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

Description대구광역시_건설기계 등록현황_2017.3월
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3074818&dataSetDetailId=30748181cd33a1b61ef4&provdMethod=FILE

Alerts

자가용 is highly overall correlated with 영업용 and 1 other fieldsHigh correlation
영업용 is highly overall correlated with 자가용 and 1 other fieldsHigh correlation
관 용 is highly overall correlated with 자가용 and 1 other fieldsHigh correlation
건설기계명 has unique valuesUnique
자가용 has 17 (50.0%) zerosZeros
영업용 has 13 (38.2%) zerosZeros
관 용 has 27 (79.4%) zerosZeros

Reproduction

Analysis started2024-04-17 10:28:06.368717
Analysis finished2024-04-17 10:28:07.826653
Duration1.46 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
2024-04-17T19:28:07.974617image/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 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%
2024-04-17T19:28:08.318669image/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 

Distinct14
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.35294
Minimum0
Maximum3747
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-17T19:28:08.419683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q38.25
95-th percentile533.5
Maximum3747
Range3747
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation660.61835
Coefficient of variation (CV)4.1718098
Kurtosis28.483999
Mean158.35294
Median Absolute Deviation (MAD)0.5
Skewness5.2272437
Sum5384
Variance436416.6
MonotonicityNot monotonic
2024-04-17T19:28:08.516781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 17
50.0%
1 4
 
11.8%
3 2
 
5.9%
6 1
 
2.9%
1047 1
 
2.9%
221 1
 
2.9%
3747 1
 
2.9%
257 1
 
2.9%
9 1
 
2.9%
12 1
 
2.9%
Other values (4) 4
 
11.8%
ValueCountFrequency (%)
0 17
50.0%
1 4
 
11.8%
2 1
 
2.9%
3 2
 
5.9%
6 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
52 1
 
2.9%
ValueCountFrequency (%)
3747 1
2.9%
1047 1
2.9%
257 1
2.9%
221 1
2.9%
52 1
2.9%
12 1
2.9%
11 1
2.9%
10 1
2.9%
9 1
2.9%
6 1
2.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.29412
Minimum0
Maximum3026
Zeros13
Zeros (%)38.2%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-17T19:28:08.617425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.5
Q3137.75
95-th percentile1233.85
Maximum3026
Range3026
Interquartile range (IQR)137.75

Descriptive statistics

Standard deviation590.05693
Coefficient of variation (CV)2.4761708
Kurtosis15.492613
Mean238.29412
Median Absolute Deviation (MAD)9.5
Skewness3.7090505
Sum8102
Variance348167.18
MonotonicityNot monotonic
2024-04-17T19:28:08.713193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 13
38.2%
141 1
 
2.9%
30 1
 
2.9%
11 1
 
2.9%
128 1
 
2.9%
12 1
 
2.9%
4 1
 
2.9%
50 1
 
2.9%
55 1
 
2.9%
93 1
 
2.9%
Other values (12) 12
35.3%
ValueCountFrequency (%)
0 13
38.2%
1 1
 
2.9%
3 1
 
2.9%
4 1
 
2.9%
8 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
16 1
 
2.9%
30 1
 
2.9%
50 1
 
2.9%
ValueCountFrequency (%)
3026 1
2.9%
1241 1
2.9%
1230 1
2.9%
913 1
2.9%
292 1
2.9%
286 1
2.9%
284 1
2.9%
278 1
2.9%
141 1
2.9%
128 1
2.9%

관 용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5294118
Minimum0
Maximum18
Zeros27
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-17T19:28:08.802723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.45
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.1431601
Coefficient of variation (CV)2.7089893
Kurtosis10.087179
Mean1.5294118
Median Absolute Deviation (MAD)0
Skewness3.2166235
Sum52
Variance17.165775
MonotonicityNot monotonic
2024-04-17T19:28:08.892081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 27
79.4%
2 2
 
5.9%
3 1
 
2.9%
18 1
 
2.9%
4 1
 
2.9%
15 1
 
2.9%
8 1
 
2.9%
ValueCountFrequency (%)
0 27
79.4%
2 2
 
5.9%
3 1
 
2.9%
4 1
 
2.9%
8 1
 
2.9%
15 1
 
2.9%
18 1
 
2.9%
ValueCountFrequency (%)
18 1
 
2.9%
15 1
 
2.9%
8 1
 
2.9%
4 1
 
2.9%
3 1
 
2.9%
2 2
 
5.9%
0 27
79.4%

Interactions

2024-04-17T19:28:07.463889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:06.781267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.231706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.550625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.081834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.305697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.622596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.149105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:28:07.380204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T19:28:08.970321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관 용
건설기계명1.0001.0001.0001.000
자가용1.0001.0000.7881.000
영업용1.0000.7881.0000.900
관 용1.0001.0000.9001.000
2024-04-17T19:28:09.055708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관 용
자가용1.0000.8400.705
영업용0.8401.0000.668
관 용0.7050.6681.000

Missing values

2024-04-17T19:28:07.720088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T19:28:07.794994image/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 용
001 불도저61413
102 굴삭기1047302618
203 로더2212784
304 지게차3747123015
405 스크레이퍼000
506 덤프트럭25712418
607 기중기92842
708 모터 그레이더0160
809 롤러122862
910 노상안정기000
건설기계명자가용영업용관 용
2425 준설선1120
2527 타워크레인01280
2651 도로보수트럭300
2752 노면파쇄기1110
2853 노면측정장비000
2954 콘크리트 믹서 트레일러000
3055 트럭지게차000
3158 수목이식기000
3259 아스팔트 콘크리트 재생기000
3360 터널용고소작업차000