Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory74.7 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description연료별(CNG, 경유, 기타연료, 수소, 엘피지, 전기, 하이브리드, 휘발유) 차종별(승용,승합,화물,특수) 용도별(비사업용,사업용) 자동차 등록현황 데이터입니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15037638&srcSe=7661IVAWM27C61E190

Alerts

시군구별 has constant value ""Constant
승 용 is highly overall correlated with 계High correlation
승 합 is highly overall correlated with 화 물 and 1 other fieldsHigh correlation
화 물 is highly overall correlated with 승 합 and 2 other fieldsHigh correlation
특 수 is highly overall correlated with 승 합 and 1 other fieldsHigh correlation
계 is highly overall correlated with 승 용 and 1 other fieldsHigh correlation
계 has unique valuesUnique
승 용 has 4 (17.4%) zerosZeros
승 합 has 11 (47.8%) zerosZeros
화 물 has 11 (47.8%) zerosZeros
특 수 has 18 (78.3%) zerosZeros

Reproduction

Analysis started2024-01-28 17:14:50.992920
Analysis finished2024-01-28 17:14:52.998508
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구별
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
남동구
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구 23
100.0%

Length

2024-01-29T02:14:53.047272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T02:14:53.115376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 23
100.0%

연료별
Text

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-29T02:14:53.241210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.8695652
Min length2

Characters and Unicode

Total characters135
Distinct characters28
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료
ValueCountFrequency (%)
cng 2
8.7%
경유 2
8.7%
기타연료 2
8.7%
수소 2
8.7%
엘피지 2
8.7%
전기 2
8.7%
하이브리드(경유+전기 2
8.7%
하이브리드(휘발유+전기 2
8.7%
휘발유 2
8.7%
휘발유(무연 2
8.7%
Other values (3) 3
13.0%
2024-01-29T02:14:53.485822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 12
 
8.9%
기 10
 
7.4%
) 9
 
6.7%
( 9
 
6.7%
전 8
 
5.9%
발 7
 
5.2%
휘 7
 
5.2%
이 6
 
4.4%
+ 6
 
4.4%
드 6
 
4.4%
Other values (18) 55
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
73.3%
Uppercase Letter 12
 
8.9%
Close Punctuation 9
 
6.7%
Open Punctuation 9
 
6.7%
Math Symbol 6
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 12
12.1%
기 10
 
10.1%
전 8
 
8.1%
발 7
 
7.1%
휘 7
 
7.1%
이 6
 
6.1%
드 6
 
6.1%
리 6
 
6.1%
브 6
 
6.1%
하 6
 
6.1%
Other values (10) 25
25.3%
Uppercase Letter
ValueCountFrequency (%)
G 4
33.3%
C 3
25.0%
N 3
25.0%
L 1
 
8.3%
P 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
73.3%
Common 24
 
17.8%
Latin 12
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 12
12.1%
기 10
 
10.1%
전 8
 
8.1%
발 7
 
7.1%
휘 7
 
7.1%
이 6
 
6.1%
드 6
 
6.1%
리 6
 
6.1%
브 6
 
6.1%
하 6
 
6.1%
Other values (10) 25
25.3%
Latin
ValueCountFrequency (%)
G 4
33.3%
C 3
25.0%
N 3
25.0%
L 1
 
8.3%
P 1
 
8.3%
Common
ValueCountFrequency (%)
) 9
37.5%
( 9
37.5%
+ 6
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
73.3%
ASCII 36
 
26.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 12
12.1%
기 10
 
10.1%
전 8
 
8.1%
발 7
 
7.1%
휘 7
 
7.1%
이 6
 
6.1%
드 6
 
6.1%
리 6
 
6.1%
브 6
 
6.1%
하 6
 
6.1%
Other values (10) 25
25.3%
ASCII
ValueCountFrequency (%)
) 9
25.0%
( 9
25.0%
+ 6
16.7%
G 4
11.1%
C 3
 
8.3%
N 3
 
8.3%
L 1
 
2.8%
P 1
 
2.8%

용도별
Categorical

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
비사업용
12 
사업용
11 

Length

Max length4
Median length4
Mean length3.5217391
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비사업용 12
52.2%
사업용 11
47.8%

Length

2024-01-29T02:14:53.586036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T02:14:53.658280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 12
52.2%
사업용 11
47.8%

승 용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10758.696
Minimum0
Maximum77806
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T02:14:53.730188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153
median1986
Q37776.5
95-th percentile59462.2
Maximum77806
Range77806
Interquartile range (IQR)7723.5

Descriptive statistics

Standard deviation21145.926
Coefficient of variation (CV)1.965473
Kurtosis4.9369418
Mean10758.696
Median Absolute Deviation (MAD)1984
Skewness2.3788231
Sum247450
Variance4.4715018 × 108
MonotonicityNot monotonic
2024-01-29T02:14:53.849541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
17.4%
33 1
 
4.3%
3049 1
 
4.3%
73 1
 
4.3%
15851 1
 
4.3%
77806 1
 
4.3%
1986 1
 
4.3%
44389 1
 
4.3%
2280 1
 
4.3%
17864 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 4
17.4%
2 1
 
4.3%
33 1
 
4.3%
73 1
 
4.3%
101 1
 
4.3%
293 1
 
4.3%
331 1
 
4.3%
1049 1
 
4.3%
1986 1
 
4.3%
2280 1
 
4.3%
ValueCountFrequency (%)
77806 1
4.3%
61137 1
4.3%
44389 1
4.3%
17864 1
4.3%
15851 1
4.3%
10077 1
4.3%
5476 1
4.3%
3049 1
4.3%
2912 1
4.3%
2741 1
4.3%

승 합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.86957
Minimum0
Maximum5097
Zeros11
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T02:14:53.946876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q344.5
95-th percentile647.7
Maximum5097
Range5097
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation1061.7942
Coefficient of variation (CV)3.5766353
Kurtosis21.502745
Mean296.86957
Median Absolute Deviation (MAD)4
Skewness4.5842692
Sum6828
Variance1127406.8
MonotonicityNot monotonic
2024-01-29T02:14:54.033288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 11
47.8%
8 1
 
4.3%
211 1
 
4.3%
5097 1
 
4.3%
653 1
 
4.3%
121 1
 
4.3%
600 1
 
4.3%
23 1
 
4.3%
17 1
 
4.3%
4 1
 
4.3%
Other values (3) 3
 
13.0%
ValueCountFrequency (%)
0 11
47.8%
4 1
 
4.3%
5 1
 
4.3%
8 1
 
4.3%
17 1
 
4.3%
23 1
 
4.3%
37 1
 
4.3%
52 1
 
4.3%
121 1
 
4.3%
211 1
 
4.3%
ValueCountFrequency (%)
5097 1
4.3%
653 1
4.3%
600 1
4.3%
211 1
4.3%
121 1
4.3%
52 1
4.3%
37 1
4.3%
23 1
4.3%
17 1
4.3%
8 1
4.3%

화 물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1463.8696
Minimum0
Maximum27890
Zeros11
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T02:14:54.113155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3277.5
95-th percentile2597.7
Maximum27890
Range27890
Interquartile range (IQR)277.5

Descriptive statistics

Standard deviation5792.0951
Coefficient of variation (CV)3.9567016
Kurtosis22.432615
Mean1463.8696
Median Absolute Deviation (MAD)1
Skewness4.71499
Sum33669
Variance33548365
MonotonicityNot monotonic
2024-01-29T02:14:54.193247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 11
47.8%
3 1
 
4.3%
27890 1
 
4.3%
2769 1
 
4.3%
456 1
 
4.3%
314 1
 
4.3%
1056 1
 
4.3%
170 1
 
4.3%
526 1
 
4.3%
241 1
 
4.3%
Other values (3) 3
 
13.0%
ValueCountFrequency (%)
0 11
47.8%
1 1
 
4.3%
3 1
 
4.3%
30 1
 
4.3%
170 1
 
4.3%
213 1
 
4.3%
241 1
 
4.3%
314 1
 
4.3%
456 1
 
4.3%
526 1
 
4.3%
ValueCountFrequency (%)
27890 1
4.3%
2769 1
4.3%
1056 1
4.3%
526 1
4.3%
456 1
4.3%
314 1
4.3%
241 1
4.3%
213 1
4.3%
170 1
4.3%
30 1
4.3%

특 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.173913
Minimum0
Maximum483
Zeros18
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T02:14:54.295292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile388.9
Maximum483
Range483
Interquartile range (IQR)0

Descriptive statistics

Standard deviation130.74669
Coefficient of variation (CV)3.0283724
Kurtosis8.4621905
Mean43.173913
Median Absolute Deviation (MAD)0
Skewness3.0816892
Sum993
Variance17094.696
MonotonicityNot monotonic
2024-01-29T02:14:54.382547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
78.3%
423 1
 
4.3%
483 1
 
4.3%
82 1
 
4.3%
3 1
 
4.3%
2 1
 
4.3%
ValueCountFrequency (%)
0 18
78.3%
2 1
 
4.3%
3 1
 
4.3%
82 1
 
4.3%
423 1
 
4.3%
483 1
 
4.3%
ValueCountFrequency (%)
483 1
 
4.3%
423 1
 
4.3%
82 1
 
4.3%
3 1
 
4.3%
2 1
 
4.3%
0 18
78.3%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12562.609
Minimum2
Maximum94547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T02:14:54.462721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19.7
Q1252
median1986
Q39191
95-th percentile74563.3
Maximum94547
Range94545
Interquartile range (IQR)8939

Descriptive statistics

Standard deviation25393.254
Coefficient of variation (CV)2.0213361
Kurtosis5.8446888
Mean12562.609
Median Absolute Deviation (MAD)1913
Skewness2.5486855
Sum288940
Variance6.4481737 × 108
MonotonicityNot monotonic
2024-01-29T02:14:54.551054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
44 1
 
4.3%
211 1
 
4.3%
73 1
 
4.3%
15856 1
 
4.3%
77888 1
 
4.3%
1986 1
 
4.3%
44641 1
 
4.3%
2280 1
 
4.3%
17865 1
 
4.3%
335 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2 1
4.3%
17 1
4.3%
44 1
4.3%
73 1
4.3%
101 1
4.3%
211 1
4.3%
293 1
4.3%
314 1
4.3%
335 1
4.3%
659 1
4.3%
ValueCountFrequency (%)
94547 1
4.3%
77888 1
4.3%
44641 1
4.3%
17865 1
4.3%
15856 1
4.3%
11736 1
4.3%
6646 1
4.3%
5669 1
4.3%
3438 1
4.3%
3049 1
4.3%

Interactions

2024-01-29T02:14:52.522331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.211135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.564280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.893136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.213781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.589347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.281579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.646973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.970339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.284341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.649551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.346761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.712853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.031350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.345304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.713474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.412306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.774703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.092225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.409768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.781068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.479088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:51.835599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.155832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:14:52.467778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T02:14:54.617990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료별용도별승 용승 합화 물특 수계
연료별1.0000.0000.0000.0000.0000.0000.000
용도별0.0001.0000.0000.0000.0000.0000.000
승 용0.0000.0001.0000.9781.0000.5661.000
승 합0.0000.0000.9781.0001.0000.7790.699
화 물0.0000.0001.0001.0001.0001.0001.000
특 수0.0000.0000.5660.7791.0001.0000.525
계0.0000.0001.0000.6991.0000.5251.000
2024-01-29T02:14:54.703171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승 용승 합화 물특 수계용도별
승 용1.0000.2310.3300.2620.9190.000
승 합0.2311.0000.5130.7180.3920.000
화 물0.3300.5131.0000.7040.5290.000
특 수0.2620.7180.7041.0000.4520.000
계0.9190.3920.5290.4521.0000.000
용도별0.0000.0000.0000.0000.0001.000

Missing values

2024-01-29T02:14:52.859694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T02:14:52.953607image/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

시군구별연료별용도별승 용승 합화 물특 수계
0남동구CNG비사업용3383044
1남동구CNG사업용021100211
2남동구경유비사업용6113750972789042394547
3남동구경유사업용274165327694836646
4남동구기타연료비사업용012145682659
5남동구기타연료사업용003140314
6남동구수소비사업용293000293
7남동구수소사업용20002
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