Overview

Dataset statistics

Number of variables5
Number of observations45
Missing cells17
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory43.9 B

Variable types

Text3
Categorical1
Numeric1

Dataset

Description인천광역시 부평구 사업장(건설)폐기물처리업 현황 데이터는 업체명, 소재지, 전화번호, 처리대상 폐기물, 허가년도에 대한 데이터를 제공합니다.
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15102564/fileData.do

Alerts

전화번호 has 17 (37.8%) missing valuesMissing

Reproduction

Analysis started2024-04-17 18:36:04.004534
Analysis finished2024-04-17 18:36:04.437712
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체명
Text

Distinct42
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-04-18T03:36:04.560350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.8444444
Min length2

Characters and Unicode

Total characters263
Distinct characters101
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)88.9%

Sample

1st row㈜미래환경
2nd row이도에코㈜
3rd row㈜삼정환경
4th row(주)피에스환경
5th row㈜세기환경
ValueCountFrequency (%)
이도에코㈜ 3
 
6.2%
주식회사 3
 
6.2%
푸른환경 2
 
4.2%
우리환경개발 1
 
2.1%
다모아환경개발㈜ 1
 
2.1%
인천폐기물 1
 
2.1%
㈜미래환경 1
 
2.1%
㈜미림이엔씨 1
 
2.1%
형제환경 1
 
2.1%
가은환경 1
 
2.1%
Other values (33) 33
68.8%
2024-04-18T03:36:04.830984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 20
 
7.6%
경 19
 
7.2%
환 19
 
7.2%
이 14
 
5.3%
에 7
 
2.7%
업 6
 
2.3%
개 6
 
2.3%
씨 6
 
2.3%
발 6
 
2.3%
산 5
 
1.9%
Other values (91) 155
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
86.7%
Other Symbol 20
 
7.6%
Uppercase Letter 4
 
1.5%
Space Separator 3
 
1.1%
Lowercase Letter 3
 
1.1%
Close Punctuation 2
 
0.8%
Open Punctuation 2
 
0.8%
Connector Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 19
 
8.3%
환 19
 
8.3%
이 14
 
6.1%
에 7
 
3.1%
업 6
 
2.6%
개 6
 
2.6%
씨 6
 
2.6%
발 6
 
2.6%
산 5
 
2.2%
앤 4
 
1.8%
Other values (79) 136
59.6%
Uppercase Letter
ValueCountFrequency (%)
S 1
25.0%
J 1
25.0%
C 1
25.0%
G 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
r 1
33.3%
s 1
33.3%
e 1
33.3%
Other Symbol
ValueCountFrequency (%)
㈜ 20
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 248
94.3%
Common 8
 
3.0%
Latin 7
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 20
 
8.1%
경 19
 
7.7%
환 19
 
7.7%
이 14
 
5.6%
에 7
 
2.8%
업 6
 
2.4%
개 6
 
2.4%
씨 6
 
2.4%
발 6
 
2.4%
산 5
 
2.0%
Other values (80) 140
56.5%
Latin
ValueCountFrequency (%)
S 1
14.3%
J 1
14.3%
C 1
14.3%
G 1
14.3%
r 1
14.3%
s 1
14.3%
e 1
14.3%
Common
ValueCountFrequency (%)
3
37.5%
) 2
25.0%
( 2
25.0%
_ 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
86.7%
None 20
 
7.6%
ASCII 15
 
5.7%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 20
100.0%
Hangul
ValueCountFrequency (%)
경 19
 
8.3%
환 19
 
8.3%
이 14
 
6.1%
에 7
 
3.1%
업 6
 
2.6%
개 6
 
2.6%
씨 6
 
2.6%
발 6
 
2.6%
산 5
 
2.2%
앤 4
 
1.8%
Other values (79) 136
59.6%
ASCII
ValueCountFrequency (%)
3
20.0%
) 2
13.3%
( 2
13.3%
S 1
 
6.7%
J 1
 
6.7%
C 1
 
6.7%
G 1
 
6.7%
r 1
 
6.7%
s 1
 
6.7%
e 1
 
6.7%

소재지
Text

Distinct41
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-04-18T03:36:05.024623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length40
Mean length33.844444
Min length17

Characters and Unicode

Total characters1523
Distinct characters108
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

Unique38 ?
Unique (%)84.4%

Sample

1st row인천광역시 부평구 후정로 4, 302호(삼산동)
2nd row인천광역시 부평구 동수로 146, 402호 (부개동)
3rd row인천광역시 부평구 영성중로37번길 32, 101호(삼산동)
4th row인천광역시 부평구 경원대로1043번길 13(십정동)
5th row인천광역시 부평구 영성동로 46, 쓰레기처리장동 2층(삼산동, 삼산농산물도매시장)
ValueCountFrequency (%)
인천광역시 45
 
15.9%
부평구 44
 
15.5%
십정동 8
 
2.8%
부평동 6
 
2.1%
2층 5
 
1.8%
부평대로 5
 
1.8%
66 4
 
1.4%
마장로 4
 
1.4%
320 4
 
1.4%
청천동 4
 
1.4%
Other values (110) 154
54.4%
2024-04-18T03:36:05.328213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
238
 
15.6%
부 63
 
4.1%
평 59
 
3.9%
동 58
 
3.8%
2 57
 
3.7%
천 55
 
3.6%
시 48
 
3.2%
4 47
 
3.1%
광 46
 
3.0%
1 46
 
3.0%
Other values (98) 806
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 810
53.2%
Decimal Number 313
 
20.6%
Space Separator 238
 
15.6%
Other Punctuation 46
 
3.0%
Open Punctuation 43
 
2.8%
Close Punctuation 43
 
2.8%
Dash Punctuation 21
 
1.4%
Uppercase Letter 9
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 63
 
7.8%
평 59
 
7.3%
동 58
 
7.2%
천 55
 
6.8%
시 48
 
5.9%
광 46
 
5.7%
인 45
 
5.6%
역 45
 
5.6%
로 44
 
5.4%
구 44
 
5.4%
Other values (79) 303
37.4%
Decimal Number
ValueCountFrequency (%)
2 57
18.2%
4 47
15.0%
1 46
14.7%
3 44
14.1%
0 38
12.1%
6 23
7.3%
8 18
 
5.8%
5 17
 
5.4%
7 12
 
3.8%
9 11
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
A 4
44.4%
L 3
33.3%
B 2
22.2%
Other Punctuation
ValueCountFrequency (%)
, 45
97.8%
. 1
 
2.2%
Space Separator
ValueCountFrequency (%)
238
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 810
53.2%
Common 704
46.2%
Latin 9
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 63
 
7.8%
평 59
 
7.3%
동 58
 
7.2%
천 55
 
6.8%
시 48
 
5.9%
광 46
 
5.7%
인 45
 
5.6%
역 45
 
5.6%
로 44
 
5.4%
구 44
 
5.4%
Other values (79) 303
37.4%
Common
ValueCountFrequency (%)
238
33.8%
2 57
 
8.1%
4 47
 
6.7%
1 46
 
6.5%
, 45
 
6.4%
3 44
 
6.2%
( 43
 
6.1%
) 43
 
6.1%
0 38
 
5.4%
6 23
 
3.3%
Other values (6) 80
 
11.4%
Latin
ValueCountFrequency (%)
A 4
44.4%
L 3
33.3%
B 2
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 810
53.2%
ASCII 713
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
238
33.4%
2 57
 
8.0%
4 47
 
6.6%
1 46
 
6.5%
, 45
 
6.3%
3 44
 
6.2%
( 43
 
6.0%
) 43
 
6.0%
0 38
 
5.3%
6 23
 
3.2%
Other values (9) 89
 
12.5%
Hangul
ValueCountFrequency (%)
부 63
 
7.8%
평 59
 
7.3%
동 58
 
7.2%
천 55
 
6.8%
시 48
 
5.9%
광 46
 
5.7%
인 45
 
5.6%
역 45
 
5.6%
로 44
 
5.4%
구 44
 
5.4%
Other values (79) 303
37.4%

전화번호
Text

MISSING 

Distinct25
Distinct (%)89.3%
Missing17
Missing (%)37.8%
Memory size492.0 B
2024-04-18T03:36:05.487844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.928571
Min length9

Characters and Unicode

Total characters334
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)82.1%

Sample

1st row032-503-5888
2nd row032-504-9280
3rd row032-327-3770
4th row1688-4515
5th row032-511-8272
ValueCountFrequency (%)
032-504-9280 3
 
10.7%
032-330-1766 2
 
7.1%
032-511-1951 1
 
3.6%
032-503-5888 1
 
3.6%
032-256-3800 1
 
3.6%
032-362-3114 1
 
3.6%
032-503-5607 1
 
3.6%
032-875-0185 1
 
3.6%
032-567-7396 1
 
3.6%
032-569-7339 1
 
3.6%
Other values (15) 15
53.6%
2024-04-18T03:36:05.746182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 55
16.5%
3 53
15.9%
0 51
15.3%
2 45
13.5%
5 26
7.8%
1 23
6.9%
7 21
 
6.3%
8 20
 
6.0%
6 16
 
4.8%
9 13
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 279
83.5%
Dash Punctuation 55
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 53
19.0%
0 51
18.3%
2 45
16.1%
5 26
9.3%
1 23
8.2%
7 21
 
7.5%
8 20
 
7.2%
6 16
 
5.7%
9 13
 
4.7%
4 11
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 55
16.5%
3 53
15.9%
0 51
15.3%
2 45
13.5%
5 26
7.8%
1 23
6.9%
7 21
 
6.3%
8 20
 
6.0%
6 16
 
4.8%
9 13
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 55
16.5%
3 53
15.9%
0 51
15.3%
2 45
13.5%
5 26
7.8%
1 23
6.9%
7 21
 
6.3%
8 20
 
6.0%
6 16
 
4.8%
9 13
 
3.9%

처리대상 폐기물
Categorical

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
수집·운반업(사업장배출시설계)
22 
수집·운반업(건설폐기물)
15 
수집·운반업(사업장비배출시설계)
7 
폐기물중간재활용업
 
1

Length

Max length17
Median length16
Mean length15
Min length9

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row수집·운반업(사업장비배출시설계)
2nd row수집·운반업(사업장비배출시설계)
3rd row수집·운반업(사업장비배출시설계)
4th row수집·운반업(사업장비배출시설계)
5th row수집·운반업(사업장비배출시설계)

Common Values

ValueCountFrequency (%)
수집·운반업(사업장배출시설계) 22
48.9%
수집·운반업(건설폐기물) 15
33.3%
수집·운반업(사업장비배출시설계) 7
 
15.6%
폐기물중간재활용업 1
 
2.2%

Length

2024-04-18T03:36:05.861640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:36:05.950772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수집·운반업(사업장배출시설계 22
48.9%
수집·운반업(건설폐기물 15
33.3%
수집·운반업(사업장비배출시설계 7
 
15.6%
폐기물중간재활용업 1
 
2.2%

허가년도
Real number (ℝ)

Distinct16
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.3333
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-04-18T03:36:06.038143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2004.2
Q12011
median2020
Q32022
95-th percentile2023
Maximum2023
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.0035068943
Kurtosis-0.76110843
Mean2016.3333
Median Absolute Deviation (MAD)3
Skewness-0.88534852
Sum90735
Variance50
MonotonicityNot monotonic
2024-04-18T03:36:06.119902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2021 8
17.8%
2022 8
17.8%
2020 6
13.3%
2023 5
11.1%
2005 4
8.9%
2015 3
 
6.7%
2009 2
 
4.4%
2011 1
 
2.2%
2007 1
 
2.2%
2006 1
 
2.2%
Other values (6) 6
13.3%
ValueCountFrequency (%)
2001 1
 
2.2%
2002 1
 
2.2%
2004 1
 
2.2%
2005 4
8.9%
2006 1
 
2.2%
2007 1
 
2.2%
2009 2
4.4%
2011 1
 
2.2%
2012 1
 
2.2%
2014 1
 
2.2%
ValueCountFrequency (%)
2023 5
11.1%
2022 8
17.8%
2021 8
17.8%
2020 6
13.3%
2016 1
 
2.2%
2015 3
 
6.7%
2014 1
 
2.2%
2012 1
 
2.2%
2011 1
 
2.2%
2009 2
 
4.4%

Interactions

2024-04-18T03:36:04.253364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:36:06.181711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체명소재지전화번호처리대상 폐기물허가년도
업체명1.0000.9961.0000.6530.957
소재지0.9961.0001.0000.9221.000
전화번호1.0001.0001.0000.6080.976
처리대상 폐기물0.6530.9220.6081.0000.000
허가년도0.9571.0000.9760.0001.000
2024-04-18T03:36:06.251852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가년도처리대상 폐기물
허가년도1.0000.000
처리대상 폐기물0.0001.000

Missing values

2024-04-18T03:36:04.325758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:36:04.402040image/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

업체명소재지전화번호처리대상 폐기물허가년도
0㈜미래환경인천광역시 부평구 후정로 4, 302호(삼산동)032-503-5888수집·운반업(사업장비배출시설계)2014
1이도에코㈜인천광역시 부평구 동수로 146, 402호 (부개동)032-504-9280수집·운반업(사업장비배출시설계)2015
2㈜삼정환경인천광역시 부평구 영성중로37번길 32, 101호(삼산동)032-327-3770수집·운반업(사업장비배출시설계)2015
3(주)피에스환경인천광역시 부평구 경원대로1043번길 13(십정동)1688-4515수집·운반업(사업장비배출시설계)2020
4㈜세기환경인천광역시 부평구 영성동로 46, 쓰레기처리장동 2층(삼산동, 삼산농산물도매시장)032-511-8272수집·운반업(사업장비배출시설계)2021
5㈜에스더블_esr인천광역시 부평구 장제로 257번길 9-3, 403호 (갈산동)<NA>수집·운반업(사업장비배출시설계)2022
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