Overview

Dataset statistics

Number of variables8
Number of observations213
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 KiB
Average record size in memory69.6 B

Variable types

Numeric5
Categorical3

Dataset

Description서울특별시 성동구 연료별 자동차등록 현황자료 입니다. 연도, 시군구별 구분, 연료별 구분, 용도별 구분, 승용 /승합/화물/특수 구분 등의 정보를 포함합니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15037596/fileData.do

Alerts

시군구 has constant value ""Constant
승합 is highly overall correlated with 화물 and 1 other fieldsHigh correlation
화물 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
특수 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
승용 has 15 (7.0%) zerosZeros
승합 has 102 (47.9%) zerosZeros
화물 has 107 (50.2%) zerosZeros
특수 has 172 (80.8%) zerosZeros

Reproduction

Analysis started2023-12-12 13:14:17.140125
Analysis finished2023-12-12 13:14:20.255487
Duration3.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct11
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.2911
Minimum2011
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T22:14:20.325158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12014
median2016
Q32019
95-th percentile2021
Maximum2021
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1815751
Coefficient of variation (CV)0.0015779344
Kurtosis-1.2340648
Mean2016.2911
Median Absolute Deviation (MAD)3
Skewness-0.10012474
Sum429470
Variance10.12242
MonotonicityIncreasing
2023-12-12T22:14:20.560059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2021 23
10.8%
2020 22
10.3%
2019 21
9.9%
2018 20
9.4%
2013 19
8.9%
2016 19
8.9%
2017 19
8.9%
2012 18
8.5%
2014 18
8.5%
2015 18
8.5%
ValueCountFrequency (%)
2011 16
7.5%
2012 18
8.5%
2013 19
8.9%
2014 18
8.5%
2015 18
8.5%
2016 19
8.9%
2017 19
8.9%
2018 20
9.4%
2019 21
9.9%
2020 22
10.3%
ValueCountFrequency (%)
2021 23
10.8%
2020 22
10.3%
2019 21
9.9%
2018 20
9.4%
2017 19
8.9%
2016 19
8.9%
2015 18
8.5%
2014 18
8.5%
2013 19
8.9%
2012 18
8.5%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
서울특별시 성동구
213 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 성동구
2nd row서울특별시 성동구
3rd row서울특별시 성동구
4th row서울특별시 성동구
5th row서울특별시 성동구

Common Values

ValueCountFrequency (%)
서울특별시 성동구 213
100.0%

Length

2023-12-12T22:14:20.710988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:21.185226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 213
50.0%
성동구 213
50.0%

연료별
Categorical

Distinct12
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
CNG
22 
경유
22 
기타연료
22 
엘피지
22 
하이브리드(휘발유+전기)
22 
Other values (7)
103 

Length

Max length13
Median length12
Mean length5.6760563
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료

Common Values

ValueCountFrequency (%)
CNG 22
10.3%
경유 22
10.3%
기타연료 22
10.3%
엘피지 22
10.3%
하이브리드(휘발유+전기) 22
10.3%
휘발유 22
10.3%
휘발유(무연) 22
10.3%
하이브리드(LPG+전기) 21
9.9%
전기 20
9.4%
휘발유(유연) 8
 
3.8%
Other values (2) 10
4.7%

Length

2023-12-12T22:14:21.329648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cng 22
10.3%
경유 22
10.3%
기타연료 22
10.3%
엘피지 22
10.3%
하이브리드(휘발유+전기 22
10.3%
휘발유 22
10.3%
휘발유(무연 22
10.3%
하이브리드(lpg+전기 21
9.9%
전기 20
9.4%
휘발유(유연 8
 
3.8%
Other values (2) 10
4.7%

용도별
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
비사업용
114 
사업용
99 

Length

Max length4
Median length4
Mean length3.5352113
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비사업용 114
53.5%
사업용 99
46.5%

Length

2023-12-12T22:14:21.502063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:21.649800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 114
53.5%
사업용 99
46.5%

승용
Real number (ℝ)

ZEROS 

Distinct149
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4271.5587
Minimum0
Maximum28702
Zeros15
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T22:14:21.783786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median104
Q32818
95-th percentile26149.6
Maximum28702
Range28702
Interquartile range (IQR)2810

Descriptive statistics

Standard deviation8393.288
Coefficient of variation (CV)1.964924
Kurtosis2.1177249
Mean4271.5587
Median Absolute Deviation (MAD)104
Skewness1.9380862
Sum909842
Variance70447284
MonotonicityNot monotonic
2023-12-12T22:14:21.993397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
7.0%
2 13
 
6.1%
4 9
 
4.2%
1 8
 
3.8%
20 4
 
1.9%
14 4
 
1.9%
28 3
 
1.4%
3 3
 
1.4%
5 3
 
1.4%
52 2
 
0.9%
Other values (139) 149
70.0%
ValueCountFrequency (%)
0 15
7.0%
1 8
3.8%
2 13
6.1%
3 3
 
1.4%
4 9
4.2%
5 3
 
1.4%
6 1
 
0.5%
7 1
 
0.5%
8 2
 
0.9%
10 1
 
0.5%
ValueCountFrequency (%)
28702 1
0.5%
28542 1
0.5%
28098 1
0.5%
28019 1
0.5%
27318 1
0.5%
26679 1
0.5%
26611 1
0.5%
26518 1
0.5%
26473 1
0.5%
26309 1
0.5%

승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.80282
Minimum0
Maximum2819
Zeros102
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T22:14:22.180614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q325
95-th percentile1907.2
Maximum2819
Range2819
Interquartile range (IQR)25

Descriptive statistics

Standard deviation590.81446
Coefficient of variation (CV)2.776347
Kurtosis10.399683
Mean212.80282
Median Absolute Deviation (MAD)1
Skewness3.3441627
Sum45327
Variance349061.73
MonotonicityNot monotonic
2023-12-12T22:14:22.406082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
47.9%
4 10
 
4.7%
3 7
 
3.3%
1 5
 
2.3%
16 4
 
1.9%
5 4
 
1.9%
14 3
 
1.4%
6 3
 
1.4%
12 3
 
1.4%
2 3
 
1.4%
Other values (61) 69
32.4%
ValueCountFrequency (%)
0 102
47.9%
1 5
 
2.3%
2 3
 
1.4%
3 7
 
3.3%
4 10
 
4.7%
5 4
 
1.9%
6 3
 
1.4%
7 1
 
0.5%
8 2
 
0.9%
9 1
 
0.5%
ValueCountFrequency (%)
2819 1
0.5%
2781 1
0.5%
2725 1
0.5%
2716 1
0.5%
2671 1
0.5%
2597 1
0.5%
2482 1
0.5%
2380 1
0.5%
2179 1
0.5%
2105 1
0.5%

화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.47418
Minimum0
Maximum8814
Zeros107
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T22:14:22.597832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q354
95-th percentile4210.8
Maximum8814
Range8814
Interquartile range (IQR)54

Descriptive statistics

Standard deviation1864.2852
Coefficient of variation (CV)3.222763
Kurtosis13.495521
Mean578.47418
Median Absolute Deviation (MAD)0
Skewness3.8431162
Sum123215
Variance3475559.2
MonotonicityNot monotonic
2023-12-12T22:14:22.839286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107
50.2%
17 7
 
3.3%
5 5
 
2.3%
7 4
 
1.9%
8 4
 
1.9%
16 3
 
1.4%
33 3
 
1.4%
3 2
 
0.9%
4 2
 
0.9%
1313 2
 
0.9%
Other values (73) 74
34.7%
ValueCountFrequency (%)
0 107
50.2%
1 1
 
0.5%
2 1
 
0.5%
3 2
 
0.9%
4 2
 
0.9%
5 5
 
2.3%
6 1
 
0.5%
7 4
 
1.9%
8 4
 
1.9%
10 2
 
0.9%
ValueCountFrequency (%)
8814 1
0.5%
8593 1
0.5%
8520 1
0.5%
8511 1
0.5%
8507 1
0.5%
8428 1
0.5%
8383 1
0.5%
8345 1
0.5%
8287 1
0.5%
8040 1
0.5%

특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0704225
Minimum0
Maximum118
Zeros172
Zeros (%)80.8%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T22:14:22.991130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile85.4
Maximum118
Range118
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.037705
Coefficient of variation (CV)2.9808649
Kurtosis7.1810937
Mean9.0704225
Median Absolute Deviation (MAD)0
Skewness2.9278088
Sum1932
Variance731.03747
MonotonicityNot monotonic
2023-12-12T22:14:23.146468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 172
80.8%
1 14
 
6.6%
2 3
 
1.4%
104 2
 
0.9%
111 2
 
0.9%
82 1
 
0.5%
43 1
 
0.5%
118 1
 
0.5%
7 1
 
0.5%
102 1
 
0.5%
Other values (15) 15
 
7.0%
ValueCountFrequency (%)
0 172
80.8%
1 14
 
6.6%
2 3
 
1.4%
7 1
 
0.5%
33 1
 
0.5%
41 1
 
0.5%
43 1
 
0.5%
48 1
 
0.5%
52 1
 
0.5%
63 1
 
0.5%
ValueCountFrequency (%)
118 1
0.5%
113 1
0.5%
111 2
0.9%
106 1
0.5%
104 2
0.9%
102 1
0.5%
100 1
0.5%
97 1
0.5%
89 1
0.5%
83 1
0.5%

Interactions

2023-12-12T22:14:19.489540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:17.737913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.222398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.630329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.053947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.574684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:17.854122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.309133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.702132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.142756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.670536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:17.944772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.388583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.787287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.240459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.769012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.035985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.474537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.872660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.320505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.869458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.111622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.545056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:18.962470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:14:19.399647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:14:23.265423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도연료별용도별승용승합화물특수
연도1.0000.0000.0000.0000.0000.0000.000
연료별0.0001.0000.0000.7020.5140.6510.560
용도별0.0000.0001.0000.3290.2560.1590.164
승용0.0000.7020.3291.0000.7750.6740.888
승합0.0000.5140.2560.7751.0000.9780.749
화물0.0000.6510.1590.6740.9781.0000.805
특수0.0000.5600.1640.8880.7490.8051.000
2023-12-12T22:14:23.392099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도별연료별
용도별1.0000.000
연료별0.0001.000
2023-12-12T22:14:23.497722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도승용승합화물특수연료별용도별
연도1.0000.0560.020-0.0120.0240.0000.000
승용0.0561.0000.3890.4320.1870.3870.323
승합0.0200.3891.0000.7600.6000.2430.192
화물-0.0120.4320.7601.0000.6740.4250.193
특수0.0240.1870.6000.6741.0000.2740.161
연료별0.0000.3870.2430.4250.2741.0000.000
용도별0.0000.3230.1920.1930.1610.0001.000

Missing values

2023-12-12T22:14:20.035709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:14:20.197891image/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

연도시군구연료별용도별승용승합화물특수
02011서울특별시 성동구CNG비사업용193170
12011서울특별시 성동구CNG사업용220200
22011서울특별시 성동구경유비사업용138852671838333
32011서울특별시 성동구경유사업용84151124383
42011서울특별시 성동구기타연료비사업용26301
52011서울특별시 성동구기타연료사업용0050
62011서울특별시 성동구엘피지비사업용7155152615340
72011서울특별시 성동구엘피지사업용253523400
82011서울특별시 성동구전기비사업용14000
92011서울특별시 성동구하이브리드(LPG+전기)비사업용83000
연도시군구연료별용도별승용승합화물특수
2032021서울특별시 성동구하이브리드(LPG+전기)사업용28000
2042021서울특별시 성동구하이브리드(경유+전기)비사업용113000
2052021서울특별시 성동구하이브리드(경유+전기)사업용13000
2062021서울특별시 성동구하이브리드(휘발유+전기)비사업용4344000
2072021서울특별시 성동구하이브리드(휘발유+전기)사업용38000
2082021서울특별시 성동구휘발유비사업용2127010681
2092021서울특별시 성동구휘발유사업용294100
2102021서울특별시 성동구휘발유(무연)비사업용2870216120
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