Overview

Dataset statistics

Number of variables4
Number of observations4110
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows863
Duplicate rows (%)21.0%
Total size in memory128.6 KiB
Average record size in memory32.0 B

Variable types

DateTime1
Categorical2
Text1

Dataset

Description한국저작권보호원이 수행하는 불법복제물 수거·폐기 및 삭제업무 관련 오프라인 불법복제물 예방현황 정보
Author(재)한국저작권보호원
URLhttps://www.data.go.kr/data/15071051/fileData.do

Alerts

Dataset has 863 (21.0%) duplicate rowsDuplicates
광역시/도 is highly overall correlated with 단속예방구분High correlation
단속예방구분 is highly overall correlated with 광역시/도High correlation
단속예방구분 is highly imbalanced (50.1%)Imbalance

Reproduction

Analysis started2023-12-12 21:56:27.893480
Analysis finished2023-12-12 21:56:28.315001
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예방일자
Date

Distinct195
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
Minimum2018-01-18 00:00:00
Maximum2019-12-18 00:00:00
2023-12-13T06:56:28.392929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:28.540177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

광역시/도
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
광주
679 
서울
560 
부산
465 
대구
410 
경북
303 
Other values (15)
1693 

Length

Max length3
Median length2
Mean length2.029927
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row경남
4th row경기
5th row울산

Common Values

ValueCountFrequency (%)
광주 679
16.5%
서울 560
13.6%
부산 465
11.3%
대구 410
10.0%
경북 303
7.4%
경기 285
6.9%
전남 278
6.8%
경남 238
 
5.8%
충남 165
 
4.0%
전북 112
 
2.7%
Other values (10) 615
15.0%

Length

2023-12-13T06:56:28.702424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광주 679
16.5%
서울 560
13.6%
부산 472
11.5%
경북 411
10.0%
대구 410
10.0%
경기 285
6.9%
전남 278
6.8%
경남 238
 
5.8%
충남 165
 
4.0%
전북 120
 
2.9%
Other values (7) 492
12.0%

시/군/구
Text

Distinct173
Distinct (%)4.2%
Missing4
Missing (%)0.1%
Memory size32.2 KiB
2023-12-13T06:56:29.058865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.7890891
Min length2

Characters and Unicode

Total characters11452
Distinct characters118
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.3%

Sample

1st row종로구
2nd row동작구
3rd row진주시
4th row안산시
5th row울주군
ValueCountFrequency (%)
북구 456
 
11.1%
남구 295
 
7.2%
동구 217
 
5.3%
광산구 139
 
3.4%
중구 138
 
3.4%
경산시 135
 
3.3%
금정구 132
 
3.2%
천안시 75
 
1.8%
부산진구 75
 
1.8%
동작구 70
 
1.7%
Other values (155) 2374
57.8%
2023-12-13T06:56:29.549330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 2449
21.4%
시 1400
 
12.2%
산 558
 
4.9%
북 518
 
4.5%
동 403
 
3.5%
남 361
 
3.2%
군 353
 
3.1%
주 336
 
2.9%
천 296
 
2.6%
성 262
 
2.3%
Other values (108) 4516
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11423
99.7%
Space Separator 29
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2449
21.4%
시 1400
 
12.3%
산 558
 
4.9%
북 518
 
4.5%
동 403
 
3.5%
남 361
 
3.2%
군 353
 
3.1%
주 336
 
2.9%
천 296
 
2.6%
성 262
 
2.3%
Other values (107) 4487
39.3%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11423
99.7%
Common 29
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2449
21.4%
시 1400
 
12.3%
산 558
 
4.9%
북 518
 
4.5%
동 403
 
3.5%
남 361
 
3.2%
군 353
 
3.1%
주 336
 
2.9%
천 296
 
2.6%
성 262
 
2.3%
Other values (107) 4487
39.3%
Common
ValueCountFrequency (%)
29
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11423
99.7%
ASCII 29
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 2449
21.4%
시 1400
 
12.3%
산 558
 
4.9%
북 518
 
4.5%
동 403
 
3.5%
남 361
 
3.2%
군 353
 
3.1%
주 336
 
2.9%
천 296
 
2.6%
성 262
 
2.3%
Other values (107) 4487
39.3%
ASCII
ValueCountFrequency (%)
29
100.0%

단속예방구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
예방활동
3659 
계도예방
451 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
예방활동 3659
89.0%
계도예방 451
 
11.0%

Length

2023-12-13T06:56:29.694418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:29.810539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
예방활동 3659
89.0%
계도예방 451
 
11.0%

Correlations

2023-12-13T06:56:29.887000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역시/도단속예방구분
광역시/도1.0000.695
단속예방구분0.6951.000
2023-12-13T06:56:29.962847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속예방구분광역시/도
단속예방구분1.0000.559
광역시/도0.5591.000
2023-12-13T06:56:30.039874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역시/도단속예방구분
광역시/도1.0000.559
단속예방구분0.5591.000

Missing values

2023-12-13T06:56:28.154982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:56:28.265619image/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

예방일자광역시/도시/군/구단속예방구분
02018-01-18서울종로구예방활동
12018-01-25서울동작구예방활동
22018-01-29경남진주시예방활동
32018-02-20경기안산시예방활동
42018-02-23울산울주군예방활동
52018-02-26부산금정구예방활동
62018-02-26부산금정구예방활동
72018-02-26부산금정구예방활동
82018-02-26경남밀양시예방활동
92018-02-26대구수성구예방활동
예방일자광역시/도시/군/구단속예방구분
41002019-07-19서울강남구예방활동
41012019-07-19서울강남구예방활동
41022019-07-19서울강남구예방활동
41032019-07-19서울강남구예방활동
41042019-07-19서울강남구예방활동
41052019-07-19서울강남구예방활동
41062019-07-19서울강남구예방활동
41072019-12-18경기고양시예방활동
41082019-12-18경기고양시예방활동
41092019-12-18경기고양시예방활동

Duplicate rows

Most frequently occurring

예방일자광역시/도시/군/구단속예방구분# duplicates
3022018-07-17광주북구예방활동35
3412018-08-27광주북구예방활동28
3152018-07-19광주동구예방활동25
5172018-09-17광주북구예방활동24
3312018-08-20광주북구예방활동21
3212018-07-19서울동작구예방활동19
1392018-03-26광주북구예방활동15
2962018-07-16광주동구예방활동15
5952019-03-05경북경산시계도예방15
6892019-03-19광주북구예방활동15