:20.624163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:21.872248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:23.136367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:24.075730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:25.280951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:26.241461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:03:27.465975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:03:33.699657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일산화탄소데이터탄화수소데이터이산화탄소데이터산소데이터공기과잉률데이터수소비율(HCV)차종구분(1CO_HC)차종구분(2LAM)무부하고속챼무부하고속HC공기과잉률데이터(고속_무부하)진동자여부이산화탄소데이터(고속_무부후)산소데이터(고속_무부하)
일산화탄소데이터1.0000.5670.3400.0000.0000.2730.1200.0000.7320.5800.6420.0000.6750.268
탄화수소데이터0.5671.0000.7500.8710.6460.0000.5640.5220.7090.7240.2790.4770.2410.000
이산화탄소데이터0.3400.7501.0000.8110.7750.4590.5860.5330.0000.0000.4500.5540.9300.000
산소데이터0.0000.8710.8111.0000.8640.2600.5160.5470.0000.0000.4280.6120.4770.445
공기과잉률데이터0.0000.6460.7750.8641.0000.0000.8020.5800.0000.0000.3150.4680.2430.000
수소비율(HCV)0.2730.0000.4590.2600.0001.0000.2240.0000.2100.0000.2450.1260.4630.515
차종구분(1CO_HC)0.1200.5640.5860.5160.8020.2241.0000.5060.2270.4990.7080.6370.6640.204
차종구분(2LAM)0.0000.5220.5330.5470.5800.0000.5061.0000.0000.1910.5460.7160.3100.000
무부하고속챼0.7320.7090.0000.0000.0000.2100.2270.0001.0000.7210.3520.0000.3360.415
무부하고속HC0.5800.7240.0000.0000.0000.0000.4990.1910.7211.0000.2350.0000.1520.690
공기과잉률데이터(고속_무부하)0.6420.2790.4500.4280.3150.2450.7080.5460.3520.2351.0000.7660.8580.294
진동자여부0.0000.4770.5540.6120.4680.1260.6370.7160.0000.0000.7661.0000.5420.000
이산화탄소데이터(고속_무부후)0.6750.2410.9300.4770.2430.4630.6640.3100.3360.1520.8580.5421.0000.196
산소데이터(고속_무부하)0.2680.0000.0000.4450.0000.5150.2040.0000.4150.6900.2940.0000.1961.000
2023-12-13T01:03:33.839068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차종구분(1CO_HC)진동자여부차종구분(2LAM)수소비율(HCV)
차종구분(1CO_HC)1.0000.5970.4410.085
진동자여부0.5971.0000.3720.094
차종구분(2LAM)0.4410.3721.0000.000
수소비율(HCV)0.0850.0940.0001.000
2023-12-13T01:03:33.945793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일산화탄소데이터탄화수소데이터이산화탄소데이터산소데이터공기과잉률데이터무부하고속챼무부하고속HC공기과잉률데이터(고속_무부하)이산화탄소데이터(고속_무부후)산소데이터(고속_무부하)수소비율(HCV)차종구분(1CO_HC)차종구분(2LAM)진동자여부
일산화탄소데이터1.0000.128-0.088-0.122-0.1320.4090.002-0.111-0.082-0.0580.1880.0810.0000.000
탄화수소데이터0.1281.000-0.0090.0530.018-0.0010.451-0.0960.017-0.0570.0000.3890.3850.342
이산화탄소데이터-0.088-0.0091.000-0.104-0.124-0.0370.3090.1940.7150.1020.3010.4090.3960.417
산소데이터-0.1220.053-0.1041.0000.962-0.049-0.1020.313-0.0680.4650.1610.3470.4100.478
공기과잉률데이터-0.1320.018-0.1240.9621.000-0.048-0.1210.304-0.0940.4420.0000.4250.5260.399
무부하고속챼0.409-0.001-0.037-0.049-0.0481.0000.2150.2130.0930.2680.1720.1870.0000.000
무부하고속HC0.0020.4510.309-0.102-0.1210.2151.0000.4070.5440.3090.0000.2060.1450.000
공기과잉률데이터(고속_무부하)-0.111-0.0960.1940.3130.3040.2130.4071.0000.5400.8760.1890.6910.2330.424
이산화탄소데이터(고속_무부후)-0.0820.0170.715-0.068-0.0940.0930.5440.5401.0000.3240.3050.4860.2050.404
산소데이터(고속_무부하)-0.058-0.0570.1020.4650.4420.2680.3090.8760.3241.0000.2350.0900.0000.000
수소비율(HCV)0.1880.0000.3010.1610.0000.1720.0000.1890.3050.2351.0000.0850.0000.094
차종구분(1CO_HC)0.0810.3890.4090.3470.4250.1870.2060.6910.4860.0900.0851.0000.4410.597
차종구분(2LAM)0.0000.3850.3960.4100.5260.0000.1450.2330.2050.0000.0000.4411.0000.372
진동자여부0.0000.3420.4170.4780.3990.0000.0000.4240.4040.0000.0940.5970.3721.000

Missing values

2023-12-13T01:03:28.765060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:03:29.145959image/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

일산화탄소데이터탄화수소데이터이산화탄소데이터산소데이터질소산화물데이터공기과잉률데이터수소비율(HCV)산소비율(OCV)차종구분(1CO_HC)차종구분(2LAM)무부하고속챼무부하고속HC공기과잉률데이터(고속_무부하)진동자여부이산화탄소데이터(고속_무부후)산소데이터(고속_무부하)
0031510800104185067031010149030
101714600099006701699014600
2001510800104185067011010153030
3010148050010218506710141010148020
40214702200110185067081010150020
5011480700103185067031010148020
6001480700103185067011010147020
7001440500102185067001010145020
8001440400102185067001010145020
900143020001101850671011010146020
일산화탄소데이터탄화수소데이터이산화탄소데이터산소데이터질소산화물데이터공기과잉률데이터수소비율(HCV)산소비율(OCV)차종구분(1CO_HC)차종구분(2LAM)무부하고속챼무부하고속HC공기과잉률데이터(고속_무부하)진동자여부이산화탄소데이터(고속_무부후)산소데이터(고속_무부하)
2900593000990067079909300
29190125030010100671701010126030
2920011704001020067001010118030
2934412604001010067721010128030
294017117040010200171321010120020
2951112404001020057000200
296061070009900670999010800
29710810100099006701299010000
2981011804001020067101010121030
299061020009900670899010300

Duplicate rows

Most frequently occurring

일산화탄소데이터탄화수소데이터이산화탄소데이터산소데이터질소산화물데이터공기과잉률데이터수소비율(HCV)산소비율(OCV)차종구분(1CO_HC)차종구분(2LAM)무부하고속챼무부하고속HC공기과잉률데이터(고속_무부하)진동자여부이산화탄소데이터(고속_무부후)산소데이터(고속_무부하)# duplicates
000125050010300570000002
10014405001021850670010101450202
211126020010000570000002