Overview

Dataset statistics

Number of variables5
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory47.8 B

Variable types

Text1
Numeric4

Dataset

Description대구광역시에 등록된 건설기계에 대한 기종별, 자가용, 영업용, 관용으로 구분한 건설기계 등록현황에 관한 정보를 제공함
URLhttps://www.data.go.kr/data/3074818/fileData.do

Alerts

자가용 is highly overall correlated with 영업용 and 2 other fieldsHigh correlation
영업용 is highly overall correlated with 자가용 and 2 other fieldsHigh correlation
관용 is highly overall correlated with 자가용 and 2 other fieldsHigh correlation
소계 is highly overall correlated with 자가용 and 2 other fieldsHigh correlation
건설기계명 has unique valuesUnique
자가용 has 20 (57.1%) zerosZeros
영업용 has 14 (40.0%) zerosZeros
관용 has 26 (74.3%) zerosZeros
소계 has 13 (37.1%) zerosZeros

Reproduction

Analysis started2023-12-12 02:43:08.464360
Analysis finished2023-12-12 02:43:10.502142
Duration2.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건설기계명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T11:43:10.635171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.8
Min length1

Characters and Unicode

Total characters203
Distinct characters87
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

Unique35 ?
Unique (%)100.0%

Sample

1st row불도저
2nd row굴착기
3rd row로더
4th row지게차
5th row스크레이퍼
ValueCountFrequency (%)
콘크리트 7
 
14.0%
아스팔트 4
 
8.0%
피니셔 2
 
4.0%
살포기 2
 
4.0%
쇄석기 1
 
2.0%
및 1
 
2.0%
항발기 1
 
2.0%
자갈채취기 1
 
2.0%
준설선 1
 
2.0%
타워크레인 1
 
2.0%
Other values (29) 29
58.0%
2023-12-12T11:43:10.988103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 18
 
8.9%
기 16
 
7.9%
15
 
7.4%
크 9
 
4.4%
콘 7
 
3.4%
리 7
 
3.4%
스 5
 
2.5%
팔 4
 
2.0%
아 4
 
2.0%
럭 4
 
2.0%
Other values (77) 114
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 188
92.6%
Space Separator 15
 
7.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.5%
크 9
 
4.8%
콘 7
 
3.7%
리 7
 
3.7%
스 5
 
2.7%
팔 4
 
2.1%
아 4
 
2.1%
럭 4
 
2.1%
레 4
 
2.1%
Other values (76) 110
58.5%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 188
92.6%
Common 15
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.5%
크 9
 
4.8%
콘 7
 
3.7%
리 7
 
3.7%
스 5
 
2.7%
팔 4
 
2.1%
아 4
 
2.1%
럭 4
 
2.1%
레 4
 
2.1%
Other values (76) 110
58.5%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 188
92.6%
ASCII 15
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.5%
크 9
 
4.8%
콘 7
 
3.7%
리 7
 
3.7%
스 5
 
2.7%
팔 4
 
2.1%
아 4
 
2.1%
럭 4
 
2.1%
레 4
 
2.1%
Other values (76) 110
58.5%
ASCII
ValueCountFrequency (%)
15
100.0%

자가용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.42857
Minimum0
Maximum5415
Zeros20
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T11:43:11.106498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile2012.1
Maximum5415
Range5415
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1101.3545
Coefficient of variation (CV)3.5593176
Kurtosis16.015792
Mean309.42857
Median Absolute Deviation (MAD)0
Skewness4.0008257
Sum10830
Variance1212981.8
MonotonicityNot monotonic
2023-12-12T11:43:11.215138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 20
57.1%
1 4
 
11.4%
1299 1
 
2.9%
231 1
 
2.9%
3676 1
 
2.9%
119 1
 
2.9%
10 1
 
2.9%
13 1
 
2.9%
44 1
 
2.9%
2 1
 
2.9%
Other values (3) 3
 
8.6%
ValueCountFrequency (%)
0 20
57.1%
1 4
 
11.4%
2 1
 
2.9%
6 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
13 1
 
2.9%
44 1
 
2.9%
119 1
 
2.9%
231 1
 
2.9%
ValueCountFrequency (%)
5415 1
2.9%
3676 1
2.9%
1299 1
2.9%
231 1
2.9%
119 1
2.9%
44 1
2.9%
13 1
2.9%
11 1
2.9%
10 1
2.9%
6 1
2.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean478.22857
Minimum0
Maximum8369
Zeros14
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T11:43:11.340598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3221.5
95-th percentile1962.7
Maximum8369
Range8369
Interquartile range (IQR)221.5

Descriptive statistics

Standard deviation1507.5669
Coefficient of variation (CV)3.1523981
Kurtosis23.498953
Mean478.22857
Median Absolute Deviation (MAD)3
Skewness4.6639238
Sum16738
Variance2272757.8
MonotonicityNot monotonic
2023-12-12T11:43:11.482179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 14
40.0%
3 2
 
5.7%
105 1
 
2.9%
1 1
 
2.9%
8369 1
 
2.9%
10 1
 
2.9%
203 1
 
2.9%
2 1
 
2.9%
63 1
 
2.9%
81 1
 
2.9%
Other values (11) 11
31.4%
ValueCountFrequency (%)
0 14
40.0%
1 1
 
2.9%
2 1
 
2.9%
3 2
 
5.7%
10 1
 
2.9%
12 1
 
2.9%
38 1
 
2.9%
63 1
 
2.9%
81 1
 
2.9%
96 1
 
2.9%
ValueCountFrequency (%)
8369 1
2.9%
3299 1
2.9%
1390 1
2.9%
1047 1
2.9%
996 1
2.9%
272 1
2.9%
263 1
2.9%
245 1
2.9%
240 1
2.9%
203 1
2.9%

관용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8285714
Minimum0
Maximum67
Zeros26
Zeros (%)74.3%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T11:43:11.588344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile19.4
Maximum67
Range67
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation12.217359
Coefficient of variation (CV)3.1911013
Kurtosis22.177372
Mean3.8285714
Median Absolute Deviation (MAD)0
Skewness4.4946862
Sum134
Variance149.26387
MonotonicityNot monotonic
2023-12-12T11:43:11.704428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 26
74.3%
1 2
 
5.7%
3 1
 
2.9%
25 1
 
2.9%
7 1
 
2.9%
17 1
 
2.9%
11 1
 
2.9%
2 1
 
2.9%
67 1
 
2.9%
ValueCountFrequency (%)
0 26
74.3%
1 2
 
5.7%
2 1
 
2.9%
3 1
 
2.9%
7 1
 
2.9%
11 1
 
2.9%
17 1
 
2.9%
25 1
 
2.9%
67 1
 
2.9%
ValueCountFrequency (%)
67 1
 
2.9%
25 1
 
2.9%
17 1
 
2.9%
11 1
 
2.9%
7 1
 
2.9%
3 1
 
2.9%
2 1
 
2.9%
1 2
 
5.7%
0 26
74.3%

소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean791.48571
Minimum0
Maximum13851
Zeros13
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T11:43:11.862346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3224
95-th percentile4761
Maximum13851
Range13851
Interquartile range (IQR)224

Descriptive statistics

Standard deviation2544.4516
Coefficient of variation (CV)3.214779
Kurtosis21.541606
Mean791.48571
Median Absolute Deviation (MAD)5
Skewness4.4506783
Sum27702
Variance6474234.1
MonotonicityNot monotonic
2023-12-12T11:43:11.991003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 13
37.1%
1 2
 
5.7%
3 2
 
5.7%
108 1
 
2.9%
13851 1
 
2.9%
11 1
 
2.9%
203 1
 
2.9%
64 1
 
2.9%
92 1
 
2.9%
102 1
 
2.9%
Other values (11) 11
31.4%
ValueCountFrequency (%)
0 13
37.1%
1 2
 
5.7%
3 2
 
5.7%
5 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
39 1
 
2.9%
64 1
 
2.9%
92 1
 
2.9%
102 1
 
2.9%
ValueCountFrequency (%)
13851 1
2.9%
5083 1
2.9%
4623 1
2.9%
1177 1
2.9%
1040 1
2.9%
478 1
2.9%
283 1
2.9%
278 1
2.9%
245 1
2.9%
203 1
2.9%

Interactions

2023-12-12T11:43:09.724070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:08.642101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:08.957739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.319432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.802158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:08.719087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.035651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.419068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:10.144143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:08.797585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.121450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.513484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:10.233230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:08.878309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.222044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:43:09.623120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:43:12.076914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관용소계
건설기계명1.0001.0001.0001.0001.000
자가용1.0001.0000.9901.0001.000
영업용1.0000.9901.0000.8940.829
관용1.0001.0000.8941.0001.000
소계1.0001.0000.8291.0001.000
2023-12-12T11:43:12.168184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관용소계
자가용1.0000.8060.6220.815
영업용0.8061.0000.6440.990
관용0.6220.6441.0000.685
소계0.8150.9900.6851.000

Missing values

2023-12-12T11:43:10.363918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:43:10.463322image/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

건설기계명자가용영업용관용소계
0불도저01053108
1굴착기12993299254623
2로더2312407478
3지게차36761390175083
4스크레이퍼0000
5덤프트럭1191047111177
6기중기102721283
7모터 그레이더012012
8롤러132632278
9노상안정기0000
건설기계명자가용영업용관용소계
25타워크레인02030203
26도로보수트럭0011
27노면파쇄기110011
28노면측정장비0000
29콘크리트 믹서 트레일러0000
30트럭지게차0000
31수목이식기0000
32아스팔트 콘크리트 재생기0000
33터널용고소작업차0000
34계541583696713851