Overview

Dataset statistics

Number of variables4
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory38.9 B

Variable types

Text1
Numeric3

Dataset

Description인천광역시 연도별 건설기계관리법에 따라 등록된 건설기계 수입니다- 출처 : 건설기계정보관리시스템 - 용도별(자가용, 영업용), 장비별 등록 수
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15055210&srcSe=7661IVAWM27C61E190

Alerts

자가용 is highly overall correlated with 영업용High correlation
영업용 is highly overall correlated with 자가용High correlation
건설기계명 has unique valuesUnique
자가용 has 18 (52.9%) zerosZeros
영업용 has 12 (35.3%) zerosZeros
관용 has 27 (79.4%) zerosZeros

Reproduction

Analysis started2024-03-18 01:38:02.642135
Analysis finished2024-03-18 01:38:03.701778
Duration1.06 second
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
2024-03-18T10:38:03.806998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.9705882
Min length2

Characters and Unicode

Total characters203
Distinct characters86
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

Unique34 ?
Unique (%)100.0%

Sample

1st row불도저
2nd row굴착기
3rd row로더
4th row지게차
5th row스크레이퍼
ValueCountFrequency (%)
콘크리트 7
 
14.3%
아스팔트 4
 
8.2%
살포기 2
 
4.1%
피니셔 2
 
4.1%
불도저 1
 
2.0%
준설선 1
 
2.0%
믹서 1
 
2.0%
트레일러 1
 
2.0%
노면측정장비 1
 
2.0%
노면파쇄기 1
 
2.0%
Other values (28) 28
57.1%
2024-03-18T10:38:04.054153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 18
 
8.9%
기 16
 
7.9%
16
 
7.9%
크 9
 
4.4%
콘 7
 
3.4%
리 7
 
3.4%
스 5
 
2.5%
레 4
 
2.0%
럭 4
 
2.0%
아 4
 
2.0%
Other values (76) 113
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
92.1%
Space Separator 16
 
7.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
콘 7
 
3.7%
리 7
 
3.7%
스 5
 
2.7%
레 4
 
2.1%
럭 4
 
2.1%
아 4
 
2.1%
팔 4
 
2.1%
Other values (75) 109
58.3%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 187
92.1%
Common 16
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
콘 7
 
3.7%
리 7
 
3.7%
스 5
 
2.7%
레 4
 
2.1%
럭 4
 
2.1%
아 4
 
2.1%
팔 4
 
2.1%
Other values (75) 109
58.3%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 187
92.1%
ASCII 16
 
7.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
콘 7
 
3.7%
리 7
 
3.7%
스 5
 
2.7%
레 4
 
2.1%
럭 4
 
2.1%
아 4
 
2.1%
팔 4
 
2.1%
Other values (75) 109
58.3%
ASCII
ValueCountFrequency (%)
16
100.0%

자가용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.76471
Minimum0
Maximum6801
Zeros18
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-18T10:38:04.145861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311.75
95-th percentile782.3
Maximum6801
Range6801
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation1180.3358
Coefficient of variation (CV)4.3592674
Kurtosis30.794015
Mean270.76471
Median Absolute Deviation (MAD)0
Skewness5.46741
Sum9206
Variance1393192.5
MonotonicityNot monotonic
2024-03-18T10:38:04.234987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 18
52.9%
2 2
 
5.9%
1 2
 
5.9%
11 1
 
2.9%
1366 1
 
2.9%
468 1
 
2.9%
6801 1
 
2.9%
346 1
 
2.9%
35 1
 
2.9%
30 1
 
2.9%
Other values (5) 5
 
14.7%
ValueCountFrequency (%)
0 18
52.9%
1 2
 
5.9%
2 2
 
5.9%
5 1
 
2.9%
9 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
30 1
 
2.9%
35 1
 
2.9%
45 1
 
2.9%
ValueCountFrequency (%)
6801 1
2.9%
1366 1
2.9%
468 1
2.9%
346 1
2.9%
72 1
2.9%
45 1
2.9%
35 1
2.9%
30 1
2.9%
12 1
2.9%
11 1
2.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.79412
Minimum0
Maximum3456
Zeros12
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-18T10:38:04.326970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q3214
95-th percentile2468.75
Maximum3456
Range3456
Interquartile range (IQR)214

Descriptive statistics

Standard deviation854.33791
Coefficient of variation (CV)2.3613925
Kurtosis7.0988769
Mean361.79412
Median Absolute Deviation (MAD)2.5
Skewness2.7909204
Sum12301
Variance729893.26
MonotonicityNot monotonic
2024-03-18T10:38:04.416763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 12
35.3%
1 3
 
8.8%
2 2
 
5.9%
86 1
 
2.9%
1429 1
 
2.9%
343 1
 
2.9%
251 1
 
2.9%
30 1
 
2.9%
103 1
 
2.9%
12 1
 
2.9%
Other values (10) 10
29.4%
ValueCountFrequency (%)
0 12
35.3%
1 3
 
8.8%
2 2
 
5.9%
3 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
12 1
 
2.9%
30 1
 
2.9%
86 1
 
2.9%
100 1
 
2.9%
ValueCountFrequency (%)
3456 1
2.9%
2992 1
2.9%
2187 1
2.9%
1429 1
2.9%
604 1
2.9%
389 1
2.9%
343 1
2.9%
300 1
2.9%
251 1
2.9%
103 1
2.9%

관용
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1764706
Minimum0
Maximum26
Zeros27
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-18T10:38:04.501614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.5
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.176547
Coefficient of variation (CV)2.8378729
Kurtosis9.808455
Mean2.1764706
Median Absolute Deviation (MAD)0
Skewness3.2202371
Sum74
Variance38.149733
MonotonicityNot monotonic
2024-03-18T10:38:04.581313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 27
79.4%
2 1
 
2.9%
23 1
 
2.9%
13 1
 
2.9%
26 1
 
2.9%
5 1
 
2.9%
1 1
 
2.9%
4 1
 
2.9%
ValueCountFrequency (%)
0 27
79.4%
1 1
 
2.9%
2 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
13 1
 
2.9%
23 1
 
2.9%
26 1
 
2.9%
ValueCountFrequency (%)
26 1
 
2.9%
23 1
 
2.9%
13 1
 
2.9%
5 1
 
2.9%
4 1
 
2.9%
2 1
 
2.9%
1 1
 
2.9%
0 27
79.4%

Interactions

2024-03-18T10:38:03.410760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:02.749826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:03.138011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:03.476286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:02.818255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:03.223982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:03.538863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:02.884427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:03.332226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T10:38:04.640941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관용
건설기계명1.0001.0001.0001.000
자가용1.0001.0001.0001.000
영업용1.0001.0001.0000.896
관용1.0001.0000.8961.000
2024-03-18T10:38:04.721597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관용
자가용1.0000.8060.463
영업용0.8061.0000.451
관용0.4630.4511.000

Missing values

2024-03-18T10:38:03.614044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T10:38:03.676440image/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

건설기계명자가용영업용관용
0불도저11862
1굴착기1366299223
2로더46838913
3지게차6801345626
4스크레이퍼000
5덤프트럭34621875
6기중기356040
7모터 그레이더051
8롤러301000
9노상안정기000
건설기계명자가용영업용관용
24준설선220
25타워크레인53430
26도로보수트럭004
27노면파쇄기110
28노면측정장비000
29콘크리트 믹서 트레일러000
30트럭지게차000
31수목이식기000
32아스팔트 콘크리트 재생기000
33터널용고소작업차020