Overview

Dataset statistics

Number of variables11
Number of observations121
Missing cells23
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory93.1 B

Variable types

Numeric4
Categorical5
Text1
DateTime1

Dataset

Description대구광역시 서구의 공용차량 운영현황 자료이며 2023. 9. 7. 기준 자료입니다. 차종, 차형, 차명, 자동차등록번호, 등록일자, 배기량, 승차인원, 적재량, 구입금액, 사용 부서의 데이터를 포함하고 있습니다.
Author대구광역시 서구
URLhttps://www.data.go.kr/data/15103325/fileData.do

Alerts

배기량(cc) is highly overall correlated with 적재량(kg) and 4 other fieldsHigh correlation
적재량(kg) is highly overall correlated with 배기량(cc) and 1 other fieldsHigh correlation
구입금액(원) is highly overall correlated with 배기량(cc) and 2 other fieldsHigh correlation
차종 is highly overall correlated with 배기량(cc) and 4 other fieldsHigh correlation
차형 is highly overall correlated with 배기량(cc) and 2 other fieldsHigh correlation
차명 is highly overall correlated with 배기량(cc) and 5 other fieldsHigh correlation
승차인원 is highly overall correlated with 차종 and 1 other fieldsHigh correlation
배기량(cc) has 23 (19.0%) missing valuesMissing
연번 has unique valuesUnique
자동차등록번호 has unique valuesUnique
적재량(kg) has 54 (44.6%) zerosZeros

Reproduction

Analysis started2023-12-12 09:59:24.890448
Analysis finished2023-12-12 09:59:28.174536
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:59:28.272073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q131
median61
Q391
95-th percentile115
Maximum121
Range120
Interquartile range (IQR)60

Descriptive statistics

Standard deviation35.073732
Coefficient of variation (CV)0.57497921
Kurtosis-1.2
Mean61
Median Absolute Deviation (MAD)30
Skewness0
Sum7381
Variance1230.1667
MonotonicityStrictly increasing
2023-12-12T18:59:28.470025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
92 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
Other values (111) 111
91.7%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%

차종
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
승용
45 
화물
40 
특수
27 
승합
9 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
승용 45
37.2%
화물 40
33.1%
특수 27
22.3%
승합 9
 
7.4%

Length

2023-12-12T18:59:28.646239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:59:28.791309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승용 45
37.2%
화물 40
33.1%
특수 27
22.3%
승합 9
 
7.4%

차형
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
중형
32 
소형
31 
청소
22 
경형
20 
다목적형
7 
Other values (4)
9 

Length

Max length4
Median length2
Mean length2.1157025
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row대형
2nd row중형
3rd row급수
4th row중형
5th row방제

Common Values

ValueCountFrequency (%)
중형 32
26.4%
소형 31
25.6%
청소 22
18.2%
경형 20
16.5%
다목적형 7
 
5.8%
대형 4
 
3.3%
급수 2
 
1.7%
구급 2
 
1.7%
방제 1
 
0.8%

Length

2023-12-12T18:59:28.945268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:59:29.120089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중형 32
26.4%
소형 31
25.6%
청소 22
18.2%
경형 20
16.5%
다목적형 7
 
5.8%
대형 4
 
3.3%
급수 2
 
1.7%
구급 2
 
1.7%
방제 1
 
0.8%

차명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
SM3 ZE
11 
봉고III 1톤
11 
그랜드 스타렉스
10 
포터 II
9 
아이오닉 일렉트릭
8 
Other values (45)
72 

Length

Max length18
Median length15
Mean length7.1157025
Min length2

Unique

Unique33 ?
Unique (%)27.3%

Sample

1st row그랜저
2nd row로체
3rd row현대급수차
4th row그랜드스타렉스 구급차
5th row한서이동방제차

Common Values

ValueCountFrequency (%)
SM3 ZE 11
 
9.1%
봉고III 1톤 11
 
9.1%
그랜드 스타렉스 10
 
8.3%
포터 II 9
 
7.4%
아이오닉 일렉트릭 8
 
6.6%
모닝 7
 
5.8%
레이 7
 
5.8%
라보롱카고 6
 
5.0%
에이엠압착식진개차 3
 
2.5%
쏘렌토 2
 
1.7%
Other values (40) 47
38.8%

Length

2023-12-12T18:59:29.344955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sm3 11
 
6.1%
봉고iii 11
 
6.1%
1톤 11
 
6.1%
ze 11
 
6.1%
포터 10
 
5.6%
ii 10
 
5.6%
일렉트릭 10
 
5.6%
스타렉스 10
 
5.6%
그랜드 10
 
5.6%
아이오닉 8
 
4.4%
Other values (50) 78
43.3%

자동차등록번호
Text

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T18:59:29.731391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0909091
Min length7

Characters and Unicode

Total characters858
Distinct characters41
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)100.0%

Sample

1st row10보3718
2nd row52부5812
3rd row85오4653
4th row73서6215
5th row85루9715
ValueCountFrequency (%)
10보3718 1
 
0.8%
89수0790 1
 
0.8%
89더0816 1
 
0.8%
83도0712 1
 
0.8%
74주8377 1
 
0.8%
85버0861 1
 
0.8%
85버0851 1
 
0.8%
82누0615 1
 
0.8%
244소6512 1
 
0.8%
164무4894 1
 
0.8%
Other values (111) 111
91.7%
2023-12-12T18:59:30.304284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 102
11.9%
0 84
9.8%
1 81
9.4%
4 79
9.2%
7 75
8.7%
6 68
7.9%
2 67
7.8%
3 62
7.2%
9 62
7.2%
5 57
6.6%
Other values (31) 121
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 737
85.9%
Other Letter 121
 
14.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
버 13
 
10.7%
나 8
 
6.6%
소 8
 
6.6%
어 6
 
5.0%
로 6
 
5.0%
너 6
 
5.0%
노 6
 
5.0%
루 5
 
4.1%
러 5
 
4.1%
더 5
 
4.1%
Other values (21) 53
43.8%
Decimal Number
ValueCountFrequency (%)
8 102
13.8%
0 84
11.4%
1 81
11.0%
4 79
10.7%
7 75
10.2%
6 68
9.2%
2 67
9.1%
3 62
8.4%
9 62
8.4%
5 57
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 737
85.9%
Hangul 121
 
14.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
버 13
 
10.7%
나 8
 
6.6%
소 8
 
6.6%
어 6
 
5.0%
로 6
 
5.0%
너 6
 
5.0%
노 6
 
5.0%
루 5
 
4.1%
러 5
 
4.1%
더 5
 
4.1%
Other values (21) 53
43.8%
Common
ValueCountFrequency (%)
8 102
13.8%
0 84
11.4%
1 81
11.0%
4 79
10.7%
7 75
10.2%
6 68
9.2%
2 67
9.1%
3 62
8.4%
9 62
8.4%
5 57
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 737
85.9%
Hangul 121
 
14.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 102
13.8%
0 84
11.4%
1 81
11.0%
4 79
10.7%
7 75
10.2%
6 68
9.2%
2 67
9.1%
3 62
8.4%
9 62
8.4%
5 57
7.7%
Hangul
ValueCountFrequency (%)
버 13
 
10.7%
나 8
 
6.6%
소 8
 
6.6%
어 6
 
5.0%
로 6
 
5.0%
너 6
 
5.0%
노 6
 
5.0%
루 5
 
4.1%
러 5
 
4.1%
더 5
 
4.1%
Other values (21) 53
43.8%

등록일자
Date

Distinct85
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2006-12-07 00:00:00
Maximum2022-06-28 00:00:00
2023-12-12T18:59:30.506891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:30.665887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

배기량(cc)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)23.5%
Missing23
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean3516.551
Minimum796
Maximum11670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:59:30.824934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum796
5-th percentile796
Q11995.75
median2497
Q35407.5
95-th percentile9960
Maximum11670
Range10874
Interquartile range (IQR)3411.75

Descriptive statistics

Standard deviation2746.3717
Coefficient of variation (CV)0.78098445
Kurtosis1.5437343
Mean3516.551
Median Absolute Deviation (MAD)1268.5
Skewness1.4570078
Sum344622
Variance7542557.4
MonotonicityNot monotonic
2023-12-12T18:59:30.982758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2497 34
28.1%
998 14
11.6%
6299 9
 
7.4%
796 6
 
5.0%
6798 5
 
4.1%
3933 4
 
3.3%
11670 4
 
3.3%
7412 3
 
2.5%
2359 2
 
1.7%
2157 2
 
1.7%
Other values (13) 15
12.4%
(Missing) 23
19.0%
ValueCountFrequency (%)
796 6
5.0%
998 14
11.6%
1396 1
 
0.8%
1685 1
 
0.8%
1798 1
 
0.8%
1995 2
 
1.7%
1998 1
 
0.8%
1999 1
 
0.8%
2157 2
 
1.7%
2199 1
 
0.8%
ValueCountFrequency (%)
11670 4
3.3%
9960 2
 
1.7%
7412 3
 
2.5%
6798 5
4.1%
6606 1
 
0.8%
6299 9
7.4%
5899 1
 
0.8%
3933 4
3.3%
3470 1
 
0.8%
2999 1
 
0.8%

승차인원
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
54 
3
34 
2
14 
6
8 
12
 
4
Other values (5)
7 

Length

Max length7
Median length1
Mean length1.1157025
Min length1

Unique

Unique3 ?
Unique (%)2.5%

Sample

1st row5
2nd row5
3rd row3
4th row12
5th row7

Common Values

ValueCountFrequency (%)
5 54
44.6%
3 34
28.1%
2 14
 
11.6%
6 8
 
6.6%
12 4
 
3.3%
7 2
 
1.7%
11 2
 
1.7%
15 1
 
0.8%
40~42 1
 
0.8%
25 1
 
0.8%

Length

2023-12-12T18:59:31.140792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:59:31.325927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 54
44.6%
3 34
28.1%
2 14
 
11.6%
6 8
 
6.6%
12 4
 
3.3%
7 2
 
1.7%
11 2
 
1.7%
15 1
 
0.8%
40~42 1
 
0.8%
25 1
 
0.8%

적재량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1331.405
Minimum0
Maximum12000
Zeros54
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:59:31.518921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median550
Q31000
95-th percentile5600
Maximum12000
Range12000
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation2121.2136
Coefficient of variation (CV)1.5932144
Kurtosis5.3276771
Mean1331.405
Median Absolute Deviation (MAD)550
Skewness2.1575373
Sum161100
Variance4499547.2
MonotonicityNot monotonic
2023-12-12T18:59:31.678191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 54
44.6%
1000 20
 
16.5%
550 6
 
5.0%
600 6
 
5.0%
5000 4
 
3.3%
4500 4
 
3.3%
400 3
 
2.5%
2500 3
 
2.5%
5600 3
 
2.5%
7500 2
 
1.7%
Other values (13) 16
 
13.2%
ValueCountFrequency (%)
0 54
44.6%
300 2
 
1.7%
400 3
 
2.5%
550 6
 
5.0%
600 6
 
5.0%
800 2
 
1.7%
1000 20
 
16.5%
1900 1
 
0.8%
2000 1
 
0.8%
2300 1
 
0.8%
ValueCountFrequency (%)
12000 1
 
0.8%
7500 2
1.7%
6000 1
 
0.8%
5600 3
2.5%
5400 1
 
0.8%
5200 1
 
0.8%
5000 4
3.3%
4800 1
 
0.8%
4600 1
 
0.8%
4500 4
3.3%

구입금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51121482
Minimum8790000
Maximum2.93267 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T18:59:31.835624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8790000
5-th percentile10268000
Q116560000
median29680000
Q353920940
95-th percentile1.98163 × 108
Maximum2.93267 × 108
Range2.84477 × 108
Interquartile range (IQR)37360940

Descriptive statistics

Standard deviation59491026
Coefficient of variation (CV)1.1637187
Kurtosis6.6958386
Mean51121482
Median Absolute Deviation (MAD)13190000
Skewness2.5655197
Sum6.1856993 × 109
Variance3.5391822 × 1015
MonotonicityNot monotonic
2023-12-12T18:59:31.978680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30708000 11
 
9.1%
37770000 6
 
5.0%
16560000 6
 
5.0%
18842000 4
 
3.3%
14750000 4
 
3.3%
15720000 3
 
2.5%
84000000 3
 
2.5%
11813450 2
 
1.7%
117500000 2
 
1.7%
15680000 2
 
1.7%
Other values (72) 78
64.5%
ValueCountFrequency (%)
8790000 1
0.8%
9217700 1
0.8%
9310000 1
0.8%
9380000 2
1.7%
9760000 1
0.8%
10268000 1
0.8%
10990000 2
1.7%
11090000 1
0.8%
11130000 1
0.8%
11813450 2
1.7%
ValueCountFrequency (%)
293267000 1
0.8%
284450000 1
0.8%
282140000 1
0.8%
271264000 1
0.8%
224486000 1
0.8%
203329000 1
0.8%
198163000 1
0.8%
171975000 1
0.8%
130500000 1
0.8%
126122000 1
0.8%

사용부서(제2차관리부서)
Categorical

Distinct35
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
환경청소과
46 
총무과
14 
도시공원과
8 
보건행정과
6 
건설안전과
5 
Other values (30)
42 

Length

Max length6
Median length5
Mean length4.5123967
Min length3

Unique

Unique24 ?
Unique (%)19.8%

Sample

1st row의회사무국
2nd row총무과
3rd row도시공원과
4th row총무과
5th row도시공원과

Common Values

ValueCountFrequency (%)
환경청소과 46
38.0%
총무과 14
 
11.6%
도시공원과 8
 
6.6%
보건행정과 6
 
5.0%
건설안전과 5
 
4.1%
교통과 5
 
4.1%
복지정책과 4
 
3.3%
문화홍보과 3
 
2.5%
의회사무국 2
 
1.7%
사회복지과 2
 
1.7%
Other values (25) 26
21.5%

Length

2023-12-12T18:59:32.123131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
환경청소과 46
38.0%
총무과 14
 
11.6%
도시공원과 8
 
6.6%
보건행정과 6
 
5.0%
건설안전과 5
 
4.1%
교통과 5
 
4.1%
복지정책과 4
 
3.3%
문화홍보과 3
 
2.5%
의회사무국 2
 
1.7%
사회복지과 2
 
1.7%
Other values (25) 26
21.5%

Interactions

2023-12-12T18:59:27.056672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:25.766340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.244278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.682279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:27.163040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:25.898284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.356743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.771967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:27.261974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.012608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.454073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.855386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:27.376214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.131027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.565253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:59:26.937544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:59:32.209538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번차종차형차명등록일자배기량(cc)승차인원적재량(kg)구입금액(원)사용부서(제2차관리부서)
연번1.0000.4370.4440.9291.0000.4130.3900.3220.3030.414
차종0.4371.0000.8630.9860.9900.9490.8750.6390.7120.654
차형0.4440.8631.0000.9990.9970.7780.7010.7250.8410.000
차명0.9290.9860.9991.0001.0001.0000.9911.0001.0000.000
등록일자1.0000.9900.9971.0001.0001.0000.9960.9941.0000.000
배기량(cc)0.4130.9490.7781.0001.0001.0000.7600.7230.8310.000
승차인원0.3900.8750.7010.9910.9960.7601.0000.5270.7260.000
적재량(kg)0.3220.6390.7251.0000.9940.7230.5271.0000.7710.000
구입금액(원)0.3030.7120.8411.0001.0000.8310.7260.7711.0000.000
사용부서(제2차관리부서)0.4140.6540.0000.0000.0000.0000.0000.0000.0001.000
2023-12-12T18:59:32.334057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차형승차인원차종사용부서(제2차관리부서)차명
차형1.0000.4110.7420.0000.785
승차인원0.4111.0000.7200.0000.684
차종0.7420.7201.0000.3370.714
사용부서(제2차관리부서)0.0000.0000.3371.0000.000
차명0.7850.6840.7140.0001.000
2023-12-12T18:59:32.439692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번배기량(cc)적재량(kg)구입금액(원)차종차형차명승차인원사용부서(제2차관리부서)
연번1.000-0.080-0.0500.0040.2510.2090.4840.1100.128
배기량(cc)-0.0801.0000.7160.8820.6200.5340.7580.4660.000
적재량(kg)-0.0500.7161.0000.3320.4920.4920.7890.2950.000
구입금액(원)0.0040.8820.3321.0000.5370.4210.7960.4320.000
차종0.2510.6200.4920.5371.0000.7420.7140.7200.337
차형0.2090.5340.4920.4210.7421.0000.7850.4110.000
차명0.4840.7580.7890.7960.7140.7851.0000.6840.000
승차인원0.1100.4660.2950.4320.7200.4110.6841.0000.000
사용부서(제2차관리부서)0.1280.0000.0000.0000.3370.0000.0000.0001.000

Missing values

2023-12-12T18:59:27.913692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:59:28.095224image/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

연번차종차형차명자동차등록번호등록일자배기량(cc)승차인원적재량(kg)구입금액(원)사용부서(제2차관리부서)
01승용대형그랜저10보37182006-12-0726565028660000의회사무국
12승용중형로체52부58122007-02-2317985016521180총무과
23특수급수현대급수차85오46532007-03-1666063500053920940도시공원과
34승합중형그랜드스타렉스 구급차73서62152008-04-29249712027690000총무과
45특수방제한서이동방제차85루97152008-07-2539337190055820140도시공원과
56특수청소한빛천연가스음식물수거차86나23222008-12-17741235000102800000환경청소과
67특수청소메가트럭CNG압착진개86나23812008-12-1974123450087170000환경청소과
78화물소형액티언스포츠85러65412009-03-191998540022200000도시공원과
89특수청소메가트럭CNG압착진개86나25342009-06-0174123450089640000환경청소과
910특수청소한빛 음식물 수거차89모64772012-06-2158993520094746000환경청소과
연번차종차형차명자동차등록번호등록일자배기량(cc)승차인원적재량(kg)구입금액(원)사용부서(제2차관리부서)
111112특수청소삼능7.5톤음식물쓰레기수거차87어41572021-06-24629937500107880000환경청소과
112113특수청소이텍3톤진공식노면청소차90오23772021-07-161167023000284450000환경청소과
113114화물소형봉고III 1톤87어15922021-09-0824973100018842000환경청소과
114115화물소형봉고III 1톤86어27102021-09-1524973100018842000환경청소과
115116승용경형레이127노60512021-10-159985015720000문화홍보과
116117승용경형레이127노60492021-10-159985015720000환경청소과
117118승용경형레이127노60932021-10-159985015720000문화홍보과
118119승용다목적형ZOE32다37142022-03-21<NA>5042300000복지정책과
119120승합중형뉴-카운티704더48582022-03-24393325085226000총무과
120121승합중형스타리아735라26832022-06-28347011033447000총무과