Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

Numeric2
Categorical1
Text2

Dataset

Description한국농업기술진흥원에서 시행하고 있는 농업기계에 대한 검정 수수료로서 분류별 기계별 세부 수수료 금액에 대한 정보를 가지고 있다.
Author한국농업기술진흥원
URLhttps://www.data.go.kr/data/15104610/fileData.do

Alerts

번호 is highly overall correlated with 수수료(원) and 1 other fieldsHigh correlation
수수료(원) is highly overall correlated with 번호High correlation
분류 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique
수수료(원) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-12 03:05:05.022562
Analysis finished2023-12-12 03:05:06.061809
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:05:06.153159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-12T12:05:06.321886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

분류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종합검정
42 
기술지도검정
41 
국제규범검정
11 
기타
 
3
안전검정
 
1
Other values (2)
 
2

Length

Max length6
Median length6
Mean length4.98
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row종합검정
2nd row종합검정
3rd row종합검정
4th row종합검정
5th row종합검정

Common Values

ValueCountFrequency (%)
종합검정 42
42.0%
기술지도검정 41
41.0%
국제규범검정 11
 
11.0%
기타 3
 
3.0%
안전검정 1
 
1.0%
변경검정 1
 
1.0%
사후검정 1
 
1.0%

Length

2023-12-12T12:05:06.515787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:05:06.669577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종합검정 42
42.0%
기술지도검정 41
41.0%
국제규범검정 11
 
11.0%
기타 3
 
3.0%
안전검정 1
 
1.0%
변경검정 1
 
1.0%
사후검정 1
 
1.0%

분석항목
Text

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T12:05:07.078954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length17
Mean length9.56
Min length2

Characters and Unicode

Total characters956
Distinct characters175
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)81.0%

Sample

1st row농업용엔진
2nd row농업용엔진
3rd row농업용엔진
4th row경운기
5th row관리기
ValueCountFrequency (%)
성능시험 21
 
11.2%
oecd 8
 
4.3%
농용트랙터표준코드 8
 
4.3%
시험 8
 
4.3%
전기용난방기 3
 
1.6%
적용 3
 
1.6%
농업용트랙터 3
 
1.6%
또는 3
 
1.6%
농업용엔진 3
 
1.6%
기종 2
 
1.1%
Other values (103) 126
67.0%
2023-12-12T12:05:07.678166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
 
9.2%
기 62
 
6.5%
시 51
 
5.3%
험 50
 
5.2%
성 34
 
3.6%
능 30
 
3.1%
용 30
 
3.1%
농 25
 
2.6%
트 18
 
1.9%
O 17
 
1.8%
Other values (165) 551
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 776
81.2%
Space Separator 88
 
9.2%
Uppercase Letter 69
 
7.2%
Open Punctuation 11
 
1.2%
Close Punctuation 11
 
1.2%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 62
 
8.0%
시 51
 
6.6%
험 50
 
6.4%
성 34
 
4.4%
능 30
 
3.9%
용 30
 
3.9%
농 25
 
3.2%
트 18
 
2.3%
터 15
 
1.9%
물 13
 
1.7%
Other values (150) 448
57.7%
Uppercase Letter
ValueCountFrequency (%)
O 17
24.6%
E 9
13.0%
C 8
11.6%
D 8
11.6%
P 8
11.6%
S 8
11.6%
F 3
 
4.3%
R 3
 
4.3%
T 2
 
2.9%
A 2
 
2.9%
Space Separator
ValueCountFrequency (%)
88
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 776
81.2%
Common 111
 
11.6%
Latin 69
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 62
 
8.0%
시 51
 
6.6%
험 50
 
6.4%
성 34
 
4.4%
능 30
 
3.9%
용 30
 
3.9%
농 25
 
3.2%
트 18
 
2.3%
터 15
 
1.9%
물 13
 
1.7%
Other values (150) 448
57.7%
Latin
ValueCountFrequency (%)
O 17
24.6%
E 9
13.0%
C 8
11.6%
D 8
11.6%
P 8
11.6%
S 8
11.6%
F 3
 
4.3%
R 3
 
4.3%
T 2
 
2.9%
A 2
 
2.9%
Common
ValueCountFrequency (%)
88
79.3%
( 11
 
9.9%
) 11
 
9.9%
_ 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 776
81.2%
ASCII 180
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
48.9%
O 17
 
9.4%
( 11
 
6.1%
) 11
 
6.1%
E 9
 
5.0%
C 8
 
4.4%
D 8
 
4.4%
P 8
 
4.4%
S 8
 
4.4%
F 3
 
1.7%
Other values (5) 9
 
5.0%
Hangul
ValueCountFrequency (%)
기 62
 
8.0%
시 51
 
6.6%
험 50
 
6.4%
성 34
 
4.4%
능 30
 
3.9%
용 30
 
3.9%
농 25
 
3.2%
트 18
 
2.3%
터 15
 
1.9%
물 13
 
1.7%
Other values (150) 448
57.7%

세부
Text

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T12:05:07.988815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length5.41
Min length2

Characters and Unicode

Total characters541
Distinct characters127
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)47.0%

Sample

1st row엔진배기량 1.5리터 미만
2nd row엔진배기량 1.5리터 이상 3.5리터 이하
3rd row엔진배기량 3.5리터 초과
4th row경운기
5th row관리기
ValueCountFrequency (%)
1항목 41
28.5%
코드 8
 
5.6%
엔진배기량 5
 
3.5%
1건 5
 
3.5%
1.5리터 4
 
2.8%
3.5리터 4
 
2.8%
kw 4
 
2.8%
이상 4
 
2.8%
이하 3
 
2.1%
발열체 3
 
2.1%
Other values (54) 63
43.8%
2023-12-12T12:05:08.522828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 55
 
10.2%
44
 
8.1%
목 41
 
7.6%
항 41
 
7.6%
기 32
 
5.9%
이 10
 
1.8%
5 9
 
1.7%
드 9
 
1.7%
리 9
 
1.7%
농 8
 
1.5%
Other values (117) 283
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 384
71.0%
Decimal Number 83
 
15.3%
Space Separator 44
 
8.1%
Uppercase Letter 12
 
2.2%
Other Punctuation 8
 
1.5%
Lowercase Letter 4
 
0.7%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
목 41
 
10.7%
항 41
 
10.7%
기 32
 
8.3%
이 10
 
2.6%
드 9
 
2.3%
리 9
 
2.3%
농 8
 
2.1%
코 8
 
2.1%
터 8
 
2.1%
용 8
 
2.1%
Other values (96) 210
54.7%
Decimal Number
ValueCountFrequency (%)
1 55
66.3%
5 9
 
10.8%
0 5
 
6.0%
3 4
 
4.8%
4 3
 
3.6%
8 3
 
3.6%
2 1
 
1.2%
7 1
 
1.2%
6 1
 
1.2%
9 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
W 4
33.3%
S 2
16.7%
O 2
16.7%
P 2
16.7%
F 1
 
8.3%
R 1
 
8.3%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 384
71.0%
Common 141
 
26.1%
Latin 16
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
목 41
 
10.7%
항 41
 
10.7%
기 32
 
8.3%
이 10
 
2.6%
드 9
 
2.3%
리 9
 
2.3%
농 8
 
2.1%
코 8
 
2.1%
터 8
 
2.1%
용 8
 
2.1%
Other values (96) 210
54.7%
Common
ValueCountFrequency (%)
1 55
39.0%
44
31.2%
5 9
 
6.4%
. 8
 
5.7%
0 5
 
3.5%
3 4
 
2.8%
( 3
 
2.1%
) 3
 
2.1%
4 3
 
2.1%
8 3
 
2.1%
Other values (4) 4
 
2.8%
Latin
ValueCountFrequency (%)
k 4
25.0%
W 4
25.0%
S 2
12.5%
O 2
12.5%
P 2
12.5%
F 1
 
6.2%
R 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 384
71.0%
ASCII 157
29.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55
35.0%
44
28.0%
5 9
 
5.7%
. 8
 
5.1%
0 5
 
3.2%
k 4
 
2.5%
3 4
 
2.5%
W 4
 
2.5%
( 3
 
1.9%
) 3
 
1.9%
Other values (11) 18
 
11.5%
Hangul
ValueCountFrequency (%)
목 41
 
10.7%
항 41
 
10.7%
기 32
 
8.3%
이 10
 
2.6%
드 9
 
2.3%
리 9
 
2.3%
농 8
 
2.1%
코 8
 
2.1%
터 8
 
2.1%
용 8
 
2.1%
Other values (96) 210
54.7%

수수료(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean772810
Minimum0
Maximum3510000
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:05:08.756862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61300
Q1325000
median530000
Q31077500
95-th percentile1794000
Maximum3510000
Range3510000
Interquartile range (IQR)752500

Descriptive statistics

Standard deviation701652.16
Coefficient of variation (CV)0.90792324
Kurtosis5.4564983
Mean772810
Median Absolute Deviation (MAD)300000
Skewness2.1165939
Sum77281000
Variance4.9231575 × 1011
MonotonicityNot monotonic
2023-12-12T12:05:08.979582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230000 7
 
7.0%
530000 4
 
4.0%
1380000 4
 
4.0%
280000 3
 
3.0%
510000 3
 
3.0%
930000 3
 
3.0%
1120000 3
 
3.0%
200000 3
 
3.0%
720000 3
 
3.0%
400000 3
 
3.0%
Other values (50) 64
64.0%
ValueCountFrequency (%)
0 2
 
2.0%
5000 1
 
1.0%
10000 2
 
2.0%
64000 1
 
1.0%
183000 1
 
1.0%
200000 3
3.0%
230000 7
7.0%
240000 1
 
1.0%
243000 2
 
2.0%
250000 1
 
1.0%
ValueCountFrequency (%)
3510000 1
 
1.0%
3380000 1
 
1.0%
3350000 1
 
1.0%
3190000 1
 
1.0%
2250000 1
 
1.0%
1770000 1
 
1.0%
1660000 1
 
1.0%
1600000 2
2.0%
1400000 1
 
1.0%
1380000 4
4.0%

Interactions

2023-12-12T12:05:05.647583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:05.403305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:05.756823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:05.518930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:05:09.133863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호분류분석항목세부수수료(원)
번호1.0000.7660.9950.8720.556
분류0.7661.0000.0000.5940.426
분석항목0.9950.0001.0000.0000.000
세부0.8720.5940.0001.0000.966
수수료(원)0.5560.4260.0000.9661.000
2023-12-12T12:05:09.280051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호수수료(원)분류
번호1.000-0.5630.519
수수료(원)-0.5631.0000.242
분류0.5190.2421.000

Missing values

2023-12-12T12:05:05.900903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:05:06.015934image/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

번호분류분석항목세부수수료(원)
01종합검정농업용엔진엔진배기량 1.5리터 미만670000
12종합검정농업용엔진엔진배기량 1.5리터 이상 3.5리터 이하730000
23종합검정농업용엔진엔진배기량 3.5리터 초과750000
34종합검정경운기경운기910000
45종합검정관리기관리기1380000
56종합검정농업용트랙터엔진배기량 1.5리터 미만3190000
67종합검정농업용트랙터엔진배기량 1.5리터 이상 3.5리터 이하3350000
78종합검정농업용트랙터엔진 배기량 3.5리터 초과3510000
89종합검정농업용로더농업용로더1300000
910종합검정농업용굴착기농업용굴착기1300000
번호분류분석항목세부수수료(원)
9091국제규범검정OECD 농용트랙터표준코드 시험코드 101060000
9192국제규범검정기존 검정 성적의 변경 또는 기 검정된 보호구조물(ROPS)을 다른 트랙터에 확장 적용1건400000
9293국제규범검정기존 검정 성적의 변경 또는 기 검정된 보호구조물(FOPS)을 다른 트랙터에 확장 적용1건400000
9394국제규범검정기타국제규범검정(ISO_ASAE 등)1대0
9495안전검정전체1대330000
9596변경검정전체1대64000
9697사후검정해당기종별 적용1건0
9798기타명의등 변경신고1건10000
9899기타성적서 사본 발급(국문)1건10000
99100기타성적서 사본 발급(영문)1페이지5000