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 memory47.9 B

Variable types

Text1
Numeric4

Dataset

Description대구지역 건설기계 등록통계(2014년08월)
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3072498&dataSetDetailId=30724981996c4e2eae28_201705311445&provdMethod=FILE

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
관 용 is highly overall correlated with 자가용 and 2 other fieldsHigh correlation
소 계 is highly overall correlated with 자가용 and 2 other fieldsHigh correlation
건설기계명 has unique valuesUnique
자가용 has 16 (47.1%) zerosZeros
영업용 has 13 (38.2%) zerosZeros
관 용 has 26 (76.5%) zerosZeros
소 계 has 12 (35.3%) zerosZeros

Reproduction

Analysis started2023-12-10 17:40:04.062876
Analysis finished2023-12-10 17:40:09.545787
Duration5.48 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
2023-12-11T02:40:09.957715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.9411765
Min length5

Characters and Unicode

Total characters304
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 row01 불도저
2nd row02 굴삭기
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%
굴삭기 1
 
1.2%
02 1
 
1.2%
노면파쇄기 1
 
1.2%
52 1
 
1.2%
Other values (62) 62
74.7%
2023-12-11T02:40:10.917410image/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 8
 
2.6%
콘 7
 
2.3%
Other values (85) 149
49.0%

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 8
 
4.3%
콘 7
 
3.7%
스 5
 
2.7%
아 4
 
2.1%
팔 4
 
2.1%
레 4
 
2.1%
럭 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 (%)
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 8
 
4.3%
콘 7
 
3.7%
스 5
 
2.7%
아 4
 
2.1%
팔 4
 
2.1%
레 4
 
2.1%
럭 4
 
2.1%
Other values (74) 108
57.8%
Common
ValueCountFrequency (%)
49
41.9%
1 14
 
12.0%
0 12
 
10.3%
5 11
 
9.4%
2 11
 
9.4%
3 4
 
3.4%
4 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%
3 4
 
3.4%
4 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 8
 
4.3%
콘 7
 
3.7%
스 5
 
2.7%
아 4
 
2.1%
팔 4
 
2.1%
레 4
 
2.1%
럭 4
 
2.1%
Other values (74) 108
57.8%

자가용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160
Minimum0
Maximum3811
Zeros16
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T02:40:11.247825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310.5
95-th percentile516.15
Maximum3811
Range3811
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation671.57711
Coefficient of variation (CV)4.197357
Kurtosis28.584325
Mean160
Median Absolute Deviation (MAD)1
Skewness5.2408134
Sum5440
Variance451015.82
MonotonicityNot monotonic
2023-12-11T02:40:11.582530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 16
47.1%
4 4
 
11.8%
1 3
 
8.8%
11 1
 
2.9%
1068 1
 
2.9%
219 1
 
2.9%
3811 1
 
2.9%
195 1
 
2.9%
19 1
 
2.9%
20 1
 
2.9%
Other values (4) 4
 
11.8%
ValueCountFrequency (%)
0 16
47.1%
1 3
 
8.8%
2 1
 
2.9%
4 4
 
11.8%
9 1
 
2.9%
11 1
 
2.9%
19 1
 
2.9%
20 1
 
2.9%
21 1
 
2.9%
46 1
 
2.9%
ValueCountFrequency (%)
3811 1
2.9%
1068 1
2.9%
219 1
2.9%
195 1
2.9%
46 1
2.9%
21 1
2.9%
20 1
2.9%
19 1
2.9%
11 1
2.9%
9 1
2.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271
Minimum0
Maximum3780
Zeros13
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T02:40:11.941207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q3184.5
95-th percentile1289.9
Maximum3780
Range3780
Interquartile range (IQR)184.5

Descriptive statistics

Standard deviation715.84131
Coefficient of variation (CV)2.6414809
Kurtosis18.241395
Mean271
Median Absolute Deviation (MAD)11
Skewness4.040487
Sum9214
Variance512428.79
MonotonicityNot monotonic
2023-12-11T02:40:12.306101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 13
38.2%
5 2
 
5.9%
206 1
 
2.9%
42 1
 
2.9%
9 1
 
2.9%
56 1
 
2.9%
15 1
 
2.9%
45 1
 
2.9%
51 1
 
2.9%
120 1
 
2.9%
Other values (11) 11
32.4%
ValueCountFrequency (%)
0 13
38.2%
1 1
 
2.9%
5 2
 
5.9%
9 1
 
2.9%
13 1
 
2.9%
15 1
 
2.9%
25 1
 
2.9%
42 1
 
2.9%
45 1
 
2.9%
51 1
 
2.9%
ValueCountFrequency (%)
3780 1
2.9%
1598 1
2.9%
1124 1
2.9%
940 1
2.9%
331 1
2.9%
312 1
2.9%
275 1
2.9%
261 1
2.9%
206 1
2.9%
120 1
2.9%

관 용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4411765
Minimum0
Maximum17
Zeros26
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T02:40:12.677346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.05
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6778714
Coefficient of variation (CV)2.5519924
Kurtosis10.430713
Mean1.4411765
Median Absolute Deviation (MAD)0
Skewness3.1668353
Sum49
Variance13.526738
MonotonicityNot monotonic
2023-12-11T02:40:13.069082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 26
76.5%
2 2
 
5.9%
3 1
 
2.9%
17 1
 
2.9%
5 1
 
2.9%
11 1
 
2.9%
8 1
 
2.9%
1 1
 
2.9%
ValueCountFrequency (%)
0 26
76.5%
1 1
 
2.9%
2 2
 
5.9%
3 1
 
2.9%
5 1
 
2.9%
8 1
 
2.9%
11 1
 
2.9%
17 1
 
2.9%
ValueCountFrequency (%)
17 1
 
2.9%
11 1
 
2.9%
8 1
 
2.9%
5 1
 
2.9%
3 1
 
2.9%
2 2
 
5.9%
1 1
 
2.9%
0 26
76.5%

소 계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432.44118
Minimum0
Maximum4946
Zeros12
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T02:40:13.503877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.5
Q3200.25
95-th percentile2873.4
Maximum4946
Range4946
Interquartile range (IQR)200.25

Descriptive statistics

Standard deviation1188.8031
Coefficient of variation (CV)2.7490515
Kurtosis11.365702
Mean432.44118
Median Absolute Deviation (MAD)13.5
Skewness3.4484024
Sum14703
Variance1413252.7
MonotonicityNot monotonic
2023-12-11T02:40:14.501719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 12
35.3%
220 1
 
2.9%
6 1
 
2.9%
10 1
 
2.9%
3 1
 
2.9%
56 1
 
2.9%
19 1
 
2.9%
5 1
 
2.9%
49 1
 
2.9%
60 1
 
2.9%
Other values (13) 13
38.2%
ValueCountFrequency (%)
0 12
35.3%
1 1
 
2.9%
3 1
 
2.9%
5 1
 
2.9%
6 1
 
2.9%
10 1
 
2.9%
17 1
 
2.9%
19 1
 
2.9%
25 1
 
2.9%
46 1
 
2.9%
ValueCountFrequency (%)
4946 1
2.9%
4865 1
2.9%
1801 1
2.9%
986 1
2.9%
536 1
2.9%
353 1
2.9%
282 1
2.9%
276 1
2.9%
220 1
2.9%
141 1
2.9%

Interactions

2023-12-11T02:40:07.933116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:04.533811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:05.773523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:06.862473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:08.184600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:04.875441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:06.033692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:07.122157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:08.439759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:05.212073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:06.280356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:07.385688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:08.793996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:05.540164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:06.546678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:40:07.657164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:40:14.724272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관 용소 계
건설기계명1.0001.0001.0001.0001.000
자가용1.0001.0000.7881.0000.648
영업용1.0000.7881.0000.9740.977
관 용1.0001.0000.9741.0000.974
소 계1.0000.6480.9770.9741.000
2023-12-11T02:40:14.949415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관 용소 계
자가용1.0000.8620.7030.896
영업용0.8621.0000.6080.989
관 용0.7030.6081.0000.651
소 계0.8960.9890.6511.000

Missing values

2023-12-11T02:40:09.185531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:40:09.410849image/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

건설기계명자가용영업용관 용소 계
001 불도저112063220
102 굴삭기10683780174865
203 로더2193125536
304 지게차38111124114946
405 스크레이퍼0000
506 덤프트럭195159881801
607 기중기192612282
708 모터 그레이더025025
809 롤러203312353
910 노상안정기0000
건설기계명자가용영업용관 용소 계
2425 준설선415019
2527 타워크레인056056
2651 도로보수트럭2013
2752 노면파쇄기19010
2853 노면측정장비0000
2954 콘크리트 믹서 트레일러0000
3055 트럭지게차0000
3158 수목이식기0000
3259 아스팔트 콘크리트 재생기0000
3360 터널용고소작업차0000