Overview

Dataset statistics

Number of variables3
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory28.2 B

Variable types

Text1
Numeric2

Dataset

Description해양쓰레기 모니터링에 대한 데이터로 (무게, 개수 등)에 대한 정보를 제공합니다. 해당 파일은 csv형식으로 제공됩니다
URLhttps://www.data.go.kr/data/15044012/fileData.do

Alerts

개수 is highly overall correlated with 무게High correlation
무게 is highly overall correlated with 개수High correlation
지역 has unique valuesUnique
개수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:20:18.530476
Analysis finished2023-12-12 05:20:19.223995
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T14:20:19.426356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.5833333
Min length4

Characters and Unicode

Total characters275
Distinct characters106
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row강화여차리
2nd row안산말부흥
3rd row태안백리포
4th row보령석대도
5th row부안변산
ValueCountFrequency (%)
강화여차리 1
 
1.7%
안산말부흥 1
 
1.7%
부산가덕도 1
 
1.7%
고흥염포 1
 
1.7%
남해유구 1
 
1.7%
거제두모 1
 
1.7%
울주나사리 1
 
1.7%
동해노봉 1
 
1.7%
영덕고래불 1
 
1.7%
포항구룡포 1
 
1.7%
Other values (50) 50
83.3%
2023-12-12T14:20:19.900205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 27
 
9.8%
안 11
 
4.0%
산 10
 
3.6%
신 8
 
2.9%
포 7
 
2.5%
해 7
 
2.5%
리 6
 
2.2%
고 6
 
2.2%
흥 6
 
2.2%
영 6
 
2.2%
Other values (96) 181
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 275
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 27
 
9.8%
안 11
 
4.0%
산 10
 
3.6%
신 8
 
2.9%
포 7
 
2.5%
해 7
 
2.5%
리 6
 
2.2%
고 6
 
2.2%
흥 6
 
2.2%
영 6
 
2.2%
Other values (96) 181
65.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 275
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 27
 
9.8%
안 11
 
4.0%
산 10
 
3.6%
신 8
 
2.9%
포 7
 
2.5%
해 7
 
2.5%
리 6
 
2.2%
고 6
 
2.2%
흥 6
 
2.2%
영 6
 
2.2%
Other values (96) 181
65.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 275
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 27
 
9.8%
안 11
 
4.0%
산 10
 
3.6%
신 8
 
2.9%
포 7
 
2.5%
해 7
 
2.5%
리 6
 
2.2%
고 6
 
2.2%
흥 6
 
2.2%
영 6
 
2.2%
Other values (96) 181
65.8%

개수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3039.5667
Minimum30
Maximum105781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T14:20:20.088932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile80.5
Q1260
median893.5
Q31635.75
95-th percentile5277.45
Maximum105781
Range105751
Interquartile range (IQR)1375.75

Descriptive statistics

Standard deviation13581.123
Coefficient of variation (CV)4.4681114
Kurtosis58.299094
Mean3039.5667
Median Absolute Deviation (MAD)668.5
Skewness7.587416
Sum182374
Variance1.8444689 × 108
MonotonicityNot monotonic
2023-12-12T14:20:20.267922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 1
 
1.7%
321 1
 
1.7%
198 1
 
1.7%
3987 1
 
1.7%
1619 1
 
1.7%
1032 1
 
1.7%
574 1
 
1.7%
2872 1
 
1.7%
663 1
 
1.7%
2835 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
30 1
1.7%
48 1
1.7%
52 1
1.7%
82 1
1.7%
86 1
1.7%
88 1
1.7%
108 1
1.7%
120 1
1.7%
143 1
1.7%
156 1
1.7%
ValueCountFrequency (%)
105781 1
1.7%
8684 1
1.7%
5305 1
1.7%
5276 1
1.7%
4438 1
1.7%
3987 1
1.7%
3237 1
1.7%
2872 1
1.7%
2835 1
1.7%
2519 1
1.7%

무게
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.323333
Minimum0.7
Maximum785.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T14:20:20.432348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.495
Q112.325
median30
Q371.975
95-th percentile259.115
Maximum785.6
Range784.9
Interquartile range (IQR)59.65

Descriptive statistics

Standard deviation133.75742
Coefficient of variation (CV)1.8494366
Kurtosis17.757273
Mean72.323333
Median Absolute Deviation (MAD)23.6
Skewness4.0001069
Sum4339.4
Variance17891.048
MonotonicityNot monotonic
2023-12-12T14:20:20.933975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.6 2
 
3.3%
61.5 2
 
3.3%
0.7 2
 
3.3%
34.1 1
 
1.7%
13.8 1
 
1.7%
24.0 1
 
1.7%
18.6 1
 
1.7%
95.2 1
 
1.7%
21.9 1
 
1.7%
7.4 1
 
1.7%
Other values (47) 47
78.3%
ValueCountFrequency (%)
0.7 2
3.3%
1.4 1
1.7%
1.5 1
1.7%
2.1 1
1.7%
2.2 1
1.7%
4.4 1
1.7%
5.2 1
1.7%
5.6 1
1.7%
7.4 1
1.7%
9.7 1
1.7%
ValueCountFrequency (%)
785.6 1
1.7%
625.6 1
1.7%
286.0 1
1.7%
257.7 1
1.7%
208.7 1
1.7%
186.7 1
1.7%
146.7 1
1.7%
124.0 1
1.7%
96.5 1
1.7%
95.2 1
1.7%

Interactions

2023-12-12T14:20:18.864198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:20:18.673751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:20:18.968113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:20:18.766971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:20:21.032906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역개수무게
지역1.0001.0001.000
개수1.0001.0001.000
무게1.0001.0001.000
2023-12-12T14:20:21.133163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개수무게
개수1.0000.608
무게0.6081.000

Missing values

2023-12-12T14:20:19.105376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:20:19.186612image/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

지역개수무게
0강화여차리65629.3
1안산말부흥300.7
2태안백리포148441.6
3보령석대도14364.2
4부안변산18686.5
5신안임자도5211.4
6진도하조도8219.9
7해남묵동리2089.7
8고흥신흥44137.9
9여수반월2885.6
지역개수무게
50여수거문도4438146.7
51여수안도8684257.7
52완도보길도125575.2
53완도평일도99661.5
54울릉현포1615186.7
55울산주전116511.2
56제주위미100821.6
57진도가사도831124.0
58통영욕지도105781625.6
59화성서신1201.5