Overview

Dataset statistics

Number of variables5
Number of observations8734
Missing cells8699
Missing cells (%)19.9%
Duplicate rows1140
Duplicate rows (%)13.1%
Total size in memory358.4 KiB
Average record size in memory42.0 B

Variable types

DateTime2
Boolean1
Categorical1
Numeric1

Dataset

Description충북농업기술원 농가 경영기록장 "바로바로"의 농업회계분석 정보제공 관련 자산, 부채, 감가상각비 등 계정과목 관리시스템으로 등록일시, 수정일시, 상태, 회계년도, 품목일련번호등을 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050289/fileData.do

Alerts

상태 has constant value ""Constant
Dataset has 1140 (13.1%) duplicate rowsDuplicates
품목일련번호 has 8699 (99.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 12:50:09.415943
Analysis finished2023-12-12 12:50:09.923086
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록일시
Date

Distinct4755
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
Minimum1900-01-01 00:00:00
Maximum2019-11-11 04:56:00
2023-12-12T21:50:10.014186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:10.206450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

Distinct3907
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
Minimum2017-03-09 19:03:00
Maximum2019-11-11 04:56:00
2023-12-12T21:50:10.383349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:10.518620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상태
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
False
8734 
ValueCountFrequency (%)
False 8734
100.0%
2023-12-12T21:50:10.641727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

회계년도
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.4 KiB
2017
6227 
2019
1566 
2018
941 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 6227
71.3%
2019 1566
 
17.9%
2018 941
 
10.8%

Length

2023-12-12T21:50:10.748090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:10.893497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 6227
71.3%
2019 1566
 
17.9%
2018 941
 
10.8%

품목일련번호
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)68.6%
Missing8699
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean107.6
Minimum1
Maximum438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.9 KiB
2023-12-12T21:50:11.028920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q129
median62
Q3154
95-th percentile324.9
Maximum438
Range437
Interquartile range (IQR)125

Descriptive statistics

Standard deviation118.71449
Coefficient of variation (CV)1.1032945
Kurtosis0.74813581
Mean107.6
Median Absolute Deviation (MAD)40
Skewness1.3617095
Sum3766
Variance14093.129
MonotonicityNot monotonic
2023-12-12T21:50:11.178022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22 4
 
< 0.1%
1 3
 
< 0.1%
35 3
 
< 0.1%
168 2
 
< 0.1%
69 2
 
< 0.1%
42 2
 
< 0.1%
29 2
 
< 0.1%
139 1
 
< 0.1%
75 1
 
< 0.1%
348 1
 
< 0.1%
Other values (14) 14
 
0.2%
(Missing) 8699
99.6%
ValueCountFrequency (%)
1 3
< 0.1%
7 1
 
< 0.1%
22 4
< 0.1%
29 2
< 0.1%
35 3
< 0.1%
38 1
 
< 0.1%
39 1
 
< 0.1%
42 2
< 0.1%
62 1
 
< 0.1%
66 1
 
< 0.1%
ValueCountFrequency (%)
438 1
< 0.1%
348 1
< 0.1%
315 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
297 1
< 0.1%
223 1
< 0.1%
168 2
< 0.1%
140 1
< 0.1%
139 1
< 0.1%

Interactions

2023-12-12T21:50:09.607931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:50:11.268388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년도품목일련번호
회계년도1.0000.000
품목일련번호0.0001.000
2023-12-12T21:50:11.373636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목일련번호회계년도
품목일련번호1.0000.000
회계년도0.0001.000

Missing values

2023-12-12T21:50:09.748710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:50:09.862132image/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

등록일시수정일시상태회계년도품목일련번호
02012-10-17 00:002017-03-09 19:04N2017<NA>
12012-10-17 00:002017-03-09 19:04N2017<NA>
22012-10-17 00:002018-09-04 10:35N2017<NA>
32012-10-17 00:002017-10-23 13:53N2017<NA>
42012-10-18 00:002018-09-04 11:00N2017<NA>
52012-10-18 00:002019-07-01 11:06N201922
62012-10-18 00:002017-03-09 19:04N2017<NA>
72012-10-18 00:002019-10-11 10:23N2017<NA>
82012-10-18 00:002017-09-08 16:52N2017<NA>
92012-10-18 00:002019-09-30 17:13N2017<NA>
등록일시수정일시상태회계년도품목일련번호
87242019-07-10 16:572019-07-10 16:57N2019<NA>
87252019-07-10 17:032019-10-30 15:02N2019<NA>
87262019-07-10 17:342019-07-10 17:34N2019<NA>
87272019-07-10 18:122019-07-10 18:12N2019<NA>
87282019-07-10 18:312019-07-10 18:31N2019<NA>
87292019-07-11 16:192019-07-11 16:19N2019<NA>
87302019-07-11 17:002019-07-11 17:00N2019<NA>
87312019-07-11 18:132019-07-11 18:13N2019<NA>
87322019-07-11 19:522019-07-11 19:52N2019<NA>
87332019-07-12 13:062019-07-12 13:06N2019<NA>

Duplicate rows

Most frequently occurring

등록일시수정일시상태회계년도품목일련번호# duplicates
7232015-11-24 00:002017-03-09 19:03N2017<NA>60
4532014-12-10 00:002017-03-09 19:03N2017<NA>42
10522017-02-03 00:002017-03-09 19:03N2017<NA>37
10192016-12-15 00:002017-03-09 19:03N2017<NA>33
9392016-08-23 00:002017-03-09 19:03N2017<NA>25
2862014-04-25 00:002017-03-09 19:04N2017<NA>24
3992014-09-11 00:002017-03-09 19:03N2017<NA>24
3352014-06-20 00:002017-03-09 19:03N2017<NA>23
2072014-01-21 00:002017-03-09 19:04N2017<NA>20
3602014-07-22 00:002017-03-09 19:03N2017<NA>20