Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory75.0 B

Variable types

Numeric5
Categorical2
Text1

Dataset

Description인천광역시 미추홀구에 등록되어 있는 차량에 대한 데이터로 연료별, 용도별,차종별로 등록된 차량에 관한 정보를 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15060812&srcSe=7661IVAWM27C61E190

Alerts

시군구명 has constant value ""Constant
연번 is highly overall correlated with 특수High correlation
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 연번 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
승용 has 2 (9.1%) zerosZeros
승합 has 12 (54.5%) zerosZeros
화물 has 11 (50.0%) zerosZeros
특수 has 17 (77.3%) zerosZeros

Reproduction

Analysis started2024-03-18 03:07:39.770563
Analysis finished2024-03-18 03:07:44.435409
Duration4.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:07:44.517523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2024-03-18T12:07:44.631155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
미추홀구
22 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미추홀구 22
100.0%

Length

2024-03-18T12:07:44.728884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:07:44.820389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미추홀구 22
100.0%

연료별
Text

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-18T12:07:44.984283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.5454545
Min length2

Characters and Unicode

Total characters122
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

Unique2 ?
Unique (%)9.1%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료
ValueCountFrequency (%)
cng 2
9.1%
경유 2
9.1%
기타연료 2
9.1%
수소 2
9.1%
엘피지 2
9.1%
전기 2
9.1%
하이브리드(경유+전기 2
9.1%
하이브리드(휘발유+전기 2
9.1%
휘발유 2
9.1%
휘발유(무연 2
9.1%
Other values (2) 2
9.1%
2024-03-18T12:07:45.275820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 12
 
9.8%
기 9
 
7.4%
( 8
 
6.6%
) 8
 
6.6%
전 7
 
5.7%
휘 7
 
5.7%
발 7
 
5.7%
연 5
 
4.1%
브 5
 
4.1%
+ 5
 
4.1%
Other values (18) 49
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
75.4%
Uppercase Letter 9
 
7.4%
Open Punctuation 8
 
6.6%
Close Punctuation 8
 
6.6%
Math Symbol 5
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 12
13.0%
기 9
 
9.8%
전 7
 
7.6%
휘 7
 
7.6%
발 7
 
7.6%
연 5
 
5.4%
브 5
 
5.4%
드 5
 
5.4%
리 5
 
5.4%
하 5
 
5.4%
Other values (10) 25
27.2%
Uppercase Letter
ValueCountFrequency (%)
G 3
33.3%
C 2
22.2%
N 2
22.2%
L 1
 
11.1%
P 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
75.4%
Common 21
 
17.2%
Latin 9
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 12
13.0%
기 9
 
9.8%
전 7
 
7.6%
휘 7
 
7.6%
발 7
 
7.6%
연 5
 
5.4%
브 5
 
5.4%
드 5
 
5.4%
리 5
 
5.4%
하 5
 
5.4%
Other values (10) 25
27.2%
Latin
ValueCountFrequency (%)
G 3
33.3%
C 2
22.2%
N 2
22.2%
L 1
 
11.1%
P 1
 
11.1%
Common
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
+ 5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
75.4%
ASCII 30
 
24.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 12
13.0%
기 9
 
9.8%
전 7
 
7.6%
휘 7
 
7.6%
발 7
 
7.6%
연 5
 
5.4%
브 5
 
5.4%
드 5
 
5.4%
리 5
 
5.4%
하 5
 
5.4%
Other values (10) 25
27.2%
ASCII
ValueCountFrequency (%)
( 8
26.7%
) 8
26.7%
+ 5
16.7%
G 3
 
10.0%
C 2
 
6.7%
N 2
 
6.7%
L 1
 
3.3%
P 1
 
3.3%

용도별
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
비사업용
12 
사업용
10 

Length

Max length4
Median length4
Mean length3.5454545
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비사업용 12
54.5%
사업용 10
45.5%

Length

2024-03-18T12:07:45.392923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:07:45.486355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 12
54.5%
사업용 10
45.5%

승용
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6553
Minimum0
Maximum52885
Zeros2
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:07:45.567116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q127
median144.5
Q32235.5
95-th percentile38736.45
Maximum52885
Range52885
Interquartile range (IQR)2208.5

Descriptive statistics

Standard deviation14540.305
Coefficient of variation (CV)2.2188776
Kurtosis5.1395381
Mean6553
Median Absolute Deviation (MAD)143.5
Skewness2.4376276
Sum144166
Variance2.1142047 × 108
MonotonicityNot monotonic
2024-03-18T12:07:45.673337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
27 2
 
9.1%
2 2
 
9.1%
0 2
 
9.1%
521 1
 
4.5%
71 1
 
4.5%
248 1
 
4.5%
52885 1
 
4.5%
40 1
 
4.5%
30100 1
 
4.5%
8120 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
0 2
9.1%
2 2
9.1%
3 1
4.5%
27 2
9.1%
40 1
4.5%
71 1
4.5%
92 1
4.5%
136 1
4.5%
153 1
4.5%
248 1
4.5%
ValueCountFrequency (%)
52885 1
4.5%
39191 1
4.5%
30100 1
4.5%
8440 1
4.5%
8120 1
4.5%
2649 1
4.5%
995 1
4.5%
521 1
4.5%
464 1
4.5%
248 1
4.5%

승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.27273
Minimum0
Maximum3874
Zeros12
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:07:45.801881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326.25
95-th percentile652.55
Maximum3874
Range3874
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation830.03214
Coefficient of variation (CV)3.5130256
Kurtosis19.923531
Mean236.27273
Median Absolute Deviation (MAD)0
Skewness4.3970652
Sum5198
Variance688953.35
MonotonicityNot monotonic
2024-03-18T12:07:45.971245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12
54.5%
1 3
 
13.6%
44 1
 
4.5%
3874 1
 
4.5%
79 1
 
4.5%
661 1
 
4.5%
492 1
 
4.5%
15 1
 
4.5%
30 1
 
4.5%
ValueCountFrequency (%)
0 12
54.5%
1 3
 
13.6%
15 1
 
4.5%
30 1
 
4.5%
44 1
 
4.5%
79 1
 
4.5%
492 1
 
4.5%
661 1
 
4.5%
3874 1
 
4.5%
ValueCountFrequency (%)
3874 1
 
4.5%
661 1
 
4.5%
492 1
 
4.5%
79 1
 
4.5%
44 1
 
4.5%
30 1
 
4.5%
15 1
 
4.5%
1 3
 
13.6%
0 12
54.5%

화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean994.86364
Minimum0
Maximum15685
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:07:46.091218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q3242
95-th percentile2708.7
Maximum15685
Range15685
Interquartile range (IQR)242

Descriptive statistics

Standard deviation3346.5337
Coefficient of variation (CV)3.3638115
Kurtosis20.074348
Mean994.86364
Median Absolute Deviation (MAD)1.5
Skewness4.4173996
Sum21887
Variance11199288
MonotonicityNot monotonic
2024-03-18T12:07:46.194090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11
50.0%
3 1
 
4.5%
15685 1
 
4.5%
2771 1
 
4.5%
356 1
 
4.5%
1525 1
 
4.5%
821 1
 
4.5%
106 1
 
4.5%
252 1
 
4.5%
212 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 11
50.0%
3 1
 
4.5%
35 1
 
4.5%
106 1
 
4.5%
121 1
 
4.5%
212 1
 
4.5%
252 1
 
4.5%
356 1
 
4.5%
821 1
 
4.5%
1525 1
 
4.5%
ValueCountFrequency (%)
15685 1
4.5%
2771 1
4.5%
1525 1
4.5%
821 1
4.5%
356 1
4.5%
252 1
4.5%
212 1
4.5%
121 1
4.5%
106 1
4.5%
35 1
4.5%

특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.863636
Minimum0
Maximum880
Zeros17
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:07:46.302822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile316.15
Maximum880
Range880
Interquartile range (IQR)0

Descriptive statistics

Standard deviation196.69902
Coefficient of variation (CV)3.3993546
Kurtosis16.11096
Mean57.863636
Median Absolute Deviation (MAD)0
Skewness3.9387192
Sum1273
Variance38690.504
MonotonicityNot monotonic
2024-03-18T12:07:46.401454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17
77.3%
330 1
 
4.5%
880 1
 
4.5%
53 1
 
4.5%
4 1
 
4.5%
6 1
 
4.5%
ValueCountFrequency (%)
0 17
77.3%
4 1
 
4.5%
6 1
 
4.5%
53 1
 
4.5%
330 1
 
4.5%
880 1
 
4.5%
ValueCountFrequency (%)
880 1
 
4.5%
330 1
 
4.5%
53 1
 
4.5%
6 1
 
4.5%
4 1
 
4.5%
0 17
77.3%

Interactions

2024-03-18T12:07:43.770814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:41.968506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.458495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.865582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.308868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.861678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.104840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.524975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.977212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.383318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.949780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.193053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.605647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.063231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.468407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:44.031653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.300533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.688421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.151981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.586631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:44.119441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.400576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:42.769021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.235300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:07:43.694756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:07:46.479195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연료별용도별승용승합화물특수
연번1.0000.9020.0000.0000.0000.0000.000
연료별0.9021.0000.0000.0000.0000.0000.000
용도별0.0000.0001.0000.2280.1280.0250.025
승용0.0000.0000.2281.0000.7250.6600.660
승합0.0000.0000.1280.7251.0000.9280.928
화물0.0000.0000.0250.6600.9281.0001.000
특수0.0000.0000.0250.6600.9281.0001.000
2024-03-18T12:07:46.607835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번승용승합화물특수용도별
연번1.0000.302-0.408-0.475-0.5310.000
승용0.3021.0000.2690.2950.0070.239
승합-0.4080.2691.0000.5860.6190.195
화물-0.4750.2950.5861.0000.7760.000
특수-0.5310.0070.6190.7761.0000.000
용도별0.0000.2390.1950.0000.0001.000

Missing values

2024-03-18T12:07:44.234263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:07:44.370954image/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

연번시군구명연료별용도별승용승합화물특수
01미추홀구CNG비사업용27130
12미추홀구CNG사업용04400
23미추홀구경유비사업용39191387415685330
34미추홀구경유사업용136792771880
45미추홀구기타연료비사업용266135653
56미추홀구기타연료사업용0015254
67미추홀구수소비사업용153000
78미추홀구수소사업용2000
89미추홀구엘피지비사업용84404928216
910미추홀구엘피지사업용264911060
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