Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1895
Duplicate rows (%)18.9%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Categorical2
DateTime2

Dataset

Description화성시 차량압류해제 현황(압류구분, 수납일자, 압류일자 등)
Author경기도 화성시
URLhttps://www.data.go.kr/data/15041546/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1895 (18.9%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 21:08:48.901811
Analysis finished2023-12-12 21:08:49.160825
Duration0.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

압류구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지방세
5899 
세외수입
4101 

Length

Max length4
Median length3
Mean length3.4101
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방세
2nd row지방세
3rd row세외수입
4th row지방세
5th row지방세

Common Values

ValueCountFrequency (%)
지방세 5899
59.0%
세외수입 4101
41.0%

Length

2023-12-13T06:08:49.220696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:08:49.302958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방세 5899
59.0%
세외수입 4101
41.0%

수납일
Date

Distinct287
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-01 00:00:00
Maximum2019-10-31 00:00:00
2023-12-13T06:08:49.403100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.567297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

압류일
Date

Distinct360
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1996-10-07 00:00:00
Maximum2019-10-17 00:00:00
2023-12-13T06:08:49.713347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.901316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-11-21
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-11-21
2nd row2019-11-21
3rd row2019-11-21
4th row2019-11-21
5th row2019-11-21

Common Values

ValueCountFrequency (%)
2019-11-21 10000
100.0%

Length

2023-12-13T06:08:50.087707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:08:50.174883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-11-21 10000
100.0%

Missing values

2023-12-13T06:08:49.045527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:08:49.124252image/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

압류구분수납일압류일데이터기준일자
1714지방세2019-02-262018-08-022019-11-21
1470지방세2019-02-202019-02-152019-11-21
11256세외수입2019-02-282018-10-102019-11-21
966지방세2019-02-082018-08-232019-11-21
2451지방세2019-03-142018-12-112019-11-21
712지방세2019-01-302018-09-142019-11-21
10714세외수입2019-02-142019-02-082019-11-21
1237지방세2019-02-152006-02-072019-11-21
2659지방세2019-03-192018-08-022019-11-21
12590세외수입2019-04-272018-11-152019-11-21
압류구분수납일압류일데이터기준일자
1385지방세2019-02-192019-02-082019-11-21
6330지방세2019-08-132019-08-052019-11-21
12792세외수입2019-05-082017-02-102019-11-21
10316세외수입2019-01-212017-01-112019-11-21
2468지방세2019-03-152018-11-122019-11-21
2288지방세2019-03-112019-02-082019-11-21
3110지방세2019-03-282018-10-052019-11-21
10212세외수입2019-01-162018-02-082019-11-21
163지방세2019-01-102018-11-122019-11-21
8001지방세2019-09-202018-08-022019-11-21

Duplicate rows

Most frequently occurring

압류구분수납일압류일데이터기준일자# duplicates
1547지방세2019-08-062019-08-052019-11-2194
1767지방세2019-10-072019-08-052019-11-2155
1119지방세2019-02-282019-02-142019-11-2150
1545지방세2019-08-052019-08-052019-11-2150
1619지방세2019-08-292019-08-052019-11-2143
1120지방세2019-02-282019-02-152019-11-2134
1639지방세2019-09-022019-08-052019-11-2134
1748지방세2019-09-302019-08-082019-11-2133
1577지방세2019-08-162019-08-052019-11-2131
1887지방세2019-10-312019-08-072019-11-2130