Overview

Dataset statistics

Number of variables3
Number of observations5452
Missing cells5452
Missing cells (%)33.3%
Duplicate rows405
Duplicate rows (%)7.4%
Total size in memory133.2 KiB
Average record size in memory25.0 B

Variable types

DateTime1
Categorical1
Unsupported1

Dataset

Description농가의 일일 영농활동(교육, 시비 작업 등), 생산, 판매 활동 등 기록 관리시스템으로 작업일시, 작업유형 비고 등을 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050313/fileData.do

Alerts

Dataset has 405 (7.4%) duplicate rowsDuplicates
비고 has 5452 (100.0%) missing valuesMissing
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-17 22:00:22.100781
Analysis finished2024-04-17 22:00:22.624840
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct4452
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size42.7 KiB
Minimum2017-03-09 16:06:00
Maximum2019-11-11 11:01:00
2024-04-18T07:00:22.679494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T07:00:22.789747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

작업유형
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.7 KiB
I
3642 
D
1145 
U
665 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowU
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I 3642
66.8%
D 1145
 
21.0%
U 665
 
12.2%

Length

2024-04-18T07:00:22.905879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T07:00:22.986837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 3642
66.8%
d 1145
 
21.0%
u 665
 
12.2%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5452
Missing (%)100.0%
Memory size48.0 KiB

Missing values

2024-04-18T07:00:22.597697image/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

작업일시작업유형비고
02017-09-18 20:34I<NA>
12017-09-20 04:00I<NA>
22017-09-22 04:50U<NA>
32017-09-26 20:44I<NA>
42017-09-27 22:29I<NA>
52017-09-28 07:18D<NA>
62017-09-28 23:36I<NA>
72017-09-29 23:34D<NA>
82017-09-29 23:39D<NA>
92017-09-29 23:55D<NA>
작업일시작업유형비고
54422018-09-04 10:36U<NA>
54432018-09-04 10:36U<NA>
54442018-09-04 10:53U<NA>
54452018-09-04 10:53U<NA>
54462018-09-04 10:55U<NA>
54472018-09-04 10:56U<NA>
54482018-09-04 10:56U<NA>
54492018-09-04 10:58U<NA>
54502018-09-04 10:59U<NA>
54512018-09-04 11:03U<NA>

Duplicate rows

Most frequently occurring

작업일시작업유형# duplicates
02017-03-10 14:11U9
12017-03-10 14:12U8
712017-09-30 04:19D7
1552017-10-23 11:38D7
1582017-10-23 11:41D7
1632017-10-23 11:46D7
1702017-10-23 13:36D7
2582018-09-04 10:20U7
632017-09-30 04:11D6
662017-09-30 04:14D6