Overview

Dataset statistics

Number of variables6
Number of observations68
Missing cells69
Missing cells (%)16.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory53.9 B

Variable types

Numeric3
Text2
Unsupported1

Dataset

Description영동군에서 임대하는 농업기계 장비 현황 자료로 장비명, 규격 ,전체수량, 신품수량,중고수량의 정보를 제공합니다.
Author충청북도 영동군
URLhttps://www.data.go.kr/data/3072326/fileData.do

Alerts

전체수량 is highly overall correlated with 중고수량High correlation
중고수량 is highly overall correlated with 전체수량High correlation
규격 has 1 (1.5%) missing valuesMissing
신품수량 has 68 (100.0%) missing valuesMissing
순번 has unique valuesUnique
장비명 has unique valuesUnique
신품수량 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 03:00:41.799471
Analysis finished2023-12-12 03:00:43.515472
Duration1.72 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.5
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T12:00:43.620100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q117.75
median34.5
Q351.25
95-th percentile64.65
Maximum68
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.5731513
Kurtosis-1.2
Mean34.5
Median Absolute Deviation (MAD)17
Skewness0
Sum2346
Variance391
MonotonicityStrictly increasing
2023-12-12T12:00:43.852388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
45 1
 
1.5%
51 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
44 1
 
1.5%
36 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
68 1
1.5%
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%

장비명
Text

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-12T12:00:44.240646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.4117647
Min length2

Characters and Unicode

Total characters436
Distinct characters122
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)100.0%

Sample

1st rowSS기
2nd row감박피기
3rd row감자파종기(트)
4th row결속기(트)
5th row경운기
ValueCountFrequency (%)
ss기 1
 
1.5%
잔가지파쇄기 1
 
1.5%
옥수수파종기(트 1
 
1.5%
운반트레일러 1
 
1.5%
이앙기(보행 1
 
1.5%
이앙기(승용 1
 
1.5%
인삼두둑기(트 1
 
1.5%
감박피기 1
 
1.5%
쟁기(트 1
 
1.5%
구굴기(관 1
 
1.5%
Other values (58) 58
85.3%
2023-12-12T12:00:44.838438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 53
 
12.2%
( 42
 
9.6%
) 42
 
9.6%
트 23
 
5.3%
수 9
 
2.1%
관 8
 
1.8%
용 7
 
1.6%
비 7
 
1.6%
동 6
 
1.4%
리 6
 
1.4%
Other values (112) 233
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 349
80.0%
Open Punctuation 42
 
9.6%
Close Punctuation 42
 
9.6%
Uppercase Letter 2
 
0.5%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 53
 
15.2%
트 23
 
6.6%
수 9
 
2.6%
관 8
 
2.3%
용 7
 
2.0%
비 7
 
2.0%
동 6
 
1.7%
리 6
 
1.7%
파 6
 
1.7%
로 6
 
1.7%
Other values (108) 218
62.5%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 349
80.0%
Common 85
 
19.5%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 53
 
15.2%
트 23
 
6.6%
수 9
 
2.6%
관 8
 
2.3%
용 7
 
2.0%
비 7
 
2.0%
동 6
 
1.7%
리 6
 
1.7%
파 6
 
1.7%
로 6
 
1.7%
Other values (108) 218
62.5%
Common
ValueCountFrequency (%)
( 42
49.4%
) 42
49.4%
1
 
1.2%
Latin
ValueCountFrequency (%)
S 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 349
80.0%
ASCII 87
 
20.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 53
 
15.2%
트 23
 
6.6%
수 9
 
2.6%
관 8
 
2.3%
용 7
 
2.0%
비 7
 
2.0%
동 6
 
1.7%
리 6
 
1.7%
파 6
 
1.7%
로 6
 
1.7%
Other values (108) 218
62.5%
ASCII
ValueCountFrequency (%)
( 42
48.3%
) 42
48.3%
S 2
 
2.3%
1
 
1.1%

규격
Text

MISSING 

Distinct67
Distinct (%)100.0%
Missing1
Missing (%)1.5%
Memory size676.0 B
2023-12-12T12:00:45.212342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.5970149
Min length4

Characters and Unicode

Total characters509
Distinct characters39
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)100.0%

Sample

1st rowASS-0433CG등
2nd rowSW-A등
3rd rowKG-PH9000
4th rowRB1000DF등
5th rowDT10DE등
ValueCountFrequency (%)
ass-0433cg등 1
 
1.5%
art-3200등 1
 
1.5%
d30s-7등 1
 
1.5%
dlk200af등 1
 
1.5%
sh-650a 1
 
1.5%
srm98등 1
 
1.5%
sgm-71b등 1
 
1.5%
sw-pn2106등 1
 
1.5%
dlk15-cr등 1
 
1.5%
sw-a등 1
 
1.5%
Other values (57) 57
85.1%
2023-12-12T12:00:45.800116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 88
17.3%
등 41
 
8.1%
- 39
 
7.7%
1 29
 
5.7%
S 28
 
5.5%
D 21
 
4.1%
2 19
 
3.7%
R 18
 
3.5%
A 17
 
3.3%
5 16
 
3.1%
Other values (29) 193
37.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 222
43.6%
Decimal Number 204
40.1%
Other Letter 41
 
8.1%
Dash Punctuation 39
 
7.7%
Close Punctuation 1
 
0.2%
Space Separator 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 28
12.6%
D 21
 
9.5%
R 18
 
8.1%
A 17
 
7.7%
H 15
 
6.8%
T 15
 
6.8%
G 13
 
5.9%
P 12
 
5.4%
C 12
 
5.4%
M 12
 
5.4%
Other values (14) 59
26.6%
Decimal Number
ValueCountFrequency (%)
0 88
43.1%
1 29
 
14.2%
2 19
 
9.3%
5 16
 
7.8%
6 14
 
6.9%
3 14
 
6.9%
7 8
 
3.9%
8 7
 
3.4%
4 6
 
2.9%
9 3
 
1.5%
Other Letter
ValueCountFrequency (%)
등 41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 246
48.3%
Latin 222
43.6%
Hangul 41
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 28
12.6%
D 21
 
9.5%
R 18
 
8.1%
A 17
 
7.7%
H 15
 
6.8%
T 15
 
6.8%
G 13
 
5.9%
P 12
 
5.4%
C 12
 
5.4%
M 12
 
5.4%
Other values (14) 59
26.6%
Common
ValueCountFrequency (%)
0 88
35.8%
- 39
15.9%
1 29
 
11.8%
2 19
 
7.7%
5 16
 
6.5%
6 14
 
5.7%
3 14
 
5.7%
7 8
 
3.3%
8 7
 
2.8%
4 6
 
2.4%
Other values (4) 6
 
2.4%
Hangul
ValueCountFrequency (%)
등 41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
91.9%
Hangul 41
 
8.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88
18.8%
- 39
 
8.3%
1 29
 
6.2%
S 28
 
6.0%
D 21
 
4.5%
2 19
 
4.1%
R 18
 
3.8%
A 17
 
3.6%
5 16
 
3.4%
H 15
 
3.2%
Other values (28) 178
38.0%
Hangul
ValueCountFrequency (%)
등 41
100.0%

전체수량
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9558824
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T12:00:45.968577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median4
Q310
95-th percentile37.15
Maximum54
Range53
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation11.965665
Coefficient of variation (CV)1.3360676
Kurtosis5.5812936
Mean8.9558824
Median Absolute Deviation (MAD)3
Skewness2.3727224
Sum609
Variance143.17713
MonotonicityNot monotonic
2023-12-12T12:00:46.179179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 17
25.0%
3 8
11.8%
10 5
 
7.4%
7 5
 
7.4%
4 5
 
7.4%
2 5
 
7.4%
6 4
 
5.9%
17 2
 
2.9%
19 2
 
2.9%
5 2
 
2.9%
Other values (11) 13
19.1%
ValueCountFrequency (%)
1 17
25.0%
2 5
 
7.4%
3 8
11.8%
4 5
 
7.4%
5 2
 
2.9%
6 4
 
5.9%
7 5
 
7.4%
8 2
 
2.9%
9 1
 
1.5%
10 5
 
7.4%
ValueCountFrequency (%)
54 1
1.5%
53 1
1.5%
44 1
1.5%
41 1
1.5%
30 1
1.5%
26 1
1.5%
25 1
1.5%
23 1
1.5%
19 2
2.9%
17 2
2.9%

신품수량
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

중고수량
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9558824
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-12T12:00:46.357687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median4
Q310
95-th percentile37.15
Maximum54
Range53
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation11.965665
Coefficient of variation (CV)1.3360676
Kurtosis5.5812936
Mean8.9558824
Median Absolute Deviation (MAD)3
Skewness2.3727224
Sum609
Variance143.17713
MonotonicityNot monotonic
2023-12-12T12:00:46.533691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 17
25.0%
3 8
11.8%
10 5
 
7.4%
7 5
 
7.4%
4 5
 
7.4%
2 5
 
7.4%
6 4
 
5.9%
17 2
 
2.9%
19 2
 
2.9%
5 2
 
2.9%
Other values (11) 13
19.1%
ValueCountFrequency (%)
1 17
25.0%
2 5
 
7.4%
3 8
11.8%
4 5
 
7.4%
5 2
 
2.9%
6 4
 
5.9%
7 5
 
7.4%
8 2
 
2.9%
9 1
 
1.5%
10 5
 
7.4%
ValueCountFrequency (%)
54 1
1.5%
53 1
1.5%
44 1
1.5%
41 1
1.5%
30 1
1.5%
26 1
1.5%
25 1
1.5%
23 1
1.5%
19 2
2.9%
17 2
2.9%

Interactions

2023-12-12T12:00:42.875443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:42.098094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:42.504135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:43.007694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:42.239519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:42.638737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:43.157156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:42.377274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:00:42.743407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:00:46.655723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번장비명규격전체수량중고수량
순번1.0001.0001.0000.3660.366
장비명1.0001.0001.0001.0001.000
규격1.0001.0001.0001.0001.000
전체수량0.3661.0001.0001.0001.000
중고수량0.3661.0001.0001.0001.000
2023-12-12T12:00:46.793088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번전체수량중고수량
순번1.0000.1030.103
전체수량0.1031.0001.000
중고수량0.1031.0001.000

Missing values

2023-12-12T12:00:43.316744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:00:43.459164image/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

순번장비명규격전체수량신품수량중고수량
01SS기ASS-0433CG등10<NA>10
12감박피기SW-A등7<NA>7
23감자파종기(트)KG-PH90001<NA>1
34결속기(트)RB1000DF등1<NA>1
45경운기DT10DE등13<NA>13
56과실수확기SC105N1<NA>1
67관리기(보행)AMC-900SM등44<NA>44
78관리기(승용)CMF1200등3<NA>3
89구굴기(관)DR-25(S)등17<NA>17
910그레이더(트)GWG-2400등3<NA>3
순번장비명규격전체수량신품수량중고수량
5859퇴비살포기(승용)CJS-1000SS등10<NA>10
5960퇴비살포기(트)HMH-6000R등4<NA>4
6061트랙터PX1000PSC등41<NA>41
6162파이프밴딩성형기HB-2532B3<NA>3
6263파종기HG10A등26<NA>26
6364풍구BH-252<NA>2
6465휴대용자동전동가위30I31등23<NA>23
6566휴립기(관)AF-300등7<NA>7
6667휴립피복기(트)DPT-140MT1<NA>1
6768휴립피복기(관)FM-120등17<NA>17