Overview

Dataset statistics

Number of variables3
Number of observations90
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)5.6%
Total size in memory2.4 KiB
Average record size in memory27.5 B

Variable types

Text1
Numeric2

Dataset

Description해양쓰레기 조사는 목적에 따라 수거처리사업 정책 수립을 위한 실태조사 사업과 수거처리 사업 시행을 위한 실시설계 사업의 형태로 수행되고 있습니다.이를 통해 수집된 조사정보는 효율적인 해양쓰레기 관리를 위하여 쓰레기 오염실태에 대한 과학적인 현장조사를 실시하고 해양쓰레기 오염분포 실태 파악 및 변화 양상을 분석하는데 활용됩니다.국민들에게 해양환경보전에 대한 중요성을 인식시키고 국가 정책자료로 활용하는데 목적이 있습니다.
Author해양환경공단
URLhttps://www.data.go.kr/data/15044010/fileData.do

Alerts

Dataset has 5 (5.6%) duplicate rowsDuplicates
조사면적 is highly overall correlated with 추정량High correlation
추정량 is highly overall correlated with 조사면적High correlation
조사면적 has 61 (67.8%) zerosZeros
추정량 has 65 (72.2%) zerosZeros

Reproduction

Analysis started2023-12-12 14:56:54.085836
Analysis finished2023-12-12 14:56:54.808636
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct81
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-12T23:56:55.023914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.5222222
Min length2

Characters and Unicode

Total characters317
Distinct characters89
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

Unique74 ?
Unique (%)82.2%

Sample

1st row평택시
2nd row안산시
3rd row시흥시
4th row김포시
5th row화성시
ValueCountFrequency (%)
창원시 5
 
5.3%
중구 3
 
3.2%
동구 3
 
3.2%
울진군 2
 
2.1%
고성군 2
 
2.1%
서구 2
 
2.1%
남구 2
 
2.1%
북구 2
 
2.1%
거제시 1
 
1.1%
남해군 1
 
1.1%
Other values (71) 71
75.5%
2023-12-12T23:56:55.732813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 34
 
10.7%
군 32
 
10.1%
구 24
 
7.6%
산 10
 
3.2%
서 9
 
2.8%
해 8
 
2.5%
원 8
 
2.5%
양 7
 
2.2%
동 7
 
2.2%
진 7
 
2.2%
Other values (79) 171
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 313
98.7%
Space Separator 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 34
 
10.9%
군 32
 
10.2%
구 24
 
7.7%
산 10
 
3.2%
서 9
 
2.9%
해 8
 
2.6%
원 8
 
2.6%
양 7
 
2.2%
동 7
 
2.2%
진 7
 
2.2%
Other values (78) 167
53.4%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 313
98.7%
Common 4
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 34
 
10.9%
군 32
 
10.2%
구 24
 
7.7%
산 10
 
3.2%
서 9
 
2.9%
해 8
 
2.6%
원 8
 
2.6%
양 7
 
2.2%
동 7
 
2.2%
진 7
 
2.2%
Other values (78) 167
53.4%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 313
98.7%
ASCII 4
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 34
 
10.9%
군 32
 
10.2%
구 24
 
7.7%
산 10
 
3.2%
서 9
 
2.9%
해 8
 
2.6%
원 8
 
2.6%
양 7
 
2.2%
동 7
 
2.2%
진 7
 
2.2%
Other values (78) 167
53.4%
ASCII
ValueCountFrequency (%)
4
100.0%

조사면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.92222
Minimum0
Maximum15226
Zeros61
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T23:56:55.884304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q328.75
95-th percentile2648.35
Maximum15226
Range15226
Interquartile range (IQR)28.75

Descriptive statistics

Standard deviation1997.2284
Coefficient of variation (CV)3.8711811
Kurtosis38.660434
Mean515.92222
Median Absolute Deviation (MAD)0
Skewness5.9221746
Sum46433
Variance3988921.2
MonotonicityNot monotonic
2023-12-12T23:56:56.014522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 61
67.8%
30 3
 
3.3%
50 3
 
3.3%
20 2
 
2.2%
10 2
 
2.2%
1500 2
 
2.2%
300 1
 
1.1%
1050 1
 
1.1%
10064 1
 
1.1%
15226 1
 
1.1%
Other values (13) 13
 
14.4%
ValueCountFrequency (%)
0 61
67.8%
10 2
 
2.2%
16 1
 
1.1%
20 2
 
2.2%
25 1
 
1.1%
30 3
 
3.3%
40 1
 
1.1%
50 3
 
3.3%
120 1
 
1.1%
300 1
 
1.1%
ValueCountFrequency (%)
15226 1
1.1%
10064 1
1.1%
3314 1
1.1%
3000 1
1.1%
2710 1
1.1%
2573 1
1.1%
2030 1
1.1%
1500 2
2.2%
1060 1
1.1%
1050 1
1.1%

추정량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.652222
Minimum0
Maximum449
Zeros65
Zeros (%)72.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T23:56:56.155644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.8
95-th percentile144.385
Maximum449
Range449
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation74.346199
Coefficient of variation (CV)3.0158011
Kurtosis17.617914
Mean24.652222
Median Absolute Deviation (MAD)0
Skewness4.0279789
Sum2218.7
Variance5527.3574
MonotonicityNot monotonic
2023-12-12T23:56:56.313119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.0 65
72.2%
4.4 2
 
2.2%
9.6 2
 
2.2%
1.0 1
 
1.1%
125.3 1
 
1.1%
212.9 1
 
1.1%
79.8 1
 
1.1%
86.3 1
 
1.1%
31.3 1
 
1.1%
376.2 1
 
1.1%
Other values (14) 14
 
15.6%
ValueCountFrequency (%)
0.0 65
72.2%
1.0 1
 
1.1%
2.2 1
 
1.1%
3.0 1
 
1.1%
4.0 1
 
1.1%
4.4 2
 
2.2%
8.5 1
 
1.1%
9.6 2
 
2.2%
11.0 1
 
1.1%
17.0 1
 
1.1%
ValueCountFrequency (%)
449.0 1
1.1%
376.2 1
1.1%
260.5 1
1.1%
212.9 1
1.1%
160.0 1
1.1%
125.3 1
1.1%
123.1 1
1.1%
100.0 1
1.1%
86.3 1
1.1%
79.8 1
1.1%

Interactions

2023-12-12T23:56:54.458296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:54.240469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:54.570618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:54.349984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:56:56.424129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분조사면적추정량
구분1.0000.9730.979
조사면적0.9731.0000.773
추정량0.9790.7731.000
2023-12-12T23:56:56.566223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사면적추정량
조사면적1.0000.923
추정량0.9231.000

Missing values

2023-12-12T23:56:54.698391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:56:54.772960image/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

구분조사면적추정량
0평택시00.0
1안산시00.0
2시흥시00.0
3김포시00.0
4화성시00.0
5군포시00.0
6안산환경재단00.0
7중구1060100.0
8남동구00.0
9동구00.0
구분조사면적추정량
80완도군271086.3
81진도군404.4
82신안군00.0
83울진군00.0
84군산시5079.8
85부안군00.0
86고창군00.0
87김제시00.0
88제주시15226212.9
89서귀포시10064125.3

Duplicate rows

Most frequently occurring

구분조사면적추정량# duplicates
1동구00.03
0고성군00.02
2북구00.02
3울진군00.02
4중구00.02