Overview

Dataset statistics

Number of variables9
Number of observations311
Missing cells930
Missing cells (%)33.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 KiB
Average record size in memory76.4 B

Variable types

Numeric4
DateTime2
Text3

Dataset

Description충청북도 농업기술원 농가경영기록장(농가의 소득을 증진시킬 수 있는 회원전용 농가경영 관리 프로그램)의 수입지출관련 이용자 접속기록, 거래, 거래처 등의 관리시스템으로 일련번호, 등록일시, 수정일시, 제목, 플랫폼종류, 앱종류, 전체수, 성공수, 실패수 등을 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050319/fileData.do

Alerts

제목 has constant value ""Constant
플랫폼종류 has constant value ""Constant
앱종류 has constant value ""Constant
전체수 is highly overall correlated with 성공수 and 1 other fieldsHigh correlation
성공수 is highly overall correlated with 전체수High correlation
실패수 is highly overall correlated with 전체수High correlation
제목 has 310 (99.7%) missing valuesMissing
플랫폼종류 has 310 (99.7%) missing valuesMissing
앱종류 has 310 (99.7%) missing valuesMissing
일련번호 has unique valuesUnique
성공수 has 35 (11.3%) zerosZeros
실패수 has 217 (69.8%) zerosZeros

Reproduction

Analysis started2023-12-12 16:15:03.496748
Analysis finished2023-12-12 16:15:05.813768
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

UNIQUE 

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162
Minimum7
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-13T01:15:05.890371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile22.5
Q184.5
median162
Q3239.5
95-th percentile301.5
Maximum317
Range310
Interquartile range (IQR)155

Descriptive statistics

Standard deviation89.922189
Coefficient of variation (CV)0.55507524
Kurtosis-1.2
Mean162
Median Absolute Deviation (MAD)78
Skewness0
Sum50382
Variance8086
MonotonicityStrictly increasing
2023-12-13T01:15:06.025242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1
 
0.3%
212 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
214 1
 
0.3%
213 1
 
0.3%
211 1
 
0.3%
Other values (301) 301
96.8%
ValueCountFrequency (%)
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
11 1
0.3%
12 1
0.3%
13 1
0.3%
14 1
0.3%
15 1
0.3%
16 1
0.3%
ValueCountFrequency (%)
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%
313 1
0.3%
312 1
0.3%
311 1
0.3%
310 1
0.3%
309 1
0.3%
308 1
0.3%

등록일시
Date

Distinct309
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2017-03-09 16:56:00
Maximum2019-11-07 11:11:00
2023-12-13T01:15:06.157947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:06.287947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

Distinct309
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2017-03-09 16:56:00
Maximum2019-11-07 11:12:00
2023-12-13T01:15:06.411784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:06.557465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제목
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing310
Missing (%)99.7%
Memory size2.6 KiB
2023-12-13T01:15:06.642180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row가
ValueCountFrequency (%)
가 1
100.0%
2023-12-13T01:15:06.855877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
가 1
100.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
가 1
100.0%

플랫폼종류
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing310
Missing (%)99.7%
Memory size2.6 KiB
2023-12-13T01:15:06.963300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row나
ValueCountFrequency (%)
나 1
100.0%
2023-12-13T01:15:07.192992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 1
100.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 1
100.0%

앱종류
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing310
Missing (%)99.7%
Memory size2.6 KiB
2023-12-13T01:15:07.293616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row다
ValueCountFrequency (%)
다 1
100.0%
2023-12-13T01:15:07.993613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 1
100.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 1
100.0%

전체수
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525.61736
Minimum0
Maximum3143
Zeros3
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-13T01:15:08.167627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median17
Q3877.5
95-th percentile2561.5
Maximum3143
Range3143
Interquartile range (IQR)869.5

Descriptive statistics

Standard deviation912.00975
Coefficient of variation (CV)1.7351211
Kurtosis0.46277812
Mean525.61736
Median Absolute Deviation (MAD)10
Skewness1.4316102
Sum163467
Variance831761.79
MonotonicityNot monotonic
2023-12-13T01:15:08.319069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 34
 
10.9%
1 32
 
10.3%
17 21
 
6.8%
20 20
 
6.4%
9 18
 
5.8%
6 17
 
5.5%
7 17
 
5.5%
10 13
 
4.2%
19 8
 
2.6%
22 8
 
2.6%
Other values (95) 123
39.5%
ValueCountFrequency (%)
0 3
 
1.0%
1 32
10.3%
2 4
 
1.3%
3 2
 
0.6%
4 2
 
0.6%
6 17
5.5%
7 17
5.5%
8 34
10.9%
9 18
5.8%
10 13
 
4.2%
ValueCountFrequency (%)
3143 1
0.3%
3138 1
0.3%
3012 1
0.3%
2884 1
0.3%
2856 1
0.3%
2844 1
0.3%
2804 1
0.3%
2709 1
0.3%
2698 1
0.3%
2690 1
0.3%

성공수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.54341
Minimum0
Maximum2638
Zeros35
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-13T01:15:08.493602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median10
Q340
95-th percentile2359
Maximum2638
Range2638
Interquartile range (IQR)34

Descriptive statistics

Standard deviation806.74158
Coefficient of variation (CV)1.8270946
Kurtosis0.74156637
Mean441.54341
Median Absolute Deviation (MAD)9
Skewness1.5356805
Sum137320
Variance650831.97
MonotonicityNot monotonic
2023-12-13T01:15:08.658633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
11.3%
8 34
 
10.9%
1 25
 
8.0%
20 20
 
6.4%
17 20
 
6.4%
7 18
 
5.8%
6 17
 
5.5%
9 17
 
5.5%
10 14
 
4.5%
19 10
 
3.2%
Other values (84) 101
32.5%
ValueCountFrequency (%)
0 35
11.3%
1 25
8.0%
2 3
 
1.0%
4 2
 
0.6%
6 17
5.5%
7 18
5.8%
8 34
10.9%
9 17
5.5%
10 14
 
4.5%
11 3
 
1.0%
ValueCountFrequency (%)
2638 1
0.3%
2633 1
0.3%
2615 1
0.3%
2593 1
0.3%
2577 1
0.3%
2553 1
0.3%
2523 1
0.3%
2520 1
0.3%
2508 1
0.3%
2467 1
0.3%

실패수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.363344
Minimum0
Maximum3143
Zeros217
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-13T01:15:08.825864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile75
Maximum3143
Range3143
Interquartile range (IQR)3

Descriptive statistics

Standard deviation438.18401
Coefficient of variation (CV)5.5212392
Kurtosis40.187514
Mean79.363344
Median Absolute Deviation (MAD)0
Skewness6.4559299
Sum24682
Variance192005.23
MonotonicityNot monotonic
2023-12-13T01:15:09.002208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 217
69.8%
1 14
 
4.5%
62 10
 
3.2%
23 7
 
2.3%
22 7
 
2.3%
52 5
 
1.6%
56 5
 
1.6%
74 5
 
1.6%
73 5
 
1.6%
51 4
 
1.3%
Other values (26) 32
 
10.3%
ValueCountFrequency (%)
0 217
69.8%
1 14
 
4.5%
2 1
 
0.3%
3 2
 
0.6%
21 1
 
0.3%
22 7
 
2.3%
23 7
 
2.3%
51 4
 
1.3%
52 5
 
1.6%
55 3
 
1.0%
ValueCountFrequency (%)
3143 1
0.3%
3138 1
0.3%
3012 1
0.3%
2884 1
0.3%
2856 1
0.3%
2844 1
0.3%
2804 1
0.3%
101 2
0.6%
98 1
0.3%
93 1
0.3%

Interactions

2023-12-13T01:15:04.844779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:03.656943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:03.976494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.320092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.972839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:03.746435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.056480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.432640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:05.106024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:03.823682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.137856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.594437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:05.231367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:03.895066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.211867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:04.706199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:15:09.123790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호전체수성공수실패수
일련번호1.0000.7760.7840.418
전체수0.7761.0000.9880.823
성공수0.7840.9881.0000.000
실패수0.4180.8230.0001.000
2023-12-13T01:15:09.238411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호전체수성공수실패수
일련번호1.0000.248-0.0710.437
전체수0.2481.0000.7380.656
성공수-0.0710.7381.0000.291
실패수0.4370.6560.2911.000

Missing values

2023-12-13T01:15:05.420222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:15:05.636976image/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.
2023-12-13T01:15:05.753435image/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

일련번호등록일시수정일시제목플랫폼종류앱종류전체수성공수실패수
072017-03-09 16:562017-03-09 16:56가나다110
182017-03-09 21:102017-03-09 21:10<NA><NA><NA>000
292017-03-09 21:112017-03-09 21:11<NA><NA><NA>110
3102017-03-09 21:132017-03-09 21:13<NA><NA><NA>110
4112017-03-09 21:172017-03-09 21:17<NA><NA><NA>110
5122017-03-10 05:092017-03-10 05:09<NA><NA><NA>110
6132017-03-10 08:252017-03-10 08:25<NA><NA><NA>110
7142017-03-10 09:502017-03-10 09:50<NA><NA><NA>58580
8152017-03-10 19:152017-03-10 19:15<NA><NA><NA>660
9162017-03-10 19:492017-03-10 19:49<NA><NA><NA>660
일련번호등록일시수정일시제목플랫폼종류앱종류전체수성공수실패수
3013082019-10-29 09:442019-10-29 09:45<NA><NA><NA>22022
3023092019-10-30 07:502019-10-30 07:50<NA><NA><NA>22022
3033102019-10-30 19:032019-10-30 19:04<NA><NA><NA>22022
3043112019-11-01 13:342019-11-01 13:35<NA><NA><NA>101
3053122019-11-01 14:092019-11-01 14:10<NA><NA><NA>101
3063132019-11-01 14:352019-11-01 14:35<NA><NA><NA>101
3073142019-11-05 09:272019-11-05 09:28<NA><NA><NA>22022
3083152019-11-05 20:232019-11-05 20:24<NA><NA><NA>313803138
3093162019-11-06 08:592019-11-06 09:00<NA><NA><NA>22022
3103172019-11-07 11:112019-11-07 11:12<NA><NA><NA>314303143