Overview

Dataset statistics

Number of variables4
Number of observations726
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.5 KiB
Average record size in memory33.2 B

Variable types

Numeric1
DateTime2
Boolean1

Dataset

Description충북농업기술원 농가 경영기록장 "바로바로"의 농업회계분석 정보제공 관련 자산, 부채, 감가상각비 등 계정과목 관리시스템으로 고용노력비일련번호, 등록일시, 수정일시, 상태를 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050295/fileData.do

Alerts

상태 has constant value ""Constant
고용노력비일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:39:37.286459
Analysis finished2023-12-12 14:39:37.623610
Duration0.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고용노력비일련번호
Real number (ℝ)

UNIQUE 

Distinct726
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509.62397
Minimum2
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:39:37.701284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile38.25
Q1187.25
median369.5
Q3833.5
95-th percentile1171.75
Maximum1250
Range1248
Interquartile range (IQR)646.25

Descriptive statistics

Standard deviation377.65164
Coefficient of variation (CV)0.7410398
Kurtosis-1.1445979
Mean509.62397
Median Absolute Deviation (MAD)268.5
Skewness0.46558363
Sum369987
Variance142620.76
MonotonicityStrictly increasing
2023-12-12T23:39:37.897970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.1%
671 1
 
0.1%
679 1
 
0.1%
681 1
 
0.1%
683 1
 
0.1%
685 1
 
0.1%
686 1
 
0.1%
689 1
 
0.1%
692 1
 
0.1%
696 1
 
0.1%
Other values (716) 716
98.6%
ValueCountFrequency (%)
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
11 1
0.1%
ValueCountFrequency (%)
1250 1
0.1%
1248 1
0.1%
1244 1
0.1%
1241 1
0.1%
1240 1
0.1%
1239 1
0.1%
1237 1
0.1%
1233 1
0.1%
1232 1
0.1%
1231 1
0.1%

등록일시
Date

Distinct358
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum1900-01-01 00:00:00
Maximum2019-11-06 22:27:57
2023-12-12T23:39:38.090940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:38.273007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

Distinct416
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Minimum1900-01-01 00:00:00
Maximum2019-11-06 22:28:15
2023-12-12T23:39:38.444247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:38.625134image/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 size858.0 B
False
726 
ValueCountFrequency (%)
False 726
100.0%
2023-12-12T23:39:38.775781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T23:39:37.353347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T23:39:37.492037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:39:37.590387image/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

고용노력비일련번호등록일시수정일시상태
021900-01-01 00:00:002019-08-20 09:04:58N
131900-01-01 00:00:002019-06-13 21:31:27N
241900-01-01 00:00:002018-04-13 06:27:19N
351900-01-01 00:00:001900-01-01 00:00:00N
461900-01-01 00:00:001900-01-01 00:00:00N
571900-01-01 00:00:002017-03-13 16:56:32N
681900-01-01 00:00:001900-01-01 00:00:00N
791900-01-01 00:00:001900-01-01 00:00:00N
8101900-01-01 00:00:001900-01-01 00:00:00N
9111900-01-01 00:00:001900-01-01 00:00:00N
고용노력비일련번호등록일시수정일시상태
71612312019-10-02 07:33:052019-10-02 07:33:05N
71712322019-10-04 10:08:292019-10-04 10:08:29N
71812332019-10-06 08:06:472019-10-06 08:06:47N
71912372019-10-09 10:51:562019-10-09 10:51:59N
72012392019-10-14 14:04:492019-10-14 14:04:49N
72112402019-10-15 13:32:262019-10-15 13:32:26N
72212412019-10-29 09:40:182019-10-29 09:40:35N
72312442019-10-30 16:19:322019-10-30 16:19:32N
72412482019-11-03 22:36:222019-11-03 22:36:22N
72512502019-11-06 22:27:572019-11-06 22:28:15N