Overview

Dataset statistics

Number of variables7
Number of observations172
Missing cells142
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory58.8 B

Variable types

DateTime2
Boolean1
Numeric2
Categorical1
Text1

Dataset

Description충북농업기술원 농가 경영기록장 "바로바로"의 농업회계분석 정보제공 관련 자산, 부채, 감가상각비 등 계정과목 관리시스템으로 등록일시, 수정일시, 상태, 은행코드, 종류, 이자율, 비고등을 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050297/fileData.do

Alerts

상태 has constant value ""Constant
이자율 has 25 (14.5%) missing valuesMissing
비고 has 117 (68.0%) missing valuesMissing
이자율 has 9 (5.2%) zerosZeros

Reproduction

Analysis started2023-12-12 11:57:11.117140
Analysis finished2023-12-12 11:57:12.159558
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록일시
Date

Distinct167
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2017-05-18 23:01:00
Maximum2019-10-30 23:36:00
2023-12-12T20:57:12.248383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:57:12.421554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

Distinct152
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2017-05-18 23:01:00
Maximum2019-10-31 20:53:00
2023-12-12T20:57:12.621904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:57:12.788107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상태
Boolean

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size304.0 B
False
172 
ValueCountFrequency (%)
False 172
100.0%
2023-12-12T20:57:12.931075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

은행코드
Real number (ℝ)

Distinct12
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.773256
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:57:13.042120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median12
Q312
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation29.462451
Coefficient of variation (CV)1.2937303
Kurtosis2.5952769
Mean22.773256
Median Absolute Deviation (MAD)0
Skewness2.1015203
Sum3917
Variance868.03601
MonotonicityNot monotonic
2023-12-12T20:57:13.504368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 91
52.9%
11 35
 
20.3%
99 18
 
10.5%
9 7
 
4.1%
1 5
 
2.9%
4 5
 
2.9%
88 5
 
2.9%
7 2
 
1.2%
2 1
 
0.6%
35 1
 
0.6%
Other values (2) 2
 
1.2%
ValueCountFrequency (%)
1 5
 
2.9%
2 1
 
0.6%
4 5
 
2.9%
7 2
 
1.2%
9 7
 
4.1%
11 35
 
20.3%
12 91
52.9%
34 1
 
0.6%
35 1
 
0.6%
45 1
 
0.6%
ValueCountFrequency (%)
99 18
 
10.5%
88 5
 
2.9%
45 1
 
0.6%
35 1
 
0.6%
34 1
 
0.6%
12 91
52.9%
11 35
 
20.3%
9 7
 
4.1%
7 2
 
1.2%
4 5
 
2.9%

종류
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
L
130 
S
42 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
L 130
75.6%
S 42
 
24.4%

Length

2023-12-12T20:57:13.666060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:57:13.831768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
l 130
75.6%
s 42
 
24.4%

이자율
Real number (ℝ)

MISSING  ZEROS 

Distinct48
Distinct (%)32.7%
Missing25
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean2.6661905
Minimum0
Maximum16.9
Zeros9
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:57:14.005335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median2
Q33.845
95-th percentile5
Maximum16.9
Range16.9
Interquartile range (IQR)2.595

Descriptive statistics

Standard deviation2.2319343
Coefficient of variation (CV)0.83712484
Kurtosis15.719516
Mean2.6661905
Median Absolute Deviation (MAD)1
Skewness3.1274071
Sum391.93
Variance4.9815306
MonotonicityNot monotonic
2023-12-12T20:57:14.203454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2.0 33
19.2%
1.0 18
 
10.5%
3.0 10
 
5.8%
0.0 9
 
5.2%
4.0 7
 
4.1%
1.5 6
 
3.5%
5.0 5
 
2.9%
1.2 4
 
2.3%
3.98 4
 
2.3%
2.5 3
 
1.7%
Other values (38) 48
27.9%
(Missing) 25
14.5%
ValueCountFrequency (%)
0.0 9
5.2%
0.03 1
 
0.6%
0.05 1
 
0.6%
0.2 1
 
0.6%
0.9 2
 
1.2%
1.0 18
10.5%
1.18 1
 
0.6%
1.2 4
 
2.3%
1.3 1
 
0.6%
1.49 2
 
1.2%
ValueCountFrequency (%)
16.9 1
 
0.6%
13.0 2
 
1.2%
6.25 1
 
0.6%
5.8 1
 
0.6%
5.5 1
 
0.6%
5.0 5
2.9%
4.81 1
 
0.6%
4.7 1
 
0.6%
4.55 1
 
0.6%
4.54 1
 
0.6%

비고
Text

MISSING 

Distinct52
Distinct (%)94.5%
Missing117
Missing (%)68.0%
Memory size1.5 KiB
2023-12-12T20:57:14.583678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length25
Mean length11.327273
Min length1

Characters and Unicode

Total characters623
Distinct characters154
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)90.9%

Sample

1st row0
2nd row귀농인창업자금(5년거치 10년분할상환_정책금리 3%→2%, 2016년부터)
3rd row후계농업경영인 창업자금(3년거치 7년분할상환)
4th row비고
5th row축사대금지급
ValueCountFrequency (%)
상호금융특별(영농 3
 
3.3%
농식품부 3
 
3.3%
대출 2
 
2.2%
메리츠보험 2
 
2.2%
농장주 2
 
2.2%
비용 2
 
2.2%
선도자금 2
 
2.2%
마이너스 2
 
2.2%
3%→2 1
 
1.1%
계약금 1
 
1.1%
Other values (71) 71
78.0%
2023-12-12T20:57:15.178154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
5.9%
0 34
 
5.5%
금 29
 
4.7%
농 22
 
3.5%
자 18
 
2.9%
, 12
 
1.9%
( 11
 
1.8%
) 11
 
1.8%
상 10
 
1.6%
년 9
 
1.4%
Other values (144) 430
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 453
72.7%
Decimal Number 82
 
13.2%
Space Separator 37
 
5.9%
Other Punctuation 25
 
4.0%
Open Punctuation 11
 
1.8%
Close Punctuation 11
 
1.8%
Dash Punctuation 2
 
0.3%
Connector Punctuation 1
 
0.2%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 29
 
6.4%
농 22
 
4.9%
자 18
 
4.0%
상 10
 
2.2%
년 9
 
2.0%
보 9
 
2.0%
영 8
 
1.8%
출 8
 
1.8%
지 8
 
1.8%
대 8
 
1.8%
Other values (124) 324
71.5%
Decimal Number
ValueCountFrequency (%)
0 34
41.5%
6 9
 
11.0%
1 8
 
9.8%
2 7
 
8.5%
5 7
 
8.5%
7 6
 
7.3%
3 4
 
4.9%
4 3
 
3.7%
8 3
 
3.7%
9 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 12
48.0%
: 6
24.0%
. 5
20.0%
% 2
 
8.0%
Space Separator
ValueCountFrequency (%)
37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Math Symbol
ValueCountFrequency (%)
→ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 453
72.7%
Common 170
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 29
 
6.4%
농 22
 
4.9%
자 18
 
4.0%
상 10
 
2.2%
년 9
 
2.0%
보 9
 
2.0%
영 8
 
1.8%
출 8
 
1.8%
지 8
 
1.8%
대 8
 
1.8%
Other values (124) 324
71.5%
Common
ValueCountFrequency (%)
37
21.8%
0 34
20.0%
, 12
 
7.1%
( 11
 
6.5%
) 11
 
6.5%
6 9
 
5.3%
1 8
 
4.7%
2 7
 
4.1%
5 7
 
4.1%
7 6
 
3.5%
Other values (10) 28
16.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 453
72.7%
ASCII 169
 
27.1%
Arrows 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
21.9%
0 34
20.1%
, 12
 
7.1%
( 11
 
6.5%
) 11
 
6.5%
6 9
 
5.3%
1 8
 
4.7%
2 7
 
4.1%
5 7
 
4.1%
7 6
 
3.6%
Other values (9) 27
16.0%
Hangul
ValueCountFrequency (%)
금 29
 
6.4%
농 22
 
4.9%
자 18
 
4.0%
상 10
 
2.2%
년 9
 
2.0%
보 9
 
2.0%
영 8
 
1.8%
출 8
 
1.8%
지 8
 
1.8%
대 8
 
1.8%
Other values (124) 324
71.5%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

Interactions

2023-12-12T20:57:11.544190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:57:11.361874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:57:11.674964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:57:11.450429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:57:15.313708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
은행코드종류이자율비고
은행코드1.0000.2850.4261.000
종류0.2851.0000.0000.499
이자율0.4260.0001.0001.000
비고1.0000.4991.0001.000
2023-12-12T20:57:15.451762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
은행코드이자율종류
은행코드1.000-0.0080.202
이자율-0.0081.0000.000
종류0.2020.0001.000

Missing values

2023-12-12T20:57:11.846615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:57:11.987145image/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.
2023-12-12T20:57:12.093656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

등록일시수정일시상태은행코드종류이자율비고
02017-05-18 23:012017-05-18 23:01N11L2.70
12017-05-26 15:562017-05-26 15:58N12L2.0귀농인창업자금(5년거치 10년분할상환_정책금리 3%→2%, 2016년부터)
22017-05-26 15:572017-05-26 15:57N11L2.0후계농업경영인 창업자금(3년거치 7년분할상환)
32017-08-27 12:312017-08-27 12:31N12L3.0<NA>
42017-09-14 23:052019-05-30 13:34N11L3.0<NA>
52017-09-14 23:142017-09-14 23:16N2S2.11비고
62017-09-14 23:252019-09-03 12:25N1L1.0<NA>
72017-09-14 23:272017-09-15 02:32N12L0.0<NA>
82018-03-08 09:442018-03-08 09:44N9L3.7축사대금지급
92018-03-09 07:032018-03-09 07:03N9L3.98사료구매
등록일시수정일시상태은행코드종류이자율비고
1622019-09-19 21:092019-09-19 21:09N12L4.5<NA>
1632019-09-19 21:102019-09-19 21:52N12L4.5<NA>
1642019-09-28 19:132019-09-28 19:13N12L4.0<NA>
1652019-09-28 19:142019-09-28 19:14N11L3.0<NA>
1662019-10-12 20:492019-10-12 20:49N12L5.8<NA>
1672019-10-29 11:192019-10-31 20:52N99S2.0<NA>
1682019-10-30 07:182019-10-30 07:18N12L0.05<NA>
1692019-10-30 13:082019-10-30 13:08N12L3.0<NA>
1702019-10-30 23:352019-10-31 20:52N88L13.0<NA>
1712019-10-30 23:362019-10-31 20:53N88L13.0<NA>