Overview

Dataset statistics

Number of variables10
Number of observations82
Missing cells231
Missing cells (%)28.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory86.6 B

Variable types

Numeric3
DateTime3
Boolean1
Unsupported2
Text1

Dataset

Description충북농업기술원 농가 경영기록장 "바로바로"의 농업회계분석 정보제공 관련 자산, 부채, 감가상각비 등 계정과목 관리시스템으로 자산현황일련번호, 자산일련번호, 등록일시, 수정일시, 상태, 구입일시, 계정과목코드, 차변전표일련번호, 대변전표일련번호, 비고등의 자료를 제공 합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050286/fileData.do

Alerts

상태 has constant value ""Constant
자산현황일련번호 is highly overall correlated with 차변전표일련번호High correlation
차변전표일련번호 is highly overall correlated with 자산현황일련번호High correlation
계정과목코드 has 82 (100.0%) missing valuesMissing
대변전표일련번호 has 82 (100.0%) missing valuesMissing
비고 has 67 (81.7%) missing valuesMissing
자산현황일련번호 has unique valuesUnique
차변전표일련번호 has unique valuesUnique
계정과목코드 is an unsupported type, check if it needs cleaning or further analysisUnsupported
대변전표일련번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 14:08:36.028824
Analysis finished2023-12-12 14:08:37.396508
Duration1.37 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자산현황일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.731707
Minimum14
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T23:08:37.485862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile22.05
Q143.25
median72.5
Q396.75
95-th percentile117.95
Maximum123
Range109
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation31.468993
Coefficient of variation (CV)0.44490645
Kurtosis-1.2219425
Mean70.731707
Median Absolute Deviation (MAD)27
Skewness-0.090072191
Sum5800
Variance990.2975
MonotonicityStrictly increasing
2023-12-12T23:08:37.643226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1
 
1.2%
98 1
 
1.2%
96 1
 
1.2%
95 1
 
1.2%
94 1
 
1.2%
93 1
 
1.2%
92 1
 
1.2%
91 1
 
1.2%
90 1
 
1.2%
89 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
14 1
1.2%
19 1
1.2%
20 1
1.2%
21 1
1.2%
22 1
1.2%
23 1
1.2%
24 1
1.2%
25 1
1.2%
26 1
1.2%
27 1
1.2%
ValueCountFrequency (%)
123 1
1.2%
122 1
1.2%
120 1
1.2%
119 1
1.2%
118 1
1.2%
117 1
1.2%
114 1
1.2%
113 1
1.2%
111 1
1.2%
110 1
1.2%

자산일련번호
Real number (ℝ)

Distinct12
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.36585
Minimum408
Maximum425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T23:08:37.812744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum408
5-th percentile408
Q1408
median409
Q3413
95-th percentile425
Maximum425
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.1774424
Coefficient of variation (CV)0.01498049
Kurtosis0.049435055
Mean412.36585
Median Absolute Deviation (MAD)1
Skewness1.3134105
Sum33814
Variance38.160795
MonotonicityNot monotonic
2023-12-12T23:08:37.936724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
408 26
31.7%
409 24
29.3%
425 11
13.4%
411 6
 
7.3%
413 5
 
6.1%
423 3
 
3.7%
412 2
 
2.4%
414 1
 
1.2%
416 1
 
1.2%
418 1
 
1.2%
Other values (2) 2
 
2.4%
ValueCountFrequency (%)
408 26
31.7%
409 24
29.3%
411 6
 
7.3%
412 2
 
2.4%
413 5
 
6.1%
414 1
 
1.2%
416 1
 
1.2%
418 1
 
1.2%
421 1
 
1.2%
422 1
 
1.2%
ValueCountFrequency (%)
425 11
13.4%
423 3
 
3.7%
422 1
 
1.2%
421 1
 
1.2%
418 1
 
1.2%
416 1
 
1.2%
414 1
 
1.2%
413 5
6.1%
412 2
 
2.4%
411 6
7.3%

등록일시
Date

Distinct76
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size788.0 B
Minimum2017-04-10 21:07:00
Maximum2019-10-27 14:40:00
2023-12-12T23:08:38.405441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.568171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

Distinct77
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
Minimum2017-04-10 21:07:00
Maximum2019-10-30 19:37:00
2023-12-12T23:08:38.762957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:38.957965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상태
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size214.0 B
False
82 
ValueCountFrequency (%)
False 82
100.0%
2023-12-12T23:08:39.094843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

구입일시
Date

Distinct54
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
Minimum1900-01-01 00:00:00
Maximum2019-10-27 00:00:00
2023-12-12T23:08:39.227704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:39.365438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계정과목코드
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82
Missing (%)100.0%
Memory size870.0 B

차변전표일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean797758.52
Minimum719152
Maximum874811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-12T23:08:39.523105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum719152
5-th percentile722259.4
Q1765205.5
median807991.5
Q3840608.5
95-th percentile865284.05
Maximum874811
Range155659
Interquartile range (IQR)75403

Descriptive statistics

Standard deviation48404.4
Coefficient of variation (CV)0.060675504
Kurtosis-1.3331481
Mean797758.52
Median Absolute Deviation (MAD)37490.5
Skewness-0.18267231
Sum65416199
Variance2.342986 × 109
MonotonicityNot monotonic
2023-12-12T23:08:39.673431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
719152 1
 
1.2%
842256 1
 
1.2%
840112 1
 
1.2%
840110 1
 
1.2%
840108 1
 
1.2%
837900 1
 
1.2%
837902 1
 
1.2%
837894 1
 
1.2%
837750 1
 
1.2%
835555 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
719152 1
1.2%
720766 1
1.2%
721082 1
1.2%
721102 1
1.2%
722259 1
1.2%
722267 1
1.2%
722275 1
1.2%
722277 1
1.2%
725652 1
1.2%
725840 1
1.2%
ValueCountFrequency (%)
874811 1
1.2%
873405 1
1.2%
873403 1
1.2%
865464 1
1.2%
865301 1
1.2%
864962 1
1.2%
864955 1
1.2%
864855 1
1.2%
844870 1
1.2%
844868 1
1.2%

대변전표일련번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82
Missing (%)100.0%
Memory size870.0 B

비고
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing67
Missing (%)81.7%
Memory size788.0 B
2023-12-12T23:08:39.913879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length21
Mean length12.133333
Min length3

Characters and Unicode

Total characters182
Distinct characters75
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row39두×4000000=약160000000
2nd row자본금
3rd row2017년영농자금 차입 무주농협안성지점
4th row차입금상환
5th row자본조정
ValueCountFrequency (%)
출자금 2
 
6.2%
단가 2
 
6.2%
구입 2
 
6.2%
양봉용벌 2
 
6.2%
새마을금고 2
 
6.2%
한국벌침양봉중앙회에서 2
 
6.2%
영농조합 1
 
3.1%
280,000 1
 
3.1%
3통 1
 
3.1%
250,000 1
 
3.1%
Other values (16) 16
50.0%
2023-12-12T23:08:40.333583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23
 
12.6%
18
 
9.9%
금 7
 
3.8%
자 6
 
3.3%
농 5
 
2.7%
입 5
 
2.7%
벌 4
 
2.2%
양 4
 
2.2%
2 4
 
2.2%
봉 4
 
2.2%
Other values (65) 102
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
64.8%
Decimal Number 38
 
20.9%
Space Separator 18
 
9.9%
Open Punctuation 2
 
1.1%
Other Punctuation 2
 
1.1%
Close Punctuation 2
 
1.1%
Math Symbol 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 7
 
5.9%
자 6
 
5.1%
농 5
 
4.2%
입 5
 
4.2%
벌 4
 
3.4%
양 4
 
3.4%
봉 4
 
3.4%
한 3
 
2.5%
출 3
 
2.5%
협 3
 
2.5%
Other values (49) 74
62.7%
Decimal Number
ValueCountFrequency (%)
0 23
60.5%
2 4
 
10.5%
3 2
 
5.3%
1 2
 
5.3%
7 2
 
5.3%
4 1
 
2.6%
6 1
 
2.6%
5 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
Math Symbol
ValueCountFrequency (%)
= 1
50.0%
× 1
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
64.8%
Common 64
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 7
 
5.9%
자 6
 
5.1%
농 5
 
4.2%
입 5
 
4.2%
벌 4
 
3.4%
양 4
 
3.4%
봉 4
 
3.4%
한 3
 
2.5%
출 3
 
2.5%
협 3
 
2.5%
Other values (49) 74
62.7%
Common
ValueCountFrequency (%)
0 23
35.9%
18
28.1%
2 4
 
6.2%
3 2
 
3.1%
( 2
 
3.1%
, 2
 
3.1%
) 2
 
3.1%
1 2
 
3.1%
7 2
 
3.1%
4 1
 
1.6%
Other values (6) 6
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
64.8%
ASCII 63
34.6%
None 1
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23
36.5%
18
28.6%
2 4
 
6.3%
3 2
 
3.2%
( 2
 
3.2%
, 2
 
3.2%
) 2
 
3.2%
1 2
 
3.2%
7 2
 
3.2%
4 1
 
1.6%
Other values (5) 5
 
7.9%
Hangul
ValueCountFrequency (%)
금 7
 
5.9%
자 6
 
5.1%
농 5
 
4.2%
입 5
 
4.2%
벌 4
 
3.4%
양 4
 
3.4%
봉 4
 
3.4%
한 3
 
2.5%
출 3
 
2.5%
협 3
 
2.5%
Other values (49) 74
62.7%
None
ValueCountFrequency (%)
× 1
100.0%

Interactions

2023-12-12T23:08:36.863477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.396410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.632607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.936841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.465873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.702673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:37.020604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.554375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:36.779718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:08:40.447097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자산현황일련번호자산일련번호등록일시수정일시구입일시차변전표일련번호비고
자산현황일련번호1.0000.4201.0001.0000.9620.9821.000
자산일련번호0.4201.0000.0000.0000.0000.0001.000
등록일시1.0000.0001.0000.9990.9991.0001.000
수정일시1.0000.0000.9991.0001.0001.0001.000
구입일시0.9620.0000.9991.0001.0000.9891.000
차변전표일련번호0.9820.0001.0001.0000.9891.0001.000
비고1.0001.0001.0001.0001.0001.0001.000
2023-12-12T23:08:40.587504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자산현황일련번호자산일련번호차변전표일련번호
자산현황일련번호1.0000.2980.992
자산일련번호0.2981.0000.303
차변전표일련번호0.9920.3031.000

Missing values

2023-12-12T23:08:37.136608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:08:37.322280image/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

자산현황일련번호자산일련번호등록일시수정일시상태구입일시계정과목코드차변전표일련번호대변전표일련번호비고
0144082017-04-10 21:072017-04-10 21:07N2017-04-10<NA>719152<NA><NA>
1194252017-04-17 22:392017-04-17 22:39N2017-01-01<NA>720766<NA>39두×4000000=약160000000
2204082017-04-19 08:202017-04-19 08:20N2017-04-04<NA>721082<NA>자본금
3214252017-04-19 09:122017-04-19 09:12N2015-09-25<NA>721102<NA><NA>
4224082017-04-25 22:342017-04-25 22:34N2017-04-25<NA>722259<NA>2017년영농자금 차입 무주농협안성지점
5234082017-04-25 22:492017-04-25 22:49N2017-04-21<NA>722267<NA><NA>
6244082017-04-25 22:562017-04-25 22:56N2017-04-25<NA>722275<NA>차입금상환
7254082017-04-25 23:022017-04-25 23:02N2017-04-26<NA>722277<NA>자본조정
8264082017-05-17 14:482017-05-17 14:48N2017-01-01<NA>725652<NA><NA>
9274082017-05-18 13:392017-05-18 13:39N2017-01-01<NA>725840<NA><NA>
자산현황일련번호자산일련번호등록일시수정일시상태구입일시계정과목코드차변전표일련번호대변전표일련번호비고
721104252019-07-02 10:292019-07-02 10:29N2019-07-02<NA>844858<NA>한우70두
731114232019-07-02 10:542019-07-02 10:54N2018-12-31<NA>844870<NA><NA>
741134092019-09-19 14:092019-10-30 19:37N2017-09-15<NA>874811<NA><NA>
751144252019-09-19 15:212019-09-19 15:21N2019-09-19<NA>864855<NA><NA>
761174092019-09-19 15:582019-09-19 15:58N2019-09-19<NA>864955<NA><NA>
771184092019-09-19 15:582019-09-19 15:58N2019-09-19<NA>864962<NA><NA>
781194112019-09-19 15:582019-09-19 21:24N2019-09-19<NA>865301<NA><NA>
791204082019-09-19 21:422019-09-19 22:07N2018-09-19<NA>865464<NA><NA>
801224092019-10-27 14:392019-10-27 14:39N2019-10-27<NA>873403<NA><NA>
811234122019-10-27 14:402019-10-27 14:40N2019-10-27<NA>873405<NA><NA>