Overview

Dataset statistics

Number of variables8
Number of observations309
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 KiB
Average record size in memory69.4 B

Variable types

Numeric5
Text1
DateTime2

Dataset

Description바다에 침적된 쓰레기의 분포실태를 지역별로 조사하고 수거 계획 및 수거실적, 정화면적 계획 및 정화실적 등 해양폐기물 정화사업 실적 정보임
Author해양환경공단
URLhttps://www.data.go.kr/data/15044296/fileData.do

Alerts

수거물량 계획 is highly overall correlated with 수거물량 실적High correlation
수거물량 실적 is highly overall correlated with 수거물량 계획High correlation
정화면적 계획 is highly overall correlated with 정화면적 실적High correlation
정화면적 실적 is highly overall correlated with 정화면적 계획High correlation
수거물량 계획 has 4 (1.3%) zerosZeros
정화면적 계획 has 24 (7.8%) zerosZeros
정화면적 실적 has 24 (7.8%) zerosZeros

Reproduction

Analysis started2023-12-12 16:15:42.375169
Analysis finished2023-12-12 16:15:45.421500
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct13
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.3139
Minimum2009
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:15:45.471221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2017
Q32020
95-th percentile2022
Maximum2022
Range13
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.2075034
Coefficient of variation (CV)0.0020867303
Kurtosis-1.1705523
Mean2016.3139
Median Absolute Deviation (MAD)3
Skewness-0.30199816
Sum623041
Variance17.703085
MonotonicityNot monotonic
2023-12-13T01:15:45.591759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2022 40
12.9%
2018 30
9.7%
2019 29
9.4%
2020 24
7.8%
2012 24
7.8%
2009 24
7.8%
2021 22
7.1%
2015 22
7.1%
2011 22
7.1%
2017 20
 
6.5%
Other values (3) 52
16.8%
ValueCountFrequency (%)
2009 24
7.8%
2010 14
4.5%
2011 22
7.1%
2012 24
7.8%
2014 18
5.8%
2015 22
7.1%
2016 20
6.5%
2017 20
6.5%
2018 30
9.7%
2019 29
9.4%
ValueCountFrequency (%)
2022 40
12.9%
2021 22
7.1%
2020 24
7.8%
2019 29
9.4%
2018 30
9.7%
2017 20
6.5%
2016 20
6.5%
2015 22
7.1%
2014 18
5.8%
2012 24
7.8%

사업명
Text

Distinct258
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-13T01:15:45.909816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.8317152
Min length2

Characters and Unicode

Total characters2420
Distinct characters194
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

Unique217 ?
Unique (%)70.2%

Sample

1st row부산 부산신항
2nd row옹진군 덕적도 주변해역
3rd row창원 마산항
4th row목포외항
5th row부산 가덕도 주변해역
ValueCountFrequency (%)
주변 34
 
5.0%
제주 19
 
2.8%
여수 14
 
2.1%
통영 13
 
1.9%
부산 11
 
1.6%
군산 10
 
1.5%
주변해역 10
 
1.5%
신안 9
 
1.3%
인천 9
 
1.3%
거제 7
 
1.0%
Other values (285) 538
79.8%
2023-12-13T01:15:46.432518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
366
 
15.1%
항 210
 
8.7%
주 84
 
3.5%
도 79
 
3.3%
포 78
 
3.2%
산 71
 
2.9%
시 55
 
2.3%
해 49
 
2.0%
변 49
 
2.0%
천 49
 
2.0%
Other values (184) 1330
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1996
82.5%
Space Separator 366
 
15.1%
Open Punctuation 21
 
0.9%
Close Punctuation 21
 
0.9%
Other Punctuation 8
 
0.3%
Math Symbol 5
 
0.2%
Decimal Number 2
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 210
 
10.5%
주 84
 
4.2%
도 79
 
4.0%
포 78
 
3.9%
산 71
 
3.6%
시 55
 
2.8%
해 49
 
2.5%
변 49
 
2.5%
천 49
 
2.5%
제 47
 
2.4%
Other values (175) 1225
61.4%
Other Punctuation
ValueCountFrequency (%)
, 6
75.0%
· 2
 
25.0%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
366
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1996
82.5%
Common 423
 
17.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 210
 
10.5%
주 84
 
4.2%
도 79
 
4.0%
포 78
 
3.9%
산 71
 
3.6%
시 55
 
2.8%
해 49
 
2.5%
변 49
 
2.5%
천 49
 
2.5%
제 47
 
2.4%
Other values (175) 1225
61.4%
Common
ValueCountFrequency (%)
366
86.5%
( 21
 
5.0%
) 21
 
5.0%
, 6
 
1.4%
~ 5
 
1.2%
· 2
 
0.5%
3 1
 
0.2%
5 1
 
0.2%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1996
82.5%
ASCII 422
 
17.4%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
366
86.7%
( 21
 
5.0%
) 21
 
5.0%
, 6
 
1.4%
~ 5
 
1.2%
A 1
 
0.2%
3 1
 
0.2%
5 1
 
0.2%
Hangul
ValueCountFrequency (%)
항 210
 
10.5%
주 84
 
4.2%
도 79
 
4.0%
포 78
 
3.9%
산 71
 
3.6%
시 55
 
2.8%
해 49
 
2.5%
변 49
 
2.5%
천 49
 
2.5%
제 47
 
2.4%
Other values (175) 1225
61.4%
None
ValueCountFrequency (%)
· 2
100.0%

수거물량 계획
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct246
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.5365
Minimum0
Maximum6515.3
Zeros4
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:15:46.623093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.718
Q140.2
median70
Q3126
95-th percentile463.8
Maximum6515.3
Range6515.3
Interquartile range (IQR)85.8

Descriptive statistics

Standard deviation509.77576
Coefficient of variation (CV)3.0982532
Kurtosis102.68197
Mean164.5365
Median Absolute Deviation (MAD)36.4
Skewness9.5196624
Sum50841.78
Variance259871.32
MonotonicityNot monotonic
2023-12-13T01:15:46.786161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.0 6
 
1.9%
31.0 4
 
1.3%
0.0 4
 
1.3%
60.0 4
 
1.3%
40.0 4
 
1.3%
30.0 3
 
1.0%
75.0 3
 
1.0%
55.0 3
 
1.0%
25.0 3
 
1.0%
253.0 2
 
0.6%
Other values (236) 273
88.3%
ValueCountFrequency (%)
0.0 4
1.3%
1.0 2
0.6%
1.34 1
 
0.3%
1.89 1
 
0.3%
2.21 1
 
0.3%
4.29 1
 
0.3%
6.0 1
 
0.3%
6.58 1
 
0.3%
6.7 1
 
0.3%
7.74 1
 
0.3%
ValueCountFrequency (%)
6515.3 1
0.3%
4777.8 1
0.3%
2770.82 1
0.3%
2500.0 1
0.3%
917.0 1
0.3%
865.0 1
0.3%
690.8 1
0.3%
584.0 1
0.3%
572.0 1
0.3%
556.0 1
0.3%

수거물량 실적
Real number (ℝ)

HIGH CORRELATION 

Distinct307
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.87495
Minimum1.34
Maximum6560.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:15:46.936543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.34
5-th percentile9.408
Q146
median85.01
Q3174.22
95-th percentile495.284
Maximum6560.7
Range6559.36
Interquartile range (IQR)128.22

Descriptive statistics

Standard deviation518.26448
Coefficient of variation (CV)2.7295042
Kurtosis98.548601
Mean189.87495
Median Absolute Deviation (MAD)50.53
Skewness9.2593368
Sum58671.36
Variance268598.08
MonotonicityNot monotonic
2023-12-13T01:15:47.112790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.98 2
 
0.6%
53.63 2
 
0.6%
381.04 1
 
0.3%
74.12 1
 
0.3%
79.49 1
 
0.3%
25.98 1
 
0.3%
58.52 1
 
0.3%
111.88 1
 
0.3%
184.03 1
 
0.3%
125.52 1
 
0.3%
Other values (297) 297
96.1%
ValueCountFrequency (%)
1.34 1
0.3%
1.36 1
0.3%
1.54 1
0.3%
1.67 1
0.3%
1.89 1
0.3%
2.21 1
0.3%
2.65 1
0.3%
4.05 1
0.3%
4.07 1
0.3%
4.29 1
0.3%
ValueCountFrequency (%)
6560.7 1
0.3%
4892.12 1
0.3%
2770.82 1
0.3%
2567.3 1
0.3%
917.0 1
0.3%
865.0 1
0.3%
751.04 1
0.3%
717.86 1
0.3%
668.14 1
0.3%
644.23 1
0.3%

정화면적 계획
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct220
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean861.12405
Minimum0
Maximum41000
Zeros24
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:15:47.279990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.5
median61.46
Q3475
95-th percentile4734
Maximum41000
Range41000
Interquartile range (IQR)457.5

Descriptive statistics

Standard deviation3108.1633
Coefficient of variation (CV)3.6094257
Kurtosis96.703886
Mean861.12405
Median Absolute Deviation (MAD)61.46
Skewness8.6343225
Sum266087.33
Variance9660679.1
MonotonicityNot monotonic
2023-12-13T01:15:47.434555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
 
7.8%
10.0 7
 
2.3%
5000.0 4
 
1.3%
20.0 4
 
1.3%
25.0 3
 
1.0%
6.0 3
 
1.0%
200.0 3
 
1.0%
30.0 3
 
1.0%
400.0 3
 
1.0%
1000.0 3
 
1.0%
Other values (210) 252
81.6%
ValueCountFrequency (%)
0.0 24
7.8%
1.5 1
 
0.3%
3.8 1
 
0.3%
3.95 1
 
0.3%
4.0 1
 
0.3%
5.0 1
 
0.3%
6.0 3
 
1.0%
6.8 2
 
0.6%
7.3 1
 
0.3%
7.5 1
 
0.3%
ValueCountFrequency (%)
41000.0 1
0.3%
20000.0 1
0.3%
14400.0 1
0.3%
12000.0 2
0.6%
9600.0 1
0.3%
8875.0 1
0.3%
8000.0 1
0.3%
7000.0 1
0.3%
6300.0 1
0.3%
5600.0 1
0.3%

정화면적 실적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct229
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean902.14097
Minimum0
Maximum41000
Zeros24
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-13T01:15:47.582518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.2
median60
Q3457.8
95-th percentile4734
Maximum41000
Range41000
Interquartile range (IQR)440.6

Descriptive statistics

Standard deviation3288.3042
Coefficient of variation (CV)3.6450004
Kurtosis80.760788
Mean902.14097
Median Absolute Deviation (MAD)60
Skewness7.971145
Sum278761.56
Variance10812944
MonotonicityNot monotonic
2023-12-13T01:15:47.745758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 24
 
7.8%
10.0 5
 
1.6%
5000.0 4
 
1.3%
700.0 3
 
1.0%
400.0 3
 
1.0%
17.0 3
 
1.0%
500.0 3
 
1.0%
1000.0 3
 
1.0%
6.0 3
 
1.0%
16.5 3
 
1.0%
Other values (219) 255
82.5%
ValueCountFrequency (%)
0.0 24
7.8%
1.5 1
 
0.3%
3.8 1
 
0.3%
3.95 1
 
0.3%
4.0 1
 
0.3%
4.9 1
 
0.3%
5.0 1
 
0.3%
6.0 3
 
1.0%
6.8 2
 
0.6%
7.3 1
 
0.3%
ValueCountFrequency (%)
41000.0 1
0.3%
21000.0 1
0.3%
20000.0 1
0.3%
14400.0 1
0.3%
12000.0 2
0.6%
9600.0 1
0.3%
8875.0 1
0.3%
7000.0 1
0.3%
6300.0 1
0.3%
5600.0 1
0.3%

사업기간(시작일)
Date

Distinct183
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2009-02-14 00:00:00
Maximum2022-12-12 00:00:00
2023-12-13T01:15:47.877296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:48.324998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사업기간(종료일)
Date

Distinct237
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2009-04-13 00:00:00
Maximum2023-04-13 00:00:00
2023-12-13T01:15:48.486103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:48.634782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T01:15:44.618378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:42.651619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.149307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.645277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.140918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.733269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:42.743535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.252558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.735994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.253584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.845858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:42.853235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.340401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.831774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.346220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.968318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:42.960089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.441001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.935804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.451700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:45.060828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.058990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:43.546271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.042981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:44.531483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:15:48.746632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도수거물량 계획수거물량 실적정화면적 계획정화면적 실적
년도1.0000.2220.2120.2950.295
수거물량 계획0.2221.0000.9990.0000.000
수거물량 실적0.2120.9991.0000.0000.000
정화면적 계획0.2950.0000.0001.0001.000
정화면적 실적0.2950.0000.0001.0001.000
2023-12-13T01:15:48.872925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도수거물량 계획수거물량 실적정화면적 계획정화면적 실적
년도1.000-0.274-0.240-0.018-0.020
수거물량 계획-0.2741.0000.8780.1010.107
수거물량 실적-0.2400.8781.0000.0030.011
정화면적 계획-0.0180.1010.0031.0000.998
정화면적 실적-0.0200.1070.0110.9981.000

Missing values

2023-12-13T01:15:45.172139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:15:45.367076image/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

년도사업명수거물량 계획수거물량 실적정화면적 계획정화면적 실적사업기간(시작일)사업기간(종료일)
02022부산 부산신항269.5381.04900.0900.02022-02-182022-05-18
12022옹진군 덕적도 주변해역72.5127.151100.01100.02022-03-242022-05-22
22022창원 마산항36.762.1931.531.52022-03-242022-05-22
32022목포외항66.599.431360.01360.02022-03-242022-05-22
42022부산 가덕도 주변해역87.4107.86902.0902.02022-05-172022-07-25
52022신안 홍도 · 장도33.635.7735.035.02022-05-182022-07-16
62022제주 성산포항26.026.158.08.02022-05-162022-07-27
72022부산남항43.241.8960.060.02022-05-172022-09-01
82022고창갯벌72.54.051500.01500.02022-06-272022-08-25
92022서천 비인항59.695.28700.0700.02022-06-282022-08-11
년도사업명수거물량 계획수거물량 실적정화면적 계획정화면적 실적사업기간(시작일)사업기간(종료일)
2992009충남 태안해역44.949.493250.03250.02009-02-142009-04-13
3002009독도해역1.891.8912000.012000.02009-06-012009-09-14
3012009서천습지690.8717.866300.06300.02009-08-132009-11-20
3022009목포 목포항2500.02567.354.054.02009-05-282009-08-11
3032009안산 북동어장106.0135.812500.02500.02009-11-132010-08-02
3042009부산 광안리174.22174.221004.01004.02009-11-132009-12-28
3052009태안 안흥항144.0207.8970.070.02009-11-132009-12-28
3062009평택당진항40.0113.8920.020.02009-11-162009-12-15
3072009부산 형제도87.087.377000.07000.02009-06-012009-08-26
3082009삼천포항187.0228.3725.025.02009-12-142009-10-22