Overview

Dataset statistics

Number of variables6
Number of observations882
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows138
Duplicate rows (%)15.6%
Total size in memory43.2 KiB
Average record size in memory50.1 B

Variable types

Text4
Numeric2

Dataset

Description전라북도 전주시내 사업장폐기물배출자를 제공하며 상호, 폐기물종류, 사업장도로명주소 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=3&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15081437

Alerts

Dataset has 138 (15.6%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-14 00:28:26.570271
Analysis finished2024-03-14 00:28:27.452428
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호
Text

Distinct248
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-03-14T09:28:27.603378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length8.8401361
Min length2

Characters and Unicode

Total characters7797
Distinct characters294
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)13.8%

Sample

1st row(사법)전북자동차검사정비사업조합
2nd row(사법)전북자동차검사정비사업조합
3rd row(사법)전북자동차정비사업조합
4th row(유)개암이엔티
5th row(유)개암이엔티
ValueCountFrequency (%)
주)전주페이퍼 70
 
7.0%
주)휴비스 48
 
4.8%
주)티에스케이워터 32
 
3.2%
천일제지(주 24
 
2.4%
전주리싸이클링에너지(주 23
 
2.3%
주식회사 22
 
2.2%
주)전주원파워 20
 
2.0%
전주파워 19
 
1.9%
효성첨단소재(주)전주공장 18
 
1.8%
삼양화성(주)전주공장 13
 
1.3%
Other values (264) 710
71.1%
2024-03-14T09:28:27.900981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
891
 
11.4%
( 632
 
8.1%
) 632
 
8.1%
394
 
5.1%
228
 
2.9%
157
 
2.0%
145
 
1.9%
131
 
1.7%
117
 
1.5%
115
 
1.5%
Other values (284) 4355
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6393
82.0%
Open Punctuation 632
 
8.1%
Close Punctuation 632
 
8.1%
Space Separator 117
 
1.5%
Uppercase Letter 12
 
0.2%
Other Punctuation 5
 
0.1%
Decimal Number 5
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
891
 
13.9%
394
 
6.2%
228
 
3.6%
157
 
2.5%
145
 
2.3%
131
 
2.0%
115
 
1.8%
104
 
1.6%
96
 
1.5%
95
 
1.5%
Other values (273) 4037
63.1%
Uppercase Letter
ValueCountFrequency (%)
C 4
33.3%
B 4
33.3%
Y 4
33.3%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
8 2
40.0%
2 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 632
100.0%
Close Punctuation
ValueCountFrequency (%)
) 632
100.0%
Space Separator
ValueCountFrequency (%)
117
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6393
82.0%
Common 1392
 
17.9%
Latin 12
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
891
 
13.9%
394
 
6.2%
228
 
3.6%
157
 
2.5%
145
 
2.3%
131
 
2.0%
115
 
1.8%
104
 
1.6%
96
 
1.5%
95
 
1.5%
Other values (273) 4037
63.1%
Common
ValueCountFrequency (%)
( 632
45.4%
) 632
45.4%
117
 
8.4%
. 5
 
0.4%
1 2
 
0.1%
8 2
 
0.1%
2 1
 
0.1%
- 1
 
0.1%
Latin
ValueCountFrequency (%)
C 4
33.3%
B 4
33.3%
Y 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6393
82.0%
ASCII 1404
 
18.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
891
 
13.9%
394
 
6.2%
228
 
3.6%
157
 
2.5%
145
 
2.3%
131
 
2.0%
115
 
1.8%
104
 
1.6%
96
 
1.5%
95
 
1.5%
Other values (273) 4037
63.1%
ASCII
ValueCountFrequency (%)
( 632
45.0%
) 632
45.0%
117
 
8.3%
. 5
 
0.4%
C 4
 
0.3%
B 4
 
0.3%
Y 4
 
0.3%
1 2
 
0.1%
8 2
 
0.1%
2 1
 
0.1%
Distinct94
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-03-14T09:28:28.146942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length55
Mean length14.527211
Min length1

Characters and Unicode

Total characters12813
Distinct characters188
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)3.6%

Sample

1st row폐합성수지류(폐염화비닐수지류는 제외한다)
2nd row폐합성수지류(폐염화비닐수지류는 제외한다)
3rd row
4th row폐합성수지류(폐염화비닐수지류는 제외한다)
5th row건축현장 폐목재(접착제_ 페인트_ 기름_ 콘크리트 등의 물질이 사용된 목재를 말한다)
ValueCountFrequency (%)
220
 
9.8%
밖의 220
 
9.8%
제외한다 194
 
8.7%
폐합성수지류(폐염화비닐수지류는 187
 
8.4%
말한다 84
 
3.8%
사업장폐기물 83
 
3.7%
소각시설 79
 
3.5%
비산재가 58
 
2.6%
폐기물 57
 
2.5%
소각재(바닥재와 56
 
2.5%
Other values (155) 1000
44.7%
2024-03-14T09:28:28.543469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1383
 
10.8%
983
 
7.7%
569
 
4.4%
527
 
4.1%
485
 
3.8%
343
 
2.7%
336
 
2.6%
335
 
2.6%
323
 
2.5%
303
 
2.4%
Other values (178) 7226
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10773
84.1%
Space Separator 1393
 
10.9%
Close Punctuation 292
 
2.3%
Open Punctuation 292
 
2.3%
Connector Punctuation 51
 
0.4%
Decimal Number 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
983
 
9.1%
569
 
5.3%
527
 
4.9%
485
 
4.5%
343
 
3.2%
336
 
3.1%
335
 
3.1%
323
 
3.0%
303
 
2.8%
288
 
2.7%
Other values (169) 6281
58.3%
Space Separator
ValueCountFrequency (%)
1383
99.3%
  10
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 288
98.6%
4
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 288
98.6%
4
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 8
66.7%
8 4
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10773
84.1%
Common 2040
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
983
 
9.1%
569
 
5.3%
527
 
4.9%
485
 
4.5%
343
 
3.2%
336
 
3.1%
335
 
3.1%
323
 
3.0%
303
 
2.8%
288
 
2.7%
Other values (169) 6281
58.3%
Common
ValueCountFrequency (%)
1383
67.8%
) 288
 
14.1%
( 288
 
14.1%
_ 51
 
2.5%
  10
 
0.5%
1 8
 
0.4%
4
 
0.2%
4
 
0.2%
8 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10712
83.6%
ASCII 2022
 
15.8%
Compat Jamo 61
 
0.5%
None 18
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1383
68.4%
) 288
 
14.2%
( 288
 
14.2%
_ 51
 
2.5%
1 8
 
0.4%
8 4
 
0.2%
Hangul
ValueCountFrequency (%)
983
 
9.2%
569
 
5.3%
527
 
4.9%
485
 
4.5%
343
 
3.2%
336
 
3.1%
335
 
3.1%
323
 
3.0%
303
 
2.8%
288
 
2.7%
Other values (168) 6220
58.1%
Compat Jamo
ValueCountFrequency (%)
61
100.0%
None
ValueCountFrequency (%)
  10
55.6%
4
 
22.2%
4
 
22.2%
Distinct222
Distinct (%)25.2%
Missing2
Missing (%)0.2%
Memory size7.0 KiB
2024-03-14T09:28:28.813632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.632955
Min length18

Characters and Unicode

Total characters18157
Distinct characters159
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)11.7%

Sample

1st row전라북도 전주시 덕진구 서귀로 23
2nd row전라북도 전주시 덕진구 서귀로 23
3rd row전라북도 전주시 덕진구 서귀로 23
4th row전라북도 전주시 덕진구 서귀로 150-4
5th row전라북도 전주시 덕진구 서귀로 150-4
ValueCountFrequency (%)
전라북도 880
20.0%
전주시 880
20.0%
덕진구 750
17.0%
팔복로 137
 
3.1%
완산구 130
 
3.0%
59 109
 
2.5%
기린대로 85
 
1.9%
서귀로 51
 
1.2%
787 48
 
1.1%
고내천변로 45
 
1.0%
Other values (278) 1286
29.2%
2024-03-14T09:28:29.216120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3521
19.4%
1802
 
9.9%
914
 
5.0%
892
 
4.9%
881
 
4.9%
880
 
4.8%
880
 
4.8%
880
 
4.8%
756
 
4.2%
751
 
4.1%
Other values (149) 6000
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11952
65.8%
Space Separator 3521
 
19.4%
Decimal Number 2500
 
13.8%
Dash Punctuation 182
 
1.0%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1802
15.1%
914
 
7.6%
892
 
7.5%
881
 
7.4%
880
 
7.4%
880
 
7.4%
880
 
7.4%
756
 
6.3%
751
 
6.3%
700
 
5.9%
Other values (135) 2616
21.9%
Decimal Number
ValueCountFrequency (%)
1 431
17.2%
5 363
14.5%
2 320
12.8%
7 281
11.2%
8 243
9.7%
3 220
8.8%
4 195
7.8%
9 158
 
6.3%
0 157
 
6.3%
6 132
 
5.3%
Space Separator
ValueCountFrequency (%)
3521
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11952
65.8%
Common 6205
34.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1802
15.1%
914
 
7.6%
892
 
7.5%
881
 
7.4%
880
 
7.4%
880
 
7.4%
880
 
7.4%
756
 
6.3%
751
 
6.3%
700
 
5.9%
Other values (135) 2616
21.9%
Common
ValueCountFrequency (%)
3521
56.7%
1 431
 
6.9%
5 363
 
5.9%
2 320
 
5.2%
7 281
 
4.5%
8 243
 
3.9%
3 220
 
3.5%
4 195
 
3.1%
- 182
 
2.9%
9 158
 
2.5%
Other values (4) 291
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11952
65.8%
ASCII 6205
34.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3521
56.7%
1 431
 
6.9%
5 363
 
5.9%
2 320
 
5.2%
7 281
 
4.5%
8 243
 
3.9%
3 220
 
3.5%
4 195
 
3.1%
- 182
 
2.9%
9 158
 
2.5%
Other values (4) 291
 
4.7%
Hangul
ValueCountFrequency (%)
1802
15.1%
914
 
7.6%
892
 
7.5%
881
 
7.4%
880
 
7.4%
880
 
7.4%
880
 
7.4%
756
 
6.3%
751
 
6.3%
700
 
5.9%
Other values (135) 2616
21.9%
Distinct224
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-03-14T09:28:29.679765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/