Overview

Dataset statistics

Number of variables5
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory46.9 B

Variable types

Text1
Numeric3
Categorical1

Dataset

Description제주도 도 건설기계 등록 관련 건설기계명,자가용,영업용,관용 정보 제공
Author제주특별자치도
URLhttps://www.data.go.kr/data/15010275/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
자가용 is highly overall correlated with 영업용 and 1 other fieldsHigh correlation
영업용 is highly overall correlated with 자가용 and 1 other fieldsHigh correlation
관용 is highly overall correlated with 자가용 and 1 other fieldsHigh correlation
건설기계명 has unique valuesUnique
자가용 has 17 (50.0%) zerosZeros
영업용 has 15 (44.1%) zerosZeros
관용 has 27 (79.4%) zerosZeros

Reproduction

Analysis started2023-12-12 22:44:04.974751
Analysis finished2023-12-12 22:44:05.828186
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건설기계명
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T07:44:05.965469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.9411765
Min length4

Characters and Unicode

Total characters270
Distinct characters95
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row01불도저
2nd row02굴삭기
3rd row03로더
4th row04지게차
5th row05스크레이퍼
ValueCountFrequency (%)
살포기 2
 
4.1%
피니셔 2
 
4.1%
01불도저 1
 
2.0%
02굴삭기 1
 
2.0%
콘크리트 1
 
2.0%
재생기 1
 
2.0%
58수목이식기 1
 
2.0%
55트럭지게차 1
 
2.0%
54콘크리트 1
 
2.0%
믹서 1
 
2.0%
Other values (37) 37
75.5%
2023-12-13T07:44:06.247195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 18
 
6.7%
기 16
 
5.9%
15
 
5.6%
1 14
 
5.2%
0 12
 
4.4%
5 11
 
4.1%
2 11
 
4.1%
크 9
 
3.3%
리 8
 
3.0%
콘 7
 
2.6%
Other values (85) 149
55.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
69.3%
Decimal Number 68
 
25.2%
Space Separator 15
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
리 8
 
4.3%
콘 7
 
3.7%
스 5
 
2.7%
럭 4
 
2.1%
팔 4
 
2.1%
레 4
 
2.1%
아 4
 
2.1%
Other values (74) 108
57.8%
Decimal Number
ValueCountFrequency (%)
1 14
20.6%
0 12
17.6%
5 11
16.2%
2 11
16.2%
3 4
 
5.9%
4 4
 
5.9%
8 3
 
4.4%
7 3
 
4.4%
9 3
 
4.4%
6 3
 
4.4%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 187
69.3%
Common 83
30.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
리 8
 
4.3%
콘 7
 
3.7%
스 5
 
2.7%
럭 4
 
2.1%
팔 4
 
2.1%
레 4
 
2.1%
아 4
 
2.1%
Other values (74) 108
57.8%
Common
ValueCountFrequency (%)
15
18.1%
1 14
16.9%
0 12
14.5%
5 11
13.3%
2 11
13.3%
3 4
 
4.8%
4 4
 
4.8%
8 3
 
3.6%
7 3
 
3.6%
9 3
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 187
69.3%
ASCII 83
30.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
리 8
 
4.3%
콘 7
 
3.7%
스 5
 
2.7%
럭 4
 
2.1%
팔 4
 
2.1%
레 4
 
2.1%
아 4
 
2.1%
Other values (74) 108
57.8%
ASCII
ValueCountFrequency (%)
15
18.1%
1 14
16.9%
0 12
14.5%
5 11
13.3%
2 11
13.3%
3 4
 
4.8%
4 4
 
4.8%
8 3
 
3.6%
7 3
 
3.6%
9 3
 
3.6%

자가용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.08824
Minimum0
Maximum2109
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:44:06.355662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q312.5
95-th percentile501.85
Maximum2109
Range2109
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation394.6722
Coefficient of variation (CV)3.6179172
Kurtosis21.636429
Mean109.08824
Median Absolute Deviation (MAD)0.5
Skewness4.5464778
Sum3709
Variance155766.14
MonotonicityNot monotonic
2023-12-13T07:44:06.464058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
50.0%
1 4
 
11.8%
2 2
 
5.9%
1003 1
 
2.9%
232 1
 
2.9%
2109 1
 
2.9%
168 1
 
2.9%
19 1
 
2.9%
3 1
 
2.9%
15 1
 
2.9%
Other values (4) 4
 
11.8%
ValueCountFrequency (%)
0 17
50.0%
1 4
 
11.8%
2 2
 
5.9%
3 1
 
2.9%
5 1
 
2.9%
15 1
 
2.9%
19 1
 
2.9%
27 1
 
2.9%
46 1
 
2.9%
74 1
 
2.9%
ValueCountFrequency (%)
2109 1
2.9%
1003 1
2.9%
232 1
2.9%
168 1
2.9%
74 1
2.9%
46 1
2.9%
27 1
2.9%
19 1
2.9%
15 1
2.9%
5 1
2.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.35294
Minimum0
Maximum2609
Zeros15
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:44:06.575194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q337.5
95-th percentile539.9
Maximum2609
Range2609
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation467.22877
Coefficient of variation (CV)3.3528447
Kurtosis25.32904
Mean139.35294
Median Absolute Deviation (MAD)4
Skewness4.8708242
Sum4738
Variance218302.72
MonotonicityNot monotonic
2023-12-13T07:44:06.690463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 15
44.1%
4 2
 
5.9%
15 2
 
5.9%
147 1
 
2.9%
2 1
 
2.9%
38 1
 
2.9%
6 1
 
2.9%
16 1
 
2.9%
8 1
 
2.9%
36 1
 
2.9%
Other values (8) 8
23.5%
ValueCountFrequency (%)
0 15
44.1%
2 1
 
2.9%
4 2
 
5.9%
6 1
 
2.9%
7 1
 
2.9%
8 1
 
2.9%
15 2
 
5.9%
16 1
 
2.9%
36 1
 
2.9%
38 1
 
2.9%
ValueCountFrequency (%)
2609 1
2.9%
887 1
2.9%
353 1
2.9%
295 1
2.9%
147 1
2.9%
130 1
2.9%
122 1
2.9%
44 1
2.9%
38 1
2.9%
36 1
2.9%

관용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8529412
Minimum0
Maximum33
Zeros27
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:44:06.788719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile24.4
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.0908941
Coefficient of variation (CV)2.8359835
Kurtosis8.0223489
Mean2.8529412
Median Absolute Deviation (MAD)0
Skewness2.9941146
Sum97
Variance65.462567
MonotonicityNot monotonic
2023-12-13T07:44:06.878814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 27
79.4%
2 2
 
5.9%
1 1
 
2.9%
33 1
 
2.9%
27 1
 
2.9%
23 1
 
2.9%
9 1
 
2.9%
ValueCountFrequency (%)
0 27
79.4%
1 1
 
2.9%
2 2
 
5.9%
9 1
 
2.9%
23 1
 
2.9%
27 1
 
2.9%
33 1
 
2.9%
ValueCountFrequency (%)
33 1
 
2.9%
27 1
 
2.9%
23 1
 
2.9%
9 1
 
2.9%
2 2
 
5.9%
1 1
 
2.9%
0 27
79.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2017-12-31
34 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-12-31
2nd row2017-12-31
3rd row2017-12-31
4th row2017-12-31
5th row2017-12-31

Common Values

ValueCountFrequency (%)
2017-12-31 34
100.0%

Length

2023-12-13T07:44:07.007527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:44:07.086717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-12-31 34
100.0%

Interactions

2023-12-13T07:44:05.511715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.118870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.324140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.569960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.190563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.381529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.628814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.260447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:44:05.450555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:44:07.134022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관용
건설기계명1.0001.0001.0001.000
자가용1.0001.0000.9381.000
영업용1.0000.9381.0000.897
관용1.0001.0000.8971.000
2023-12-13T07:44:07.226579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관용
자가용1.0000.8680.635
영업용0.8681.0000.558
관용0.6350.5581.000

Missing values

2023-12-13T07:44:05.723782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:44:05.800139image/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

건설기계명자가용영업용관용데이터기준일자
001불도저1412017-12-31
102굴삭기10032609332017-12-31
203로더23244272017-12-31
304지게차2109295232017-12-31
405스크레이퍼0002017-12-31
506덤프트럭16888792017-12-31
607기중기1912202017-12-31
708모터 그레이더3722017-12-31
809롤러1513022017-12-31
910노상안정기0002017-12-31
건설기계명자가용영업용관용데이터기준일자
2425준설선0002017-12-31
2527타워크레인03802017-12-31
2651도로보수트럭0002017-12-31
2752노면파쇄기11502017-12-31
2853노면측정장비0002017-12-31
2954콘크리트 믹서 트레일러0002017-12-31
3055트럭지게차0202017-12-31
3158수목이식기0002017-12-31
3259아스팔트 콘크리트 재생기0002017-12-31
3360터널용고소작업차0002017-12-31