Overview

Dataset statistics

Number of variables5
Number of observations288
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory41.5 B

Variable types

Numeric1
Text2
Categorical2

Dataset

Description전기전자제품및자동차의재활용시스템내 시스템 사용관련 도움말 정보를 제공(순번, 화면위치, 화면명, 등록일, 수정일 등)
Author환경부
URLhttps://www.data.go.kr/data/15092122/fileData.do

Alerts

순번 is highly overall correlated with 등록일High correlation
등록일 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
수정일 is highly overall correlated with 등록일High correlation
순번 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:28:27.902621
Analysis finished2024-04-06 08:28:29.519745
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.44792
Minimum1
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-06T17:28:29.687802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.35
Q172.75
median144.5
Q3251.25
95-th percentile308.65
Maximum323
Range322
Interquartile range (IQR)178.5

Descriptive statistics

Standard deviation98.54837
Coefficient of variation (CV)0.61805994
Kurtosis-1.3706449
Mean159.44792
Median Absolute Deviation (MAD)89.5
Skewness0.076238297
Sum45921
Variance9711.7812
MonotonicityStrictly increasing
2024-04-06T17:28:30.069634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
146 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
231 1
 
0.3%
230 1
 
0.3%
229 1
 
0.3%
228 1
 
0.3%
227 1
 
0.3%
226 1
 
0.3%
Other values (278) 278
96.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
323 1
0.3%
322 1
0.3%
321 1
0.3%
320 1
0.3%
319 1
0.3%
318 1
0.3%
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%

화면위치
Text

Distinct65
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-06T17:28:30.478712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length24.895833
Min length11

Characters and Unicode

Total characters7170
Distinct characters124
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)5.9%

Sample

1st row제도소개 > null
2nd row전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수
3rd row전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수
4th row전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수
5th row전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수
ValueCountFrequency (%)
569
38.9%
전기전자제품관리 141
 
9.6%
자동차관리 130
 
8.9%
전자관리표 105
 
7.2%
사전예방관리 39
 
2.7%
재활용실적 33
 
2.3%
폐자동차 29
 
2.0%
회수실적 27
 
1.8%
파쇄잔재물 24
 
1.6%
재활용결과보고 24
 
1.6%
Other values (70) 343
23.4%
2024-04-06T17:28:31.122458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1176
16.4%
> 569
 
7.9%
리 539
 
7.5%
관 536
 
7.5%
자 441
 
6.2%
전 441
 
6.2%
차 203
 
2.8%
동 185
 
2.6%
재 178
 
2.5%
제 162
 
2.3%
Other values (114) 2740
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5314
74.1%
Space Separator 1176
 
16.4%
Math Symbol 569
 
7.9%
Other Punctuation 39
 
0.5%
Close Punctuation 32
 
0.4%
Open Punctuation 32
 
0.4%
Lowercase Letter 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 539
 
10.1%
관 536
 
10.1%
자 441
 
8.3%
전 441
 
8.3%
차 203
 
3.8%
동 185
 
3.5%
재 178
 
3.3%
제 162
 
3.0%
기 161
 
3.0%
용 148
 
2.8%
Other values (105) 2320
43.7%
Lowercase Letter
ValueCountFrequency (%)
l 4
50.0%
u 2
25.0%
n 2
25.0%
Other Punctuation
ValueCountFrequency (%)
· 37
94.9%
/ 2
 
5.1%
Space Separator
ValueCountFrequency (%)
1176
100.0%
Math Symbol
ValueCountFrequency (%)
> 569
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5314
74.1%
Common 1848
 
25.8%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 539
 
10.1%
관 536
 
10.1%
자 441
 
8.3%
전 441
 
8.3%
차 203
 
3.8%
동 185
 
3.5%
재 178
 
3.3%
제 162
 
3.0%
기 161
 
3.0%
용 148
 
2.8%
Other values (105) 2320
43.7%
Common
ValueCountFrequency (%)
1176
63.6%
> 569
30.8%
· 37
 
2.0%
) 32
 
1.7%
( 32
 
1.7%
/ 2
 
0.1%
Latin
ValueCountFrequency (%)
l 4
50.0%
u 2
25.0%
n 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5314
74.1%
ASCII 1819
 
25.4%
None 37
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1176
64.7%
> 569
31.3%
) 32
 
1.8%
( 32
 
1.8%
l 4
 
0.2%
u 2
 
0.1%
n 2
 
0.1%
/ 2
 
0.1%
Hangul
ValueCountFrequency (%)
리 539
 
10.1%
관 536
 
10.1%
자 441
 
8.3%
전 441
 
8.3%
차 203
 
3.8%
동 185
 
3.5%
재 178
 
3.3%
제 162
 
3.0%
기 161
 
3.0%
용 148
 
2.8%
Other values (105) 2320
43.7%
None
ValueCountFrequency (%)
· 37
100.0%

화면명
Text

Distinct123
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-06T17:28:31.597236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length7.1180556
Min length2

Characters and Unicode

Total characters2050
Distinct characters116
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)26.4%

Sample

1st rowㅅㄷㄴㅅ
2nd row목록
3rd row상세
4th row입력
5th row일괄입력
ValueCountFrequency (%)
목록 39
 
12.4%
입력 27
 
8.6%
상세 21
 
6.7%
폐자동차 8
 
2.5%
인계관리표(목록 6
 
1.9%
인계관리표(상세 6
 
1.9%
재활용관리표(입력 6
 
1.9%
재활용관리표(상세 6
 
1.9%
재활용관리표(목록 6
 
1.9%
인수관리표(상세 6
 
1.9%
Other values (123) 183
58.3%
2024-04-06T17:28:32.931129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 128
 
6.2%
) 128
 
6.2%
록 98
 
4.8%
목 98
 
4.8%
관 82
 
4.0%
리 82
 
4.0%
상 77
 
3.8%
표 74
 
3.6%
입 73
 
3.6%
세 72
 
3.5%
Other values (106) 1138
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1753
85.5%
Open Punctuation 128
 
6.2%
Close Punctuation 128
 
6.2%
Space Separator 26
 
1.3%
Decimal Number 8
 
0.4%
Uppercase Letter 6
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
록 98
 
5.6%
목 98
 
5.6%
관 82
 
4.7%
리 82
 
4.7%
상 77
 
4.4%
표 74
 
4.2%
입 73
 
4.2%
세 72
 
4.1%
회 71
 
4.1%
조 70
 
4.0%
Other values (95) 956
54.5%
Decimal Number
ValueCountFrequency (%)
2 2
25.0%
0 2
25.0%
1 2
25.0%
4 2
25.0%
Uppercase Letter
ValueCountFrequency (%)
M 2
33.3%
E 2
33.3%
O 2
33.3%
Open Punctuation
ValueCountFrequency (%)
( 128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 128
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1753
85.5%
Common 291
 
14.2%
Latin 6
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
록 98
 
5.6%
목 98
 
5.6%
관 82
 
4.7%
리 82
 
4.7%
상 77
 
4.4%
표 74
 
4.2%
입 73
 
4.2%
세 72
 
4.1%
회 71
 
4.1%
조 70
 
4.0%
Other values (95) 956
54.5%
Common
ValueCountFrequency (%)
( 128
44.0%
) 128
44.0%
26
 
8.9%
2 2
 
0.7%
0 2
 
0.7%
1 2
 
0.7%
4 2
 
0.7%
/ 1
 
0.3%
Latin
ValueCountFrequency (%)
M 2
33.3%
E 2
33.3%
O 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1749
85.3%
ASCII 297
 
14.5%
Compat Jamo 4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 128
43.1%
) 128
43.1%
26
 
8.8%
2 2
 
0.7%
0 2
 
0.7%
1 2
 
0.7%
4 2
 
0.7%
M 2
 
0.7%
E 2
 
0.7%
O 2
 
0.7%
Hangul
ValueCountFrequency (%)
록 98
 
5.6%
목 98
 
5.6%
관 82
 
4.7%
리 82
 
4.7%
상 77
 
4.4%
표 74
 
4.2%
입 73
 
4.2%
세 72
 
4.1%
회 71
 
4.1%
조 70
 
4.0%
Other values (92) 952
54.4%
Compat Jamo
ValueCountFrequency (%)
ㅅ 2
50.0%
ㄷ 1
25.0%
ㄴ 1
25.0%

등록일
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2016-11-16
157 
2018-12-27
58 
2018-12-28
37 
2018-12-26
25 
2016-11-24
 
5
Other values (5)
 
6

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)1.4%

Sample

1st row2016-12-03
2nd row2016-11-16
3rd row2016-11-16
4th row2016-11-16
5th row2016-11-16

Common Values

ValueCountFrequency (%)
2016-11-16 157
54.5%
2018-12-27 58
 
20.1%
2018-12-28 37
 
12.8%
2018-12-26 25
 
8.7%
2016-11-24 5
 
1.7%
2016-11-25 2
 
0.7%
2016-12-03 1
 
0.3%
2018-01-10 1
 
0.3%
2018-07-03 1
 
0.3%
2019-12-03 1
 
0.3%

Length

2024-04-06T17:28:33.209272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:28:33.503245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-11-16 157
54.5%
2018-12-27 58
 
20.1%
2018-12-28 37
 
12.8%
2018-12-26 25
 
8.7%
2016-11-24 5
 
1.7%
2016-11-25 2
 
0.7%
2016-12-03 1
 
0.3%
2018-01-10 1
 
0.3%
2018-07-03 1
 
0.3%
2019-12-03 1
 
0.3%

수정일
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2019-04-10
113 
<NA>
71 
2016-11-24
61 
2019-12-03
15 
2019-04-11
 
10
Other values (7)
18 

Length

Max length10
Median length10
Mean length8.5208333
Min length4

Unique

Unique2 ?
Unique (%)0.7%

Sample

1st row<NA>
2nd row2019-04-10
3rd row2016-11-24
4th row2019-04-10
5th row2016-11-24

Common Values

ValueCountFrequency (%)
2019-04-10 113
39.2%
<NA> 71
24.7%
2016-11-24 61
21.2%
2019-12-03 15
 
5.2%
2019-04-11 10
 
3.5%
2020-01-02 5
 
1.7%
2019-01-02 4
 
1.4%
2018-12-27 3
 
1.0%
2020-04-17 2
 
0.7%
2018-12-26 2
 
0.7%
Other values (2) 2
 
0.7%

Length

2024-04-06T17:28:33.806558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-04-10 113
39.2%
na 71
24.7%
2016-11-24 61
21.2%
2019-12-03 15
 
5.2%
2019-04-11 10
 
3.5%
2020-01-02 5
 
1.7%
2019-01-02 4
 
1.4%
2018-12-27 3
 
1.0%
2020-04-17 2
 
0.7%
2018-12-26 2
 
0.7%
Other values (2) 2
 
0.7%

Interactions

2024-04-06T17:28:28.658243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:28:34.069259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번화면위치등록일수정일
순번1.0000.9130.9480.750
화면위치0.9131.0000.8760.679
등록일0.9480.8761.0000.784
수정일0.7500.6790.7841.000
2024-04-06T17:28:34.268631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일수정일
등록일1.0000.520
수정일0.5201.000
2024-04-06T17:28:34.558510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번등록일수정일
순번1.0000.6170.441
등록일0.6171.0000.520
수정일0.4410.5201.000

Missing values

2024-04-06T17:28:29.022850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:28:29.407590image/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

순번화면위치화면명등록일수정일
01제도소개 > nullㅅㄷㄴㅅ2016-12-03<NA>
12전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수목록2016-11-162019-04-10
23전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수상세2016-11-162016-11-24
34전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수입력2016-11-162019-04-10
45전기전자제품관리 > 사전예방관리 > 유해물질함유기준준수일괄입력2016-11-162016-11-24
56전기전자제품관리 > 사전예방관리 > 재활용정보제공관리요청서목록2016-11-162019-04-10
67전기전자제품관리 > 사전예방관리 > 재활용정보제공관리상세2016-11-162016-11-24
78전기전자제품관리 > 사전예방관리 > 재활용정보제공관리입력2016-11-162019-04-10
89전기전자제품관리 > 사전예방관리 > 재질구조개선사항평가목록2016-11-162019-04-10
910전기전자제품관리 > 사전예방관리 > 재질구조개선사항평가상세2016-11-162016-11-25
순번화면위치화면명등록일수정일
278314자동차관리 > 재활용결과보고 > 파쇄재활용목록2018-12-28<NA>
279315자동차관리 > 재활용결과보고 > 파쇄재활용상세2018-12-28<NA>
280316자동차관리 > 재활용결과보고 > 파쇄재활용입력2018-12-28<NA>
281317자동차관리 > 재활용결과보고 > 파쇄잔재물 재활용목록2018-12-28<NA>
282318자동차관리 > 재활용결과보고 > 파쇄잔재물 재활용상세2018-12-28<NA>
283319자동차관리 > 재활용결과보고 > 파쇄잔재물 재활용입력2018-12-28<NA>
284320자동차관리 > 재활용결과보고 > 폐가스류 재활용목록2018-12-28<NA>
285321자동차관리 > 재활용결과보고 > 폐가스류 재활용상세2018-12-28<NA>
286322자동차관리 > 재활용결과보고 > 폐가스류 재활용입력2018-12-28<NA>
287323전기전자제품관리 > 재활용실적 > 재활용의무이행계획입력2019-12-03<NA>