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 memory38.8 B

Variable types

Text1
Numeric3

Dataset

Description대구광역시_건설기계 등록현황_2019.4월
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3074818&dataSetDetailId=30748181e42ae729ed05&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 20 (58.8%) zerosZeros
영업용 has 14 (41.2%) zerosZeros
관 용 has 27 (79.4%) zerosZeros

Reproduction

Analysis started2024-04-21 08:14:43.804501
Analysis finished2024-04-21 08:14:46.776155
Duration2.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건설기계명
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
2024-04-21T17:14:47.440114image/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%
2024-04-21T17:14:48.683465image/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 

Distinct13
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.94118
Minimum0
Maximum3810
Zeros20
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-04-21T17:14:49.042281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.25
95-th percentile513.55
Maximum3810
Range3810
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation672.74899
Coefficient of variation (CV)4.2326917
Kurtosis28.379874
Mean158.94118
Median Absolute Deviation (MAD)0
Skewness5.2209324
Sum5404
Variance452591.21
MonotonicityNot monotonic
2024-04-21T17:14:49.422775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 20
58.8%
4 2
 
5.9%
1 2
 
5.9%
1094 1
 
2.9%
201 1
 
2.9%
3810 1
 
2.9%
196 1
 
2.9%
7 1
 
2.9%
11 1
 
2.9%
45 1
 
2.9%
Other values (3) 3
 
8.8%
ValueCountFrequency (%)
0 20
58.8%
1 2
 
5.9%
2 1
 
2.9%
4 2
 
5.9%
7 1
 
2.9%
9 1
 
2.9%
11 1
 
2.9%
19 1
 
2.9%
45 1
 
2.9%
196 1
 
2.9%
ValueCountFrequency (%)
3810 1
2.9%
1094 1
2.9%
201 1
2.9%
196 1
2.9%
45 1
2.9%
19 1
2.9%
11 1
2.9%
9 1
2.9%
7 1
2.9%
4 2
5.9%

영업용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.52941
Minimum0
Maximum3022
Zeros14
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-04-21T17:14:49.781408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q3139.5
95-th percentile1176.5
Maximum3022
Range3022
Interquartile range (IQR)139.5

Descriptive statistics

Standard deviation582.87985
Coefficient of variation (CV)2.4959591
Kurtosis16.370651
Mean233.52941
Median Absolute Deviation (MAD)7.5
Skewness3.8121824
Sum7940
Variance339748.92
MonotonicityNot monotonic
2024-04-21T17:14:50.142061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 14
41.2%
284 2
 
5.9%
10 2
 
5.9%
132 1
 
2.9%
2 1
 
2.9%
142 1
 
2.9%
4 1
 
2.9%
57 1
 
2.9%
74 1
 
2.9%
93 1
 
2.9%
Other values (9) 9
26.5%
ValueCountFrequency (%)
0 14
41.2%
2 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
10 2
 
5.9%
12 1
 
2.9%
33 1
 
2.9%
57 1
 
2.9%
74 1
 
2.9%
93 1
 
2.9%
ValueCountFrequency (%)
3022 1
2.9%
1274 1
2.9%
1124 1
2.9%
821 1
2.9%
285 1
2.9%
284 2
5.9%
272 1
2.9%
142 1
2.9%
132 1
2.9%
93 1
2.9%

관 용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6764706
Minimum0
Maximum20
Zeros27
Zeros (%)79.4%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-04-21T17:14:50.486866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.8
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.530841
Coefficient of variation (CV)2.7026069
Kurtosis10.248163
Mean1.6764706
Median Absolute Deviation (MAD)0
Skewness3.2191852
Sum57
Variance20.52852
MonotonicityNot monotonic
2024-04-21T17:14:50.851557image/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%
20 1
 
2.9%
6 1
 
2.9%
16 1
 
2.9%
8 1
 
2.9%
ValueCountFrequency (%)
0 27
79.4%
2 2
 
5.9%
3 1
 
2.9%
6 1
 
2.9%
8 1
 
2.9%
16 1
 
2.9%
20 1
 
2.9%
ValueCountFrequency (%)
20 1
 
2.9%
16 1
 
2.9%
8 1
 
2.9%
6 1
 
2.9%
3 1
 
2.9%
2 2
 
5.9%
0 27
79.4%

Interactions

2024-04-21T17:14:45.535952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:44.001616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:44.778420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:45.801785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:44.262608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:45.034803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:46.053870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:44.513216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:14:45.277691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T17:14:51.106231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설기계명자가용영업용관 용
건설기계명1.0001.0001.0001.000
자가용1.0001.0001.0001.000
영업용1.0001.0001.0000.899
관 용1.0001.0000.8991.000
2024-04-21T17:14:51.356357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가용영업용관 용
자가용1.0000.7580.727
영업용0.7581.0000.678
관 용0.7270.6781.000

Missing values

2024-04-21T17:14:46.395727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T17:14:46.670669image/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 불도저41323
102 굴삭기1094302220
203 로더2012846
304 지게차3810127416
405 스크레이퍼000
506 덤프트럭19611248
607 기중기72852
708 모터 그레이더0120
809 롤러112722
910 노상안정기000
건설기계명자가용영업용관 용
2425 준설선0100
2527 타워크레인01420
2651 도로보수트럭200
2752 노면파쇄기1100
2853 노면측정장비000
2954 콘크리트 믹서 트레일러000
3055 트럭지게차000
3158 수목이식기000
3259 아스팔트 콘크리트 재생기000
3360 터널용고소작업차000