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 17 (50.0%) zerosZeros
영업용 has 12 (35.3%) zerosZeros
관용 has 27 (79.4%) zerosZeros

Reproduction

Analysis started2024-03-18 01:38:07.835562
Analysis finished2024-03-18 01:38:08.910661
Duration1.08 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
2024-03-18T10:38:09.045196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.9411765
Min length5

Characters and Unicode

Total characters304
Distinct characters96
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 row01 불도저
2nd row02 굴착기
3rd row03 로더
4th row04 지게차
5th row05 스크레이퍼
ValueCountFrequency (%)
콘크리트 7
 
8.4%
아스팔트 4
 
4.8%
피니셔 2
 
2.4%
살포기 2
 
2.4%
01 1
 
1.2%
53 1
 
1.2%
굴착기 1
 
1.2%
02 1
 
1.2%
노면파쇄기 1
 
1.2%
52 1
 
1.2%
Other values (62) 62
74.7%
2024-03-18T10:38:09.326581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
16.1%
트 18
 
5.9%
기 16
 
5.3%
1 14
 
4.6%
0 12
 
3.9%
5 11
 
3.6%
2 11
 
3.6%
크 9
 
3.0%
리 7
 
2.3%
콘 7
 
2.3%
Other values (86) 150
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
61.5%
Decimal Number 68
 
22.4%
Space Separator 49
 
16.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
리 7
 
3.7%
콘 7
 
3.7%
스 5
 
2.7%
아 4
 
2.1%
팔 4
 
2.1%
럭 4
 
2.1%
레 4
 
2.1%
Other values (75) 109
58.3%
Decimal Number
ValueCountFrequency (%)
1 14
20.6%
0 12
17.6%
5 11
16.2%
2 11
16.2%
4 4
 
5.9%
3 4
 
5.9%
8 3
 
4.4%
7 3
 
4.4%
9 3
 
4.4%
6 3
 
4.4%
Space Separator
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 187
61.5%
Common 117
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
리 7
 
3.7%
콘 7
 
3.7%
스 5
 
2.7%
아 4
 
2.1%
팔 4
 
2.1%
럭 4
 
2.1%
레 4
 
2.1%
Other values (75) 109
58.3%
Common
ValueCountFrequency (%)
49
41.9%
1 14
 
12.0%
0 12
 
10.3%
5 11
 
9.4%
2 11
 
9.4%
4 4
 
3.4%
3 4
 
3.4%
8 3
 
2.6%
7 3
 
2.6%
9 3
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 187
61.5%
ASCII 117
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
41.9%
1 14
 
12.0%
0 12
 
10.3%
5 11
 
9.4%
2 11
 
9.4%
4 4
 
3.4%
3 4
 
3.4%
8 3
 
2.6%
7 3
 
2.6%
9 3
 
2.6%
Hangul
ValueCountFrequency (%)
트 18
 
9.6%
기 16
 
8.6%
크 9
 
4.8%
리 7
 
3.7%
콘 7
 
3.7%
스 5
 
2.7%
아 4
 
2.1%
팔 4
 
2.1%
럭 4
 
2.1%
레 4
 
2.1%
Other values (75) 109
58.3%

자가용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.44118
Minimum0
Maximum6810
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-18T10:38:09.419842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q311
95-th percentile799.5
Maximum6810
Range6810
Interquartile range (IQR)11

Descriptive statistics

Standard deviation1182.2719
Coefficient of variation (CV)4.3555362
Kurtosis30.745275
Mean271.44118
Median Absolute Deviation (MAD)0.5
Skewness5.4621255
Sum9229
Variance1397766.8
MonotonicityNot monotonic
2024-03-18T10:38:09.505348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 17
50.0%
1 3
 
8.8%
11 2
 
5.9%
1378 1
 
2.9%
488 1
 
2.9%
6810 1
 
2.9%
330 1
 
2.9%
35 1
 
2.9%
29 1
 
2.9%
71 1
 
2.9%
Other values (5) 5
 
14.7%
ValueCountFrequency (%)
0 17
50.0%
1 3
 
8.8%
2 1
 
2.9%
3 1
 
2.9%
5 1
 
2.9%
9 1
 
2.9%
11 2
 
5.9%
29 1
 
2.9%
35 1
 
2.9%
44 1
 
2.9%
ValueCountFrequency (%)
6810 1
2.9%
1378 1
2.9%
488 1
2.9%
330 1
2.9%
71 1
2.9%
44 1
2.9%
35 1
2.9%
29 1
2.9%
11 2
5.9%
9 1
2.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.05882
Minimum0
Maximum3519
Zeros12
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-18T10:38:09.593873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q3222.25
95-th percentile2482.75
Maximum3519
Range3519
Interquartile range (IQR)222.25

Descriptive statistics

Standard deviation857.76425
Coefficient of variation (CV)2.3822892
Kurtosis7.4329028
Mean360.05882
Median Absolute Deviation (MAD)2.5
Skewness2.8422201
Sum12242
Variance735759.51
MonotonicityNot monotonic
2024-03-18T10:38:09.680293image/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%
89 1
 
2.9%
1313 1
 
2.9%
297 1
 
2.9%
259 1
 
2.9%
34 1
 
2.9%
112 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%
34 1
 
2.9%
89 1
 
2.9%
100 1
 
2.9%
ValueCountFrequency (%)
3519 1
2.9%
2980 1
2.9%
2215 1
2.9%
1313 1
2.9%
606 1
2.9%
386 1
2.9%
301 1
2.9%
297 1
2.9%
259 1
2.9%
112 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
2024-03-18T10:38:09.799095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.2
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.2302808
Coefficient of variation (CV)2.8625615
Kurtosis10.192045
Mean2.1764706
Median Absolute Deviation (MAD)0
Skewness3.2766735
Sum74
Variance38.816399
MonotonicityNot monotonic
2024-03-18T10:38:09.917894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 27
79.4%
2 1
 
2.9%
24 1
 
2.9%
12 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%
12 1
 
2.9%
24 1
 
2.9%
26 1
 
2.9%
ValueCountFrequency (%)
26 1
 
2.9%
24 1
 
2.9%
12 1
 
2.9%
5 1
 
2.9%
4 1
 
2.9%
2 1
 
2.9%
1 1
 
2.9%
0 27
79.4%

Interactions

2024-03-18T10:38:08.593476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:07.957705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.156473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.665042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.027444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.223195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.733474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.092283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T10:38:08.522097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T10:38:09.982297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관용
건설기계명1.0001.0001.0001.000
자가용1.0001.0001.0000.648
영업용1.0001.0001.0000.893
관용1.0000.6480.8931.000
2024-03-18T10:38:10.062545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관용
자가용1.0000.8460.453
영업용0.8461.0000.451
관용0.4530.4511.000

Missing values

2024-03-18T10:38:08.821478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T10:38:08.884561image/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
001 불도저11892
102 굴착기1378298024
203 로더48838612
304 지게차6810351926
405 스크레이퍼000
506 덤프트럭33022155
607 기중기356060
708 모터 그레이더051
809 롤러291000
910 노상안정기000
건설기계명자가용영업용관용
2425 준설선220
2527 타워크레인52970
2651 도로보수트럭004
2752 노면파쇄기110
2853 노면측정장비000
2954 콘크리트 믹서 트레일러000
3055 트럭지게차000
3158 수목이식기000
3259 아스팔트 콘크리트 재생기000
3360 터널용고소작업차020