Overview

Dataset statistics

Number of variables16
Number of observations205
Missing cells1831
Missing cells (%)55.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.2 KiB
Average record size in memory140.6 B

Variable types

Categorical3
Numeric12
Text1

Dataset

Description서울특별시_송파구_연료별 자동차 등록현황에 대한 데이터로 동, 차종, 용도, CNG, 경유, 기타연료, 수소, 엘피지 등에 항목으로 제공합니다.
Author서울특별시 송파구
URLhttps://www.data.go.kr/data/15037659/fileData.do

Alerts

CNG is highly overall correlated with 기타연료 and 1 other fieldsHigh correlation
경유 is highly overall correlated with 수소 and 7 other fieldsHigh correlation
기타연료 is highly overall correlated with CNG and 1 other fieldsHigh correlation
수소 is highly overall correlated with CNG and 6 other fieldsHigh correlation
엘피지 is highly overall correlated with 경유 and 3 other fieldsHigh correlation
전기 is highly overall correlated with 경유 and 5 other fieldsHigh correlation
하이브리드(LPG-전기) is highly overall correlated with and 2 other fieldsHigh correlation
하이브리드(경유-전기) is highly overall correlated with 경유 and 5 other fieldsHigh correlation
하이브리드(휘발유-전기) is highly overall correlated with 경유 and 7 other fieldsHigh correlation
휘발유 is highly overall correlated with 경유 and 6 other fieldsHigh correlation
휘발유(무연) is highly overall correlated with 경유 and 7 other fieldsHigh correlation
휘발유(유연) is highly overall correlated with 경유 and 2 other fieldsHigh correlation
is highly overall correlated with 하이브리드(LPG-전기)High correlation
차종 is highly overall correlated with 수소 and 4 other fieldsHigh correlation
용도 is highly overall correlated with 기타연료 and 3 other fieldsHigh correlation
CNG has 170 (82.9%) missing valuesMissing
경유 has 16 (7.8%) missing valuesMissing
기타연료 has 126 (61.5%) missing valuesMissing
수소 has 177 (86.3%) missing valuesMissing
엘피지 has 54 (26.3%) missing valuesMissing
전기 has 120 (58.5%) missing valuesMissing
하이브리드(CNG-전기) has 203 (99.0%) missing valuesMissing
하이브리드(LPG-전기) has 181 (88.3%) missing valuesMissing
하이브리드(경유-전기) has 180 (87.8%) missing valuesMissing
하이브리드(휘발유-전기) has 170 (82.9%) missing valuesMissing
휘발유 has 124 (60.5%) missing valuesMissing
휘발유(무연) has 132 (64.4%) missing valuesMissing
휘발유(유연) has 178 (86.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:35:40.077029
Analysis finished2023-12-12 17:35:56.365784
Duration16.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables


Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
송파1동
 
8
석촌동
 
8
가락본동
 
8
마천1동
 
8
마천2동
 
8
Other values (22)
165 

Length

Max length4
Median length4
Mean length3.7804878
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가락1동
2nd row가락1동
3rd row가락1동
4th row가락1동
5th row가락1동

Common Values

ValueCountFrequency (%)
송파1동 8
 
3.9%
석촌동 8
 
3.9%
가락본동 8
 
3.9%
마천1동 8
 
3.9%
마천2동 8
 
3.9%
삼전동 8
 
3.9%
문정2동 8
 
3.9%
방이2동 8
 
3.9%
풍납2동 8
 
3.9%
문정1동 8
 
3.9%
Other values (17) 125
61.0%

Length

2023-12-13T02:35:56.446632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파1동 8
 
3.9%
문정1동 8
 
3.9%
오금동 8
 
3.9%
풍납1동 8
 
3.9%
장지동 8
 
3.9%
석촌동 8
 
3.9%
잠실3동 8
 
3.9%
잠실2동 8
 
3.9%
송파2동 8
 
3.9%
잠실본동 8
 
3.9%
Other values (17) 125
61.0%

차종
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
승용
54 
화물
54 
특수
52 
승합
45 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row승용
2nd row승용
3rd row승합
4th row특수
5th row특수

Common Values

ValueCountFrequency (%)
승용 54
26.3%
화물 54
26.3%
특수 52
25.4%
승합 45
22.0%

Length

2023-12-13T02:35:56.568688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:35:56.681846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승용 54
26.3%
화물 54
26.3%
특수 52
25.4%
승합 45
22.0%

용도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
비사업용
108 
사업용
97 

Length

Max length4
Median length4
Mean length3.5268293
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비사업용
2nd row사업용
3rd row비사업용
4th row비사업용
5th row사업용

Common Values

ValueCountFrequency (%)
비사업용 108
52.7%
사업용 97
47.3%

Length

2023-12-13T02:35:56.888072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:35:57.040365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 108
52.7%
사업용 97
47.3%

CNG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)31.4%
Missing170
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean23.828571
Minimum1
Maximum568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:57.187693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile61.5
Maximum568
Range567
Interquartile range (IQR)3

Descriptive statistics

Standard deviation96.345727
Coefficient of variation (CV)4.0432859
Kurtosis32.439336
Mean23.828571
Median Absolute Deviation (MAD)1
Skewness5.6195963
Sum834
Variance9282.4992
MonotonicityNot monotonic
2023-12-13T02:35:57.389007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 12
 
5.9%
2 10
 
4.9%
4 3
 
1.5%
3 3
 
1.5%
51 1
 
0.5%
50 1
 
0.5%
86 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
(Missing) 170
82.9%
ValueCountFrequency (%)
1 12
5.9%
2 10
4.9%
3 3
 
1.5%
4 3
 
1.5%
7 1
 
0.5%
9 1
 
0.5%
10 1
 
0.5%
50 1
 
0.5%
51 1
 
0.5%
86 1
 
0.5%
ValueCountFrequency (%)
568 1
 
0.5%
86 1
 
0.5%
51 1
 
0.5%
50 1
 
0.5%
10 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
4 3
 
1.5%
3 3
 
1.5%
2 10
4.9%

경유
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct135
Distinct (%)71.4%
Missing16
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean462.39153
Minimum1
Maximum6009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:57.532684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median83
Q3454
95-th percentile2437.8
Maximum6009
Range6008
Interquartile range (IQR)445

Descriptive statistics

Standard deviation845.23427
Coefficient of variation (CV)1.8279623
Kurtosis10.84877
Mean462.39153
Median Absolute Deviation (MAD)79
Skewness2.8574384
Sum87392
Variance714420.97
MonotonicityNot monotonic
2023-12-13T02:35:57.730883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
3.9%
9 8
 
3.9%
2 6
 
2.9%
3 6
 
2.9%
8 5
 
2.4%
5 5
 
2.4%
14 4
 
2.0%
6 4
 
2.0%
4 3
 
1.5%
7 3
 
1.5%
Other values (125) 137
66.8%
(Missing) 16
 
7.8%
ValueCountFrequency (%)
1 8
3.9%
2 6
2.9%
3 6
2.9%
4 3
 
1.5%
5 5
2.4%
6 4
2.0%
7 3
 
1.5%
8 5
2.4%
9 8
3.9%
11 1
 
0.5%
ValueCountFrequency (%)
6009 1
0.5%
3189 1
0.5%
3002 1
0.5%
2997 1
0.5%
2808 1
0.5%
2750 1
0.5%
2623 1
0.5%
2553 1
0.5%
2521 1
0.5%
2505 1
0.5%

기타연료
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)25.3%
Missing126
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean6.1772152
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:57.891635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile19.1
Maximum29
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.4066462
Coefficient of variation (CV)1.0371415
Kurtosis2.8883994
Mean6.1772152
Median Absolute Deviation (MAD)3
Skewness1.7667046
Sum488
Variance41.045115
MonotonicityNot monotonic
2023-12-13T02:35:58.032578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 17
 
8.3%
1 15
 
7.3%
3 7
 
3.4%
5 6
 
2.9%
6 5
 
2.4%
4 4
 
2.0%
7 4
 
2.0%
9 3
 
1.5%
8 3
 
1.5%
19 2
 
1.0%
Other values (10) 13
 
6.3%
(Missing) 126
61.5%
ValueCountFrequency (%)
1 15
7.3%
2 17
8.3%
3 7
3.4%
4 4
 
2.0%
5 6
 
2.9%
6 5
 
2.4%
7 4
 
2.0%
8 3
 
1.5%
9 3
 
1.5%
11 1
 
0.5%
ValueCountFrequency (%)
29 1
0.5%
28 1
0.5%
23 1
0.5%
20 1
0.5%
19 2
1.0%
18 1
0.5%
15 2
1.0%
14 2
1.0%
13 2
1.0%
12 1
0.5%

수소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)25.0%
Missing177
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean4.1428571
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:58.148658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.35
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7787696
Coefficient of variation (CV)0.42935817
Kurtosis-0.91679424
Mean4.1428571
Median Absolute Deviation (MAD)1
Skewness0.023430458
Sum116
Variance3.1640212
MonotonicityNot monotonic
2023-12-13T02:35:58.298456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 6
 
2.9%
3 6
 
2.9%
6 5
 
2.4%
5 3
 
1.5%
2 3
 
1.5%
7 3
 
1.5%
1 2
 
1.0%
(Missing) 177
86.3%
ValueCountFrequency (%)
1 2
 
1.0%
2 3
1.5%
3 6
2.9%
4 6
2.9%
5 3
1.5%
6 5
2.4%
7 3
1.5%
ValueCountFrequency (%)
7 3
1.5%
6 5
2.4%
5 3
1.5%
4 6
2.9%
3 6
2.9%
2 3
1.5%
1 2
 
1.0%

엘피지
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)63.6%
Missing54
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean111.1457
Minimum1
Maximum819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:58.459065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median39
Q3127.5
95-th percentile446
Maximum819
Range818
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation162.28631
Coefficient of variation (CV)1.4601223
Kurtosis3.6626642
Mean111.1457
Median Absolute Deviation (MAD)34
Skewness1.9749067
Sum16783
Variance26336.845
MonotonicityNot monotonic
2023-12-13T02:35:58.633417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
5.9%
2 7
 
3.4%
7 6
 
2.9%
13 6
 
2.9%
12 5
 
2.4%
16 4
 
2.0%
14 3
 
1.5%
6 3
 
1.5%
44 3
 
1.5%
27 3
 
1.5%
Other values (86) 99
48.3%
(Missing) 54
26.3%
ValueCountFrequency (%)
1 12
5.9%
2 7
3.4%
3 1
 
0.5%
4 2
 
1.0%
5 2
 
1.0%
6 3
 
1.5%
7 6
2.9%
9 2
 
1.0%
10 2
 
1.0%
11 2
 
1.0%
ValueCountFrequency (%)
819 1
0.5%
660 1
0.5%
642 1
0.5%
627 1
0.5%
566 1
0.5%
522 1
0.5%
472 1
0.5%
452 1
0.5%
440 1
0.5%
418 1
0.5%

전기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)36.5%
Missing120
Missing (%)58.5%
Infinite0
Infinite (%)0.0%
Mean12.647059
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:58.809913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q322
95-th percentile54.2
Maximum74
Range73
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.04595
Coefficient of variation (CV)1.3478193
Kurtosis2.9561175
Mean12.647059
Median Absolute Deviation (MAD)2
Skewness1.8386261
Sum1075
Variance290.56443
MonotonicityNot monotonic
2023-12-13T02:35:58.965444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 16
 
7.8%
2 16
 
7.8%
3 11
 
5.4%
4 7
 
3.4%
24 3
 
1.5%
22 3
 
1.5%
10 2
 
1.0%
33 2
 
1.0%
55 2
 
1.0%
26 2
 
1.0%
Other values (21) 21
 
10.2%
(Missing) 120
58.5%
ValueCountFrequency (%)
1 16
7.8%
2 16
7.8%
3 11
5.4%
4 7
3.4%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
10 2
 
1.0%
11 1
 
0.5%
ValueCountFrequency (%)
74 1
0.5%
70 1
0.5%
56 1
0.5%
55 2
1.0%
51 1
0.5%
45 1
0.5%
36 1
0.5%
35 1
0.5%
33 2
1.0%
29 1
0.5%
Distinct2
Distinct (%)100.0%
Missing203
Missing (%)99.0%
Memory size1.7 KiB
2023-12-13T02:35:59.067004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row
2nd row4
ValueCountFrequency (%)
4 1
100.0%
2023-12-13T02:35:59.333964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
50.0%
4 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 1
50.0%
Decimal Number 1
50.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
50.0%
4 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
50.0%
4 1
50.0%

하이브리드(LPG-전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)45.8%
Missing181
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean5.375
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:59.482855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q37.25
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.9312781
Coefficient of variation (CV)0.54535407
Kurtosis-0.84796642
Mean5.375
Median Absolute Deviation (MAD)1.5
Skewness0.60149644
Sum129
Variance8.5923913
MonotonicityNot monotonic
2023-12-13T02:35:59.622537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 6
 
2.9%
3 4
 
2.0%
10 3
 
1.5%
5 2
 
1.0%
7 2
 
1.0%
2 2
 
1.0%
6 1
 
0.5%
9 1
 
0.5%
11 1
 
0.5%
8 1
 
0.5%
(Missing) 181
88.3%
ValueCountFrequency (%)
1 1
 
0.5%
2 2
 
1.0%
3 4
2.0%
4 6
2.9%
5 2
 
1.0%
6 1
 
0.5%
7 2
 
1.0%
8 1
 
0.5%
9 1
 
0.5%
10 3
1.5%
ValueCountFrequency (%)
11 1
 
0.5%
10 3
1.5%
9 1
 
0.5%
8 1
 
0.5%
7 2
 
1.0%
6 1
 
0.5%
5 2
 
1.0%
4 6
2.9%
3 4
2.0%
2 2
 
1.0%

하이브리드(경유-전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)32.0%
Missing180
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean3.76
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:35:59.771911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7730849
Coefficient of variation (CV)0.73752259
Kurtosis1.7414287
Mean3.76
Median Absolute Deviation (MAD)2
Skewness1.289626
Sum94
Variance7.69
MonotonicityNot monotonic
2023-12-13T02:35:59.921730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 7
 
3.4%
1 5
 
2.4%
5 3
 
1.5%
6 3
 
1.5%
3 2
 
1.0%
4 2
 
1.0%
8 2
 
1.0%
12 1
 
0.5%
(Missing) 180
87.8%
ValueCountFrequency (%)
1 5
2.4%
2 7
3.4%
3 2
 
1.0%
4 2
 
1.0%
5 3
1.5%
6 3
1.5%
8 2
 
1.0%
12 1
 
0.5%
ValueCountFrequency (%)
12 1
 
0.5%
8 2
 
1.0%
6 3
1.5%
5 3
1.5%
4 2
 
1.0%
3 2
 
1.0%
2 7
3.4%
1 5
2.4%

하이브리드(휘발유-전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)80.0%
Missing170
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean268.25714
Minimum1
Maximum668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:36:00.080564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q1116
median254
Q3406
95-th percentile595.5
Maximum668
Range667
Interquartile range (IQR)290

Descriptive statistics

Standard deviation204.12256
Coefficient of variation (CV)0.76092125
Kurtosis-0.94773086
Mean268.25714
Median Absolute Deviation (MAD)144
Skewness0.22213797
Sum9389
Variance41666.02
MonotonicityNot monotonic
2023-12-13T02:36:00.241518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 6
 
2.9%
1 2
 
1.0%
249 2
 
1.0%
529 1
 
0.5%
340 1
 
0.5%
125 1
 
0.5%
378 1
 
0.5%
244 1
 
0.5%
192 1
 
0.5%
415 1
 
0.5%
Other values (18) 18
 
8.8%
(Missing) 170
82.9%
ValueCountFrequency (%)
1 2
 
1.0%
2 6
2.9%
115 1
 
0.5%
117 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
192 1
 
0.5%
229 1
 
0.5%
244 1
 
0.5%
249 2
 
1.0%
ValueCountFrequency (%)
668 1
0.5%
634 1
0.5%
579 1
0.5%
559 1
0.5%
542 1
0.5%
529 1
0.5%
469 1
0.5%
415 1
0.5%
414 1
0.5%
398 1
0.5%

휘발유
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)48.1%
Missing124
Missing (%)60.5%
Infinite0
Infinite (%)0.0%
Mean695.79012
Minimum1
Maximum4112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:36:00.417456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q31477
95-th percentile2911
Maximum4112
Range4111
Interquartile range (IQR)1475

Descriptive statistics

Standard deviation1101.3589
Coefficient of variation (CV)1.5828895
Kurtosis0.8766122
Mean695.79012
Median Absolute Deviation (MAD)4
Skewness1.3910561
Sum56359
Variance1212991.3
MonotonicityNot monotonic
2023-12-13T02:36:00.584803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 12
 
5.9%
2 10
 
4.9%
5 9
 
4.4%
3 8
 
3.9%
6 4
 
2.0%
7 3
 
1.5%
4 3
 
1.5%
1382 1
 
0.5%
1916 1
 
0.5%
2337 1
 
0.5%
Other values (29) 29
 
14.1%
(Missing) 124
60.5%
ValueCountFrequency (%)
1 12
5.9%
2 10
4.9%
3 8
3.9%
4 3
 
1.5%
5 9
4.4%
6 4
 
2.0%
7 3
 
1.5%
8 1
 
0.5%
11 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
4112 1
0.5%
3911 1
0.5%
3477 1
0.5%
3001 1
0.5%
2911 1
0.5%
2664 1
0.5%
2396 1
0.5%
2337 1
0.5%
2251 1
0.5%
2215 1
0.5%

휘발유(무연)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)57.5%
Missing132
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean997.82192
Minimum1
Maximum4367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:36:00.746987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median11
Q32133
95-th percentile3681.8
Maximum4367
Range4366
Interquartile range (IQR)2131

Descriptive statistics

Standard deviation1405.8879
Coefficient of variation (CV)1.4089567
Kurtosis-0.55825182
Mean997.82192
Median Absolute Deviation (MAD)10
Skewness0.99097735
Sum72841
Variance1976520.7
MonotonicityNot monotonic
2023-12-13T02:36:00.907876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 12
 
5.9%
2 10
 
4.9%
3 8
 
3.9%
45 2
 
1.0%
4 2
 
1.0%
5 2
 
1.0%
11 2
 
1.0%
3803 1
 
0.5%
4272 1
 
0.5%
14 1
 
0.5%
Other values (32) 32
 
15.6%
(Missing) 132
64.4%
ValueCountFrequency (%)
1 12
5.9%
2 10
4.9%
3 8
3.9%
4 2
 
1.0%
5 2
 
1.0%
6 1
 
0.5%
11 2
 
1.0%
12 1
 
0.5%
14 1
 
0.5%
21 1
 
0.5%
ValueCountFrequency (%)
4367 1
0.5%
4272 1
0.5%
3803 1
0.5%
3701 1
0.5%
3669 1
0.5%
3527 1
0.5%
3501 1
0.5%
3299 1
0.5%
3088 1
0.5%
3023 1
0.5%

휘발유(유연)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)33.3%
Missing178
Missing (%)86.8%
Infinite0
Infinite (%)0.0%
Mean3.7777778
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-13T02:36:01.052450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8.4
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5469942
Coefficient of variation (CV)0.67420435
Kurtosis3.2516309
Mean3.7777778
Median Absolute Deviation (MAD)1
Skewness1.625911
Sum102
Variance6.4871795
MonotonicityNot monotonic
2023-12-13T02:36:01.224938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 9
 
4.4%
2 4
 
2.0%
1 4
 
2.0%
5 3
 
1.5%
6 2
 
1.0%
4 2
 
1.0%
12 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
(Missing) 178
86.8%
ValueCountFrequency (%)
1 4
2.0%
2 4
2.0%
3 9
4.4%
4 2
 
1.0%
5 3
 
1.5%
6 2
 
1.0%
7 1
 
0.5%
9 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
12 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
6 2
 
1.0%
5 3
 
1.5%
4 2
 
1.0%
3 9
4.4%
2 4
2.0%
1 4
2.0%

Interactions

2023-12-13T02:35:54.481373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:40.733908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.826015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.443919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.070413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.189618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.429845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.841589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.641218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.680738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.661359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.589005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.543227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:40.807387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.911495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.568615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.173153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.294595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.513095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.987670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.722119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.770802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.735607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.654126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.614820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:40.901641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.990945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.689778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.270879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.396966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.620697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:49.144067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.819489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.868140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.813213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.726151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.694305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.014313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:42.515146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.812592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.371836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.493071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.746614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:49.322975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.903822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.946967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.899087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.800286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.774935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.098710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:42.627847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.955725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.464526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.598486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.867450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:49.479635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.001523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.024028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.983052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.878514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.851713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.184374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:42.767818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.186038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.549963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.719346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.964640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:49.572137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.083768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.098186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.063785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.953590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.941624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.259997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:42.872720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.320871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.645418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.844264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.048120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:49.675586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.182080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.171557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.147069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.027564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:55.005928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.357048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:42.945217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.427030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.731615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.931861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.142175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.140886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.278961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.251158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.227187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.110152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:55.080691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.452916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.027682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.548150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.813896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.029649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.230474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.232625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.357456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.335009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.303195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.189989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:55.157083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.540454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.151358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.690968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:45.912026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.126478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.378271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.345481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.445024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.423768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.380600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.269506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:55.231857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.656675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.250334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.831157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.010381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.239500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.520110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.440873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.531496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.507845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.445604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.338508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:55.587637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:41.747360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:43.346719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:44.936894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:46.092062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:47.338621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:48.704785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:50.550402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:51.602886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:52.590778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:53.514586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:35:54.416036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:36:01.350760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차종용도CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)
1.0000.0000.0000.0000.0000.0000.7840.0000.5590.0001.0000.8870.8670.3780.7250.000
차종0.0001.0000.0000.6450.5760.398NaN0.6160.4010.000NaNNaNNaN0.5780.697NaN
용도0.0000.0001.0000.2730.4020.5160.5430.1510.6570.000NaN0.0001.0000.0000.0810.000
CNG0.0000.6450.2731.0000.000NaNNaN0.000NaNNaNNaNNaNNaNNaNNaNNaN
경유0.0000.5760.4020.0001.0000.4790.5750.6060.6850.0000.2540.6540.8550.8350.8510.575
기타연료0.0000.3980.516NaN0.4791.000NaN0.0000.000NaNNaNNaNNaN0.0000.000NaN
수소0.784NaN0.543NaN0.575NaN1.0000.2570.570NaN0.0000.0000.4850.5280.5090.557
엘피지0.0000.6160.1510.0000.6060.0000.2571.0000.622NaN0.6060.0000.4990.7980.8040.375
전기0.5590.4010.657NaN0.6850.0000.5700.6221.000NaN0.0000.3790.7970.7280.6930.000
하이브리드(CNG-전기)0.0000.0000.000NaN0.000NaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaN
하이브리드(LPG-전기)1.000NaNNaNNaN0.254NaN0.0000.6060.000NaN1.0000.6340.0000.0000.0000.000
하이브리드(경유-전기)0.887NaN0.000NaN0.654NaN0.0000.0000.379NaN0.6341.0000.0000.7360.0000.690
하이브리드(휘발유-전기)0.867NaN1.000NaN0.855NaN0.4850.4990.797NaN0.0000.0001.0000.9240.7840.000
휘발유0.3780.5780.000NaN0.8350.0000.5280.7980.728NaN0.0000.7360.9241.0000.9020.577
휘발유(무연)0.7250.6970.081NaN0.8510.0000.5090.8040.693NaN0.0000.0000.7840.9021.0000.360
휘발유(유연)0.000NaN0.000NaN0.575NaN0.5570.3750.000NaN0.0000.6900.0000.5770.3601.000
2023-12-13T02:36:01.581382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도차종
용도1.0000.0000.000
0.0001.0000.000
차종0.0000.0001.000
2023-12-13T02:36:02.057158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CNG경유기타연료수소엘피지전기하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)차종용도
CNG1.000-0.1330.5780.549-0.2880.124-0.2210.1850.395-0.104-0.0080.3600.0000.3050.435
경유-0.1331.0000.4700.6520.5690.720-0.0140.7300.9510.7730.5840.6410.0000.4320.425
기타연료0.5780.4701.000NaN0.1590.152NaNNaNNaN0.2100.202NaN0.0000.2640.506
수소0.5490.652NaN1.0000.4430.3970.1070.2350.5820.6040.6720.3600.0001.0000.519
엘피지-0.2880.5690.1590.4431.0000.6130.0220.0950.3400.6800.8470.3330.0000.4100.111
전기0.1240.7200.1520.3970.6131.0000.1160.7320.8790.8170.8400.3730.2180.2680.480
하이브리드(LPG-전기)-0.221-0.014NaN0.1070.0220.1161.0000.018-0.117-0.002-0.0270.0511.0001.0001.000
하이브리드(경유-전기)0.1850.730NaN0.2350.0950.7320.0181.0000.6920.7010.7040.4020.2131.0000.000
하이브리드(휘발유-전기)0.3950.951NaN0.5820.3400.879-0.1170.6921.0000.9610.9280.4610.2821.0000.870
휘발유-0.1040.7730.2100.6040.6800.817-0.0020.7010.9611.0000.8730.4870.1090.4010.000
휘발유(무연)-0.0080.5840.2020.6720.8470.840-0.0270.7040.9280.8731.0000.6220.2740.3860.066
휘발유(유연)0.3600.641NaN0.3600.3330.3730.0510.4020.4610.4870.6221.0000.0001.0000.000
0.0000.0000.0000.0000.0000.2181.0000.2130.2820.1090.2740.0001.0000.0000.000
차종0.3050.4320.2641.0000.4100.2681.0001.0001.0000.4010.3861.0000.0001.0000.000
용도0.4350.4250.5060.5190.1110.4801.0000.0000.8700.0000.0660.0000.0000.0001.000

Missing values

2023-12-13T02:35:55.762514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:35:55.967535image/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.
2023-12-13T02:35:56.201181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

차종용도CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)
0가락1동승용비사업용<NA>2623<NA>429970<NA>45668291135016
1가락1동승용사업용<NA><NA><NA><NA>482<NA><NA><NA><NA><NA><NA><NA>
2가락1동승합비사업용<NA>1018<NA>11<NA><NA><NA><NA><NA>11<NA>
3가락1동특수비사업용<NA>142<NA>1<NA><NA><NA><NA><NA><NA><NA><NA>
4가락1동특수사업용<NA>6<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5가락1동화물비사업용489812<NA>3912<NA><NA><NA><NA>53<NA>
6가락1동화물사업용<NA>61<NA><NA>2<NA><NA><NA><NA><NA><NA><NA><NA>
7가락2동승용비사업용12553<NA>644033<NA>63469266436692
8가락2동승용사업용<NA><NA><NA><NA>833<NA><NA><NA><NA><NA><NA><NA>
9가락2동승합비사업용<NA>1562<NA>41<NA><NA><NA><NA><NA>12<NA>
차종용도CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)
195풍납1동화물비사업용<NA>3502<NA>341<NA><NA><NA><NA>32<NA>
196풍납1동화물사업용<NA>773<NA>172<NA><NA><NA><NA><NA><NA><NA>
197풍납2동승용비사업용21837<NA>436629<NA>1<NA>340183526223
198풍납2동승용사업용<NA>2<NA><NA>2211<NA><NA><NA>1<NA><NA><NA>
199풍납2동승합비사업용<NA>1473<NA>27<NA><NA><NA><NA><NA><NA>1<NA>
200풍납2동승합사업용<NA><NA><NA><NA>7<NA><NA><NA><NA><NA><NA><NA><NA>
201풍납2동특수비사업용<NA>3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
202풍납2동특수사업용<NA>5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
203풍납2동화물비사업용24137<NA>321<NA><NA><NA><NA>43<NA>
204풍납2동화물사업용<NA>652<NA>51<NA><NA><NA><NA><NA><NA><NA>